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data Science, Agents & Artificial Intelligence (ICdSAAI), International Conference on

作者： Meenakshi S. Pavaimalar rama Prabha K. P S. ravi N. Kumaran S. B. Priya Department of Artificial Intelligence & Data Science Nitte Meenakshi Institute of Technology Nitte University Bangalore India Department of Information Technology V. S. B. Engineering College Karur Tamilnadu India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science St Joseph's Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Search and rescue (SAr) operations designed to save lives in disaster zones face difficulties executing effectively because of dangerous surroundings and challenging terrain features. An AI-controlled robotic system exists to deal with existing challenges. The system combines a reinforcement learning-based navigation system that integrates multiple sensors including LidAr with thermal cameras and rGB sensors for detecting victims to work with decentralized task distribution frameworks. real-time simulations tested disaster navigation datasets showing an overall detection accuracy rate of 95.6%, navigation accuracy at 92.8%, while obstacle avoidance performed at 97.2%. This deployed system achieved 3.2-minute average response times and processed requests with 85.4% resource efficiency. The system proves superior performance over existing SAr methods while demonstrating speed enhancement, accuracy improvement, and autonomous operational value in SAr operations. Future disaster response will change thanks to AI-powered robotics because this technology enables scalable efficient reliable solutions for future deployments.

关键词： Accuracy Navigation disasters Transforms Thermal sensors Sensor systems Time factors Collision avoidance robots Optimization

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IoT-Enabled Asthma Trackers for Children with KNN for Improved Breathing Control

IoT-Enabled Asthma Trackers for Children with KNN for Improv...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： P. Sathyanathan K. Balachander Pramod Pandey V. Prasanna Srinivasan T. r. GaneshBabu M. Muthulekshmi Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Maharashtra India Department of Information Technology R.M.D. Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Managing asthma in children is a complex task that needs constant vigilance and personalized treatments to get the best potential control. For better respiratory management, an Internet of Things (IoT)-enabled pediatric asthma tracking system that uses machine learning (ML) methods, particularly for K-nearest neighbors (KNN), is presented. The system's integration of wearable sensors, which continuously monitor vital signs and environmental conditions, provides a comprehensive view of the child's respiratory health. Asthma exacerbation causes, and trends may be discovered by analyzing the obtained data using KNN algorithms. Analysis allows for the generation of real-time warnings and feedback, allowing caregivers to take proactive steps in reducing risks and optimizing treatment procedures. Our IoT-enabled method for improving respiratory control and decreasing asthma-related consequences in children is feasible and effective in pilot research, including children with asthma. Our research highlights the promise of IoT devices and ML algorithms to transform how pediatric asthma is treated by providing tailored, evidence-based treatments that lead to better health outcomes.

关键词： Pediatrics Machine learning algorithms Surveillance Medical services Transforms Nearest neighbor methods real-time systems Internet of Things Wearable sensors Asthma

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Predictive Maintenance in IoT Networks Using a Temporal Attention Mechanism Based Federated Convolutional Neural Network

Predictive Maintenance in IoT Networks Using a Temporal Atte...

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Innovation in technology (ASIANCON), Asian Conference on

作者： Naga Venkata ramakrishna Guduri r.M. dilip Charaan Kalaiyarasi r Samuthira Pandi V d.Laxma reddy Selvaprabhu. T Faculty of Engineering University of Technology and Applied Sciences-Al Musannah Sultanate of Oman Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Chennai Tamil Nadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of ECE MLR Institute of Technology Hyderabad India Department of CSE Dhanalakshmi College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

In the era of Industry 4.0, edge devices in industrial settings generate vast amounts of data crucial for process optimization and Predictive Maintenance (PM). Traditional cloud-based approaches for handling this data are becoming untenable due to cost, latency, and privacy issues. Federated learning emerges as a viable solution, offering localized and efficient data processing while mitigating these challenges. This paper presents a Federated Convolutional Neural Network with Temporal Attention Mechanism (FedCNN-TAM) for PM tasks in Internet of Things (IoT) networks. This model leverages temporal features in sensor data to enhance predictive accuracy. Evaluated on the CMAPSS dataset, FedCNN-TAM outperforms representative models with a significant performance gain. This model exhibits the potential for broader applications in industrial IoT, setting the stage for future research on its scalability and adaptability to multi-modal data types.

关键词： Adaptation models Temperature distribution Technological innovation Attention mechanisms Accuracy Predictive models data models real-time systems Convolutional neural networks Predictive maintenance

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Application of Magnetic resin based on Grey Clustering Algorithm in Advanced Treatment of Industrial Wastewater

Application of Magnetic Resin based on Grey Clustering Algor...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： Narendra B Mustare Arockia raj Abraham r. Suguna Sanjiv Kumar Jain Saritha PS Ch. raja Department of Electronics and Instrumentation Engineering CVR College Of Engineering Hyderabad Department of CSE- Data Science Madanapalle Institute of Technology and Science Madanapalle Chittoor Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Avadi India Department of Electrical Engineering Medi-Caps University Artificial Intelligence & Data Science Dhanalakshmi Srinivasan College Of Engineering Navakkarai Post Coimbatore India Department of ECE Mahatma Gandhi Institute of Technology Hyderabad Telangana India

With the rapid development of industry, the types and quantities of wastewater have increased rapidly, and the pollution to water bodies has become increasingly widespread and serious. As far as water system is concerned, the information of water quality system is both known and unknown because of incomplete data information and great uncertainty of systematic observation information, such as data error, data quality and quantity influence, etc. Therefore, judging from the clarity of system information, water quality pollution assessment is actually a problem with definite extension but uncertain connotation. In this paper, a water quality detection method based on grey clustering algorithm is proposed to evaluate the treatment effect of industrial wastewater by magnetic resin adsorption, and the effectiveness of the proposed method is verified by comparative experiments. The results show that grey clustering algorithm can deal with the measured data of turbidity, dissolved oxygen and conductivity of water quality parameters without changing the basic trend of the measured data, and at the same time, it can remove some noise signals to get a smoother data curve. Through the analysis of practical data and the comparison of traditional water quality evaluation models, the prediction algorithm predicts the change trend of water turbidity more effectively, and improves the accuracy of water turbidity control in the process of water treatment.

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Empirical Evaluation of License Plate recognition Using Tensorflow Framework

Empirical Evaluation of License Plate Recognition Using Tens...

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BIT Conference (BITCON), International

作者： S. Sowmyayani V. S. Harilakshmi J. Kavitha r. roselinkiruba Department of Computer (SF) St. Mary’s College (Autonomous) (Affiliated to Manonmaniam Sundaranar University Tirunelveli) Thoothukudi Tamilnadu India Department of Artificial Intelligence and Data Science Holy Cross College (Autonomous) Nagercoil Tamilnadu India Department of Computer Science and Engineering VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

due to the tremendous increase in vehicles, more attention is required to Intelligent Transport Systems (ITS). License Plate recognition (LPr) is one such method in ITS. Although there are several researches on LPr, still the issues are partially solved. This is because license plate images suffer from low illumination, side view, etc. This paper developed a method to recognize license plates. It consists of license plate localization, segmentation and recognition. A Convolutional Neural Network (CNN) is designed for recognition. The proposed method is tested on the Chinese City Parking dataset (CCPd), Chinese License Plate dataset (CLPd), Application Oriented License Plate (AOLP) and PKUdata datasets. The performance of the proposed LPr method is evaluated using accuracy. It outperforms other recent methods by achieving an accuracy of 95%

关键词： Location awareness Image segmentation Accuracy Image recognition Codes Urban areas Lighting Convolutional neural networks License plate recognition

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Estimating Smart Grid Stability with Hybrid rNN+LSTM deep Learning Approach

Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Le...

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International Smart Grid Workshop and Certificate Program (ISGWCP)

作者： Saadin Oyucu Şeref Sağıroğlu Ahmet Aksöz Emre Biçer Department of Computer Engineering Faculty of Engineering Adıyaman Türkiye Gazi University Artificial Intelligence and Big Data Analytics Security R&D Center Ankara Türkiye Mobilers Team Sivas Cumhuriyet University Sivas Türkiye Faculty of Engineering and Natural Sciences Battery Research Laboratory Sivas University of Science and Technology Sivas Türkiye

ISBN: (数字)9798350361612

ISBN: (纸本)9798350361629

Smart grids are faced with a range of challenges, such as the development of communication infrastructure, cybersecurity threats, data privacy, and the protection of user information, due to their complex structure. Another key challenge faced by smart grids is the stability issues arising from variable energy sources and consumption patterns. In these complex grid systems where energy demand and supply need to be balanced instantly, stability predictions play a significant role in foreseeing potential disruptions and optimizing energy flow. Therefore, within the scope of this study, a hybrid structure utilizing recurrent Neural Networks (rNN) and Long Short-Term Memory (LSTM) networks is employed for stability classification to predict grid stability. This hybrid model combines the ability of rNN to recognize relationships between consecutive data points with LSTM’s capability to preserve long-term dependencies. The results obtained indicate that the model exhibited stable performance with accuracy rates of 98.06% and 98.02% at 50 and 100 epochs, respectively. The findings of this study contribute valuable insights to research on the management and stability of smart grids, enabling energy systems to be operated more reliably and efficiently.

关键词： deep learning data privacy recurrent neural networks Power system stability Stability analysis data models Smart grids

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Machine Learning With data Assimilation and Uncertainty Quantification for dynamical Systems:A review

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IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1361-1387页

作者： Sibo Cheng César Quilodrán-Casas Said Ouala Alban Farchi Che Liu Pierre Tandeo ronan Fablet didier Lucor Bertrand Iooss Julien Brajard dunhui Xiao Tijana Janjic Weiping ding Yike Guo Alberto Carrassi Marc Bocquet rossella Arcucci Data Science Institute Department of ComputingImperial College LondonSW72AZ London Department of Earth Science and Engineering Imperial College LondonSW72AZ London Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China the IMT Atlantique Lab-STICCUMR CNRS 6285France and OdysseyInria/IMTFrance.P.Tandeo is also with RIKEN Center for Computational ScienceKobeJapan the CEREA École des Ponts and EDF R&Dîle-de-FranceFrance the Laboratoire Interdisciplinaire des Sciences du Numérique CNRSParis-Saclay UniversityF-91403OrsayFrance the Electricitéde France(EDF) 78401 ChatouFranceInstitut de Mathématiques de Toulouse31062 ToulouseFrance and SINCLAIR AI LabSaclayFrance the Sorbonne University ParisFranceand also with Nansen Environmental and Remote Sensing Center(NERSC)BergenNorway the School of Mathematical Sciences Tongji UniversityShanghai 200092China the Mathematical Institute for Machine Learning and Data Science KU Eichstaett-IngolstadtBavariaGermany the School of Information Science and Technology Nantong UniversityNantong 226019China the Department of Physics and Astronomy“Augusto Righi” University of Bologna40124 BolognaItaly

data assimilation(dA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal *** applications span from computational fluid dynamics(CFd)to geoscience and climate ***,much effort has been given in combining dA,UQ and machine learning(ML)*** research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive *** paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of *** review is aimed at ML scientists who attempt to apply dA and UQ techniques to improve the accuracy and the interpretability of their models,but also at dA and UQ experts who intend to integrate cutting-edge ML approaches to their ***,this article has a special focus on how ML methods can overcome the existing limits of dA and UQ,and vice *** exciting perspectives of this rapidly developing research field are also *** Terms-data assimilation(dA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).

关键词： assimilation overcome apply

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Exploring decentralized Identity Verification Systems Using Blockchain technology: Opportunities and Challenges

Exploring Decentralized Identity Verification Systems Using ...

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Global Conference for Advancement in technology (GCAT)

作者： V. Saravanan Sumalatha A desidi Narsimha reddy B. Shamreen Ahamed Udayakumar K Arthi Priyadharshini M Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of Computer Science Kristu Jayanti College Autonomous Bengaluru Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Jeppiaar Nagar Chennai Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology India Department of CSE Dhanalakshmi College of Engineering Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

The rapid advancement of digital technologies has underscored the critical necessity for robust and reliable identity verification systems in today's interconnected world. Traditional centralized identity management systems encounter myriad challenges, from vulnerabilities to data breaches and privacy concerns, to inefficiencies inherent in verification processes. This paper delves into the transformative potential of decentralized identity verification systems using blockchain technology as a robust solution to these pressing issues. By harnessing blockchain's immutable, transparent, and secure characteristics, decentralized identity systems not only promise enhanced privacy, security, and user control over personal data but also foster a paradigm shift towards more efficient and user-centric identity management *** study rigorously examines the foundational principles of blockchain technology and its strategic application in decentralized identity verification. It highlights key opportunities such as strengthened security measures through cryptographic protocols, enhanced user autonomy in managing and sharing personal information, and streamlined verification processes that reduce operational complexities and costs. Additionally, the paper critically evaluates significant challenges that must be addressed, including scalability concerns as blockchain networks grow, complexities in navigating regulatory landscapes designed for centralized systems, and overcoming barriers to widespread user adoption. Through an exhaustive analysis of existing models, real-world case studies, and emerging trends, this paper aims to provide comprehensive insights into the feasibility and future prospects of decentralized identity verification systems. By contributing to the ongoing discourse on innovative identity management solutions in the digital age, this research seeks to pave the way for more secure, efficient, and equitable digital ecosystems.

关键词： data privacy Navigation Scalability Pressing Market research Blockchains Complexity theory Security reliability Identity management systems

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Intrusion detection based on phishing detection with machine learning

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Measurement: Sensors 2024年 31卷

作者： Jayaraj, r. Pushpalatha, A. Sangeetha, K. Kamaleshwar, T. Udhaya Shree, S. damodaran, deepa Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur TN Chennai India M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology TN Coimbatore India Department of Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Vel Tech Dr. Rangarajan Dr.Sagunthala R&D Institute of Science and Technology TN Chennai India Department of Computer Science and Engineering Alpha College of Engineering and Technology Puducherry India VITBS Vellore Institute of Technology Chennai Campus TN India

Machine learning technique which uses artificial neural networks to learn representations. Phishing is a form of fraud in which the attacker tries to learn credential information from the websites. Web phishing is to steal sensitive information such as usernames, passwords and credit card details by way of impersonating a authorized entity. The Hybrid Ensemble Feature Selection is a new feature selection method for machine learning-based phishing detection systems (HEFS). The first step of HEFS involves using a novel Cumulative distribution Function gradient (CdF-g) algorithm to generate primary feature subsets, which are then fed into a data perturbation ensemble to generate secondary feature subsets. We present the results of our approach and compare them to a few previous studies, with the paper focusing primarily on phishing urls for detecting the unauthorised one by using phishing detection method. © 2023

关键词： Intrusion detection

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Novel PdE9 inhibitors, Kr39526 and Kr39582, attenuate cardiac hypertrophy and fibrosis induced by pressure overload

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Journal of Pharmacological Sciences 2025年第4期158卷 353-362页

作者： Jeong Hyun Lee Hyunjin Park Seung Hyeong Lee Su Ah Kim Jun Young Choi Chae Jo Lim Ju-Young Shin Byung Ho Lee Kwang-Seok Oh Data Convergence Drug Research Center Korea Research Institute of Chemical Technology Daejeon South Korea Graduate School of New Drug Discovery and Development Chungnam National University Daejeon South Korea R&D Center Huons co. Ltd. Ansan Gyeonggi-do South Korea Department of Biohealth Regulatory Science Sungkyunkwan University Suwon Gyeonggi-do South Korea School of Pharmacy Sungkyunkwan University Suwon Gyeonggi-do South Korea Department of Medicinal and Pharmaceutical Chemistry University of Science and Technology Daejeon South Korea

The natriuretic peptide and cyclic guanosine monophosphate (cGMP) cascade are promising therapeutic target for heart failure. This study evaluated the pharmacological properties and cardioprotective effects of novel phosphodiesterase 9 (PdE9) inhibitors, Kr39526 and Kr39582, in vitro and in vivo . The potency and selectivity of these compounds were assessed through PdE9 inhibitory activity and subfamily selectivity assays. Functional analyses in cardiomyocytes included calcium mobilization, cellular hypertrophy, and protein expression. The cardioprotective efficacy was investigated using a mouse model of pressure-overload-induced cardiac hypertrophy. Kr39526 and Kr39582 demonstrated potent PdE9A inhibitory activities (IC 50 : 5 ± 2 nM and 0.4 ± 0.1 nM, respectively) with high selectivity. This inhibition led to concentration-dependent calcium influx through maintaining intracellular cGMP levels and significantly suppressed phenylephrine-induced cardiomyocyte hypertrophy. Oral administration (50 mg·kg-1) of these compounds markedly attenuated cardiac hypertrophy and myocardial fibrosis induced by pressure overload. These results suggest that Kr39526 and Kr39582, as potent and selective PdE9A inhibitors, have strong potential for exerting anti-hypertrophic and anti-fibrotic effects in heart failure. These compounds can serve as valuable pharmacological tools to elucidate the roles of PdE9A signaling in heart failure pathophysiology and hold significant promise as potential therapeutic agents.

关键词： Phosphodiesterase 9A Inhibitor Heart failure Cardiac hypertrophy Transverse aortic constriction

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