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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Dhabliya, Dharmesh Anandhasilambarasan, D. Ojha, Varun Primmia, D.R. Kalra, Hitesh Sahana, B.S. Vishwakarma Institute of Technology Department of Information Technology Maharashtra Pune India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Karnataka Bangalore India

ISBN: (纸本)9798350370249

Adaptive blunders management coding strategies have become increasingly essential to ensure reliable communique in wireless systems. This paper presents an evaluation of the overall performance of adaptive error control coding strategies in noisy channels. Specifically, the authors offer a theoretical and numerical analysis of the error probability, throughput, and channel ability of adaptive error management codes over Rayleigh, Rican, and Z-diversity channels. The theoretical evaluation is based on the discrete-time finite-country Markov chains and the belief in perfect channel country records. The final results of the theoretical analysis famous that the error opportunity decreases while the channel fading is slower and that the error performance may be notably improved by the usage of more than one diversity technique. The numerical analysis is based totally on Monte-Carlo simulations of adaptive error control coding systems with frequency-hopping spread spectrum modulation. The consequences of the numerical evaluation show that the adaptive error management coding systems can efficiently enhance the overall mistake performance in Rayleigh and Rican fading channels. Moreover, the authors conclude that the Z-variety coding device substantially improves the channel capacity and throughput in comparison to other coding strategies. © 2024 IEEE.

关键词： Numerical Analysis Rayleigh Reliable Rican Theoretical Z-Variety

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A Method to Improve the Accuracy of Personal Information Detection

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Journal of computer and communications 2023年第6期11卷 131-141页

作者： Chih-Chieh Chiu Chu-Sing Yang Ce-Kuen Shieh Institute of Computer and Communication Engineering Department of Electrical Engineering National Cheng Kung University Tainan Miin Wu School of Computing National Cheng Kung University Tainan

It is necessary to confirm the personal data factors and the rules of verification before conducting personal data detection. So that the detection method can be written in the subsequent implementation of the automatic detection tool. This paper will conduct experiments on common personal data factor rules, including domestic personal identity numbers and credit card numbers with checksums. We use ChatGPT to test the accuracy of identifying personal information like ID card identification numbers or credit card numbers. And then use personal data correlation to reduce the time for personal data identification. Although the number of personal information factors found has decreased, it has had a better effect on the actual manual personal data identification. The result shows that it saves about 45% of the calculation time, and the execution efficiency of the accuracy is also improved with the original method by about 22%, which is about 2.2 times higher than the general method. Therefore, the method proposed in this paper can accurately and effectively find out the leftover personal information in the enterprise. .

关键词： Data Leakage Privacy Personal Information Factors

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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KSKV:Key-Strategy for Key-Value Data Collection with Local Differential Privacy

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computer Modeling in engineering & Sciences 2024年第6期139卷 3063-3083页

作者： Dan Zhao Yang You Chuanwen Luo Ting Chen Yang Liu Artificial Intelligence Development Research Center Institute of Scientific and Technical Information of ChinaBeijing100038China Industry Development Department NSFOCUS Inc.BeijingChina School of Information Science and Technology Beijing Forestry UniversityBeijing100083China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China Institute of Computing Technology China Academy of Railway Sciences Corporation LimitedBeijing10081China

In recent years,the research field of data collection under local differential privacy(LDP)has expanded its focus fromelementary data types to includemore complex structural data,such as set-value and graph ***,our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data *** studies would simultaneously collect the frequencies of keys and the mean of values associated with each ***,the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility *** the importance of obtaining accurate key frequencies and mean estimations for key-value data collection,this paper presents a novel framework:the Key-Strategy Framework forKey-ValueDataCollection under ***,theKey-StrategyUnary Encoding(KS-UE)strategy is proposed within non-interactive frameworks for the purpose of privacy budget allocation to achieve precise key frequencies;subsequently,the Key-Strategy Generalized Randomized Response(KS-GRR)strategy is introduced for interactive frameworks to enhance the efficiency of collecting frequent keys through group-anditeration *** strategies are adapted for scenarios in which users possess either a single or multiple key-value ***,we demonstrate that the variance of KS-UE is lower than that of existing *** claims are substantiated through extensive experimental evaluation on real-world datasets,confirming the effectiveness and efficiency of the KS-UE and KS-GRR strategies.

关键词： Key-value local differential privacy frequency estimation mean estimation data perturbation

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Transformer Encoder and Decoder Method for Forest Estimation and Change Detection 2

Transformer Encoder and Decoder Method for Forest Estimation...

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2nd IEEE International Conference on Integrated Circuits and communication Systems, ICICACS 2024

作者： Kotin, Kiran Kashinath Kumar, Sharan Alabdeli, Haider Kumar, Gotte Ranjith Ramachandra, A.C. Smt. Kamala and Sri Venkappa M. Agadi College of Engineering and Technology Department of Ece Karnataka Lakshmeshwar India H.K.E. Society's S.M.V. College of Engineering Department of Ece Karnataka Raichur India The Islamic University Najaf Iraq Sr University School of Computer Science and Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350317558

Forest Change Detection (FCD) is difficult component of natural assess monitoring and conservation strategy, enabled informing decision-making. Different techniques using strength of Artificial Intelligence (AI) has implemented to detect and classify modifications in forest cover by Remote Sensing (RS) data. In this research, proposed a Transformer Encoder and Decoder (TED) method for forest estimation and change detection. The Multi Feature Fusion (MFF) is used for effective combine features of various scales. Next, Multilevel decoding named as Multi Scale and Multi Decoder (MSMD) is used at fine the scales much excessively. At last, loss is function is used for minimizing loss of MSMD. The performance of proposed method is analysed with performance measures of Intersection over Union (IoU) and Accuracy. The proposed method attained IoU of 86.23% and 87.45% in forest and background Attained mIoU of 85.21% and 88.92% in forest and background respectively. The proposed method attained accuracy of 92.89% and 92.33% in forest and background. Attained mAcc of 90.54% and 91.48% in forest and background respectively. The proposed method performed effective than previous methods like Multiscale Channelwise Cross Attention Network (MCCANet), UNet and Generative Adversarial Networks (GAN). © 2024 IEEE.

关键词： Forest Change Detection Multi Feature Fusion Multi Scale and Multi Decoder Remote Sensing and Transformer Encoder and Decoder

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Enhancing Image Segmentation in the Marine Atmosphere for Time-Sensitive Contextualisation using Convolutional Long Short-Term Memory

Enhancing Image Segmentation in the Marine Atmosphere for Ti...

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Advances in Computation, communication and Information Technology (ICAICCIT), International Conference on

作者： A. Reethika R. Rajeshwari S. Vijayalakshmi S. Malathy S. Gnanasaravanan M.Suresh Kumar Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Information Technolog KPR Institute of Engineering and Technology Coimbatore India Department of Biomedical Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Information Technology Sri Ranganathar Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

The work investigates autonomous surface vehicle obstacle avoidance and detection using computer vision techniques. It recognizes boundaries in marine environments by using the temporal context from video feeds from unmanned surface vehicles. The study shows how successful LSTM cells are in the CNN topologies that are currently in use, emphasizing how they might perform better. Nevertheless, datasets and network training techniques are not perfect. The work also uses a benchmarking approach to compare new network configurations.

关键词： Training Image segmentation computer vision Network topology Atmospheric modeling Topology Convolutional neural networks Long short term memory Autonomous vehicles Water resources

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Analysis of Spam Messages Using Various Machine Learning Classifier

Analysis of Spam Messages Using Various Machine Learning Cla...

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2022 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2022

作者： Nagaraj, P. Gopal, R. Sunethra, B. Sumathi, R. Muneeswaran, V. Vignesh, K. Kalasalingam Academy of Research and Education Krishnankoil Department of Computer Science and Engineering Virudhunagar India Bannari Amman Institute of Technology Sathyamangalam Department of Information Science and Engineering Erode India Kalasalingam Academy of Research and Education Krishnankoil Department of Electronics and Communication Engineering Virudhunagar India

ISBN: (纸本)9798350333848

Background:As people using social media increases the data generation also increases and the data generated may be safe or unsafe. If we see some applications like Twitter and mail. We get a lot of emails or twits that include all dangerous and useful things. Here to be safe from the threats and dangers we need a filter that separates useful messages from spam and helps us not to drown in a trap. And one of the approaches to do this is explained in this paper. In this paper, the algorithm followed is the Naïve Bayes classifier. This also provides the comparison between using Naïve Bayes, KNN, and Logistic Regression to solve the same problem that is spam filtering and term frequency-inverse document frequency (TFIDF). © 2022 IEEE.

关键词： Inverse problems

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LIRSRN: A Lightweight Infrared Image Super-Resolution Network

LIRSRN: A Lightweight Infrared Image Super-Resolution Networ...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Chun-An Lin Tsung-Jung Liu Kuan-Hsien Liu Department of Electrical Engineering and Graduate Institute of Communication Engineering National Chung Hsing University Taichung Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Infrared imaging is crucial for many applications, such as night vision and environmental monitoring. However, it often suffers from lower spatial resolution compared to visible light imaging, which hinders its effectiveness in scenes demanding fine detail discernment. This paper introduces a Lightweight Infrared Image Super-Resolution Network (LIRSRN), which is a novel architecture designed to enhance the resolution of IR images with minimal computation overhead. The core of LIRSRN is the Attention Enhancement Module, which synergizes various attention mechanisms to emphasize important features across both channel and spatial domains. It is further enhanced by spatial frequency processing, which helps to extract key image attributes. The training is conducted on the DIV2K dataset, and the test results from "results-A" and "results-C" demonstrate the superior performance of LIRSRN in terms of PSNR and SSIM. Ablation studies compare the trade-offs between performance and computational cost using different attention mechanisms. Overall, the proposed model strikes a balance between performance and complexity. The source code and trained model are available at https://***/yYDnA6.

关键词： Training Computational modeling Source coding Scalability Superresolution Refining computer architecture

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Enhancing House Price Prediction Accuracy using Deep Learning and Hyperparameter Tuning with AEDA 7

Enhancing House Price Prediction Accuracy using Deep Learnin...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Priya, R.Padma Raghuveer, K. Srinivas, K. Basi Reddy, A. Shriidhar, P.J. Shirisha, N. Avinashilingam Institute For Home Science And Higher Education For Women School of Engineering Department of Electronics And Communication Engineering Tamilnadu Coimbatore India V.R.Siddhartha Engineering College Department of Business Management Andhra Pradesh Kanuru20007 India Secundrabad Telangana Dhulapally India Mohan Babu University School of Computing Department of Computer Science And Engineering Andhra Pradesh Tirupati India Vinayaka Mission's Kirupananda Variyar Engineering College Department of Cse Tamilnadu Salem India Mlr Institute of Technology Department of Cse Hyderabad Telangana Dundigal India

ISBN: (纸本)9798350371406

Traditionally, real estate agents and appraisers used their expertise and market knowledge to estimate house prices. However, due to the expansion of data sources and developments in data science, there was a growing interest in developing predictive models to automate and enhance the house price prediction process. This research introduces the Adaptive Exploratory Data Analysis (AEDA) tool, specifically the AEDA by frequency for categorical values, implemented in Python. The tool provides an API that maps usual AEDA tasks in statistical modeling for generating plots and statistics. It automatically generates insights and establishes standards and procedures. The frequency of users in various variables highlights the challenges in data processing pipelines for predicting house prices using Machine Learning (ML) and Deep Learning (DL) techniques. The chapter also discusses the evaluation of the hyperparameter tuning model for house pricing in the subsequent chapter. Model building, in the context of ML, involves creating a model that can learn from data without explicit instructions. Regression analysis is utilized to observe and resolve prediction issues. The suggested house price prediction model combines a joint self-attention mechanism with a specific feature parameter representation. While buyers and sellers focus on external features like building area, location, and construction year, the proposed model emphasizes internal features. It emphasizes the importance of hyperparameter tuning in ML and DL algorithms to improve model accuracy by minimizing residual errors during validation. © 2023 IEEE.

关键词： Forecasting

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An Adaptive Hybrid Particle Swarm Optimizer for Constrained Optimization Problem

An Adaptive Hybrid Particle Swarm Optimizer for Constrained ...

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2021 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2021

作者： Khosla, Tejna Verma, Om Prakash Delhi Technological University Computer Science Engineering Department Delhi India Delhi Technological University Electronic and Communication Engineering Department Delhi India

ISBN: (纸本)9781665425063

Particle Swarm Optimization (PSO) is a well-known nature-inspired algorithm that is inspired by the social behavior of bird flocking and fish schooling. It has proved efficient in solving real-time problems due to its simplicity and good exploitation ability. However, due to poor exploration of search space, PSO gets trapped in local optima and gives less accurate results. On the other hand, Butterfly Optimization Algorithm (BOA) is good in exploration but converges slowly. This paper proposes a novel hybrid based on PSO and BOA, namely PSOBOA with few improvements based on the advantage and uniqueness of these algorithms. Firstly, a parameter-free penalty function is used to handle constraint violations so that the search process does not slacken when handling the constraints. Secondly, a self-adaptive approach has been adopted in PSO as well as BOA to ensure a smooth transition from exploration to exploitation, and no user interference. Thirdly, to improve the convergence rate and avoid local optima stagnation, a conditional approach has been used in the local and global search of BOA. The proposed algorithm PSOBOA overcomes the shortcomings of PSO and BOA and maximizes the performance. It is applied to solve structural optimization problems, such as pressure vessel design and welded-beam design problem, where the objectives, decision variables, and constraints are different. The experimental results and the convergence curves demonstrate better optimization performance of PSOBOA compared with quite a few state-of-the-art algorithms. © 2021 IEEE.

关键词： Constrained optimization

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