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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Ramaprabha Marimuthu Gurusigaamani Ayyanar Muthulingam Vinoth N.A. S Sivasathiya Ganesan Kardeepa Ponnuchamy Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Computer science and Engineering Kalasalingam Academy of Research and Education Virudhunagar India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai India Department of Artificial Intelligence and Data Science Easwari Engineering College Ramapuram Chennai India

In recent years, social media communication plays an important role in sharing messages, thoughts, comments and ideas around the world. Among them, Twitter is one of the most popular communication mediums. Twitter provides free services for sharing information's that are limited to 140 characters. Each month nearly 40 million new twitter accounts were created. Since usage of twitter increases rapidly, fake accounts were created for posting the spam messages and links for redirecting to phishing websites. Researchers proposed various machine learning methodologies for filtering the spam to handle and maintain the social network security problems. But still detecting spam tweets, malicious link in real world scenario is still a complex task. In the proposed work, a novel deep learning technique is introduced to address the above mention issues. Word Vector model is used for learning the syntax of each tweet. Later by using proceeding representation data set, binary classifier is developed. For experimental purpose 1-month twitter datasets are collected. Proposed work outperforms when compared with various existing methodologies and also to detect the non-text based as well. The ratio of spam is determined using the precision, recall, and F-measure metrics.

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A new (p;q)- rung orthopair fuzzy SIR method with a multi-criteria decision-making approach

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Granular Computing 2024年第2期9卷

作者： Shahzadi, Gulfam Luqman, Anam Shahzadi, Sundas Deveci, Muhammet Department of Mathematics University of Management and Technology Sialkot Campus Pakistan Division of Science and Technology Department of Mathematics University of Education Lahore Pakistan Department of Industrial Engineering Turkish Naval Academy National Defence University Istanbul Tuzla34942 Turkey Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

In comparison to Fermatean, Pythagorean, and intuitionistic fuzzy sets, (p,q)-rung orthopair fuzzy sets have a wider range of displaying membership grades and can therefore provide more uncertain situations. In this work, the accuracy of (p,q)-rung orthopair fuzzy numbers is investigated using sine trigonometric functions. First, the (p,q)-rung orthopair fuzzy data are extended to the sine trigonometric operational laws (STOLs). In this study, we suggest a novel (p,q)-rung orthopair fuzzy superiority and inferiority ranking (SIR) approach to address the uncertainty group multiple-attribute decision-making (MADM) problem. This strategy handles unclear information, incorporates individual perspectives into group viewpoints, makes conclusions based on many criteria, and ultimately structures a specific decision map. The proposed SIR method utilizes two kinds of information, the superiority and the inferiority information, to obtain two types of flows, including the superiority and the inferiority flows. Then, these flows are utilized to rank the set of alternatives partially or completely. Using sine trigonometric functions and the flexibility of (p,q)-rung orthopair fuzzy sets, novel STOLs have been created. Further, we conduct a case study of the selection of the best journal to demonstrate the feasibility and applicability of the developed technique. The main contributions of this article are as follows: (1) The aggregation operators for (p,q)-rung orthopair fuzzy numbers and their characteristics have been studied under sine trigonometric functions. (2) The SIR approach has been developed under (p,q)-rung orthopair fuzzy sets. The proposed technique is explained through a step-by-step Algorithm. (3) Then, a case study of journal selection is considered to apply the developed technique. (4) The results obtained have been compared with the ranking obtained through various existing techniques. © The Author(s), under exclusive licence to Springer Nature Switzerland AG

关键词： Decision making

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An Investigation Using Structural Equation Modelling on Customer Adoption of Internet of Things Assistance 3

An Investigation Using Structural Equation Modelling on Cust...

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3rd International Conference on Advance Computing and Innovative technologies in Engineering, ICACITE 2023

作者： Mir, Aasim Sankar, M. Kafila, Kafila Akram, Shaik Vaseem Devi, D Praveena Barthwal, Shivangi Baba Ghulam Shah Badshah University Department of Management Studies Rajouri India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Computer Science and Engineering India Sr University School of Business Telangana Warangal India Uttaranchal University Uttaranchal Institute of Technology India Koneru Lakshmaiah Education Foundation Guntur AP India Graphic Era Deemed to Be University Department of Ece Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

The development of Internet of Things-based technologies in India includes home automation, fitness trackers devices, and smart buildings. The Internet of Things has had very little uptake thus far. Scholars and professionals alike agree that a country's economic and social growth may benefit greatly from the introduction of IoT. Several theories have been applied to study the diffusion of IoT, including the technology admission model, the theory of reasoned action, and the theory of patterned behaviors. This research fills a significant knowledge gap by utilizing a structured equation model strategy to evaluate and contrast three competing models in the context of internet of things in India. This research, when compared to the existing literature, opens the door to exploration of a wide variety of internet of things application domains, such as healthcare, senior assistance, intelligent buildings, intelligent supply chains, etc. © 2023 IEEE.

关键词： Wireless networks

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Prediction of Loan Approval in Banks Using Machine Learning Algorithm

Prediction of Loan Approval in Banks Using Machine Learning ...

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Smart technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： Mathan Kumar Mounagurusamy P A Manoharan J. Anvar Shathik A. Pandiaraj N. Ramshankar Naveen G Department of Computing Technologies SRM Institute of Science and Technology Chennai Chennai Institute of Technology Chennai AIML and Data science Anjuman Institute of Technology and Management Karnataka India Department of Computer Science and Engineering R.M. D. Engineering College Department of Electronics and Communication Madha Institute of Engineering and Technology

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

Normally, the banking sector gains profit from different kinds of sources, but the main factor in their profitable growth is giving loans to their customers. A bank's profit or loss is determined by the quantity of the loan. A huge number of people are taking loans from banks and not clearing their dues. So there is a tedious process for banks to identify them and collect them. This in turn leads to following up on a complex verification process and issuing loans to their customers. Customer satisfaction is mandatory in every field, but in the banking sector, it is essential to identify genuine applicants and satisfy their needs, as well as to keep bankers free from non-payable customers. The manual method of identifying the right person and providing a loan to them may cause misconceptions. Several studies in this field suggest various kinds of approaches to identifying applicant status. Therefore, the use of machine learning (ML) approaches in the banking sector to resolve difficulties faced by bankers is becoming more popular. This reduces the problems faced by bankers and makes their applicants satisfactory. The time taken for this process is too long. It is essential to select the best ML approach from the different kinds of classifier algorithms available. The attributes chosen for this study include the applicant's education, type of work, applicant income, loan amount, co-applicant income, credit history, and property area. The proposed study reviews ML approaches such as K-nearest Neighboring (KNN), Logistic Regression, Decision Tree and Support vector Mechanism (SVM). Among those, KNN outperforms the other three algorithms, and the efficiency obtained by KNN is 87.50%, Logistic Regression is 83.512%, Decision Tree is 77.09%, and Random forest (RF) is 74.49%.

关键词： Support vector machines Logistic regression Machine learning algorithms Reviews Banking Nearest neighbor methods Vectors Sustainable development Recommender systems Testing

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Leveraging Deep Learning for Accurate User Journey Prediction and Revenue Enhancement on Online Platforms

Leveraging Deep Learning for Accurate User Journey Predictio...

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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Selvarani V. Kanpur Rani Sakar Fatah Sulaiman V. Selvi Ali Ali Saber Mohammed M. Karthik Ministry of Education's Innovation Cell New Delhi India Department of EEE Hindustan Institute of Technology and Science Chennai India Department of Business Administration College of Administrative and Financial Sciences Knowledge University Erbil Iraq Department of computer science Mother Teresa Women's University Kodaikanal India Department of Computer Engineering Technologies Al Kitab University Kirkuk Iraq Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

The rapid evolution of online platforms has transformed how businesses engage with customers, making accurate user journey prediction crucial for enhancing revenue. This study investigates the application of deep learning techniques, particularly Multi-Layer Perceptron-Gated Recurrent Unit (MLP-GRU) models, to analyze and predict user behavior effectively. By leveraging a comprehensive dataset collected from e-commerce sales, we implement a systematic approach that includes data collection, preprocessing through Min-Max normalization, feature extraction using attention mechanisms, and revenue enhancement strategies. The attention mechanism allows the model to focus on significant user interactions, dynamically weighing the importance of various features to improve predictive accuracy. Our findings demonstrate that the MLP-GRU model not only captures intricate patterns in user behavior but also enables personalized recommendations that significantly boost conversion rates. By integrating deep learning with user journey analytics, businesses can optimize their marketing strategies, tailor user experiences, and ultimately drive higher revenue. The proposed method is implemented in Python and has an accuracy of about 99.11% which is higher than other existing methods like CNN, BiLSTM and LSTM. This research contributes to the growing body of knowledge in the field of artificial intelligence in e-commerce and provides actionable insights for practitioners aiming to leverage data-driven strategies to enhance customer engagement and satisfaction.

关键词： Deep learning Accuracy Attention mechanisms Systematics Predictive models Data collection Smart systems Feature extraction Electronic commerce Business

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Deep Learning and MRI Improve Carotid Arterial Tree Reconstruction

Deep Learning and MRI Improve Carotid Arterial Tree Reconstr...

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Data science and Network Security (ICDSNS), International Conference on

作者： Narasimha Murthy M S Gunti Spandan Inakollu Aswani Shankar Nayak Bhukya Ramachandra A C Department of Information Science & Engineering BMS Institute of Technology & Management Bengaluru India Department of Computer Science GST GITAM University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India Department of CSE (Data Science) CMR Technical Campus Hyderabad India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

This study suggests using magnetic resonance imaging (MRI) scan data to segment and rebuild the carotid artery tree using a deep learning-based method. In the proposed method, the UNET architecture is used to divide an image into parts, and then a 3D level set method is used to make the parts even better. The method was compared to seven different approaches to segmentation and reconstruction, and it outperformed all but one of them. It had a 94% accuracy rate and took 12 hours to process. Deep learning's capacity to reliably segment and categorise pictures, the UNET architecture's ability to maintain spatial information through skip connections, and the level set approach's ability to improve segmentation further all contribute to the proposed method's effectiveness. The proposed approach has the potential to be a useful tool for clinicians in evaluating the status of the carotid artery and finding any irregularities. Stroke is one of the most devastating symptoms of cardiovascular disease, and recognising and treating carotid artery stenosis in its early stages can help avoid or reduce the risk of having a stroke. The success of the proposed method shows that deep learning techniques have the potential to improve the accuracy of carotid arterial tree reconstruction from MRI data. Better identification and treatment of carotid artery stenosis may increase the overall effectiveness of the proposed technique, ultimately leading to better patient outcomes.

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Wireless/wired integrated transmission for industrial cyber-physical systems: risk-sensitive co-design of 5G and TSN protocols

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science China(Information sciences) 2022年第1期65卷 56-71页

作者： Yajing ZHANG Qimin XU Xinping GUAN Cailian CHEN Mingyan LI Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management College of Computer Science Chongqing University

The growing popularity of intelligent manufacturing is driven by deterministic transmission demands of applications in industrial cyber-physical systems(ICPS). However, the ossified shortages of industrial wireless communication such as diverse quality of service(Qo S) and complex signaling processes incur a severe long tail of transmission delay distribution. As a solution, the 5 th generation(5 G) wireless communication technology provides ultra-reliable and low-latency communication(URLLC) for industry scenarios. Moreover, the newly proposed time-sensitive networking(TSN) standards guarantee the transmission determinacy by gate mechanism. In this paper, we propose a heterogeneous time-sensitive network(HTSN) co-designed by 5 G and TSN. We first develop a predictive multi-priority wireless scheduling mechanism based on semi-persistent scheduling(SPS) to reduce signaling delay by reserving resources in *** we propose an adaptive data injection mechanism for TSN based on per-stream filtering and policing(PSFP), which dynamically adjusts the priority of data for queue injection in TSN. To further reduce the long tail of delay, we employ a risk-sensitive learning method to improve the worst-case delay. Simulations on a hot rolling production scenario demonstrate that the proposed mechanisms under HTSN achieve great performance in terms of integrated delay and resource utilization.

关键词： industrial cyber-physical systems (ICPS) 5G-TSN co-design wireless/wired integrated network risk-sensitive reinforcement learning deterministic communication

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A Hierarchical DAG-based Neural Network Approach to Aspect-based Sentiment Analysis 2

A Hierarchical DAG-based Neural Network Approach to Aspect-b...

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2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： Garg, Anvesha Suresh, Vaibhav Goutham Mishra, V.A. Yuvaraj, K. Saini, Pavas Sathi, G. Quantum University Research Center Quantum University Uttarakhand Roorkee247667 India Industry Liasion School of Mangement - Pg Management Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India

ISBN: (纸本)9798350388916

The DNN-BSA (DAG based Neural Networks for Sentiment Analysis) technique, which is used in the context of sentiment analysis, focuses on reliably recognizing opinion aspects and the sentiment polarity that correlate to those opinion features. Both stand-alone and integrated approaches of the past have proven unsuccessful in their attempts to accurately capture the intrinsic link that exists between these two goals. In order to overcome this issue, the study that we have conducted suggests the use of an innovative dynamic DAG graph that explicitly reflects the relationship that exists between aspect extraction and sentiment identification. The DAG network that we use comprises both regular words and emotion labels as linked nodes. This is how we tackle the problem. This approach enables interaction between aspect words and emotion information to take place in a fluid manner. During the process of real-time prediction, the graph is dynamically changed after being initialized with many distinct types of dependencies. The performance of our model is superior to that of other state-of-the-art models, as demonstrated by numerous tests conducted on benchmark datasets. In addition, in-depth examination suggests that our model exhibits considerable gains when dealing with difficult scenarios that involve multiple-opinion aspects as well as circumstances in which there is no opinion element present. This demonstrates how resilient and successful our methodology is in dealing with difficult sentiment analysis assignments. © 2024 IEEE.

关键词： Neural network models

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STEM course on circuit design accomplished to enhance scientific and technological capabilities of students 3

STEM course on circuit design accomplished to enhance scient...

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3rd International Conference on sciences and Technology Applications, ICoSTA 2021

作者： Vexler, V. Aibashev, M. Saipbekova, A. Fominykh, I. Bazhenov, R. Department of Information Systems and Technologies in Education Saratov State University 83 Astrakhanskaya Saratov410012 Russia Department of Accounting and Economic Analysis Osh State University 331 Lenina Osh723500 Kyrgyzstan Department of Applied Mathematics Computer Science Osh State University 331 Lenina Osh723500 Kyrgyzstan Department of Methods of Teaching Mathematics Computer Science and Natural Science Disciplines Mari State University 6 Eshkinina Yoshkar-Ola424033 Russia Department of Information Systems and Mathematics Sholom-Aleichem Priamursky State University 70A Shirokaya Birobidzhan679015 Russia

The aim of the research is to develop an interdisciplinary course of extracurricular activities integrating knowledge in science, technology, engineering and mathematics for students to develop their scientific and technological skills. 52 students from secondary schools of Engels, Yoshkar-Ola (Russia), Sholom-Aleichem Priamursky State University (Birobidzhan, Russia), Osh State University College (Osh, Kyrgyz Republic) took part in the educational experiment. The theme-based course Basic Concepts of Arduino Systems Engineering is developed based on the proprietary technology of simple projects of practical relevance. The projects render the general concept from knowledge to competence. The course consists of four modules: Start, Big Workshop/Hands-on Training, Go Ahead/Step forward, Group Design. Arduino applied in design modeling allows students to create electrical devices that would be productive in one or another area of life at a low cost. The course program is aimed at engaging students in innovative design technologies, computer programming and using robotic devices. The findings of the study were observed as part of an interview with students, educational achievement, subsequent STEM literacy test activities and the performance of game summarizing projects. The recommended method has shown the efficiency in mastering the required core set of skills of circuit design and computer programming. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence

关键词： Students

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Secure data communication framework based on fuzzy based secure clustering and hybridized metaheuristic algorithms 4

Secure data communication framework based on fuzzy based sec...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Vennila, C. Sidhu, Amritpal Preetham, K. Mahajan, Rupali A. Vigenesh, M. Samrat, Bhavuk Prince Shri Venkateshwara Padmavathy Engineering College Department of S&h Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - Ug Management Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India

ISBN: (纸本)9798331518981

The Internet of Things (IoT) has made real-time applications more flexible and deployable. IoT has security and energy efficiency issues despite its benefits. This study addresses these issues with a secure data transmission architecture using fuzzy-based secure clustering and hybridised metaheuristic algorithms. The suggested framework contains three phases. The first phase presents an energy-efficient and secure IoT data transfer infrastructure. Secure route selection is done using fuzzy-based secure clustering and the Whale Optimization-based Harris Hawks Optimisation (W-HHO) method. Parallel cypher block chaining and advanced encryption strengthen data security. Secure cluster-based routing is needed for the Internet of Medical Things (IoMT) in the second phase. An adaptive Gaussian mutation-based Sine Cosine Optimisation (SCO) approach clusters nodes, and mutual authentication assures security. Final phase of research delivers optimal and secure fuzzy inference route selection policy. The Levy Flight Distributed optimization-based Henry Gas Solubility Optimisation Algorithm (LFD-HGSO) clusters data, while concurrent encryption and cypher block chaining secure it. Fuzzy inference selects safest paths. Performance tests show that the proposed solutions improve packet delivery rate, throughput, energy efficiency, and network lifetime over conventional methods. These results demonstrate the framework's safe and energy-efficient data transfer for IoT and IoMT applications. Experimental findings show that the suggested strategy outperforms various QoS metrics like packet delivery ratio, throughput, network longevity, and energy usage. Time efficiency is also improved by the PAES-CBC key generation algorithm. © 2024 IEEE.

关键词： Heuristic algorithms

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