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8th IEEE Inter.a.ional Conference on Infor.a.ion and Communication technology, CICT 2024

作者： Vyas, Avani Shar.a. Shivani Dr B R Ambedkar National Institute of Technology Jalandhar Department of Information Technology Jalandhar India Thapar Institute of Engineering and Technology Department of Computer Science Engineering Patiala India

ISBN: (纸本)9798331531539

In today's enter.a.nment era, several movies are produced over.a.year. We all try to get a review of any movie before planning to watch it. Much work has been done in the past for early movie success prediction but most of them are from the production perspective. However, the audience perspective is still unexplored. Further, most of the predictive models work with some basic features such as genre, cast, comments, reviews, and production house. However, a few impor.a.t factors such as current audience interest shift, the social influence of the star, and personal liking of a person are much more impor.a.t in predicting the rating of a movie. In this work, we propose an advanced movie rating prediction cross model, which could rate movies according to general as well as some fine-tuned features. This helps in providing a fair prediction of a movie according to global and personal audience interests. We collected a wide range of data from the Web, and social media along with the IMDb dataset. A comparative analysis of the proposed model with existing benchmark schemes has been done. Significant improvement in movie rating and success prediction is observed with an accuracy of 87%. © 2024 IEEE.

关键词： Prediction models

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A deep learning-based early alzheimer’s disease detection using magnetic resonance images

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Multimedia Tools and Applications 2025年第16期84卷 16561-16582页

作者： Suchitra, S. Krishnasamy, Lalitha Poovaraghan, r.J. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R &D Institute of Science and Technology Avadi Tamil Nadu Chennai India Department of Information Technology Nandha Engineering College Tamil Nadu Erode India Department of Information Technology Jaya Engineering College Tamil Nadu Thiruninravur Chennai India

Alzheimer’s Disease (AD) is a degenerative, chronic condition of the brain for which there is now no effective tr.a.ment. However, there are medications that can slow its development. In order to stop and control the development of AD, earlier diagnosis of the disease is quintessential. Our proposed method’s primary objective is to establish a comprehensive model for the prior detection of Alzheimer’s disease and the categorization of distinct AD stages. This work employs a deep learning methodology, especially CNN. The proposed appr.a.h makes use of well-known models that have alr.a.y been trained to classify medical images, like the EfficentNetB7 model, by applying the transfer learning principle. In order to achieve gr.a.er.a.curacy, convolutional neural networks (CNNs) are frequently scaled up as new resources become available at a fixed cost throughout the construction phase. A compound coefficient is used by the CNN architecture and scaling appr.a.h, which is the foundation of the pre-trained EfficientNetB7 model, to scale the dimensions equally. This proposed EfficientNetB7 model is quicker, easier, and more effective than other pre-trained models like VGG19 and InceptionV3. The proposed model includes simple structures that have memory requirements, provide manageable time, overfitting, and low computational complexity as well as training and inference speeds. The Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset was employed for.a.comprehensive assessment of the method proposed, utilizing well-known perfor.a.ce metrics including sensitivity, specificity, and accuracy. The findings revealed that the improvised results achieved the accuracy metric when compared to existing methods. The EfficientNetB7 model has been enhanced, and this model achieves a sensitivity of 98.08%, specificity-98%, accuracy-98.2%, and F-score-98.95%, for multi-class AD stage classifications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, par

关键词： Magnetic resonance

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Semi-analytical finite element method applied for characterizing micropolar fibrous composites

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Applied Mathematics and Mechanics(English Edition) 2024年第12期45卷 2147-2164页

作者： J.A.OTErO Y.ESPINOSA-ALMEYDA r.rODrIGUEZ-rAMOS J.MErODIO School of Engineering and Sciences Monterrey Institute of TechnologyEstado de Mexico 52926Mexico Institute of Engineering and Technology Autonomous University of Ciudad JuarezCiudad JuarezChihuahua 32310Mexico Postgraduate Program in Computational Modeling in Science and Technology Federal Fluminense UniversityRıo de Janeiro 27255-125Brazil Faculty of Mathematics and Computer Science University of HavanaLa Habana 10400Cuba Department of Mathematics Applied to ICT School of Informatics Systems EngineeringTechnical University of MadridMadrid 28031Spain

A semi-analytical finite element method(SAFEM),based on the two-scale asymptotic homogenization method(AHM)and the finite element method(FEM),is implemented to obtain the effective properties of two-phase fiber-reinforced composites(FrCs).The fibers are periodically distributed and unidirectionally aligned in a homogeneous *** framework addresses the static linear elastic micropolar problem through partial differential equations,subject to boundary conditions and perfect inter.a.e contact *** mathematical formulation of the local problems and the effective coefficients are presented by the *** local problems obtained from the AHM are solved by the FEM,which is denoted as the *** numerical results are provided,and the accuracy of the solutions is analyzed,indicating that the formulas and results obtained with the SAFEM may serve as the reference points for validating the outcomes of experimental and numerical computations.

关键词： semi-analytical appr.a.h fiber-reinforced composite(FrC) effective property finite element method(FEM) asymptotic homogenization method(AHM) micropolar elasticity

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Segmentation Of Intracerebral Haemorr.a.e CT-Scan Images Using N-Net Architecture 3

Segmentation Of Intracerebral Haemorrhage CT-Scan Images Usi...

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3rd Inter.a.ional Conference on Electrical, Electronics, Infor.a.ion and Communication Technologies, ICEEICT 2024

作者： Santhi, P. Maheswari, S. Deepa, K. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tiruchirapalli Avadi India

ISBN: (纸本)9798350369083

In recent years, image segmentation has emerged as a critical task in computer vision and various applications such as medical image processing, autonomous driving, and satellite imagery analysis. Deep learning techniques, particularly convolutional neural networks (CNNs), have played a pivotal role in revolutionizing image segmentation by offering unprecedented accuracy and efficiency. Many algorithms have used for the segmentation such as U-Net, V-Mesh, U-Net++ and etc. But, U-Net having the challenges in parameter transfer, model size and in speed. To overcome these issues, this paper explores the evolution of image segmentation techniques into ICH (Intracerebral Haemorr.a.e) for better diagnosis. This model is tested using ICH dataset available in Kaggle. In which, N-Net have produced the better perfor.a.ce in terms of precision, Dice, recall and F1 Score. © 2024 IEEE.

关键词： Mesh generation

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Machine Learning and Vehicle Fault Diagnosis System with IoT Enabled Data

Machine Learning and Vehicle Fault Diagnosis System with IoT...

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2023 Inter.a.ional Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Kannan, S. rajes Partheeban, Pachaivannan ramesh, ramamoorthy Elamparithi, P. Navin Somasundaram, Krishnan Selvi, r. Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Chennai Institute of Technology Department of Civil Engineering Chennai India National Institute of Technology Department of Computer Science and Engineering Tiruchirappalli India Sri Muthukumaran Institute of Technology Department of Computer Scinece and Engineering Chennai India Dr.M.G.R. Educational and Research Institute Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348910

Vehicle systems are difficult for both Hardware implementation and Software implementation, so it is safeguarding is a difficult task. In vehicle trade, the maintenance process being utilized that effect in a fall of vehicle lifetime and also loss of capital. Predictive maintenance is needed to conquer these problems. The fault diagnosis system is proposed using PIC Microcontroller with efficient manner. The several parts in the vehicle such as vehicle engine, Gearbox, Power train unit, Air.a., Engine Control Units (ECUs), Chassis, ABS are establishing to apply the microcontroller. This article aims to clarify and review the ML frontiers involved in modern vehicle condition monitoring systems. The microcontroller operation helps to infor.a.ion about the individual health of the vehicle and gives/saves on the Internet of Things (IoT). Then, the web page receives the data and infor.a.ion will apply to the fault code diagnosis system. The fault diagnosis system helps to find the faults in the vehicle, such as the faults are precisely identified and establish the proper establishment of fault infor.a.ion in the fault diagnosis system. This work is combined effect of IoT data with machine learning to reduce the vehicle demages or fault condition monitoring. The perfor.a.ce analysis of the proposed system attains better perfor.a.ce than the other existing appr.a.hes. © 2023 IEEE.

关键词： Microcontrollers

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Invoice Processing Automation Using UI Path Studio 5

Invoice Processing Automation Using UI Path Studio

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5th IEEE Inter.a.ional Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： ramyadevi, r. Anbuselvan, A. Julian, Anitha Selvi, S. Murali Dhar, M.S. Saveetha Engineering College Department of Computer Science and Engineering Chennai India R.M.K Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Invoices are the fundamental document that play a vital role in encapsulating the details of the products and the business transactions between the seller.a.d the buyer. We cannot check our invoices at any time for emergencies. Substituting the case, by storing the basic details of multiple invoices in an excel sheet might be used for future purposes. This paper provides the advancement of data extraction through automation technologies. We begin this research by the technique Optical Character recognition (OCr) in automation of data extraction from invoices. This reduces the manual errors during extraction as the entire process works with the automation and provides an accuracy of 95%. The paper discusses the design of the automation that has to be processed for the data extraction including the invoice details, line items and relevant metadata and stored in a structured and accessible for.a.. This storage allows the customer to get through all their product details in a document. Furthermore, the study investigates the integration of extracted data from the invoice to the customer. This integration acts as the strategic role in transforming invoice data into a dynamic asset, enabling customers to glean insights. The paper incorporates r.a.-world examples and case studies to illustrate implementations of scrapping techniques using automation. By addressing challenges such as technological limitations, integration complexities, and data security concerns, the paper provides practical understanding for customers in processing the automation. © 2024 IEEE.

关键词： Accuracy Automation Technologies Data Extractions Data Storage Invoice Processing Optical Character recognition (OCr)

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Analysis and Estimation of Discounts on Product Prices in E-Commerce Sites 5

Analysis and Estimation of Discounts on Product Prices in E-...

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5th IEEE Inter.a.ional Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Julian, Anitha Maheswar, Challa ramyadevi, r. Murali Dhar, M.S. Selvi, S. Saveetha Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Chennai India R.M.K Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Sales on e-commerce platforms are significantly influenced by discounts. When a discount is offered, 80% of buyers who initially show disinterest end up making a purchase, per.a.retail Me-not survey. This incr.a.es revenue and draws in new clients who might not have otherwise thought about making a purchase. Another survey by Accenture reveals that 75% of consumers are more inclined to choose a brand that provides personalized discounts. To retain customers, e-commerce websites can implement tailored discount strategies based on customers' past purchases or preferences. The decision-making process for discounts involves various factors. Pricing strategies and discounts, as identified in the study, are pivotal in influencing consumer behavior.a.d encouraging purchases, particularly among new customers. This research delves into the complexities of discounting practices and their impact on the online retail ecosystem. Employing a comprehensive methodology that combines quantitative and qualitative techniques, the study investigates the frequency, trends, and effectiveness of discounts across different e-commerce platforms. Through extensive data collection, including details on discount percentages, durations, and consumer feedback, the research aims to reveal insights into the strategic implementation of discounts. The study's main objective is to analyze discounts that are given on a variety of products by gathering infor.a.ion from an online store and using the characteristics, reviews, and ratings of the products as factors to calculate the discount value. © 2024 IEEE.

关键词： data analysis discount analysis EDA estimation techniques feedback and reviews predictive modelling

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rehabPal: Automated Physical Therapy Assistance

RehabPal: Automated Physical Therapy Assistance

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Inter.a.ional Conference on Computing and Machine Learning, CML 2024

作者： Ar.a. P. Shar.a. Treya Jatain, Aman Bhaskar, Shalini Bajaj Manju Kumar, Sandeep Department of Computer Science & Technology Amity University Gurgaon India School of Engineering & Technology K R Mangalam University Gurgaon India Department of Computer Science & Engineering and Technology Jaypee Institute of Information Technology Noida India CHRIST University Bengaluru India

ISBN: (纸本)9789819778386

This innovative research project transforms automated physical therapy support by combining 2D and 3D pose assessment appr.a.hes with tailored feedback systems. Utilizing Mediapipe technology, the apparatus attentively observes patients’ movements in a two-dimensional space and gives r.a.time data regarding their gait, exercise compliance, and range of motion. Accurate data on joint angles and body segment alignment are provided by the system's sophisticated 3D posture estimation algorithms, which enhance tracking precision. The system incorporates customized feedback systems that include individual patients’ goals, progress, and conditions and delivery and reward schemes. By incr.a.ing user engagement and adherence, the integration of gamification elements has the potential to revolutionize automated physical therapy support. This comprehensive appr.a.h aims to enhance patients’ quality of life while simultaneously enhancing long-term rehabilitation outcomes by providing more conveniently accessible, affor.a.ly priced, and specially designed therapies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Patient rehabilitation

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Adaptable Individualized Investment recommendation System

Adaptable Individualized Investment Recommendation System

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2024 IEEE Inter.a.ional Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Sabiyath Fatima, N. Noor.a.leema, N. Mahesh, C. Umanesan, r. Senthil, K. Santhosh Kumar, P. B.S.Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Information Technology Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Chennai India

ISBN: (纸本)9798331522988

Internetization has played a massive role in the rapid development and growth of the financial investment industry. Due to the rapid development, there is a demand for more financial knowledge services to help make well-informed investments. Accessing and conducting investments through online financial platforms has made the field highly accessible. This has made the investment recommendation system a popular research ar.a. Personalized recommendations will help the user make investments that are safe for their financial situation and also suit their particular needs and interests. This work proposes a method where investment recommendations are given based on three key factors. The user interest sectors, risk appetite-based clustering, and market sentiment of the stock. These factors will be calculated by the use of Lasso regression, SVM, stepwise regression, generalized boosted regression, random forest, ridge regression and fuzzy clustering. Based on these three factors, the proposed method results in investment recommendations made for users that are personalized to the specific user. This allows existing investors to gain new opportunities and new investors to start investing with lower.a.ount of risk using the infor.a.ion available. © 2024 IEEE.

关键词： Decentralized finance

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A Federated Learning Using Probabilistic Data Structures 1

A Federated Learning Using Probabilistic Data Structures

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1st Inter.a.ional Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Kaur, Simrandeep Singh, Amritpal Dr. B R Ambedkar National Institute of Technology Department of Computer Science and Engineering Jalandhar India

ISBN: (纸本)9798350365016

In this era, most of the prominent or.a.izations are developing the chat-bots like Gemini and GPT and humanoid robots such as Ameca and Sophia. They have their capability to think and make their own independent decisions based on their extreme learning models. The data used to train these models is gathered from various sources in different for.a.s. Federated learning is one of techniques using which the heterogeneous devices having non-identical independent data collaborate to train a global learning model without compromising with their privacy. In this case, the traditional data structures are not efficient enough to work on this r.a. time big data, thus probabilistic data structures come into a picture which shows their feasibility to generate the result of queries within a stipulated time and space. This work aims to give an extensive overview of various probabilistic data structures and federated learning. The field of federated learning is fully investigated, covering its characteristics, classifications, challenges, evolution, and applications, along with research gaps. The primary goal of this work is to present a thorough overview of federated learning to r.a.ers and scholars who want to dive deeper into the topic, as well as possible future paths for research in this ar.a. © 2024 IEEE.

关键词： Federated learning

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