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3rd International Conference on computing and Communication Networks, ICCCN 2023

作者： Gayathri, R. Sheela Sobana Rani, K. Aravindhan, K. Department of Electronics and Communication Engineering Sri Ramakrishna Institute of Technology Coimbatore India Karpagam College of Engineering Coimbatore India School of Computing and Information and Technology Reva University Bangalore India

ISBN: (纸本)9789819726707

Speech categorisation is a broad study that has attracted a lot of interest recently. Understanding other people's emotions and responding appropriately is the largest distinction between robots and people. Speech signals are categorised in this article using both conventional machine learning methods and deep learning algorithms. Speech emotion is categorised using the interactive emotional binary motion capture (IEMOCAP) and Ryerson affective voice and audio-visual database of songs (RAVDESS) databases. The expanded dataset is mixed with the voice signals once they have been transformed into spectrograms. This model incorporates CNN + LSTM, attention, and a novel type of spectrogram frequency distribution based on the spectrogram’s properties. Mel-frequency cepstral coefficients guarantee improved speech feature performance in tasks requiring emotion categorisation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Spectrographs

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CPIE: A Spatiotemporal Visual Analytic Tool to Explore the Impact of Coal Pollution

CPIE: A Spatiotemporal Visual Analytic Tool to Explore the I...

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2024 IEEE Workshop on Energy Data Visualization, EnergyVis 2024

作者： Jin, Sichen Henneman, Lucas Roberts, Jessica Georgia Institute of Technology School of Interactive Computing United States George Mason University College of Engineering and Computing Sid and Reba Dewberry Department of Civil Environmental and Infrastructure Engineering United States

ISBN: (纸本)9798350379235

This paper introduces CPIE (Coal Pollution Impact Explorer), a spatiotemporal visual analytic tool developed for interactive visualization of coal pollution impacts. CPIE visualizes electricitygenerating units (EGUs) and their contributions to statewide Medicare deaths related to coal P M2.5 emissions. The tool is designed to make scientific findings on the impacts of coal pollution more accessible to the general public and to raise awareness of the associated health risks. We present three use cases for CPIE: 1) the overall spatial distribution of all 480 facilities in the United States, their statewide impact on excess deaths, and the overall decreasing trend in deaths associated with coal pollution from 1999 to 2020;2) the influence of pollution transport, where most deaths associated with the facilities located within the same state and neighboring states but some deaths occur far away;and 3) the effectiveness of intervention regulations, such as installing emissions control devices and shutting down coal facilities, in significantly reducing the number of deaths associated with coal pollution. © 2024 IEEE.

关键词： Coal transportation

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Innovative Video Classification Method based on Deep Learning Approach

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Recent Patents on engineering 2025年第2期19卷 95-107页

作者： Hemamalini, V. Jayasutha, D. Vinothini, V.R. Devi, R. Manjula Kumar, A. Anitha, E. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Arjun College of Technology Coimbatore Thamaraikulam India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Erode638401 India Department of Computer Science and Design Kongu Engineering College Perundurai Erode638060 India Department of Civil Engineering University Institute of Engineering & Technology Maharshi Dayanand University Rohtak124001 India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore641202 India

Background: The automated classification of videos through artificial neural networks is addressed in this work. To explore the concepts and measure the results, the data set UCF101 is used, consisting of video clips taken from YouTube to recognize actions. The study is carried out with the authors' resources to determine the feasibility of independent research in the area. Methods: This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken. Results: Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art. Conclusion: Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent-based outcomes. © 2025 Bentham Science Publishers.

关键词： Convolutional neural networks

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Edge-Enabled Metaverse:The Convergence of Metaverseand Mobile Edge computing

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Tsinghua Science and technology 2024年第3期29卷 795-805页

作者： Nyothiri Aung Sahraoui Dhelim Liming Chen Huansheng Ning Luigi Atzori Tahar Kechadi Information Engineering College Xinjiang Institute of EngineeringUrumqi 830023Chinaand also with School of Computer ScienceUniversity College DublinDublinD04 V1W8Ireland School of Computing Ulster UniversityBelfastBT151EDUK School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Department of Electrical and Electronic Engineering University of CagliariSardinia 09124Italy School of Computer Science University College DublinDublinD04 V1W8Ireland

Metaverse is a virtual environment where users are represented by their avatars to navigate a virtual world having strong links with its physical *** state-of-the-art Metaverse architectures rely on a cloud-based approach for avatar physics emulation and graphics rendering *** current centralized architecture of such systems is unfavorable as it suffers from several drawbacks caused by the long latency of cloud access,such as low-quality *** this end,we propose a Fog-Edge hybrid computing architecture for Metaverse applications that leverage an edge-enabled distributed computing *** applications leverage edge devices’computing power to perform the required computations for heavy tasks,such as collision detection in the virtual universe and high-computational 3D physics in virtual *** computational costs of a Metaverse entity,such as collision detection or physics emulation,are performed at the device of the associated physical *** validate the effectiveness of the proposed architecture,we simulate a distributed social Metaverse *** simulation results show that the proposed architecture can reduce the latency by 50%when compared with cloud-based Metaverse applications.

关键词： industrial Metaverse edge computing Blockchain virtual environment task offloading fog computing

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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Computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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Enhancing Earthquake Prediction through Hybrid Deep Learning Approach 2

Enhancing Earthquake Prediction through Hybrid Deep Learning...

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

作者： Kandan, M. Sarkar, Swagata Sharma, Meenakshi Rajeswari, V. Poonkuzhali, P. Srimathi, S. SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Kattankulathur India Sri Sairam Engineering College Artificial Intelligence and Data Science Chennai India Global Group of Institute Department of CSE Amritsar India Panimalar Engineering College Information Technology India R.M.D Engineering College Kavaraipettai India Saveetha School of Engineering SIMATS Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350317558

Earthquakes, one of the greatest natural catastrophes, kill people and harm the economy. Heavy earthquakes and tsunamis may kill thousands and cost millions. Globally, predicting major earthquakes is important. Because earthquake prediction is difficult, researchers employ machine learning to improve forecasts. Many countries have early earthquake warning systems to save lives. Temporal rather than geographical characteristics dominated previous research. A similarity between earthquake and non-seismic signals makes real-time earthquake detection with cellphones or IoT devices problematic. This system may change entity interactions using ML and an IoT network. EEWS metrics are tracked using IoT. However, ML can assess these actions and determine smart city risk reduction and catastrophe management strategies. A deep learning approach for continuous earthquake forecasting utilising past seismic events is proposed. This research predicts earthquakes by position and depth using machine learning and deep learning. ConvLSTM, a Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), extracts important waveform elements to make the classifier reliable in earthquake parameters. © 2024 IEEE.

关键词： Convolutional Neural Network Deep Learning Earthquake LSTM Machine Learning

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User Centric Block-Level Attribute Based Encryption in Cloud Using Blockchains

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Computer Systems Science & engineering 2022年第8期42卷 605-618页

作者： S.Godfrey Winster A.Siva Kumar R.Ramesh Department of Computer Science and Engineering School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyChengalpattu603203India Department of Information Technology Sri Sai Ram Engineering CollegeChennai600044India Department of Electronics and Communication Engineering Saveetha Engineering CollegeChennai602105India

Cloud computing is a collection of distributed storage Network which can provide various services and store the data in the efficient *** advantages of cloud computing is its remote access where data can accessed in real time using Remote Method Innovation(RMI).The problem of data security in cloud environment is a major concern since the data can be accessed by any time by any *** to the lack of providing the efficient security the cloud computing they fail to achieve higher performance in providing the efficient *** improve the performance in data security,the block chains are used for securing the data in the cloud ***,the traditional block chain technique are not suitable to provide efficient security to the cloud data stored in the *** this paper,an efficient user centric block level Attribute Based Encryption(UCBL-ABE)scheme is presented to provide the efficient security of cloud data in cloud *** proposed approach performs data transaction by employing the block *** proposed system provides efficient privacy with access control to the user access according to the behavior of cloud user using Data Level Access Trust(DLAT).Based on DLAT,the user access has been restricted in the cloud *** proposed protocol is implemented in real time using Java programming language and uses IBM *** implementation results justifies that the proposed system can able to provide efficient security to the data present in and cloud and also enhances the cloud performance.

关键词： Cloud blockchain data security ABE block-level

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D^(2)PAM:Epileptic seizures prediction using adversarial deep dual patch attention mechanism

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CAAI Transactions on Intelligence technology 2023年第3期8卷 755-769页

作者： Arfat Ahmad Khan Rakesh Kumar Madendran Usharani Thirunavukkarasu Muhammad Faheem Department of Computer Science College of ComputingKhon Kaen UniversityKhon KaenThailand Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennaiIndia Department of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(Deemed to be University)ChennaiIndia School of Technology and Innovations University of VaasaVaasaFinland

Epilepsy is considered as a serious brain disorder in which patients frequently experience *** seizures are defined as the unexpected electrical changes in brain neural activity,which leads to *** researches made an intense effort for predicting the epileptic seizures using brain signal ***,they faced difficulty in obtaining the patients'characteristics because the model's distribution turned to fake predictions,affecting the model's *** addition,the existing prediction models have severe issues,such as overfitting and false positive *** overcome these existing issues,we propose a deep learning approach known as Deep dual‐patch attention mechanism(D^(2)PAM)for classifying the pre‐ictal signals of people with Epilepsy based on the brain *** neural network is integrated with D^(2)PAM,and it lowers the effect of differences between patients to predict *** multi‐network design enhances the trained model's generalisability and stability ***,the proposed model for processing the brain signal is designed to transform the signals into data blocks,which is appropriate for pre‐ictal *** earlier warning of epilepsy with the proposed model obtains the auxiliary *** data of real patients for the experiments provides the improved accuracy by D2PAM approximation compared to the existing *** be more distinctive,the authors have analysed the performance of their work with five patients,and the accuracy comes out to be 95%,97%,99%,99%,and 99%***,the numerical results unveil that the proposed work outperforms the existing models.

关键词： artificial intelligence techniques classification learning(artificial intelligence)

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Optimizing high-utility item mining using hybrid dolphin echolocation and Boolean grey wolf optimization

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Journal of Ambient Intelligence and Humanized computing 2023年第3期14卷 2327-2339页

作者： Pazhaniraja, N. Sountharrajan, S. Suganya, E. Karthiga, M. School of Computing and Science VIT Bhopal University Madhya Pradesh Bhopal India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Information Technology Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai India Bannari Amman Institute of Technology Tamilnadu Sathyamangalam India

High-utility item-set mining has become a very crucial topic of research in recent times. It is used to find profitable products utilizing both profit factors and the quantity as opposed to the association rule mining or the frequent item-set mining. There are many algorithms that are proposed for mining, such high-utility item-sets, and almost all algorithms will need to grip an exponential search space for learning the high-utility item-sets. To handle the exponential search space, metaheuristic algorithms are used to find optimal solutions. Here, an optimization method depending on the grey wolf optimization and dolphin echolocation-optimization has been proposed for finding high-utility item-sets. The grey wolf optimization algorithm can be an interesting one owing to the strategy of group foraging. All conventional grey wolf optimization will deal with the problems of continuous optimization. In dolphin echolocation-optimization, as soon as the dolphin manages to reach the food, the search is reduced, and there is an increasing click made that directs food to the site where food is. The procedure also feigns dolphin echolocation by means of preventive exploration, and this is relational its target distance. In connection to the problems in optimization, there can be a particular features-set, which include progress of various hunting preys that sing echo sounding among dolphins, those having problems in answering. A hybrid dolphin echolocation-optimization (hybrid dolphin echolocation-binary grey wolf optimization) is proposed with a novel mechanism for finding global solutions. In this work, hybrid dolphin echolocation-binary grey wolf optimization algorithm is proposed. The dolphin echolocation-optimization had taken as it can be a promising solution and the Boolean format of grey wolf optimization is used. It works as a global search solution to distinguish the high-utility item-sets. The results prove that the dolphin echolocation-binary grey wolf optimizat

关键词： Data mining

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Advanced Glaucoma Detection Using CNN Models Integrated with Random Forest Classifier for Enhanced Diagnosis 2

Advanced Glaucoma Detection Using CNN Models Integrated with...

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2nd International Conference on Computer Vision and Internet of Things, ICCVIoT 2024

作者： Nallasivam, Manikanda Prabu Brindha, S. Vijayalakshmi, N. Thirrunavukkarasu, R.R. Jegan, S. Rajasekar, P. Sri Krishna College of Technology Coimbatore India Nandha Engineering College Erode India Alliance School of Advanced Computing Bangalore India Sri Eshwar College of Engineering Coimbatore India Kongu Engineering College Erode India

ISBN: (纸本)9781837242672

Glaucoma is a prevalent retinal disease worldwide, leading to irreversible blindness if not diagnosed early. This research aims to distinguish glaucomatous eyes from normal eyes using an innovative glaucoma detection framework that integrates a Random Forest classifier with VGG-19 and ResNet50 models. The proposed method aggregates classification outcomes from Random Forest, VGG-19, and ResNet50 models through a post-processing decision block to deliver the final classification. This approach addresses limitations of existing techniques, significantly enhancing diagnostic accuracy and robustness by leveraging the strengths of these models. The framework, developed through ensemble modeling, achieves an accuracy of 96.10%, a precision rate of 99.10%, a recall rate of 89.00%, and an F1-score of 94.20%. Our research underscores the importance of integrating conventional optic disc measurement methods with image shape-derived features in machine learning models, enabling early and precise glaucoma diagnosis. © The Institution of engineering & technology 2024.

关键词： Ophthalmology

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