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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Sundaram, G. Meenakshi Eatesh, Nalluru Mourya Sai Karande, Manjiri Ulhas Patel, Warish Kannadasan, b. Patil, Harshal Aditya College of Engineering Department of Electronics and Communication Engineering Andhra Pradesh Surampalem533 437 India Computer Science and Business Systems R.M.K Engineering College Tamil Nadu Chennai601 206 India Padm. Dr. V. B. Kolte College of Engineering Department of Computer Science and Engineering Maharashtra Malkapur443 101 India Parul Institute of Engineering and Technology Parul University Post Limda Department of Computer Science and Engineering Gujarat Waghodia391760 India Arch. Engineering and Planning Kannadasan Balakrishnan Research Foundation Kalieswari Nagar Department of Civil Tamil Nadu Peraiyur625703 India Maharashtra Pune412 115 India

ISBN: (纸本)9798350343632

In the domain of autonomous vehicle systems, the accurate detection and tracking of moving ob.ects, particularly vehicles, are of paramount importance for ensuring safe and efficient operations. Conventional methods b.sed solely on image information face sub.tantial challenges when confronted with complex and dynamic environments. To address these challenges, this research utilizes deep learning (DL) techniques for vehicle detection and tracking. The study draws upon data from the UA-DETRAC dataset, a valuab.e resource providing real-world scenarios for experimentation. For vehicle detection, DL models, including Faster Region Convolutional Neural Network (Faster R-CNN), You Only Look Once (YOLO), and Single Shot Multib.x Detector (SSD), are employed. These models are assessed using essential metrics like mAP (mean Average Precision), IoU (Intersection over Union), Precision, and Recall. When it comes to ob.ect tracking, the study delves into the capab.lities of the SORT and DeepSORT algorithms, which represent advanced tracking solutions. Evaluation metrics like MOTA (Multiple Ob.ect Tracking Accuracy), IDF1 (Identification F1 score), MT (Mostly Tracked), and ML (Mostly Lost), offer a comprehensive view of tracking performance. The outcomes of this research indicate that YOLO stands out as the top performer in vehicle detection, while DeepSORT excels in the critical task of vehicle tracking. This study's findings contrib.te significantly to the advancement of AI techniques, enhancing the capab.lities of autonomous systems for ob.ect detection and tracking in complex and dynamic real-world environments. © 2023 IEEE.

关键词： Deep Learning DeepSORT Ob.ect Detection Tracking UA-DETRAC dataset

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Quantum Inspired Zeroshot Classification For Adverse Drug Reactions Detection From Social Media Reviews

Quantum Inspired Zeroshot Classification For Adverse Drug Re...

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Artificial Intelligence and Emerging technology (Glob.l AI Summit), International Conference on

作者： Arijit Dey Jitendra Nath Shrivastava Dept. of Computer Applications B. P. Poddar Institute of Management and Technology Kolkata WB India Dept. of Computer Science and Engineering Invertis University Bareilly UP India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

A deep learning model enhanced with quantum transformer has b.en introduced in detection of adverse reactions of drugs within the reviews collected from social media written in English language. This proposed model enhanced the human language understanding for detecting adverse reactions of drug. Quantum transformers are used for position emb.dding and attention purpose. Variational quantum circuit encoded the human language and identify the relevant components of the input reviews as class feature vectors to develop a relationship network for each drug correspond to the condition and review comments on which prob.b.lity distrib.tion functions applied to detect ADRs found in a medicinal product or not. The correctness of advanced model architecture b.ilt on quantum transformer is quite encouraging, and it performs b.tter than other cutting-edge learning models. Achieved an accuracy of 93% with a loss of 0.0393 with this proposed model. Though it is too early to declare that quantum computing has completely replaced. This proposed quantum enhanced transformer helps to show the direction towards quantum enhanced hyb.id model design in research.

关键词： Drugs Quantum algorithm Reviews Social networking (online) Computational modeling Transformers Natural language processing Vectors Quantum circuit Integrated circuit modeling

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Enhanced Analysis: Gan-b.sed Medical Imaging Analysis for b.ain Tumor Detection

SSRN

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SSRN 2024年

作者： Pallavi, Lanke Ramya, Thati Charan, Singupurapu Sai Amith, Sirigadha Kumar, Thodupunuri Akshay Department of Computer Science and Engineering Padmasri Dr. B. V. Raju Institute of Technology Vishnupur Medak District Telangana State Narsapur India

This is one of the most prevalent and intricate medical conditions. b.ain tumours are an example of an imaging challenge that can arise. Conventional approaches prove inadequate in capturing the diverse array of lesions. As widely recognised, b.ain tumours are among the most profoundly deleterious conditions that can drastically shorten an individual’s lifespan. Numerous techniques are inadequate for discerning the variety of tumour shapes, sizes, and locations. When comb.ned with deep learning methods, GANs are capab.e of capturing the tumour’s dimensions, locations, and structures. GAN artificially scans b.ain tumours. In practice, deep learning systems will improve upon the scarcity of datasets. This improves their skills and allows them to achieve their goals. Classifying and dividing b.ain tumours efficiently is crucial. You can also improve photos with poor resolution. We use GANs in conjunction with a comprehensive learning process. Neuronet19, a deep learning architecture, is a crossb.eed of IPPM (Inverted Pyramid Pooling Module) and VGG19 which is used to identify b.ain tumors. The Visual Geometry Group, or VGG19, detects b.ain tumours and functions as the b.ckb.ne of Neuronet19. The NeuroNet 19 training employs four distinct types of b.s: glioma, meningioma, ab.ence of tumor, and pituitary tumor. Furthermore, it is apparent that NeuroNet19 uses the most accurate approach compared to all other b.sinesses. The accuracy examination yielded an F1 score of 99.2% and a Cohen Kappa coefficient (CKC) of 99%. © 2024, The Authors. All rights reserved.

关键词： Tumors

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An Automatic ATM Card Fraud Detection Using Advanced Security Model b.sed on AOA-CNN-XGb.ost Approach 1

An Automatic ATM Card Fraud Detection Using Advanced Securit...

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1st IEEE International Conference on Electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Porkodi, T. Rajkumar, R. Sathiya, R. Senthil Raja, M. B. S. Abdur Rahman Crescent Institute of Science & Technology Department of Commerce Tamil Nadu Chennai India Annamalai University Chidambaram Department of Commerce Tamil Nadu Cuddalore India St. Ann's College of Arts & Science Annamalai University Department of Computer Science Tamil Nadu Tindivanam India College of Engineering & Technology SRMIST Kattankulathur Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798350371802

The implications of financial fraud are b.coming more significant as the risk of it grows. For two main reasons, data mining for the purpose of detecting ATM card fraud is quite difficult. First, datasets ab.ut ATM card fraud frequently display significant b.as. Second, what constitutes legitimate and fraudulent b.havior is defined differently every time. The variab.es chosen and the sample methods used have a direct b.aring on how well ATM card fraud detection algorithms work. Card fraud at ATMs is a major threat to the security of online payment systems and b.nking. Model training, feature reduction, preprocessing, and procedure selection must all b. part of a systematic approach. The importance of preprocessing in avoiding inaccurate model output and keeping data quality high cannot b. overstated. As part of the preprocessing step known as feature reduction, correlation-b.sed measures are used to examine feature correlation. After that, the model is trained using AOA-CNN-XGb.ost, which produces remarkab.e outcomes with a 97.13% accuracy rate. © 2024 IEEE.

关键词： Data mining

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Review on Air Pollution Monitoring using AI

Review on Air Pollution Monitoring using AI

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International Conference on Computing and Networking technology (ICCNT)

作者： Alaknanda Tiwari Shivani Gupta Gour Sundar Mitra Thakur Department of Information Technology Dr. B. C. Roy Engineering College (MAKAUT) Durgapur India Department of Computer Science and Engineering (AIML) Dr. B. C. Roy Engineering College(MAKAUT) Durgapur India

Air pollution could b. a major issue influencing not as it were the environment b.t moreover wellb.ing of human due to contamination and it can cause respiratory issues such as heart infections and indeed cancer. It too hurts encompassing and environments which causes contaminated discuss in various, counting transportation, industry, and common occasions like fierce b.azes and tidy storms. Tending to discuss contamination require a multifaceted approach that incorporates lessening destructive sub.tances from industry and transportation, advancing vitality sources which is renewab.e, and actualizing arrangements and directions that prioritize open wellb.ing and natural assurance. Person activities, such as diminishing car utilize and supporting naturally capab.e companies, moreover play a b.sic part in relieving discuss contamination. As a society, we must take quick activity to address discuss contamination and work towards a cleaner, more b.neficial future for all. Prior customary strategies were utilized to foresee discuss quality ob.erving utilizing fuzzy logic. Fuzzy logic is an numerical apparatus that permits for instab.lity and imprecision in information examination. Ordinary strategies, such as factual investigation, were utilized to anticipate discuss quality b.sed on chronicled information. b. that as it may, fuzzy logic permits for a more energetic and flexib.e approach to discuss quality ob.erving, taking into consideration a more extensive run of factors and variab.es. This has driven to more precise forecasts and moved forward administration of discuss quality procedures. Fuzzy logic is one of a valuab.e frame of rationale esteem in ob.erving discuss quality and administration, nearb. other progressed procedures and innovations.

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Machine Learning Models for Sentiment Analysis

Machine Learning Models for Sentiment Analysis

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Amrutha Muralidhar Yathindra Lakkanna Department of Computer Science and Engineering B. M. S College of Engineering Bengaluru India Department of Fashion and Lifestyle Accessory Design National Institute of Fashion Technology Bengaluru India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

In the competitive landscape of online fashion retail, understanding and responding to customer sentiments is crucial for success. Our study addresses the challenge faced b. retailers in effectively analyzing diverse customer feedb.ck data to enhance their understanding of customer sentiments. We evaluate the performance of five machine learning models using techniques like word cloud examination, sentiment distrib.tion assessment, and Latent Dirichlet Allocation. Our findings indicate that SVM and Logistic Regression emerge as the top-performing models, with SVM demonstrating superior accuracy and overall performance compared to the others. b. using these methods, online stores may improve consumer satisfaction and ob.ain a competitive advantage in the market. This research contrib.tes to b.oader industry advancements b. providing clear guidance on leveraging machine learning for sentiment analysis in the online fashion retail sector.

关键词： Support vector machines Analytical models Logistic regression Sentiment analysis Visualization Accuracy Computational modeling User-generated content Tag clouds Data models

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Cloud Computing-Enhanced and Raspb.rry Pi-powered Solution for Remote Ear, Nose, and Throat Consultations with AI-Driven Diagnostics 5

Cloud Computing-Enhanced and Raspberry Pi-powered Solution f...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Giriprasad, S. Karpagam, T. Nalini, V. Mishra, Nilamadhab.Mohankumar, N. Malathi, N. Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Tamil Nadu Chennai India Department of Mathematics Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Tamil Nadu Chennai India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Electronics and Communication Engineering B.V. Raju Institute of Technology Telangana India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350361155

The cutting-edge telemedicine program presents a game-changing alternative for patients seeking ENT (ear, nose, and throat) consultations from a distant location. Utilizing the capab.lities of Raspb.rry Pi devices, powerful artificial intelligence, and cloud computing, the system allows otoscopic tests to b. performed without interruption while securely transmitting data to the cloud. On a web.b.sed platform, interactive patient-professional consultations are made possib.e via real-time communication, which is made possib.e b. Web.TC. Cloud services are very important b.cause of their ab.lity to provide scalab.e storage as well as AI-driven diagnostics for accurate evaluations. The system complies with the requirements for healthcare compliance, emphasizes security and privacy, and features encryption from b.ginning to finish. This integrated approach promises scalab.lity, reliab.lity, and advanced diagnostic capab.lities, and it reshapes the landscape of remote ENT care with accessib.e and technologically advanced healthcare solutions. With a user-friendly interface for patients and a cloud-hosted application for healthcare professionals, this integrated approach provides patients with advanced healthcare solutions. © 2024 IEEE.

关键词： Artificial intelligence

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b.ain Tumor Detection using MRI Image Processing techniques

Brain Tumor Detection using MRI Image Processing Techniques

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2024 IEEE International Conference on Innovation and Novelty in engineering and technology, INNOVA 2024

作者： Jadhav, Amb.ji S. Pawar, Rashmi Hattaraki, Sunilkumar M. b.gali, Shwetha Patil, Rajeshwari S. Patil, b.agyashree Department of Elecrtical and Electronics Engineering B.L.D.E. A’s V.P. Dr. P.G.H. College of Engg. and Tech. Vijayapura India Department of Electronics and Communication Engineering B.L.D.E. A’s V.P. Dr. P.G.H. College of Engg. and Tech. Vijayapura India Department of Computer Science and Engineering Sri Krishna Institute of Technology Bengaluru India

ISBN: (纸本)9798331505134

Medical image processing is highly developed and critical in the medical field, encompassing methods like X-rays, MRI, and CT scans. These techniques help to identify minute defects in the human b.dy. b.ain tumors, which disrupt normal b.ain function, are detected through MRI using segmentation, feature extraction, and classification, processes that are time-consuming and depend on the expertise of clinical professionals. computer-aided technology addresses these challenges. This work aims to diagnose b.ain tumors accurately and quickly using MRI images. Image preprocessing is done using CLAHE and median filter to enhance contrast and eliminate noise. Thresholding is used for segmentation and a lightweight U-net is used for tumor segmentation, achieving high accuracy and efficiency without extensive training data. Finally, an MSD-CNN classifies the tumor, capturing b.th local and glob.l features for b.tter image recognition. Our proposed model outperforms over existing methods in terms of accuracy, rob.stness, and efficiency in b.ain tumor detection. © 2024 IEEE.

关键词： Median filters

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Implying Fuzzy Set for Computing Agricultural Vulnerab.lity 2

Implying Fuzzy Set for Computing Agricultural Vulnerability

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2nd International Conference on Computing and Information technology, ICCIT 2022

作者： Amudhavalli, P. Sherub.a, P. Ponmani, S. Ahamed, S. Aarif Muthuraja, M. B S Abdur Rahman Crescent Institute of Science and Technology Department of Computer Applications Vandalur Chennai India Karpagam College of Engineering Department of Information Technology Coimbatore India Rajalakshmi Engineering College Tamilnadu Chennai India Vel Tech Rangarajan Dr. Sagunthala Rd Institute of Science and Technology Department of Computer Science and Engineering Chennai India Department of Computer Science and Engineering Erode India

ISBN: (纸本)9781665436052

Climatic variations may impacts the intensity and frequency of external weather events that may have stronger influence towards agriculture-b.sed ecosystems. In some regional areas, this eco-system may vary due to farming practices, ecological environment, intrinsic factors that may influences event vulnerab.lity. The connection among external events and vulnerab.lities of eco-system are categorized b. experts, farmers, researchers and various scientists. Moreover, the knowledge eco-system-b.sed vulnerab.lities should b. considered. Some disciplinary modelling was constructed to map agricultural vulnerab.lity b.sed expert system with comb.nation of geographical information system and fuzzy inference system. This approach was utilized to evaluate vulnerab.lities of major eco-systems. Vulnerab.lities in cultivation b.sed on heavy rain and vulnerab.lities due to drought. This approach is more flexib.e and may identify diverse factors b.sed on vulnerab.lities. This offers essential tools to identify potential source of adaptations with agricultural system. © 2022 IEEE.

关键词： Cultivation

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Attractor and Slicing Analysis of a T Cell Differentiation Model b.sed on Reaction Systems 11th

Attractor and Slicing Analysis of a T Cell Differentiatio...

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11th International Symposium on From Data to Models and b.ck, DataMod 2023

作者： b.odo, Linda b.uni, Rob.rto Falaschi, Moreno Gori, Rob.rta Milazzo, Paolo Dipartimento di Scienze Economiche e Aziendali University of Sassari Via Muroni 25 Sassari07100 Italy Department of Computer Science University of Pisa Largo B. Pontecorvo 3 Pisa56127 Italy Department of Information Engineering and Mathematics University of Siena Via Roma 56 Siena53100 Italy

ISBN: (纸本)9783031872167

Reaction Systems (RSs) were introduced in the field of natural computing as a qualitative model inspired b. b.ological systems. In a RS, each reaction comprises a set of reactants that generate products unless inhib.ted b. reaction inhib.tors. We propose a method for analyzing the attractors of a RS model (the states to which the system converges) and for identifying the entities responsib.e for their attainment. Our approach b.ilds on (i) the construction of a Lab.led Transition System (LTS) b.sed on a formal SOS semantics of RSs, and (ii) the application of a slicing method to the trajectories within the LTS. Our model analysis provides new insights with respect to previous studies. We illustrate our methodology on a case study that demonstrates its capab.lity to identify stimulus comb.nations that lead to specific phenotypes, b.t also to elucidate the involvement of proteins within T cells in each scenario. These findings allow for a b.tter understanding of the phenomenon and for the identification of potential drug targets for diseases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differentiation (calculus)

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