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3rd International Conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Josphineleela, R. Gupta, Rupal Misra, Neeti Malik, Mohit Somasundaram, K. Gangodkar, Durgaprasad Panimalar Engineering College Computer Science and Engineering India Teerthanker Mahaveer University College of Computing Science and Information Technology Uttar Pradesh Moradabad India University of Petroleum and Energy Studies Uttaranchal Institute of Management Dehradun Management School of Business India Amity University Rajasthan Jaipur India Alpha College of Engineering CSE Thirumazhisai Chennai India Graphic Era Deemed to be University Department of CSE Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

Why block chain enables cutting-edge protection. This article recommends a number of co secured internet-of-things communication architecture based on block chains, so let's talk about how to set up a network authentication process with many degrees of security using that innovation. By merging the Internet of Things into a bridges connecting and implementing the cryptocurrency structure at every point in the network, the method removes the obstacles to really adopting chain while keeping the humankind's strict privacy and valid assurance. It provides huge internet as an alternative for the Internet of Things. Bricks that include data make form a block chain. The amount of data in a blocks varies dramatically depending on the kind of bitcoin blockchain. For example, a brick of digital money contains the Sender, the Receiver, and the quantity of virtual money than will be sent. The chain's first block is called the New block. Making information accessible to everybody using an application is the aim of the block chain. Learning to read access to this ledger may be either unlimited (sometimes referred to as "password less") or constrained (called "permission"). computer code may now be sent, retrieved, and stored securely thanks the block company currently. Additionally, it secures every transaction using cryptographic encryption. Banks may increase the threshold on our modern security and disclosure by putting this into practise. © 2023 IEEE.

关键词： Network security

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Hyperparameter Optimization of Machine Learning Models for Predicting Actual Evapotranspiration

SSRN

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SSRN 2025年

作者： Liyew, Chalachew Muluken Di Nardo, Elvira Ferraris, Stefano Meo, Rosa Department of Computer Science University of Turin Italy Department of Mathematics"G. Peano" University of Turin Italy Interuniversity Department of Regional and Urban Studies and Planning Politecnico di Torino University of Turin Italy Faculty of Computing Institute of Technology Bahir Dar University Bahir Dar Ethiopia

Actual evapotranspiration (AET) is a critical component of the water cycle, essential for effective water resource planning and management. While meteorological variables can predict AET, determining the reduction of potential evapotranspiration due to soil dryness remains challenging. AET data are often unavailable, necessitating reliance on more commonly measured variables. This study uses two groups of predictor variables for AET prediction. The first group, selected through Pearson correlation, tolerance, and VIF scores to address multicollinearity, includes net CO₂, sensible heat flux, air temperature, relative humidity, and wind speed. The second group, chosen for practical applicability, consists of soil surface temperature, air temperature, relative humidity, and wind *** predictive approaches are proposed: (i) deep learning models (LSTM, GRU, CNN) and (ii) classical machine learning models (SVR, RF). Hyperparameters were optimized using Bayesian optimization and compared with grid search. Bayesian optimization demonstrated higher performance and reduced computation time. Model performance was evaluated using statistical indicators (RMSE, MSE, MAE, R²). Deep learning methods outperformed classical methods, with LSTM achieving the best results (Bayesian optimization: RMSE=0.0230, MSE=0.0005, MAE=0.0139, R²=0.8861).Performance decreased with fewer predictors. LSTM maintained superiority, achieving R²=0.8861 with five predictors and R²=0.8467 with four. LSTM also slightly outperformed SVR with fewer predictors. Overall, deep learning methods, especially with Bayesian optimization, proved more effective than classical methods for AET prediction. Future research could explore additional predictors and machine learning models to further improve AET prediction accuracy. © 2025, The Authors. All rights reserved.

关键词： Deep learning

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Identifying Time Patterns of Highland and Lowland Air Temperature Trends in Italy and UK across monthly and annual scales

arXiv

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arXiv 2024年

作者： Liyew, Chalachew Muluken Di Nardo, Elvira Meo, Rosa Ferraris, Stefano Department of Computer Science University of Turin Italy Department of Mathematics "G. Peano" University of Turin Italy Interuniversity Department of Regional and Urban Studies and Planning Politecnico Università of Turin Italy Faculty of Computing Institute of Technology Bahir Dar University Bahir Dar Ethiopia

This paper presents a statistical analysis of air temperature data from 32 stations in Italy and the UK up to 2000 m above sea level, from 2002 to 2021. The data came from both highland and lowland areas, in order to evaluate both the differences due to location, and elevation. The analysis focused on detecting trends at annual and monthly time scales, employing both ordinary least squares (OLS), robust S-estimator regression, and Mann-Kendall (MK) and Sen's slope methods. Then hierarchical clustering using Dynamic Time Warping (DTW) was applied to the monthly data to analyze the intra-annual pattern similarity of trends within and across the groups. Two different regions of Europe were chosen because of the different climate and temperature trends, namely the Northern UK (smaller trends) and the North-West Italian Alps (larger trends). The main novelty of the work is to show that stations having similar locations and altitudes have similar monthly slopes by quantifying them using DTW and clustering. These results reveal the nonrandomness of different trends along the year and among different parts of Europe, with a modest influence of altitude in wintertime. The findings revealed that group average trends were close to the NOAA values for the areas in Italy and the UK, confirming the validity of analyzing a small number of stations. More interestingly, intra-annual patterns were detected commonly at the stations of each of the groups, and clearly different between them. Confirming the different climates, most highland and lowland stations in Italy exhibit statistically significant positive trends, while in the UK, both highland and lowland stations show statistically nonsignificant negative trends. Hierarchical clustering in combination with DTW showed consistent similarity between monthly patterns of means and trends within the group of stations and inconsistent similarity between patterns across groups. The use of the twelve distance correlation matrices (dCor) (

关键词： Sea level

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Integrating machine learning models for real-time pollution monitoring in smart cities

Integrating machine learning models for real-time pollution ...

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8th IET Smart Cities Symposium (SCS 2024)

作者： R. Ravi Matheswaran Saravanan Veni Jayakumar V. Sivaraman V. Amirthalingam Prabaharan Sengodan Department of CSE J.J College of Engineering and Technology India R&D Institute of Science and Technology India India Department of Management Studies J.J College of Engineering and Technology India Department of Computer Science & Engineering Cyber Security Madanapalle Institute of Technology & Science Andhra Pradesh India Computer Science and Engineering Vinayaka Mission's Kirupananda Variyar Engineering College Salem India School of Computing Galgotias University Noida India

ISBN: (数字)9781837243105

The integration of machine learning (ML) models into smart city infrastructures offers a robust solution for real-time pollution monitoring. This paper presents innovative approaches, including data collection, preprocessing, model deployment, and adaptive learning techniques. Through comprehensive experiments, our study demonstrates the efficacy of various ML models. The Gradient Boosting model achieved the highest accuracy with an R-squared value of 0.93, MAE of 2.9 µg/m³, and RMSE of 4.2 µg/m³. The DBSCAN algorithm effectively identified pollution hotspots with a silhouette score of 0.71 and a Davies-Bouldin index of 0.35. The Deep Q-Network (DQN) optimized pollution control strategies, achieving a cumulative reward of 1400 and a convergence rate of 90%. An ensemble model combining Random Forest and Gradient Boosting achieved an R-squared value of 0.94. These results underscore the potential of ML models to enhance pollution monitoring and management in smart cities, improving urban air quality and public health.

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

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IEEE Transactions on Mobile computing 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing China College of Computing and Data Science Singapore University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral Gables33146 United States University of Miami Department of Industrial and Systems Engineering Coral Gables33146 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing Advanced Information Network Laboratory Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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Diabetes Prediction Model for Better Clarification by using Machine Learning

Diabetes Prediction Model for Better Clarification by using ...

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International Conference on Inventive Computation Technologies (ICICT)

作者： J. Lysa Eben R. Jayasudha S. Ramya S. Kaliappan Shobha Aswal Khalid Ali Salem Al-Salehi Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Chennai Tamilnadu India Department of Mathematics Dr.N.G.P.Institute of Technology Coimbatore India Department of Computer Science and Engineering Kongu Engineering College Erode Tamilnadu India Division of Research and Development Lovely Professional University Phagwara Punjab India School of Computing Graphic Era Hill University Dehradun India Biomedical engineering study program faculty of science and technology Airlangga University Surabaya Indonesia

Diabetes mellitus is one of the most pressing health concerns because so many people are afflicted by its disabling symptoms. Factors such as age, excess body fat, insufficient physical activity, a history of diabetes in one's family, a sedentary lifestyle, an unhealthy diet, hypertension, etc., all increase the likelihood of developing diabetes mellitus. Health complications are more common in people with diabetes, including cardiovascular disease, renal failure, stroke, blindness, and nerve injury. To validate a diagnosis of diabetes, hospitals typically perform a battery of procedures on the patient. Big data analytics has many vital applications in the healthcare sector. Numerous large computer systems are used in the healthcare sector. With the help of big data analytics, researchers can sift through mountains of data in search of previously unseen patterns and insights. Current techniques have a poor degree of precision in classification and forecast. While previous research has focused on factors such as glucose, body mass index, age, insulin, etc., the proposed model takes these into account and also the other factors that may be more relevant to the development of diabetes. The newer sample is superior to the older one based on categorization accuracy. A workflow algorithm for diabetes prognosis is also required to improve the accuracy.

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Co‑packaged optics(CPO):status,challenges,and solutions

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Frontiers of Optoelectronics 2023年第1期16卷 1-40页

作者： Min Tan Jiang Xu Siyang Liu Junbo Feng Hua Zhang Chaonan Yao Shixi Chen Hangyu Guo Gengshi Han Zhanhao Wen Bao Chen Yu He Xuqiang Zheng Da Ming Yaowen Tu Qiang Fu Nan Qi Dan Li Li Geng Song Wen Fenghe Yang Huimin He Fengman Liu Haiyun Xue Yuhang Wang Ciyuan Qiu Guangcan Mi Yanbo Li Tianhai Chang Mingche Lai Luo Zhang Qinfen Hao Mengyuan Qin School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong KongChina HKUST Fok Ying Tung Research Institute Guangzhou 511462China The Hong Kong University of Science and Technology(Guangzhou) Guangzhou 511462China Chongqing United Micro-Electronics Center(CUMEC) Chongqing 401332China Hisense Broadband Multimedia Technologies Co. Ltd.Qingdao 266000China Institute of Microelectronics Chinese Academy of SciencesBeijing 100029China State Key Laboratory of Superlattices and Microstructures Institute of SemiconductorsChinese Academy of SciencesBeijing 100083China School of Microelectronics Xi’an Jiaotong UniversityXi’an 710049China Zhangjiang Laboratory Shanghai 201210China The State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China Huawei Technologies Co. Ltd.Shenzhen 440307China College of Computer National University of Defense TechnologyChangsha 410073China Institute of Computing Technology Chinese Academy of SciencesBeijing 100086China

Due to the rise of 5G,IoT,AI,and high-performance computing applications,datacenter trafc has grown at a compound annual growth rate of nearly 30%.Furthermore,nearly three-fourths of the datacenter trafc resides within *** conventional pluggable optics increases at a much slower rate than that of datacenter *** gap between application requirements and the capability of conventional pluggable optics keeps increasing,a trend that is *** optics(CPO)is a disruptive approach to increasing the interconnecting bandwidth density and energy efciency by dramatically shortening the electrical link length through advanced packaging and co-optimization of electronics and *** is widely regarded as a promising solution for future datacenter interconnections,and silicon platform is the most promising platform for large-scale *** international companies(e.g.,Intel,Broadcom and IBM)have heavily investigated in CPO technology,an inter-disciplinary research feld that involves photonic devices,integrated circuits design,packaging,photonic device modeling,electronic-photonic co-simulation,applications,and *** review aims to provide the readers a comprehensive overview of the state-of-the-art progress of CPO in silicon platform,identify the key challenges,and point out the potential solutions,hoping to encourage collaboration between diferent research felds to accelerate the development of CPO technology.

关键词： Co-packaged optics Silicon photonics High-performance computing advanced packaging External laser Optical power delivery Co-simulation Standardization Transmitter Receiver

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Analysis and Detection of Fake News Using Machine Learning

Analysis and Detection of Fake News Using Machine Learning

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： P Sathish Kumar P. Suthanthiradevi C Arul Stephen B Ebenezer Abishek S. Sivakumar M Mathiyarasu Computer and Communication Engineering Rajalakshmi institute of technology Chennai Data Science and Business Systems School of Computing SRM institute of Science and Technology Kattankulathur Chennai Electronics and Communication Engineering Vels Institute of science Technology and Advanced studies Chennai Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai Electronics and Communication Engineering BIHER Chennai

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

The Fake news becomes a more critical issue nowadays, it may reduce the trust of the public about the particular information. This project focuses on the advancement of fake news detection model using machine learning. The main goal of this project is to develop, analyze a model that can effectively classify the news articles based on their Subject or contents. Natural preprocessing Algorithm and features such as Stemming, Tokenization, Stop word and TF-IDF Vectorization are used to train the machine learning model. Then the Machine learning Algorithms likeLogistic Regression and Random Forest are used to classifying the dataset which gives 98% accuracy and 99% of accuracy score. For this Project we have taken a dataset of 22413 Real news and 23481 Fake News from the online resources. This model has a potential to reduce the impact of fake news by grant the promotion of accurate and reliable information in a society.

关键词： Support vector machines Logistic regression Accuracy Machine learning algorithms Tokenization Reliability Internet of Things

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Remote Patient Monitoring System with Wearable IoT Devices and Biosensors for Vital Signs 3

Remote Patient Monitoring System with Wearable IoT Devices a...

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3rd IEEE International Conference on Device Intelligence, computing and Communication Technologies, DICCT 2025

作者： Naveen, S. Sivakumar, T.B. Supreeth, B.R. Niranjana, R. Vishwa Priya, V. Saranya, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences-SIMATS Saveetha University Department of Mechanical Engineering Tamil Nadu Chennai India School of Computing Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Cse Tamil Nadu Chennai India Oxford Degree and Pg College Department of Computer Science Sir. M. Visveshwaraiah Layout Ullal Bangalore India Velammal College of Engineering and Technology Department of Cse Tamil Nadu Madurai India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Information Technology Tamil Nadu Chennai India Kpr Institute of Engineering and Technology Department of English Tamil Nadu Coimbatore India

ISBN: (纸本)9798331543358

Due to infrequent patient monitoring and the reactive character of the traditional healthcare system, delayed diagnoses and inadequate management of chronic diseases are common outcomes. The study presents a solution to these drawbacks: a Remote Patient Monitoring (RPM) system that combines advanced biosensors with wearable IoT devices to track vital signs continuously. By ensuring proactive healthcare through real-time data collecting and analysis, the entire system overcomes the drawbacks of conventional techniques. Early anomaly detection and timely medical interventions are made possible by the method's utilization of scalable cloud infrastructure and advanced machine learning (ML) methods, such as CNNs (Convolutional Neural Networks) and Long Short-Term Memory (LSTMs). The proposed system achieves a 90% early detection rate, 95% sensitivity, 97% specificity in anomaly detection, and 92% patient satisfaction, which is a substantial improvement over existing techniques, according to the results. It also exhibits better vital sign measurement accuracy than existing systems, indicating its potential to completely transform patient care through better results and economical resource utilization. © 2025 IEEE.

关键词： Remote patient monitoring

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Remote Patient Monitoring System with Wearable IoT Devices and Biosensors for Vital Signs

Remote Patient Monitoring System with Wearable IoT Devices a...

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Device Intelligence, computing and Communication Technologies, (DICCT), International Conference on

作者： S. Naveen T.B. Sivakumar B. R. Supreeth R. Niranjana Vishwa Priya V Saranya R Department of Mechanical Engineering Saveetha School of Engineering_Saveetha Institute of Medical and Technical Sciences_SIMATS Saveetha University Chennai Tamil Nadu India Department of CSE School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science Oxford Degree and PG College Sir. M. Visveshwaraiah Layout Ullal India Department of CSE Velammal College of Engineering and Technology Madurai Tamil Nadu India Department of Computer Science and Information Technology Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India Department of English KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

关键词： Patient monitoring Accuracy Medical services Real-time systems Biosensors Internet of Things Biomedical monitoring Medical diagnostic imaging Anomaly detection Diseases

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