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2nd International Conference on business Data Analytics, ICbDA 2023

作者： saxena, virat Raj Kumar, babloo Goel, Navnish sharma, Yogesh Kumar CSE-AI&M Department Raj Kumar Goel Institute of Technology Ghaziabad India Department of Computer Science and Engineering Dewan V S Institute of Engineering and Technology Meerut India Department of CSE IIMT Engineering College Meerut India

ISBN: (纸本)9783031807770

The success and quality of a project are directly impacted by the software development process’ crucial step of prioritising software requirements. The fuzzy Analytic Hierarchy Process (AHP) and fuzzy Technique for Order of Preference by similarity to Ideal solution (TOPsIs) are combined in this study to provide a thorough method for the methodical prioritisation of software requirements. We demonstrate the efficacy of this strategy using a real-world case study that makes use of an Institute Examination system (IEs). The technique is used to assess Functional Requirements after first identifying Non-Functional Requirements (NFRs), which include things like security, dependability, and economy and serve as crucial criteria for doing so. Pairwise comparison matrices (PCMs) are used to represent the various stakeholder perspectives. The Extent Fuzzy AHP is then used to calculate the weights of NFRs while accounting for decision-making’s inherent subjectivity. The Objective-based Requirements Elicitation Technique (ObRET) is used to elicit FRs, which are then evaluated in light of linguistic considerations. Our plan is based on the fuzzy TOPsIs method, which prioritises FRs by methodically processing the data. The process is completed by calculating Closeness Coefficients (cc) for each FR, which can be used to determine whether software is required. With the help of this integrated approach, project managers and software developers can use data to organise and prioritise needs, ensuring that software systems satisfy both crucial functional requirements and quality standards. In the end, it improves software project success by balancing the perspectives of various stakeholders, managing uncertainty, and raising software quality and user satisfaction. © The Author(s), under exclusive license to springer Nature switzerland AG 2025.

关键词： software quality

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Detection of Knee Osteoarthritis from Magnetic Resonance Imaging Using a 3-D Independent Component Analysis Method in Machine Learning 1st

Detection of Knee Osteoarthritis from Magnetic Resonance Ima...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Karve, swagat Dhope, Tanuja satish Kaushal, Rajesh Kumar, Naveen Chippalkatti, Pranav Jadhav, Akshay S. B. Patil College of Engineering Maharashtra Indapur India College of Engineering Maharashtra Pune India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science and Engineering School of Computing MIT Art Design and Technology University Pune India MIT College of Railway Engineering Maharashtra Barshi India

ISBN: (纸本)9783031860683

Osteoarthritis (OA) is a widespread condition without a currently available treatment, significantly affecting individuals’ quality of life as this is a progressive condition that deteriorates with time, frequently leading to persistent Joint pain and stiffness can become severe enough to make daily tasks difficult. Magnetic resonance (MR) images are commonly employed in OA diagnosis, where medical experts assess changes, especially within the tibio-femoral cartilage compartment for knee OA. This study presents a new diagnostic technique for knee OA that detects the illness from MR images using a support vector Machine (svM) algorithm. Our suggested method employs the Independent Component Analysis (ICA) technique on 3-D MR imaging data from a real-world cohort. The experimental results show that our ICA-svM machine learning model obtained 86% testing accuracy and 72% specificity. and sensitivity of 100%, having been trained on a limited MR image dataset. © The Author(s), under exclusive license to springer Nature switzerland AG 2025.

关键词： support vector machines

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selective Oxidation of benzyl Alcohol to benzaldehyde under solvent Free Aerobic Oxidation Condition Using Pd-supported CeO2 Catalyst

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Russian Journal of General Chemistry 2025年第5期95卷 1270-1279页

作者： suryam, N. Jagadish, T. Krishna, T. R.s.D, Anitha swapna, s. Rakesh, C. Mallesh, D. Reddy, K. Laxma Department of Chemistry University College of Science Osmania University Hyderabad India Department of chemistry (H&S) CVR College of Engineering Hyderabad India Department of Chemistry Nizam College Osmania University Hyderabad India B.V. Raju Institute of Tecchnology Hyderabad India

In this research, the homogenous PdNPs were developed with varying weight percentages (1 to 4 wt %) on the CeO2 support using a simple impregnation process with a mean size of 5 nm. Furthermore, homogenous PdNPs were examined for the selective oxidation of benzyl alcohol (bA) to benzaldehyde (bZA) in a solvent-free oxidation technique. The developed Pd/CeO2 heterogeneous nanocatalysts were thoroughly studied utilizing various techniques such as P-XRD, TEM, and XPs analysis. Among various Pd loadings, 3 wt % (3Pd/CeO2) catalysts exhibit enhanced catalytic activity for the bA to bZA oxidation process, with 9.5 times greater bA conversion than bare CeO2. Additionally, experiments were conducted to determine the effect of temperature, catalyst weight, reaction time, and catalyst recyclability. The highest benzyl alcohol conversion (95%), benzaldehyde selectivity (99%) and benzaldehyde yield (94%) achieved for 3Pd/CeO2. The 3Pd/CeO2 catalyst not only exhibited superior catalytic activity, but also showed stable activity during recyclability.

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Artificial Intelligence based smart vision based on segmentation of Image Processing

Artificial Intelligence based Smart vision based on Segmenta...

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Data science, Agents & Artificial Intelligence (ICDsAAI), International Conference on

作者： M. Amina begum Jayaraj Ramasamy Er. s. John Pimo veeramalai sankaradass Tushar v Mohite Patil v.Jayalakshmi Department of ECE Sri Venkateswaraa College of Technology Chennai India Faculty of Engineering and Technology Botho University Botswana Department of CSE St. Xavier's Catholic College of Engineering India Dept of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Industrial Electronics V.P.M’s Polytechnic Thane Easwari Engineering College Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Evaluating the simulation process of digital image processing and application in various fields with its image analysis technique is the essence of this article. The upcoming problems of segmentation of digital images for artificial intelligent vision are focused. The wide range of problems of computer vision can be solved by simulating digital images. This article discusses the proper evaluation of the different methods of segmentation and its importance in today's application. It discussed how algorithms help in digital image processing for face recognition, Irish recognition, and fingerprint. This study goanna help you improve the problem faced by digital image processing and create its various application in the upcoming future. Also, helps you to detect primary objective, improve performance, and perform better quality work in the field of image processing.

关键词： Image segmentation computer vision Digital images Face recognition Morphology Object detection Fingerprint recognition Artificial intelligence biomedical imaging Iris recognition

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Optimizing blood bank Management with Cloud-Hosted Long short-Term Memory Models for Inventory Forecasting and Utilization

Optimizing Blood Bank Management with Cloud-Hosted Long Shor...

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Electronics and Renewable systems (ICEARs), International Conference on

作者： R. Aiyshwariya Devi sree southry s s. Rajanarayanan s. Chandra sekaran Audithan sivaraman s. srinivasan Department of Artificial Intelligence and Data Science R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sona College of Technology Salem Tamil Nadu India Department of Computer Science and Engineering Vinayaka Mission’s Research Foundation (DU) Vinayaka Mission’s Kirupananda Variyar Engineering College Salem Tamil Nadu India Department of Computer Science and Engineering P.S.V College of Engineering and Technology Krishnagiri Tamil Nadu India Department of Information Technology Netaji Subhas University of Technology (Govt of NCTDelhi) Jafferpur New Delhi 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)9798331509675

ISBN: (纸本)9798331509682

This research presents an innovative method for blood bank management using Cloud-based Long short-Term Memory (LsTM) models for precise inventory forecasting and optimization. The objective of this research is to increase blood bank efficiency by utilizing LsTM models to accurately estimate demand, optimize inventory levels, and enhance overall resource utilization. The system uses cloud computing to provide real-time demand forecasting, minimizing blood waste and assuring a reliable supply for healthcare requirements. LsTM models use historical data to identify long-term trends and variations in blood demand, facilitating accurate inventory forecasting. This predictive system optimizes the allocation process, resulting in improve resource utilization and increased operational efficiency. In compared to traditional techniques, the proposed system provides a more scalable and flexible solution by using cloud technology to provide continuous monitoring and dynamic modifications of inventory levels. It provides a robust framework for management of blood supply and enhancing patient care for improved resource management. Future enhancements may include broadening the system to incorporate real-time multi-source data integration and investigating hybrid machine learning models to better optimize demand forecasts.

关键词： Cloud computing Computational modeling Demand forecasting Medical services Predictive models Real-time systems Resource management blood Long short term memory Optimization

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Enhanced Monitoring and security Measures with video Analysis

Enhanced Monitoring and Security Measures with Video Analysi...

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Multi-Agent systems for Collaborative Intelligence (ICMsCI), International Conference on

作者： v. Esther Jyothi G. Rakesh vipparla Aruna shobana Gorintla Yalanati Ayyappa U Ganesh Naidu Department of Computer Applications Velagapudi Ramakrishna Siddhartha Engineering College Deemed to be University Vijayawada A.P. India Department of Computer Science and Engineering NRI Institute of Technology (Autonomous) Pothavarapadu A.P India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Tamilnadu India B V Raju Institute of Technology Narsapur Telangana India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

In modern surveillance, activities have increasingly become dependent on the continuous observation offered by CCTv systems. still, with massive amounts of video data generated in a minute, sifting through this information manually to pick up any anomalies would be extremely burdensome and nearly impossible without significant human labor and vigilant watchfulness. CNN and CLIP models changed everything with regards to the working of surveillance systems. It makes use of CNN's capability for video frame processing and analysis to detect and classify these activities in the frames as either normal or suspicious. At the same time, it improves this by adding the CLIP model's capability of understanding textual descriptions and visual content together for better nuance detection in suspicious activities. This methodology transforms video surveillance by breaking video streams into frames and analyzing the behavior and interactions of persons in those frames. The synergy of CNN and CLIP models not only ensures real-time, efficient, and accurate surveillance but also minimizes dependency on extensive manual labor for monitoring. This paper aims to provide a contribution toward developing better security infrastructures in public spaces, transportation hubs, and private facilities. The introduction of this anomaly-detection mechanism can hugely improve the capability of implemented surveillance systems with a proactive response based on real-time detection and response capabilities against suspicious activities.

关键词： visualization Transportation Transforms Manuals streaming media video surveillance Real-time systems security Feeds streams

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Machine Learning-Driven Hybrid Adaptive beamforming for Efficient Interference Mitigation in Dense Wireless sensor Networks

Machine Learning-Driven Hybrid Adaptive Beamforming for Effi...

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2025 International Conference on visual Analytics and Data visualization, ICvADv 2025

作者： Loganathan, P. Priyeshkumar, A.T. Kodeeswari, K. Reena, K Jose Poonguzhali, I. balaji, M. Department of Electronics and Communication Engineering Namakkal India Mahendra College of Engineering Department of Electronics and Communication Engineering Salem India Department of Electronics and Communication Engineering Erode India B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Applications Vandalur India Panimalar Engineering College Department of Electronics and Communication Engineering Chennai India Vivekananda College of Technology for Women Department of Electronics and Communication Engineering Tiruchengode India

ISBN: (纸本)9798331521394

The Adaptive beamforming Algorithm (AbA) represents a promising approach to suppress interference in dense wireless sensor networks (WsNs), enhancing signal fidelity and network throughput. In dense WsNs, the proximity of sensor nodes leads to high interference levels, which degrade energy efficiency and communication reliability. This is exacerbated by static beamforming methods that oversimplify time and frequency utilization. This work proposes a set of adaptive beamforming solutions, including digital, machine learning (ML)-empowered, and hybrid beamforming approaches. The Adaptive Array signal Processing (AAsP) method introduces real-time interference analysis with null steering to suppress unwanted signals and maximize the signal-to-interference-plus-noise ratio (sINR). Hybrid beamforming, combining analog and digital domains, balances computational cost with performance. Additionally, a deep learning-based beam steering module dynamically adjusts beam patterns based on interference forecasts. Extensive simulations demonstrate the proposed methods' superiority over conventional beamforming, achieving significant sINR improvement, enhanced network throughput, and improved energy efficiency. In ultra-dense environments, the results indicate a 10-fold reduction in interference power alongside better scalability. Proactive interference mitigation using ML models further ensures adaptability to varying network conditions. These techniques establish a baseline for deploying adaptive beamformers in dense WsNs, addressing scalability and real-time computational challenges. They also pave the way for implementing advanced 5G/6G-capable sensor systems, enabling resilient communication in high-interference environments. Key-words: signal to interference plus noise ratio(sINR), adaptive beamforming, interference mitigation, hybrid beamforming, null steering, beam steering, computational efficiency. © 2025 IEEE.

关键词： beamforming

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Enhanced Fire Detection for Industrial and Environmental safety Using IoT-Integrated vision sensors and Residual Clutch Attention Network and Depthwise Distinguishable Convolutional Neural Networks

Enhanced Fire Detection for Industrial and Environmental Saf...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： D.Christy sujatha Priyanshu singh C. vallikannu Ravi varman skanda M G T. Gavaskar Department of Software Engineering Periyar Maniammai Institute of Science & Technology Thanjavur Tamil Nadu India Department of Computer Science and Engineering R.V college of engineering Bangalore Karnataka India Department of Humanities and Sciences Rajalakshmi Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Karpagam academy of Higher Education Coimbatore Tamil Nadu India Department of Industrial and Production Engineering SJCE JSS Science and Technology University Mysore Karnataka India Department of Mechanical Engineering St. Joseph's College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Fire detection is an important factor in improved safety in industrial and environmental conditions. The conventional flame detectors, largely relying on heat and smoke detectors, have drawbacks of time lag, low sensitivity, and low coverage when compared with our requirement in large industrial scenes and vast natural terrains vulnerable to fire occurrence. These systems are highly unsuitable for dynamic and high-risk situations as they require a higher accuracy and a faster reaction time for the alarms to be efficient as is now the case due to the aforementioned reasons. In an attempt to remedy these limitations, this research introduces a fire detection system using IoT and vision sensors that combines a RCAN with DDCNN. This architecture is tuned using the Crayfish Optimization Algorithm (COA), which seeks to improve model parameter to improve response time and accuracy. by incorporating feedbacks from sensors placed at its different parts and reading from the environment, the RCAN-DDCNN model can quickly identify the fire patterns and respond by activating the sprinkler systems as well as setting off alarms. The proposed system presents a superior option for fire detecting because it is scalable and adaptive and it provides high degree of precision, making it an optimal development in fire detection technology for industry and environment. The introduced approach attains higher accuracy as 99%.

关键词： support vector machines Accuracy vision sensors sensor systems stability analysis safety Convolutional neural networks Time factors Optimization smoke detectors

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Integration of Continuous Improvement Approach in sustainable E-Government services Towards Citizen satisfaction in Health sector Development of Conceptual Model

Integration of Continuous Improvement Approach in Sustainabl...

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Automation and Computation (AUTOCOM), International Conference on

作者： R. Anandhakrishnan D. vimala s. vasantha Department of Biotechnology V.S.B. College of Engineering Karur Tamil Nadu India School of Management Studies Vels Institute of Science Technology & Advanced Studies(VISTAS) Chennai Tamil Nadu India Department of MBA Saveetha Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

E-government success relies on high-quality e-services. Understanding e-government service quality is crucial for improving existing and future implementations. As the globe advances in communication and information technology, study on e-government services especially in health sector since covid-19 is gaining momentum. This study introduces a conceptual model for measuring the impact of continuous improvement approach on sustainable e-government services in health sector. The suggested model identifies five stream dimensions to assess the quality of e-government services based on a survey of relevant literature. This article focuses on the five dimension of continuous improvement approach the efficiency, accessibility, and sustainability of e-government services. This research provides a conceptual model for integrating continuous improvement approach to promote sustainable long-term e-government services in the health care area. Governments may use AI-driven analytics, predictive modeling, and automation to improve administrative procedures, personalise healthcare services, and enable data-driven decision-making. The concept of continuous improvement approach is central to the model, in which AI algorithms continuously analyze data from various sources, such as electronic health records, patient feedback and performance metrics. The outcome of the conceptual model is to identify the effect of integrating continuous improvement approach in the healthcare sector to achieve sustainable e-government services that optimizes resource allocation, improves service quality, and fosters innovation for Citizen satisfaction. This research adds to the literature on e-government and healthcare management by presenting a new conceptual model that combines ideas of continuous improvement. Future study should concentrate on empirical validation and implementation methodologies to determine the suggested model's usefulness and scalability in real-world scenarios. Overall, incorpor

关键词： Analytical models Technological innovation Automation Medical services Predictive models Data models vaccines Continuous improvement sustainable development Electronic government

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Theoretical bound and Compensation for Residual Error Probability of GRAND-CRC-based Functional safety Communication

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IEEE Transactions on Communications 2025年

作者： Zhan, Ming Pang, Zhibo Wu, Fang Zhou, Qiang Zhang, shiqing Zhang, Jianwu Yu, Kan Taizhou University College of Electronic and Information Engineering Zhejiang Taizhou318000 China ABB Corporate Research Forskargränd 7 VästeråsSE-721 78 Sweden School of Electrical Engineering and Computer Science Department of Intelligent Systems StockholmSE-114 28 Sweden Southwest University College of Economics and Management Chongqing400715 China Hangzhou Dianzi University College of Telecommunication Engineering Hangzhou310018 China La Trobe University Bendigo Campus School of Engineering and Mathematical Sciences Department of Computer Science and Information Technology Edwards Rd Flora HillVIC3552 Australia

In modern Industrial Internet of Things (IIoT) ecosystems, functional safety protocols are increasingly utilized to transmit safety protocol data unit (sPDU). Integrating the universal guessing random additive noise decoding (GRAND) algorithm with cyclic redundancy check (CRC)-coded sPDU can minimize sPDU retransmissions. However, it introduces residual error probability (REP) degradation that requires careful consideration. Using the IEC 61784-3 standard and CRC assumptions, we derive a closed-form REP evaluation formula specific to sPDU length and maximum error correction capability. Our analysis reveals that, under the worst industrial conditions and for an sPDU length of 128 bits, the REP performance degrades by approximately 2:2 × 102 times when the CRC signature length is 24 bits and up to one bit is guessing decoded. This degradation becomes more pronounced with increased error correction capability. To address this, we propose to compensate the REP degradation by adopting longer CRC signature. This paper provides a theoretical framework for adopting the GRAND algorithm in functional safety communication, setting a foundation for enhancing reliability in IIoT applications. © 1972-2012 IEEE.

关键词： Decoding

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