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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： E. G. Satish Komuravelly Sudheer Kumar Myasar M. Adnan Krithika M MandapatiVenkata Rama Sundari Department of CSE Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science & Artificial Intelligence SR University Warangal India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of BCA/IT Vels Institute of Science Technology and Advanced Studies Pallavaram India Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Identifying encrypted traffic from emerging applications is important and challenging as traditional traffic classification approaches fail to achieve the desired level of accuracy. This necessitates the elaborate strategies to learn about the encrypted network flow. The research focuses on developing a cloud native hybrid Deep Learning (DL) pipeline for Network Traffic Classification (NTC). Initially, the ISCXVPN2016 dataset is considered that consists encrypted and non-encrypted traffic where the raw data is pre-processed with packet length normalization. The features are extracted applying Convolutional Neural Networks (CNNs) to define statistical flow features including inter-arrival time, packet size and flow duration. The data from network traffic is processed and classified using Convolutional Recurrent Neural Network (CRNN) with microservices executed in serverless containers. The hyper parameters in the DL models are then tuned using grid search and the random search algorithms. Experimental results showed that the proposed cloud native CRNN pipeline achieved accuracy of 98.67%, precision of 95.46%, and 94.32% when compared to the existing NTC models Stacked Autoencoders (SAE) based CNN (SAE-CNN) and Autoencoders based CNN (AE-CNN).

关键词： Deep learning Recurrent neural networks Accuracy Pipelines Microservice architectures Telecommunication traffic Containers Feature extraction Cryptography Convolutional neural networks

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Evaluating Machine Learning and Statistical Models for Greenland Subglacial Bed Topography 22

Evaluating Machine Learning and Statistical Models for Green...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Yi, Katherine Dewar, Angelina Tabassum, Tartela Lu, Jason Chen, Ray Alam, Homayra Faruque, Omar Li, Sikan Morlighem, Mathieu Wang, Jianwu Purdue University Department of Computer Science West LafayetteIN United States University of Oregon Department of Physics EugeneOR United States University of Maryland Baltimore County Department of Information Systems BaltimoreMD United States University of Maryland College Park Department of Information Studies College ParkMD United States Marriotts Ridge High School Ellicott City MarylandMD United States Texas Advanced Computing Center University of Texas at Austin AustinTX United States Dartmouth College Department of Earth Sciences HanoverNH United States

ISBN: (纸本)9798350345346

The purpose of this research is to study how different machine learning and statistical models can be used to predict bedrock topography under the Greenland ice sheet using ice-penetrating radar and satellite imagery data. Accurate bed topography representations are crucial for understanding ice sheet stability and vulnerability to climate change. We explore nine predictive models including dense neural network, long-short term memory, variational auto-encoder, extreme gradient boosting (XGBoost), gaussian process regression, and kriging based residual learning. Model performance is evaluated with mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R2), and terrain ruggedness index (TRI). In addition to testing various models, different interpolation methods, including nearest neighbor, bilinear, and kriging, are also applied in preprocessing. The XGBoost model with kriging interpolation exhibit strong predictive capabilities but demands extensive resources. Alternatively, the XGBoost model with bilinear interpolation shows robust predictive capabilities and requires fewer resources. These models effectively capture the complexity of the terrain hidden under the Greenland ice sheet with precision and efficiency, making them valuable tools for representing spatial patterns in diverse landscapes. © 2023 IEEE.

关键词： Greenland Interpolation Machine Learning Applications Model Evaluation Subglacial Bed Topography

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Robust Spectral Efficiency Maximization in 5G Networks Using Non-Linear Stochastic Precoding

Robust Spectral Efficiency Maximization in 5G Networks Using...

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Disruptive Technologies (ICDT), International Conference on

作者： Geetha Ponnaian Jayanthi. N R. Sarasu Sajitha. L.P R. Ramalakshmi Manimegalai L Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Chennai India School of Management Studies Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai Tamil Nadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and business system R. M. K Engineering College Kavaraipettai Tamilnadu India Department of Electronics and Communication Engineering Ramco Institute of Technology Rajapalayam Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Chennai India

This research focuses on enhancing the spectral efficiency of 5G networks through the utilization of nonlinear stochastic precoding techniques. The proposed approach aims to mitigate the detrimental effects of channel impairments and noise by employing advanced precoding schemes that exploit the statistical properties of the wireless channel. By incorporating nonlinear precoding algorithms and stochastic optimization methods, the system can dynamically adapt to varying channel conditions, resulting in improved data transmission rates and overall spectral efficiency. Through extensive simulations and analysis, the effectiveness of the proposed method is demonstrated, showcasing its potential to significantly enhance the performance of 5G networks in real-world scenarios.

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Development of Machine Learning Framework for the Protection of IoT Devices

Development of Machine Learning Framework for the Protection...

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International Conference on computing Methodologies and Communication (ICCMC)

作者： R. Mohandas N. Sivapriya A. Sanyasi Rao K. Radhakrishna Madona B Sahaai Department of Electronics and Communication Engineering Balaji Institute of Technology & Science Warangal Telangana India Department of Computer Applications Cauvery College of Women Trichy Tamil Nadu India Department of Electronics and Communication Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Tamil Nadu India

Internet of Things (IoT) has a wide range of threats to businesses, according to security experts. Organizations need an intelligent system that can automatically detect suspicious IoT devices linked to their networks. This study introduces a unique security framework powered by machine learning (ML) that automatically adapts to the growing security needs of the IoT sector. There should be a way to identify IoT devices that aren't on a trusted white list. In this article, a machine learning method has been used to recognize IoT device types from a white list by using network traffic data. Seventeen separate IoT devices, each representing one of nine different categories of IoT devices, were manually tagged to train and assess multi-class classifiers. The majority rule was used to classify block listed devices accurately using unidentified in 86% of trial forms, while authorized expedient categories stayed appropriately identified through the real kinds with 88% of forms. The detection times varied for different types of IoT devices. In addition, it shows how the machine learning-based IoT white-listing system can defend itself against hostile attacks.

关键词： Wireless communication Accesslists System performance Machine learning Telecommunication traffic Media Routing

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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 Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (A*STAR) 2 Fusionopolis Way #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

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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Panel Discussion on“Development Trends of computer science in the New Era”

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计算机教育 2024年第3期 26-29页

作者： Andrew Yao Nancy M.Amato Ann Copestake Sukyoung Ryu Yike Guo Yaqin Zhang Institute for Interdisciplinary Information Sciences Tsinghua UniversityBeijing100084China Department of Computing University of IllinoisUrbana61801U.S Department of Computer Science and Technology University of CambridgeCambridgeCB30FDUK Korea Advanced Institute of Science and Technology(KAIST) Yuseong-gu Daejeon34141Korea Hong Kong University of Science and Technology Hong Kong999077China Institute for AI Industry Research(AIR) Tsinghua UniversityBeijing100084China

At the panel session of the 3rd Global Forum on the Development of computer science,attendees had an opportunity to deliberate recent issues affecting computer science departments as a result of the recent growth in the field.6 heads of university computer science departments participated in the discussions,including the moderator,Professor Andrew *** first issue was how universities are managing the growing number of applicants in addition to swelling class *** approaches were suggested,including increasing faculty hiring,implementing scalable teaching tools,and working closer with other departments through degree programs that integrate computer science with other *** second issue was about the position and role of computer science within broader *** generally agreed that all fields are increasingly relying on computer science techniques,and that effectively disseminating these techniques to others is a key to unlocking broader scientific progress.

关键词： Development trends computer science

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An Assessment of the Supremizer and Aggregation Methods of Stabilization for Reduced-Order Models

arXiv

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arXiv 2023年

作者： Davie, Kayla D. Elman, Howard C. Applied Mathematics & Statistics Scientific Computation Program University of Maryland College ParkMD20742 United States Department of Computer Science Institute for Advanced Computer Studies University of Maryland College ParkMD20742 United States

MSC Codes 65N22, 65K10, 65F45We explore the features of two methods of stabilization, aggregation and supremizer methods, for reduced-order modeling of parametrized optimal control problems. In both methods, the reduced basis spaces are augmented to guarantee stability. For the aggregation method, the reduced basis approximation spaces for the state and adjoint variables are augmented in such a way that the spaces are identical. For the supremizer method, the reduced basis approximation space for the state-control product space is augmented with the solutions of a supremizer equation. We implement both of these methods for solving several parametrized control problems and assess their performance. Results indicate that the number of reduced basis vectors needed to approximate the solution space to some tolerance with the supremizer method is much larger, possibly double, that for aggregation. There are also some cases where the supremizer method fails to produce a converged solution. We present results to compare the accuracy, efficiency, and computational costs associated with both methods of stabilization which suggest that stabilization by aggregation is a superior stabilization method for control problems. © 2023, CC BY.

关键词： Stabilization

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Tumor Infiltration of Microrobot using Magnetic torque and AI Technique

Tumor Infiltration of Microrobot using Magnetic torque and A...

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International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN)

作者： R. Shalini Laxmi Mishra Athulya S Abhijeet Gajanan Chimankar Sunanda Ratna. Kandavalli Keshav Kumar K R. Senthamil Selvan Department of BCA & IT School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Chennai Department of Electronics and Communication Engineering Khwaja Moinuddin Chishti Language University Uttar Pradesh Department of computer science and engineering Faculty of engineering Christ deemed to be University Bangalore Department of B.Sc. Computer Science Chatrapati Shivaji BCA And BCS College Maharashtra Department of Electronics and Communication Engineering Sri Padmavati Mahila Visva Vidyalayam Tirupati Department of Humanities and Mathematics G. Narayanamma Institute of Technology and Science Hyderabad Department of Electronics and Communication Engineering Annamacharya Institute of Technology & Sciences Tirupati

Because of their surroundings and lifestyle alternatives, human beings, these days be afflicted by a huge style of illnesses. thus, early contamination prediction will become crucial. on the other hand, primarily based just on signs, docs warfare to make correct forecasts. The most challenging issue is accurately forecasting illnesses, which is why machine learning is essential to accomplish this task. To identify concealed patterns within vast amounts of medical data, disease information is processed using data mining techniques. We evolved a extensive contamination prediction primarily based on the affected person's signs. We rent the device getting to know techniques Convolutional Neural network (CNN) and ANFIS to exactly count on sickness (adaptive community-based totally fuzzy inference machine). For an correct forecast, this trendy illness prediction considers the character's way of life picks and fitness history. ANFIS outperforms CNN's set of rules in phrases of popular infection prediction, with an accuracy price of 96.7%. additionally, CNN consumes extra memory and processing energy than ANFIS because it trains and assessments on facts from the UCI repository. The Anaconda notebook is a suitable tool for implementing Python programming as it contains a range of libraries and header files that enhance the accuracy and precision of the process.

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Wireless Power Transfer Technologies: State-Of-The-Art and Future Prospects

Wireless Power Transfer Technologies: State-Of-The-Art and F...

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Communication, computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Vivek Justus S.Viveka M.Sumithra Purushothaman V V.Samuthira Pandi Desidi Narsimha Reddy Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Wireless Power Transfer (WPT) technologies represent a transformative approach to energy transmission, offering advantages over traditional wired systems such as convenience, flexibility, and reduced maintenance. This paper presents a comprehensive review of current advancements and future prospects in WPT. The review encompasses three primary technologies: electromagnetic induction, magnetic resonance, and microwave transmission, each offering unique benefits and challenges. Recent developments have significantly enhanced WPT efficiency, extended transmission distances, and enabled integration with emerging technologies like Internet of Things (IoT) devices and electric vehicle (EV) charging infrastructure. Looking forward, the paper identifies several key research directions and challenges. These include improving efficiency through advanced materials and metamaterials, standardization efforts to promote interoperability and safety, and exploration of new applications such as wireless charging in smart cities and space-based applications. The potential impact of WPT on reducing reliance on fossil fuels and enabling sustainable energy practices underscores its importance in future energy landscapes. By synthesizing current knowledge and highlighting future opportunities, this study aims to guide researchers, engineers, and policymakers in accelerating the development and adoption of WPT technologies, paving the way for a moreefficient and interconnected energy ecosystem.

关键词： Costs Reviews Smart cities Magnetic resonance Wireless power transfer Standardization Telecommunication computing Smart grids Internet of Things Tuning

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A Hybrid Solution for Secure Privacy-Preserving Cloud Storage & Information Retrieval

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Human-centric computing and Information sciences 2023年 13卷

作者： Kumar, Ankit Aljrees, Turki Hsieh, Sun-Yuan Singh, Kamred Udham Singh, Teekam Raja, Linesh Samriya, Jitendra Kumar Mundotiya, Rajesh Kumar Department of Computer Engineering & Applications GLA University UP Mathura India College of Computer Science and Engineering University of Hafr Al-Batin Eastern Province Saudi Arabia Department of Computer Science and Information Engineering Institute of Medical Information Institute of Manufacturing Information and Systems Center for Innovative FinTech Business Models and International Center for the Scientific Development of Shrimp Aquaculture National Cheng Kung University Tainan Taiwan School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Application Manipal University Jaipur Jaipur India Department of Computer Science and Engineering National Institute of Technology Delhi Delhi India School of Computer Science University of Petroleum and Energy Studies Dehradun India

Cloud storage is an emerging archetype used by businesses for data storage. Clients require easy access to data in the cloud, which helps clients with limited computing power move their high-value, high-risk principle jobs to the cloud. The primary concern of this study is towards flawless verifying, checking data bundles, and determining modules to carry out the project. As discussed at several research platforms, each client is concerned about data storage and retrieval. Data security is critical in cloud computing, and thus monitoring has evolved into a vigilance that abstracts the monitoring process. Request made by clients for security planning is always computationally exclusive. As a result, each customer is concerned as to their verified condition. This paper will highlight a secure cloud communication method for client data to address the afore-mentioned issues. Synchronization creates secret hare keys, combined with a model-encrypted data packet sent to the cloud. These secure data are then sent over a public network, subject to monitoring to avert data leakage to an unauthorized party. They lack the cohesion to outsource data storage to the cloud deliberately. As an outcome of this model's distribution, the perception of authorized data owners would be changed regarding cloud storage up to 97.6%, addressing the trust issues between data owners and cloud ace affiliations © 2023, Human-centric computing and Information *** Rights Reserved.

关键词： Cryptography

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