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Optimized featured swarm convolutional neural network (OFSCNN) model based dialect recognition system for Bagri Rajasthani language

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International Journal of Information technology (Singapore) 2025年第1期17卷 121-133页

作者： Kukana, Poonam Sharma, Pooja Bhardwaj, Neeru Department of Computer Science and Engineering University School of Engineering & amp Technology Rayat-Bahra University Punjab Mohali India Department of Computer Science and Engineering Chandigarh University Punjab Mohali India

The dialects of a language hold a significant place in speech processing (SP) applications. The objective of dialect identification is to categorize speech sample data into a specific dialect of a speaker's spoken language. A dialect recognition system must effectively distinguish between different dialects of a standard language, as they tend to possess many similarities. The dialect of a language is not a distinct characteristic, as it can be impacted by the utterer's birthplace. Researchers in the domain of automatic speech recognition (ASR) face difficulties in identifying the speech patterns unique to each dialect or language. The proposed work recognizes the dialects of the Bagri Rajasthani language from undefined expressions of speech. Rajasthani language is one of the eldest and most famous languages in the Indo-Aryan languages. It comprises the different dialects and for recognizing the dialects, it used dissimilar phases of acoustic and spectral characteristics of the speech signal (SS). The spectral and acoustic features of SSs are measured to design the system. As there is no specific speech dataset for Bagri dialects, the database is built, to verify the Bagri dialects of the Rajasthani language. To improve the accuracy rate, and error rate in recognizing the Bagri dialects, the acoustic and spectral characteristics of speech expressions are joined. To verify several Bagri dialects of the Rajasthani language, different simulations for classification and investigation are carried out i.e., OFSCNN model, GA-NN, etc. The outcomes are important and the accuracy of 96.95% for the OFSCNN model, 80.63% for GA-NN, and 93.45% for the SVM method is an achievement. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： CNN (convolutional neural network Dialects MFCC (Mel frequency co-efficient cepstral) OFSCNN (optimized featured swarm convolutional neural network) PSO (particle swarm optimization) Speech Dialect Recognition

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Real-Time Implementation of an Automated Student Attendance Monitoring System with computer Vision technology 5

Real-Time Implementation of an Automated Student Attendance ...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Lakshmi, M. Vidhya SivaPriya, M.S. Manivannan, Kirankumar Vivekanandan, M. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350379945

computer vision and the Internet of Things have led to significant advancements in student attendance monitoring. In this work, an innovative system has been designed to facilitate real-time, automated student attendance tracking in educational environments. Utilizing face recognition algorithms and IOT. The aim of the system is to optimize attendance management in educational institutions. IoT-connected, high-resolution cameras are strategically positioned across classrooms to capture visual data for analysis. By utilizing advanced computer vision algorithms, the system identifies and tracks students in real time, facilitating precise and efficient attendance monitoring. Student Faces are detected using a Haar cascade classifier and recognized using the LBPH (Local Binary Pattern Histogram) Algorithm. Then, Histogram data is checked against an established dataset. If a Match is found, the device automatically labels attendance. The Integration of IoT helps in seamless data transmission, storage and ensures accessibility and analysis of attendance records by administrators and educators. The primary objective is to offer an intelligent, automated solution for attendance management, that will significantly reduce manual workload and enhances accuracy. This system addresses the limitations of conventional attendance-taking methods, which are often laborintensive, error-prone, and time-consuming. © 2024 IEEE.

关键词： Local binary pattern

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Human Detection and Tracking for Drone based Marine Surveillance 15

Human Detection and Tracking for Drone based Marine Surveill...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Micheal, Ancy A. Sivaramakrishnan, Sneha School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350370249

Effective object detection and tracking are essential for prompt and precise rescue operations in water-based emergency response scenarios. Acknowledging this necessity, this paper introduces an innovative method using deep learning methodologies. At the heart of this project is the MobDrone dataset, selected to document various situations that occur during water crises. By carefully reannotating a subset of this dataset, new labels of swimmers, rescue victims, and deceased individuals were generated, making it more useful for training and assessing. In this context, the research assesses the effectiveness of YOLOv9 using this specific dataset. By conducting thorough experiments, the study highlights YOLOv9’s exceptional accuracy and resilience, particularly in comparison with its predecessor YOLOv8. This research highlights the possibility of combining advanced object detection algorithms such as YOLOv9 with effective tracking methods like BoT-SORT to transform autonomous systems in water-based emergency response, leading to improved and quicker rescue operations. © 2024 IEEE.

关键词： Object tracking

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Heart Attack Prediction Using Ensemble Learning

Heart Attack Prediction Using Ensemble Learning

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Mali, Saurabh Veeramani, Karthika School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331540685

Heart attacks are a leading cause of death worldwide, making it a necessity to develop accurate prediction systems for timely intervention and prevention. This paper addresses the challenge of heart attack prediction through ensemble learning methodologies. Previous research has primarily focused on individual machine learning algorithms, often overlooking the potential of ensemble techniques to improve predictive accuracy. In this study, we employed a dataset comprising various clinical features and medical indicators. After preprocessing steps such as encoding categorical variables, scaling, and balancing data using SMOTE, we explored multiple machine learning models, including logistic regression, SVM, KNN, decision trees, random forests, gradient boosting, and artificial neural networks. Additionally, feature selection techniques and hyperparameter optimization were utilized to enhance model performance. Finally, we employed ensemble learning, specifically Stacking, to combine the strengths of individual models, resulting in a higher predictive accuracy of 93%. The results demonstrate the high efficiency of ensemble learning in heart attack prediction, showcasing a significant improvement over individual models. © 2024 IEEE.

关键词： Heart

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From Clusters to Scores: Unsupervised Acne Detection and Severity Assessment with Deep Learning 4

From Clusters to Scores: Unsupervised Acne Detection and Sev...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Khandelwal, Animan Santhi, H. Wagh, Devdatta School of Computer Science and Engineering Vellore Institute Of Technology Vellore India

ISBN: (纸本)9798331540364

Acne detection and severity scoring remain challenging due to the scarcity of labeled data and inconsistencies caused by varying skin tones and lighting conditions. While recent advancements using deep learning models like CNNs have shown promise, they often rely heavily on large labeled datasets. This dependency limits their applicability, particularly in real-world settings where such data is scarce. Moreover, ensuring accurate classification across diverse image conditions continues to be a significant hurdle. To address these challenges, an unsupervised learning framework is proposed, utilizing the Kaggle Acne dataset of 680 unlabeled images. Our approach combines image normalization through NumPy and OpenCV with feature extraction using a pretrained ResNet model. The extracted features are clustered into five severity levels using K-Means, and clusters are labeled based on acne-related characteristics like redness intensity. This method bypasses the need for labeled data, offering a scalable solution for acne severity scoring that can support further dermatological research and clinical applications. © 2024 IEEE.

关键词： Unsupervised learning

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Enhancing User Authentication for Financial Application Using Multi-Modal Behavioural Biometrics - A Survey 1

Enhancing User Authentication for Financial Application Usin...

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1st International Conference on Data, Computation and Communication, ICDCC 2024

作者： Sriram, Ajay Premanand, V. Navin Kumar, L. Jayanth Kumar, S. School of Computer Science & Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331532956

It is essential to ensure secure and easy user authentication in the domain of financial applications. Conventional techniques frequently fall short of offering strong security without sacrificing user pleasure. This research integrates fingerprint, signature, and eye movement patterns to provide a sophisticated multi-modal behavioral biometric identification system. In current systems, all three biometric modalities are infrequently integrated, especially for financial applications where security and usability are critical. The suggested solution promises to provide continuous authentication for mobile devices, improve authentication accuracy, and address issues such as template ageing. The multi-modal behavioral biometric is fused using Image-level fusion. It creates a single image map of fingerprint, signature, and eye movement patterns and matches it with the input image the system receives. In case of a perfect match the user is authenticated to use the service. A survey of 50 participants showed strong support for the multimodal approach, with 90% perceiving it as necessary and effective. By utilizing their combined capabilities and minimizing individual biometric weaknesses, the integrated system exhibits better security. This work also looks at the system's practical application and computing effectiveness on mobile devices with limited resources. This study offers a comprehensive solution for secure, effective, and user-friendly authentication in financial applications by filling in the gaps in the current research. © 2024 IEEE.

关键词： Biometrics

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Selected Replay with Distillation for Continual Semantic Segmentation 3

Selected Replay with Distillation for Continual Semantic Seg...

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3rd International Conference on Cloud Computing, Big Data Application and Software engineering, CBASE 2024

作者： Luo, Yuan Liu, Jun Zeng, Shuqi Hu, Zhuoya Wuhan Institute of Technology School of Computer Science and Engineering Wuhan China

ISBN: (纸本)9798331506582

Although deep neural networks have demonstrated exceptional performance in various fields, especially in image processing, they still face some significant unresolved challenges. Catastrophic forgetting is one of the typical challenges commonly encountered in deep learning models. This issue becomes apparent when new training data introduces new categories to adapt to environmental changes, while the old training data is no longer available. As a result, the model tends to forget previously learned knowledge. In recent years, a variety of solutions and incremental learning frameworks have been proposed to address this issue, particularly showing effectiveness in classification and detection tasks. In this paper, we extend the problem to semantic segmentation, which involves pixel-level classification. We conducted an in-depth analysis of the root causes of catastrophic forgetting and identified two key issues arising from the unavailability of old data samples during the new training phase. First, previously learned knowledge is often mistakenly labeled as background in the new training phase, causing a 'background shift' phenomenon. Second, the data distribution of old and new classes becomes highly imbalanced. To tackle these issues, we propose a strategy that combines replay and knowledge distillation to effectively alleviate catastrophic forgetting. © 2024 IEEE.

关键词： Deep reinforcement learning

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A Robust Road Lane Detection Using computer Vision Approach for Autonomous Vehicles

A Robust Road Lane Detection Using Computer Vision Approach ...

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

作者： Bhavadharini, R.M. Sutha, J. School Of Computer Science And Engineering Vellore Institute Of Technology Chennai India

ISBN: (纸本)9798350364828

The quality of human life is improving over a period of time with the technological advancements in the arena of Artificial Intelligence and Internet of Things. Various fields adopt and implement automation process for eco-nomic growth. The recent advancement and transition to automated vehicles is one of the key paradigms in Intelligent Systems. In order to improve the safety and road accidents, detection of road lanes is required to be done with more accuracy. Utilizing computer vision is a potential method to aid drivers in challenging situations, enhancing both their safety and comfort. The essential components of road lane detection include Line parameter estimation, selection of the Region of Interest (ROI) and astute edge detection. This research work aims to use computer vision techniques for the detection of road lane lines. It utilizes image processing algorithms to identify and track the road lane markings in real-time. The proposed system makes use of edge detection techniques and Hough transform to detect straight lines on the road. It then combines this information with other image processing techniques to accurately determine the lane boundaries. The algorithm is able to accurately detect and classify road lane lines in real-time, achieving a high detection accuracy of 97.5%. The performance achieved demonstrate the efficiency of the proposed method in enabling autonomous vehicles to navigate safely and stay within designated boundaries. © 2024 IEEE.

关键词： Autonomous vehicles

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Revolutionizing Healthcare: Blockchain's Transformative Applications for Data Security, Privacy, and Interoperability 9

Revolutionizing Healthcare: Blockchain's Transformative Appl...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Raj, Riya Raja, S.P. Vellore Institute of Technology School of Computer Science And Engineering Vellore India

ISBN: (纸本)9798350394474

In a rapidly evolving digital era, the convergence of blockchain technology and healthcare is poised to revolutionize the industry in unprecedented ways. This research paper presents an innovative exploration of the transformative potential of blockchain within healthcare, unveiling a new paradigm of patient-centric, secure, and interoperable healthcare systems. Blockchain, renowned for its decentralized architecture and cryptographic security, addresses the perennial challenges of data integrity, patient privacy, and healthcare system interoperability. In this paper, we embark on a journey to unveil the diverse applications of blockchain technology in healthcare, ranging from enhancing medical records management (MRMs) to enabling groundbreaking advances in telemedicine, drug traceability, clinical trials and organ transplantation. Our research deploys a multidimensional analysis, we delve into blockchain's ability to empower patients with unprecedented control over their health data, fostering transparency and trust within the healthcare ecosystem. As we navigate the landscape of blockchain in healthcare, we also confront the formidable challenges that lie ahead, such as regulatory compliance, scalability, and the need for industry-wide adoption. In conclusion, this research paper is an innovative exploration into the fusion of blockchain technology and healthcare, reimagining the possibilities of a digitally transformed healthcare landscape. By unveiling novel applications, addressing challenges, and presenting practical solutions, this work inspires a new wave of research and innovation in the intersection of blockchain and healthcare, paving the way for a healthier, more secure, and patient-centric future. © 2024 IEEE.

关键词： Blockchain

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Adopting Ensemble Learning and Machine Learning Techniques for Predictive Modeling in Traffic Data Analysis 3

Adopting Ensemble Learning and Machine Learning Techniques f...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Santhosh Narayanan, B. Shyamala, L. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

In this study, the trends of traffic data are analyzed using predictive analysis techniques applied to the NYC taxi dataset. Various models were tested for forecasting travel time, fare, and speed. Notably, the Decision Tree Regression model demonstrated outstanding performance with an impressive R2 score of 0.9995 for travel time prediction. Additionally, an ensemble model consisting of Linear Regression and Decision Tree Regression achieved an accuracy of 0.93 for fare prediction. Furthermore, another ensemble model combining LSTM and DBN attained an R2 score of 0.90 for speed prediction. This study highlights the effectiveness of diverse predictive modeling techniques in accurately forecasting these features. © 2024 IEEE.

关键词： Long short-term memory

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