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IEEE International Conference on Big Data

作者： Xinyang Wu Andrew Horner Dept. Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Music mixing is a process that involves fine-tuning the levels, dynamics, and frequency content of musical elements to ensure clarity and harmony in the final music production. In this paper, we present an automatic mixing system based on diffusion models to correct imbalances in music mixes. We manipulate the well mixed stems’ short-time Fourier transform randomly to simulate the frequency and level imbalances commonly encountered in real-world scenarios. The difference between the imbalanced mix and the original mix is treated as noise for the diffusion model to predict, enabling the reverse denoising process to generate an automated mix. We evaluate our model’s performance by calculating the signal-to-distortion ratio between the original and predicted mixes. These results are compared with baseline automatic mixing models, demonstrating significant improvements. Test set results in audio: https://***/diffmusicmix/

关键词： Fourier transforms Noise reduction Music Production Predictive models Diffusion models Data models Delays Reverberation Signal to noise ratio

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Dynamic Road Management in the Era of CAV

arXiv

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arXiv 2024年

作者： Younis, Mohamed Lee, Sookyoung Lalouani, Wassila Tan, Dayuan Gupte, Sanket Dept. of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD United States Dept. of Computer Science and Engineering Ewha Womans University Seoul Korea Republic of

Traffic management and on-road safety have been a concern for the transportation authorities and the engineering communities for many years. Most of the implemented technologies for intelligent highways focus on safety measures and increased driver awareness, and expect a centralized management for the vehicular traffic flow. Leveraging recent advances in wireless communication, researchers have proposed solutions based on vehicle-to-vehicle (V2V) and vehicle-to-Infrastructure (V2I) communication in order to detect traffic jams and better disseminate data from on-road and on-vehicle sensors. Moreover, the development of connected autonomous vehicles (CAV) have motivated a paradigm shift in how traffic will be managed. Overall, these major technological advances have motivated the notion of dynamic traffic management (DTM), where smart road reconfiguration capabilities, e.g., dynamic lane reversal, adaptive traffic light timing, etc. will be exploited in real-time to improve traffic flow and adapt to unexpected incidents. This chapter discusses what the challenges in realizing DTM are and covers how CAV has revolutionized traffic management. Moreover, we highlight the issues for handling human-driven vehicles while roads are transitioning to CAV only traffic. Particularly, we articulate a new vision for inter-vehicle communication and assessment of road conditions, and promote a novel system for traffic management. Vehicle to on-road sensors as well as inter-vehicle connectivity will be enabled through the use of hand-held devices such as smartphones. This not only enables real-time data sharing but also expedites the adoption of DTM without awaiting the dominant presence of autonomous vehicle on the road. The proposed traffic management system incorporates computation and communication capabilities in traffic lights and road-side units, and accounts for the human-factor in controlling the traffic flow. The goal is to allow autonomous and driver-centric routing decis

关键词： Traffic congestion

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Enhanced Predictive Modeling Techniques for Early Detection of COPD Utilizing 1D Convolutional Neural Networks

Enhanced Predictive Modeling Techniques for Early Detection ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Sneha Jha Prakash Sahu Santosh Kumar Dept. of Data Science CSVTU Chhattisgarh Computer Science & Engineering IIIT-Naya Raipur

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Artificial intelligence (AI) breakthroughs have created new opportunities in the field of medical diagnostics, especially for the early identification of respiratory conditions like Chronic Obstructive Pulmonary Disease (COPD), the most prevalent respiratory condition, is characterized by breathing difficulties, mucus (sputum) coughing, and wheezing. Early verification is critical for efficient administration. A one-dimensional convolutional neural network (1D CNN) model for COPD detection, optimized using Adam and RMSprop algorithms is proposed for this purpose. Utilizing the ICBHI 2017 dataset, the performance of each optimizer based on training and validation accuracy is evaluated. The Adam optimizer achieved a training accuracy of ${9 4 \%}$ and a validation accuracy of ${9 0 \%}$. In comparison, the RMSprop optimizer yielded a training accuracy of ${9 2 \%}$ and a validation accuracy of ${8 8 \%}$. These results demonstrated that the Adam optimizer surpassed RMSprop in terms of accuracy. It showcased the potential of the proposed 1D CNN model as a reliable diagnostic tool, contributing to the advancement of respiratory health analytics.

关键词： Training Analytical models Accuracy Computational modeling Predictive models Chronic obstructive pulmonary disease Convolutional neural networks Reliability Medical diagnosis Artificial intelligence

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Recognition of Human Anomalies Using Convolution Neural Network

Recognition of Human Anomalies Using Convolution Neural Netw...

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

作者： Teja Parella Balakrishna Ponnam Santhiya P Praveen. A Sujihelen. L dept. of Computer Science and engineering Sathyabama Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

By instantly identifying suspicious occurrences, people, and things, smart CCTV systems greatly improve monitoring capacities. In this research, we investigate how to enhance CCTV video stream anomaly detection using state-of-the-art machine-vision and machine-learning techniques, namely UNET and ALEXNET. In contrast to more conventional methods that depend on supervised learning, this research makes use of uncontrolled and semi-supervised learning techniques to tackle the variety and complexity of anomalies. Using algorithms like UNET and ALEXNET, the system can learn to spot outliers on its own, with little to no help from humans. Because it can tell the difference between normal and abnormal situations, this method improves monitoring efficiency and decreases false alarms. In addition, the system maintains original quality for unusual occurrences while keeping typical video at a lower level, optimizing storage economy. By combining UNET and ALEXNET algorithms, smart CCTV systems may detect suspicious events before they happen, which increases security and allows for faster reactions to any dangers.

关键词： Training System performance Supervised learning Streaming media Semisupervised learning Recording Security

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Exploratory Data Analysis on Student Alcohol Consumption 4

Exploratory Data Analysis on Student Alcohol Consumption

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4th International Conference on Information Management and Machine Intelligence, ICIMMI 2022

作者： Verma, Yash Gupta, Aakansha Katarya, Rahul Dept. of Mechanical Engineering Delhi Technological University Delhi India Department of Computer Science and Engineering Delhi Technological University Delhi India

ISBN: (纸本)9781450399937

Alcohol is very common in youth these days;it is consumed by most individuals, especially Gen Z. Alcohol consumption is now taken as a status symbol in this millennial world where people only think about what makes them cooler for their fellow peers and indulge in such activities, but the overuse of alcohol has led to various drastic consequences in normal student's life. There is a common saying that excess of everything is bad;excessive consumption of alcohol has turned out to be a negative factor in any student's life, hindering their academics as well as psychological conditions. In this study, we have taken data through questionnaires from 2 different classes of Maths & Portuguese of Senior Secondary College. A total of 700 entries were taken and analyzed using various statistical and analytical tools. The results showed an evident relation between the alcohol consumption by the students and their academic performance;some factors were also seen affecting the alcohol consumption habits of the students and the trends in their alcohol consumption. Finally, the results concluded the impacts of excessive alcohol consumption and how we can work in this aspect to find out more about the psychological impacts of alcohol consumption on the students. © 2022 ACM.

关键词： Students

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User Authentication on Smart Speakers Leveraging Acoustic Imaging

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IEEE Transactions on Mobile Computing 2025年

作者： Ren, Yanzhi Xia, Zhiliang Li, Siyi Liu, Hongbo Yu, Jiadi Li, Shuai Li, Hongwei University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China Shanghai Jiao Tong University Dept. of Computer Science and Engineering Shanghai200240 China University of Oulu Finland

The user authentication has drawn increasingly attention as the smart speaker becomes more prevalent. For example, smart speakers that can verify who is sending voice commands can mitigate various types of attacks such as replay attack or impersonation attack. Existing user authentication solutions either cannot be applicable to smart speakers directly or require certain additional user-device interaction or pre-installed infrastructure, which may severely affect the user experience and create extra burdens to users. In this work, we propose a user authentication system utilizing acoustic images, which are derived from the smart speaker by emitting beep signals and sensing echoes from the user's body with its microphone array, as the proof for user authentication. Given the acoustic samplings of the reflected beep signal, our system designs a distance estimation component by applying a correlation based technique on the beamformed signal to estimate the distance between the user and microphone array. Our image construction component then constructs a virtual imaging plane using the estimated distance and steers the array towards each grid of the plane to generate an acoustic image of the user. We also propose a transfer learning-based method to derive efficient features from the constructed images, and employ SVM classifiers for accurate user authentication. Moreover, to ensure the accuracy of our system, our user direction estimation scheme could further estimate the DoA of the user's voice command to make sure that the user stands in front of the smart speaker during the authentication process. Our extensive experiments demonstrate that our system is robust and accurate across various scenarios. © 2002-2012 IEEE.

关键词： Acoustic imaging

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Enhancing Interpretability, Reliability and Trustworthiness: Applications of Explainable Artificial Intelligence in Medical Imaging, Financial Markets, and Sentiment Analysis

Enhancing Interpretability, Reliability and Trustworthiness:...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Kriti Srivastava Afreen Sorathiya Jinal Mehta Vineet Chotaliya Dept. of Computer Science & Engineering (Data Science) SVKM’S Dwarkadas J Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

In today’s technological era, as AI systems become more integral to critical decision-making, the importance of Explainable Artificial Intelligence (XAI) has become more pronounced. It addresses the challenge of understanding complex machine learning and deep learning models, ensuring transparency, interpretability, and accountability. This research paper provides a comprehensive analysis of XAI, focusing on its significance, methodologies, challenges, and future prospects. Theoretical foundations of XAI are elucidated, clarifying key concepts such as interpretability, transparency, and accountability. We differentiate between model-agnostic and model-specific XAI methods, outlining their strengths and limitations. A range of recent XAI techniques, including Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Gradient-weighted Class Activation Mapping (Grad-CAM), are scrutinized. Through case studies in Healthcare (Pneumonia Classification), Finance (Stock Price Prediction), and Entertainment (Sentiment Analysis), we demonstrate how XAI enhances the understandability and trustworthiness of AI systems. Additionally, a comparative study of all three methods on all three case studies has been conducted, and the results are compared. Challenges such as scalability issues and ethical considerations, including biases and fairness, are discussed. Looking ahead, we offer insights into future XAI research trajectories, aiming to foster public trust and shape a future where AI systems are both intelligent and comprehensible.

关键词： Sentiment analysis Ethics Pneumonia Explainable AI Decision making Finance Entertainment industry

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Comparative Analysis of Deep Learning Models for Structural Defect Segmentation in Bridges

Comparative Analysis of Deep Learning Models for Structural ...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： O'Neal, Kiara Hu, Da Kennesaw State University Dept. of Computer Science MariettaGA United States Kennesaw State University Dept. of Civil and Environmental Engineering MariettaGA United States

ISBN: (纸本)9798331504847

Automated structural defect detection is essential for ensuring the safety and maintenance of civil infrastructure, particularly in bridges where defects such as cracks, spalling, and corrosion can compromise structural integrity. This paper presents a comparative study of three semantic segmentation models - U-Net, Feature Pyramid Network (FPN), and DeepLabv3+ - for detecting and classifying structural defects in bridge imagery. Each model was evaluated using two encoder architectures, EfficientNet B3 and MobileOne S4, to assess the impact of different feature extraction strategies on segmentation accuracy. The experiments were conducted using the DACL benchmark dataset, which includes a wide range of defect classes. FPN paired with EfficientNet B3 demonstrated the highest mean accuracy across most defect categories, outperforming the other combinations, particularly in detecting common defects such as cracks and graffiti. However, certain defect types, such as hollow areas and cavities, presented challenges for all models. These results highlight the effectiveness of deep learning models in automated defect detection, while also identifying areas where further refinement is needed to improve performance in more complex defect scenarios. © 2025 IEEE.

关键词： Semantic Segmentation

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Comparative Analysis of Epileptic Seizure Prediction: Exploring Diverse Pre-Processing Techniques and Machine Learning Models 10

Comparative Analysis of Epileptic Seizure Prediction: Explor...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Hasan Talukder, Md. Simul Sulaiman, Rejwan Bin Chhabra, Gunjan Yadav, Suman Avdhesh Haque, Rezaul Islam, Md Babul Kaushik, Keshav Nuclear Safety Security and Safegurad Division Bangladesh Atomic Energy Regulatory Authority Dhaka Bangladesh School of Computer Science and Technology Northumbria University Newcastle United Kingdom Graphic Era Hill University Dept. of Cse Dehradun India Amity School of Engineering & Technology Department of Computer Science and Engineering Noida India East West University Dept of Computer Science and Engineering Dhaka1212 Bangladesh Calabria Italy School of Computer Science University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9798350382471

Precise prediction and detection of epilepsy, a common neurological condition marked by sporadic and repeated seizures, is necessary for efficient management and patient care. To do accurate and reliable seizure detection, machine learning (ML) on electroencephalogram (EEG) recordings is a good choice because of its capacity to provide insightful information about brain activity during seizures. In this work, we do a thorough comparative analysis of five machine learning methods for the prediction of epileptic seizures using EEG data: Random Forest (RF), Decision Tree (DT), Extra Trees (ET), Logistic Regression (LR), and Gradient Boosting (GB). Firstly, to assure the quality of the data and enable precise model training, the dataset underwent extensive preprocessing, which included cleaning, normalization, treatment of outliers, and oversampling. These preprocessing techniques significantly enhanced the models' performance. In our study, we evaluate the accuracy of each model;the LR classifier obtains 56.95% accuracy, while both GB and DT achieve 97. 17% accuracy. Outperforming the findings of previous investigations, the ET model showed the best performance with an accuracy of 99.29%. Achieving 98.99% accuracy was another accomplishment for the RF model. Our results imply that the ET model not only performed better than the other models in the comparison analysis but also more effectively than the most advanced outcomes from earlier research. Regarding the accurate and dependable prediction of epileptic seizures using EEG data, the ET model is a solid option due to its remarkable performance. © 2023 IEEE.

关键词： EEG epileptic seizure Extra Tree hyperparameter tuning machine learning

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Evaluating Resource Allocation Strategies in Mobile Ad Hoc Networks with Cloud Computing Algorithms

Evaluating Resource Allocation Strategies in Mobile Ad Hoc N...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Pillai, Sanjaikanth E. Vadakkethil Somanathan Jindal, Mayank Mimani, Sushant Jiwani, Nasmin Kiruthiga, T. School of Electrical Engineering and Computer Science University of North Dakota Grand Forks United States San Jose CA United States GoDaddy SnohomishWA United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Vetri Vinayaha College of Engineering and Technology Dept. of Ece Trichy India

ISBN: (纸本)9798350394399

Cellular advert Hoc Networks (MANETs) is self-organizing wireless networks that lack the physical infrastructure and centralized manage of conventional networks. With a purpose to enable powerful communiqué, it's miles important to deal with the resource allocation problem in MANETs. Recently, Cloud Computing algorithms were used to evaluate aid allocation techniques in MANETs. Those algorithms contain sampling strategies, which include nearest neighbor, Monte Carlo, and hybrid strategies, to optimize the useful resource utilization levels. This allows for a more correct assessment of the to be had assets in MANETs. Moreover, these algorithms also can be carried out to autonomously control and time table communication flows with a view to improve standard network performance. This paper affords an outline of the existing aid allocation techniques and their evaluation in MANETs the usage of cloud computing algorithms. It also discusses the impacts of the useful resource control selections in MANETs and the challenges confronted in deploying cloud computing algorithms in these settings. Lastly, a fixed of future studies directions is proposed to in addition improve resource allocation techniques in MANETs. © 2024 IEEE.

关键词： Cloud computing

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