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Comprehensive Earth System Analysis for Advancing Community Resilience: Integrating Remote Sensing with Hybrid GAN-RNN Techniques

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Remote Sensing in Earth Systems Sciences 2024年第4期7卷 788-803页

作者： Sundar, Ganapathy Patchaiammal, Pitchaimuthu Lakkshmanan, Ajanthaa Ramya, Saminathan Pallavi, Lanke Thiagarajan, Ranganathan Department of Computer Applications Sindhi College Chennai India Department of Computing Technologies SRM Institute of Science and Technology Kattankaluthur Chengalpattu District India Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamil Nadu Erode India Department of CSE B V Raju Institute Of Technology Narsapur India Department of Information Technology Prathyusha Engineering College Thiruvallur India

Effective disaster prediction is essential for climate resilience, especially in areas prone to extreme weather events. Conventional models often face challenges in predicting complex or uncommon events, largely due to data scarcity and the difficulty of modeling temporal dependencies in sequential data. This paper introduces an innovative hybrid GAN-RNN model that leverages remote sensing and IoT sensor data to improve disaster prediction accuracy. By combining generative adversarial networks (GANs) for generating synthetic data and recurrent neural networks (RNNs) for recognizing temporal patterns, this model forms a potent predictive tool. Achieving a remarkable testing accuracy of 98.16%, the hybrid GAN-RNN model significantly surpasses traditional predictive methods. Comprehensive evaluations demonstrate its enhanced capability to predict complex and rare events effectively, establishing it as a crucial resource for disaster risk management and environmental monitoring. This research advances AI-driven methodologies in climate resilience, offering a solid framework for future innovations in the field. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： AI-driven solutions Climate resilience Data augmentation Disaster prediction Environmental monitoring Hybrid GAN-RNN IoT sensors Remote sensing Synthetic data Temporal pattern recognition

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Optimal Wavelet Neural Network-Based Intrusion Detection in Internet of Things Environment

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computers, Materials & Continua 2023年第5期75卷 4467-4483页

作者： Heba G.Mohamed Fadwa Alrowais Mohammed Abdullah Al-Hagery Mesfer Al Duhayyim Anwer Mustafa Hilal Abdelwahed Motwakel Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box84428Riyadh 11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box84428Riyadh 11671Saudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

As the Internet of Things(IoT)endures to develop,a huge count of data has been *** IoT platform is rather sensitive to security challenges as individual data can be leaked,or sensor data could be used to cause *** typical intrusion detection system(IDS)studies can be frequently designed for working well on databases,it can be unknown if they intend to work well in altering network *** learning(ML)techniques are depicted to have a higher capacity at assisting mitigate an attack on IoT device and another edge system with reasonable *** article introduces a new Bird Swarm Algorithm with Wavelet Neural Network for Intrusion Detection(BSAWNN-ID)in the IoT *** main intention of the BSAWNN-ID algorithm lies in detecting and classifying intrusions in the IoT *** BSAWNN-ID technique primarily designs a feature subset selection using the coyote optimization algorithm(FSS-COA)to attain ***,to detect intrusions,the WNN model is *** last,theWNNparameters are optimally modified by the use of *** experiment is performed to depict the better performance of the BSAWNNID *** resultant values indicated the better performance of the BSAWNN-ID technique over other models,with an accuracy of 99.64%on the UNSW-NB15 dataset.

关键词： Internet of things wavelet neural network security intrusion detection machine learning

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Investigation of DNA discontinuity for detecting tuberculosis

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Journal of Ambient Intelligence and Humanized computing 2024年第1期15卷 1149-1163页

作者： Nimmy, Sonia Farhana Sarowar, Md. Golam Dey, Nilanjan Ashour, Amira S. Santosh, K.C. Department of Computer Science and Engineering Notre Dame University Dhaka Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Information Technology Techno India College of Technology Kolkata India Department of Electronics and Electrical Communications Engineering Faculty of Engineering Tanta University Tanta Egypt Vermillion United States

Discontinuity in long Deoxyribonucleic Acid (DNA) sequences creates harmful diseases. Changes in the DNA structure refers to changes in the human immunity system. Tuberculosis is a critical disease that causes coughing, fatigue, unintentional weight loss and fever on aged people due to the disorder in the DNA. Breaks or mutations over long DNA sequences are the pivotal reasons for this fatal disease. This study developed an automated machine learning technique to assess the total number of such breaks in the long DNA sequences. Data cleansing and deep neural network techniques are applied to handle this big data. The National Center for Biotechnology Information (NCBI) database has been used to extract the amino acid sequences for Tuberculosis disease from the big DNA datasets. Results reveal that the proposed automated approach is significantly effective for the determination of DNA sequence breaks for the tuberculosis diseases due to the high sensitivity of Markov chain as well as the effective normalization techniques. This approach fixed the size of the training datasets and recursively divide the whole dataset into certain length. The study also adopts multiple predictions approaches, such as the hidden Markov chain, Box-Cox transformation and linear transformation to forecast about the breaks for any long positions of the training and testing datasets. The results demonstrated that hidden the Markov chain model provided faster analysis with more accurate and reliable results. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： DNA

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Neuroscheduling for Remote Estimation 58

Neuroscheduling for Remote Estimation

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Vasconcelos, Marcos M. Zhang, Yifei FAMU-FSU College of Engineering Florida State University Department of Electrical and Computer Engineering TallahasseeFL32306 United States

ISBN: (纸本)9798350354058

Many modern distributed systems consist of devices that generate more data than what can be transmitted via a communication link in near real time with high-fidelity. We consider the scheduling problem in which a device has access to multiple data sources, but at any moment, only one of them is revealed in real-time to a remote receiver. Even when the sources are Gaussian, and the fidelity criterion is the mean squared error, the globally optimal data selection strategy is not known. We propose a data-driven methodology to search for the elusive optimal solution using linear function approximation approach called neuroscheduling and establish necessary and sufficient conditions for the optimal scheduler to not over fit training data. Additionally, we present several numerical results that show that the globally optimal scheduler and estimator pair to the Gaussian case are nonlinear. © 2024 IEEE.

关键词： Mean square error

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A Representation Learning Induced Property Inference Attack on Machine Learning Models for E-Health

A Representation Learning Induced Property Inference Attack ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Bukhari, Moomal Iqbal, Asif Aman, Muhammad Naveed Sikdar, Biplab University of Nebraska-Lincoln School of Computing LincolnNE68588 United States National University of Singapore Department of Electrical and Computer Engineering Singapore

ISBN: (纸本)9798350351255

Privacy concerns have become increasingly prominent as machine learning (ML) models are adopted in an increasing number of sectors. The potential of unintended or malicious exposure of sensitive data, especially in E-Health solutions, has increased as these models are shared and deployed more broadly. In order to highlight the important problem of property inference attacks, which can result in privacy and data confidentiality breaches, this study focuses on inferring global characteristics of the underlying datasets used to train the ML models. Building upon the intriguing work by Ateniese et al. on property inference attacks on ML models, we present a novel property inference attack using Variational Auto-Encoders (VAEs). VAEs offer a strong answer to the difficult problem of inferring dataset attributes because of their reputation for being successful in modeling complex data distributions and producing synthetic data samples. Experiments on three healthcare and the US census datasets show that the proposed attack can effectively reveal underlying patterns in the training dataset with up to 94.29% accuracy. A comparison with the popular meta-classifier based property inference attacks shows that the proposed attack not only has better success rate, but can do so with half training data and a smaller number of shadow models. © 2024 IEEE.

关键词： Electronic health record

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Faba Bean Disease Detection Using Deep Learning Techniques

Faba Bean Disease Detection Using Deep Learning Techniques

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2023 International Conference on Cyber Management and engineering, CyMaEn 2023

作者： Salau, Ayodeji Olalekan Abeje, Bekalu Tadele Faisal, Abeer Naser Asfaw, Tesfa Tegegne Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Haramaya University College of Computing and Informatics Department of Information Technology Ethiopia Institute of Technology Faculty of Computing Bahir Dar University Department of Computer Science Ethiopia University of Sumer College of Computer Science and Information Technology Al-Rifai Iraq

ISBN: (纸本)9781665493291

Faba bean (Vicia faba L.) is an important cash crop for animal and human consumption in many countries, especially Ethiopia due to its high protein content and high rate of production. It also improves soil fertility with the help of bacteria, which forms nodules on its roots. Ethiopia is the second largest faba bean producer;however, according to research, the yield is generally low. Among the constraints contributing to low productivity of the crop, disease is the most important. Even though there are countermeasures such as the use of chemical sprays, which is used to reduce the damage or eliminate the disease, an effective disease identification technique is still required. Chocolate spot, faba bean gal, and rust are the most common diseases of faba beans. To effectively detect the disease, in this paper, we propose an end-to-end CNN model for training before and after the dataset is preprocessed. The CNN end-to-end model is trained with raw images and achieves 92.1% training accuracy. Also, the model is trained with preprocessed data and it achieves a training accuracy of 98.14% for the end-to-end CNN classifier. The results show that the proposed approach is suitable for faba bean disease detection and can be employed to detect diseases of other crops. © 2023 IEEE.

关键词： Convolutional neural networks

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SVM Machine Learning Model for Detection of Etherlock Vulnerability in Solidity Smart Contracts 6

SVM Machine Learning Model for Detection of Etherlock Vulner...

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6th International Conference on Blockchain computing and Applications, BCCA 2024

作者： Almobaideen, Wesam Alnuman, Rashed Sajid, Tayyab Hasan, Qusai Department of Electrical Engineering and Computing Sciences Rochester Institute of Technology Dubai Dubai United Arab Emirates Department of Computer Science The University of Jordan Amman Jordan

ISBN: (纸本)9798350351538

Blockchain is a disruptive technology that changed how financial operations and ownership exist over the internet. With the implementation of smart contracts in blockchains, facilitation of transactions without intermediaries is possible and fully automated. Smart contracts allow relatively safe transactions and transfer of ownership, but as with any piece of software, it proved to have vulnerabilities and challenges. Unfortunately, considering that smart contracts hold and facilitate valuable asset and funds transfer, the damage caused by exploitation of these contracts is devastating with billions of dollars’ worth of cryptocurrency and other assets lost or stolen up until now. One attack on Ethereum smart contracts is the Etherlock attack, which exploits bad logic in the code to stall the transaction indefinitely, effectively sealing user funds. In this paper, we propose a high-accuracy Support Vector Machine model for detection of Etherlock vulnerability in Solidity smart contracts. The model is developed into a standalone tool and achieves 98.44% accuracy accompanied with 87% recall using a generated dataset of Smart Contracts from the Ethereum blockchain. Our promising results still show the necessity of more sophisticated accurate detection models for protection of crypto assets on the blockchain. © 2024 IEEE.

关键词： Smart contract

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AI based Compact Controller for Wind farms Economical Operation, Control and Monitoring 2

AI based Compact Controller for Wind farms Economical Operat...

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

作者： Sasikala, C. Palpandian, P. Muthuram, G. Kumar, S. Siva Karthik, S. Sam Raj, C. Mohan Arjun College of Technology Department of Electronics and Communication Engineering Coimbatore642 120 India Karpagam College of Engineering Department of Electrical and Electronics Engineering Coimbatore641 032 India Hindusthan College of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India Nehru Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore641 105 India Dhanalakshmi Srinivasan College of Engineering Department of Electrical and Electronics Engineering Coimbatore641 105 India Sri Eshwar College of Engineering Department of Electrical and Electronics Engineering Coimbatore641 202 India

ISBN: (纸本)9798350368413

In India, 42.6 GW power is generated by wind turbine generators for supplying electricity to the load. Permanent Magnet DC generator, Synchronous generator and Induction generator are the most commonly used generators in the wind mill. Though the generators are designed for producing the power with maximum efficiency, monitoring, operation and control of wind mill plays an important role. The instantaneous faults like open circuit fault and least resistance fault can be easily detected and removed by the protection scheme. But the inter turn fault, core saturation condition, loss of dielectric due to ageing of machines cannot be detected until the fault occurs. The economic operation of the wind mill is based on the fault detection and prediction, uninterrupted operation of the wind mill, monitoring of parameters from the remote location. The AI based compact controller R04CPU with remote I/O module is used to monitor, control the windmill and ensures the economic operation/fault detection. © 2024 IEEE.

关键词： Windmill

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Innovative Deep Learning Techniques for Solving Complex computer Vision Challenges and Enhancing Pattern Recognition Accuracy

Innovative Deep Learning Techniques for Solving Complex Comp...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Jayachitra, T. Sathya, S. Ramesh, P.S. Jothikumar, R. Gouri, M.S. Hussain, Mohammed Waheeduddin Sharda University Department of Electrical Electronics and Communication Engineering Greater Noida India Grt Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Tiruttani India Vellore Institute of Technology Department of Smart Computing Vellore India Shadan College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Baselios Mathews - Ii College of Engineering Department of Computer Science and Engineering Sasthamcotta Kollam India Shadan College of Engineering and Technology Department of Information Technology Peerancheru Hyderabad India

ISBN: (纸本)9798350375442

The proposed work provides an in-depth analysis of DL techniques to enhance pattern recognition accuracy in difficult computer vision applications. This background explains the present status of the problems in the area and motivates the development of a complex framework that blends innovative neural network topologies, state-of-the-art optimization approaches, and inventive data augmentation strategies. Among the methods are CNNs enhanced with residual blocks and attention mechanisms, which are implemented using the TensorFlow deep learning framework. The results demonstrate significant improvements over baseline models on a number of measures. Using the CIFAR-10 dataset, the proposed system achieves an impressive 92.5% accuracy and 92.1% F1-score for classification tasks. With an IoU of 78.2% and a mAP of 81.4% in object identification tasks, the system shows its robustness in accurately localizing and recognizing things within photos. These findings have practical ramifications for industries needing accurate visual identification, such autonomous driving and medical imaging, in addition to advancing theoretical knowledge. The efficacy of the architecture highlights its potential to serve as a benchmark for next deep learning studies and applications. © 2024 IEEE.

关键词： Stochastic systems

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Row-Efficient Pruning for In-Memory Convolutional Weight Mapping 21

Row-Efficient Pruning for In-Memory Convolutional Weight Map...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Rhe, Johnny Ko, Jong Hwan Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of Sungkyunkwan University College of Information and Communication Engineering Korea Republic of

ISBN: (纸本)9798350377088

In this paper, we propose a mapping-aware weight pruning method for in-memory computing (IMC) architectures that operate on a row-by-row basis. Our proposed method can dynamically skip unnecessary row operations to minimize energy consumption while maintaining the inference accuracy of pre-trained model. To achieve this, it calculates the importance of weight elements considering the weight mapping method, which helps preserve critical weights in pre-trained model, thereby minimizing inference accuracy loss. The simulation results demonstrate that our method not only enhances scalability by allowing users to select the number of row operations to be skipped, but also achieves superior inference accuracy compared to existing pruning method in both ResNet-20 and WRN16-4 networks. © 2024 IEEE.

关键词： Mapping

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