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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： More, Amruta Patil, Satchal Savale, Aditya Satle, Prafull Tilwane, Krishna Shinde, Sanket Ajeenkya DY Patil School of Engineering Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350355611

This Research aimed at the challenge of finding college for students, but this is complex task as students are unable to find best institutes based on entrance exam scores in India. To bounce on that, we focus on the development of a College Predictor Bot, which uses some key factors notably JEE and CET scores and predicts admission to different Indian colleges. You are trained on years of admissions data from hundreds of colleges, with students of all shapes and sizes and performance levels. In terms of methodology used in the study, a combination of random forest and decision tree models were used for analysing the exam scores and make findings. The ML-model is subject to a lot of statistics and testing, and the analysis results in highly valuable co-relations between performance on entrance exams and admitting results. Our results show that College Predictor Bot accurately predicts admissions, therefore helping students know what colleges are suitable. The larger implications [of this tool] are that this might make the college selection process easier and provide a slightly more transparent and informed way of getting deserved seat in the Eductaion system of Bharat. © 2025 IEEE.

关键词： Students

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Ethereum Smart Contract in Supply Chain Management 4

Ethereum Smart Contract in Supply Chain Management

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Muthamilselvan, S. Mariam Bobby, K. Shobana, R. Sujitha, J. Varsha, K. Sri Sairam Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

'Ethereum Smart Contracts: Transparency and Efficiency Revolution in Supply Chain Management'. The revolutionary effect of incorporating Ethereum Smart Contracts into supply chain management procedures is examined in this abstract. Ethereum smart contracts improve supply chain transparency, traceability, and trust by utilizing blockchain technology. These smart contracts optimize overall efficiency by reducing the risk of fraud and errors through the automation and execution of specific contractual agreements. Because Ethereum is decentralized, information may move freely throughout the supply chain network and is protected from tampering. This abstract explores the advantages of reduced costs, more stakeholder engagement, and real-time visibility. The paper also highlights the paradigm change that Ethereum smart contracts bring to conventional supply chain approaches, discussing potential obstacles and suggesting solutions. © 2024 IEEE.

关键词： Efficiency

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Intelligent Human Behaviour Detection Leveraging ConvLSTM and LRCN Deep Learning Approaches 10

Intelligent Human Behaviour Detection Leveraging ConvLSTM an...

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10th International Conference on Signal Processing and Communication, ICSC 2025

作者： Chakradhar Amingad, S. Janmanchi, Harika Sultan, Mohammed Shalini, K. Hariharan, S. Maddala, Bhanu Prakash Artificial Intelligence and Data Science Vardhaman College of Engineering Telangana Hyderabad India Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India

ISBN: (纸本)9798331511890

This research delves into intelligent human behavior detection from video sequences by employing advanced deep learning methodologies. The study harnesses the capabilities of Convolutional Long Short-Term Memory (ConvLSTM) and Long-term Recurrent Convolutional Networks (LRCN) architectures, which are adept at capturing both temporal and spatial dynamics in video data. Using the extensive Kinetics dataset, which includes a wide range of human activities and contexts, the video data was converted into sequential image frames for model training. The ConvLSTM and LRCN models were implemented and trained with the TensorFlow/Keras framework on GPU-accelerated systems. During training, both models exhibited effective learning, with improved accuracy and reduced loss. However, validation metrics revealed fluctuations, indicating possible overfitting and challenges in generalizing to new data. To address these issues, regularization techniques such as dropout and early stopping were applied, alongside comprehensive hyperparameter tuning. Additionally, the study investigated the use of transfer learning to enhance model performance. This research lays the groundwork for sophisticated video-based human behavior detection systems, with applications in surveillance, healthcare, and human-computer interaction. Future work will aim to bolster model robustness and broaden the scope of action recognition. © 2025 IEEE.

关键词： Feature Selection

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Proliferative Diabetic Retinopathy Detection and Progression Analysis 4

Proliferative Diabetic Retinopathy Detection and Progression...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Rathinasamy, Dhivya Logesh, S. Shri Guru Varshini, S. Senthilkumari, P. PSNA College of Engineering and Technology Department of Artificial Intelligence and Data Science Dindigul India

ISBN: (纸本)9798331515720

Proliferative Diabetic Retinopathy (PDR) is a severe complication of diabetes that can lead to vision loss if not detected and managed promptly. This data analysis project aims to develop an advanced diagnostic system for PDR detection using retinal images. Going beyond conventional approaches, proposed a novel methodology that integrates multi-modal data and employs explainable artificial intelligence (AI) techniques to enhance accuracy and interpretability. The project utilizes a diverse dataset, including retinal images, patient histories, genetics, and lifestyle data. A multi-modal fusion framework extracts features capturing subtle Proliferative Diabetic Retinopathy (PDR) variations. Alongside precise diagnosis, the study introduces longitudinal analysis for PDR progression *** model not only detects PDR but also provides transparent insights into its decision-making process, empowering clinicians to validate and understand the predictions. The proposed design involves using a Convolutional Neural Network (CNN) to classify images of the retina into different stages of Diabetic Retinopathy. The model will be evaluated using the Confusion Matrix and True Value and Predicted Value Comparison. The outcomes of this project extend beyond conventional PDR detection by offering an interpretable AI-powered tool for clinicians. The system holds promise for revolutionizing PDR management, promoting early detection, and providing tailored interventions for patients. © 2024 IEEE.

关键词： Convolutional neural networks

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Cloud-Based AIOT Soil Monitoring System for Earthworms 9

Cloud-Based AIOT Soil Monitoring System for Earthworms

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

作者： Varma, S. Asha Mangamuri, Meghana Kalagara, Anusha Kurukanti, Kezia Velagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350377972

Determining the soil conditions that are favourable to earthworm species is vital for maintaining soil health and improving agricultural yield. Timely reactions are constrained by the work-intensive nature of conventional soil monitoring techniques and their lack of real-time insights into characteristics of the soil. In order to address these issues, we created an Internet of Things (IoT) cloud-based soil monitoring system that tracks pH and moisture levels, two factors that are essential for earthworm survival, while utilizing clustering algorithms for categorizing soil conditions and Random Forest machine learning algorithms for predicting optimal habitats. This state-of-the-device enables more informed soil management decisions by offering cloud-based trend analysis visuals and fast feedback via an LCD display. The method effectively improves the detection of suitable circumstances for earthworm habitats by combining innovative sensor technology with real-time data transmission and processing. Compared to conventional techniques, our system reveals significant gains in graphical representation and data collection as well as automated choices. Collectively, these features promote a more proactive approach to soil management, improving the fertility of the soil and creating optimal conditions for earthworms. With potential uses across various precision farming domains, our methodology provides a significant instrument for sustainable agriculture. © 2024 IEEE.

关键词： Soil surveys

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Personalized Music Recommendation: A Comparative Study of Machine Learning and Deep Learning Approaches 11

Personalized Music Recommendation: A Comparative Study of Ma...

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Iqbal, Nafla Mohan, Aiswarya Syjo, Ann Rose Rajagiri School of Engineering and Technology Dept of Artificial Intelligence and Data Science Kakkanad India

ISBN: (纸本)9798350379136

The rapid advancement of music playback technology, particularly within mobile platforms, has fundamentally transformed user engagement with music. Despite significant improvements in music retrieval techniques over the past decade, the evolution of music recommendation systems remains in its early stages. With users having access to extensive online music libraries, the task of selecting preferred tracks from an overwhelming array of options presents a considerable challenge. Individual musical tastes vary widely, and the influx of new users and music necessitates a system that can adapt *** music recommendation systems face the dual challenge of managing vast amounts of data while delivering accurate results, making them resource-intensive. However, the high precision of these models drives their increasing application in commercial settings. This paper proposes a framework aimed at enhancing recommendation accuracy by tailoring suggestions to align more closely with user preferences.A comprehensive analysis of traditional and advanced music recommendation methodologies is presented, focusing on two user-centric approaches: user preference and emotion-based metrics. These methodologies are critically compared, leveraging the Million Song dataset (MSD) for training the recommendation system. Additionally, an Android application has been developed to collect empirical data on user experiences, facilitating a deeper understanding of user interactions and satisfaction with the recommendation framework. This research aims to improve user experience in music discovery by providing more relevant and personalized recommendations. © 2024 IEEE.

关键词： Contrastive Learning

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Deep Learning Technique for Medicinal Plant Leaf Identification: Using Fine-Tuning of Transfer Learning Model 4

Deep Learning Technique for Medicinal Plant Leaf Identificat...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Kalaiselvi, P. Esther, C. Aburoobha, A. Nishanth, J. Gopika, S. Kabilan, M. Sri Sairam Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Ayurvedic medicines are used to treat various acute and chronic conditions with minimal side effects. They have played a significant role in global health systems. Numerous health issues, including pneumonia, cancer, diabetes mellitus, heart disease, asthma, hypertension, and others, can be naturally treated using plants. Because of their diverse pharmacological characteristics, plants rich in bioactive phytomedicine components, such as alkaloids, flavonoids, and polyphenols, are widely used to treat disorders. India is renowned for its rich medicinal plants repository, with ayurvedic medicinal practices considered a 'living tradition.' Their identification is a manual and time-consuming process, often requiring extensive knowledge of Ayurvedic and botanical classification. Thus,aiming to automate the identification of medicinal plants, our system utilizes a ResNet-18 model to extract key features from plant images, achieving high accuracy in identifying plant species. This method simplifies identification and also broadens the reach of traditional medicine knowledge. © 2024 IEEE.

关键词： Medicinal chemistry

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Text-Image Feature Fine-Grained Learning for Joint Multimodal Aspect-Based Sentiment Analysis

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Computers, Materials & Continua 2025年第1期82卷 279-305页

作者： Tianzhi Zhang Gang Zhou Shuang Zhang Shunhang Li Yepeng Sun Qiankun Pi Shuo Liu School of Data and Target Engineering Information Engineering UniversityZhengzhou450001China Information Engineering Department Liaoning Provincial College of CommunicationsShenyang110122China School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou450000China

Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal *** this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for ***,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect ***,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature ***,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and *** on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.

关键词： Multimodal sentiment analysis aspect-based sentiment analysis feature fine-grained learning graph convolutional network adjective-noun pairs

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Certain Investigations on Cognitive based Movie Recommendation system using Pairwise Cosine Similarity 9

Certain Investigations on Cognitive based Movie Recommendati...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Sumathi, S. Madhumitha, S. Sushma, R. Suriya, M. Subitsha, M. Swetha, S. Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India Sri Eshwar College of Engineering Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350397376

The Internet serves as a major source in various sectors to communicate and share data. It is being used by every individual across the world for several purposes. The main objective of the recommender system is to provide users with suitable products and information. The movie recommendation system also helps in providing customized decisions to the user, thus improving the experience of customer or user. Content-based filtering manipulates item attributes to suggest other items that are relevant to user likes, this suggestion is made using past actions of customers or users. Cosine Similarity is an evaluation metric that helps in finding how similar the items are regardless of their dimensions. The proposed study, furnish users with suggestions that rest on the prevalence and category of a movie. Content based recommendation system is built using cosine similarity. The movie recommendation system has its separate algorithms for making suggestions by examining users' past data. Many attributes are considered like rating of each movie, name of the director and genre of the film. The system make recommendation by considering one attribute or mixture of attributes. For the proposed system, two different datasets used namely, the credits dataset and the movie dataset. The analysis of data is done with help of Python. © 2023 IEEE.

关键词： Python

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SourcePlag - Source Code Plagiarism Detection Based on Abstract Syntax Trees 3

SourcePlag - Source Code Plagiarism Detection Based on Abstr...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Sharma, Nakul Shinde, Siddharth Bhosale, Swarup Patil, Suyog Vishwakarma Institute of Information Technology Faculty of Engineering Department of Artificial Intelligence and Data Science Pune India Vishwakarma Institute of Information Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350354348

Code plagiarism poses a significant challenge in programming communities, necessitating effective detection mechanisms. This paper introduces a novel system that employs Abstract Syntax Trees (ASTs) for code representation and comparison. The system utilizes ASTs to capture the structural essence of code, facilitating a comprehensive analysis of code similarity, it offers flexibility for application across multiple programming languages. In addition to leveraging the Levenshtein Distance Algorithm for Python code comparison, the system incorporates node counting for other languages such as Java and C/C++. By integrating AST-based representation and a combination of comparison techniques, the system offers a robust solution for identifying plagiarized code instances accurately across various programming environments. Through a detailed exploration of the system's methodology, this paper underscores its potential to address the pervasive issue of code plagiarism in programming communities. © 2024 IEEE.

关键词： C++ (programming language)

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