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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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"traditional fraud detection",
"a method",
"present rules",
"patterns",
"skilled scammer",
"machine learning",
"deep learning algorithms",
"prom... |
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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"concerns",
"data security",
"privacy",
"the sharing",
"reuse",
"data",
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"existi... |
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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"orchards",
"advanced computational techniques",... |
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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"sie kam zu dem s... |
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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"this study profoundly investigates",
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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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"the deadliest and common cancers",
"breast cancer",
"patients",
"recurrence",
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"modern technology",
"disease recurrence",
"an ear... |
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