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Remote Sensing–Based UAV Imaging in Heat Pattern Analysis Impact on Climate Change Detection Using Fuzzy Stacked Lasso Elastic-Net Model

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Remote Sensing in Earth systems sciences 2024年第4期7卷 699-708页

作者： Sailaja, M. Kumar, M. Prema Jyothi, B. Swarna Vanguri, G. L. Narasamba Manjula, S. Priya, D. Divya Department of Computer Science and Engineering Ravindra College of Engineering for Women Andhra Pradesh Kurnool India Dept. of ECE Shri Vishnu Engineering College for Women (A) Andhra Pradesh Bhimavaram 534202 India Department of Computer Science & amp Engineering Madanapalle Institute of Technology & amp Science Andhra Pradesh Madanapalle India Information Technology Aditya College of Engineering & amp Technology(A) Andhra Pradesh Surampalem 533437 India Department of Computer Science and Business Systems Nehru Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Urban heat islands raise surface temperatures, which has an effect on city dwellers’ health and welfare. Urbanisation-related changes to the land surface, which are especially notable right after sunset, have an impact on radiative forcing. Recently, there has been a surge in the usage of unmanned aerial vehicle (UAV) technology, particularly for precision agriculture and plant phenotyping. This is due to the declining cost and increased accessibility of both UAVs and thermal imaging sensors. This interruption affects subsequent operations like target item categorisation and water quality parameter inversion. The goal of this study was to evaluate the potential for combining machine learning (ML) with unmanned aerial vehicle (UAV)–based imaging technology. In this research, the novel technique in UAV image–based temperature pattern detection of various regions of earth and their sea level rise analysis using image classification by machine learning model. Here, the input is collected as UAV images which have been processed for noise removal and normalisation. These image features have been extracted using convolutional active contour–based kernel component analysis and classified using region-based fuzzy stacked lasso elastic-net model. The classified output shows temperature analysis with sea level rise based on image analysis. The experimental analysis has been carried out for various UAV image dataset in terms of detection accuracy, RMSE, F-measure, mean average precision (MAP), and recall. Proposed technique F-measure of 89%, detection accuracy of 96%, MAP of 94%, recall of 92%, and RMSE of 60%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional active contour Heat pattern detection Kernel component analysis Lasso elastic-net model Remote sensing

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Street Light Cum Garbage System Optimal Design Based on IOT 2

Street Light Cum Garbage System Optimal Design Based on IOT

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Rajesh, G. Deepa, J. Ezilarasan, M.R. Syed Fiaz, A. Siva Rama Lingham, N. Jagannathan, J. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Information Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Software and Systems Engineering Vellore India

ISBN: (纸本)9798350328202

Street lights currently use more energy than other types of lighting because of an inefficient mechanism that makes the bulbs use a lot of electricity. The suggested approach uses various sensors on intelligent streetlights to keep an eye on and manage the lights. It has sensors for measuring temperature, brightness, and power that control dimming levels and keep track of status. One Zigbee network serves as the connection point for these lights. In the modification, a cloud- and IoT-based installation was made. Through Zigbee -based data transmission, sensor values are recorded on a distant server known as the Cloud. Additionally, it lowers electricity theft. We include a trash can notice in addition to this street light idea. When the garbage can is whole, a machine automatically notifies the business as people approach the sun, and the brightness of the street lights changes or is increased. © 2024 IEEE.

关键词： Light emitting diodes

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Manjula, R. Jagadeesan, S. Ramalingam, Venkadeshan Vetriselvi, T. Johnpeter, T. Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamilnadu Vellore India Information Technology Department University of Technology and Applied Sciences-Shinas Branch Sultanate of Oman Oman Vellore Institute of Technology School of Computer Science and Engineering Department of Iot Tamilnadu Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350313987

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user. © 2023 IEEE.

关键词： Gesture analysis Hand gesture recognition Hand tracking Sign language

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Generating Adversarial Patterns in Facial Recognition with Visual Camouflage

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Journal of Shanghai Jiaotong University (science) 2024年 1-12页

作者： Bao, Qirui Mei, Haiyang Wei, Huilin Lü, Zheng Wang, Yuxin Yang, Xin Key Laboratory of Social Computing and Cognitive Intelligence of Ministry of Education School of Control Science and EngineeringSchool of Computer Science and Technology Dalian University of Technology Liaoning Dalian116024 China Systems Engineering Institute Academy of Military Sciences Beijing100141 China

Deep neural networks, especially face recognition models, have been shown to be vulnerable to adversarial examples. However, existing attack methods for face recognition systems either cannot attack black-box models, are not universal, have cumbersome deployment processes, or lack camouflage and are easily detected by the human eye. In this paper, we propose an adversarial pattern generation method for face recognition and achieve universal black-box attacks by pasting the pattern on the frame of goggles. To achieve visual camouflage, we use a generative adversarial network (GAN). The scale of the generative network of GAN is increased to balance the performance conflict between concealment and adversarial behavior, the perceptual loss function based on VGG19 is used to constrain the color style and enhance GAN’s learning ability, and the fine-grained meta-learning adversarial attack strategy is used to carry out black-box attacks. Sufficient visualization results demonstrate that compared with existing methods, the proposed method can generate samples with camouflage and adversarial characteristics. Meanwhile, extensive quantitative experiments show that the generated samples have a high attack success rate against black-box models. © 2023, Shanghai Jiao Tong University.

关键词： Face recognition

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Pose-Critical Keypoint Attention model for Dynamic Yoga Pose Classification 2

Pose-Critical Keypoint Attention model for Dynamic Yoga Pose...

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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Meghana, J.H. Chethan, H.K. Prakash, S.P. Shiva Department of Studies in Computer Science University of Mysore Karnataka Mysore India Department of Computer Science and Engineering Maharaja Institute of Technology Karnataka Mysore India Department of Information Science and Engineering & Computer Science and Business Systems JSS Science and Technology University Karnataka Mysuru India

ISBN: (纸本)9781837242672

Yoga pose detection and classification have garnered considerable attention in recent years due to their significant applications in various domains, including fitness, health monitoring, and rehabilitation programs. Accurate detection and classification of yoga poses not only enhance the effectiveness of fitness regimes but also facilitate the assessment of practitioners' health and recovery processes. However, existing methods often struggle with the challenges posed by high-dimensional data processing, particularly in real-time applications. This paper introduces the Pose-Critical Keypoint Attention (PCKA) model, a novel approach designed to overcome the limitations of traditional methods in yoga pose analysis. The PCKA model employs an advanced attention mechanism to dynamically prioritize the most relevant keypoints from a comprehensive set of 33 extracted keypoints. This dynamic focus allows for efficient classification without the drawbacks associated with static keypoint reduction, ensuring that the model remains adaptable and robust across various poses. Extensive experiments evaluate the performance of the PCKA model against conventional classification frameworks. The results indicate that this model significantly outperforms traditional approaches, achieving an impressive accuracy of 91.08%, precision of 89.70%, recall of 91.93%, and an F1-score of 90.78%. These metrics demonstrate the model's capability to deliver accurate pose classification while providing realtime feedback for corrective measures, which is crucial for users striving to improve their yoga practices. © The Institution of engineering & Technology 2024.

关键词： Data handling

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software Defect Prediction Using Ensemble Technique 9

Software Defect Prediction Using Ensemble Technique

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Wicaksono, Pandu Arisaputra, Panji Yunanda, Rezky Kristian, Willy Mujhid, Almuzhidul School of Computer Science Bina Nusantara University Software Engineering Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

software defect prediction is the methodical process of identifying code segments that are likely to have problems. This is done by analyzing software metrics and using categorization algorithms. This work introduces an alternate approach that utilizes ensemble learning techniques to improve the effectiveness of fault detection models. The models utilized in this study are CodeGPT and CodeBERT, both of which are transformer-based deep learning models that can extract code features from source code. The models are trained using the PROMISE dataset, which consists of Java projects that have been labeled with defects. The test results show that using ensemble learning methods can improve the accuracy, precision, recall, and F1 score of a single model by around 1-3%. This study contributes to the progress of software dependability and quality by utilizing advanced software fault prediction algorithms. © 2024 IEEE.

关键词： Prediction models

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Lockstep Vs Microarchitecture: A Comparison 37

Lockstep Vs Microarchitecture: A Comparison

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37th IEEE International System-on-Chip Conference, SOCC 2024

作者： Mach, Jan Kohutka, Lukas Cicak, Pavel Slovak University of Technology Institute of Computer Engineering and Applied Informatics Bratislava842 16 Slovakia Slovak University of Technology Institute of Informatics Information Systems and Software Engineering Bratislava842 16 Slovakia

ISBN: (纸本)9798350377569

Safety/mission-critical applications require high dependability of the control systems. Their state-of-the-art protection approach is a system-level lockstep. This paper compares the system-level dual and triple lockstep technique to the microarchitecture-level protection of the Hardisc RISC-V core. For a fair comparison, each system is based on the same core(s) and integrates protection against bit-flips in memory and transient faults in the bus. We propose a fault injection methodology, combining pre-synthesis simulation with synthesis data to analyse the vulnerability of a system to faults. The fault injection campaigns show that the Hardisc can withstand fault rates orders of magnitude higher than the dual-core lockstep system while preserving the same area and power consumption. It comes with a 5% frequency penalty. We have shown that system failures are more frequent once the fault rate reaches an application-specific threshold (one fault in 100 clock cycles). © 2024 IEEE.

关键词： Failure rate

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Accelerating the cryo-EM structure determination in RELION on GPU cluster

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Frontiers of computer science 2022年第3期16卷 21-39页

作者： Xin YOU Hailong YANG Zhongzhi LUAN Depei QIAN Sino-German Joint Software Institute School of Computer Science and EngineeringBeihang UniversityBeijing 100191China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125

The cryo-electron microscopy(cryo-EM)is one of the most powerful technologies available today for structural *** RELION(Regularized Likelihood Optimization)implements a Bayesian algorithm for cryo-EM structure determination,which is one of the most widely used software in this *** researchers have devoted effort to improve the performance of RELION to satisfy the analysis for the ever-increasing volume of *** this paper,we focus on performance analysis of the most time-consuming computation steps in RELION and identify their performance bottlenecks for specific *** propose several performance optimization strategies to improve the overall performance of RELION,including optimization of expectation step,parallelization of maximization step,accelerating the computation of symmetries,and memory affinity *** experiment results show that our proposed optimizations achieve significant speedups of RELION across representative *** addition,we perform roofline model analysis to understand the effectiveness of our optimizations.

关键词： cryo-EM structure determination performance optimization GPU acceleration RELION

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A Deployment of Ensemble-Based IDS on Raspberry Pi 13

A Deployment of Ensemble-Based IDS on Raspberry Pi

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Musthafa, Muhammad Bisri Huda, Samsul Shamim, S.M. Kodera, Yuta Mwaura, Jedidah Araki, Shunsuke Nogami, Yasuyuki Okayama University Graduate School of Environmental Life Natural Science and Technology Japan Okayama University Green Innovation Center Japan Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering Fukuoka Japan Kyushu Institute of Technology Faculty of Computer Science and Systems Engineering Fukuoka Japan

ISBN: (纸本)9798350355079

Intrusion Detection systems (IDS) are essential for safeguarding IoT networks against various attacks. Our previously developed ensemble-based IDS model, which combines stacked Long Short-Term Memory (LSTM) networks with ANOVA feature selection, achieved a remarkable accuracy of 97.46% and an overfitting value of 0.22% in classifying network attacks. However, deploying this powerful model on resource-constrained devices like Raspberry Pi poses challenges due to limited computational resources. In this paper, we tackle these challenges by converting the original model to TensorFlow Lite (TFLite) and applying quantization techniques to optimize the model size for deployment on a Raspberry Pi 3b+. Our approach significantly reduces the model size by 90.9% while maintaining an impressive accuracy of 96.96%, only a 0.5% reduction from the original model. The optimized model demonstrates efficient performance on the Raspberry Pi, with a loading time of 100.2ms and an inference time of 1.2ms. © 2024 IEEE.

关键词： Network intrusion

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A novel deep learning approach for automated grading of knee osteoarthritis severity

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Multimedia Tools and Applications 2024年 1-20页

作者： Kaur, PrabSimran Kohli, Guneet Singh Bedi, Jatin Wasly, Saud Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Electrical and Computer Engineering Department King Abdulaziz University Jeddah22254 Saudi Arabia Center of Excellence in Intelligent Engineering Systems King Abdulaziz University Jeddah22254 Saudi Arabia

Knee Osteoarthritis (OA) is a prevalent musculoskeletal disorder that affects the knee joint that causes pain, stiffness, and reduced mobility. It is also known as "Degenerative Joint Disease" and is caused by the degeneration of cartilage in the knee joint, leading to bone-on-bone contact and further damage. Knee OA is prevalent in the population, affecting around 22% to 39% of people in India, and there is currently no treatment available that can halt the progression of the disease. Therefore, early diagnosis and management of symptoms are essential to reduce its impact on an individual’s quality of life. To address this issue, have introduced a framework that leverages ConvNeXt architecture, a modernization of ResNets (ResNet-50) architecture towards Hierarchical Transformers (Swin Transformers), to provide accurate identification and classification of knee osteoarthritis. The classification of knee osteoarthritis was done using the Kellgren and Lawrence (KL) graded X-ray images. These images of the damaged knees are preprocessed and augmented, creating a scaled, enhanced, and varied version of the features, thus making the data fitter and more significant for classification. The performance estimation of the proposed strategy is conducted on the Osteoarthritis Initiative (OAI), a research project focused on knee osteoarthritis that works in partnership with NIH and other private industries to develop a public domain dataset that can facilitate research and evaluation. It involves training the prepared data using various hyper-tuned versions of ConvNeXt. The different fine-tuned results of the ConvNeXt models on each KL Grade are evaluated against the other state-of-the-art models and vision transformers. The comparative assessment of widely used performance measures shows that the proposed approach outperforms the conventional models by generating the highest score for all the KL grades. Lastly, an approach is employed to statistically confirm the validity of t

关键词： Joints (anatomy)

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