ADKU/ResearchGPT_model_dataset huggingface dataset:
A comparative analysis of deep learning and deep transfer learning approaches for identification of rice varieties
Authors: 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 identification system. India is a significant player in the global rice market, and this extensive study delves into the importance of rice there. The study uses state-of-the-art deep learning and TL classifiers to tackle the problems of rice variety detection. An enormous dataset consisting of more than 600,000 rice photographs divided into 22 different classes is presented in the study to improve classification accuracy. With a training accuracy of 96% and a testing accuracy of 80.5%, ResNet50 stands well among other deep learning models compared by the authors. These models include CNN, Deep CNN, AlexNet2, Xception, Inception V3, DenseNet121, and ResNet50. Finding the best classifiers to identify varieties accurately is crucial, and this work highlights their possible uses in rice seed production. This paper lays the groundwork for future research on image-based rice categorization by suggesting areas for development and investigating ensemble strategies to improve performance.
Interpretable deep learning methods for multiview learning
Authors: 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 Learning Method for Multiview Learning) to learn nonlinear relationships in data from multiple views while achieving feature selection. iDeepViewLearn combines deep learning flexibility with the statistical benefits of data and knowledge-driven feature selection, giving interpretable results. Deep neural networks are used to learn view-independent low-dimensional embedding through an optimization problem that minimizes the difference between observed and reconstructed data, while imposing a regularization penalty on the reconstructed data. The normalized Laplacian of a graph is used to model bilateral relationships between variables in each view, therefore, encouraging selection of related variables. iDeepViewLearn is tested on simulated and three real-world data for classification, clustering, and reconstruction tasks. For the classification tasks, iDeepViewLearn had competitive classification results with state-of-the-art methods in various settings. For the clustering task, we detected molecular clusters that differed in their 10-year survival rates for breast cancer. For the reconstruction task, we were able to reconstruct handwritten images using a few pixels while achieving competitive classification accuracy. The results of our real data application and simulations with small to moderate sample sizes suggest that iDeepViewLearn may be a useful method for small-sample-size problems compared to other deep learning methods for multiview learning.ConclusioniDeepViewLearn is an innovative deep learning model capable of capturing nonlinear relationships between data from multiple views while achieving feature selection. It is fully open source and is freely available at https://github.com/lasandrall/iDeepViewLearn.
Deep-learning-based image reconstruction with limited data: generating synthetic raw data using deep learning
Authors: 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 quality.Materials and methodsAn adversarial auto-encoder was trained to generate phase and coil sensitivity maps from magnitude images, which were combined into synthetic raw data.On a fourfold accelerated MR reconstruction task, deep-learning-based reconstruction networks were trained with varying amounts of training data (20 to 160 scans). Test set performance was compared between baseline experiments and experiments that incorporated synthetic training data.ResultsTraining with synthetic raw data showed decreasing reconstruction errors with increasing amounts of training data, but importantly this was magnitude-only data, rather than real raw data. For small training sets, training with synthetic data decreased the mean absolute error (MAE) by up to 7.5%, whereas for larger training sets the MAE increased by up to 2.6%.DiscussionSynthetic raw data generation improved reconstruction quality in scenarios with limited training data. A major advantage of synthetic data generation is that it allows for the reuse of magnitude-only datasets, which are more readily available than raw datasets.
Topic- and learning-related predictors of deep-level learning strategies
Authors: 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 seasonal changes. The study was completed electronically. Topic-related knowledge was assessed via an open question about seasonal changes, and learning-related knowledge was assessed via scenario-based tasks. Expectancies, interest, and utility values related to learning astronomy and using deep-level learning strategies were assessed via questions based on the Situated Expectancy-Value Theory. Deep-level learning strategies (using drawings in addition to reading and self-testing) were assessed while completing the reading task. Among topic-related variables, prior knowledge and utility value—but not interest or expectancy in learning astronomy—were related to using deep-level learning strategies. Among learning-related variables, interest and utility value of effective learning—but not metacognitive knowledge of learning strategies or expectancy in using deep-level learning strategies—were related to using deep-level learning strategies. This study confirms that it is not enough to examine students’ knowledge and skills in using learning strategies with general or hypothetical questions, instead, it is of crucial importance to study students in real learning situations.
Urban traffic signal control optimization through Deep Q Learning and double Deep Q Learning: a novel approach for efficient traffic management
Authors: 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 intersection. The RL agents are trained using a reward function based on minimizing waiting times, enabling them to learn effective traffic signal control policies. The study focuses on comparing the performance of a simple non-reinforcement learning (Non RL) agent, a Deep Q-Network (DQN) agent, and an improved Double Deep Q-Learning (DDQN) agent in different traffic scenarios. The Non RL agent, which follows a fixed order of traffic phases, demonstrates limitations in both low and high traffic situations, leading to inefficiencies and imbalanced queue lengths. On the other hand, the DQN agent exhibits promising results in low traffic conditions but struggles in high traffic due to its greedy behavior. The DDQN agent, with an extended green light base time, outperforms both the Non RL agent and the original DQN agent in high traffic scenarios, making it more suitable for real-world traffic conditions. However, it shows some inefficiencies in low traffic scenarios. Future research is recommended to address multi-agent deep reinforcement learning challenges, incorporate attention mechanisms and hierarchical reinforcement learning, explore graph theory applications, and develop efficient communication protocols among agents to further enhance traffic control solutions.
Research trends in deep learning and machine learning for cloud computing security
Authors: 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 cloud computing security. Accordingly, this paper aims to provide the most updated statistics on the development and research in cloud computing security utilizing deep learning and machine learning. Up to the middle of December 2023, 4051 publications were identified after we searched the Scopus database. This paper highlights key trend solutions for cloud computing security utilizing machine learning and deep learning, such as anomaly detection, security automation, and emerging technology's role. However, challenges such as data privacy, scalability, and explainability, among others, are also identified as challenges of using machine learning and deep learning for cloud security. The findings of this paper reveal that deep learning and machine learning for cloud computing security are emerging research areas. Future research directions may include addressing these challenges when utilizing machine learning and deep learning for cloud security. Additionally, exploring the development of algorithms and techniques that comply with relevant laws and regulations is essential for effective implementation in this domain.
Distributed Deep Reinforcement Learning: A Survey and a Multi-player Multi-agent Learning Toolbox
Authors: 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 methods have been developed for sample efficient deep reinforcement learning, such as environment modelling, experience transfer, and distributed modifications, among which distributed deep reinforcement learning has shown its potential in various applications, such as human-computer gaming and intelligent transportation. In this paper, we conclude the state of this exciting field, by comparing the classical distributed deep reinforcement learning methods and studying important components to achieve efficient distributed learning, covering single player single agent distributed deep reinforcement learning to the most complex multiple players multiple agents distributed deep reinforcement learning. Furthermore, we review recently released toolboxes that help to realize distributed deep reinforcement learning without many modifications of their non-distributed versions. By analysing their strengths and weaknesses, a multi-player multi-agent distributed deep reinforcement learning toolbox is developed and released, which is further validated on Wargame, a complex environment, showing the usability of the proposed toolbox for multiple players and multiple agents distributed deep reinforcement learning under complex games. Finally, we try to point out challenges and future trends, hoping that this brief review can provide a guide or a spark for researchers who are interested in distributed deep reinforcement learning.
Deep learning in rheumatological image interpretation
Authors: 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-dimensional numerical data. With images as input, however, deep learning has become so successful that it has already outperformed the majority of conventional image-processing techniques developed during the past 50 years. As with any new imaging technology, rheumatologists and radiologists need to consider adapting their arsenal of diagnostic, prognostic and monitoring tools, and even their clinical role and collaborations. This adaptation requires a basic understanding of the technical background of deep learning, to efficiently utilize its benefits but also to recognize its drawbacks and pitfalls, as blindly relying on deep learning might be at odds with its capabilities. To facilitate such an understanding, it is necessary to provide an overview of deep-learning techniques for automatic image analysis in detecting, quantifying, predicting and monitoring rheumatic diseases, and of currently published deep-learning applications in radiological imaging for rheumatology, with critical assessment of possible limitations, errors and confounders, and conceivable consequences for rheumatologists and radiologists in clinical practice.
Loss of plasticity in deep continual learning
Authors: 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 the network is used or evaluated. This contrasts with natural learning and many applications, which require continual learning. It has been unclear whether or not deep learning methods work in continual learning settings. Here we show that they do not—that standard deep-learning methods gradually lose plasticity in continual-learning settings until they learn no better than a shallow network. We show such loss of plasticity using the classic ImageNet dataset and reinforcement-learning problems across a wide range of variations in the network and the learning algorithm. Plasticity is maintained indefinitely only by algorithms that continually inject diversity into the network, such as our continual backpropagation algorithm, a variation of backpropagation in which a small fraction of less-used units are continually and randomly reinitialized. Our results indicate that methods based on gradient descent are not enough—that sustained deep learning requires a random, non-gradient component to maintain variability and plasticity.
Comparative approach on crop detection using machine learning and deep learning techniques
Authors: 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 farming community is facing challenges in sustaining its practices due to the swift alterations in climatic conditions. Therefore, machine learning algorithms have replaced traditional methods in predicting agricultural productivity in recent years. To guarantee optimal precision through a specific machine learning approach. Authors extend their approach not limited to Machine Learning but also with Deep Learning Techniques. We use machine and deep learning algorithms to predict crop outcomes accurately. In this proposed model, we utilise machine learning algorithms such as Naive Bayes, decision tree, and KNN. It is worth noting that the decision tree algorithm demonstrates superior performance compared to the other algorithms, achieving an accuracy rate of 83%. In order to enhance the precision, we have suggested implementing a deep learning technique, specifically a convolutional neural network, to identify the crops. Achieving an accuracy of 93.54% was made possible by implementing this advanced deep-learning model.
A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer’s disease
Authors: 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, it is required to accurately diagnose whether a mild cognitive impaired (MCI) patient will convert to AD (namely MCI converter MCI-C) or not (namely MCI non-converter MCI-NC), during early diagnosis. There are two modalities, positron emission tomography (PET) and magnetic resonance image (MRI), used by a physician for the diagnosis of Alzheimer’s disease. Machine learning and deep learning perform exceptionally well in the field of computer vision where there is a requirement to extract information from high-dimensional data. Researchers use deep learning models in the field of medicine for diagnosis, prognosis, and even to predict the future health of the patient under medication. This study is a systematic review of publications using machine learning and deep learning methods for early classification of normal cognitive (NC) and Alzheimer’s disease (AD).This study is an effort to provide the details of the two most commonly used modalities PET and MRI for the identification of AD, and to evaluate the performance of both modalities while working with different classifiers.
Exploring the connection between deep learning and learning assessments: a cross-disciplinary engineering education perspective
Authors: 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 learning. They persist despite challenges and take delight in accomplishing their work. As student deep learning is affected by the assessment system, it is important to explore the relationship between assessment systems and factors affecting deep learning. This study identifies the factors associated with deep learning and examines the relationships between different assessment systems those factors. A conceptual model is proposed, and a structured questionnaire was designed and directed to 600 Queensland University of Technology (QUT) multidisciplinary engineering students, with 243 responses received. The gathered data were analyzed using both SPSS and SEM. Exploratory factor analysis revealed that deep learning is strongly associated with learning environment and course design and content. Strong influence of both summative and formative assessment on learning was established in this study. Engineering educators can facilitate deep learning by adopting both assessment types simultaneously to make the learning process more effective. The proposed theoretical model related to the deep learning concept can support the key practices and modern learning methodologies currently adopted to enhance the learning and teaching process.
Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR
Authors: 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 improvements in computational power have supported the emergence of a new field of QSAR applications that we term ‘deep QSAR’. Marking a decade from the pioneering applications of deep QSAR to tasks involved in small-molecule drug discovery, we herein describe key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning and the application of deep QSAR models in structure-based virtual screening. We also reflect on the emergence of quantum computing, which promises to further accelerate deep QSAR applications and the need for open-source and democratized resources to support computer-aided drug design.
Deep learning for water quality
Authors: 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 scarcity. Traditional process-based and statistical models often fall short in predicting water quality. In this Review, we posit that deep learning represents an underutilized yet promising approach that can unravel intricate structures and relationships in high-dimensional data. We demonstrate that deep learning methods can help address data scarcity by filling temporal and spatial gaps and aid in formulating and testing hypotheses via identifying influential drivers of water quality. This Review highlights the strengths and limitations of deep learning methods relative to traditional approaches, and underscores its potential as an emerging and indispensable approach in overcoming challenges and discovering new knowledge in water-quality sciences.