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4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Verma, Apurv Rastogi, Shailesh Meenakshi India Srv Media Pvt. Ltd India Faculty of Management It & Business Analytics India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Pune India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350360165

Many electronic infection detection systems often use binary classification techniques, which identify patient data as either pathology-indicating or normal in relation to different forms of infections. The Moni-ICU system, which is used in the critical care units of Vienna General Hospital, is noteworthy for introducing a novel third category called borderline. This categorization indicates patient data that partially satisfies the corresponding needs by closely aligning with virus-related clinical concepts or infections related to healthcare (HAI) surveillance categories. The probability of classifying patient data into normal, borderline, of pathological categories with respect to infection-related medical ideas and HAI definitions was calculated by integrating fuzzy sets of propositional fuzzy rules. This deviation from dichotomous approaches prevents the possible misunderstanding of borderline findings with normal ones, a major difficulty in binary classifications. Data collection included a thorough analysis of 5099 individuals treated over a two-year period, including adult patients across 10 ICUs at Vienna General Hospital. The Moni-ICU knowledge base, which includes fuzzy sets and rules for four top-level HAI definitions and 10 infection-related clinical concepts, made the categorization process easier. In order to verify the accuracy and dependability of the system, the assessment focused on how suitable borderline classifications were in relation to normal values for related HAI criteria. © 2024 IEEE.

关键词： Fuzzy rules

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Selection of Rendezvous Points Using Fuzzy Logic for Mobile Data Collection in Wireless Sensor Networks

Selection of Rendezvous Points Using Fuzzy Logic for Mobile ...

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2024 International Conference on Knowledge Engineering and Communication systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India Srv Media Pvt. Ltd India Biib Faculty of Management It & Business Analytics India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

Wireless Sensor Networks (WSNs) are a cornerstone of modern IoT applications, enabling data collection across various environments. The efficiency of these networks, however, is hampered by the limited energy resources of sensor nodes and the challenges inherent in mobile data collection. This paper proposes a novel approach to address these challenges through the strategic selection of rendezvous points (RPs) using fuzzy logic. Our method leverages the flexibility and decision-making process of fuzzy logic to evaluate and integrate multiple dynamic variables, including node energy levels, collector mobility patterns, and data priority, to dynamically determine optimal RPs. This approach not only facilitates efficient data transmission but also significantly conserves the energy of sensor nodes, thereby extending the network's lifespan. We present a comprehensive model incorporating a fuzzy inference system for RP selection, tailored to adapt to the changing conditions of WSNs. Through extensive simulations, our results demonstrate a marked improvement in network performance, showcasing reduced energy consumption and minimized data collection latency. The proposed fuzzy logic-based selection method offers a robust and adaptive solution to the challenges of mobile data collection in WSNs, promising enhanced efficiency and prolonged operational longevity of sensor networks. © 2024 IEEE.

关键词： Sensor nodes

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Content Based Classification of Short Messages using Recurrent Neural Networks in NLP

Content Based Classification of Short Messages using Recurre...

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2024 International Conference on Artificial Intelligence, computer, Data sciences, and Applications, ACDSA 2024

作者： Bennet, Deepthi Tabitha Bennet, Preethi Samantha Thiagarajan, Priya Sundarakantham, K. Thiagarajar College of Engineering Dept. of Applied Mathematics and Computational Science Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Business Systems Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350394528

Short message spam poses a significant threat for all mobile phone users, as it can act as an efficient tool for cyberattacks including spreading malware and phishing. Traditional anti-spam measures are only minimally effective against modern spammers. Intelligent analysis of the content to categorise the messages as Spam (unwanted and unsolicited) or Ham (useful messages) is therefore essential to safeguard the user from such attacks. Various artificial intelligence (AI) techniques are proving to be useful in the analysis of the content of such short messages to classify and filter spam. We have trained, validated and tested seven such AI techniques on the SMS spam collection dataset to identify the best model for designing and developing a content based classification system. Recurrent Neural Networks (RNN) have shown the highest performance metrics (Test Accuracy: 99.28%) and hence our proposed system includes a RNN model for classification. A web app of this system has also been deployed where a single SMS can be input and the designed system can classify it as Spam or Ham. The designed system is compared against existing systems and is found to be significantly better. © 2024 IEEE.

关键词： Recurrent neural networks

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A Decision Support System for Enhancing Transportation Services Using Aspect-Based Sentiment Analysis

A Decision Support System for Enhancing Transportation Servi...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Benaddi, Lamya Ouaddi, Charaf Naimi, Lahbib Souha, Adnane Jakimi, Abdeslam Ouchao, Brahim Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Department of Computer Sciences Morocco

ISBN: (纸本)9798350369106

This paper proposes an innovative decision support system based on sentiment analysis, specifically designed for the transportation sector. The system employs an aspect-based sentiment analysis approach, which accurately identifies and classifies customer opinions on key transportation service aspects such as comfort, driver behavior, punctuality, and vehicle condition. By integrating the BERT model, known for its advanced natural language processing capabilities, our system provides an in-depth and reliable analysis of customer sentiments. This analysis enables managers and decision-makers to better understand the strengths and weaknesses of the provided transportation services and to precisely target areas that require improvement. By offering actionable insights, our approach aims to enhance the user experience, improve service quality, and increase overall customer satisfaction. © 2024 IEEE.

关键词： Customer satisfaction

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A Retrospective Study on Obesity to Evaluate Omnipotence of Physical Condition Feature Set

A Retrospective Study on Obesity to Evaluate Omnipotence of ...

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2022 IEEE International Conference on Data science and Information System, ICDSIS 2022

作者： Sengupta, Diganta Mondal, Subhash Banerjee, Susanta Navin, Harsh Meghnad Saha Institute of Technology Department of Computer Science & Engineering Department of Computer Science & Business Systems Kolkata India

ISBN: (数字)9781665498012

ISBN: (纸本)9781665498012

One of the growing medical concerns globally is obesity. The age old popular notion for the disease lies in physical conditions (PC), and eating habits (EH), leading to much observed debate for the root cause of obesity. This study establishes the omnipotence of PC over EH as a leading cause of obesity. The dataset used for the study comprised of 16 features which were divided into two feature subsets (FSS);one FSS containing 9 PC features, and the other FSS containing features related to EH. Initially obesity was classified using the complete feature dataset, followed by classification using the PC and EH FSSs respectively. Eight Machine Learning (ML) algorithms were used for the study. Regular performance metrics were used to evaluate the results. It was observed that the PC features unanimously contributed to obesity in contrast to EH features. Moreover, boosting was done using six algorithms, and results reflected that all the boosting algorithms enhanced the results. Of all the boosting algorithms, Hist-Gradient Boost generated the best results. The prime focus of the study is to analyze the major features for obesity using ML algorithms including boosting. This study computationally concludes that physical conditions have a greater impact on obesity with respect to eating habit conditions. © 2022 IEEE.

关键词： Classification (of information)

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Blockchain driven Evidence Management System 3

Blockchain driven Evidence Management System

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3rd International Conference on Artificial Intelligence and Signal Processing, AISP 2023

作者： Mehta, Shyam Kumari, K. Shantha Jain, Paras Raikwar, Harshal Gore, Shubham SRM Institute of Science & Technology Data Science and Business Systems Chennai India SRM Institute of Science & Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798350320749

When a cognizable violation like murder, abduction, rape, theft, etc. is committed, a victim or someone acting on their behalf must submit an electronic first information report (e-FIR) to the police station. Due to the centralized nature of the e-FIR database, it is possible for the offense's record to be hacked, and it is also possible for fake e-FIRs to be purposefully registered. Data transparency and integrity are therefore major issues with the e-FIR database. Indian government launched nation-wide Crime and Criminal Tracking Network and systems (CCTNS) during 2009 and it is an efficient e-governance system. This paper provides a blockchain solution to handle the complaints given on both cognizable and non-cognizable complaints. Using real-world examples from previous events, the technology and security underlying the use of blockchain will be discussed. Police will file an e-FIR, which will be validated by the authorities, and on acceptance of the FIR, it will be encrypted and stored as a hash along with the timestamp and hash of the next block. Now, how blockchain makes it secure is that it doesn't let anybody make changes to the FIR without proof of work and a vote of consensus in which a majority of the blockchain must agree to the change. The hash will be stored in smart contracts using Ethereum. Our findings demonstrate a trade-off between the number of transactions contained in a single block on the blockchain ledger and the security level of various hashing algorithms for the offence data. © 2023 IEEE.

关键词： Blockchain

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The Design of Security System in Transmission of Healthcare Cloud Data System to Secure from Attacks 4

The Design of Security System in Transmission of Healthcare ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Raman, Ramakrishnan Pillai, Biju G. Rastogi, Shailesh Kumar, Vikram Verma, Apurv Meenakshi India Faculty of Management It & Business Analytics India Symbiosis Institute of Business Management Nagpur Pune India Srv Media Pvt. Ltd. India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350360165

Harmonizing both safety and ease of use in healthcare software is a top priority in current security research. The balance between security and usability is still elusive, despite the constant efforts of developers and security experts. More thorough research into practical approaches to assess and improve the usable security of medical technology is urgently needed. The main goal of this research is to evaluate healthcare software's usable security and, using empirical analysis as a basis, provide approaches for optimum security that don't sacrifice usability. Decision-makers in the development of software have a difficulty when it comes to effortlessly integrating exceptionally safe software with optimal usability. In order to evaluate usability and security, this study proposes merging the Analytic Network Process (ANP), Fuzzy Set (FS), and Technique for Priority of Priority by Similarity to the Ideal Response (TOPSIS). This innovative technique promotes the integration of user and safety qualities. The FSAHP-TOPSIS hybrid solution's effectiveness in assessing usable-security is shown by the study's application to a hospital management system in Mecca, Saudi Arabia. These results provide insightful advice for security management that doesn't sacrifice end-user usability. © 2024 IEEE.

关键词： Cloud security

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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A Hybrid Approach for Automated GUI Testing Using Quasi-Oppositional Genetic Sparrow Search Algorithm

A Hybrid Approach for Automated GUI Testing Using Quasi-Oppo...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Jayalakshmi, N. Sakthivel, K. Psna College of Engineering and Technology Department of Computer Applications Tamil Nadu Dindigul India K.S Rangasamy College of Technology Department of Computer Science and Business Systems Tamil Nadu Tiruchengode India

ISBN: (纸本)9798331543617

Testing of GUI is crucial for assessing software reliability, usability, and functionality;however, classical approaches are not sustainable in contemporary applications. It proposes the Quasi-Oppositional Genetic Sparrow Search Algorithm (OOGSSA) to auto-generate efficient GUI test cases. It combines genetic algorithms, sparrow search dynamics, and quasi-oppositional learning to optimize test suites, minimize redundant test cases, and improve the testing fault. The result obtained for the model OOGSSA is 95.8 % in test coverage, 85.3 % in redundancy, and 92.6 % in fault detection. Furthermore, test generation time was minimized to forty minutes and the overall testing time to seventy-five minutes, which is more efficient than conventional genetic or model-based testing. The outcomes presented above show that OOGSSA is highly efficient and effective in solving the issues of the current GUI testing and improving the overall quality and reliability of software. More enhancements to the algorithm will be done in future work as well as leveraging the algorithm to cover a wider GUI space including mobile and web. © 2024 IEEE.

关键词： Software reliability

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A Novel Machine Learning-Enhanced Swarm Intelligence Algorithm for Cost-Effective Cloud Load Balancing

A Novel Machine Learning-Enhanced Swarm Intelligence Algorit...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Satheesh, Nimmati Sakthivel, K. Psna College of Engineering and Technology Department of Computer Applications Tamil Nadu Dindigul India K.S Rangasamy College of Technology Department of Computer Science and Business Systems Tamil Nadu Tiruchengode India

ISBN: (纸本)9798331543617

Cloud computing refers to the instant accessibility of shared computer resources over the internet on-demand, thereby revolutionizing the way organizations deploy and manage their IT infrastructure. Efficient load balancing in a cloud environment scales up, utilizing resources with optimized cost-effectiveness. This paper introduces a new Machine Learning-guided Improved Swarm Intelligence Algorithm for reliable load balancing in cloud computing. Methodology It spans on three main elements. The first is the data center controller for the management of tasks. VM allocation uses an Improved Beetle Swarm Optimization Algorithm and one-dimensional convolutional probabilistic neural network is used for host load prediction. To propose an approach that looks to optimize the key metrics such as make span, average cost of VM, resource utilization, and value response time through iterative optimization process. The results of the experiments reflect the success of the proposed algorithm, because an improved performance of load balancing is obtained in comparison with those achieved using PSO, Grey Wolf Optimization, and Genetic Algorithm. The convergence diagram displays the iterative optimization process achieved by the proposed algorithm in obtaining a balance among various performance metrics. Overall, the methodology proposed in this work will provide promising facilitation in the increased efficiency, scalability, and cost-effectiveness of environments based on cloud computing with a reliable load balancing. © 2024 IEEE.

关键词： Genetic algorithms

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