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Audio Content Processing System for Automatic Music Classification using Mask R-CNN

Audio Content Processing System for Automatic Music Classifi...

引用

2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Putta, Narsaiah Srinivas, R. Ravi Prasad, Dasu Vaman Rawoof, S.M. Suvarna Kumari, T. Dept. of Computer Science & Engg. Vasavi College of Engineering Telangana Hyderabad India Dept. of Computer Science & Engg. Mahatma Gandhi Institute of Technology TS Hyderabad India Dept. of Computer Science & Engg. Anurag University Telangana Hyderabad India Anwarul Uloom College Telangana Hyderabad India Dept. of Computer Science & Engg. Chaitanya Bharathi Institute of Technology TS Hyderabad India

ISBN: (纸本)9798331533137

The definition of a music genre is a subjective category that aids in identifying audio tracks within a shared culture or set of conventions. These genres are necessary for classifying various music-related metadata and tracks into domains that share similar musical qualities. The categorization of audio data according to their genre holds significant importance in the field of information retrieval of music. The current retrieval of various musical databases over computer networks necessitates the development of specialized methods for searching and organizing these data sets. The paper presents a model for genre classification, developed and automated using deep neural networks and the Mask R-CNN method. We performed a dimensional reduction of the model's target labels to enhance the previously mentioned method. Lastly, we will predict the behavior of the classification task through a qualitative analysis of the results using Keras. We conducted experiments using various pre-existing models such as FCN, Timbre CNN, end-to-end, and CNN. We conducted experiments using both raw amplitude data and Mel spectrograms from the raw audio data, which yielded more accurate results. At 86% and 53%, respectively, the suggested model's recall rates for hip-hop and rock music demonstrate a noteworthy level of accuracy. ©2024 IEEE.

关键词： Convolutional neural networks

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Classification of Mental Workload Using 1D-CNN with Transfer Learning 1

Classification of Mental Workload Using 1D-CNN with Transfer...

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1st International Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Chaturvedi, Sushil Ahirwal, Mitul Kumar Maulana Azad National Institute of Technology Dept. of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350365016

Mental workload (MWL) can be explained as the ratio of a participant's mental effort to their brain capability when performing activities. A significant challenge in the field of MWL classification for brain-computer interfaces (BCIs) based on electroencephalograms (EEGs) is the variability among individuals. It commonly takes unnecessary time to retrain the classification model from starting for a new subject. To address the individual variations among different subjects, a one-dimensional convolutional neural network along with transfer learning (1D CNN-TL) is proposed in this work. The end-to-end 1D-CNN learns the common features of EEG signals and, to tackle the problem of difference in EEG for individuals, a transfer learning technique is used that does fine-tuning on fully connected (FC) layers for a new participant having less training data. The average test accuracy (mean±stdv) of 65.18± 8.37 is achieved over the Cross-session mental workload dataset. The suggested method can achieve satisfying results for new unknown data with less time and fewer training data. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparative Analysis of Machine Learning Classification Algorithms for Cardiovascular Disease Prediction 9

A Comparative Analysis of Machine Learning Classification Al...

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9th IEEE International Conference on Adaptive Science and technology, ICAST 2024

作者： Attipoe, Elliot Kojo Yussiff, Alimatu Saadia Asante-Mensah, Maame Gyamfua University of Cape Coast Dept. of Computer Science and Information Technology Cape Coast Ghana

ISBN: (纸本)9798350385403

Cardiovascular disease is a life-threatening condition accounting for over 17 million deaths globally every year. Several studies have revealed the advancement of machine learning algorithms in predictive modeling and risk assessment, leading to improved chronic disease management and preventive measures. The algorithms analyze complex medical datasets to uncover hidden patterns, trends, and correlations that might be difficult for the medical practitioner, thereby improving the efficiency and accuracy of the medical decision-making process. This study utilizes a cardiovascular disease dataset comprising 70,000 instances to compare the performance of ten classification algorithms in predicting cardiovascular diseases. A new machine learning model is proposed, which performs better than all existing models using the same dataset. The results show the XGBoost classifier as the best model for predicting cardiovascular diseases, with an accuracy of 73.3% and an AUC score of 80%. © 2024 IEEE.

关键词： Contrastive Learning

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Epileptic Seizure Detection Using Quantum Support Vector Classifier

Epileptic Seizure Detection Using Quantum Support Vector Cla...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Meesala, Gopichand Soni, Barkha Pandey, Manish Khare, Nilay Maulana Azad National Institute of Technology Dept. of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350381887

Epilepsy is the second most prevalent neurological disorder. It poses significant challenges to both diagnosis and treatment. The information obtained from electroencephalography serves as crucial for understanding the dynamics of epileptic brain activity. It offers a non-invasive means for monitoring and detecting epileptic seizures. This paper explores the use of quantum machine learning algorithms to improve the precision and effectiveness of utilizing EEG data for epileptic seizure detection. Quantum machine learning, leveraging principles from quantum mechanics, holds promise for addressing these challenges by harnessing quantum computational power and exploiting quantum entanglement to process information more efficiently. Quantum features and quantum kernels are employed to capture intricate patterns within the EEG signals, allowing for improved discrimination between normal brain activity and seizure events. Results indicate that the Quantum Support Vector Classifier exhibit a satisfactory classification accuracy compared to traditional techniques despite having constraints with resources. The findings suggest that quantum computing holds great promise in revolutionizing the field of epilepsy diagnosis by providing more robust and accurate tools for distinguishing between epileptic and non-epileptic EEG patterns. © 2024 IEEE.

关键词： Quantum entanglement

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To Kill a Mockingbird: Cryptanalysis of an Authenticated Key Exchange Scheme for Drones 15

To Kill a Mockingbird: Cryptanalysis of an Authenticated Key...

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15th International Conference on Information and Knowledge technology, IKT 2024

作者： Toghraee, Neda Mala, Hamid Dept. of Information Technology Engineering Faculty of Computer Engineering University of Isfahan Isfahan Iran

ISBN: (纸本)9798331522254

Drones have evolved into sophisticated autonomous systems with a multitude of applications, including military operations, environmental monitoring, traffic oversight, data transmission, package delivery, fire targeting, and film production. As the Internet of Drones (IoD) ecosystem expands, ensuring secure and real-time access for authorized users becomes increasingly vital. User and drone authentication is crucial for this reason. In response to these challenges, Srinivas et al. proposed a lightweight three-factor authentication protocol designed for the IoD. While the authors assert that their protocol is resilient against known cyber threats, our research identifies critical vulnerabilities that challenge this claim. Although this scheme has already been cryptanalyzed in previous studies, in this paper we propose two new attacks on this scheme. Firstly, we present a concrete attack against the perfect forward secrecy of this scheme, and then we show that it is vulnerable to unauthorized access attack by a valid user to an unauthorized area's information. These weaknesses highlight the pressing need for the development of more secure authentication mechanisms in the IoD environment. Moving forward, addressing these vulnerabilities will be essential for fostering trust and ensuring the safe integration of drones into various applications, ultimately contributing to the advancement of IoD technology. © 2024 IEEE.

关键词： Authentication Protocol

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Exploring the Efficacy of Modified Transfer Learning in Identifying Parkinson’s Disease Through Drawn Image Patterns 2

Exploring the Efficacy of Modified Transfer Learning in Iden...

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2nd International Conference on Information and Communication technology, ICICT 2024

作者： Daiyan, Nabil Haque, Md Rakibul Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh

ISBN: (纸本)9798331508227

Parkinson’s disease (PD) is a progressive neurodegenerative condition characterized by the death of dopaminergic neurons, leading to various movement disorder symptoms. Early diagnosis of PD is crucial to prevent adverse effects, yet traditional diagnostic methods are often cumbersome and costly. In this study, a machine learning-based approach is proposed using hand-drawn spiral and wave images as potential biomarkers for PD detection. Our methodology leverages convolutional neural networks (CNNs), transfer learning, and attention mechanisms to improve model performance and resilience against overfitting. To enhance the diversity and richness of both spiral and wave categories, the training dataset undergoes augmentation to increase the number of images. The proposed architecture comprises three phases: utilizing pre-trained CNNs, incorporating custom convolutional layers, and ensemble voting. Employing hard voting further enhances performance by aggregating predictions from multiple models. Experimental results show promising accuracy rates. For spiral images, weighted average precision, recall, and F1-score are 90%, and for wave images, they are 96.67%. After combining the predictions through ensemble hard voting, the overall accuracy is 93.3%. These findings underscore the potential of machine learning in early PD diagnosis, offering a non-invasive and cost-effective solution to improve patient outcomes. ©2024 IEEE.

关键词： Convolutional neural networks

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Twitter Scraping and Sentiment Analysis on Stock Price

Twitter Scraping and Sentiment Analysis on Stock Price

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

作者： Iniyan, S. Thangakumar, J. Rajendran, P. Selvi Hindustan Institute Of Science And Technology Dept. Of Computer Science And Engineering Chennai603103 India

ISBN: (纸本)9798350364828

social media is useful in the community for sharing and discussing various events with others. The study's main objective is to predict the sign of the stock by sentimental analysis regarding the Hindenburg report on Adani group stocks by obtaining tweets from Twitter. The study's findings will aid in understanding the effect of the Hindenburg report on the stocks of the Adani group. Excel files are downloaded from Twitter containing tweets from social media users. Tweets are cleaned up by eliminating noise data after extraction. Machine learning algorithms, such as Logistic Regression (LR), Linear support classification (SVC), Random Forest (RF), and voting, were used to classify the data into positive, Neutral, and negative groups. © 2024 IEEE.

关键词： Logistic regression

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Generative-AI in E-Commerce: Use-Cases and Implementations 20

Generative-AI in E-Commerce: Use-Cases and Implementations

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Ghaffari, Shervin Yousefimehr, Behnam Ghatee, Mehdi University of Technology Dept. of Math. & Computer Sci. Amirkabir Tehran Iran

ISBN: (纸本)9798350383942

In recent years, the advancement of generative AI has profoundly influenced its application across various industries. One such industry is e-commerce where this technology can enhance both customer experience as well as merchants' productivity and profitability. In this paper, our objective is to review some of the potential use cases of generative AI technology in different areas of an online store. Specifically, we will focus on the use cases of product description generation, sentiment analysis of product reviews, and product tagging and categorization. We utilize various prompt engineering techniques to suggest exemplary implementations of these applications using large language models (LLMs). Lastly, the paper will discuss the possible risks and challenges that come with using generative AI in these contexts. © 2024 IEEE.

关键词： Sentiment analysis

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A Deep Learning Based Hybrid Structure for the Intrusion Detection 15

A Deep Learning Based Hybrid Structure for the Intrusion Det...

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

作者： Verma, Pranit Sharma, T.P. National Institute of Technology Hamirpur Dept. of Computer Science and Eng Himachal Pradesh India

ISBN: (纸本)9798350370249

In the current digital landscape, in order to protect sensitive data and vital infrastructure from cyber assaults, computer network security is essential. Conventional IDS, which often rely on signature-based and rule-based techniques, face challenges in detecting novel and sophisticated attacks. This paper presents a new approach detect intrusion using a DL-based hybrid model that integrates CNNs, BiLSTM, BiGRU, and attention mechanisms. The proposed model leverages the strengths of these architectures to identify temporal and spatial patterns in the data of traffic of network Our hybrid model begins with a CNN component to extract high-level facets from raw network traffic data. These features are then processed by BiLSTM and BiGRU layers to capture patterns and temporal dependencies. The inclusion of bidirectional layers allows the model to learn from both past and future contexts, improving its ability to detect subtle and sophisticated attacks. Attention mechanism further refines the model's pivot on pertinent characteristics of data, enhancing its detection capabilities. Various benchmark datasets commonly employed in the intrusion detection domain are used to evaluate the model. Obtained results demonstrate that the proposed model achieves an accuracy of approximately 98%, outperforming several advanced methods. The model's robustness and generalizability to different network configurations and attack types are also validated. With its more precise and dependable approach for identifying and reducing cyber risks, this research advances intrusion detection systems. The results have important ramifications for the creation and application of cutting-edge security systems capable of proactively safeguarding critical infrastructure and confidential data in today's interconnected world. © 2024 IEEE.

关键词： Network intrusion

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1 AI-Powered Facial Expression Analysis In Real Time: Unveiling Emotions 1

1 AI-Powered Facial Expression Analysis In Real Time: Unveil...

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1st IEEE International Conference on Electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Aishwarya, D. Sandeep, Ponduru Akhilesh Varma, Penmatsa V.P.S. Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350371802

This study introduces a novel facial deconstruction method for face emotion identification. After identifying facial landmarks with the IntraFace algorithm, seven regions of interest (ROI) are extracted, representing the principal featuresof the face: the mouth, nose, an area between the eyebrows, right and left eyes, and eyebrows. Next, a variety of local descriptors, such as Local Binary Patterns, Completed Local Binary Patterns. Local Ternary Patterns, and Dynamic Local Ternary Patterns, are employed to capture distinctive features. Inputting the feature vector which represents a face image into a multi-class support vector machine completes the recognition process. The suggested method outperforms previous facial deconstruction techniques, according on experimental findings on two publicly available datasets. © 2024 IEEE.

关键词： Local binary pattern

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