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4th International Conference on Machine Intelligence and Signal Processing, MISP 2022

作者： Krishna Munjuluri Vamsi, V.S. Telukuntla, Yashwanth Reddy Kumar, Parimi Sanath Gutjahr, Georg Department of Computer Science and Engineering Amritapuri India Department of Electronics and Communication Amritapuri India Center for Research in Analytics & Technologies for Education Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789819900466

The Capacitated Vehicle Routing Problem (CVRP) is an important combinatorial optimization problem in which a fleet of vehicles must be routed to a set of customers. Many exact and heuristic methods have been proposed to solve this problem. Recently, efficient machine learning algorithms have been developed for solving the VRP. A major component of these algorithms is the Attention Mechanism which enables the vehicle to make more informed decisions on which customer to serve next. This paper compares various topologies for implementing such attention mechanisms when solving VRPs. Using simulations, it is found that Single-Head Attention works better with larger node instances, whereas Multi-Head Attention is superior for smaller node instances. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heuristic methods

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Analysis of COVID-19 Datasets Using Statistical Modelling and Machine Learning Techniques to Predict the Disease

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SN computer science 2024年第1期5卷 181页

作者： Nramban Kannan, Senthil Kumar Kolla, Bhanu Prakash Sengan, Sudhakar Muthusamy, Rajendiran Manikandan, Raja Patel, Kanubhai K. Dadheech, Pankaj Department of Computer Science and Engineering School of Computing Institute of Science and Technology (Deemed to Be University) Vel Tech Rangarajan Dr. Sagunthala R&D Tamil Nadu Chennai 600062 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur 522302 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India Department of Computer Science and Engineering Panimalar Engineering College Chennai 600123 India Department of Electronics and Communication Engineering K. Ramakrishnan College of Technology Tamil Nadu Trichy 621112 India Charotar University of Science and Technology Gujarat 388421 India Department of Computer Science and Engineering Management and Gramothan (SKIT) Swami Keshvanand Institute of Technology Rajasthan Jaipur 302017 India

Sustainable development is crucial for a prosperous future, but epidemic diseases like Coronavirus Disease 2019 (COVID-19) pose real and complex challenges. The global pandemic, declared by the WHO on March 11, 2020, has led to significant loss of life and challenges in public health, economy, food systems, and social life. This research article aims to develop a Machine Learning (ML) model to predict a patient’s COVID-19 diagnosis, as early diagnosis is fundamental for controlling the spread of COVID-19. An investigation of the literature and a research study is conducted to find suitable mathematical methods and measure how they impact prediction models. This paper analyses COVID-19 pandemic deaths, confirmed cases, and recovered individuals using Time Series Analysis (TSA) to study the disease's impacts and understand the TAS. A forecasting model can predict future COVID cases by analyzing trends in time-series and connecting global changes with government restrictions. Since higher predictive accuracy is the limitation of ensemble learning algorithms, better ML approaches are proposed here. Autocorrelation plots clearly showed the results executed for the considered objectives. The hybrid ARIMA algorithm proposed in this work proved adequate results. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Analysis and visualization Autocorrelation COVID-19 Machine learning Time-series analysis Time-series forecasting

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Implementation of ABC & WOA-Based Security Defense Mechanism for Distributed Denial of Service Attacks 6

Implementation of ABC & WOA-Based Security Defense Mechanism...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Devi, Suman Sharma, Yogesh Kumar Athithan, Senthil Sachi, Savya Singh, Ajay Kumar Jain, Arpit Chaudhary Devi Lal University Department of Computer Science and Engineering Haryana Sirsa India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering AP Guntur India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Andhra Pradesh Vaddeswaram India Lalit Narayan Mishra College of Business Management Department of Information Technology Bihar Muzaffarpur India Allen House Institute of Technology India

ISBN: (纸本)9798350304480

computer systems are vulnerable to information theft as a result of the extensive use of the internet, which has caused an increase in security defence mechanisms. In order to overcome these theft various approaches have been proposed like Machine Learning, Swarm Intelligent Algorithm, Bayesian-based algorithms etc. For choosing effective and efficacious features and improve the Security Defense Mechanism. In order to lessen Distributed Denial of Service (DDO) assaults, we suggested a novel hybrid classification approach in this study based on Artificial Bee Colony (ABC) and Whale Optimisation Algorithm (WOA). The results are compared with other algorithms. These algorithms have shown promising results. © 2023 IEEE.

关键词： ABC DDO'S Defense Mechanism WOA

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Behavioural Intentions to Adopt Artificial Intelligence in Healthcare: Exploring the Perception of Healthcare Professionals

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Journal of Technology in Behavioral science 2025年 1-11页

作者： Swathi, K.S. Aswathy, S. Kavitha, T.C. Chadaga, Krishnaraja Sampathila, Niranjana Department of Healthcare & Hospital Management Prasanna School of Public Health Manipal Academy of Higher Education Manipal India Manipal Institute of Management MAHE Manipal 576104 India Department of Commerce Manipal Academy of Higher Education Manipal India Department of Computer Science Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal 576104 India Department of Biomedical Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal 576104 India

Artificial intelligence (AI) technologies are used in healthcare to detect diseases faster, provide personalized treatment, automate some processes, and enhance hospital safety. Yet, one of the critical challenges is users’ hesitation and unwillingness to adopt technological applications in medical practice. Hence, this research aims to identify the important factors affecting professionals’ behavioural intentions to use AI in healthcare. This cross-sectional survey was conducted in Kerala, India. We proposed a framework grounded on the literature on technology acceptance theories, and the conceptual model was tested using a 20-item questionnaire with a 5-point Likert scale. The structured questionnaire was converted into a Google Form and shared with the respondents. A total of 347 respondents, including doctors, nurses, and allied healthcare professionals from 5 different private hospitals in India, participated in this study. The partial least square structural equation modelling approach was used to evaluate the proposed framework upon confirming the reliability and validity of the measurement items. The study findings reveal that perceived ease of use (PEU), perceived usefulness (PU), and facilitating conditions (FC) are the critical factors that influence the behavioural intentions (BI) of healthcare professionals to use AI. Though healthcare professionals perceived the risk of using AI, it did not affect their choice to use AI. This study gives an understanding of healthcare professionals’ behavioural intentions to use AI so that the administrators can plan and improve their strategies to implement AI in hospitals. © The Author(s) 2025.

关键词： AI adoption Artificial intelligence Behavioural intentions Healthcare professionals

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Ensuring Web Application Quality: The Role of Software Testing as a Form of Quality Assurance 3

Ensuring Web Application Quality: The Role of Software Testi...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Deshpande, Maneesh Vilash Soitkar, Priyanka Ambar Tripathi, Diwakar Ramanuj Agarmore, Sudhir Balwant Tai Golwalkar Mahavidyalaya Department of Computer Science Ramtek Nagpur India Dr. Ambedkar College Department of Computer Science Nagpur India S.S. Maniar College of Computer & Management Rtmnu Nagpur India Datta Meghe Institute of Higher Education & Research Faculty of Science & Technology School of Allied Science Wardha India

ISBN: (纸本)9798350343274

Software quality is a precise characteristic that identifies the characteristics that every software should have. Quality is the primary determinant of the success or failure of a software project and a software-related company. Software quality has been the subject of several studies. To produce high-quality software, organizations involved in the software industry adhere to requirements set out by Capability Maturity Model Integration. Software Quality Assurance, Software Quality Plans, and Software Quality Control are the three key levels that make up quality. Therefore, in this research, we'll talk about the concepts and quality standards that are applied to software projects in the Bangalore software industry, as well as how these standards are monitored and maintained. We will examine how many software companies use the CMMI guidelines while producing software in this research. How many companies evaluate the quality of their initiatives, and how many are meeting international standards. The discoveries exhibit that specific organizations in Bangalore are utilizing software quality assurance strategies. © 2023 IEEE.

关键词： Software testing

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A Consumption-Based Picture Extraction Procedure for Static Images Using a User Opinion Choice Method 12th

A Consumption-Based Picture Extraction Procedure for Static ...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Saravanan, D. Kaushik, S. Handaragall, Vijayalaxmi C. Raja Vikram, G. Sudha, C. Kalaivani, J. Hyderabad Hyderabad India ICFAI Foundation for Higher Education Hyderabad Hyderabad India School of Computing and Information Technology REVA University Bengaluru Bengaluru India Department of Computer Science & Engineering Vignan Institute of Technology and Science Hyderabad India Hyderabad India Department of Computing Technologies SRM Institute of Science and Technologies Kattankulathur Chennai India

ISBN: (纸本)9789819601387

Extracting the necessary information from the stored database is one of the most demanded research areas today, especially in the field of subject-based information extraction. This application is a crucial area of focus for many researchers. The existing technique in this area relies on user observation-based responses. In this approach, each time a user submits a query, they need to select the most relevant content from the query extraction and then rank the items. The search is then refined, and the process continues in the next iteration. This method is time-consuming and requires domain knowledge about the system. To overcome these issues in subject-based information extraction, creating an index of datasets can improve search time and enhance information extraction efficiency. Initially, this process may take some time to train the input datasets, but the extraction will yield the most relevant data. Experimental results and practical applications indicate that the proposed technique performs well overall. The outputs confirm that even if the query is designed by an unprofessional user or lacks specific query points, the technique produces effective results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Creation of evidence Evidence-based search Identify the patterns Response Subject-based content User Assessment

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User story clustering in agile development:a framework and an empirical study

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Frontiers of computer science 2023年第6期17卷 43-59页

作者： Bo YANG Xiuyin MA Chunhui WANG Haoran GUO Huai LIU Zhi JIN School of Information Science and Technology Beijing Forestry UniversityBeijing 100083China Engineering Research Center for Forestry Oriented Intelligent Information Processing National Forestry and Grassland AdministrationBeijing 100083China School of Information Science and Technology North China University of TechnologyBeijing 100144China College of Computer Science and Technology Inner Mongolia Normal UniversityHohhot 010020China Department of Computer Science and Software Engineering Swinburne University of TechnologyHawthorn VIC 3122Australia Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of EducationBeijing 100871China Institute of Software School of Computer SciencePeking UniversityBeijing 100871China

Agile development aims at rapidly developing software while embracing the continuous evolution of user requirements along the whole development *** stories are the primary means of requirements collection and elicitation in the agile development.A project can involve a large amount of user stories,which should be clustered into different groups based on their functionality’s similarity for systematic requirements analysis,effective mapping to developed features,and efficient ***,the current user story clustering is mainly conducted in a manual manner,which is time-consuming and subjective to human *** this paper,we propose a novel approach for clustering the user stories automatically on the basis of natural language ***,the sentence patterns of each component in a user story are first analysed and determined such that the critical structure in the representative tasks can be automatically extracted based on the user story *** similarity of user stories is calculated,which can be used to generate the connected graph as the basis of automatic user story *** evaluate the approach based on thirteen datasets,compared against ten baseline *** results show that our clustering approach has higher accuracy,recall rate and F1-score than these *** is demonstrated that the proposed approach can significantly improve the efficacy of user story clustering and thus enhance the overall performance of agile *** study also highlights promising research directions for more accurate requirements elicitation.

关键词： user story agile development user story mapping clustering

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Optimizing Task Execution in Robotic Swarms: Coordination Strategies for Enhanced Efficiency 7

Optimizing Task Execution in Robotic Swarms: Coordination St...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Bansla, Vandna Kaur, Inderjeet Bhutia, Rudramani Bisht, Girish Singh Sharma, Khemraj Shivalik Engineering College U.K Dehradun India Ajay Kumar Garg Engineering College Department of Computer Science and Engineering U.P. Ghaziabad India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering A.P. India Tula's Institute Department of Computer Science & Engineering Dehradun248001 India Nanhua University Program in Management Sciences Department of Business Administration Chiayi Dalin62248 Taiwan

ISBN: (纸本)9798350350067

The 'swarm robotics' is a relatively recent field of study that investigates the ways in which several robot systems interact with one another throughout the course of time. The organization is able to accomplish its goals by delegating work to a large number of robots that are capable of doing activities that are beyond the capabilities of a single robot. This is accomplished via the use of a broad variety of different methods. It is possible to make use of optimization algorithms in robotic swarms in order to discover answers to difficult trans disciplinary challenges that are encountered in real-world environments. The fact that algorithms for swarm intelligence are able to adjust to changing circumstances makes the potential of this happening a reality. In order to achieve the highest possible level of performance, this study analyses a broad range of different swarm optimization algorithms that may be used to robotic swarm scenarios. This category contains a variety of methodologies, including PSO, GPSO, CSO, ACO, and GSO, among others. Within this section, a comprehensive analysis is provided for each of the strategies that came before it, and an inquiry into the potential future course of action for swarm optimization is furthermore carried out. The ramifications of these tactics are also discussed in this section, which is included in the overall debate. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Copy and Move Forged Image Detection by Deep Learning

Copy and Move Forged Image Detection by Deep Learning

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2023 IEEE World Conference on Communication and Computing, WCONF 2023

作者： Sharma, Yogesh Kumar Athithan, Senthil Sachi, Savya Singh, Ajay Kumar Jain, Arpit Devi, Suman Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Vaddeswaram AP Greenfield Guntur India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Lalit Narayan Mishra College of Business Management Department of Information Technology Bihar Muzaffarpur842001 India Allenhouse Institute of Technology Kanpur India Koneru Lakshmaiah Education Foundation Greenfield Vaddeswaram Department of Computer Science & Engineering A.P. Guntur India Chaudhary Devi Lal University Department of Computer Science and Engineering Haryana Sirsa India

ISBN: (纸本)9798350311204

Copy-Move forgery detection (CMFD) is one of the most active study topics in blind image forensics. Most known algorithms are based on block and key-point approaches or a mix. Recently, various deep convolutional neural network approaches have been utilised in image classification, image forensics, image hashing retrieval, and other areas, and they have outperformed the previous way. The paper proposes a unique copy-move forgery detection system based on a convolutional neural network. The suggested technique takes a trained model from an extensive database such as ImageNet and minimally modifies the net structure using tiny training examples. The experimental findings indicate that the approach we presented outperforms the counterfeit picture created automatically by the computer using a simple image copy-move operation. © 2023 IEEE.

关键词： Convolutional neural networks

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Industrial IoT Smart Manufacturing Systems based on Cloud Computing Energy Efficiency Analysis using Machine Learning Algorithms 3

Industrial IoT Smart Manufacturing Systems based on Cloud Co...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Khan, Patan Feridoz Sasikala, C. Swathi, Mavalluru Kumar, M. Prema Yadav, Ajay Kumar Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Mohan Babu University School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India Dept of Cse Andhra Pradesh Ananthapuramu India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of Ece Andhra Pradesh Bhimavaram India United Institute of Management Department of Computer Application Prayagraj India

ISBN: (纸本)9798331509675

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time. © 2025 IEEE.

关键词： Smart manufacturing

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