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Enhancing Hyperspectral Image Compression Through Stacked Autoencoder Approach 6

Enhancing Hyperspectral Image Compression Through Stacked Au...

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6th International Conference on Electrical engineering and Information and communication Technology, ICEEICT 2024

作者： Afrin, Afsana Haque, Md. Rakibul Mamun, Md. Al Inst. of Information and Communication Technology Rajshahi University of Engineering Technology Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350385779

Hyperspectral Image (HSI) consisting of numerous high-resolution spectral bands creates challenges of the high dimensionality problem in HSI classification which hinder real-life applications despite their abundance of information. We present a lossy compression approach using stacked autoencoders to reduce high dimensionality problem in this paper. The pro-posed method utilizes stacked autoencoders to extract features from HSIs, allowing compression and subsequent reconstruction. The study demonstrates improved Peak Signal-to-Noise Ratio (PSNR) of 70.43, 60.87 and 61.36 on three distinct HSI datasets-Salinas, Botswana, and KSC respectively compared to previous autoencoder-based compression method. Additionally, the impact of compression on classification accuracy is assessed using a 3D-2D CNN model, achieving an average accuracy of 99 % across the datasets. Improved compression along with high classification accuracy shows our proposed method's usefulness. © 2024 IEEE.

关键词： Image enhancement

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LIVES: XAI-based Secure UAV communication Framework for Essential Items Delivery

LIVES: XAI-based Secure UAV Communication Framework for Esse...

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Artificial Intelligence and Data engineering (AIDE), International Conference on

作者： Vidhi Ruparelia Sneh Shah Lakshin Pathak Rajesh Gupta Sudeep Tanwar Sahil Sawhney Ankur Gupta Department of Electronics and Communication Engineering Inst. of Technology Nirma University Ahmedabad India Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Department of Computer Science & Engineering Model Institute of Engineering & Technology Jammu & Kashmir India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

The application of Unmanned Aerial Vehicles (UAVs) for securely delivering essential items during extreme conditions includes numerous challenges, particularly in ensuring identification of potential threats. To solve this challenge, we propose a strong framework that makes use of Boosting algorithms, with a particular focus on XGBoost to enhance attack classification in UAV-based delivery systems. The XGBoost model achieved an accuracy of 93%, a precision of 0.9312, a recall of 0.9311, and an F1 score of 0.9283 , which makes it a highly suitable model for security of UAVs. Explainable AI (XAI) is used to further enhance classification including SHAP and LIME to identify the most influential features, such as the barometer and battery levels. Our framework provides a comprehensive solution for secure UAV-based deliveries. The experimental results proves the effectiveness of the proposed model which shows its potential for real-life applications.

关键词： Accuracy Explainable AI Autonomous aerial vehicles Boosting Data engineering Barometers Classification algorithms Batteries Security

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LIVES: XAI-based Secure UAV communication Framework for Essential Items Delivery

LIVES: XAI-based Secure UAV Communication Framework for Esse...

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2025 International Conference on Artificial Intelligence and Data engineering, AIDE 2025

作者： Ruparelia, Vidhi Shah, Sneh Pathak, Lakshin Gupta, Rajesh Tanwar, Sudeep Sawhney, Sahil Gupta, Ankur Nirma University Inst. of Technology Department of Electronics and Communication Engineering Ahmedabad India Nirma University Institute of Technology Department of Computer Science and Engineering Ahmedabad India Model Institute of Engineering & Technology Department of Computer Science & Engineering Jammu & Kashmir India

ISBN: (纸本)9798331527518

关键词： Drones

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A Non-normal Warning System for Dam Operation Using Machine Learning 24

A Non-normal Warning System for Dam Operation Using Machine ...

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24th IEEE/ACIS International Winter Conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2022

作者： Chang, Meng-Wei Liu, I-Hsien Liu, Chuan-Kang Lin, Wei-Min Su, Zhi-Yuan Li, Jung-Shian Inst. Computer and Communication Engineering National Cheng Kung University Dept. of Electrical Engineering Tainan Taiwan National Chin-Yi University of Technology Dept. of Artificial Intelligence and Computer Engineering Taichung Taiwan Chia Nan University of Pharmacy and Science Dept. of Multimedia and Game Development Tainan71710 Taiwan

ISBN: (纸本)9798350310412

A country's critical infrastructures are heavily related to the quality of life and safety of the people. As a result, the security protection aspect of critical infrastructure has gained more and more attention nowadays, especially the security of its industrial control system (ICS). To avoid the abnormal condition happening in the critical infrastructure which could put people in great danger, a system that is capable of detecting any abnormal state of the ICS promptly is needed. Fortunately, due to the dramatic growth of the applications of machine learning in recent years, some researchers have already proposed anomaly detection methods with machine learning to provide inst.nt warning and protection for ICS. However, most of the existing anomaly detection research tends to only target one cause that harms the system, such as attacks on the network or physical equipment failures. The ICS will be more comprehensively secured if the anomaly detection system can cover multiple aspects of the ICS. Therefore, we have established a non-normal warning system with the Generative Adversarial Network (GAN) for dam operations in this study, which can detect various types of non-normal operations and notify relevant personnel right away. Note that we use real historical data to make predictions and verify our warning system, and we improve it even more by implementing the visual analysis method, which makes up the indecipherable results often found in unsupervised learning. © 2022 IEEE.

关键词： Critical infrastructures

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Threat Analysis of Cyber Security Exercise for Reservoir Testbed Based on Attack Tree

Threat Analysis of Cyber Security Exercise for Reservoir Tes...

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International Symposium on Computing and Networking Workshops (CANDARW)

作者： Sú-Goân Tân I-Hsien Liu Jung-Shian Li Dept. of Electrical Engineering Inst. of Computer and Communication Engineering National Cheng Kung University Tainan Taiwan

As operational technology (OT) and information technology (IT) are getting closer in industrial control systems (ICS) nowadays, the security of critical infrastructure (CI) has become an important issue, which may have a great influence on people's livelihood, economy, and even safety. Therefore, governments around the world have conducted a series of cyber security exercises, which is one of the most effective ways to maintain and optimize the security, so as to cultivate the information security capabilities of government agencies and enterprises in the face of cyber attacks. In this paper, we present the framework and situations of the cyber security exercise with two scenarios on our reservoir testbed, and then analyze the threats based on the attack tree model and give the security recommendations to protect the critical infrastructure according to the results of the practical exercise and analysis.

关键词： Integrated circuits Measurement Analytical models Industrial control Knowledge based systems Government Reservoirs

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A Method for Predicting Cardiovascular Disorder using Machine Learning Techniques

A Method for Predicting Cardiovascular Disorder using Machin...

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Recent Advances in Science and engineering Technology (ICRASET), International Conference on

作者： Ramakrishna Hegde D R Pavithra S Shivashankara G Prasanna Kumar S M Soumyasri S Nagashree Computer Science and Engg. Vidyavardaka College of Engineering Affiliated to Visvesvaraya Technological University Belagavi India Electronics & Communication Engg. Sri Jayachamarajendra College of Engineering JSS Science and Technology University Mysuru India Computer Science and Engg. KR Pete Krishna Government Engineering College Krishnarajapet India Information Science & Engg. The National Institute of Engineering Mysore India Dept. of Computer Applications Vidya Vikas Inst. of Engg. and Tech Affiliated to Visvesvaraya Technological University Belagavi India Electronics and communication Engg Vidya Vikas Institute of Engineering and Technology Mysuru India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

Drastic increase of cardiovascular disease has led to a lot of adult’s death. As per “News 18” very year, 12 million young people in India die from heart disease. In most of these cases people experience cardiac arrest, some of which are normal. First, families of patients are vulnerable because it takes only a few minutes for a person to die of a heart attack, and it is difficult to get medical help in time. Second, most victims are under the age of 45. According to Dr. Maninder Sandhu, cardiologist and cardiologist at Artemis Hospital Gurgaon, 30 percent of people who suffer from heart disease are under the age of 45. This paper proposes a method that maybe used by laboratory technicians, doctors and users to understand whether there are any chances of having cardiovascular disease or not. The proposed system takes live data of patients and their lifestyle attributes these data are processed by the trained model to produce the output. In this model we haveused random forest classifier, linear regression, $\mathbf{k}$-neighbors classifier & x gradient boost classifier. This model has attained maximum accuracy and least type one error in “X- Gradient Boost”. We have concentrated more on type one error. The developed system shows whether the patient has any chances of cardiovascular disease or not.

关键词： Heart Accuracy Linear regression Cardiac arrest Predictive models Nearest neighbor methods Feature extraction Prediction algorithms Classification algorithms Random forests

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A Non-normal Warning System for Dam Operation Using Machine Learning

A Non-normal Warning System for Dam Operation Using Machine ...

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ACIS International Conference on Software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Meng-Wei Chang I-Hsien Liu Chuan-Kang Liu Wei-Min Lin Zhi-Yuan Su Jung-Shian Li Dept. of Electrical Engineering Inst. Computer and Communication Engineering National Cheng Kung University Tainan Taiwan dept. of Artificial Intelligence and Computer Engineering National Chin-Yi University of Technology Taichung Taiwan dept. of multimedia and Game Development Chia Nan University of Pharmacy and Science Tainan Taiwan

关键词： Integrated circuits Training Visualization Dams Machine learning Alarm systems Generative adversarial networks

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Optimization of Stride Prefetching Mechanism and Dependent Warp Scheduling on GPGPU

Optimization of Stride Prefetching Mechanism and Dependent W...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Tsung-Han Tsou Dun-Jie Chen Sheng-Yang Hung Yu-Hsiang Wang Chung-Ho Chen Inst. of Computer and Communication Engineering Dept. of Electrical Engineering National Cheng-Kung University Taiwan

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

In this paper, we propose a data prefetching scheme, History-Awoken Stride (HAS) prefetching, optimized with a warp scheduler, Prefetched-Then-Executed (PTE), and evaluate the performance on the platform that we developed. Our platform is a single inst.uction, multiple thread (SIMT) GPGPU environment, supporting OpenCL 1.2 runtime and TensorFlow framework with CUDA-on-CL technology. Enormous amount of executing threads in GPU demands critical memory performance. HAS exploits history table of related memory accesses in intra-warp and inter-warp of the same workgroup as well as among workgroups, and uses address strides and warp status to monitor the prefetching progress of the executed warp. PTE precisely issues warps according to prefetching status from HAS. The experimental results of LeNet-5 inference and 11 PolyBench test programs on CAS-GPU show that our mechanism can achieve an average IPC performance improvement of 10.4%, and 7.8% reduction in data cache miss rate. The prefetch accuracy can reach 67.7%, and the proportion of prefetch request arrived at the appropriate time reaches 48.2%.

关键词： Prefetching History Kernel Graphics processing units Benchmark testing Central Processing Unit

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A Comparative Study of Z- Transform and Fourier Transform applied on Medical Images for Detection of Cancer Segments 3

A Comparative Study of Z- Transform and Fourier Transform ap...

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3rd International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2019

作者： Santra, Soumen Mandal, Surajit Das, Kunal Bhattacharjee, Joy Deyasi, Arpan Dept. of Computer Application Techno India International Kolkata India B.P.Poddar Inst. of Mgmt. and Technology Dept. of Electronics and Communication Engineering Kolkata India Narula Institute of Technology Dept. of Computer Science and Communication Engineering Kolkata India A.P.C College Dept. of Electronic Science Barasat India RCC Institute of Information Technology Dept. of Electronics and Communication Engineering Kolkata India

ISBN: (纸本)9781728155432

A preliminary investigation is carried out by applying Z-transform technique on different types of images with sole intention to record the z-score of any binary image. Bandwidth of the images are computed, and compared with that obtained from Fourier technique. Length of z-score matrix is graphically represented. Result is also analyzed for smooth, shiny and rough images. This preliminary investigation will help in sending multimedia content, precisely images through network and in signal processing which helps for the segments of cancerous portion. © 2019 IEEE.

关键词： Medical imaging

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Multi-Objective Discrete Migratory Bird Optimizer for Stochastic Disassembly Line Balancing Problem

Multi-Objective Discrete Migratory Bird Optimizer for Stocha...

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IEEE International Conference on Systems, Man and Cybernetics

作者： GuiBin Qin XiWang Guo MengChu Zhou ShiXin Liu Liang Qi College of Computer and Communication Engineering Liaoning Shihua University Fushun China New Jersey Inst. of Tech. Newark NJ USA Tongji University Shanghai China College of Information Science and Engineering Northeastern University Shenyang China Shandong University of Science and Technology Qingdao China

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Timely and proper recycling of end-of-life products promotes the sustainable development of our human society. Cost-effective and energy-efficient disassembly is a crucial step to realize such recycling. A disassembly process is usually uncertain because of the different quality of subassemblies in an end-of-life product. This work considers resource constraints and operation failure in such a process. A novel mathematical model is formulated with the objectives to maximize profit, minimize energy consumption, and minimize the total need for disassembly resources. A new solution method called a multi-objective discrete migratory bird optimizer is proposed to solve it. A real-life case is given to show its effectiveness and feasibility. Experimental results show that the superiority of the proposed algorithm over a well-known nondominated sorting genetic algorithm II and a multi-objective grey wolf optimizer.

关键词： Energy consumption Uncertainty Stochastic processes Birds Recycling Sustainable development Sorting

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