title stringlengths 31 206 | authors listlengths 1 85 | abstract stringlengths 428 3.21k | doi stringlengths 21 31 | cleaned_title stringlengths 31 206 | cleaned_abstract stringlengths 428 3.21k | key_phrases listlengths 19 150 |
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A comparative analysis of deep learning and deep transfer learning approaches for identification of rice varieties | [
"Komal Sharma",
"Ganesh Kumar Sethi",
"Rajesh Kumar Bawa"
] | Rice is an essential staple food for human nutrition. Rice varieties worldwide have been planted, imported, and exported. During production and trading, different types of rice can be mixed. Due to rice impurities, rice importers and exporters may lose trust in each other, requiring the development of a rice variety id... | 10.1007/s11042-024-19126-7 | a comparative analysis of deep learning and deep transfer learning approaches for identification of rice varieties | rice is an essential staple food for human nutrition. rice varieties worldwide have been planted, imported, and exported. during production and trading, different types of rice can be mixed. due to rice impurities, rice importers and exporters may lose trust in each other, requiring the development of a rice variety id... | [
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Interpretable deep learning methods for multiview learning | [
"Hengkang Wang",
"Han Lu",
"Ju Sun",
"Sandra E. Safo"
] | BackgroundTechnological advances have enabled the generation of unique and complementary types of data or views (e.g. genomics, proteomics, metabolomics) and opened up a new era in multiview learning research with the potential to lead to new biomedical discoveries.ResultsWe propose iDeepViewLearn (Interpretable Deep L... | 10.1186/s12859-024-05679-9 | interpretable deep learning methods for multiview learning | backgroundtechnological advances have enabled the generation of unique and complementary types of data or views (e.g. genomics, proteomics, metabolomics) and opened up a new era in multiview learning research with the potential to lead to new biomedical discoveries.resultswe propose ideepviewlearn (interpretable deep l... | [
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Deep-learning-based image reconstruction with limited data: generating synthetic raw data using deep learning | [
"Frank Zijlstra",
"Peter Thomas While"
] | ObjectDeep learning has shown great promise for fast reconstruction of accelerated MRI acquisitions by learning from large amounts of raw data. However, raw data is not always available in sufficient quantities. This study investigates synthetic data generation to complement small datasets and improve reconstruction qu... | 10.1007/s10334-024-01193-4 | deep-learning-based image reconstruction with limited data: generating synthetic raw data using deep learning | objectdeep learning has shown great promise for fast reconstruction of accelerated mri acquisitions by learning from large amounts of raw data. however, raw data is not always available in sufficient quantities. this study investigates synthetic data generation to complement small datasets and improve reconstruction qu... | [
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Topic- and learning-related predictors of deep-level learning strategies | [
"Eve Kikas",
"Gintautas Silinskas",
"Eliis Härma"
] | The aim of this study was to examine which topic- and learning-related knowledge and motivational beliefs predict the use of specific deep-level learning strategies during an independent learning task. Participants included 335 Estonian fourth- and sixth-grade students who were asked to read about light processes and s... | 10.1007/s10212-023-00766-6 | topic- and learning-related predictors of deep-level learning strategies | the aim of this study was to examine which topic- and learning-related knowledge and motivational beliefs predict the use of specific deep-level learning strategies during an independent learning task. participants included 335 estonian fourth- and sixth-grade students who were asked to read about light processes and s... | [
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Urban traffic signal control optimization through Deep Q Learning and double Deep Q Learning: a novel approach for efficient traffic management | [
"Qazi Umer Jamil",
"Karam Dad Kallu",
"Muhammad Jawad Khan",
"Muhammad Safdar",
"Amad Zafar",
"Muhammad Umair Ali"
] | Traffic congestion remains a persistent challenge in urban areas, necessitating efficient traffic control strategies. This research explores the application of advanced reinforcement learning techniques, specifically Deep Q-Learning (DQN) and Double Deep Q-Learning (DDQN), to address this issue at a four-way traffic in... | 10.1007/s11042-024-20060-x | urban traffic signal control optimization through deep q learning and double deep q learning: a novel approach for efficient traffic management | traffic congestion remains a persistent challenge in urban areas, necessitating efficient traffic control strategies. this research explores the application of advanced reinforcement learning techniques, specifically deep q-learning (dqn) and double deep q-learning (ddqn), to address this issue at a four-way traffic in... | [
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Research trends in deep learning and machine learning for cloud computing security | [
"Yehia Ibrahim Alzoubi",
"Alok Mishra",
"Ahmet Ercan Topcu"
] | Deep learning and machine learning show effectiveness in identifying and addressing cloud security threats. Despite the large number of articles published in this field, there remains a dearth of comprehensive reviews that synthesize the techniques, trends, and challenges of using deep learning and machine learning for... | 10.1007/s10462-024-10776-5 | research trends in deep learning and machine learning for cloud computing security | deep learning and machine learning show effectiveness in identifying and addressing cloud security threats. despite the large number of articles published in this field, there remains a dearth of comprehensive reviews that synthesize the techniques, trends, and challenges of using deep learning and machine learning for... | [
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Distributed Deep Reinforcement Learning: A Survey and a Multi-player Multi-agent Learning Toolbox | [
"Qiyue Yin",
"Tongtong Yu",
"Shengqi Shen",
"Jun Yang",
"Meijing Zhao",
"Wancheng Ni",
"Kaiqi Huang",
"Bin Liang",
"Liang Wang"
] | With the breakthrough of AlphaGo, deep reinforcement learning has become a recognized technique for solving sequential decision-making problems. Despite its reputation, data inefficiency caused by its trial and error learning mechanism makes deep reinforcement learning difficult to apply in a wide range of areas. Many ... | 10.1007/s11633-023-1454-4 | distributed deep reinforcement learning: a survey and a multi-player multi-agent learning toolbox | with the breakthrough of alphago, deep reinforcement learning has become a recognized technique for solving sequential decision-making problems. despite its reputation, data inefficiency caused by its trial and error learning mechanism makes deep reinforcement learning difficult to apply in a wide range of areas. many ... | [
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Deep learning in rheumatological image interpretation | [
"Berend C. Stoel",
"Marius Staring",
"Monique Reijnierse",
"Annette H. M. van der Helm-van Mil"
] | Artificial intelligence techniques, specifically deep learning, have already affected daily life in a wide range of areas. Likewise, initial applications have been explored in rheumatology. Deep learning might not easily surpass the accuracy of classic techniques when performing classification or regression on low-dime... | 10.1038/s41584-023-01074-5 | deep learning in rheumatological image interpretation | artificial intelligence techniques, specifically deep learning, have already affected daily life in a wide range of areas. likewise, initial applications have been explored in rheumatology. deep learning might not easily surpass the accuracy of classic techniques when performing classification or regression on low-dime... | [
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Loss of plasticity in deep continual learning | [
"Shibhansh Dohare",
"J. Fernando Hernandez-Garcia",
"Qingfeng Lan",
"Parash Rahman",
"A. Rupam Mahmood",
"Richard S. Sutton"
] | Artificial neural networks, deep-learning methods and the backpropagation algorithm1 form the foundation of modern machine learning and artificial intelligence. These methods are almost always used in two phases, one in which the weights of the network are updated and one in which the weights are held constant while th... | 10.1038/s41586-024-07711-7 | loss of plasticity in deep continual learning | artificial neural networks, deep-learning methods and the backpropagation algorithm1 form the foundation of modern machine learning and artificial intelligence. these methods are almost always used in two phases, one in which the weights of the network are updated and one in which the weights are held constant while th... | [
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Comparative approach on crop detection using machine learning and deep learning techniques | [
"V. Nithya",
"M. S. Josephine",
"V. Jeyabalaraja"
] | Agriculture is an expanding area of study. Crop prediction in agriculture is highly dependent on soil and environmental factors, such as rainfall, humidity, and temperature. Previously, farmers had the authority to select the crop to be farmed, oversee its development, and ascertain the optimal harvest time. The farmin... | 10.1007/s13198-024-02483-9 | comparative approach on crop detection using machine learning and deep learning techniques | agriculture is an expanding area of study. crop prediction in agriculture is highly dependent on soil and environmental factors, such as rainfall, humidity, and temperature. previously, farmers had the authority to select the crop to be farmed, oversee its development, and ascertain the optimal harvest time. the farmin... | [
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A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer’s disease | [
"Akhilesh Deep Arya",
"Sourabh Singh Verma",
"Prasun Chakarabarti",
"Tulika Chakrabarti",
"Ahmed A. Elngar",
"Ali-Mohammad Kamali",
"Mohammad Nami"
] | Alzheimer’s disease (AD) is a brain-related disease in which the condition of the patient gets worse with time. AD is not a curable disease by any medication. It is impossible to halt the death of brain cells, but with the help of medication, the effects of AD can be delayed. As not all MCI patients will suffer from AD... | 10.1186/s40708-023-00195-7 | a systematic review on machine learning and deep learning techniques in the effective diagnosis of alzheimer’s disease | alzheimer’s disease (ad) is a brain-related disease in which the condition of the patient gets worse with time. ad is not a curable disease by any medication. it is impossible to halt the death of brain cells, but with the help of medication, the effects of ad can be delayed. as not all mci patients will suffer from ad... | [
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Exploring the connection between deep learning and learning assessments: a cross-disciplinary engineering education perspective | [
"Sabrina Fawzia",
"Azharul Karim"
] | It is widely accepted that student learning is significantly affected by assessment methods, but a concrete relationship has not been established in the context of multidisciplinary engineering education. Students make a physiological investment and internalize learning (deep learning) if they see high value in their l... | 10.1057/s41599-023-02542-9 | exploring the connection between deep learning and learning assessments: a cross-disciplinary engineering education perspective | it is widely accepted that student learning is significantly affected by assessment methods, but a concrete relationship has not been established in the context of multidisciplinary engineering education. students make a physiological investment and internalize learning (deep learning) if they see high value in their l... | [
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Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR | [
"Alexander Tropsha",
"Olexandr Isayev",
"Alexandre Varnek",
"Gisbert Schneider",
"Artem Cherkasov"
] | Quantitative structure–activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic... | 10.1038/s41573-023-00832-0 | integrating qsar modelling and deep learning in drug discovery: the emergence of deep qsar | quantitative structure–activity relationship (qsar) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. in recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic... | [
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Deep learning for water quality | [
"Wei Zhi",
"Alison P. Appling",
"Heather E. Golden",
"Joel Podgorski",
"Li Li"
] | Understanding and predicting the quality of inland waters are challenging, particularly in the context of intensifying climate extremes expected in the future. These challenges arise partly due to complex processes that regulate water quality, and arduous and expensive data collection that exacerbate the issue of data ... | 10.1038/s44221-024-00202-z | deep learning for water quality | understanding and predicting the quality of inland waters are challenging, particularly in the context of intensifying climate extremes expected in the future. these challenges arise partly due to complex processes that regulate water quality, and arduous and expensive data collection that exacerbate the issue of data ... | [
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Comparative Analysis of Machine Learning, Ensemble Learning and Deep Learning Classifiers for Parkinson’s Disease Detection | [
"Palak Goyal",
"Rinkle Rani"
] | A progressive neurodegenerative ailment called Parkinson's disease (PD) is marked by the death of dopamine-producing cells in the substantia nigra area of the brain. The exact etiology of PD remains elusive, but it is believed to involve the presence of Lewy bodies, abnormal protein aggregates, in affected brain region... | 10.1007/s42979-023-02368-x | comparative analysis of machine learning, ensemble learning and deep learning classifiers for parkinson’s disease detection | a progressive neurodegenerative ailment called parkinson's disease (pd) is marked by the death of dopamine-producing cells in the substantia nigra area of the brain. the exact etiology of pd remains elusive, but it is believed to involve the presence of lewy bodies, abnormal protein aggregates, in affected brain region... | [
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Employing deep learning and transfer learning for accurate brain tumor detection | [
"Sandeep Kumar Mathivanan",
"Sridevi Sonaimuthu",
"Sankar Murugesan",
"Hariharan Rajadurai",
"Basu Dev Shivahare",
"Mohd Asif Shah"
] | Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process large amounts of data. Magnetic resonance imaging stands as the gold standard for brain tumor diagnosis using machine vision, surpassing computed tomography, ultrasound, an... | 10.1038/s41598-024-57970-7 | employing deep learning and transfer learning for accurate brain tumor detection | artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process large amounts of data. magnetic resonance imaging stands as the gold standard for brain tumor diagnosis using machine vision, surpassing computed tomography, ultrasound, an... | [
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Detecting Suicidality in Arabic Tweets Using Machine Learning and Deep Learning Techniques | [
"Asma Abdulsalam",
"Areej Alhothali",
"Saleh Al-Ghamdi"
] | Social media platforms have revolutionized traditional communication techniques by allowing people to connect instantaneously, openly, and frequently. As people use social media to share personal stories and express their opinions, negative emotions such as thoughts of death, self-harm, and hardship are commonly expres... | 10.1007/s13369-024-08767-3 | detecting suicidality in arabic tweets using machine learning and deep learning techniques | social media platforms have revolutionized traditional communication techniques by allowing people to connect instantaneously, openly, and frequently. as people use social media to share personal stories and express their opinions, negative emotions such as thoughts of death, self-harm, and hardship are commonly expres... | [
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Deep learning for code generation: a survey | [
"Huangzhao Zhang",
"Kechi Zhang",
"Zhuo Li",
"Jia Li",
"Jia Li",
"Yongmin Li",
"Yunfei Zhao",
"Yuqi Zhu",
"Fang Liu",
"Ge Li",
"Zhi Jin"
] | In the past decade, thanks to the powerfulness of deep-learning techniques, we have witnessed a whole new era of automated code generation. To sort out developments, we have conducted a comprehensive review of solutions to deep learning-based code generation. In this survey, we generally formalize the pipeline and proc... | 10.1007/s11432-023-3956-3 | deep learning for code generation: a survey | in the past decade, thanks to the powerfulness of deep-learning techniques, we have witnessed a whole new era of automated code generation. to sort out developments, we have conducted a comprehensive review of solutions to deep learning-based code generation. in this survey, we generally formalize the pipeline and proc... | [
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Privacy enhanced course recommendations through deep learning in Federated Learning environments | [
"Chandra Sekhar Kolli",
"Sreenivasu Seelamanthula",
"Venkata Krishna Reddy V",
"Padamata Ramesh Babu",
"Mule Rama Krishna Reddy",
"Babu Rao Gumpina"
] | The increasing concerns around data security and privacy among users have significantly pushed the interest of the research community towards developing privacy-preserving recommendation systems. Amidst this backdrop, our study introduces a novel course recommendation methodology leveraging Federated Learning (FL) coup... | 10.1007/s41870-024-02087-3 | privacy enhanced course recommendations through deep learning in federated learning environments | the increasing concerns around data security and privacy among users have significantly pushed the interest of the research community towards developing privacy-preserving recommendation systems. amidst this backdrop, our study introduces a novel course recommendation methodology leveraging federated learning (fl) coup... | [
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DEEP-squared: deep learning powered De-scattering with Excitation Patterning | [
"Navodini Wijethilake",
"Mithunjha Anandakumar",
"Cheng Zheng",
"Peter T. C. So",
"Murat Yildirim",
"Dushan N. Wadduwage"
] | Limited throughput is a key challenge in in vivo deep tissue imaging using nonlinear optical microscopy. Point scanning multiphoton microscopy, the current gold standard, is slow especially compared to the widefield imaging modalities used for optically cleared or thin specimens. We recently introduced “De-scattering w... | 10.1038/s41377-023-01248-6 | deep-squared: deep learning powered de-scattering with excitation patterning | limited throughput is a key challenge in in vivo deep tissue imaging using nonlinear optical microscopy. point scanning multiphoton microscopy, the current gold standard, is slow especially compared to the widefield imaging modalities used for optically cleared or thin specimens. we recently introduced “de-scattering w... | [
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Harnessing deep learning for population genetic inference | [
"Xin Huang",
"Aigerim Rymbekova",
"Olga Dolgova",
"Oscar Lao",
"Martin Kuhlwilm"
] | In population genetics, the emergence of large-scale genomic data for various species and populations has provided new opportunities to understand the evolutionary forces that drive genetic diversity using statistical inference. However, the era of population genomics presents new challenges in analysing the massive am... | 10.1038/s41576-023-00636-3 | harnessing deep learning for population genetic inference | in population genetics, the emergence of large-scale genomic data for various species and populations has provided new opportunities to understand the evolutionary forces that drive genetic diversity using statistical inference. however, the era of population genomics presents new challenges in analysing the massive am... | [
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An enhanced deep learning method for multi-class brain tumor classification using deep transfer learning | [
"Sohaib Asif",
"Ming Zhao",
"Fengxiao Tang",
"Yusen Zhu"
] | Multi-class brain tumor classification is an important area of research in the field of medical imaging because of the different tumor characteristics. One such challenging problem is the multiclass classification of brain tumors using MR images. Since accuracy is critical in classification, computer vision researchers... | 10.1007/s11042-023-14828-w | an enhanced deep learning method for multi-class brain tumor classification using deep transfer learning | multi-class brain tumor classification is an important area of research in the field of medical imaging because of the different tumor characteristics. one such challenging problem is the multiclass classification of brain tumors using mr images. since accuracy is critical in classification, computer vision researchers... | [
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A Procedural Constructive Learning Mechanism with Deep Reinforcement Learning for Cognitive Agents | [
"Leonardo de Lellis Rossi",
"Eric Rohmer",
"Paula Dornhofer Paro Costa",
"Esther Luna Colombini",
"Alexandre da Silva Simões",
"Ricardo Ribeiro Gudwin"
] | Recent advancements in AI and deep learning have created a growing demand for artificial agents capable of performing tasks within increasingly complex environments. To address the challenges associated with continuous learning constraints and knowledge capacity in this context, cognitive architectures inspired by huma... | 10.1007/s10846-024-02064-9 | a procedural constructive learning mechanism with deep reinforcement learning for cognitive agents | recent advancements in ai and deep learning have created a growing demand for artificial agents capable of performing tasks within increasingly complex environments. to address the challenges associated with continuous learning constraints and knowledge capacity in this context, cognitive architectures inspired by huma... | [
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Classification of Different Plant Species Using Deep Learning and Machine Learning Algorithms | [
"Siddharth Singh Chouhan",
"Uday Pratap Singh",
"Utkarsh Sharma",
"Sanjeev Jain"
] | In the present situation, a lot of research has been directed towards the potency of plants. These natural resources contain characteristics valuable in combat against a number of diseases. But due to lack of familiarity of these plants among human beings, an appropriate advantage of their significance cannot be drawn ... | 10.1007/s11277-024-11374-y | classification of different plant species using deep learning and machine learning algorithms | in the present situation, a lot of research has been directed towards the potency of plants. these natural resources contain characteristics valuable in combat against a number of diseases. but due to lack of familiarity of these plants among human beings, an appropriate advantage of their significance cannot be drawn ... | [
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A deep learning model for anti-inflammatory peptides identification based on deep variational autoencoder and contrastive learning | [
"Yujie Xu",
"Shengli Zhang",
"Feng Zhu",
"Yunyun Liang"
] | As a class of biologically active molecules with significant immunomodulatory and anti-inflammatory effects, anti-inflammatory peptides have important application value in the medical and biotechnology fields due to their unique biological functions. Research on the identification of anti-inflammatory peptides provides... | 10.1038/s41598-024-69419-y | a deep learning model for anti-inflammatory peptides identification based on deep variational autoencoder and contrastive learning | as a class of biologically active molecules with significant immunomodulatory and anti-inflammatory effects, anti-inflammatory peptides have important application value in the medical and biotechnology fields due to their unique biological functions. research on the identification of anti-inflammatory peptides provides... | [
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Fraud Detection Using Machine Learning and Deep Learning | [
"Akash Gandhar",
"Kapil Gupta",
"Aman Kumar Pandey",
"Dharm Raj"
] | Detecting fraudulent activities is a major worry for businesses and financial organizations because they can result in significant financial losses and reputational harm. Traditional fraud detection a method frequently depend on present rules and patterns that skilled scammer can easily circumvent. Machine learning and... | 10.1007/s42979-024-02772-x | fraud detection using machine learning and deep learning | detecting fraudulent activities is a major worry for businesses and financial organizations because they can result in significant financial losses and reputational harm. traditional fraud detection a method frequently depend on present rules and patterns that skilled scammer can easily circumvent. machine learning and... | [
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Robot autonomous grasping and assembly skill learning based on deep reinforcement learning | [
"Chengjun Chen",
"Hao Zhang",
"Yong Pan",
"Dongnian Li"
] | This paper proposes a deep reinforcement learning-based framework for robot autonomous grasping and assembly skill learning. Meanwhile, a deep Q-learning-based robot grasping skill learning algorithm and a PPO-based robot assembly skill learning algorithm are presented, where a priori knowledge information is introduce... | 10.1007/s00170-024-13004-0 | robot autonomous grasping and assembly skill learning based on deep reinforcement learning | this paper proposes a deep reinforcement learning-based framework for robot autonomous grasping and assembly skill learning. meanwhile, a deep q-learning-based robot grasping skill learning algorithm and a ppo-based robot assembly skill learning algorithm are presented, where a priori knowledge information is introduce... | [
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Enabling business sustainability for stock market data using machine learning and deep learning approaches | [
"S. Divyashree",
"Christy Jackson Joshua",
"Abdul Quadir Md",
"Senthilkumar Mohan",
"A. Sheik Abdullah",
"Ummul Hanan Mohamad",
"Nisreen Innab",
"Ali Ahmadian"
] | This paper introduces AlphaVision, an innovative decision support model designed for stock price prediction by seamlessly integrating real-time news updates and Return on Investment (ROI) values, utilizing various machine learning and deep learning approaches. The research investigates the application of these techniqu... | 10.1007/s10479-024-06118-x | enabling business sustainability for stock market data using machine learning and deep learning approaches | this paper introduces alphavision, an innovative decision support model designed for stock price prediction by seamlessly integrating real-time news updates and return on investment (roi) values, utilizing various machine learning and deep learning approaches. the research investigates the application of these techniqu... | [
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Deep learning for lungs cancer detection: a review | [
"Rabia Javed",
"Tahir Abbas",
"Ali Haider Khan",
"Ali Daud",
"Amal Bukhari",
"Riad Alharbey"
] | Although lung cancer has been recognized to be the deadliest type of cancer, a good prognosis and efficient treatment depend on early detection. Medical practitioners’ burden is reduced by deep learning techniques, especially Deep Convolutional Neural Networks (DCNN), which are essential in automating the diagnosis and... | 10.1007/s10462-024-10807-1 | deep learning for lungs cancer detection: a review | although lung cancer has been recognized to be the deadliest type of cancer, a good prognosis and efficient treatment depend on early detection. medical practitioners’ burden is reduced by deep learning techniques, especially deep convolutional neural networks (dcnn), which are essential in automating the diagnosis and... | [
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A brief review of hypernetworks in deep learning | [
"Vinod Kumar Chauhan",
"Jiandong Zhou",
"Ping Lu",
"Soheila Molaei",
"David A. Clifton"
] | Hypernetworks, or hypernets for short, are neural networks that generate weights for another neural network, known as the target network. They have emerged as a powerful deep learning technique that allows for greater flexibility, adaptability, dynamism, faster training, information sharing, and model compression. Hype... | 10.1007/s10462-024-10862-8 | a brief review of hypernetworks in deep learning | hypernetworks, or hypernets for short, are neural networks that generate weights for another neural network, known as the target network. they have emerged as a powerful deep learning technique that allows for greater flexibility, adaptability, dynamism, faster training, information sharing, and model compression. hype... | [
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Learning Dynamic Batch-Graph Representation for Deep Representation Learning | [
"Xixi Wang",
"Bo Jiang",
"Xiao Wang",
"Bin Luo"
] | Recently, batch-based image data representation has been demonstrated to be effective for context-enhanced image representation. The core issue for this task is capturing the dependences of image samples within each mini-batch and conducting message communication among different samples. Existing approaches mainly adop... | 10.1007/s11263-024-02175-8 | learning dynamic batch-graph representation for deep representation learning | recently, batch-based image data representation has been demonstrated to be effective for context-enhanced image representation. the core issue for this task is capturing the dependences of image samples within each mini-batch and conducting message communication among different samples. existing approaches mainly adop... | [
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Relay learning: a physically secure framework for clinical multi-site deep learning | [
"Zi-Hao Bo",
"Yuchen Guo",
"Jinhao Lyu",
"Hengrui Liang",
"Jianxing He",
"Shijie Deng",
"Feng Xu",
"Xin Lou",
"Qionghai Dai"
] | Big data serves as the cornerstone for constructing real-world deep learning systems across various domains. In medicine and healthcare, a single clinical site lacks sufficient data, thus necessitating the involvement of multiple sites. Unfortunately, concerns regarding data security and privacy hinder the sharing and ... | 10.1038/s41746-023-00934-4 | relay learning: a physically secure framework for clinical multi-site deep learning | big data serves as the cornerstone for constructing real-world deep learning systems across various domains. in medicine and healthcare, a single clinical site lacks sufficient data, thus necessitating the involvement of multiple sites. unfortunately, concerns regarding data security and privacy hinder the sharing and ... | [
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Predicting Apple Plant Diseases in Orchards Using Machine Learning and Deep Learning Algorithms | [
"Imtiaz Ahmed",
"Pramod Kumar Yadav"
] | Apple cultivation in the Kashmir Valley is a cornerstone of the region’s agriculture, contributing significantly to the economy through substantial annual apple exports. This study explores the application of machine learning and deep learning algorithms for predicting apple plant diseases in orchards. By leveraging ad... | 10.1007/s42979-024-02959-2 | predicting apple plant diseases in orchards using machine learning and deep learning algorithms | apple cultivation in the kashmir valley is a cornerstone of the region’s agriculture, contributing significantly to the economy through substantial annual apple exports. this study explores the application of machine learning and deep learning algorithms for predicting apple plant diseases in orchards. by leveraging ad... | [
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Deep Learning zur Kariesdiagnostik | [
"Norbert Krämer",
"Roland Frankenberger"
] | Deep-Learning-Modelle spielen auch in der Zahnheilkunde eine zunehmend größere Rolle und werden in unterschiedlichen Feldern eingesetzt. Vor diesem Hintergrund wurde in der vorliegenden Literaturübersicht eine systematische Übersichtsarbeit einer internationalen Autorengruppe vorgestellt, die Deep-Learning-Modelle zur ... | 10.1007/s44190-023-0647-4 | deep learning zur kariesdiagnostik | deep-learning-modelle spielen auch in der zahnheilkunde eine zunehmend größere rolle und werden in unterschiedlichen feldern eingesetzt. vor diesem hintergrund wurde in der vorliegenden literaturübersicht eine systematische übersichtsarbeit einer internationalen autorengruppe vorgestellt, die deep-learning-modelle zur ... | [
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Deep doubly robust outcome weighted learning | [
"Xiaotong Jiang",
"Xin Zhou",
"Michael R. Kosorok"
] | Precision medicine is a framework that adapts treatment strategies to a patient’s individual characteristics and provides helpful clinical decision support. Existing research has been extended to various situations but high-dimensional data have not yet been fully incorporated into the paradigm. We propose a new precis... | 10.1007/s10994-023-06484-w | deep doubly robust outcome weighted learning | precision medicine is a framework that adapts treatment strategies to a patient’s individual characteristics and provides helpful clinical decision support. existing research has been extended to various situations but high-dimensional data have not yet been fully incorporated into the paradigm. we propose a new precis... | [
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Topological deep learning: a review of an emerging paradigm | [
"Ali Zia",
"Abdelwahed Khamis",
"James Nichols",
"Usman Bashir Tayab",
"Zeeshan Hayder",
"Vivien Rolland",
"Eric Stone",
"Lars Petersson"
] | Topological deep learning (TDL) is an emerging area that combines the principles of Topological data analysis (TDA) with deep learning techniques. TDA provides insight into data shape; it obtains global descriptions of multi-dimensional data whilst exhibiting robustness to deformation and noise. Such properties are des... | 10.1007/s10462-024-10710-9 | topological deep learning: a review of an emerging paradigm | topological deep learning (tdl) is an emerging area that combines the principles of topological data analysis (tda) with deep learning techniques. tda provides insight into data shape; it obtains global descriptions of multi-dimensional data whilst exhibiting robustness to deformation and noise. such properties are des... | [
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Deep learning in computational mechanics: a review | [
"Leon Herrmann",
"Stefan Kollmannsberger"
] | The rapid growth of deep learning research, including within the field of computational mechanics, has resulted in an extensive and diverse body of literature. To help researchers identify key concepts and promising methodologies within this field, we provide an overview of deep learning in deterministic computational ... | 10.1007/s00466-023-02434-4 | deep learning in computational mechanics: a review | the rapid growth of deep learning research, including within the field of computational mechanics, has resulted in an extensive and diverse body of literature. to help researchers identify key concepts and promising methodologies within this field, we provide an overview of deep learning in deterministic computational ... | [
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OpBench: an operator-level GPU benchmark for deep learning | [
"Qingwen Gu",
"Bo Fan",
"Zhengning Liu",
"Kaicheng Cao",
"Songhai Zhang",
"Shimin Hu"
] | Operators (such as Conv and ReLU) play an important role in deep neural networks. Every neural network is composed of a series of differentiable operators. However, existing AI benchmarks mainly focus on accessing model training and inference performance of deep learning systems on specific models. To help GPU hardware... | 10.1007/s11432-023-3989-3 | opbench: an operator-level gpu benchmark for deep learning | operators (such as conv and relu) play an important role in deep neural networks. every neural network is composed of a series of differentiable operators. however, existing ai benchmarks mainly focus on accessing model training and inference performance of deep learning systems on specific models. to help gpu hardware... | [
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Diabetes detection based on machine learning and deep learning approaches | [
"Boon Feng Wee",
"Saaveethya Sivakumar",
"King Hann Lim",
"W. K. Wong",
"Filbert H. Juwono"
] | The increasing number of diabetes individuals in the globe has alarmed the medical sector to seek alternatives to improve their medical technologies. Machine learning and deep learning approaches are active research in developing intelligent and efficient diabetes detection systems. This study profoundly investigates a... | 10.1007/s11042-023-16407-5 | diabetes detection based on machine learning and deep learning approaches | the increasing number of diabetes individuals in the globe has alarmed the medical sector to seek alternatives to improve their medical technologies. machine learning and deep learning approaches are active research in developing intelligent and efficient diabetes detection systems. this study profoundly investigates a... | [
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Deep-kidney: an effective deep learning framework for chronic kidney disease prediction | [
"Dina Saif",
"Amany M. Sarhan",
"Nada M. Elshennawy"
] | Chronic kidney disease (CKD) is one of today’s most serious illnesses. Because this disease usually does not manifest itself until the kidney is severely damaged, early detection saves many people’s lives. Therefore, the contribution of the current paper is proposing three predictive models to predict CKD possible occu... | 10.1007/s13755-023-00261-8 | deep-kidney: an effective deep learning framework for chronic kidney disease prediction | chronic kidney disease (ckd) is one of today’s most serious illnesses. because this disease usually does not manifest itself until the kidney is severely damaged, early detection saves many people’s lives. therefore, the contribution of the current paper is proposing three predictive models to predict ckd possible occu... | [
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Predicting Potato Crop Yield with Machine Learning and Deep Learning for Sustainable Agriculture | [
"El-Sayed M. El-Kenawy",
"Amel Ali Alhussan",
"Nima Khodadadi",
"Seyedali Mirjalili",
"Marwa M. Eid"
] | Potatoes are an important crop in the world; they are the main source of food for a large number of people globally and also provide an income for many people. The true forecasting of potato yields is a determining factor for the rational use and maximization of agricultural practices, responsible management of the res... | 10.1007/s11540-024-09753-w | predicting potato crop yield with machine learning and deep learning for sustainable agriculture | potatoes are an important crop in the world; they are the main source of food for a large number of people globally and also provide an income for many people. the true forecasting of potato yields is a determining factor for the rational use and maximization of agricultural practices, responsible management of the res... | [
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Prediction of glycopeptide fragment mass spectra by deep learning | [
"Yi Yang",
"Qun Fang"
] | Deep learning has achieved a notable success in mass spectrometry-based proteomics and is now emerging in glycoproteomics. While various deep learning models can predict fragment mass spectra of peptides with good accuracy, they cannot cope with the non-linear glycan structure in an intact glycopeptide. Herein, we pres... | 10.1038/s41467-024-46771-1 | prediction of glycopeptide fragment mass spectra by deep learning | deep learning has achieved a notable success in mass spectrometry-based proteomics and is now emerging in glycoproteomics. while various deep learning models can predict fragment mass spectra of peptides with good accuracy, they cannot cope with the non-linear glycan structure in an intact glycopeptide. herein, we pres... | [
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Deep learning application in diagnosing breast cancer recurrence | [
"Zeinab Jam",
"Amir Albadvi",
"Alireza Atashi"
] | Patients' lives can always be saved when diseases, especially special diseases, are detected early. The chances of a patient surviving can be increased by early detection. Breast cancer is one of the deadliest and common cancers. After recovering from breast cancer, patients are always worried about recurrence and retu... | 10.1007/s11042-024-19423-1 | deep learning application in diagnosing breast cancer recurrence | patients' lives can always be saved when diseases, especially special diseases, are detected early. the chances of a patient surviving can be increased by early detection. breast cancer is one of the deadliest and common cancers. after recovering from breast cancer, patients are always worried about recurrence and retu... | [
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CNN-Transformer: A deep learning method for automatically identifying learning engagement | [
"Yan Xiong",
"Guo Xinya",
"Junjie Xu"
] | Learning engagement is an essential indication to define students' learning pacification in the class, and its automated identification technique is the foundation for exploring how to effectively explain the motive of learning impact modifications and making intelligent teaching choices. Current research have demonstr... | 10.1007/s10639-023-12058-z | cnn-transformer: a deep learning method for automatically identifying learning engagement | learning engagement is an essential indication to define students' learning pacification in the class, and its automated identification technique is the foundation for exploring how to effectively explain the motive of learning impact modifications and making intelligent teaching choices. current research have demonstr... | [
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Deep learning based features extraction for facial gender classification using ensemble of machine learning technique | [
"Fazal Waris",
"Feipeng Da",
"Shanghuan Liu"
] | Accurate and efficient gender recognition is an essential for many applications such as surveillance, security, and biometrics. Recently, deep learning techniques have made remarkable advancements in feature extraction and have become extensively implemented in various applications, including gender classification. How... | 10.1007/s00530-024-01399-5 | deep learning based features extraction for facial gender classification using ensemble of machine learning technique | accurate and efficient gender recognition is an essential for many applications such as surveillance, security, and biometrics. recently, deep learning techniques have made remarkable advancements in feature extraction and have become extensively implemented in various applications, including gender classification. how... | [
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Ensemble of Deep Learning Architectures with Machine Learning for Pneumonia Classification Using Chest X-rays | [
"Rupali Vyas",
"Deepak Rao Khadatkar"
] | Pneumonia is a severe health concern, particularly for vulnerable groups, needing early and correct classification for optimal treatment. This study addresses the use of deep learning combined with machine learning classifiers (DLxMLCs) for pneumonia classification from chest X-ray (CXR) images. We deployed modified VG... | 10.1007/s10278-024-01201-y | ensemble of deep learning architectures with machine learning for pneumonia classification using chest x-rays | pneumonia is a severe health concern, particularly for vulnerable groups, needing early and correct classification for optimal treatment. this study addresses the use of deep learning combined with machine learning classifiers (dlxmlcs) for pneumonia classification from chest x-ray (cxr) images. we deployed modified vg... | [
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Instance segmentation on distributed deep learning big data cluster | [
"Mohammed Elhmadany",
"Islam Elmadah",
"Hossam E. Abdelmunim"
] | Distributed deep learning is a promising approach for training and deploying large and complex deep learning models. This paper presents a comprehensive workflow for deploying and optimizing the YOLACT instance segmentation model as on big data clusters. OpenVINO, a toolkit known for its high-speed data processing and ... | 10.1186/s40537-023-00871-9 | instance segmentation on distributed deep learning big data cluster | distributed deep learning is a promising approach for training and deploying large and complex deep learning models. this paper presents a comprehensive workflow for deploying and optimizing the yolact instance segmentation model as on big data clusters. openvino, a toolkit known for its high-speed data processing and ... | [
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Deep learning-based personalized learning recommendation system design for "T++" Guzheng Pedagogy | [
"Xingyue Wang"
] | This study investigates the development and impact of a deep learning-based personalized learning recommendation system designed specifically for 'T++' Guzheng pedagogy. In the realm of music education, particularly in the context of the traditional Chinese Guzheng instrument, technology-driven personalization has the ... | 10.1007/s41870-024-01871-5 | deep learning-based personalized learning recommendation system design for "t++" guzheng pedagogy | this study investigates the development and impact of a deep learning-based personalized learning recommendation system designed specifically for 't++' guzheng pedagogy. in the realm of music education, particularly in the context of the traditional chinese guzheng instrument, technology-driven personalization has the ... | [
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Deep learning in two-dimensional materials: Characterization, prediction, and design | [
"Xinqin Meng",
"Chengbing Qin",
"Xilong Liang",
"Guofeng Zhang",
"Ruiyun Chen",
"Jianyong Hu",
"Zhichun Yang",
"Jianzhong Huo",
"Liantuan Xiao",
"Suotang Jia"
] | Since the isolation of graphene, two-dimensional (2D) materials have attracted increasing interest because of their excellent chemical and physical properties, as well as promising applications. Nonetheless, particular challenges persist in their further development, particularly in the effective identification of dive... | 10.1007/s11467-024-1394-7 | deep learning in two-dimensional materials: characterization, prediction, and design | since the isolation of graphene, two-dimensional (2d) materials have attracted increasing interest because of their excellent chemical and physical properties, as well as promising applications. nonetheless, particular challenges persist in their further development, particularly in the effective identification of dive... | [
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Revitalizing Arabic Character Classification: Unleashing the Power of Deep Learning with Transfer Learning and Data Augmentation Techniques | [
"Marwa Amara",
"Nadia Smairi",
"Sami Mnasri",
"Abdelmalek Zidouri"
] | Deep learning techniques have demonstrated remarkable success in various domains, including character classification tasks. However, the performance of deep learning models heavily relies on the availability of large-annotated datasets. This research work is motivated by the need to overcome the difficulties associated... | 10.1007/s13369-024-08818-9 | revitalizing arabic character classification: unleashing the power of deep learning with transfer learning and data augmentation techniques | deep learning techniques have demonstrated remarkable success in various domains, including character classification tasks. however, the performance of deep learning models heavily relies on the availability of large-annotated datasets. this research work is motivated by the need to overcome the difficulties associated... | [
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A systematic survey of fuzzy deep learning for uncertain medical data | [
"Yuanhang Zheng",
"Zeshui Xu",
"Tong Wu",
"Zhang Yi"
] | Intelligent medical industry is in a rapid stage of development around the world, followed by are the expanding market size and basic theories of intelligent medical diagnosis and decision-making. Deep learning models have achieved good practical results in medical domain. However, traditional deep learning is almost c... | 10.1007/s10462-024-10871-7 | a systematic survey of fuzzy deep learning for uncertain medical data | intelligent medical industry is in a rapid stage of development around the world, followed by are the expanding market size and basic theories of intelligent medical diagnosis and decision-making. deep learning models have achieved good practical results in medical domain. however, traditional deep learning is almost c... | [
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Predicting discrete-time bifurcations with deep learning | [
"Thomas M. Bury",
"Daniel Dylewsky",
"Chris T. Bauch",
"Madhur Anand",
"Leon Glass",
"Alvin Shrier",
"Gil Bub"
] | Many natural and man-made systems are prone to critical transitions—abrupt and potentially devastating changes in dynamics. Deep learning classifiers can provide an early warning signal for critical transitions by learning generic features of bifurcations from large simulated training data sets. So far, classifiers hav... | 10.1038/s41467-023-42020-z | predicting discrete-time bifurcations with deep learning | many natural and man-made systems are prone to critical transitions—abrupt and potentially devastating changes in dynamics. deep learning classifiers can provide an early warning signal for critical transitions by learning generic features of bifurcations from large simulated training data sets. so far, classifiers hav... | [
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Machine learning and deep learning techniques for poultry tasks management: a review | [
"Thavamani. Subramani",
"Vijayakumar. Jeganathan",
"Sruthi. Kunkuma Balasubramanian"
] | In recent years the poultry production industry has adopted automation with the help of different kinds of technological advancements like verities of monitoring and sensing tools, IoT devices, sensors, monitoring devices, and more. These advanced techniques will offer numerous advantages in poultry product production.... | 10.1007/s11042-024-18951-0 | machine learning and deep learning techniques for poultry tasks management: a review | in recent years the poultry production industry has adopted automation with the help of different kinds of technological advancements like verities of monitoring and sensing tools, iot devices, sensors, monitoring devices, and more. these advanced techniques will offer numerous advantages in poultry product production.... | [
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Explaining deep learning-based leaf disease identification | [
"Ankit Rajpal",
"Rashmi Mishra",
"Sheetal Rajpal",
"Kavita",
"Varnika Bhatia",
"Naveen Kumar"
] | Crop diseases adversely affect agricultural productivity and quality. The primary cause of these diseases is the presence of biotic stresses such as fungi, viruses, and bacteria. Detecting these causes at early stages requires constant monitoring by domain experts. Technological advancements in machine learning and dee... | 10.1007/s00500-024-09939-x | explaining deep learning-based leaf disease identification | crop diseases adversely affect agricultural productivity and quality. the primary cause of these diseases is the presence of biotic stresses such as fungi, viruses, and bacteria. detecting these causes at early stages requires constant monitoring by domain experts. technological advancements in machine learning and dee... | [
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Model-based deep reinforcement learning for accelerated learning from flow simulations | [
"Andre Weiner",
"Janis Geise"
] | In recent years, deep reinforcement learning has emerged as a technique to solve closed-loop flow control problems. Employing simulation-based environments in reinforcement learning enables a priori end-to-end optimization of the control system, provides a virtual testbed for safety-critical control applications, and a... | 10.1007/s11012-024-01808-z | model-based deep reinforcement learning for accelerated learning from flow simulations | in recent years, deep reinforcement learning has emerged as a technique to solve closed-loop flow control problems. employing simulation-based environments in reinforcement learning enables a priori end-to-end optimization of the control system, provides a virtual testbed for safety-critical control applications, and a... | [
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Deep learning algorithms applied to computational chemistry | [
"Abimael Guzman-Pando",
"Graciela Ramirez-Alonso",
"Carlos Arzate-Quintana",
"Javier Camarillo-Cisneros"
] | Recently, there has been a significant increase in the use of deep learning techniques in the molecular sciences, which have shown high performance on datasets and the ability to generalize across data. However, no model has achieved perfect performance in solving all problems, and the pros and cons of each approach re... | 10.1007/s11030-023-10771-y | deep learning algorithms applied to computational chemistry | recently, there has been a significant increase in the use of deep learning techniques in the molecular sciences, which have shown high performance on datasets and the ability to generalize across data. however, no model has achieved perfect performance in solving all problems, and the pros and cons of each approach re... | [
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Deep learning for named entity recognition: a survey | [
"Zhentao Hu",
"Wei Hou",
"Xianxing Liu"
] | Named entity recognition (NER) aims to identify the required entities and their types from unstructured text, which can be utilized for the construction of knowledge graphs. Traditional methods heavily rely on manual feature engineering and face challenges in adapting to large datasets within complex linguistic context... | 10.1007/s00521-024-09646-6 | deep learning for named entity recognition: a survey | named entity recognition (ner) aims to identify the required entities and their types from unstructured text, which can be utilized for the construction of knowledge graphs. traditional methods heavily rely on manual feature engineering and face challenges in adapting to large datasets within complex linguistic context... | [
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Deep Kernel learning for reaction outcome prediction and optimization | [
"Sukriti Singh",
"José Miguel Hernández-Lobato"
] | Recent years have seen a rapid growth in the application of various machine learning methods for reaction outcome prediction. Deep learning models have gained popularity due to their ability to learn representations directly from the molecular structure. Gaussian processes (GPs), on the other hand, provide reliable unc... | 10.1038/s42004-024-01219-x | deep kernel learning for reaction outcome prediction and optimization | recent years have seen a rapid growth in the application of various machine learning methods for reaction outcome prediction. deep learning models have gained popularity due to their ability to learn representations directly from the molecular structure. gaussian processes (gps), on the other hand, provide reliable unc... | [
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Colour fusion effect on deep learning classification of uveal melanoma | [
"Albert K. Dadzie",
"Sabrina P. Iddir",
"Mansour Abtahi",
"Behrouz Ebrahimi",
"David Le",
"Sanjay Ganesh",
"Taeyoon Son",
"Michael J. Heiferman",
"Xincheng Yao"
] | BackgroundReliable differentiation of uveal melanoma and choroidal nevi is crucial to guide appropriate treatment, preventing unnecessary procedures for benign lesions and ensuring timely treatment for potentially malignant cases. The purpose of this study is to validate deep learning classification of uveal melanoma a... | 10.1038/s41433-024-03148-4 | colour fusion effect on deep learning classification of uveal melanoma | backgroundreliable differentiation of uveal melanoma and choroidal nevi is crucial to guide appropriate treatment, preventing unnecessary procedures for benign lesions and ensuring timely treatment for potentially malignant cases. the purpose of this study is to validate deep learning classification of uveal melanoma a... | [
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Predicting DNA structure using a deep learning method | [
"Jinsen Li",
"Tsu-Pei Chiu",
"Remo Rohs"
] | Understanding the mechanisms of protein-DNA binding is critical in comprehending gene regulation. Three-dimensional DNA structure, also described as DNA shape, plays a key role in these mechanisms. In this study, we present a deep learning-based method, Deep DNAshape, that fundamentally changes the current k-mer based ... | 10.1038/s41467-024-45191-5 | predicting dna structure using a deep learning method | understanding the mechanisms of protein-dna binding is critical in comprehending gene regulation. three-dimensional dna structure, also described as dna shape, plays a key role in these mechanisms. in this study, we present a deep learning-based method, deep dnashape, that fundamentally changes the current k-mer based ... | [
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Machine learning and deep learning for classifying the justification of brain CT referrals | [
"Jaka Potočnik",
"Edel Thomas",
"Aonghus Lawlor",
"Dearbhla Kearney",
"Eric J. Heffernan",
"Ronan P. Killeen",
"Shane J. Foley"
] | ObjectivesTo train the machine and deep learning models to automate the justification analysis of radiology referrals in accordance with iGuide categorisation, and to determine if prediction models can generalise across multiple clinical sites and outperform human experts.MethodsAdult brain computed tomography (CT) ref... | 10.1007/s00330-024-10851-z | machine learning and deep learning for classifying the justification of brain ct referrals | objectivesto train the machine and deep learning models to automate the justification analysis of radiology referrals in accordance with iguide categorisation, and to determine if prediction models can generalise across multiple clinical sites and outperform human experts.methodsadult brain computed tomography (ct) ref... | [
"objectivesto",
"the machine",
"deep learning models",
"the justification analysis",
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"human experts.methodsadult brain computed tomography (ct) referrals",
"scans",
"three ct centres",... |
Applications of deep learning method of artificial intelligence in education | [
"Fan Zhang",
"Xiangyu Wang",
"Xinhong Zhang"
] | Intersection of education and deep learning method of artificial intelligence (AI) is gradually becoming a hot research field. Education will be profoundly transformed by AI. The purpose of this review is to help education practitioners understand the research frontiers and directions of AI applications in education. T... | 10.1007/s10639-024-12883-w | applications of deep learning method of artificial intelligence in education | intersection of education and deep learning method of artificial intelligence (ai) is gradually becoming a hot research field. education will be profoundly transformed by ai. the purpose of this review is to help education practitioners understand the research frontiers and directions of ai applications in education. t... | [
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A systematic literature review of visual feature learning: deep learning techniques, applications, challenges and future directions | [
"Mohammed Abdullahi",
"Olaide Nathaniel Oyelade",
"Armand Florentin Donfack Kana",
"Mustapha Aminu Bagiwa",
"Fatimah Binta Abdullahi",
"Sahalu Balarabe Junaidu",
"Ibrahim Iliyasu",
"Ajayi Ore-ofe",
"Haruna Chiroma"
] | Visual Feature Learning (VFL) is a critical area of research in computer vision that involves the automatic extraction of features and patterns from images and videos. The applications of VFL are vast, including object detection and recognition, facial recognition, scene understanding, medical image analysis, and auton... | 10.1007/s11042-024-19823-3 | a systematic literature review of visual feature learning: deep learning techniques, applications, challenges and future directions | visual feature learning (vfl) is a critical area of research in computer vision that involves the automatic extraction of features and patterns from images and videos. the applications of vfl are vast, including object detection and recognition, facial recognition, scene understanding, medical image analysis, and auton... | [
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Deep Learning Challenges and Prospects in Wireless Sensor Network Deployment | [
"Yaner Qiu",
"Liyun Ma",
"Rahul Priyadarshi"
] | This paper explores the transformative integration of deep learning applications in the deployment of Wireless Sensor Networks (WSNs). As WSNs continue to play a pivotal role in diverse domains, the infusion of deep learning techniques offers unprecedented opportunities for enhanced data processing, analysis, and decis... | 10.1007/s11831-024-10079-6 | deep learning challenges and prospects in wireless sensor network deployment | this paper explores the transformative integration of deep learning applications in the deployment of wireless sensor networks (wsns). as wsns continue to play a pivotal role in diverse domains, the infusion of deep learning techniques offers unprecedented opportunities for enhanced data processing, analysis, and decis... | [
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Wheat crop classification using deep learning | [
"Harmandeep Singh Gill",
"Bikramjit Singh Bath",
"Rajanbir Singh",
"Amarinder Singh Riar"
] | Crop yield forecasting is becoming more essential in the present environment, when food security must be maintained despite climate, population, and climate change concerns. Machine learning is a useful decision-making tool for predicting agricultural yields, as well as for deciding what crops to plant and what to do t... | 10.1007/s11042-024-18617-x | wheat crop classification using deep learning | crop yield forecasting is becoming more essential in the present environment, when food security must be maintained despite climate, population, and climate change concerns. machine learning is a useful decision-making tool for predicting agricultural yields, as well as for deciding what crops to plant and what to do t... | [
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HARNet in deep learning approach—a systematic survey | [
"Neelam Sanjeev Kumar",
"G. Deepika",
"V. Goutham",
"B. Buvaneswari",
"R. Vijaya Kumar Reddy",
"Sanjeevkumar Angadi",
"C. Dhanamjayulu",
"Ravikumar Chinthaginjala",
"Faruq Mohammad",
"Baseem Khan"
] | A comprehensive examination of human action recognition (HAR) methodologies situated at the convergence of deep learning and computer vision is the subject of this article. We examine the progression from handcrafted feature-based approaches to end-to-end learning, with a particular focus on the significance of large-s... | 10.1038/s41598-024-58074-y | harnet in deep learning approach—a systematic survey | a comprehensive examination of human action recognition (har) methodologies situated at the convergence of deep learning and computer vision is the subject of this article. we examine the progression from handcrafted feature-based approaches to end-to-end learning, with a particular focus on the significance of large-s... | [
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... |
Deep learning bulk spacetime from boundary optical conductivity | [
"Byoungjoon Ahn",
"Hyun-Sik Jeong",
"Keun-Young Kim",
"Kwan Yun"
] | We employ a deep learning method to deduce the bulk spacetime from boundary optical conductivity. We apply the neural ordinary differential equation technique, tailored for continuous functions such as the metric, to the typical class of holographic condensed matter models featuring broken translations: linear-axion mo... | 10.1007/JHEP03(2024)141 | deep learning bulk spacetime from boundary optical conductivity | we employ a deep learning method to deduce the bulk spacetime from boundary optical conductivity. we apply the neural ordinary differential equation technique, tailored for continuous functions such as the metric, to the typical class of holographic condensed matter models featuring broken translations: linear-axion mo... | [
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Deep learning: systematic review, models, challenges, and research directions | [
"Tala Talaei Khoei",
"Hadjar Ould Slimane",
"Naima Kaabouch"
] | The current development in deep learning is witnessing an exponential transition into automation applications. This automation transition can provide a promising framework for higher performance and lower complexity. This ongoing transition undergoes several rapid changes, resulting in the processing of the data by sev... | 10.1007/s00521-023-08957-4 | deep learning: systematic review, models, challenges, and research directions | the current development in deep learning is witnessing an exponential transition into automation applications. this automation transition can provide a promising framework for higher performance and lower complexity. this ongoing transition undergoes several rapid changes, resulting in the processing of the data by sev... | [
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Colour fusion effect on deep learning classification of uveal melanoma | [
"Albert K. Dadzie",
"Sabrina P. Iddir",
"Mansour Abtahi",
"Behrouz Ebrahimi",
"David Le",
"Sanjay Ganesh",
"Taeyoon Son",
"Michael J. Heiferman",
"Xincheng Yao"
] | BackgroundReliable differentiation of uveal melanoma and choroidal nevi is crucial to guide appropriate treatment, preventing unnecessary procedures for benign lesions and ensuring timely treatment for potentially malignant cases. The purpose of this study is to validate deep learning classification of uveal melanoma a... | 10.1038/s41433-024-03148-4 | colour fusion effect on deep learning classification of uveal melanoma | backgroundreliable differentiation of uveal melanoma and choroidal nevi is crucial to guide appropriate treatment, preventing unnecessary procedures for benign lesions and ensuring timely treatment for potentially malignant cases. the purpose of this study is to validate deep learning classification of uveal melanoma a... | [
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Machine learning and deep learning for classifying the justification of brain CT referrals | [
"Jaka Potočnik",
"Edel Thomas",
"Aonghus Lawlor",
"Dearbhla Kearney",
"Eric J. Heffernan",
"Ronan P. Killeen",
"Shane J. Foley"
] | ObjectivesTo train the machine and deep learning models to automate the justification analysis of radiology referrals in accordance with iGuide categorisation, and to determine if prediction models can generalise across multiple clinical sites and outperform human experts.MethodsAdult brain computed tomography (CT) ref... | 10.1007/s00330-024-10851-z | machine learning and deep learning for classifying the justification of brain ct referrals | objectivesto train the machine and deep learning models to automate the justification analysis of radiology referrals in accordance with iguide categorisation, and to determine if prediction models can generalise across multiple clinical sites and outperform human experts.methodsadult brain computed tomography (ct) ref... | [
"objectivesto",
"the machine",
"deep learning models",
"the justification analysis",
"radiology referrals",
"accordance",
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"prediction models",
"multiple clinical sites",
"human experts.methodsadult brain computed tomography (ct) referrals",
"scans",
"three ct centres",... |
General deep learning framework for emissivity engineering | [
"Shilv Yu",
"Peng Zhou",
"Wang Xi",
"Zihe Chen",
"Yuheng Deng",
"Xiaobing Luo",
"Wangnan Li",
"Junichiro Shiomi",
"Run Hu"
] | Wavelength-selective thermal emitters (WS-TEs) have been frequently designed to achieve desired target emissivity spectra, as a typical emissivity engineering, for broad applications such as thermal camouflage, radiative cooling, and gas sensing, etc. However, previous designs require prior knowledge of materials or st... | 10.1038/s41377-023-01341-w | general deep learning framework for emissivity engineering | wavelength-selective thermal emitters (ws-tes) have been frequently designed to achieve desired target emissivity spectra, as a typical emissivity engineering, for broad applications such as thermal camouflage, radiative cooling, and gas sensing, etc. however, previous designs require prior knowledge of materials or st... | [
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Forecasting VIX using Bayesian deep learning | [
"Héctor J. Hortúa",
"Andrés Mora-Valencia"
] | Recently, deep learning techniques are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. In this paper, we leverage probabilistic deep learning for inferring the volatility index VIX. We employ the probabilistic counterpart of WaveNet, Temporal Conv... | 10.1007/s41060-024-00562-5 | forecasting vix using bayesian deep learning | recently, deep learning techniques are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. in this paper, we leverage probabilistic deep learning for inferring the volatility index vix. we employ the probabilistic counterpart of wavenet, temporal conv... | [
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Review of Deep Learning Techniques for Neurological Disorders Detection | [
"Akhilesh Kumar Tripathi",
"Rafeeq Ahmed",
"Arvind Kumar Tiwari"
] | Neurological disease is one of the most common types of dementia that predominantly concerns the elderly. In clinical approaches, identifying its premature stages is complicated, and no biomarker is comprehended to be thorough in witnessing neurological disorders in their earlier stages. Deep learning approaches have a... | 10.1007/s11277-024-11464-x | review of deep learning techniques for neurological disorders detection | neurological disease is one of the most common types of dementia that predominantly concerns the elderly. in clinical approaches, identifying its premature stages is complicated, and no biomarker is comprehended to be thorough in witnessing neurological disorders in their earlier stages. deep learning approaches have a... | [
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Enhancing Cardiovascular Health Monitoring Through IoT and Deep Learning Technologies | [
"Huu-Hoa Nguyen",
"Tri-Thuc Vo"
] | Monitoring cardiovascular conditions is crucial in healthcare due to their significant impact on overall wellness and their role in mitigating heart-related diseases. To address this pressing issue, the research community has introduced various methodologies, among which deep learning approaches have shown notable effe... | 10.1007/s42979-024-02962-7 | enhancing cardiovascular health monitoring through iot and deep learning technologies | monitoring cardiovascular conditions is crucial in healthcare due to their significant impact on overall wellness and their role in mitigating heart-related diseases. to address this pressing issue, the research community has introduced various methodologies, among which deep learning approaches have shown notable effe... | [
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Image recognition algorithm based on hybrid deep learning | [
"Tang Xiangdong"
] | As we embrace the information age, our lives have experienced revolutionary transformations. With the continuous advancement of computer technology, data sharing and information exchange have become increasingly robust. Consequently, the widespread adoption of hybrid deep learning algorithms has been prioritized. The e... | 10.1007/s13198-023-02134-5 | image recognition algorithm based on hybrid deep learning | as we embrace the information age, our lives have experienced revolutionary transformations. with the continuous advancement of computer technology, data sharing and information exchange have become increasingly robust. consequently, the widespread adoption of hybrid deep learning algorithms has been prioritized. the e... | [
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Deep Ensemble learning and quantum machine learning approach for Alzheimer’s disease detection | [
"Abebech Jenber Belay",
"Yelkal Mulualem Walle",
"Melaku Bitew Haile"
] | Alzheimer disease (AD) is among the most chronic neurodegenerative diseases that threaten global public health. The prevalence of Alzheimer disease and consequently the increased risk of spread all over the world pose a vital threat to human safekeeping. Early diagnosis of AD is a suitable action for timely interventio... | 10.1038/s41598-024-61452-1 | deep ensemble learning and quantum machine learning approach for alzheimer’s disease detection | alzheimer disease (ad) is among the most chronic neurodegenerative diseases that threaten global public health. the prevalence of alzheimer disease and consequently the increased risk of spread all over the world pose a vital threat to human safekeeping. early diagnosis of ad is a suitable action for timely interventio... | [
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A Review on Machine Learning and Deep Learning Based Systems for the Diagnosis of Brain Cancer | [
"Prottoy Saha",
"Shanta Kumar Das",
"Rudra Das"
] | Brain cancer is a disease of the brain caused by a brain tumor. A brain tumor is the development of cells in the brain that grow in an unregulated and unnatural manner. Patients may suffer irreversible brain damage or even death if these tumors are not detected and treated properly. As with all types of treatment, Posi... | 10.1007/s42979-023-02360-5 | a review on machine learning and deep learning based systems for the diagnosis of brain cancer | brain cancer is a disease of the brain caused by a brain tumor. a brain tumor is the development of cells in the brain that grow in an unregulated and unnatural manner. patients may suffer irreversible brain damage or even death if these tumors are not detected and treated properly. as with all types of treatment, posi... | [
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Ensemble deep learning for Alzheimer’s disease characterization and estimation | [
"M. Tanveer",
"T. Goel",
"R. Sharma",
"A. K. Malik",
"I. Beheshti",
"J. Del Ser",
"P. N. Suganthan",
"C. T. Lin"
] | Alzheimer’s disease, which is characterized by a continual deterioration of cognitive abilities in older people, is the most common form of dementia. Neuroimaging data, for example, from magnetic resonance imaging and positron emission tomography, enable identification of the structural and functional changes caused by... | 10.1038/s44220-024-00237-x | ensemble deep learning for alzheimer’s disease characterization and estimation | alzheimer’s disease, which is characterized by a continual deterioration of cognitive abilities in older people, is the most common form of dementia. neuroimaging data, for example, from magnetic resonance imaging and positron emission tomography, enable identification of the structural and functional changes caused by... | [
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Prediction of crop yield in India using machine learning and hybrid deep learning models | [
"Krithikha Sanju Saravanan",
"Velammal Bhagavathiappan"
] | Crop yield prediction is one of the burgeoning research areas in the agriculture domain. The crop yield forecasting models are developed to enhance productivity with improved decision-making strategies. The highly efficient crop yield forecasting model assists farmers in determining when, what and how much to plant on ... | 10.1007/s11600-024-01312-8 | prediction of crop yield in india using machine learning and hybrid deep learning models | crop yield prediction is one of the burgeoning research areas in the agriculture domain. the crop yield forecasting models are developed to enhance productivity with improved decision-making strategies. the highly efficient crop yield forecasting model assists farmers in determining when, what and how much to plant on ... | [
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Deep learning CT reconstruction improves liver metastases detection | [
"Achraf Kanan",
"Bruno Pereira",
"Constance Hordonneau",
"Lucie Cassagnes",
"Eléonore Pouget",
"Léon Appolinaire Tianhoun",
"Benoît Chauveau",
"Benoît Magnin"
] | ObjectivesDetection of liver metastases is crucial for guiding oncological management. Computed tomography through iterative reconstructions is widely used in this indication but has certain limitations. Deep learning image reconstructions (DLIR) use deep neural networks to achieve a significant noise reduction compare... | 10.1186/s13244-024-01753-1 | deep learning ct reconstruction improves liver metastases detection | objectivesdetection of liver metastases is crucial for guiding oncological management. computed tomography through iterative reconstructions is widely used in this indication but has certain limitations. deep learning image reconstructions (dlir) use deep neural networks to achieve a significant noise reduction compare... | [
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Predicting Renal Toxicity of Compounds with Deep Learning and Machine Learning Methods | [
"Bitopan Mazumdar",
"Pankaj Kumar Deva Sarma",
"Hridoy Jyoti Mahanta"
] | Renal toxicity prediction plays a vital role in drug discovery and clinical practice, as it helps to identify potentially harmful compounds and mitigate adverse effects on the renal system. Compound with inherent renal-toxic potential is one of the major concerns for drug development as it leads to failure in drug disc... | 10.1007/s42979-023-02258-2 | predicting renal toxicity of compounds with deep learning and machine learning methods | renal toxicity prediction plays a vital role in drug discovery and clinical practice, as it helps to identify potentially harmful compounds and mitigate adverse effects on the renal system. compound with inherent renal-toxic potential is one of the major concerns for drug development as it leads to failure in drug disc... | [
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A deep ensemble learning method for cherry classification | [
"Kiyas Kayaalp"
] | In many agricultural products, information technologies are utilized in classification processes at the desired quality. It is undesirable to mix different types of cherries, especially in export-type cherries. In this study on cherries, one of the important export products of Turkey, the classification of cherry speci... | 10.1007/s00217-024-04490-3 | a deep ensemble learning method for cherry classification | in many agricultural products, information technologies are utilized in classification processes at the desired quality. it is undesirable to mix different types of cherries, especially in export-type cherries. in this study on cherries, one of the important export products of turkey, the classification of cherry speci... | [
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"turkey",
"the classification",
"cherry species",
"ensemble... |
The application of deep learning technology in integrated circuit design | [
"Lihua Dai",
"Ben Wang",
"Xuemin Cheng",
"Qin Wang",
"Xinsen Ni"
] | This study addresses the intricate challenge of circuit layout optimization central to integrated circuit (IC) design, where the primary goals involve attaining an optimal balance among power consumption, performance metrics, and chip area (collectively known as PPA optimization). The complexity of this task, evolving ... | 10.1186/s42162-024-00380-w | the application of deep learning technology in integrated circuit design | this study addresses the intricate challenge of circuit layout optimization central to integrated circuit (ic) design, where the primary goals involve attaining an optimal balance among power consumption, performance metrics, and chip area (collectively known as ppa optimization). the complexity of this task, evolving ... | [
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"a multidimensional problem",
"mu... |
Alzheimer Disease Detection Using MRI: Deep Learning Review | [
"Pallavi Saikia",
"Sanjib Kumar Kalita"
] | Deep learning for Alzheimer disease detection using MRI is an emerging area of research in medical image processing. With the advent of new technologies based on methods of Deep Learning, medical diagnosis of certain diseases has become possible. Alzheimer’s is a disease which till date has no cure but the progression ... | 10.1007/s42979-024-02868-4 | alzheimer disease detection using mri: deep learning review | deep learning for alzheimer disease detection using mri is an emerging area of research in medical image processing. with the advent of new technologies based on methods of deep learning, medical diagnosis of certain diseases has become possible. alzheimer’s is a disease which till date has no cure but the progression ... | [
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Enhancing Cardiovascular Health Monitoring Through IoT and Deep Learning Technologies | [
"Huu-Hoa Nguyen",
"Tri-Thuc Vo"
] | Monitoring cardiovascular conditions is crucial in healthcare due to their significant impact on overall wellness and their role in mitigating heart-related diseases. To address this pressing issue, the research community has introduced various methodologies, among which deep learning approaches have shown notable effe... | 10.1007/s42979-024-02962-7 | enhancing cardiovascular health monitoring through iot and deep learning technologies | monitoring cardiovascular conditions is crucial in healthcare due to their significant impact on overall wellness and their role in mitigating heart-related diseases. to address this pressing issue, the research community has introduced various methodologies, among which deep learning approaches have shown notable effe... | [
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"effe... |
Medical Image Analysis Through Deep Learning Techniques: A Comprehensive Survey | [
"K. Balasamy",
"V. Seethalakshmi",
"S. Suganyadevi"
] | Deep learning has been the subject of a significant amount of research interest in the development of novel algorithms for deep learning algorithms and medical image processing have proven very effective in a number of medical imaging tasks to help illness identification and diagnosis. The shortage of large-sized datas... | 10.1007/s11277-024-11428-1 | medical image analysis through deep learning techniques: a comprehensive survey | deep learning has been the subject of a significant amount of research interest in the development of novel algorithms for deep learning algorithms and medical image processing have proven very effective in a number of medical imaging tasks to help illness identification and diagnosis. the shortage of large-sized datas... | [
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Deep-SEA: a deep learning based patient specific multi-modality post-cancer survival estimation architecture | [
"Ibtihaj Ahmad",
"Saleem Riaz"
] | Cancer survival estimation is essential for post-cancer patient care, cancer management policy building, and the development of tailored treatment plans. Existing survival estimation methods use censored data; therefore, standard machine learning methods can not be used directly. Some censoring-based semi-machine learn... | 10.1007/s10489-024-05794-3 | deep-sea: a deep learning based patient specific multi-modality post-cancer survival estimation architecture | cancer survival estimation is essential for post-cancer patient care, cancer management policy building, and the development of tailored treatment plans. existing survival estimation methods use censored data; therefore, standard machine learning methods can not be used directly. some censoring-based semi-machine learn... | [
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"these m... |
A survey on deep learning and machine learning techniques over histopathology image based Osteosarcoma Detection | [
"K. V. Deepak",
"R. Bharanidharan"
] | Osteosarcoma is a common type of cancer that occurs in the cells and spreads to the bones. Osteosarcoma can develop due to genetic mutations, but most cases are not inherited. It often starts at the ends of long bones in the arms and legs during periods of rapid growth. Osteosarcoma can be diagnosed by Histopathologica... | 10.1007/s11042-024-19554-5 | a survey on deep learning and machine learning techniques over histopathology image based osteosarcoma detection | osteosarcoma is a common type of cancer that occurs in the cells and spreads to the bones. osteosarcoma can develop due to genetic mutations, but most cases are not inherited. it often starts at the ends of long bones in the arms and legs during periods of rapid growth. osteosarcoma can be diagnosed by histopathologica... | [
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"histopathological examination",
"microscopic images",
... |
Prediction of crop yield in India using machine learning and hybrid deep learning models | [
"Krithikha Sanju Saravanan",
"Velammal Bhagavathiappan"
] | Crop yield prediction is one of the burgeoning research areas in the agriculture domain. The crop yield forecasting models are developed to enhance productivity with improved decision-making strategies. The highly efficient crop yield forecasting model assists farmers in determining when, what and how much to plant on ... | 10.1007/s11600-024-01312-8 | prediction of crop yield in india using machine learning and hybrid deep learning models | crop yield prediction is one of the burgeoning research areas in the agriculture domain. the crop yield forecasting models are developed to enhance productivity with improved decision-making strategies. the highly efficient crop yield forecasting model assists farmers in determining when, what and how much to plant on ... | [
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"farmers",
"their cultivable land",
"the main objective",
"the prop... |
Learning sparse and smooth functions by deep Sigmoid nets | [
"Xia Liu"
] | To pursue the outperformance of deep nets in learning, we construct a deep net with three hidden layers and prove that, implementing the empirical risk minimization (ERM) on this deep net, the estimator can theoretically realize the optimal learning rates without the classical saturation problem. In other words, deepen... | 10.1007/s11766-023-4309-4 | learning sparse and smooth functions by deep sigmoid nets | to pursue the outperformance of deep nets in learning, we construct a deep net with three hidden layers and prove that, implementing the empirical risk minimization (erm) on this deep net, the estimator can theoretically realize the optimal learning rates without the classical saturation problem. in other words, deepen... | [
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"the satu... |
Parametric RSigELU: a new trainable activation function for deep learning | [
"Serhat Kiliçarslan",
"Mete Celik"
] | Activation functions are used to extract meaningful relationships from real-world problems with the help of deep learning models. Thus, the development of activation functions which affect deep learning models’ performances is of great interest to researchers. In the literature, mostly, nonlinear activation functions a... | 10.1007/s00521-024-09538-9 | parametric rsigelu: a new trainable activation function for deep learning | activation functions are used to extract meaningful relationships from real-world problems with the help of deep learning models. thus, the development of activation functions which affect deep learning models’ performances is of great interest to researchers. in the literature, mostly, nonlinear activation functions a... | [
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"researchers",
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"nonlinear activation functions",
"lin... |
Deep-learning-enabled antibiotic discovery through molecular de-extinction | [
"Fangping Wan",
"Marcelo D. T. Torres",
"Jacqueline Peng",
"Cesar de la Fuente-Nunez"
] | Molecular de-extinction aims at resurrecting molecules to solve antibiotic resistance and other present-day biological and biomedical problems. Here we show that deep learning can be used to mine the proteomes of all available extinct organisms for the discovery of antibiotic peptides. We trained ensembles of deep-lear... | 10.1038/s41551-024-01201-x | deep-learning-enabled antibiotic discovery through molecular de-extinction | molecular de-extinction aims at resurrecting molecules to solve antibiotic resistance and other present-day biological and biomedical problems. here we show that deep learning can be used to mine the proteomes of all available extinct organisms for the discovery of antibiotic peptides. we trained ensembles of deep-lear... | [
"-",
"extinction",
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"antibiotic peptides",
"we",
"ensembles",
"deep-learning models",
"a peptide-sequen... |
Investigations on machine learning, deep learning, and longitudinal regression methods for global greenhouse gases predictions | [
"S. D. Yazd",
"N. Gharib",
"J. F. Derakhshandeh"
] | Combating climate change is one of the key topics and concerns that our community is currently facing these days. Since a few decades ago, greenhouse gases emissions gradually started to increase. Thus, the researchers attempted to find a permanent solution for this challenge. In this paper, different methods of machin... | 10.1007/s13762-024-06014-8 | investigations on machine learning, deep learning, and longitudinal regression methods for global greenhouse gases predictions | combating climate change is one of the key topics and concerns that our community is currently facing these days. since a few decades ago, greenhouse gases emissions gradually started to increase. thus, the researchers attempted to find a permanent solution for this challenge. in this paper, different methods of machin... | [
"climate change",
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"machine learning",
"deep learning models",
"their effectiveness",
"accuracy",
"g... |
Deep ensemble transfer learning framework for COVID-19 Arabic text identification via deep active learning and text data augmentation | [
"Abdullah Y. Muaad",
"Hanumanthappa Jayappa Davanagere",
"Jamil Hussain",
"Mugahed A. Al-antari"
] | Since the declaration of COVID-19 as an epidemic by the World Health Organization in September 2019, the task of monitoring and managing the spread of misinformation related to COVID-19 on social media has become increasingly challenging. Particularly, when it comes to Arabic text recognition, tracking and identifying ... | 10.1007/s11042-024-18487-3 | deep ensemble transfer learning framework for covid-19 arabic text identification via deep active learning and text data augmentation | since the declaration of covid-19 as an epidemic by the world health organization in september 2019, the task of monitoring and managing the spread of misinformation related to covid-19 on social media has become increasingly challenging. particularly, when it comes to arabic text recognition, tracking and identifying ... | [
"the declaration",
"covid-19",
"an epidemic",
"the world health organization",
"september",
"the task",
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"the spread",
"misinformation",
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"social media",
"it",
"arabic text recognition",
"tracking",
"misleading information",
"covid-19",
"social media platforms... |
A comprehensive review of image denoising in deep learning | [
"Rusul Sabah Jebur",
"Mohd Hazli Bin Mohamed Zabil",
"Dalal Adulmohsin Hammood",
"Lim Kok Cheng"
] | Deep learning has gained significant interest in image denoising, but there are notable distinctions in the types of deep learning methods used. Discriminative learning is suitable for handling Gaussian noise, while optimization models are effective in estimating real noise. However, there is limited research that summ... | 10.1007/s11042-023-17468-2 | a comprehensive review of image denoising in deep learning | deep learning has gained significant interest in image denoising, but there are notable distinctions in the types of deep learning methods used. discriminative learning is suitable for handling gaussian noise, while optimization models are effective in estimating real noise. however, there is limited research that summ... | [
"deep learning",
"significant interest",
"image denoising",
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"discriminative learning",
"gaussian noise",
"optimization models",
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"limited research",
"that",
"the different deep learning techniques",
"image denoising... |
Fisheye freshness detection using common deep learning algorithms and machine learning methods with a developed mobile application | [
"Muslume Beyza Yildiz",
"Elham Tahsin Yasin",
"Murat Koklu"
] | AbstractFish is commonly ingested as a source of protein and essential nutrients for humans. To fully benefit from the proteins and substances in fish it is crucial to ensure its freshness. If fish is stored for an extended period, its freshness deteriorates. Determining the freshness of fish can be done by examining i... | 10.1007/s00217-024-04493-0 | fisheye freshness detection using common deep learning algorithms and machine learning methods with a developed mobile application | abstractfish is commonly ingested as a source of protein and essential nutrients for humans. to fully benefit from the proteins and substances in fish it is crucial to ensure its freshness. if fish is stored for an extended period, its freshness deteriorates. determining the freshness of fish can be done by examining i... | [
"abstractfish",
"a source",
"protein",
"essential nutrients",
"humans",
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"its freshness",
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"its freshness",
"the freshness",
"fish",
"its eyes",
"smell",
"skin",
"gills",
"this study",
"artificial intelli... |
Deep learning for transesophageal echocardiography view classification | [
"Kirsten R. Steffner",
"Matthew Christensen",
"George Gill",
"Michael Bowdish",
"Justin Rhee",
"Abirami Kumaresan",
"Bryan He",
"James Zou",
"David Ouyang"
] | Transesophageal echocardiography (TEE) imaging is a vital tool used in the evaluation of complex cardiac pathology and the management of cardiac surgery patients. A key limitation to the application of deep learning strategies to intraoperative and intraprocedural TEE data is the complexity and unstructured nature of t... | 10.1038/s41598-023-50735-8 | deep learning for transesophageal echocardiography view classification | transesophageal echocardiography (tee) imaging is a vital tool used in the evaluation of complex cardiac pathology and the management of cardiac surgery patients. a key limitation to the application of deep learning strategies to intraoperative and intraprocedural tee data is the complexity and unstructured nature of t... | [
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"a key limitation",
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"to intraoperative and intraprocedural tee data",
"the complexity... |
Automated optical inspection based on synthetic mechanisms combining deep learning and machine learning | [
"Chung-Ming Lo",
"Ting-Yi Lin"
] | The quality inspection of products before delivery plays a critical role in ensuring manufacturing quality. Quick and accurate inspection of samples is realized by highly automated inspection based on pattern recognition in smart manufacturing. Conventional ensemble methods have been demonstrated to be effective for de... | 10.1007/s10845-024-02474-4 | automated optical inspection based on synthetic mechanisms combining deep learning and machine learning | the quality inspection of products before delivery plays a critical role in ensuring manufacturing quality. quick and accurate inspection of samples is realized by highly automated inspection based on pattern recognition in smart manufacturing. conventional ensemble methods have been demonstrated to be effective for de... | [
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"conventional ensemble methods",
"defect detection",
"this study",
"further... |
Deep ensemble transfer learning framework for COVID-19 Arabic text identification via deep active learning and text data augmentation | [
"Abdullah Y. Muaad",
"Hanumanthappa Jayappa Davanagere",
"Jamil Hussain",
"Mugahed A. Al-antari"
] | Since the declaration of COVID-19 as an epidemic by the World Health Organization in September 2019, the task of monitoring and managing the spread of misinformation related to COVID-19 on social media has become increasingly challenging. Particularly, when it comes to Arabic text recognition, tracking and identifying ... | 10.1007/s11042-024-18487-3 | deep ensemble transfer learning framework for covid-19 arabic text identification via deep active learning and text data augmentation | since the declaration of covid-19 as an epidemic by the world health organization in september 2019, the task of monitoring and managing the spread of misinformation related to covid-19 on social media has become increasingly challenging. particularly, when it comes to arabic text recognition, tracking and identifying ... | [
"the declaration",
"covid-19",
"an epidemic",
"the world health organization",
"september",
"the task",
"monitoring",
"the spread",
"misinformation",
"covid-19",
"social media",
"it",
"arabic text recognition",
"tracking",
"misleading information",
"covid-19",
"social media platforms... |
A comprehensive review of image denoising in deep learning | [
"Rusul Sabah Jebur",
"Mohd Hazli Bin Mohamed Zabil",
"Dalal Adulmohsin Hammood",
"Lim Kok Cheng"
] | Deep learning has gained significant interest in image denoising, but there are notable distinctions in the types of deep learning methods used. Discriminative learning is suitable for handling Gaussian noise, while optimization models are effective in estimating real noise. However, there is limited research that summ... | 10.1007/s11042-023-17468-2 | a comprehensive review of image denoising in deep learning | deep learning has gained significant interest in image denoising, but there are notable distinctions in the types of deep learning methods used. discriminative learning is suitable for handling gaussian noise, while optimization models are effective in estimating real noise. however, there is limited research that summ... | [
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"discriminative learning",
"gaussian noise",
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"the different deep learning techniques",
"image denoising... |
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