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Music Emotion Classification using Harris Hawk Optimization based LightGBM Classifier 10

Music Emotion Classification using Harris Hawk Optimization ...

引用

10th International Conference on Biosignals, Images and Instrumentation, ICBSII 2024

作者： Patil, Hemprasad Yashwant Bhagwan Patil, Priyanka Balasubramaniam, Anishkaa Shanmugasundaram, Lackesh Vellore Institute of Technology School of Electronics Engineering Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350350951

This research investigates the relationship between music and human emotions, focusing on emotion classification in Turkish music and the role of acoustics in music emotion recognition (MER). Leveraging a unique dataset stored in the UCI-MLR, the study employs machine learning techniques, including Linear Discriminant Analysis and Gradient Boost Classifier, to build a robust model. The accuracy enhancement is primarily driven by the feature selection process, which is powered by the Harris Hawks optimizer. This optimizer, when used with the LightGBM model, enables a more precise selection of features, thereby significantly improving the model's accuracy. The research achieves notable accuracies - 81.25% for Linear Discriminant Analysis and 83.75% for the Gradient Boosting Classifier - surpassing previous records. The paper discusses findings in the context of affective computing, emphasizing potential applications in music recommendation systems and therapy, concludes with a comprehensive analysis, and outlines future possibilities. This research contributes to advancing MER, laying the groundwork for innovations in diverse domains, including the potential integration with IoT-based intelligent systems for real-time emotion analysis and personalized music experiences based on camera frames. © 2024 IEEE.

关键词： Emotion Recognition

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Deep Learning-based Text News Classification using Bi-directional LSTM Model 3

Deep Learning-based Text News Classification using Bi-direct...

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Anand, Shreyas Prakasam, P. Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Electronics Engineering Vellore India

ISBN: (纸本)9798350372120

The advancement of technology has resulted in increased news consumption through various online platforms and social media channels. Hence, there is a requirement for intelligent systems that may categorize the news that exists on the social media/online platform. Hence, this paper explores creating a news classifier utilizing a Bidirectional Long ShortTerm Memory (Bi-LSTM) deep learning model, which is a type of recurrent neural network (RNN) specifically designed to overcome the challenge of vanishing gradient often faced in deep learning. The machine learning was performed on a Kaggle dataset with over 400,000 records of news stories from various publishers. Natural language processing techniques such as stemming, one-hot encoding, vectorization, and embedding are used to prepare the data for machine learning. The multiclass classification is achieved and the performance of the model is analyzed. © 2024 IEEE.

关键词： Classification (of information)

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Text Normalization and Summarization Using Advanced Natural Language Processing 2

Text Normalization and Summarization Using Advanced Natural ...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Khekare, Ganesh Masudi, Chandrasekhar Chukka, Yaswanth Kumar Koyyada, Dhanik Prashanth Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350317558

Large quantities of data have been generated through various applications such as social media, online platforms, etc., and analyzing these data requires a significant amount of time and effort. Additionally, when encountering complex vocabulary, it would take even longer to comprehend the word and its context within a sentence. Normalization and summarization of this data is the need of the hour. Natural Language Processing (NLP) is used to process data and provide meaningful insight. The summarization technique can effectively condense lengthy texts into simpler and more concise language, as well as identify the main context and themes present in the data. Two ways for summarization are available: one is abstractive and the other one is extractive. Each model has its own set of benefits and drawbacks and can be employed depending on the task's requirements. The proposed model uses its own words and sentences to frame the context of data. This proposed text summarization system is implemented using extractive models, and through a thorough comparison of these two models, the highest efficiency output has been achieved. A strong text-summarization tool is presented through this research article which can collect the main points of big paragraphs. Through results, it has been found that Abstractive Summarization techniques have low scores when compared to Extractive Summarization techniques. For extractive summarization, the 'Text Ranking' method provided better results. © 2024 IEEE.

关键词： Abstractive Text Summarization Efficiency Output Extractive Models Language Identification Natural Language Processing Text Summarization

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Collision Avoidance System using YOLO-based Object Detection and Distance Estimation 3

Collision Avoidance System using YOLO-based Object Detection...

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

作者： Raj, Shiv Akash Dhinakar Singh, Ashita Shekhawat, Tarakshraj Singh Manav Muthanna, M. Sivasankaran, K. School of Electronics Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331527549

Road accidents are a significant global issue, with human error causing a substantial number of collisions. This emphasizes the need for advanced collision avoidance systems to improve road safety. In response, this study integrates object detection and distance estimation techniques to enhance vehicle safety. Utilizing recent iterations of the YOLO architecture—YOLOv6n, YOLOv8n, YOLOv9t, and YOLOv10n—the models were trained and evaluated on the KITTI dataset, a benchmark suite for driving applications. This research study provides a comparative analysis of these models, focusing on performance metrics such as precision, mean average precision, recall, and F1 score. A novel single-camera distance estimation method was also implemented to assess object proximity accurately. Among the evaluated models, YOLOv8n achieved the best results, with a F1 score of 0.74 and a mAP@0.5 of 0.782, demonstrating superior detection accuracy and reliability. Its swift convergence and low training losses make it well-suited for real-world use cases. While YOLOv9t and YOLOv10n also performed well, they were slightly less effective. The system enhances its capabilities by accurately estimating distances, which improves collision avoidance and road safety applications. Overall, YOLOv8n is the most efficient and reliable option for developing advanced safety features in vehicle systems. © 2025 IEEE.

关键词： Vehicle safety

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Enhancing Diagnostic Accuracy in Medical Imaging: Integrating GAN-Based Data Augmentation for Balanced Dataset Creation 2

Enhancing Diagnostic Accuracy in Medical Imaging: Integratin...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Karnati, Hemanth SujayKumar Reddy, M. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350383652

Advancements in medical imaging have been substantially driven by deep learning technologies, particularly Convolutional Neural Networks (CNNs). A critical hurdle in this domain is the imbalance of datasets, where certain medical conditions are underrepresented, leading to potential biases in diagnostic models. This research addresses the imbalance in medical imaging datasets, specifically in chest radiography, by leveraging Generative Adversarial Networks (GANs) for data augmentation. The study utilizes the ChestXray2017 dataset, which is skewed towards pneumonia cases, resulting in a dearth of normal chest X-ray images. To counter this, Deep Convolution Generative Adversarial Networks (DCGAN) were employed to generate synthetic images of normal chest X-rays, thus aiming to balance the dataset. In this study, we conducted a comparative analysis of a Convolutional Neural Network's (CNN) performance on a chest radiography dataset, before and after augmenting it with Deep Convolution Generative Adversarial Network (DCGAN)-generated images. Initially, the CNN trained on the un-augmented dataset achieved 93% training accuracy and 87% validation accuracy. After integrating 400 synthetic normal chest X-ray images, the training accuracy slightly increased to 95%, while the validation accuracy notably improved to 89%. This enhancement in validation accuracy demonstrates the model's improved generalization capabilities due to a more balanced training dataset. Our results indicate that GAN-based data augmentation effectively addresses class imbalances in medical imaging datasets, potentially leading to more accurate and reliable diagnostic models. However, the study also underscores the need for further research into the quality and ethical implications of using synthetic images in medical diagnostics. Overall, the integration of GAN-generated images into CNN training presents a promising method for improving classification performance in medical imaging, offering a practical

关键词： Convolutional neural networks

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Securing the Threads: In-Depth Analysis of IoT Architecture and Threat Mitigation 13

Securing the Threads: In-Depth Analysis of IoT Architecture ...

引用

13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Saxena, Akshat Vijayashree, J. Jayashree, J. Jha, Vaibhavi Kakrani, Sidharth School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350380583

The rapid proliferation of the Internet of Things is changing industries by making connectivity seamless in nearly every object and letting them exchange data. The major problem inherent to the complexity and decentralization of IoT architectures is security, thereby making them vulnerable to a wide arrayof threats. This paper discusses in detail the architecture of IoT, the vulnerabilities at each layer, starting from device hardware and moving on to communication protocols and cloud services. We probed into a very wide and increasing threat landscape that includes a number of attacks such as Distributed Denial of Service, man-in-the-middle, and firmware tampering that have been exploiting these vulnerabilities. In the paper, we discussthe current security measures against these threats and also propose a holistic framework of threat mitigation by incorporating advanced encryption techniques, machine learning-based anomaly detection, and blockchain for secure data transactions. The paper, by hitting at the very security issues within the lo$T$, aims at contributing to the development of more resilient and trustworthy IoT systems that ensure safe and efficient operation in critical sectors. © 2024 IEEE.

关键词： Firmware

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From Inception to Efficiency: Evaluating State-of-the-Art CNNs for Fake Currency Identification 4

From Inception to Efficiency: Evaluating State-of-the-Art CN...

引用

4th IEEE International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2024

作者： Aryan, Raj Kumar, Kakelli Anil Gupta, Anjaneya Parikh, Manav School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331528614

This paper presents a comprehensive comparison of five state-of-the-art deep learning models for fake currency detection using the Indian Currency Dataset. The performance of Inception V3, ResNet50, Xception and EfficientNet B0 models are evaluated, with the experimental findings indicating that ResNet significantly surpasses the performance of other models, achieving an accuracy of 98.78%, while EfficientNet B0 got 97.82% and Xception got 97.09%. An in-depth analysis of each model's performance is provided, discussing their architectural differences and their impact on currency authentication tasks. This study advances the field of automated banknote verification and provides key insights for integrating advanced neural networks into financial security frameworks. ©2024 IEEE.

关键词： Convolutional neural networks

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Automated Kidney Stone Detection using Deep Learning Technique 2

Automated Kidney Stone Detection using Deep Learning Techniq...

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

作者： Guruarunachalam, G. Prabhath Sai, G.B. Hilda, J. Jabanjalin School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

Convolutional Neural Networks(CNN) are created to work mostly on the image datasets and have revolutionized image classification and object detection by introducing versatile architectures which can be modified according to the requirements needed with help of modifiable hyperparameters like architecture specifications, batch size, kernel, stride size, loss function, learning rate etc.,. The use of ResNet architecture introduces residual pathways which accelerates weight convergence compared to the traditional neural networks and other CNN architectures like AlexNet, LeNet, GoogLeNet by effectively preserves much patterns and informations contained in the images and giving a good accuracy in almost all cases considering the dataset size, quality and task complexity. In this proposed work, the CT images of Kidneys which are divided into train (1453 images) and test (346 images), encompassing both stones and non-stone cases. Employing ResNet50 architecture with meticulously configured hyperparameters and tailored preprocessing methods are made to learn the train data with a specific number of epochs and suggested learning rate. After training the model for 50 epochs, applied to detect stone's presence in the test set and achieved an accuracy of 93%. The limitations of conventional machine learning models in image classification tasks by considering Support Vector Machine, Logistic Regression and RandomForest demonstrates their challenges in capturing complex image patterns and features, often results in lower accuracy. And this deep learning model must need Graphics Processing Unit (GPU) for training to reduce the computation time and memory management. © 2024 IEEE.

关键词： Image classification

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Improved Transfer Learning based Deep Convolutional Neural Network for Lung Disease Classification using CT image 3

Improved Transfer Learning based Deep Convolutional Neural N...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Shafi, Syed Mohammed Chinnappan, Sathiya Kumar Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350354171

One of the most terrible diseases that affect many nations on the globe is lung-related diseases, and early diagnosis of the disease is still a difficult task. It takes time and extra human effort for the oncologists to evaluate the lung disease based on the findings of blood tests and CT scans. To lower mortality, an automated approach for accurate identification of lung diseases should be proposed. Even though several researchers have put out approaches for detecting lung diseases, the methods now in use for early-stage disease detection still do not fulfil the expectations. This research developed a new lung disease classification (LDC) approach in CT images using the Transfer learning concept. At first, the CT image is pre-processed using the wiener filter (WF) method. The classification of lung disease is performed using Transfer Learning based Improved Deep CNN (TL-IDCNN) framework. © 2024 IEEE.

关键词： Lung cancer

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Valluvan: Processing Name Board Images to Enhance Communication for Native Tamil Speakers 1

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Ramesh, Sriramachandran Prasanna Kumar, N. Murali Krishnan, D.A. Parveen Sultana, H. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9789819718412

ISBN: (纸本)9789819718405

The Valluvan app is a language solution for native Tamil speakers. The system emphasizes the recognition of name boards, translation, and speech output to enhance communication and access to information. The app utilizes advanced technology to analyze images and text on name boards, transforming it into readable text for machines using sophisticated algorithms, the text is then translated to Tamil. The Valluvan app utilizes text-to-speech conversion methods to deliver speech output in Tamil, enhancing users’ understanding of translated content. The paper presents an overview of the app, discussing its methodology, implementation details, experimental results, and analysis. An app with potential to empower native Tamil speakers by facilitating access to and comprehension of information from diverse language sources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image enhancement

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