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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Divya, R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Parkinson's disease (PD) is a neurodegenerative disorder of progressive degeneration, with motor and speech impairments. In this study, we investigate the potential of vowel-based acoustic biomarkers for early detection of PD. The study analyses different vocal features including fundamental frequency, jitter, shimmer, and harmonic-to-noise ratio to identify small changes in voice based on the presence of PD. In the research, we use a dataset of voice recordings from PD patients and healthy controls and explore the effectiveness of machine learning models, including traditional feature engineering approaches. It is found that acoustic analysis, in particular explorations through glottal features and hybrid feature extraction techniques, can radically increase the accuracy of PD diagnosis. This kind of approach looks promising to develop affordable and non-invasive diagnostic tools, particularly in remote or resource-limited environments, thus allowing for earlier intervention and tailored treatment intervention for PD patients. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Li-Fi Enabled Gas Detection: A Novel Approach for Navy Ship Safety 3

Li-Fi Enabled Gas Detection: A Novel Approach for Navy Ship ...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ashwanth, G.P.N. Reddy, Gajjala Venkat Rushith Abhiram, Prayaga Shashank Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The major risk of gas leakages to naval ships and vessels is to both personnel and machinery. Traditional types of gas detectors use fixed wired sensors, which tend to be extremely challenging for maintenance in complex ship contexts. This work addresses applying Li-Fi technology for successful wireless detection of gas leaks. Li-Fi has been employed to measure the gas level in real time, hence enabling fast reaction in case of possible leaks or damage. The foundation of this project is to establish a LiFi-driven communication system in areas where Radio Frequency(RF) communication is restricted and provides safer and more reliable detection system for navy ships. Preliminary results show that in this Li-Fi-based system, accurate real-time gas detection with high speed data transfer in RF challenged environments should be possible. This type of approach not only will increase response times but the need for vast wiring diminishes, hence enhancing effectiveness in maintenance and operational dependability in complex ship spaces. © 2025 IEEE.

关键词： Warships

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Comparison of CNN-based deep learning architectures for rice diseases classification

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Artificial Intelligence in Agriculture 2023年第3期9卷 22-35页

作者： Md Taimur Ahad Yan Li Bo Song Touhid Bhuiyan Department of Computer Science and Engineering Daffodil International UniversitySavarBangladesh School of Mathematics Physics and Computing University of Southern QueenslandToowoombaAustralia School of Engineering University of Southern Queensland ToowoombaAustralia Department of Computer Science and Engineering Faculty of Science and Information TechnologyDaffodil International UniversityBangladesh

Although convolutional neural network(CNN)paradigms have expanded to transfer learning and ensemble models from original individual CNN architectures,few studies have focused on the performance comparison of the applicability of these techniques in detecting and localizing rice ***,most CNN-based rice disease detection studies only considered a small number of diseases in their *** these shortcomings were addressed in this *** this study,a rice disease classification comparison of six CNN-based deep-learning architectures(DenseNet121,Inceptionv3,MobileNetV2,resNext101,Resnet152V,and Seresnext101)was conducted using a database of nine of the most epidemic rice diseases in *** addition,we applied a transfer learning approach to DenseNet121,MobileNetV2,Resnet152V,Seresnext101,and an ensemble model called DEX(Densenet121,EfficientNetB7,and Xception)to compare the six individual CNN networks,transfer learning,and ensemble *** results suggest that the ensemble framework provides the best accuracy of 98%,and transfer learning can increase the accuracy by 17%from the results obtained by Seresnext101 in detecting and localizing rice leaf *** high accuracy in detecting and categorisation rice leaf diseases using CNN suggests that the deep CNN model is promising in the plant disease detection domain and can significantly impact the detection of diseases in real-time agricultural *** research is significant for farmers in rice-growing countries,as like many other plant diseases,rice diseases require timely and early identification of infected diseases and this research develops a rice leaf detection system based on CNN that is expected to help farmers to make fast decisions to protect their agricultural yields and quality.

关键词： Deep learning Convolutional neural networks(CNNs) Transfer learning Plant leaf disease detection

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Advanced Traffic Violation Detection with Tesseract OCR and computer Vision

Advanced Traffic Violation Detection with Tesseract OCR and ...

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2024 International Conference on Circuit, Systems and Communication, ICCSC 2024

作者： Gnanakumar, D. Jairus Ponrabi, S.V. Ravindran, N. Razvi, Ahmed Gokulakannan, K. School of Computing Department of Computer Science Engineering Virudhunagar Dist Tamilnadu Krishnankoil India

ISBN: (纸本)9798350365306

The paper introduces an intelligent system that utilizes machine learning to effectively detect traffic infractions. It incorporates adaptive stop line detection, robust licence plate extraction, and real-time traffic light monitoring. The system's machine learning component accurately recognizes traffic signal colors, facilitating better traffic flow management. Adaptive stop line detection adjusts to various traffic situations, enhancing system flexibility. Licence plates are accurately extracted using Optical Character Recognition (OCR), which is powered by image processing and Haar Cascade classifiers. The system's effectiveness in identifying traffic violations, especially during red lights, is demonstrated through video data testing. Additionally, the integration of a MySQL database enables comprehensive logging and tracking of penalized licence plates, providing a detailed overview of traffic infractions and keeping track of all the violating vehicles. This technological integration promises significant improvements in traffic enforcement and road safety. The combination of adaptive stop line detection, real-time traffic light analysis, and licence plate identification makes this system an invaluable tool for traffic control authorities. By improving enforcement and monitoring, the system has the potential to revolutionize traffic management and enhance safety measures. This proposed work suggests that such an intelligent system can be a sophisticated asset in addressing traffic-related challenges and ensuring safer road conditions. © 2024 IEEE.

关键词： Optical character recognition

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Safeguarding Financial Transactions using Customer Profiles

Safeguarding Financial Transactions using Customer Profiles

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Kumar, Anand Sountharrajan, S. School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331542344

Credit card fraud is thereby a big challenge which results to enormous losses to both the individuals and the financial institutions. It is often the case that conventional measures of approach to fraud detection often fail to offer what is needed because they rely on broad characteristics of behaviour and not characteristics of the specific customer. The present work proposes a sophisticated fraud detection system equipped with a transaction-processing engine defining the fraud detection technology coupled with machine learning algorithms used to assess the credibility of transactions. To enhance the detection accuracy, the system incorporates Support Vector Classifier (SVC) and the MCC-based merchant profiles. To conduct a more realistic experiment, the synthetic dataset of 50,000 credit and debit card transactions with a minimal portion of fraudulent transactions was used. To assess the effectiveness of the Support Vector Machine (SVC), Decision Tree, and Random Forest algorithms, the study was carried out. The findings indicated that the use of MCC based profiles the category of Support Vector Machines (SVC) achieved an accuracy of 91 % which is remarkably higher than basic methods. To avoid the high risk of fraud and significant losses, the proposed system is designed to continuously check, verify, and process transaction requests. Further advancements will focus on enhancing the algorithm to adapt to the emerging techniques in fraudulent practices and enhance the security of transactions to a new level. © 2024 IEEE.

关键词： Support vector machines

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A review on the energy efficient resource allocation schemes with improved quality of services in green cloud computing

A review on the energy efficient resource allocation schemes...

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International Conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Fiaz, A.S. Syed Sonai, Veeramani Computer Science and Engineering Amrita Viswa Vidhyapeetham School of Computing Chennai India Department of Computer Science and Engineering School of Engineering Shiv Nadar University Chennai India

ISBN: (纸本)9781032669663

The vast changes in the computing utilization have created a need with the improvised resource availability and reliability. The virtualization of servers and data centers to increase their efficiency has transformed how enormous data is stored and handled. Cloud computing has created a supreme and impressive approach to virtualize servers and data centers and to make them energy efficient in order to leverage on diverse IT resources. Massive power and energy consumption by IT resources leads to an energy crisis and a change in the planet’s climate. But because these IT resources use so much energy and power, they end up being a significant source of CO2 emissions. Due to this, green cloud computing is now required in order to make IT resources cost-and energy-effective. It is necessary to do a thorough analysis of the cloud’s power efficiency in order to arrive at the best options for green cloud computing. Green Cloud computing is well known to be a broad domain and a popular field for research because of the increasing rise of enormous data storage and computational demand. It can generate solutions that not only make IT resources energy efficient but also reduce operational expenses. This review’s objective is to highlight some of the best practices for achieving green cloud computing, including workload clustering, Auction-Based Resource Optimization techniques, Hardware and Software controlled solutions, Minimize cooling requirements scheduling, clustering, virtualization and other energy-saving techniques. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Carbon footprint

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Multimodal Deep Learning with XAI for Transparent and Accurate Doctor Prescription Recognition 2

Multimodal Deep Learning with XAI for Transparent and Accura...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Sunil Reddy, J. Norbu, Tenzin Sungrab Kumar, Pavan Ajjanakatt, Praveen Vitthal Harsha, Reddyam Ananthanagu, U. Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798331534950

This paper presents a deep learning framework to automate recognition of medical prescriptions by using handwritten content, feature extraction from CNNs like VGG16, Alex Net, and MobileNetV2, and sequential data utilizing LSTMs. A multi-modal approach captures even the spatial and temporal details in a prescription, which assists in an accurate interpretation of complex handwriting. Specifically, key pre- processing like grayscale conversion, normalization and edge detection improving the quality of the image, reducing noise and bringing forth the prominent features that aid in the improvement of recognition accuracy and also implementation of XAI concepts to further enhance the interpretation. This model reduces transcriptions, making it easier for the patients as well as the practitioners to understand the handwriting through the computer screen. The method therefore assists in safer patient-centered healthcare by automating prescription processing thus triggering clearer communication towards more informed healthcare decisions. © 2024 IEEE.

关键词： Deep learning

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Predicting Coral Bleaching: Forecasting Degree Heating Weeks using Sea Surface Temperature 8

Predicting Coral Bleaching: Forecasting Degree Heating Weeks...

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8th IEEE International Conference on Information and Communication Technology, CICT 2024

作者： Raaj Eshwar, S. Krishnan, Sanjay Prabu, M. Amrita School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331531539

Coral reefs are some of Earth's most varied and critical ecosystems however, they face an increasing threat because of climate change rising sea surface temperatures (SST). Long-term exposure to high SST can result in coral bleaching, which is when corals lose their symbiotic algae, causing them to lose their color and functionality, which may result in losing many corals. Thus for purposes of reducing the impact of Coral Bleaching it is important to devise predictive models that would be able to provide early warning to coral reef thermal stress. The proposed work focuses on forecasting Degree Heating Weeks (DHW), a statistic used in quantifying cumulative thermal stress upon corals over a period of 12 weeks including analysing past data and also monitoring current SST Levels with an aim to knowing how many DHWs there will be in spatial time frames, so as one can be prepared beforehand if they should happen at all. This method incorporates other factors besides sea surface temperature such as intensity and duration that affect bleeding more efficiently than just relying on sea temperature. © 2024 IEEE.

关键词： Bleaching

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Privacy-preserving recommendation with coarse-grained spatiotemporal contexts

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science China(Information sciences) 2025年第4期68卷 66-81页

作者： Lei CHEN Chen GAO Jiahuan LEI Xiaoyi DU Xinlei SHI Hengliang LUO Depeng JIN Yong LI Meng WANG Department of Electronic Engineering BNRist Tsinghua University Meituan Inc. School of Computer Science and Information Engineering Hefei University of Technology

The behavior of users on online life service platforms like Meituan and Yelp often occurs within specific finegrained spatiotemporal contexts(i.e., when and where). Recommender systems, designed to serve millions of users, typically operate in a fully server-based manner, requiring on-device users to upload their behavioral data, including fine-grained spatiotemporal contexts, to the server, which has sparked public concern regarding privacy. Consequently, user devices only upload coarse-grained spatiotemporal contexts for user privacy protection. However, previous research mostly focuses on modeling fine-grained spatiotemporal contexts using knowledge graph convolutional models, which are not applicable to coarse-grained spatiotemporal contexts in privacy-constrained recommender systems. In this paper, we investigate privacy-preserving recommendation by leveraging coarse-grained spatiotemporal contexts. We propose the coarse-grained spatiotemporal knowledge graph for privacy-preserving recommendation(CSKG), which explicitly models spatiotemporal co-occurrences using common-sense knowledge from coarse-grained contexts. Specifically, we begin by constructing a spatiotemporal knowledge graph tailored to coarse-grained spatiotemporal contexts. Then we employ a learnable metagraph network that integrates common-sense information to filter and extract co-occurrences. CSKG evaluates the impact of coarsegrained spatiotemporal contexts on user behavior through the use of a knowledge graph convolutional network. Finally, we introduce joint learning to effectively learn representations. By conducting experiments on two real large-scale datasets,we achieve an average improvement of about 11.0% on two ranking metrics. The results clearly demonstrate that CSKG outperforms state-of-the-art baselines.

关键词： privacy-preserveing coarse-grained spatiotemporal contexts recommender systems

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Bias analysis of AI models for undergraduate student admissions

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Neural computing and Applications 2025年第12期37卷 7785-7795页

作者： Van Busum, Kelly Fang, Shiaofen Department of Computer Science and Software Engineering Butler University IndianapolisIN United States Computing and Engineering Indiana University Luddy School of Informatics Indiana University Indianapolis IndianapolisIN United States

Bias detection and mitigation is an active area of research in machine learning. This work extends previous research done by the authors Van Busum and Fang (Proceedings of the 38th ACM/SIGAPP Symposium on Applied computing, 2023) to provide a rigorous and more complete analysis of the bias found in AI predictive models. Admissions data spanning six years was used to create an AI model to determine whether a given student would be directly admitted into the school of science under various scenarios at a large urban research university. During this time, submission of standardized test scores as part of a student’s application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We developed and analyzed AI models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted. We then evaluated the predictive models to detect and analyze biases these models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his/her family to attend college. We also extended our analysis to show that the biases detected were persistent. Finally, we included several fairness metrics in our analysis and discussed the uses and limitations of these metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Students

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