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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. rajesh Vanithamani, S. Karunakaran, P. Pandiaraja, P. Tamilarasi, N. Nithin, P. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India M. Kumarasamy College of Engineering Department of Master of Computer Applications Tamil Nadu Karur India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala Department of Computer Science and Engineering R&D Institute of Science and Technology Tamil Nadu Chennai India Nehru Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Efficient and precise traffic incident detection is essential to reduce casualties and property damage. To address the issue of unbalanced event data, this work offers a novel methodology known as FA-WrF (Factor Analysis and Weighted random Forest). This approach combines dimensionality reduction through factor analysis with classification using weighted random forests, data preparation through the Synthetic Minority Over-Sampling Technique (SMOTE), and data preparation with SMOTE. The included feature of severity detection is highlighted, as are the performance metrics of the FA-WrF model using well-established metrics such as detection level rate, number of false count alarm rate, classification type rate, and area specification under the receiver operating characteristic curve (AUC). The superiority of the FA-WrF model is illustrated using real-world expressway traffic data represented by imbalanced incidents through thorough comparisons with various machine learning-based algorithms such as Support Vector Machine, k-nearest neighbours algorithms, Logistic regression algorithms, and decision-making trees. In addition to advancing incident detection, this technique has encouraging prospects for enhancing traffic management procedures and well-informed decision-making procedures in transportation networks. © 2024 IEEE.

关键词： dimensionality reduction

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Implementing data Science and Big data Analytics to Their Full Potential: Protocols for Gaining Useful Understanding and Promoting data-driven Innovation 5

Implementing Data Science and Big Data Analytics to Their Fu...

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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Arthy, M. Nivethitha, S.B. Karuppiah, S.P. Vijayanandh, T. Sumalatha, A. ramesh, T. School of Computing College of Engineering and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Rajalakshmi Engineering College Mechatronics Engineering Tamil Nadu Chennai India St.Joseph's College of Engineering Department of Mba India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science & Engineering Tamil Nadu Chennai India Kristu Jayanti College Autonomous Department of Computer Science Bengaluru India R.M.K Engineering College Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376685

The proper implementation of data science and big data analytics has become an essential component for gaining valuable insight and driving innovation across a variety of industries in this era, which is characterized by the constant presence of data. The strategies and protocols that are necessary to fully exploit the promise of these transformational technologies are investigated in depth by this research. A complete framework for enterprises to harness their data assets is the goal of the study, which will be accomplished by analyzing the fundamental principles of data science as well as the difficulties connected with big data. A number of advanced analytics methods, including machine learning, predictive modeling, and natural language processing, are being investigated as potential integrations into the decision-making process as part of this research. The relevance of data governance, quality, and the ethical considerations that surround data usage are also discussed in this document. It is demonstrated in this study that data-driven insights may lead to strategic advantages, operational efficiencies, and the development of creative goods and services. This is accomplished through a number of case studies and practical examples. In order for businesses to make the most of the potential that are given by data science and big data analytics, it is vital for them to have a culture of data literacy as well as a robust infrastructure, according to the results made. At the end of the study, a collection of best practices and recommendations for businesses and governments to implement in order to cultivate a data-driven environment that encourages innovation and usage of data in a responsible manner are presented. In light of the findings of this study, it is clear that data science and big data analytics require a more comprehensive approach, one that strikes a balance between technological capabilities, organizational readiness, and the influence on society. © 2024

关键词： Big data Analytics data Governance data Infrastructure data Literacy data Quality data Science data-driven decision Making data-driven Innovation Ethical Considerations Machine Learning Natural Language Processing Predictive Modeling Strategic Advantage

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A Proportional-Phase Lag Compensation Strategy Enhancing robustness Against Grid Impedance for LCL-Type Grid-Connected Inverter

A Proportional-Phase Lag Compensation Strategy Enhancing Rob...

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Power Electronics and Power System Conference (PEPSC)

作者： Jinping Huang damin Zhang Xianjin Su Yichuan Zhong Weidong Xu Zexiang ding School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China Kehua data R&D Center Kehua Data Co. Ltd Xiamen China School of Mechanical and Electrical Engineering and Automation Xiamen University Tan Kah Kee College Xiamen China

Compared to analog control, the adoption of digital control in LCL-type grid-connected inverters introduces control delays, which significantly alters the frequency characteristics of open-loop transfer functions of systems and imposes stricter stability requirements on the grid-connected inverter. Furthermore, in weak grid with wide-ranging variations in grid impedance, the robustness of the system is severely compromised. To bolster the resilience of the grid-connected inverter amidst fluctuations in grid impedance, this paper proposes a proportional-phase lag compensation strategy. By considering the control delays, stability analysis of the system is conducted within the bounds of grid impedance fluctuations, and stable regions are identified based on the Nyquist stability criterion, and then phase lag compensators and proportional parameters are properly designed. The resulting improved system adheres to the Nyquist stability criterion in weak grid, ensuring the grid-connected inverter to maintain stable operation, even amidst variations in grid impedance. Ultimately, the simulation outcomes confirm the viability and efficacy of the method proposed.

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Predicting Crops Based on Soil and Environmental Characteristics Using ArIMA and LSTM Model 4

Predicting Crops Based on Soil and Environmental Characteris...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Naresh Kumar, U. Madhana Mohan, K. Pragadeeswaran, S. Scaria, Jobin M. Baseera, A. Seenivasan, d. Annamacharya University Department of Artificial intelligence & Data science Rajampet India Sri Sairam Engineering College Department of Electronics and Instrumentation Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India School of Information Technology Ibs University Mt. Eriama Campus Port Moresby Papua New Guinea School of Computing Science and Engineering Vit Bhopal University Sehore India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India

ISBN: (纸本)9798350352931

Food producers are under pressure to meet rising demand as the world's population and natural resources diminish. Overuse of pesticides and fertilizers has caused soil erosion and land degradation. Future food needs will require environmentally sustainable farming strategies. The suggested strategy includes model training, feature extraction, sensor integration, and model preparation. data preparation involves processing data before storing it machine-readable. SFFS and rFE select features. The model was trained using a hybrid ArIMA-LSTM technique, which blends deep learning and time-series forecasting. The ArIMA-LSTM model outperforms solo ArIMA and LSTM models with an average accuracy of 93.54%. Increased accuracy shows the model's capacity to anticipate sustainable agriculture approaches. data-driven and environmentally sustainable ways are needed to overcome global food production concerns, according to this study. When paired with good preprocessing and feature selection, ArIMA-LSTM can boost agricultural productivity while minimizing environmental impact. © 2024 IEEE.

关键词： crop productivity long short-term memory (lstm) soil management

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data-Centric Evolution in Autonomous driving: A Comprehensive Survey of Big data System, data Mining, and Closed-Loop Technologies

arXiv

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arXiv 2024年

作者： Li, Lincan Shao, Wei dong, Wei Tian, Yijun Yang, Kaixiang Zhang, Wenjie Data and Knowledge Research Group University of New South Wales Sydney Australia Data61 CSIRO ClaytonVIC Australia BYD Intelligent Driving R&D Centre Shenzhen China University of Notre Dame South Bend United States School of Computer Science & Engineering South China University of Technology Guangzhou China

The aspiration of the next generation’s autonomous driving (Ad) technology relies on the dedicated integration and interaction among intelligent perception, prediction, planning, and low-level control. There has been a huge bottleneck regarding the upper bound of autonomous driving algorithm performance, a consensus from academia and industry believes that the key to surmount the bottleneck lies in data-centric autonomous driving technology. recent advancement in Ad simulation, closed-loop model training, and Ad big data engine have gained some valuable experience. However, there is a lack of systematic knowledge and deep understanding regarding how to build efficient data-centric Ad technology for Ad algorithm self-evolution and better Ad big data accumulation. To fill in the identified research gaps, this article will closely focus on reviewing the state-of-the-art data-driven autonomous driving technologies, with an emphasis on the comprehensive taxonomy of autonomous driving datasets characterized by milestone generations, key features, data acquisition settings, etc. Furthermore, we provide a systematic review of the existing benchmark closed-loop Ad big data pipelines from the industrial frontier, including the procedure of closed-loop frameworks, key technologies, and empirical studies. Finally, the future directions, potential applications, limitations and concerns are discussed to arouse efforts from both academia and industry for promoting the further development of autonomous driving. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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A multilevel downscaling model for enhancing nocturnal aerosol optical depth reanalysis from CAMS over the Beijing-Tianjin-Hebei region, China

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Environmental technology and Innovation 2025年 39卷

作者： Wang, Shiyao Li, Fuxing de Leeuw, Gerrit Fan, Cheng Li, Zhengqiang Hebei Key Laboratory of Environmental Change and Ecological Construction Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change School of Geographical Sciences Hebei Normal University Shijiazhuang050024 China State Environmental Protection Key Laboratory of Satellite Remote Sensing Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China R&D Satellite Observations De Bilt3730AE Netherlands

Nocturnal aerosol optical depth (AOd) serves a critical indicator for investigating the diurnal aerosol's climatic and environmental effects. However, the nocturnal AOd product is lacking due to that current daytime AOd retrieval algorithms are inapplicable to nighttime. despite important contribution of spatiotemporal continuous global reanalysis datasets to producing atmospheric composition forecasts and analyses, its feasibility for the characterization of nocturnal aerosol variation over small scales is still a major challenge due to its coarse resolution. In this study, a multilevel two-stage downscaled (TSd) model by integrating a linear mixed effect (LME) and a geographic weight regression (GWr) model is proposed to improve the spatial resolution of Copernicus Atmosphere Monitoring Service reanalysis (CAMSrA) nocturnal AOd. The multilevel downscaled model is referred to as the M_TSd model. The M_TSd model is employed over the Beijing-Tianjin-Hebei (BTH) region in China for the years from 2018 to 2022. Cross-validation of the retrieval results versus original CAMSrA data shows good performance of the M_TSd model with a determination coefficient (r2) of daily nocturnal AOd of 0.9569, root mean squared prediction error (rMSE) and the relative prediction error (rPE) of 0.0939 and 15.1 %, respectively. The inter-annual average nocturnal AOd indicate significant spatial variation with high value in southeastern plain and low value in the northwestern mountainous and plateau areas of BTH. Meanwhile, the nocturnal AOd is subject to seasonal variability. The M_TSd model may serve as a valuable reference to provide nocturnal AOd data with high spatial resolution for small scale. © 2025 The Authors

关键词： Mean square error

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Individual Intelligent recognition Method Based on Fingerprint Features of radar Emitter

Individual Intelligent Recognition Method Based on Fingerpri...

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2021 CIE International Conference on radar, radar 2021

作者： Liu, Zhongwei Gao, Hongwei Chen, Jie Zhou, dongming Li, Yingchun Sun, Shuyan Xiang, rongrong School of Electronic and Information Engineering Beihang University Beijing China Beijing Institute of Radio Measurement Radar R&d Center Beijing China Beijing Institute of Computer Technology and Application Department of Data Technology and Application Beijing China

ISBN: (纸本)9781665498142

Aiming at the problem of radar emitter individual recognition with unintentional modulation feature, a method of emitter fingerprint identification based on deep learning is proposed. In this method, the deep fingerprint features of radar emitter are extracted by deep Convolutional neural network, and the fingerprint feature library is constructed. Through similarity calculation, the individual recognition results of radar emitter signal are obtained. The results show that the new method can effectively identify both known and unknown emitter individuals. © 2021 IEEE.

关键词： deep learning Fingerprint features Individual identification radar emitter

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Hybrid Visual Verification

Hybrid Visual Verification

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International Conference on Confluence The Next Generation Information technology Summit (Confluence)

作者： S.Nikkath Bushra A. Syed Ismail P. Sasigresa Edwin Singh C Department of Computing Technologies SRM Institute of Scienece and Technology KattankulathurChengalpattu India Department of Data Science and Business Systems SRM Institute of Scienece and Technology KattankulathurChengalpattu India Department: Computer Science and Engineering Veltech University Department: Computer Science and Engineering Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

data security is becoming increasingly important as cloud computing advances. data security is the fundamental problem of all distributed computing systems. Cloud computing enables access to distributed applications and services belonging to various organizations spread across different locations. Cloud computing raises security concerns, because it shares data with distributed users via network in an open terrain. In this system, some of the most important security services like image-based security are handled by the cloud computing system. The proposed scheme takes care of several attacks like botnets, malware and guessing attacks. This scheme is contrasted withseveral established schemesand a new form of authentication is used for the purpose of storing files in a secure manner.

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Cross-Language Connect: A Chat App for Smooth Multilingual Communication

Cross-Language Connect: A Chat App for Smooth Multilingual C...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： S Sathiya Priya Manivannan d Gayathri. r Manikandann. K Kujani T Bharathi S Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Data Science Aalim Muhammed Salegh College of Engineering Chennai Department of Computer Science & Engineering SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

The rise of social media has firmly established chat as a cornerstone of online interaction. However, language barriers continue to hinder effective communication among users from diverse linguistic backgrounds. Many traditional chat platforms restrict users to a single language, often requiring the use of external translation tools for cross-lingual conversations, a process that can be cumbersome and inefficient. To address this challenge, a groundbreaking chat application designed to shatter language barriers is proposed. By eliminating the need for a shared language, the solution enables seamless communication between individuals from various linguistic backgrounds. Existing chat platforms frequently confine users to one language, necessitating external translation tools for multilingual interactions. It introduces a cutting-edge chat application that seamlessly integrates machine translation into the chat experience, removing the need for additional applications and facilitating unrestricted multilingual communication. To support this vision, an advanced Neural Machine Translation (NMT) model has been developed that effectively addresses the complexities of cross-lingual communication. The model outperforms commercial alternatives such as Google and Bing Translators in terms of BLEU scores (BLEU-1: 0.87 vs. 0.91/0.83; BLEU-2: 0.77 vs. 0.81/0.73), demonstrating superior fluency and accuracy. Additionally, the model achieves a competitive F1 score of 0.87 compared to Google Translator's 0.92, indicating its strong performance in capturing the nuances of translated text. This technological advancement empowers the chat application to deliver seamless and precise multilingual conversations, enabling global connections and enhancing cultural understanding. Through this innovative approach, a future where language serves as a bridge connecting people from all corners of the world, rather than a barrier that divides them, is envisioned.

关键词： Accuracy System performance Speech recognition Oral communication Linguistics Streaming media real-time systems User experience Global communication Videos

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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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