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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Muhammad Shameem, P. Malarvel, Muthukumaran Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The most recent advances in deep learning have demonstrated considerable promise for medical image analysis, especially for the segmentation of thin blood smears, which are microscopic and used to diagnose malaria. This paper presents an improved Convolutional Neural Network (CNN) model for the segmentation of malaria parasites in thin blood smear images, which is based on the U-Net architecture. Our model aims to address the problems that are frequently present in microscopic images, such as overlapping cells and variable stain intensity. Specifically, our model is quite good at overcoming the inherent difficulties in microscopy imaging, especially when it comes to segmentation in conditions with overlapping cells, poor staining quality, and low contrast. Our model stands out for its ability to handle these complex scenarios, which is a major advancement over traditional segmentation strategies. The U-Net architecture is a perfect fit for this purpose because of its efficiency in segmenting biomedical images. We provide an extensive analysis of the model's performance, contrasting it with conventional segmentation techniques in terms of accuracy, sensitivity, and specificity. © 2024 IEEE.

关键词： Medical imaging

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AI-Enhanced Learning Assistant Platform: An Advanced System for Q&A Generation from Provided Content, Answer Evaluation, Identification of Students' Weak Areas, Recursive Testing for Strengthening Knowledge, Integrated Query Forum, and Expert Chat Support 2

AI-Enhanced Learning Assistant Platform: An Advanced System ...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Redhu, Vivek Singh, Abhishek Kumar Saravanan, M. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350349221

The AI-Enhanced Learning Assistant Platform is a revolutionary system designed to enhance learning, with cutting-edge features like question-and-answer generation, answer evaluation, identification of weak areas, recursive testing, an integrated query forum, and expert chat support. This platform makes use of artificial intelligence (AI) technology to try to satisfy the many needs that students and teachers have. Using natural language processing and machine learning, the platform's question and answer generating feature generates relevant questions on its own from the provided content. This encourages participation and in-depth subject understanding. The answer evaluation section provides quick feedback for improvement by utilizing AI algorithms to assess the accuracy and caliber of student responses. One of this platform's key advantages is its capacity to identify students' areas of weakness. Through the analysis of performance patterns and root causes, the system can generate customized recommendations and learning materials to help overcome those constraints. The property of recurring testing facilitates continuous assessment and reinforcement of knowledge. Through repeated practice, the program gradually pushes students to increase their understanding of the material by creating adaptive exams. Through' the integrated query forum, students can collaborate and ask for assistance from others by asking questions and receiving answers from teachers and their peers. Furthermore, by enabling real-time communication between users and subject matter experts, the expert chat support tool fosters an engaging and motivating learning environment. To sum up, the AI-Enhanced Learning Assistant Platform offers a wide range of features designed to maximize learning. With AI technology, it helps students learn more effectively and retain what they have learned, promotes active learning, and provides the support they need for a good educational experience. © 2024 IEEE.

关键词： Students

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Morphological Classification of Galaxies using Enhanced Deep Learning Model 5

Morphological Classification of Galaxies using Enhanced Deep...

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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Nemade, Rohan Jha, Suraj Anand, Harsh Chandrasekaran, Saravanan Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350379884

Galaxies in astrophysics are one of its fundamental, their morphological classification remains an essential key to unlock countless doors about the structure, formation and history of our Universe. For nearly a century, astronomers have assigned galaxies to perceptual categories such as ellipticals, spirals and irregulars. However, the extraordinary expansion of the galaxy database catalysed and instigated by sky surveys of ever-increasing scales rendered this classification more manual than practical. Because of the learning capability and its scaling nature, to learn hierarchical abstract features from the visual data Convolutional Neural Networks (CNN) are powerful tools to recognize complex patterns and structures in images, such as spiral arms or central bulges that contribute significantly to accurate morphological classification. That is why CNN are particularly well suited to the problem of galaxy classification. While traditional classification work used to solve the problem, it is not feasible for today's huge amounts of generated data. This attempt to build and train a CNN model using galaxy images as our domain. The Enhanced CNN model can achieve the high accuracy to morphological type of galaxies. The Enhanced CNN increase both efficiency and classification accuracy, particularly when processing large dataset. The Galaxy Zoo dataset indicated that our study can provide a tool for other astronomical research in future. The Enhanced CNN gain the overall accuracy rate are 95.29% with the performance indicator were calculated including average precision, recall, F1-score and Area under curve (AUC) is 0.9513, 0.9531, 0.9525 and 0.9853 respectively. The Enhanced CNN outperforms the various existing state-of-the models. © 2024 IEEE.

关键词： Convolutional neural networks

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Machine Learning for Forecasting Depression and Anxiety in University Students 1

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8th International Conference on Information and Communication technology for Competitive Strategies, ICTCS 2023

作者： Biswas, Tamal Bhattacharya, Diptendu Rudrapal, Dwijen Roy, Srijan National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India M Tech Student National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India

ISBN: (数字)9789819702107

ISBN: (纸本)9789819702091

The timely identification of mental health issues enables experts to more effectively provide treatment and enhance the well-being of patients. Mental health pertains to an individual’s emotional, mental, and interpersonal state, influencing their thoughts, emotions, and behaviors. It remains crucial across all life phases, spanning childhood, adolescence, and adulthood. Historically, categorizing mental health problems among college students demanded significant effort and time from psychologists. This research engaged five machine learning methods to classify such issues swiftly. The effectiveness of these methods was evaluated based on different standards. The five methods included logistic regression, KNN classification, decision tree classification, random forest, and stacking. A comparison and implementation of these methods revealed that stacking yielded the highest accuracy, predicting 82.20. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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LLM-Enhanced Deepfake Detection: Dense CNN and Multi-Modal Fusion Framework for Precise Multimedia Authentication

LLM-Enhanced Deepfake Detection: Dense CNN and Multi-Modal F...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Samesh Enathe, V.P. Chandra Mouli, S. Dheepthi, R. Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The increasing ubiquity of deepfake technology presents a serious risk to the legitimacy and reliability of multimedia material across a number of sectors, from identity verification to news distribution. Current deepfake detection methods frequently have trouble identifying minor manipulations and are unable to keep up with the latest generation techniques, which creates a serious vulnerability in the defence against the improper use of synthetic media. Using Dense Convolutional Neural Networks (Dense CNN) and Multi-Modal Fusion, this study presents a novel method for deepfake detection. Our Dense CNN architecture which draws inspiration from Dense Net improves sensitivity to complex manipulations while optimizing feature reuse through dense connectivity patterns, thereby mitigating the shortcomings of existing systems. Our suggested approach dynamically combines temporal and visual modalities, enhanced by Multi-Modal Fusion, to offer a comprehensive contextual knowledge that enhances detection accuracy. After thorough tests on several datasets, our method performs exceptionally well and is particularly good at identifying advanced deepfake variations. Our suggested methodology improves the state-of-the-art in deepfake identification by addressing the shortcomings of current systems and providing a reliable response to the urgent problems brought on by the malicious usage of synthetic media in practical applications. Adding Large Language Models (LLMs) is essential to improving the accuracy of the system. To add another level of scrutiny, LLMs are deliberately used to characterize and analyze portions of multimedia information that are vulnerable to manipulation. © 2024 IEEE.

关键词： Convolutional neural networks

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Generative AI-assisted Standalone Wearable Reading Glasses for the Visually Impaired 3

Generative AI-assisted Standalone Wearable Reading Glasses f...

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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Islam, Md. Repon Sheikh Sadi, Muhammad Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798331534400

This paper presents a novel, low-cost, wearable Optical Character Recognition (OCR) reading assistant glasses designed for the low-vision and visually impaired community. This standalone system uses a Radxa Zero 3W single board computer and a 5MP RGB camera. PaddleOCR engine, optimized through a series of preprocessing and postprocessing algorithms, used for effective text recognition. A finetuned lightweight 0.5B Large Language Model deployed on-device as a Generative AI chat assistant (Ask-LLM) to respond to the user query with full privacy. To convert the generated response into speech, the Festival Text-to-Speech (TTS) engine has been used. Also, Whisper CT2 Automatic Speech Recognition (ASR) has been utilized for effective and real-time voice transcription. The system recognizes English text with character recognition accuracy of 95.25% and Ask-LLM generates responses at 2~4 tokens/second. The hardware cost of the whole system is only $50 while works completely offline utilizing cutting-edge technologies. This paper contributes to the field of assistive technology by showcasing how carefully selected off-the-shelf components and open-source software can be engineered into a powerful, affordable, and practical solution. © 2024 IEEE.

关键词： Open source software

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An underwater explorer remotely operated vehicle: Unraveling the secrets of the ocean

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IEEE Potentials 2023年第3期42卷 31-36页

作者： Huang, Xiang-Rui Chen, Liang-Bi National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Magong880011 Taiwan

In recent years, underwater robots have been an essential focus of marine science and technology applications. Whether it is the application of military tasks or general civil affairs, underwater robots have played a significant role. For example, unmanned underwater vehicles (UUVs) can work in the sea for a long time, have high maneuverability, and perform various underwater tasks. There are many subcategories of UUVs, such as autonomous underwater vehicles (AUVs), remotely operated vehicles (ROVs), and autonomous and remotely operated vehicles (ARVs) (Kong et al., 2020). © 1988-2012 IEEE.

关键词： Remotely operated vehicles

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Paillier Cryptography Based Message Authentication Code for IoMT Security

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computer Systems science & engineering 2023年第3期44卷 2209-2223页

作者： S.Siamala Devi Chandrakala Kuruba Yunyoung Nam Mohamed Abouhawwash Department of Computer Science and Engineering Sri Krishna College of TechnologyCoimbatore641042India Department of Computer Science and Engineering Vignan Nirula Institute of Technology and ScienceGuntur522009India Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Health care visualization through Internet of Things(IoT)over wireless sensor network(WSN)becomes a current research attention due to medical sensor evolution of *** digital technology-based communication system is widely used in all *** of medical thing(IoMT)assisted health-care application ensures the continuous health monitoring of a patient and provides the early awareness of the one who is suffered without human *** smart medical devices may consume with limited resources and also the data generated by these devices are large in *** IoMT based applications suffer from the issues such as security,anonymity,privacy,and *** overcome these issues,data aggregation methods are the solution that can concatenate the data generated by the sensors and forward it into the base station through fog node with efficient encryption and *** article proposed a well-organized data aggregation and secured transmission *** data generated by the sensor are collected and *** nodes(AN)received the compressed data and concatenate *** concatenated and encrypted data is forward to fog node using the enhanced Paillier cryptogra-phy-based encryption with Message Authentication code(MAC).Fog node extracts the forwarded data from AN using Fog message extractor method(FME)with *** proposed system ensures data integrity,security and also protects from security *** proposed model is simulated in Net-work Simulator 2.35 and the evaluated simulation results proves that the aggregation with MAC code will ensures the security,privacy and also reduces the communication *** node usages in between Aggregator and base station,will reduce the cloud server/base station computational overhead and storage *** proposed ideology is compared with existing data aggregation schemes in terms of computational cost,storage cost,communication cost and energy *** of communica

关键词： Fog IoMT wireless sensor network cloud aggregation encryption decryption energy

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A Deep Learning Based System to Predict the Plant Disease Using Streamlit 4

A Deep Learning Based System to Predict the Plant Disease Us...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Warbhe, Mohan K. Bore, Joy Jordan Chaudari, Shiv Nath Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Sawangi Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering MaharashtraSawangi Wardha442001 India

ISBN: (纸本)9798331523923

The proposed web application for tomato leaf disease detection exemplifies the transformative power of Artificial Intelligence and computer Vision in modern agriculture. Addressing the critical issue of early and accurate disease detection, this solution bridges the gap between advanced technological innovations and the practical needs of farming. The application is designed using Streamlit and Python to combine the usability of a friendly interface with the power of robust analytical capabilities in CNN, offering real-time reliable diagnosis and actionable insights to farmers. This application provides the highest possible accuracy and adaptability through the thorough training and evaluation of models and is a good asset for disease management. Deployment on scalable cloud platforms ensures access, reliability, and global reach. Future improvements, including extending disease detection to other crops, multilingual support, real-time recommendations, and integration with IoT devices, promise to expand its reach and utility. This initiative is a shining example of how AI-based solutions can transform agricultural practices by enhancing productivity, reducing losses, and ensuring sustainability. The application empowers farmers and contributes to a resilient agricultural ecosystem, addressing the pressing need for innovative and sustainable solutions in a rapidly evolving world. © 2025 IEEE.

关键词： Smart agriculture

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Predicting Macro-Economic Factors of WTI Crude Oil Using Monte Carlo Simulation

Predicting Macro-Economic Factors of WTI Crude Oil Using Mon...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Divyaa, Sharon Saravanane, Roshan Kumar, Mohan Hindustan Institute Of Technology And Science Department Of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

In this paper, we use regression equations and Monte Carlo simulations to study the dynamics of WTI crude oil prices. Our research takes the price of WTI crude oil as the dependent variable and looks at how it relates to nine key macroeconomic variables, most of which have to do with supply. We recognize the importance of these factors affecting the price of crude oil by using the knowledge from earlier studies. In order to make our study easier, we compile historical WTI crude oil price data from the last 60 months and combine it with the chosen macroeconomic indicators. The choice of the variables stem from their critical role in shaping crude oil markets. Through a multiple regression model, we aim to establish a comprehensive understanding of the dependencies between WTI crude oil prices and the macroeconomic factors. To ensure the robustness of our model, we assess multicollinearity among the independent variables, emphasizing that while they are related to WTI crude prices, they should not exhibit high intercorrelations. Our research provides a valuable framework for scenario generation, allowing us to explore potential future price movements based on the identical relationships. By unveiling the intricate interplay between these factors and WTI crude oil prices our study contributes to informed decision-making for investors and policymakers. © 2024 IEEE.

关键词： Crude oil

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