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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Patel, Omkumar J. Verma, Kshitij Vyas, Chirayu Pandey, Trilok Nath Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Applications Kiit Deemed to be University Bhubaneswar India

ISBN: (纸本)9798331540364

The human eye is a complex organ that can perceive a wide variety of visual inputs. However, its functioning can be severely impaired by diseases such as cataract and diabetic retinopathy which have the potential to cause permanent blindness which not only disrupts the productivity and mobility of patients but also causes a socio-economic impact that decreases the quality of life. This paper reviews the recent advancements in deep learning for diagnosing retinal diseases such as diabetic retinopathy (DR), cataract and glaucoma. The paper presents an extensive comparative analysis of 4 definite deep learning models - Vision Transformers (ViT), ResNet, GoogLeNet and Convolutional Neural Network (CNN). The results from the conducted experiments show that ViT has the highest accuracy followed by CNN. The outcomes highlights the potential of deep learning models in revolutionizing eye disease detection technology. © 2024 IEEE.

关键词： Convolutional neural networks

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AI-Driven Solutions for Predictive Maintenance in Smart Transportation Systems

AI-Driven Solutions for Predictive Maintenance in Smart Tran...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Hundekari, Sheela Mittal, Tanusha Dutt, Amit Bhavana, Munugapati Kumar, Rakesh Nijhawan, Ginni School of Computer Applications Pimpri Chinchwad University Pune India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering & Applications Mathura India

ISBN: (纸本)9798331512965

Predictive maintenance: A game changer in infrastructure and vehicle management, and able to cut maintenance expenses and downtime of smart transportation systems. This study explores ways in which smart transportation systems might use AI in order to plan maintenance schedules and forecast equipment breakdowns. Predictive algorithms, that can detect potential problems before raising to expensive repair or system breakdown, use real time sensor data from infrastructure and vehicles. Decision trees and neural networks are two of these models. By allowing AI driven solutions, transportation systems may transition from a reactive to a proactive maintenance practice which may support efficiency, safety, and reliability. Unsupervised learning methods are used to do anomaly detection for real time operations, and we examine the patterns in previous maintenance data with supervised learning approaches. The use of AI for predictive maintenance is also being studied for use in smart cities, where transportation networks are becoming more interconnected by IoT. The paper also considers challenges on data integration as well as the importance of scaling the algorithms and guaranteeing cybersecurity in these systems. Based on our research, smart transportation systems could benefit from AI to increase operational efficiency, reduce the cost of material and labor to maintain and repair equipment systems, and decrease the rate of disruptions. © 2025 IEEE.

关键词： Predictive maintenance

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Advance Chess Engine: an use of ML Approach 3

Advance Chess Engine: an use of ML Approach

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Srivastava, Kushagra Pandey, Trilok Nath Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Mishra, Manoj Ranjan De, Utpal Chandra Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350381931

Several researchers have sought to create a software that can learn to play cognitive games with little or no prior understanding of the game's rules. A typical chess playing machine investigates all of the possible moves from a chessboard arrangement before deciding on the next best move. The Deep Blue chess engine's brute-force searching strategy has had a significant influence on artificial intelligence, although it has been discovered to be resource intensive. Using the evolutionary and adaptive computing strategy on learning from human grandmasters, this study proposes a relatively easy and fast approach to developing an intelligent chess engine that can aid and hint at probable moves inside the game. Chess-playing machines may now easily surpass human talent thanks to the introduction of Machine Learning, Deep Learning and Artificial Intelligence. The game of chess is examined through the prism of these algorithms in this study. The comparison of the model performance with the existing ones has been performed to show the efficacy of the model. © 2024 IEEE.

关键词： Engines

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Improved Resource Allocation in 6G Networks Through Artificial Intelligence 2

Improved Resource Allocation in 6G Networks Through Artifici...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Gupta, Rolly Kumar Sagar, Pramod Malhotra, Sonal Kulshreshtha, Kushagra Srm Institute of Science and Technology Computer Science & Engineering UP Modinagar India Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Gla University Department of Computer Engineering & Applications Up Mathura India

ISBN: (纸本)9798350371055

This paper proposes a unique resource allocation technique for 6G networks the use of synthetic intelligence (AI). To assist the evolution of cellular networks, aid allocation mechanisms must be designed to successfully allocate network assets and assist the large wide variety of to be had offerings. Thus far, this has been implemented with the aid of traditional algorithms which possess an upper restrict on their talents. AI-based useful resource allocation offers superior adaptively and scalability to such algorithms, through gaining knowledge of from historic records and feedback from the person terminals. On this paper, an improved aid allocation algorithm primarily based on the integration of AI technology is proposed, underneath the perspective of optimizing both electricity performance and network overall performance. The proposed set of rules relies on a deep Q-learning framework, taking gain from deep neural networks to dynamically examine the most reliable resource allocation strategy. Simulation consequences display an improvement in network performance and energy performance in comparison to traditional optimization schemes. © 2024 IEEE.

关键词： dynamically mechanisms propose schemes technique

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Dragonfly Algorithm and Variants for Feature Selection: A Review

Dragonfly Algorithm and Variants for Feature Selection: A Re...

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2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Prinson, P.T. Geetha, Angelina Hindustan Institute of Technology and Science Department of Computer Applications Chennai India Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350314946

Ultimately, the key to successfully analysing real-world problems is to be diligent and thorough. It is important to carefully consider all available data, remove any redundant or unnecessary information, and approach the analysis with an open mind. By doing so, it is possible to gain valuable insights and make informed decisions that can help solve complex real-world problems. By removing redundant, irrelevant, and noisy data, feature selection (FS), a difficult machine learning problem, attempts to lessen the number of features while maintaining a decent degree of classification accuracy. Feature selection is intended to reduce features. This immediately reduces the search space and aids machine learning algorithms in using only the most significant features. A review of Dragonfly algorithms and variants is presented in this paper, emphasizing their main characteristics. Eventually, the conclusion concentrates on the present research on Dragonfly Algorithm, emphasizing its limitations with suggested solutions that lead to future directions © 2023 IEEE.

关键词： Swarm intelligence

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Deep Learning Empowered Natural Language Understanding Voicebot's for farmers: Kissan Samvaad Initiative 8

Deep Learning Empowered Natural Language Understanding Voice...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Jayalakshmi, V. Deepa, R. Ganesan, R. Sriram, N. Surendran, R. Srm Institute of Science and Technology Department of Computer Science and Applications Ramapuram Tamilnadu Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Avadi Computer Science and Engineering chennai India Kg College of Arts and Science Department of Computer Applications Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

The 'Kissan Samvaad' work focuses on implementing a closed-domain Chatbot using the RASA Open Source Framework to address the lack of timely access to expert agricultural advice for farmers. Despite the rapid growth of Deep Learning and NLU based Chatbots in various fields, farmers often rely on peer knowledge to solve agricultural problems. Kissan Samvaad aims to bridge this gap by allowing farmers to interact with the Chatbot to specify farmers need. By identifying intents and entities in user inputs, the Chatbot retrieves relevant information from its database to provide remedies and solutions using BERT. Initial testing of Kissan Samvaad has shown promising results, indicating its potential to serve as a valuable resource for farmers. © 2024 IEEE.

关键词： agriculture chatbot kissan samvaad natural language understanding (nlu) RASA RNN

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IAOMDR: Improved Aquila Optimized Hybrid deep learning Model for Classification of Diabetic Retinopathy 2

IAOMDR: Improved Aquila Optimized Hybrid deep learning Model...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Saju, Binju Barwal, Sahil Asha, V. Kumar, Shubham Tressa, Neethu Nikhil, T. Adi Shankara Institute of Engineering & Technology Department of Computer Science-AI Engineering Kalady India New Horizon College of Engineering Department of Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350328202

One of the scariest illnesses that causes irreversible blindness is Diabetic Retinopathy (DR). As a result, early exposure to Diabetic Retinopathy can help to preserve vision. The study proposes a hybrid model to classify Diabetic Retinopathy images. Initially, pre-processing of the photographs includes resizing them. Grey scale translation and noise removing using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is as opposed to other studies in literature. The proposed model has acquired F1 score of 98.98%, accuracy of 99.2%, sensitivity of 98.9%, specificity of 99.1%, and precision of 99%. © 2024 IEEE.

关键词： Median filters

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Enhancing Medical Image Segmentation with Recurrent Neural Network Architectures 2

Enhancing Medical Image Segmentation with Recurrent Neural N...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Masson, Palak Sharma, Deepak Yadav, Kanchan Sethi, Tarun Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Srm Institute of Science and Technology Department of Computer Applications UP Modinagar India Gla University Department of Mechanical Engineering UP Mathura India Iamr Department of Computer Applications UP Ghaziabad India

ISBN: (纸本)9798350371055

Scientific imaging is a swiftly evolving and developing vicinity of healthcare. Greater accurate segmentation of clinical images is paramount for correct diagnosis. Recent breakthroughs in profound mastering studies have allowed researchers to faucet into a powerful but unstructured information form - images. With the usage of recurrent neural network architectures (RNNs), researchers are exploring the capacity of this shape of facts to acquire better consequences from scientific photograph segmentation. RNNs are regarded for their flexibility in encoding temporal relationships and processing established statistics. In medical image segmentation, this flexibility aids in recognizing complicated styles in photographs and helps the model to localize features inside the image better. Furthermore, the networks can run on images of various sizes, ensuring that models can handle photos created using unique imaging modalities. Additionally, RNNs can examine large annotated datasets and, consequently, help supply meaningful segmentation outcomes. Recent advances in using RNNs for medical photo segmentation have shown promising effects. RNNs have been used to phase colon anatomy, mind tumors, and lymph nodes. U-internet-primarily based segmentation fashions that include RNNs and feature-achieved high segmentation accuracy for medical photograph segmentation responsibilities have also evolved. Within destiny, RNN-based total models will remain superior and could be implemented in various medical imaging segmentation tasks. As such, RNNs will make scientific imaging segmentation extra green and accurate, allowing faster and more accurate analysis and treatment. © 2024 IEEE.

关键词： Achieved Recognizing Relationships Segmentation

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Hyperparameter Optimization of Software Quality Model Using Improvised Genetic Algorithm and EEM 1

Hyperparameter Optimization of Software Quality Model Using ...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Singh, Rakhi Sharma, Vishnu Kaushik, Neeraj Malhotra, Ritu Rauthan, Hemlata Delhi Institute of Higher Education Department of Computer Application Greater Noida India ITS Engineering College Department Of Computer Science and Engineering Greater Noida India Galgotias College of Engineering and Tehnology Department of Computer Applications Greater Noida India GNIOT Institute of Professional Studies Department of Computer Applications Greater Noida India Delhi Institute of Higher Education Department of Computer Applications Greater Noida India

ISBN: (纸本)9798350356816

Software quality analysis has become one of the topmost concerns in the modern world and requires the massive attention of developers and testers during software development. Software quality analysis is a multidimensional notion and one attribute may not describe the software product's overall quality. Software developer's objective is to build a software product that attains the highest quality which depends on multifarious attributes namely functional suitability, maintainability, etc. However, analysis of software product quality is challenging in both the development and maintenance phases. This research is aimed at hyperparameter optimization of the software quality model using an improvised genetic algorithm and an enhanced ensemble model (EEM) approach. In this research, there has been shown the optimal possibility of software quality model hyperparameter optimization using the modified architecture of genetic algorithm along with EEM. To validate the performance of this suggested approach, PROMISE Repository datasets have been utilized. As a result, this proposed approach achieves all performance metrics very optimal. The evaluated values of accuracy, precision, recall, and F1- score are 99.45%, 98.83%, 97.28%, and 99.37%, respectively. In the future, this approach may be integrated with modified architectures of software quality models for automated software product quality analysis. © 2024 IEEE.

关键词： Software quality

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Improving recommendations utilizing users’ demographic information

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Quality and Quantity 2024年第6期58卷 5559-5575页

作者： Dey, Avick Kumar Dutta Pramanik, Pijush Kanti Singh, Pradeep Kumar Choudhury, Prasenjit Department of Computer Applications DSMS College West Bengal Durgapur India School of Computer Science & amp Engineering Galgotias University Greater Noida India Department of Computer Applications & amp Technology Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science & amp Engineering National Institute of Technology Durgapur West Bengal India

The exponential increase in digital data has increased the amount of available online information. This complicates the user’s decision-making. Most online merchants and service providers utilize recommendation systems to solve this problem and meet customer needs. The traditional collaborative filtering based approach faces enormous challenges in providing potential personalized recommendation results. The demographic information of users may improve personalized recommendation results. This research proposes an improved recommendation approach based on users’ demographic information. Compared with traditional collaborative filtering-based approaches, this approach provides improved results. The experimental results show the enhanced prediction accuracy of the proposed approach and significantly lower errors when experimenting with the MovieLens dataset. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

关键词： Collaborative filtering Demographic information MAE Recommendation system RMSE

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