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Unveiling Hidden Water Resources: Deep Learning and Remote Sensing for Subsurface Hydrology for Environmental Health

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 352-364页

作者： Babu, Jampani Satish G, Dinesh Suganya, R. Radhakrishnan, P. Sutar, Anand S, Alex David Rao, L. Malleswara Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur AP Vaddeswaram India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur (KTR) Chennai India Department of Computer Science and Engineering (Data Science) New Horizon College of Engineering Karnataka Bengaluru India Department of ECE Tagore Engineering College Chennai India Department of Electronics and Telecommunication Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R & d Institute of Science and Technology Chennai India Department of Physics Sri Y N College (A) West Godavari District Andhra Pradesh Narsapur India

Water scarcity in arid regions necessitates innovative solutions to uncover and manage subsurface water resources for environmental health. This study explores the potential of GeoHydroNet, a specialized deep learning framework, in identifying hidden water resources within arid regions. Utilizing remote sensing data such as multispectral and hyperspectral imagery, geological maps, and soil moisture datasets, GeoHydroNet combines graph neural Networks (GNNs), AutoEncoders, and generative adversarial networks (GANs) to address challenges in subsurface hydrology. The model achieved an accuracy of 93.5%, surpassing traditional models like CNNs (85.4%), LSTMs (88.1%), and RNNs (82.6%). GeoHydroNet demonstrates significant precision (0.88), recall (0.87), and sensitivity to data imbalance (0.91). Its physics-informed learning approach incorporates hydrological principles, ensuring realistic predictions. By analyzing subsurface water patterns, recharge zones, and geological influences, the model not only enhances prediction accuracy but also aids in sustainable groundwater management. GeoHydroNet’s performance in identifying groundwater levels, with minimal errors ranging from 0.62 to 3.07%, indicates its robustness in tackling water scarcity challenges in data-constrained environments. These findings highlight its transformative role in hydrological modeling and sustainable water resource management in arid regions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Aquifers Arid regions Deep learning Generative adversarial networks GeoHydroNet Physics-informed learning Remote sensing Subsurface hydrology Sustainable water management

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UNDERSTANDING THE GENERALIZATION OF ADAM IN LEARNING NEURAL NETWORKS WITH PROPER REGULARIZATION 11

UNDERSTANDING THE GENERALIZATION OF ADAM IN LEARNING NEURAL ...

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11th International Conference on Learning Representations, ICLR 2023

作者： Zou, Difan Cao, Yuan Li, Yuanzhi Gu, Quanquan Department of Computer Science Institute of Data Science The University of Hong Kong Hong Kong Department of Statistics & Actuarial Science The University of Hong Kong Hong Kong Machine Learning Department Carnegie Mellon University United States Department of Computer Science University of California Los Angeles United States

Adaptive gradient methods such as Adam have gained increasing popularity in deep learning optimization. However, it has been observed in many deep learning applications such as image classification, Adam can converge to a different solution with a worse test error compared to (stochastic) gradient descent, even with a fine-tuned regularization. In this paper, we provide a theoretical explanation for this phenomenon: we show that in the nonconvex setting of learning over-parameterized two-layer convolutional neural networks starting from the same random initialization, for a class of data distributions (inspired from image data), Adam and gradient descent (GD) can converge to different global solutions of the training objective with provably different generalization errors, even with weight decay regularization. In contrast, we show that if the training objective is convex, and the weight decay regularization is employed, any optimization algorithms including Adam and GD will converge to the same solution if the training is successful. This suggests that the generalization gap between Adam and SGD in the presence of weight decay regularization is closely tied to the nonconvex landscape of deep learning optimization, which cannot be covered by the recent neural tangent kernel (NTK) based analysis. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Gradient methods

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An Efficient Algorithm for Hamiltonian Path Embedding of k-Ary n-Cubes Under the Partitioned Edge Fault Model

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IEEE Transactions on Parallel and Distributed Systems 2023年第6期34卷 1802-1815页

作者： Zhuang, Hongbin Li, Xiao-Yan Chang, Jou-Ming Wang, Dajin Fuzhou University College of Computer and Data Science Fuzhou350108 China National Taipei University of Business Institute of Information and Decision Sciences Taipei10051 Taiwan Montclair State University Department of Computer Science MontclairNJ07043 United States

The k-ary n-cube Qnk is one of the most important interconnection networks for building network-on-chips, data center networks, and parallel computing systems owing to its desirable properties. Since edge faults grow rapidly and the path structure plays a vital role in large-scale networks for parallel computing, fault-tolerant path embedding and its related problems have attracted extensive attention in the literature. However, the existing path embedding approaches usually only focus on the theoretical proofs and produce an n-related linear fault tolerance since they are based on the traditional fault model, which allows all faults to be adjacent to the same node. In this paper, we design an efficient fault-tolerant Hamiltonian path embedding algorithm for enhancing the fault-tolerant capacity of k-ary n-cubes. To facilitate the algorithm, we first introduce a new conditional fault model, named Partitioned Edge Fault model (PEF model). Based on this model, for the k-ary n-cube Qnk with n ≥ 2 and odd k ≥ 3, we explore the existence of a Hamiltonian path in Qnk with large-scale edge faults. Then we give an O(N) algorithm, named HP-PEF, to embed the Hamiltonian path into Qnk under the PEF model, where N is the number of nodes in Qnk. The performance analysis of HP-PEF shows the average path length of adjacent node pairs in the Hamiltonian path constructed by HP-PEF. We also make comparisons to show that our result of edge fault tolerance has exponentially improved other known results. We further experimentally show that HP-PEF can support the dynamic degradation of average success rate of constructing Hamiltonian paths when increasing faulty edges exceed the fault tolerance. © 1990-2012 IEEE.

关键词： Fault tolerance

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Security and Energy Efficiency Enhancement for the Internet of Things: Challenges, Architecture and Future Research

Security and Energy Efficiency Enhancement for the Internet ...

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International Conference on data science and Network Engineering, ICDSNE 2023

作者： Dewan, Ritu Nagpal, Tapsi Ahmad, Sharik Rana, Arun Kumar Islam, Sardar M. N. Department of Computer Science and Engineering Lingayas Vidyapeeth Faridabad India Department of Computer Science and Engineering Galgotias College of Engineering & Technology Gr. Noida India SILC Decision Sciences and Modelling Program Victoria University Victoria Australia

ISBN: (纸本)9789819967544

Thanks to constantly advancing technology, the world is changing rapidly. One such idea that has contributed to the reality of automation is the Internet of Things (IoT). IoT links various non-living objects to the internet and enables them to communicate with their local network to automate processes and simplify people’s lives. The IoT’s potential needs to be completely realised despite the enormous efforts of standards, organisations, coalitions, businesses, academics and others. A number of problems remain. The enabling technology, applications and business models, as well as the social and environmental repercussions, should all be taken into consideration while analysing these difficulties. This article’s emphasis is on unresolved issues and challenges from a technological perspective. The main objective is to provide a thorough evaluation of IoT in terms of energy and security, as well as unresolved problems and obstacles that need further study. We provide some perspectives on some new concepts to help future research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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A GoogLeNet Performance Approach for COVID-19 Detection using Chest X-ray Images 15

A GoogLeNet Performance Approach for COVID-19 Detection usin...

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15th International Conference on Knowledge and Smart Technology, KST 2023

作者： Rattanawin, Patipan Pakinsee, Tidatep Songmuang, Pokpong Thammasat University College of Interdisciplinary Studies Data Science and Innovation Pathum Thani Thailand Thammasat University Faculty of Science and Technology Department of Computer Science Pathum Thani Thailand

ISBN: (纸本)9781665477123

Coronavirus disease (COVID-19) is a major pandemic disease that has already infected millions of people worldwide and affects many aspects, especially public health. There are many clinical techniques for the diagnosis of this disease, such as RT-PCR and CT-Scan. X-ray image is one of the important techniques for medical diagnosis and easily accessible in classifying suspected cases of COVID-19 infection. In this study, we classified COVID-19 images with four classes: COVID-19, Normal, Lung opacity and Viral pneumonia by compared three models: EfficientNetB0, MobileNet and GoogLeNet for the performance of classification using 1,000 chest X-ray images from Kaggle dataset within scenario of resource limitations. The experiment reveals that GoogLeNet shows superiority over other models that produced the highest accuracy results of 88% and F1 score of 0.88 with a total time of 1 hour and 15 minutes. Along with its confusion matrix that shows model can better classify images than other models. © 2023 IEEE.

关键词： COVID-19

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Evaluation of Deep Learning Technique on Working Model of Self-driving Car—A Review 8th

Evaluation of Deep Learning Technique on Working Model of...

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8th International Conference on Soft Computing: Theories and Applications, SoCTA 2023

作者： Sangwan, Somin Singh, Gurpreet Bangia, Aashima Goswami, Vishwajeet Shankar Data Science Department of Mathematics Chandigarh University Mohali India Department of Computer Science and Engineering Chandigarh University Mohali India Department of Mathematics Chandigarh University Mohali India

ISBN: (纸本)9789811998577

Artificial intelligence is revolutionising the way we are living. Autonomous vehicles are going to play a major role in that. According to US department of Transportation, Ohio University and The UK Economic Opportunity self-driving cars will reduce traffic deaths up to 90%, drop of 60% in harmful emissions and reduction in travel time by 40%, respectively. Its a key technology which will help us out in reducing various problems to a larger extent. This is a self-explanatory paper which provides an overview of the functioning of self-driving cars with the use of deep learning. This paper will first make you understand what basically we mean by when we call a car self-driving along with the meaning of deep learning. This will be followed by providing the details on how deep learning is involved at various steps like how the car is able to perceive, localise, use various deep learning algorithms like Markov decision process (MDP) and neural networks like convolutional neural networks (CNNs), recurrent neural networks (RNNs), etc., to predict various possible actions it can take based on the situation and finally with the help of Bayesian optimisation choosing the best possible outcome. This paper will easily make you understand the functioning of self-driving cars and technology behind them. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision making

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Optimizing Blister Detection With CNN-based Infrared Shape Recognition 2

Optimizing Blister Detection With CNN-based Infrared Shape R...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Dakshana, M. Gunalan, K. Anandan, P. Saveetha, R. Nithya, M. Satheeshkumar, S. Sathyabama Institute of Science and Technology Department of Physics Tamilnadu India Bannari Amman Institute of Technology Department of Computer Technology Tamilnadu India Bannari Amman Institute of Technology Department of Artificial intelligence and data science Tamilnadu India

ISBN: (纸本)9798350368413

The present research employs texture analysis and a Convolutional Neural Network to discover anomalous temperature patterns related with plantar foot ulcers in diabetes patients. Diabetic foot ulcers are a serious health danger if not managed. The suggested approach distinguishes diabetic foot ulcers from healthy tissue based on their distinct textural properties. The study tries to accurately detect these ulcers based on their individual thermal textures by using multiple layers within a CNN model. The suggested method's efficacy is assessed and compared to existing methodologies for identifying diabetic foot ulcers, revealing the possibility for earlier diagnosis and care in this essential health problem. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimation Cavacity Vehicle Routing Problem with K-Nearest Neighbor in Classification of Goods Ditribution Route 7

Optimation Cavacity Vehicle Routing Problem with K-Nearest N...

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7th IEEE International Conference of computer science and Information Technology, ICOSNIKOM 2023

作者： Novelan, Muhammad Syahputra Efendi, Syahril Sihombing, Poltak Mawengkang, Herman Universitas Sumatera Utara Doctoral Program of Computer Science Faculty of Computer Science and Information Technology Medan Indonesia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Computer Science Medan Indonesia Universitas Sumatera Utara Faculty of Mathematics and Natural Sciences Department of Mathematics Medan Indonesia

ISBN: (纸本)9798350360752

Industrial and distribution service problems that belong to combinatorial optimization include vehicle routing with Vehicle Routing Problem. This research builds a framework and implements it in a multi-class optimization model to reduce overfitting and misclassification results caused by unbalanced multiclassification in the influence of the number of 'nodes' on vehicle routes with machine learning. The problem of imbalance in vehicle route classification has been a challenge in the classification process and attracted the attention of a number of researchers. The purpose of the model in general is to gain an understanding of the mechanism in the problem so as to classify the imbalanced vehicle route data based on JNE delivery routes. The solution method that will be used by applying k-nearest neighbor to determine the amount of carrying capacity of the goods will then determine the delivery location point with the vehicle routing problem. So that this model can be a model in determining vehicle routes based on the capacity limit of the number of shipments of goods. © 2023 IEEE.

关键词： Machine learning

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Recommendation System and Its Techniques in Machine Learning: A Survey 1

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8th International Conference on Soft Computing: Theories and Applications, SoCTA 2023

作者： Banwala, Neeru Singh, Gurpreet Singh, Jaspreet Goswami, Vishwajeet Shankar Bagnia, Aashima Data Science Department of Mathematics Chandigarh University Mohali India Department of Computer Science and Engineering Chandigarh University Mohali India Department of Mathematics Chandigarh University Mohali India

ISBN: (数字)9789811998584

ISBN: (纸本)9789811998577

Machine learning-based recommendation systems are formidable tools that target specific customers with tailored product and content recommendations based on their user data and behavioural patterns. Due to the abundance of information available today, it is now challenging to separate out and provide the user with the most pertinent information. Therefore, using recommendation systems to reduce time and costs for both the business and the user is now necessary to address the issue of information overload. Based on a user’s interest and past preferences, these systems might suggest suitable products to them, thus increasing revenue. This paper seeks to clarify the idea of the recommendation systems built upon, the many methods used to create these systems and the necessity of using these systems in the current and future environments. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Collaborative filtering

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An Ambiguous Edge Detection Method for Computed Tomography Scans of Coronavirus Disease 2019 Cases

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2024年第1期54卷 352-364页

作者： Singh, Pritpal Huang, Yo-Ping Central University of Rajasthan Quantum Optimization Research Lab Department of Data Science and Analytics Ajmer305817 India Department of Electrical Engineering National Penghu University of Science and Technology Magong88046 Taiwan The Department of Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan The Department of Computer Science and Information Engineering National Taipei University New Taipei City23741 Taiwan The Department of Information and Communication Engineering Chaoyang University of Technology Taichung41349 Taiwan

Recently, the coronavirus disease of 2019 (COVID-19), as named by the World Health Organization (WHO), has spread to over 200 countries. The WHO has declared this disease as a worldwide public health emergency. One of the most difficult tasks in combating this epidemic is to identify and segregate the afflicted people. The reverse transcription-polymerase chain reaction test (RT-PCR) is the most common pathology test used to diagnose this infection. Studies show that the RT-PCR test has a low-positive rate and sometimes becomes ineffective in diagnosing infection. In some cases, computed tomography (CT) scans reveal acute pneumonia and pulmonary anomalies. Therefore, CT scans are used together with RT-PCR tests to confirm infected people. Existing artificial intelligence and machine learning techniques require a large number of CT scans for training, which is a time-consuming process. Visual inspection shows that most CT scans of COVID-19 cases have broken, blurred, and ambiguous edges for infectious areas. Another major issue with these images is the heterogeneous intensity of the pixels, high noise, and low resolution. As a result of all these issues, the problem of effective edges/boundaries of various areas of CT scans of COVID-19 cases cannot be resolved by the current edge detection approach. Indeed, improper selection of edges can lead to an incorrect diagnosis of diseases through CT scans of COVID-19 cases. Therefore, there is an urgent need for a diagnostic method in addition to the RT-PCR test that can extract useful information from the minimum number of chest CT scans of suspected COVID-19 cases. This study introduces a new ambiguous edge detection method (AEDM) for identifying the edges/boundaries of different regions in CT scans of COVID-19 cases. The proposed AEDM is developed on the basis of ambiguous set (AS) theory, which is highly efficient in processing ambiguous pixel information. For simulation purposes, various CT scans of COVID-19 cases are c

关键词： Coronavirus

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