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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Manickaraj, K. Aravind, S. Ramakrishnan, T. Sudha, N. Ramamoorthi, R. Nithyanandhan, T. CMS College of Engineering and Technology Department of Mechanical Engineering Tamilnadu Coimbatore India Karpagam Academy of Higher Education Department of Mechanical Engineering Tamilnadu Coimbatore India Sri Eshwar College of Engineering Department of Mechanical Engineering Tamilnadu Coimbatore India CMS College of Engineering and Technology Department of Computer Science Engineering Tamilnadu Coimbatore India Sri Krishna College of Engineering and Technology Department of Mechanical Engineering Tamilnadu Coimbatore India Sri Krishna College of Technology Department of Mechanical Engineering Tamilnadu Coimbatore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

The revolutionary effects of computational learning and artificial intelligence (AI) on materials science and engineering - particularly in the field of high-performance fiber -reinforced polymer (FRP) composites - are examined in this article. Efficiency, accuracy, and innovation have significantly increased as a result of the integration of AI and machine learning (ML) approaches across the product lifecycle, from material selection and development to manufacturing process optimization and property prediction. AI-powered deep generative models in material design speed up the development of novel composite formulations, while automated fiber placement and Physics-Informed Machine Learning (PIML) improve manufacturing processes to guarantee consistent quality and cut costs. The large-scale dataset-based predictive models have significantly enhanced mechanical property prediction, simplified material selection and cut down on experimental testing expenses and time. Sustainability, efficiency, and performance in material research and production will be fueled by the ongoing advancements in AI and computational learning, which will further transform material science by allowing the design and production of superior composites suited for contemporary industrial applications. © 2024 IEEE.

关键词： Life cycle

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Machine Learning Algorithms for Predicting House Prices in a Smart city using its Real-Time Data 2

Machine Learning Algorithms for Predicting House Prices in a...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Joel, M. Robinson Navaneethakrishnan, M. Sriram, K.P. Manonmani, S. Priskilla Kings Engineering College Department of Information Technology Chennai India St. Joseph College of Engineering Sriperumbudur Department of Computer Science and Engineering Chennai India Meenakshi Sundararajan Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350346640

Housing prices are an important economic indicator, wherein the property values are important to both buyers and sellers. One of the least transparent industries across the world is real estate. In order to conduct the research, real-time housing data has been collected from Chennai City. Sine this study deals with the continuous outcome variable, regression techniques are highly preferred. The major goal of this research study is to predict housing values by using real-time information. This study performs various house price evaluation process based on various factors. Several regression models like Lasso and Ridge are utilized and their performances are compared in order to select the best model for resolving a specific issue. This study intends to develop an effective machine learning model to predict the price of a property based on input parameters and display the information in a website to reach out to people. © 2023 IEEE.

关键词： Machine learning

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Customer Segmentation Using Hierarchical Clustering 9

Customer Segmentation Using Hierarchical Clustering

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Afzal, Areeba Khan, Laiba Hussain, Muhammad Zunnurain Zulkifl Hasan, Muhammad Mustafa, Muzzamil Khalid, Aqsa Awan, Rimsha Ashraf, Farhan Khan, Zohaib Ahmed Javaid, Arslan Information Technology University Lahore Department of Computer Engineering Punjab Pakistan Bahria University Lahore Campus Dept. of Computer Science Pakistan University of Central Punjab Lahore Faculty of Information Technology Department of Computer Science Pakistan National College of Business Administration And Economics Department of Computer Science Lahore Pakistan Information Technology University Lahore Pakistan

ISBN: (纸本)9798350394474

In the dynamic landscape of retail, understanding customer behavior is paramount for businesses seeking to optimize marketing strategies and enhance the shopping experience. This study explores the utilization of hierarchical clustering techniques for mall customer segmentation, with a focus on the paper titled 'MALL CUSTOMER SEGMENTATION USING MACHINE LEARNING' as the benchmark. Our dataset encompasses a diverse range of mall customers, spanning demographics and behavioral attributes. Hierarchical clustering systematically groups customers into clusters, revealing distinct segments within the mall's customer base. A comprehensive analysis of these clusters unveils profound insights into customer tendencies, preferences, and purchasing habits. These insights form a solid foundation for tailored marketing campaigns, personalized recommendations, and resource allocation within the mall. The study contributes significantly to customer analytics, providing retailers with a powerful tool to gain a competitive edge in the retail sector. By leveraging hierarchical clustering for mall customer segmentation, businesses can enhance customer satisfaction, drive sales, and foster lasting customer relationships. This paper underscores the importance of data-driven methodologies in understanding customer behavior and offers a practical framework for businesses to harness the potential of hierarchical clustering for strategic decision-making in the retail industry. © 2024 IEEE.

关键词： Consumer behavior

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Identification of Diabetic Retinopathy by Transfer Learning Based Retinal Images 9

Identification of Diabetic Retinopathy by Transfer Learning ...

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

作者： Sagili, Sreenivas Reddy Goswami, Chandrashekhar Bharathi, V.C. Ananthi, S. Rani, K. Sathya, R. Rochester Institute Of Technology Department of computer science Lomb Memorial Drive RochesterNY14623 United States Mit School of Computing Mit Art Design and Technology University Department of Computer Science & Engineering Maharashtra Pune India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Advanced Research Centre for Future Networks and Digital Twin Sri Eshwar College of Engineering Tamil Nadu Coimbatore India M. Kumarasamy College of Engineering Department of Information Technology Tamilnadu Karur India Kongunadu College of Engineering and Technology Department of It Tamil Nadu Trichy India

ISBN: (纸本)9798350377972

The human eye is frequently considered to deliver a space into a person's strength because it is only with diabetic retinopathy (DR), a devastating eye illness caused by diabetes mellitus, that one may understand the subject's unprotected surface without invasive operations. There are certain disorders, notably vascular illness, that authority telltale indicators in the eye retina. Microaneurysms (MAs) are primary indicators of diabetic retinopathy (DR), making their detection crucial for an effective screening program that adheres to clinical procedures. Retinal examinations can reveal pathological deviations caused by limited ocular diseases such as arteriosclerosis, diabetes, cardiovascular disease hypertension and stroke. The proposed work introduces image processing algorithms, such as shady object identification, to predict the disorder or improve the contribution frame, thereby improving the perceptibility of vessels in color fundus frames. Following this enhancement, an automated classification algorithm, specifically a Convolutional Neural Network (CNN), is executed. The CNN architecture is designed to efficiently extract features from retinal frames, capturing complex patterns connected with diabetic retinopathy. The trained CNN is then assessed on a separate test condition, with its performance system of measurement-accuracy, specificity, recall, and Fl measure-being reported. © 2024 IEEE.

关键词： Convolutional neural networks

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HDL Environment for the Synthesis of 2-Dimensional and 3- Dimensional Network on Chip Mesh Router Architecture

HDL Environment for the Synthesis of 2-Dimensional and 3- Di...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Kumari, Shweta Rajput, Kapil Singh, Gurwinder Jain, Arpit Sachi, Savya Manwal, Manika Shri Fanishwar Nath Renu Engineering College Department Of Computer Science & Engineering Bihar Araria India Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Chandigarh University Department Of Aitcse Punjab Mohali India Koneru Lakshmaiah Education Foundation Department Of Computer Science & Engineering AP Vaddeswaram India L N Mishra College Of Business Management Department Of Information Technology Bihar Muzaffarpur India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350366846

Application specific Network on Chip (NoC) designs are quickly becoming the technology of choice for solving the problem of multiprocessor system architecture. Broadband, interposes communication, deadlock avoidance, port bandwidth, router architecture, and bespoke NoCs may all be improved by modelling and simulation of multilayer network structures and synthesis. Topology-based organization of 2D and 3D routers allows for the construction and laDyout of NoCs. The articles compare and contrast the design and analysis of two-dimensional and three-dimensional network-on-chip routers for setting up mesh topological nodes in a network. Xilinx 14.2 is used to execute the code. The most recent version of modalism, 10.0 student edition, is used to functionally mimic the program and modules. The experimental work and thorough testing on the Virtex-5 FPGA confirm the chip design. © 2024 IEEE.

关键词： Network-on-chip

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Blockchain Driven Metaheuristic Route Planning in Secure Wireless Sensor Networks

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computers, Materials & Continua 2023年第1期74卷 933-949页

作者： M.V.Rajesh T.Archana Acharya Hafis Hajiyev E.Laxmi Lydia Haya Mesfer Alshahrani Mohamed K Nour Abdullah Mohamed Mesfer Al Duhayyim Department of Computer Science and Engineering Aditya Engineering CollegeSurampalem533437India Department of Business Administration Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Accounting and Audit Azerbaijan State University of Economics(UNEC)BakuRepublic of Azerbaijan Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Recently,Internet of Things(IoT)has been developed into a field of research and it purposes at linking many sensors enabling devices mostly to data collection and track *** sensor network(WSN)is a vital element of IoT paradigm since its inception and has developed into one of the chosen platforms for deploying many smart city application regions such as disaster management,intelligent transportation,home automation,smart buildings,and other such IoT-based *** routing approaches were extremely-utilized energy efficient approaches with an initial drive that is,for balancing the energy amongst sensor *** clustering and routing procedures assumed that Non-Polynomial(NP)hard problems but bio-simulated approaches are utilized to a recognized time for resolving such *** this motivation,this paper presents a new blockchain with Enhanced Hunger Games Search based Route Planning(BCEHGS-RP)scheme for IoT assisted *** presented BCEHGS-RP model majorly employs BC technology for secure communication in the IoT supportedWSN *** addition,an effective multihop route planning approach was designed by the use of EHGS *** proposed EHGS technique is derived from the concept of Hill Climbing strategy(HCS)and HGS ***,a fitness function with two parameters namely residual energy(RE)and intercluster distance to elect optimal *** performance validation of the BCEHGS-RP model is experimented with under diverse number of *** experimental outcomes highlighted the better performance of the BCEHGS-RP technique on recent approaches.

关键词： Internet of things wireless sensor networks routing metaheuristics blockchain

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Deep Learning Analysis for Skin Cancer Detection

Deep Learning Analysis for Skin Cancer Detection

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International Conference on VLSI, Signal Processing, Power Electronics, IoT, Communication and Embedded Systems, VSPICE-2022

作者： Singh, Chandra Nischitha Shetty, Shailesh S. Bekal, Anush Bhat, Sandeep Badiger, Manjunatha Department of Electronics & Communication Engineering Sahyadri College of Engineering & Management Mangaluru India Department of Electronics & Communication Engineering Shree Devi Institute of Technology Mangaluru India Department of Computer Science Engineering Sahyadri College of Engineering & Management Mangaluru India

ISBN: (纸本)9789819944439

In the present world, skin cancer is the most widely recognized reason for death among people. Skin malignancy is an unusual development of skin cells. Frequently created on the body part exposed to the sunlight;however, it can happen on any place on the body. The majority of the skin malignancy is treatable at the beginning phase. So an early and quick identification of skin disease can spare the patient's life. With the new innovation, early identification of skin malignancy is conceivable at the introductory stage utilizing picture handling. The skin cancer detection using image processing is based on the detection of skin cancer types at its earliest stage. There are many types of skin cancers found. It is difficult to identify the type of skin cancer at the earlier stage, manual identification can often be time consuming and inaccurate. Doctors are able to identify the symptoms of skin cancer but are unable to identify the type of skin cancer in the initial stage. So the doctors will wait until it gets blotted but by that time the disease will become out of control. So a software is developed to help the Skin Cancer Detection at its earliest stage by passing valid input images. So this chapter explains about a method to identify and classify the skin cancer using images using Convolutional Neural Network (CNN) algorithm. The accuracy obtained by using the proposed method is 89%. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Disease control

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Optimal Structure Exploration of Bayesian Network Based on Dealing with the Route Choice Model 6

Optimal Structure Exploration of Bayesian Network Based on D...

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6th International Conference on computer Information science and Application technology, CISAT 2023

作者： Wang, Ziyi Zeng, Yufei Wang, Rui College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China College of Computer Science and Technology Harbin Institute of Technology Yichun China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

ISBN: (纸本)9781510668546

Route selection in residents' travel is a key link in travel decision-making in transportation systems. In existing studies, discrete choice models are often used to describe residents' travel path choices. However， with today's highly developed big data, the traditional discrete choice model is too simple to represent residents' travel path choices, ignoring the connection between various factors. Therefore, a big data-driven approach is proposed to solve this problem in this paper. Driven by research data and with the introduction of the Bayesian network model, the paper adopts a hill-climbing search strategy to improve the search efficiency, uses a variety of scoring functions to train data, which optimizes the Bayesian network structure. The experimental results show that the model fully considers the causation between the independent variables, which is more logical and makes the construction of the resident travel route selection model more accurate and efficient. © 2023 SPIE.

关键词： Bayesian networks

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An Adaptive Intrusion Detection System in Industrial Internet of Things(IIoT) using Deep Learning 1

An Adaptive Intrusion Detection System in Industrial Interne...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Jyothi, E.V.N. Kranthi, M. Sailaja, S. Sesadri, U. Koka, Sridhar N. Reddy, Pundru Chandra Shaker Rise Krishna Sai Prakasam Group of Institutions Computer Science and Engineering A.P Ongole India Karunya Institute of Technology and Sciences Computer Science and Engineering Tamilnadu Coimbattore India Rise Krishna Sai Prakasam Group of Institutions A.P Ongole India Vardhaman College of Engineering Computer Science and Engineering Telangana Hyderabad India Andhra University Computer Science and Engineering Andhra Pradesh Visakhapatnam India School of Computer Science and Artificial Intelligence Sr University Telangana Warangal India

ISBN: (纸本)9798350384246

The Industrial-Internet-of-Things (IIoT) is a product of the extensive use of the Internet-of-Things(IoT) in vital industries including manufacturing and industrial production. To improve industrial and manufacturing processes, the IIoT integrates sensors, actuators, and smart tools that can interact with each other. There are many advantages to IIoT for service providers and customers alike, but privacy and security are still major concerns. Cyberattacks in such a network have been reduced with the use of an intrusion detection system (IDS). Nevertheless, several current IIoT intrusion detection systems (IDS) suffer from issues such as an incomplete list of the network's attack kinds, an excessive number of features, models constructed using outdated datasets, and an absence of attention to the issue of imbalanced datasets. Our proposed intelligent recognition system can spot cyberattacks in IIoT-networks, which will help with the difficulties. Singular value decomposition (SVD) is employed by the suggested model to decrease data characteristics and enhance detection outcomes. If we want to keep our classifications from being biased due to over-fitting or under-fitting, we employ the SMOTE method. Data has been classified using a number of deep learning and machine learning techniques for both binary and multi-class purposes. We test the suggested intelligent model on the ToN_ IoT dataset to see how well it performs. With the suggested method, we were able to achieve a 99.98% accuracy rate and a lowered error rate of 0.016% for multi-class classification, and a 0.001 % reduction in the error rate for binary classification. © 2024 IEEE.

关键词： Internet of things

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Neuromorphic Computing Architectures for EnergyEfficient Edge Devices in Autonomous Vehicles 3

Neuromorphic Computing Architectures for EnergyEfficient Edg...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Pradhan, Rahul Gummadi, Annapurna Tanusha, P. Sowmya, Gandla Vaitheeshwari, S. Ponni Valavan, M. Gla University Department of Computer Science and Engineering Uttar Pradesh Mathura India Hyderabad India Kl University Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Nri Institute of Technology Department of Computer Science and Engineering Andhra Pradesh Vijayawada India Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science Chennai Avadi India K. Ramakrishnan College of Engineering Department of Ai & Ds Tamil Nadu Trichy India

ISBN: (纸本)9798350361186

Autonomous vehicles hold the promise of significantly reducing road accidents caused by human error, such as distracted driving or impaired judgment. However, the widespread adoption of autonomous vehicles faces challenges, including regulatory frameworks, liability concerns, ethical considerations, and the need for comprehensive infrastructure updates. This research focuses on the development of neuromorphic computing architectures tailored for energyefficient edge devices in autonomous vehicles. The significance of this study lies in addressing the escalating computational demands of real-time data processing in autonomous driving scenarios while mitigating the inherent power constraints of edge computing environments. The conventional approaches in autonomous vehicle computing, often centralized and powerintensive, pose challenges in terms of energy consumption and real-time responsiveness. The proposed neuromorphic computing architectures leverage spiking neural networks and event-driven processing to emulate the brain's efficiency, offering a novel solution to enhance both computational performance and energy efficiency. The research addresses prior issues related to the power-hungry nature of traditional computing paradigms in autonomous vehicles and emphasizes the need for more sustainable and responsive edge computing solutions. The novelty of this research lies in the integration of neuromorphic principles into edge devices, enabling onboard processing that is not only energy-efficient but also well-suited for the sporadic and dynamic nature of real-world driving environments. This study paves the way for advancements in autonomous vehicle technology, promising enhanced energy efficiency, reduced latency, and improved overall system performance. © 2024 IEEE.

关键词： Energy efficiency

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