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International Conference on Smart Communications and Networking (SmartNets)

作者： Prakash Agarwal Rajendra Hegadi Prathamesh P Pai Sudeepto Chatterjee Siddhant Dixit Computer Science and Engineering Indian Institute of Information Technology Dharwad Dharwad Karnataka India Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad Dharwad Karnataka India

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

Water supply management is a critical aspect of ensuring sustainable access to clean water resources. However, challenges such as water scarcity, aging infrastructure, and inefficient resource allocation persists. This research explores the potential of utilizing a digital twin framework combined with blockchain for effective water supply management. The proposed framework offers numerous benefits, including optimized water allocation, early detection of leaks and anomalies, predictive maintenance, and efficient decision-making. Incorporation of smart contracts in the blockchain infrastructure enables automated governance mechanisms, ensuring compliance with predefined rules and agreements. By demonstrating the feasibility and advantages of combining digital twin technology with blockchain in water supply management systems, this research contributes to the existing body of knowledge. The findings can inform policymakers, water utility operators, and researchers on the practical implementation and potential impact of this innovative approach in addressing water management challenges and fostering sustainable water resource utilization.

关键词： Technological innovation Smart contracts Decision making Aging Blockchains Digital twins Resource management

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Enhancing the Efficiency of the Grouping-Scoring-Modeling Framework with Statistical Pre-Scoring Component for Transcriptomic data Analysis

Enhancing the Efficiency of the Grouping-Scoring-Modeling Fr...

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18th International Joint Conference on Biomedical engineering systems and Technologies, BIOSTEC 2025

作者： Khokhar, Maham Bakir-Gungor, Burcu Yousef, Malik Department of Data Science Social Sciences Institute Abdullah Gul University Kayseri38080 Turkey Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri38080 Turkey Department of Information Systems Galilee Digital Health Research Center Zefat Academic College Zefat13206 Israel

The advent of high-throughput transcriptomic technologies has generated vast transcriptomic datasets, challenging current analytical methodologies with their sheer volume and complexity. The Grouping-Scoring Modeling (G-S-M) approach is one of the recent approaches that treat groups of genes (or clusters of genes) by embedding prior biological knowledge with machine learning in order to detect the most significant groups for classification tasks. The G-S-M might need to treat thousand ten thousand of groups (scoring those groups) which might affect the speed and performance of the algorithm. In response, this study introduces the Pre Scoring G-S-M model, an enhancement of the established Grouping-Scoring-Modeling (G-S-M) framework. This approach incorporates a Pre-Scoring component that leverages the Limma package for its empirical Bayes methods to optimize initial transcriptomic data evaluation through a percentage-based selection of statistically significant gene groups. Aimed at reducing computational demand and streamlining feature selection, the model also addresses data redundancy by eliminating duplicate gene-disease associations. Application to nine human gene expression datasets from the GEO database showed promising results. It demonstrated improvements in computational efficiency and analytical precision while reducing the number of features selected per dataset compared to the traditional G-S-M approach, without compromising accuracy. These initial findings highlight the Pre-Scoring G-S-M model's potential to enhance transcriptomic data analysis, indicating a promising direction for future bioinformatics research. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

关键词： data accuracy

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A Model for Predicting Chronic Kidney Diseases Based on Medical data Using Reinforcement Learning

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SN Computer science 2024年第4期5卷 353页

作者： Nramban Kannan, Senthil Kumar Aseervatham, Joshi Moholkar, Kavita Palanimuthu, Mithun Marappan, Saranya Narendran, Muthusamy Sathar, Banu Sengan, Sudhakar Department of Computer Science & amp Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai 600123 India Department of Computer Science and Business Systems Engineering JSPM’S Rajarshi Shahu College of Engineering Savitribai Phule Pune University Pune 411033 India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai 600119 India Department of Information Technology Paavai Engineering College Tamil Nadu Namakkal 637018 India Department Information Technology KCG College of Technology Tamil Nadu Chennai 600097 India Department of Master of Computer Application T. J. Institute of Technology Chennai 600100 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627451 India

Kidney diseases (KD) are a global public health concern affecting millions. Early detection and prediction are crucial for effective treatment. Artificial intelligence (AI) techniques have been used in KDP to analyze past medical records, applying patients’ Electronic Medical Record (EHR) data. However, conventional statistical analysis methods conflict with fully comprehending the complexity of EHR data. AI algorithms have helped early KDP learn and identify complex data patterns. However, challenges include training heterogeneous historical data, protecting privacy and security, and developing monitoring system regulations. This study addresses the primary challenge of training heterogeneous datasets for real-world evaluation. Early detection and diagnosis of chronic kidney disease (CKD) is crucial for improved outcomes, reduced healthcare costs, and reliable treatment. Early treatments are crucial for CKD, as it often develops without apparent symptoms. Predictive models, particularly those using reinforcement learning (RL), can identify significant trends in complex healthcare information, which standard techniques may struggle with. The study makes KDP more accurate and reliable using RL methods on clinical data. This lets doctors find diseases earlier and treat them better by looking at static and changing health measurements. Machine learning (ML) algorithms can enhance the accuracy of AI systems over time, enhancing their effectiveness in detecting and diagnosing diseases. In the current investigation, the RL-ANN model is implemented for performing enforceable CKD by assessing the outcomes of multiple neural networks, which include FNN, RNN, and CNN, according to parameters such as accuracy, sensitivity, specificity, prediction error, prediction rate, and kidney failure rate (KFR). The recommended RL-ANN method has a lower failure rate of 70% based on the KFR data. Further, the proposed approach earned 95% in PR and 70% in analysis of errors. However, the RL

关键词： Alerting systems and diagnostic assistance Chronic kidney disease Machine learning Medical history RL-ANN

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Health information Broadcast Distributed Pattern Association based on Estimated Volume 3

Health Information Broadcast Distributed Pattern Association...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Dhanalakshmi, R. Kalpana, A.V. Umamageswaran, J. Kumar, B. Praveen Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Kattankulathur Department of Data Science and Business Systems Tamil Nadu Chennai India R.M.K Engineering College Department of Information Technology Tamil Nadu Chennai India K L University Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9781665462167

Any study on dataset and inadequate representation are featured in the conventional data mining process. Consequently, data mining findings reveal high chances of challenges and huge root-mean-square approximation errors besides long utilization. The proposed algorithm for associating distribution patterns in streams of medical data is based on density calculations. The distance approach will be utilized to identify any orphaned or questionable data within the data stream, and the gathering of medical data will be used to achieve the aim. data stream duplicates are compiled by sector match quality, and both the out-of-the-ordinary and the redundant data are eventually eliminated. The density of the information stream is then calculated based on histogram calculations. This investigates the flow of medical information from the viewpoints of concentration and dispersion, as well as the physical characteristics of information distribution, in combination with the results of data density estimate. The model is then built upon a neural network composite, the data distribution parameters in the clustering layer of the model are entered, and in-depth testing is carried out via the neural B.P. network (Back Propagation) on the mining layer of the model. Temporarily, all laws under the grouping of the hidden layer operation value besides the resulting output value are derived, and all rules under the grouping of the hidden layer value besides the conforming contribution value in the medical information stream pattern. The new findings show a curve of contour contiguous to the true curve of the likelihood density, a fair range of medical data dispersion and high medical data consistency, a lower probability of data redundancy, a low RMSEA for mining, a lower time for data recovery. © 2023 IEEE.

关键词： data mining

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Enabling Protection for Critical Infrastructure through Security and Privacy in the Industrial Internet of Things 9

Enabling Protection for Critical Infrastructure through Secu...

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9th International Conference on Communication and Electronics systems, ICCES 2024

作者： Yasotha, B. Vijayanandh, T. Thirumoorthy, P. Priya, M.Sangeetha Sasidaran, S. Prakalya, S.B. Srmist Department of Data Science and Business Systems Kattankulathur India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Avadi Tamil Nadu Chennai India Dhanalakshmi College of Engineering Department of Electrical and Electronics Engineering Chennai India Chennai India Panimalar Engineering College Department of Information Technology Chennai600123 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350377972

Industrial Internet of Things can improve critical infrastructure in energy, transportation, and manufacturing. However, IIoT device and system integration opened security weaknesses that bad actors may exploit, causing huge disruptions and financial losses. IoT network security and privacy must be prioritized to protect key infrastructure. IIoT security issues and solutions are examined in this research. First, we take a look at the state of security and the unique risks that industrial IoT devices are up against, including hacking, illegal access, and cyber-physical assaults. Then, to lessen the impact of these dangers, we investigate cutting-edge security solutions, such as authentication, encryption, and access control systems. Building a robust security architecture specifically for IIoT applications is one of the main goals of the project. To improve safety and anonymity, this system uses cutting-edge innovation like blockchain, AI, and edge computing. We also talk about how privacy-preserving methods like differential privacy and anonymization are important for keeping sensitive information safe while allowing data sharing for analysis and operations. We assess the efficacy of the suggested security protocols and procedures using a battery of case studies and simulations. Reducing the attack surface and improving the resilience of IIoT systems can be achieved by a multi-layered security approach that includes effective privacy protections. We conclude by outlining our research's larger implications, which include the importance of standardization, regulatory compliance, and stakeholder collaboration in ensuring the safe and privacy-protecting deployment of IIoT technologies in vital infrastructure. Protecting vital infrastructure from cyber threats is our ultimate goal, and our work helps build IIoT systems that are more secure and dependable. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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An Intellectual Fusion Classification Prototypical for an Imbalanced Electrocardiogram data

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SN Computer science 2023年第6期4卷 721页

作者： Ruth Isabels, K. Mrudula Devi, K. Anand, R. Athe, Ramesh Chowdhury, Sumit Singha Pund, Sachin S. Department of Mathematics Saveetha Engineering College (Autonomous) Chennai India BS&H Vignans Institute of Information Technology (A) Visakhapatnam India Department of MCA Acharya Institute of Technology Bangalore India Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad Dharwad India Acharya Institute of Technology Bangalore India Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Maharashtra Nagpur India

One of the greatest threats posed by progress around the world is cardiovascular diseases (CVD). Each year, CVD accounts for the vast majority of deaths worldwide. The vast majority of people who die from CVD do so suddenly, and their families have no hope of getting them medical help in time. Because of this, there is a pressing need for a real-time, intelligent system that can keep tabs on cardiac patients round the clock. Internet of Things (IoT)-enabled systems are a viable choice for real-time data monitoring. Internet-based smart healthcare has several potential uses, including monitoring physiological data, such as electrocardiograms (ECGs), heart rates (HRs), blood pressures (BPs), and more. IoT-based smart healthcare paradigms present significant issues, not the least of which is selecting the suitable model. This research proposes a heart rate sensor and a sophisticated hybrid classification algorithm as components of an IoT-based electrocardiogram (ECG) monitoring system. The goal of this study is to aid in the development of an automated key for social happiness by creating an intelligent hybrid classification model that can handle the class imbalance problem with more precision. No matter what validation criterion was used (twofold, fivefold, or tenfold cross-validation), the results of the empirical evaluation showed that the proposed hybrid model achieved better outcomes than the additional ingrained classification models in terms of accuracy and efficiency for both classes. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： BP CVD ECG HR IoT

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An Anomaly Detection Algorithm for Estimating Interannual Variation in Crop Yields

An Anomaly Detection Algorithm for Estimating Interannual Va...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： R. Vinoth Kumar Malathy M. Manjula J. Veerandeswari M. Indumathy B S Raghul Information technology Department Rajiv Gandhi College of Engineering & Technology Kriumambakkam Puduherry Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur India

Crop production is a vital aspect of human survival. Reducing global poverty heavily relies on increasing the robustness in crop yields. The quantity of crops harvested from a given agricultural land, also known as crop yields of that land is the important parameter that helps to increase the smartness in adopting novel agricultural plans. Measuring this parameter can be done through supervised machine learning techniques which helps with accurate predictions given the historical data of crop yield. This paper proposes an ideation, that uses Support Vector Machines (SVMs) because of its strength being accuracy, robust and flexible. SVM can predict reliable crop yield estimates that help to allocate resources and smart planning for agricultural activities. Also, SVMs are good enough to handle the imperfections in historical crop yield datasets, outliers, and inconsistencies. Furthermore, SVMs can bring out the relationships and any nonlinear patterns in the crop yields which are influenced by uncertain environmental factors over various seasons. This helps to identify the anomalies in the historical data. The anomalies could be exceptionally high yield or the failures which happen to be pinpointed as abnormal years. The identified outliers are excluded by the SVM, thus bringing out a prediction model that focusses on “normal years”. By this, it is possible to represent more accurate inter-annual trends of crop yields avoiding the anomalies aiding the farmer to make better decisions for much more productivity in the crop yield. Based on the ideation, simulations are performed that showcase the success of the SVM in predicting good for the crop yields in inter-annual period.

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Extracting Events Using Spans Enhanced with Trigger-Argument Interaction 18

Extracting Events Using Spans Enhanced with Trigger-Argument...

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18th International Conference on Intelligent systems and Knowledge engineering, ISKE 2023

作者： Xu, Chi Zeng, Zefan Duan, Jiashun Qi, Qi Zhang, Xin National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China National University of Defense Technology Big Data and Decision Laboratory Changsha China War Studies College The Academy of Military Sciences Beijing China

ISBN: (纸本)9798350318401

Event extraction (EE) aims to detect event triggers and extract their corresponding arguments. In more realistic situations, events often come in a more complicated way in which multiple events may appear simultaneously and arguments may have overlaps in a sentence. For languages without delimiters like Chinese, most existing EE models are token-level and thus may have difficulties in solving multi-event or overlapping problems. What's more, they are prone to overlook the word-level or span-level features and the rich interactions between triggers and arguments. To tackle these problems, we propose a span-based model for multiple and overlapped EE with trigger-argument interaction enhanced span representations. Experimental results show that our approach outperforms other mainstream baselines on the DuEE dataset. © 2023 IEEE.

关键词： multiple event overlapped event span-based model trigger-argument interaction

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Learning and Estimation of Latent Structural Models Based on between-data Metrics

Learning and Estimation of Latent Structural Models Based on...

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Mikawa, Kenta Kobayashi, Manabu Goto, Masayuki Hirasawa, Shigeichi Tokyo City University Department of Information Systems Kanagawa Japan Waseda University Center for Data Science Tokyo Japan Waseda University School of Creative Science and Engineering Tokyo Japan Waseda University Research Institute for Science and Engineering Tokyo Japan

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

With the development of information technology, a wide variety of data have been accumulated, and there are many methods for analyzing such data. In this study, we model the input data and the metrics between the data based on the assumption that each metric is generated from a continuous latent variable. Specifically, we assume that the input data are generated using low-dimensional latent variables and their projection matrices. We describe a method for estimating the latent variables. Because the generative model defined in this study cannot obtain the Q function analytically, we use the Monte Carlo EM algorithm to approximate the Q function and investigate an efficient parameter estimation method. Experiments using artificial data and the 20 newsgroups dataset demonstrate the effectiveness of the proposed method. © 2022 IEEE.

关键词： Input output programs

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Development of Fast Prediction System of User Action for a Fully Autonomous Smart Home System 4

Development of Fast Prediction System of User Action for a F...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rajalakshmi, N.R. Sharmila, L. Prabu, S. Veeramakali, T. Arulkumaran, G. Chourasia, Pranshu Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India School of Computing and Information Technology Reva University Karnataka Bangalore560064 India

ISBN: (纸本)9798331523923

The HVAC system, energy storage building, distributed power supply, and other equipment are integrated into the scheduling algorithm, which is aimed at reducing household electricity consumption. It is also assumed that users can provide energy to the grid according to their own conditions. Taking electricity cost and comfort level as optimization targets, a home energy optimization control model for the coordinated management of hybrid energy sources is built. A smart scheduling mechanism based on the improved adaptive particle swarm optimization approach is proposed in order to derive the best time intervals for electric appliances, necessary power for the control of the room temperature for every time frame, and power for charging and discharging of the storage battery at various moments. Simulation results show that through the incorporation of distributed photovoltaic power generation, backup storage by battery, and home energy optimization control, the system efficiently balances between user comfort and electricity consumption. This offers great technical support to the development of home energy management systems. By using time-of-use electricity price for energy acquisition and supply, the optimization control goal is minimizing both power use and cost as well as preserving comfort levels. The hybrid energy management's proposed home energy optimization control model uses an adaptive particle swarm optimization algorithm to find the optimal operation schedules of the electrical appliances, the required power for temperature control in a room, and the charge/discharge power level of the storage battery at each time interval. As per the optimization principle, the proposed dynamic programming algorithm converts the multi-stage problem into a sequence of single-stage problems and solves them separately. This method successfully resolves intricate problems that cannot be addressed through greedy algorithms or divide-and-conquer. In this research, management ac

关键词： Decision making

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