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A geometric-approach based Combinatorial Transformative Scalogram analysis for multiclass identification of pathologies in a voice signal

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Multimedia Tools and Applications 2024年 1-21页

作者： Khumukcham, Ranita Nongmeikapam, Kishorjit Department of Electronics and Communication Engineering Indian Institute of Information Technology Mantripukhri Manipur Imphal795002 India Department of Computer Science and Engineering Indian Institute of Information Technology Mantripukhri Manipur Imphal795002 India

Many researchers have preferred non-invasive techniques for recognizing the exact type of physiological abnormality in the vocal tract by training machine learning algorithms with feature descriptors extracted from the voice signal. However, until now, most techniques have been limited to classifying whether a voice is normal or abnormal. It is crucial that the trained Artificial Intelligence (AI) be able to identify the exact pathology associated with voice for implementation in a realistic environment. Another issue is the need to suppress the ambient noise that could be mixed up with the spectra of the voice. Current work proposes a robust, less time-consuming and non-invasive technique for the identification of pathology associated with a laryngeal voice signal. More specifically, a two-stage signal filtering approach that encompasses a score-based geometric approach and a glottal inverse filtering method is applied to the input voice signal. The aim here is to estimate the noise spectra, to regenerate a clean signal and finally to deliver a completely fundamental glottal flow-derived signal. For the next stage, clean glottal derivative signals are used in the formation of a novel fused-scalogram which is currently referred to as the "Combinatorial Transformative Scalogram (CTS)." The CTS is a time-frequency domain plot which is a combination of two time-frequency scalograms. There is a thorough investigation of the performance of the two individual scalograms as well as that of the CTS *** classification metrics are used to investigate performance, which are: sensitivity, mean accuracy, error, precision, false positive rate, specificity, Cohen’s kappa, Matthews Correlation Coefficient, and F1 score. Implementation of the VOice ICar fEDerico II (VOICED) standard database provided the highest mean accuracy of 94.12% with a sensitivity of 93.85% and a specificity of 97.96% against other existing techniques. The current method performed well despite the d

关键词： Frequency domain analysis

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Survey on Task Scheduling Optimization Strategy under Multi-Cloud Environment

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computer Modeling in engineering & Sciences 2023年第6期135卷 1863-1900页

作者： Qiqi Zhang Shaojin Geng Xingjuan Cai School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan030024China College of Electronics and Information Engineering Tongji UniversityShanghai201804China

Cloud computing technology is favored by users because of its strong computing power and convenient *** the same time,scheduling performance has an extremely efficient impact on promoting carbon ***,scheduling research in the multi-cloud environment aims to address the challenges brought by business demands to cloud data centers during peak ***,the scheduling problem has promising application prospects under themulti-cloud *** paper points out that the currently studied scheduling problems in the multi-cloud environment mainly include independent task scheduling and workflow task scheduling based on the dependencies between *** paper reviews the concepts,types,objectives,advantages,challenges,and research status of task scheduling in the multi-cloud *** scheduling strategies proposed in the existing related references are analyzed,discussed,and summarized,including research motivation,optimization algorithm,and related ***,the research status of the two kinds of task scheduling is compared,and several future important research directions of multi-cloud task scheduling are proposed.

关键词： Cloud computing task scheduling workflow review multi-cloud environment

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SmokerBeacon: Hybrid Real-Time Smoking Detection Using Object Detection and Gas Sensor

SmokerBeacon: Hybrid Real-Time Smoking Detection Using Objec...

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2025 IEEE International Students' Conference on Electrical, electronics and computer Science, SCEECS 2025

作者： Harshavardhan, S. Upender, Patri Ganesh, N. Giftsy, Sharon Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Electronics Engineering Chennai India

ISBN: (纸本)9798331529833

Enforcing smoking bans in public areas remains a challenge due to limitations in conventional monitoring methods. This paper introduces SmokerBeacon, an innovative system for real-time detection of smoking activities in restricted zones. SmokerBeacon uses gas sensors and advanced image classification - YOLO to identify smoking activities. The system catego-rizes detection scenarios into three types: (i) instances where smoking actions are detected by YOLO without corresponding signals from gas sensors, (ii) cases where YOLO fails to detect smoking actions despite gas sensors detecting smoking-related substances, and (iii) situations where both smoking activities and corroborating signals from gas sensors confirm smoking. Each scenario triggers specific actions to improve enforcement and foster healthier environments. By generating timely alerts and enabling proactive intervention, SmokerBeacon mitigates risks associated with smoking in prohibited areas, leveraging IoT technology. © 2025 IEEE.

关键词： Chemical sensors

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Review of AI & XAI-based breast cancer diagnosis methods using various imaging modalities

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Multimedia Tools and Applications 2025年第5期84卷 2209-2260页

作者： Karthiga, R. Narasimhan, K. Thanikaiselvan, V. Hemalatha, M. Amirtharajan, Rengarajan Department of ECE SRM Institute of Science and Technology Ramapuram Chennai600089 India School of Electrical & Electronics Engineering SASTRA Deemed University Thanjavur613 401 India School of Electronics Engineering Vellore Institute of Technology Vellore India Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia

Breast cancer is a significant health concern;early detection and treatment are critical to improving patient outcomes. Artificial Intelligence has the potential to assist healthcare professionals in the diagnosis and treatment of breast cancer, and clinical decision support systems using AI algorithms can help to improve the accuracy and speed of breast cancer detection and diagnosis. By analysing large amounts of data from various imaging modalities, AI algorithms can help identify breast cancer early, increasing the chances of successful treatment. This survey aims to briefly overview the most prevalent breast cancer types, their staging, and the methods and modalities for their diagnosis to address these problems. Our investigation showed that, when compared to ML, DL techniques achieved the highest accuracy, with an approximate 2% improvement in accuracy. This review article mainly focuses on four commonly used imaging modalities: Mammogram, Thermogram, Ultrasound imaging and histopathology images. This survey critically analyzes breast cancer detection approaches and modalities, as well as a cost-to-accuracy comparison. Explainable AI (XAI) techniques and their application for breast cancer diagnosis are critically examined, as well as their advantages and limitations. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Lung cancer

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Multi-Objective Computation Offloading based on Decentralized Deep Reinforcement Learning in Industrial Internet of Things

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1940-1953页

作者： Zhao, Yingjie Chai, Zhengyi Li, Yalun Huang, Hao Kang, Hongshen Tiangong University School of Computer Science and Technology Tianjin300387 China Tiangong University School of Electronics and Information Engineering Tianjin300387 China

With the increasing scale of industrial equipments, delay and energy consumption have emerged as critical concerns within the Industrial Internet of Things (Industrial IoT). Mobile edge computing (MEC) offloads tasks to nearby edge servers to meet the demands of delay-sensitive applications. However, the limitations of edge computing resources can lead to significant processing delays or even task failures when offloading numerous tasks. Furthermore, it is difficult for existing centralized algorithms to acquire global information within large-scale industrial environment. To tackle these challenges, the computation offloading problem is transformed into a decentralized partially observable Markov decision process (Dec-POMDP) with rewards for delay and energy consumption, and a decentralized multi-objective computation offloading method is proposed to achieve the long-term reward maximization. Specifically, two deep neural networks, namely the delay and energy network, are designed to estimate the expected rewards for each offloading decision in terms of delay and energy consumption. Meanwhile, to achieve dynamic offloading, gated recurrent unit (GRU) is introduced to predict the occupancy of edge computing resources, and an adaptive weight network is devised to dynamically adjust the weights of optimization objectives based on historical information. Comprehensive experiments demonstrate that the proposed method effectively meets the requirements of delay-sensitive tasks, as well as minimizing long-term delay and energy consumption. © 2015 IEEE.

关键词： Deep reinforcement learning

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Event-Driven Attention Network:A Cross-Modal Framework for Efficient Image-Text Retrieval in Mass Gathering Events

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computers, Materials & Continua 2025年第5期83卷 3277-3301页

作者： Kamil Yasen Heyan Jin Sijie Yang Li Zhan Xuyang Zhang Ke Qin Ye Li School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Information and Software Engineering University of Electronic Science and Technology ofChinaChengdu611731China Kashi Institute of Electronics and Information Industry Kashi844508China

Research on mass gathering events is critical for ensuring public security and maintaining social ***,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting,and there is a relative lack of research on mass gathering *** believe real-time detection and monitoring of mass gathering behaviors are essential formigrating potential security risks and ***,it is imperative to develop a method capable of accurately identifying and localizing mass gatherings before disasters occur,enabling prompt and effective *** address this problem,we propose an innovative Event-Driven Attention Network(EDAN),which achieves image-text matching in the scenario of mass gathering events with good results for the first *** image-text retrieval methods based on global alignment are difficult to capture the local details within complex scenes,limiting retrieval *** local alignment-based methods aremore effective at extracting detailed features,they frequently process raw textual features directly,which often contain ambiguities and redundant information that can diminish retrieval efficiency and degrade model *** overcome these challenges,EDAN introduces an Event-Driven AttentionModule that adaptively focuses attention on image regions or textual words relevant to the event *** calculating the semantic distance between event labels and textual content,this module effectively significantly reduces computational complexity and enhances retrieval *** validate the effectiveness of EDAN,we construct a dedicated multimodal dataset tailored for the analysis of mass gathering events,providing a reliable foundation for subsequent *** conduct comparative experiments with other methods on our dataset,the experimental results demonstrate the effectiveness of *** the image-to-text retrieval task,EDAN achieved the best performance on the R@5 metric,w

关键词： Mass gathering events image-text retrieval attention mechanism

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Gaze-Assisted Autism Spectrum Disorder Identification: A Fusion of Machine Learning and Deep Learning Approaches for Preemptive Identification 5

Gaze-Assisted Autism Spectrum Disorder Identification: A Fus...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Simon, Judy Kapileswar, N. Datchinamoorthi, M. Muthukumar, S. Keerthana Devi, G. SRM Institute of Science and Technology Dept of Electronics and Communication Engineering Chennai India SRM Institute of Science and Technology Dept of Electronics and Computer Engineering Chennai India

ISBN: (纸本)9798350395235

This research proposes an all-encompassing method for diagnosing Autism Spectrum Disorder (ASD) by leveraging eye-tracking data and advanced machine learning techniques. In this study, gaze-tracking data was collected from individuals diagnosed with Autism Spectrum Disorder and those not exhibiting ASD characteristics as they participated in various tasks and stimuli. Principal Component Analysis was employed to streamline the eye-tracking data, retaining crucial information while enhancing computational efficiency. These refined features were then integrated into Convolutional Neural Networks as the primary modelling approach. CNNs excel in recognizing relevant spatial and temporal patterns within the visual data, leading to intricate feature extraction and classification. Additionally, we explored the efficacy of transfer learning by enhancing pre-trained CNN models with our eye-tracking dataset. This approach enables us to leverage knowledge from large-scale visual tasks, potentially enhancing the model's performance in ASD classification. The research findings highlight the success of this multifaceted approach in accurately categorizing individuals with ASD. The synergy of ML, CNN, and transfer learning models significantly enhances the accuracy of ASD diagnosis, offering a promising avenue for early identification and intervention. Consequently, this research has the potential to aid in the creation of accurate noninvasive instruments for diagnosing ASD, thereby accelerating timely assistance and treatment for individuals impacted by the condition. © 2024 IEEE.

关键词： Image classification

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Long-term solar radiation forecasting in India using EMD, EEMD, and advanced machine learning algorithms

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Environmental Monitoring and Assessment 2025年第3期197卷 1-36页

作者： RajasundrapandiyanLeebanon, T. Murugan, N. S. Sakthivel Kumaresan, K. Jeyabose, Andrew Department of Electrical and Electronics Engineering TamilNadu College of Engineering Tamil Nadu Coimbatore India Department of Mechanical Engineering Park College of Engineering & Technology TamilNadu Coimbatore India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Solar radiation plays a critical role in the carbon sequestration processes of terrestrial ecosystems, making it a key factor in environmental sustainability among various renewable energy sources. This study integrates two advanced signal processing techniques—empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD)—with machine learning (ML) algorithms, including multilayer perceptron (MLP), random forest regression (RFR), support vector regression (SVR), and ridge regression, to forecast long-term solar radiation. Meteorological data spanning 13 years (2000–2012) from seven locations across India (Bhubaneswar, Chennai, Delhi, Hyderabad, Nagpur, Patna, and Trivandrum) were used for training and testing. The optimal model was identified based on performance metrics, including the highest linear correlation coefficient (R), and the lowest mean absolute error (MAE) and root mean square error (RMSE). The results indicate that EEMD integrated with ML algorithms consistently outperformed EMD-based approaches. Among the ML models evaluated, EEMD integrated with MLP achieved the best performance across all locations, with RMSE = 0.332, MAE = 0.26, and R2 = 0.99. Furthermore, a comparative analysis with previous studies demonstrated that the proposed approach provides superior accuracy, underscoring its efficacy in solar radiation forecasting. © The Author(s) 2025.

关键词： Empirical mode decomposition

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Filtering Methods, Assessment Criteria, and Prospects for Image Denoising

Filtering Methods, Assessment Criteria, and Prospects for Im...

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2025 IEEE International Students' Conference on Electrical, electronics and computer Science, SCEECS 2025

作者： Wandile, Piyush S Singh Kirar, Bhupendra Kirar, Seema Indian Institute of Information Technology Computer Science and Engineering Bhopal India Indian Institute of Information Technology Electronics and Communications Engineering Bhopal India Bansal Institute of Science and Technology Electronics and Communications Engineering Bhopal India

ISBN: (纸本)9798331529833

Image denoising is a crucial step in image acquisition and processing that helps improve the image quality by removing the unwanted noise. In this paper Gaussian and median filters are used as denoiser and performance is evaluated using some of the key metrics like Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Feature Similarity Index Measure (FSIM), Structural Similarity Index Measure (SSIM), Mean Absolute Error (MAE), and Correlation Coefficient. A denoiser with low MSE, high PSNR, values of 1 for SSIM, FSIM, and Correlation Coefficient. Reported results are better in comparison to existing for better image quality. © 2025 IEEE.

关键词： Wiener filtering

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Cultural Heritage Monitoring and Predictive Maintenance using Internet of Things: Assessment and Future Aspects

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Journal of Mobile Multimedia 2024年第6期20卷 1211-1250页

作者： Um-E-habiba Shaikh, Faisal Karim Chowdhry, Bhawani Shankar Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Hyderabad Pakistan Telecommunication Engineering Department Mehran University of Engineering and Technology Jamshoro Pakistan Faculty of Electrical Electronics and Computer Engineering Mehran University of Engineering and Technology Jamshoro Pakistan

This study presents a detailed survey on the use of the Internet of Things (IoT) for predictive maintenance and monitoring of cultural heritage, focusing on museums and exhibitions. The integration of IoT in these fields offers significant potential for optimizing maintenance and supervision of heritage resources while also enhancing maintenance strategies. This review examines the application of IoT devices, data analysis, and predictive algorithms in monitoring the state of heritage resources to improve tourist experiences and conserve cultural resources. Key enabling technologies can enable adaptive interactions and engagement with enhanced navigation. Visualization technologies are also common, offering hands-on displays. The study also emphasizes the value of accessibility and interactivity, providing personalization and content sharing features. Several initiatives prioritize the delivery of digital content through mobile applications, internet browsers, and downloadable *** study examines how IoT-enabled predictive maintenance systems can recognize potential hazards, like resource decay or environmental dangers, enabling immediate action. This survey also highlights trends, challenges, and future research in IoT and cultural heritage monitoring. © 2025 River Publishers.

关键词： heritage monitoring IoTs predictive maintenance trends

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