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Exploring the Effectiveness of Deep Learning in Audio Compression and Restoration 2

Exploring the Effectiveness of Deep Learning in Audio Compre...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Pushparani, S. Rekha, K. Sashi Sivagami, V.M. Usharani, R. Jothi, M. Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology SriperumbudurTamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Tamil Nadu Kattankulathur India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India

ISBN: (纸本)9798350384277

Modern audio processing requires compression and restoration for streaming services and data storage and transmission. This study examines how deep learning can address these difficulties and includes an architecture flow diagram. The suggested complex design has numerous integral stages optimized for audio compression and restoration. The input layer contains raw audio data. The pre-processing module normalizes and extracts features from raw data, frequently using Mel-frequency cepstral coefficients (MFCC) to capture audio characteristics. This module improves data quality by reducing noise and filtering. The architecture's heart is the compression module, where deep learning is used. This module compresses audio data using neural networks, maybe CNNs or RNNs. This compression is quantified by compression ratio and bitrate, with hypothetical values representing audio quality trade-offs. The compression module compacts audio data for storage or transmission. To minimize data size, quantization and encoding are common here. To accurately decompress audio, the decompression module mimics the compression module's architecture. Depending on needs, error correction or quality enhancement layers may be added. Filtering, equalization, and dynamic range compression may be used in the post-processing module to improve audio quality. If needed, a restoration module uses deep learning models to minimize noise and artifacts. This study presents four hypothetical scenarios to demonstrate different results. The scenarios emphasize high compression, balanced approaches, quality prioritization, and low resource utilization. Deep learning approaches can adapt to varied audio processing needs by balancing compression ratio, bitrate, audio quality (measured by PSNR), and restoration accuracy in each case. © 2024 IEEE.

关键词： Audio streaming

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Leveraging Exponential Smoothing for Time Series Analysis of Wireless Sensor Networks 5th

Leveraging Exponential Smoothing for Time Series Analysis of...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Alam, Intekhab Ojha, Ananta Verma, Tushar K. Venkatachalam, Amirtha Preeya Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

The abstract should summarize the contents of the paper in short terms, i.e. 150–250. This paper explores the applications of exponential smoothing techniques in evaluating time collection records generated using Wireless Sensor Networks (WSNs). Empirical results demonstrate that exponential smoothing outperforms conventional time series models, supplying users with extra accurate analysis and forecasting of WSNs. The essential blessings of this method encompass decreased errors in predicting destiny values, progressed reliability, and stepped forward performance in developing forecasts. This paper affords a comprehensive review of exponential smoothing and its packages in WSNs, including an assessment of the outcomes of applying those techniques and the capability of future research avenues. In addition, it gives hints for practitioners trying to leverage exponential smoothing for time series analysis of WSNs. Exponential smoothing is a critical time series forecasting method that may be applied to evaluate Wi-Fi sensor Networks (WSN). It is miles primarily based on the belief that the fashion in a time collection is a clean continuation of past values. The rules work by calculating an exponentially weighted shifting average, wherein facts points are exponentially weighted and given more importance the closer they are to the present-day time point words. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： exponentially smoothing weighted

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Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models 38

Inflationary Flows: Calibrated Bayesian Inference with Diffu...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： de Albuquerque, Daniela Pearson, John Department of Electrical & Computer Engineering School of Medicine Duke University DurhamNC27708 United States Department of Neurobiology Department of Electrical & Computer Engineering Center for Cognitive Neuroscience Duke University DurhamNC27708 United States

Beyond estimating parameters of interest from data, one of the key goals of statistical inference is to properly quantify uncertainty in these estimates. In Bayesian inference, this uncertainty is provided by the posterior distribution, the computation of which typically involves an intractable high-dimensional integral. Among available approximation methods, sampling-based approaches come with strong theoretical guarantees but scale poorly to large problems, while variational approaches scale well but offer few theoretical guarantees. In particular, variational methods are known to produce overconfident estimates of posterior uncertainty and are typically non-identifiable, with many latent variable configurations generating equivalent predictions. Here, we address these challenges by showing how diffusion-based models (DBMs), which have recently produced state-of-the-art performance in generative modeling tasks, can be repurposed for performing calibrated, identifiable Bayesian inference. By exploiting a previously established connection between the stochastic and probability flow ordinary differential equations (pfODEs) underlying DBMs, we derive a class of models, inflationary flows, that uniquely and deterministically map high-dimensional data to a lower-dimensional Gaussian distribution via ODE integration. This map is both invertible and neighborhood-preserving, with controllable numerical error, with the result that uncertainties in the data are correctly propagated to the latent space. We demonstrate how such maps can be learned via standard DBM training using a novel noise schedule and are effective at both preserving and reducing intrinsic data dimensionality. The result is a class of highly expressive generative models, uniquely defined on a low-dimensional latent space, that afford principled Bayesian inference. © 2024 Neural information processing systems foundation. All rights reserved.

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Emotion Recognition in Visual Art on Color and Texture Analysis using Improved Vision Transformer 1

Emotion Recognition in Visual Art on Color and Texture Analy...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Suma, K.G. Balasm, Zaed Sasirekha, N. Veera Manikandan, P. Sandhya, K. Vellore Institute of Technology School of Computer Science and Engineering Department of Ai & Ml Andhra Pradesh India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Sona College of Technology Department of Electronics and Communication Engineering Salem India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Electrical and Electronics Engineering Mamallapuram India Tirupati India

ISBN: (纸本)9798331529246

Nowadays, Emotion Recognition plays a key role in social communication interaction in which human emotions are interpreted from facial expressions and speech. Several challenges occur in emotion recognition such as capturing context of any emotion also facial attributes make the recognition process complex. To overcome these drawbacks Improved Vision Transformer (IViT) classifier is proposed for emotion recognition. Initially, the input data is collected from the Japanese Female Facial Expression (JAFFE) to collect color features and visual features. Then, the input data is processed using min max scaling method which normalizes pixel values. After that, colour and texture-based features are extracted by utilizing Rotation Invariant Local Binary Patterns (RI-LBP) with Hue Saturation and Value (HSV) and Red Green Blue (RGB). Finally, classification is performed by IViT, which processes images as sequences of patches. Hence, the proposed IViT model achieved better output when compared to existing Visual-Audio Attention Network (VAANet) in terms of accuracy by obtaining 96.6% respectively. © 2025 IEEE.

关键词： Local binary pattern

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Identifying Acute Disseminated Encephalomyelitis with Time Series Analysis 5th

Identifying Acute Disseminated Encephalomyelitis with Time S...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Pallavi, M. Mishra, Awakash Kudari, Jayashree M. Belwal, Ankit School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

Acute disseminated encephalomyelitis (ADEM) is an extraordinary immune-mediated disease that influences the mind and spinal cord, central to the unexpected onset of neurologic symptoms. Time series evaluation can be used to become aware of ADEM in patients. Mainly, statistical analysis of the temporal styles of mind imaging and immunological parameters has proven effective for diagnosing ADEM in medical settings. Such material styles offer essential clues about disease development, which can be leveraged to diagnose ADEM patients appropriately. Acute disseminated encephalomyelitis (ADEM) is a critical degenerative sickness of the imperative nervous device, characterized by a rapid onset of neurologic signs and significant infection of the mind and spinal twine. Early analysis of ADEM is critical to initiate appropriate remedies and reduce long-term incapacity. In this look, we advocate the usage of time collection analysis to discover neurological patterns related to ADEM from the brain indicators of sufferers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Time series

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Predicting Song Popularity Through Machine Learning and Sentiment Analysis on Social Networks 13th

Predicting Song Popularity Through Machine Learning and Sen...

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13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Rompolas, Gerasimos Smpoukis, Athanasios Kafeza, Eleanna Makris, Christos Computer Engineering and Informatics Department University of Patras Patras26504 Greece College of Interdisciplinary Studies Zayed University Dubai144534 United Arab Emirates

ISBN: (纸本)9783031632266

In this paper, we delve into the intricate relationship between technology, music, and success. Our research focuses on leveraging the capabilities of machine learning algorithms to forecast the success and popularity of songs. By combining audio features, social media data, and emotion analysis, our machine learning-based model demonstrates its capability as a reliable tool for artists, amateurs, and music industry stakeholders to make informed and targeted decisions. Our analysis encompassed not only the inherent audio characteristics of each track but also the extent of audience engagement and emotional response of the fans. Through the integration of textual emotion analysis, we quantified the reactions of the audience and performed an emotion labelling, leveraging our social information to uncover the emotions surrounding each track’s release. The experimental results show that the feature data from which the classification algorithms were trained, unfolded to be qualitative and precise, while they also show that the use of the social media features improves the classifiers’ performance in predicting the popularity of the tracks. © IFIP International Federation for Information Processing 2024.

关键词： Emotion Recognition

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Memory-Augmented Deep Deterministic Policy Gradient 16th

Memory-Augmented Deep Deterministic Policy Gradient

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16th International Conference on Social Robotics, ICSR + BioMed 2024

作者： Qiu, Qian Zeng, Fanyu Yang, Haigen Xing, Guanyu Ge, Shuzhi Sam School of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China College of Computer Science Sichuan University Chengdu China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

ISBN: (纸本)9789819789627

With the advancement of intelligent technologies, particularly deep reinforcement learning, Unmanned Aerial Vehicles (UAVs) are being widely deployed across various scenarios. However, current remote-controlled UAV methods and existing intelligent decision-making algorithms struggle to effectively respond to dynamic and partially observable situations. To tackle this issue, we propose the Memory-augmented Deep Deterministic Policy Gradient (MemDDPG) algorithm. The MemDDPG algorithm leverages a Long Short-Term Memory (LSTM) network to use past state information for current decision-making, enabling UAVs to perform accurately in dynamic scenarios. By incorporating memory-based mechanisms, MemDDPG addresses the limitations of conventional methods, enhancing UAVs’ ability to manage complex environments. This algorithm improves decision-making and ensures UAVs operate more autonomously and efficiently in unpredictable situations. Experiments demonstrate the effectiveness and feasibility of the MemDDPG algorithm in enhancing the intelligent decision-making capabilities of UAVs, showing significant improvements in adaptability and decision-making accuracy in dynamic situations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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Advanced VANET Technology for Emergency Vehicle Safety in Time-Critical Scenario 8

Advanced VANET Technology for Emergency Vehicle Safety in Ti...

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8th IEEE International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2023

作者： Murali, N. Beulah David, D. Saveetha School of Engineering Simats Department of Computer Science and Engineering Tamilnadu Chennai India Institute of Information Technology Saveetha School of Engineering Simats Department of Data Analytics Tamilnadu Chennai India

ISBN: (纸本)9798350347791

The most pressing issue with ITS is minimal latency in communication between vehicles, especially given that unstable traffic flow and road safety are two of their main concerns. Because to these problems, I have concentrated my study on employing a vehicular ad hoc network to track speedy object detection and provide the quickest warning to drivers. These systems require ongoing monitoring to function properly, making it possible to use machine learning techniques on vast amounts of data produced by various VANET applications. Machine learning is a technique used to prevent accidents for emergency vehicles by automatically learning and improving the system's rapid object detection stage in situations with high traffic. Our methods effectively implement the VANET aim by providing efficient unsupervised learning of the collected data. In this research, we suggested a unique real-time (DT-YOLO) algorithm-based automatic discovering vehicle gap approach for detecting and identifying objects in congested metropolitan regions. By using VANET technologies, we were able to address traffic, communication, and safety concerns in metropolitan areas. We investigated, using DT-YOLO technology, how these problems may be resolved in a transparent object-tracking system via machine learning techniques. Fast-moving EVs have a high probability of finding a gap to let the driver to make a decision. Moreover, a case study demonstrating a VANET-based scenario of nearest-object and gap-finding to flee from congested traffic with the ideal vehicle moment is shown. The paper examined current challenges and future directions. © 2023 IEEE.

关键词： Object detection

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A Machine Learning Approach for Detecting Fake News in Hindi 10

A Machine Learning Approach for Detecting Fake News in Hindi

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

作者： Nagesh, Shreyas Sampath, Aditya N Guruprasad, Pradhumna Salai Sanjay, S. Jeyakumar, G. Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Coimbatore India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Engineering Coimbatore India

ISBN: (纸本)9798350382471

Several solutions exist to detect whether English news articles are fake or not. However, in a country such as India, news articles are published in numerous regional languages too. There has been little research work done on regional languages such as Hindi. This work proposes a framework to detect Hindi fake news articles. A dataset of nearly 4500 news articles has been collected in Hindi, written using the Devanagari Script. These news articles are scraped using BeautifulSoup and Scrapy libraries. The IndicBERT and MuRIL libraries, developed earlier in Hindi, are used to generate the word embedding. SVM, Logistic Regression, and a simple CNN are used as classifiers. Extensive experiments have been methodically conducted in order to find the best combination to detect fake news in Hindi. The best combination was found to be MuRIL and SVM, yielding 98.58% classification accuracy. A standalone GUI running on a local host is built to demonstrate the working model. © 2023 IEEE.

关键词： Hindi Fake News IndicBERT Machine Learning MuRIL Natural Language Processing StanfordNLP

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Predicting Tulip Leaf Diseases: A Integrated CNN and Random Forest Approach

Predicting Tulip Leaf Diseases: A Integrated CNN and Random ...

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2023 IEEE World Conference on Communication and Computing, WCONF 2023

作者： Banerjee, Deepak Kukreja, Vinay Hariharan, Shanmugasundaram Jain, Vishal Jindal, Varun Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering UP Greater Noida India

ISBN: (纸本)9798350311204

In this paper, the methods of Random Forest (RF) & Convolutional Neural Network (CNN) algorithms are combined to present a novel approach for classifying bacterial infections in plant species. The proposed model extracts characteristics from the input photos using three layers of convolution, three layers that max-pool features, and a layer that is completely connected before categorizing the images using RF into various bacterial disease classifications. Recall, F1-Score, support, and accuracy measures were used to evaluate the model's performance using a dataset of plant image data that included seven different types of bacterial illnesses. The experimental results demonstrate that the proposed CNN-RF methodology provides remarkable precision in categorizing the images when compared to other state-of-the-art techniques. The model specifically achieves an average weighted recall of 81.54%, an F1-Score of 81.54%, and an accuracy of 81.54%. The recommended approach is effective at identifying various bacterial illnesses in plants because the model outperforms rival models when it comes to of macro & micro-average F1-Scores. The recommended method for diagnosing bacterial diseases in plants makes use of machine learning as well as deep learning techniques. Because bacterial illnesses in plants can have a substantial influence on agricultural production, a model's high precision, and efficacy can help identify and stop the spread of these infections. © 2023 IEEE.

关键词： Diseases

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