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MediAI: An AI Based Guide for Medical Content Authenticity Analysis

MediAI: An AI Based Guide for Medical Content Authenticity A...

2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Jadhav, Dhiraj Dighe, Aneesh Walunj, Ajinkya Sagri, Anish Bhandari, Akash Ashtagi, Rashmi Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798331517953

In the Internet age, it is critical to identify the legitimacy of accurate medical content. This proposed research offers a thorough NLP-driven architecture intended to improve the veracity and correctness of online medical material. The proposed system effectively differentiates AI-generated content from non-AI content by leveraging Machine Learning (ML) models for testing and Deep Learning (DL) models for implementation, particularly Long-Short Term Memory (LSTM) and Bidirectional Long-Short Term Memory (Bi-LSTM). This will help decrease the dissemination of medical misinformation with the use of higher techniques of ML and DL, hence greatly improving the level of public health literacy. The proposed system will be user-friendly and will provide real-time verification;hence it would be very useful for healthcare professionals, the general public, in effect leading to a better-informed and health-conscience society. © 2024 IEEE.

关键词： Electronic health record

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Automatic Recognition of Non-standard Number Plates using YOLOv8 18

Automatic Recognition of Non-standard Number Plates using YO...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Tupsounder, Asaavi Patwari, Renuka Ambati, Roja Chavarkar, Arya Shirsat, Kavita Vidyalankar Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9789380544519

A critical feature of vehicle movement applications is the detection and identification of a vehicle's License Plate (LP). Despite technological and algorithm developments, differences in LP properties by nation, such as number system, colors, character language, fonts, and size, necessitate more study to enhance detection and recognition. Despite substantial study, many systems function in well-defined contexts, necessitating the use of expensive equipment to capture photos from slow-moving vehicles or generate high-quality shots. This study describes a deep learning-based automated approach for LP detection and recognition, which is further separated into detection and character recognition. Yolov8 (You Only Look Once Version-8) was utilized for automatic pixel separation and gradient computation, making complicated model training easier. Using deep learning, Easy OCR (Optical Character Recognition) was utilized to retrieve text from number plates. Easy OCR (Optical Character Recognition) was utilized to extract text from license plates, with deep learning algorithms employed to effectively read text in difficult settings. For image and video processing, OpenCV (Open-Source computer Vision Library) was employed. The project titled 'Automatic Number Plate Using YOLOv8 Model' leverages the advanced capabilities of the YOLOv8 model to develop a robust system for automated number plate recognition, serving as the core component for real-time object detection and number plate localization within images and video streams. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： ANPR (Automatic Number Plate Recognition) Deep learning Image Recognition Non-Standard OpenCV YOLOv8

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Knee Osteoarthritis detection and classification using a customized CenterNet with DenseNet201 3

Knee Osteoarthritis detection and classification using a cus...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Shirisha, N. Shilpa, K. Meghana, C.H. Rahman, Mohammed Abdul MLR Institute of Technology Dept. of Computer Science and Engineering Hyderabad India MLR Institute of Technology Dept. of Computer Science and Engineering Nalgonda India

ISBN: (纸本)9798331523893

Knee osteoarthritis (KOA) is a prevalent joint disorder diagnosed using imaging modalities like MRI, CT scans, and X-rays, with X-rays being the most cost-effective. Early detection is crucial for effective management. This study presents an automated deep learning approach to detect and classify KOA severity based on the Kellgren-Lawrence (KL) grading system using single posteroanterior standing knee X-ray images. Utilizing the Osteoarthritis Initiative dataset, we employed transfer learning to fine-tune DenseNet-201, enhancing model performance. Additionally, knowledge distillation was applied to reduce computational complexity while maintaining accuracy. Our model achieved over 95% accuracy on both testing and cross-validation datasets, outperforming existing methods. This approach offers a reliable tool for early KOA diagnosis and grading, potentially aiding clinical decision-making © 2025 IEEE.

关键词： Diagnostic radiography

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Automated Exam Script Checking using Zero-Shot LLM and Adaptive Generative AI

Automated Exam Script Checking using Zero-Shot LLM and Adapt...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Himi, Shinthi Tasnim Tanzila Monalisa, Natasha Sultana, Shirin Afrin, Anika Hasib, Khan Md Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Dhaka Bangladesh Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

The manual grading of exam scripts is a labor-intensive process, often plagued by subjectivity and inconsistency. This study presents an innovative approach to automating the evaluation of exam scripts using the Large Language Model (LLM) integrated with Zero-Shot Learning (ZSL) and Generative AI. Our system leverages the capabilities of GPT-4 to generate and evaluate answers, applying similarity measures to ensure accuracy and fairness in grading. The model's adaptability through ZSL allows it to assess new questions without additional training. The system also incorporates iterative refinement techniques to enhance the quality of standard answers over time. Performance evaluations demonstrate a low error rate, with an average relative error of 1.29% for annotated questions and 1.67% for non-annotated questions compared to human graders. The performance underscores the automated system's transformative potential in revolutionizing educational assessments, offering a consistent, fair, and highly efficient alternative to traditional grading methods. © 2024 IEEE.

关键词： Zero-shot learning

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Communication through Indian Sign Language: An Approach for Data Preprocessing

Communication through Indian Sign Language: An Approach for ...

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2023 International Conference on computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023

作者： Khadkikar, Prajakta Shah, Himanshu Chaudhary, Shubham Kalekar, Shlok Chavda, Jenish Pune Institute of Computer Technology Dept. of Computer Engg Pune India

ISBN: (纸本)9798350338003

People who are specially impaired with regards to speaking and hearing find it difficult to communicate with people who are not impaired. The same is true vice-versa. For this very reason, Sign Language is necessary to be known and understood by both the parties. However, there are many people who are completely unaware of Sign Language and its importance. Hence, this project is aimed at bridging this communication gap between the two parties, and eventually, have an awareness on this importance of Sign Language. The paper presents an introductory analysis for the development of the Indian Sign Language (ISL) Recognition System. The primary focus of the paper is to discuss various steps involved in the formation, preprocessing, and augmentation of the dataset. The paper also briefly discusses the various steps performed after preprocessing to train and implement the model, with the focus on the systems based on Artificial Neural Networks. © 2023 IEEE.

关键词： Image classification

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Detecting Feet's Bending Patterns With Smart Insoles 5

Detecting Feet's Bending Patterns With Smart Insoles

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Zhong, Yuchen Ying, Qijun Cai, Xiaohui dept. of Computer Science and Technology Hefei China dept. of Data Science Hefei China

ISBN: (纸本)9798350355925

Foot bending is a basic component of walking. Although bending is important for foot-ground interaction and ensures the speed and balance of the whole walking cycle, there is still no practical method for recording and analyzing foot bending under everyday scenarios. In this study we propose to use a smart insole, with two embedded Inertial Measurement Units, to acquire the movements of the fore-foot and the hind-foot and deduce the bending, represented by the bending point's position. We use the deep learning approach to estimate the bending position and its changes during different walking patterns. On the dataset containing 8 test subjects and 6 walking patterns, our method can achieve an overall mean absolute error accuracy of 2.58 cm. The smart insole can be with the subject all the day, bring in a new method for continuous foot bending monitoring. © 2024 IEEE.

关键词： Wearable technology

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Deep Learning Assisted Intelligent Human computer Interaction for Next Generation Internet Applications

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第2期48卷 145-146页

作者： Hasan, Al Mehedi Hwang, Yong Seok Shin, Jungpil Dept. of Computer Science and Engineering Rajshahi University of Engineering and Technology Bangladesh Kwangwoon University Seoul Korea Republic of School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan

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Transformer-Based Embeddings for Greek Language Categorization 24

Transformer-Based Embeddings for Greek Language Categorizati...

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24th IEEE/ACIS International Conference on computer and Information Science, ICIS 2024

作者： Liapis, Charalampos M. Kyritsis, Konstantinos Perikos, Isidoros Paraskevas, Michael University of Patras Computer Technology Institute & Press 'Diophantus' Mathematics Dept. Patras26504 Greece University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350376647

The Greek School Network (GSN) provides support to students, teachers, and school units in secondary education across Greece. Handling numerous user queries manually can be challenging, necessitating the development of an automated system for accurate categorization of these queries. This paper presents a comparative study of various transformer-based models for multi-class text categorization of Greek language queries submitted to the GSN helpdesk. We introduce a new experimental balanced dataset and extract vector representations from eleven transformer-based models. These representations are evaluated using ten classic machine learning classifiers. Our findings highlight the superior performance of the Multilingual E5 Text Embeddings model, particularly when paired with the extreme gradient-boosting classifier. This combination demonstrates a clear advantage in accurately categorizing user queries, paving the way for more efficient automated helpdesk systems. © 2024 IEEE.

关键词： Contrastive Learning

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IOT Based Multicrop Soil Moisture Control and Monitoring System 1

IOT Based Multicrop Soil Moisture Control and Monitoring Sys...

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1st International Conference on AIML-Applications for Engineering and technology, ICAET 2025

作者： Barhate, Madhusudan Desai, Devam Mishra, Gaurav Phadke, Gargi Das, Siuli Bhuran, Supriya Yogesh Ramrao Adik Institute of Technology D.Y. Patil Deemed to be University Dept. of Instrumentation Engg Dept. of Computer Engg Dept. of Computer Science and Engg Navi Mumbai India

ISBN: (纸本)9798350355611

Agriculture is crucial for the economy, with China and India leading in production. Conventional irrigation methods require precise timing and quantity, varying for different crops. This paper proposes an IoT-enabled intelligent irrigation system to enhance crop productivity. The system uses a moisture sensor to activate the pump motor, and IoT technology enables remote monitoring through a web interface or mobile app. The system stores moisture data customized to specific crop requirements, reducing human intervention while ensuring optimal water supply. © 2025 IEEE.

关键词： Moisture control

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COMPARING WSN PERFORMANCE: TRADITIONAL VS. SDN APPROACH

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical Engineering and computer Science 2024年第4期86卷 81-94页

作者： Hamad, Diyar Jamal Yalda, Khirota Gorgees Okumus, Ibrahim Taner Tapus, Nicolae Faculty of Automatic Control and Computer Science University of Science and Technology POLITEHNICA Bucharest Soran Technical college Erbil Polytechnic University Erbil Iraq Computer Engineering Kahramanmaras Sutcu Imam University Kahramanmaras Turkey Dept. of Computer Science University of Science and Technology POLITEHNICA Bucharest Romania

The proliferation of Wireless Sensor Networks (WSN) in various applications has necessitated the exploration of network architectures that can ensure efficient, scalable, and reliable communication. This study presents a comparative performance evaluation of traditional networking paradigms and Software-Defined Networking (SDN)-based architectures within WSN. Utilizing the Floodlight SDN controller and Mininet-WiFi emulator, we conducted a series of experiments across three different network topologies, varying the number of hosts and access points. The performance metrics focused on are average latency, packet loss percentage, and throughput. The results demonstrate a considerable improvement in all measured parameters under the SDN-based environment compared to the traditional setup. Specifically, SDN environments consistently showcased lower latency, minimal packet loss, and higher throughput, with the most notable difference observed in smaller network configurations. The findings suggest that SDN, with its centralized control plane, can significantly enhance the performance of WSNs, particularly in scenarios demanding rapid scalability and dynamic traffic management. This study underscores the potential of SDN architectures in optimizing WSN operations and lays the groundwork for future research in this domain. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Floodlighting

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