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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Mahendar, A. Senthamarai Kannan, S. Anantha Sivaprakasam, S. Santhosh Kumar, P.N. Pradeep Ghantasala, G.S. Kallam, Suresh CMR Technical Campus Telangana Hyderabad India Department of AI and Data Science SRM TRP Engineering College Irungalur Tamilnadu Thiruchirapalli India Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Tamilnadu Chennai India CMR Technical Campus Hyderabad Telangana Hyderabad India Department of Computer Science and Engineering Alliance University Bengaluru India School of Computer Science and Engineering Jain Deemed to Be University Bengaluru India

ISBN: (纸本)9783031810855

The use of information technology by companies and individuals in cloud computing revolutionizes. Cloud computing offers virtualized tools over the Internet for dynamic services. This ensures that end-users with resources-virtualization maintained and controlled can create, deploy and manage applications "on the cloud." The preparation of tasks in cloud computing systems is one of the most challenging issues in fulfilling user-defined service quality while minimizing the cost of executing tasks. Several cost optimization approaches have been proposed to improve the economic aspect of task scheduling in cloud computing. A full analysis of approaches to promoting cost optimization in connection with task planning in cloud computing has not yet been published in the literature. This paper is intended to examine the problems of cost optimization in task scheduling through a detailed analysis of current task scheduling methods in cloud computing and to identify aspects and parameters of cost optimization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cost benefit analysis

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Classification of PC Baseband Signals from Wireless Egress 26

Classification of PC Baseband Signals from Wireless Egress

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26th International ITG Workshop on Smart Antennas, WSA 2023 and 13th Conference on Systems, Communications, and Coding, SCC 2023

作者： Leghari, Mohammad Ahmed Pralle, Sina Mei Peik, Sören F. Lütje, Sebastian Henkel, Werner Computer Science and Engineering Jacobs University Bremen Bremen Germany Electrical Engineering and Computer Science Bremen University of Applied Sciences Bremen Germany Development and TEMPEST Aerospace Data Security GmbH Bremen Germany

ISBN: (纸本)9783800760510

Being related to so-called TEMPEST activities, we show some possibilities to classify standard signal emissions from a PC environment, such as Ethernet, USB, or HDMI. Hereto, we make use of time-frequency analysis followed by deep learning or alternatively, by looking into individual signal properties, such as symbol rates, special fixed patterns, common-mode presence, or amplitude histograms. We will see that the distinction of such standard signals can be very efficient, even in the case of 100Base-T and 1000Base-T Ethernet that are designed to use the same frequency range and hence have a very similarly looking time-frequency representation. Under noise-free conditions, 100 % correct classification is achieved. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Ethernet

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Advancing Anomaly Detection in Time Series data: A Knowledge Distillation Approach with LSTM Model

Advancing Anomaly Detection in Time Series Data: A Knowledge...

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2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023

作者： Kilinc, Sena Camlidere, Bora Yildiz, Ekrem Guler, Ali Kerem Alsan, Huseyin Fuat Arsan, Taner Kadir Has University Department of Computer Engineering Istanbul Turkey Turknet Department of Data Science Istanbul Turkey

ISBN: (纸本)9798350306590

This paper focuses on enhancing anomaly detection in time series data using deep learning techniques. Particularly, it investigates the integration of knowledge distillation with LSTM-based models for improved precision, efficiency, and interpretability. The study outlines objectives such as dataset preprocessing, developing a novel LSTM-knowledge distillation framework, incorporating Grafana, InfluxDB, Flask API with Docker, performance assessment, and practical implications. Results highlight the efficacy of knowledge distillation in enhancing student model performance. The proposed approach enhances anomaly detection, offering a viable solution for real-world applications. © 2023 IEEE.

关键词： Distillation

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Recognition of data Breach Method from Story Using Advanced Deep Learning 3

Recognition of Data Breach Method from Story Using Advanced ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Jeba, Samiha Maisha Aurpa, Tanjim Taharat Alom, Md Samiul Joy, Md. Wadud Mahmud Ahamed, Asm Shakil Dash, Satya Ranjan Ahmed, Md Shoaib International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Bangladesh Bangabandhu Sheikh Mujibur Rahman Digital University Department of Data Science Bangladesh Kiit University School of Computer Applications Odisha India Boise State University Department of Computer Science BoiseID United States

ISBN: (纸本)9798350381931

We often open our eyes in the morning with the appalling news of data breaches of different popular companies. This is a significant threat not only to giant companies but also to the general people's privacy. Various methods are maneuvered for data breaches, and identifying these methods in a short can help understand the loss and take proper steps against these incidents. Researchers nowadays focus on the data breaches issue to reduce its threats to the tech industry. In this research, we have used advanced deep learning methods(e.g., BERT, XLNet, and Electra) to predict data breach methods using the data breach story. The proposed classifier has provided an accuracy of 92.86%. © 2024 IEEE.

关键词： Deep learning

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Attention based ConVnet-Recurrent Neural Network for Facial Recognition and Emotion Detection

Attention based ConVnet-Recurrent Neural Network for Facial ...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Kumar, S. Vinod Sunil, G. Hussein, Ramy Riad Manju Vidhya, S. Sundaram, S. Meenakshi Department of Computer Science and Engineering Secunderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Excel Engineering College Department of Artificial Intelligence and Data Science Komarapalayam India Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India

ISBN: (纸本)9798350360660

Facial Emotion Recognition (FER) plays a crucial role in analysing and understanding individual's emotions in the form of non-verbal communication. Existing Deep Learning (DL) based FER methods offered diverse contribution in analysing human emotions but several challenges like extracting accurate spatial and temporal features for accurate classification of emotions is a major concern. To address this, a DL based Facial Recognition and Emotion Detection (FRED) system is proposed by using extended Cohn-Kanade (CK+) dataset as the input. The data is pre-processed first where the Histogram Equalization (HE) is applied and then Convolutional Neural Network (CNN) is used to extract the spatial features while reducing the dimensionality. The extracted features are given to Recurrent Neural Network (RNN) to learn long term dependencies from a temporal sequence of data. The final classification is performed where the Attention Mechanism (AM) is integrated in the RNN to determine the higher weights for better outcome. The results of proposed AMConVnet-RNN showed a superior classification accuracy of 99.54%, precision of 98.63%, and f1-score of 97.39% when compared to existing FER methods like Residual Network-50 (ResNet-50) and Channel Based Attention Mechanism (CBAM) based ResNet50. © 2024 IEEE.

关键词： Convolutional neural networks

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An efficient speech emotion recognition based on a dual-stream CNN-transformer fusion network

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International Journal of Speech Technology 2023年第2期26卷 541-557页

作者： Tellai, Mohammed Gao, Lijian Mao, Qirong Department of Computer Science and Communication Engineering Jiangsu University Jiangsu Zhenjiang212013 China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Jiangsu Zhenjiang212013 China

The use of machine learning and artificial intelligence enables us to create intelligent systems. Speech emotion recognition system analyzes the speaker’s speech to determine his/her emotional state. Speech emotion recognition is a challenging pattern recognition task. This paper proposes a new robust and lightweight speech emotion recognition system based on a dual-stream CNN-Transformer fusion network that effectively captures spatial and temporal information disentangled in long-distance raw features by exploring both MFCCs and Mel-spectrograms in a parallel mechanism. Experiments are performed on the widely used emotional benchmark datasets. Our approach has shown to be quite efficient. It outperforms the best-known state-of-the-art models with an accuracy of 97.64%, 99.42%, and 97.53% on RAVDESS, TESS, and EMO-DB datasets respectively. The results demonstrate the significant advantages of the proposed model and the ability of this architecture to learn to recognize emotional data features accurately. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Emotion Recognition

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An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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Massively parallel algorithms for fully dynamic all-pairs shortest paths

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Frontiers of computer science 2024年第4期18卷 201-203页

作者： Chilei WANG Qiang-Sheng HUA Hai JIN Chaodong ZHENG National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

1 Introduction In recent years,the Massively Parallel Computation(MPC)model has gained significant ***,most of distributed and parallel graph algorithms in the MPC model are designed for static graphs[1].In fact,the graphs in the real world are constantly *** size of the real-time changes in these graphs is smaller and more *** graph algorithms[2,3]can deal with graph changes more efficiently[4]than the corresponding static graph ***,most studies on dynamic graph algorithms are limited to the single machine ***,a few parallel dynamic graph algorithms(such as the graph connectivity)in the MPC model[5]have been proposed and shown superiority over their parallel static counterparts.

关键词： dynamic shortest gained

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Feature Selection for Classification on High- Dimensional data Using Swarm Optimization Algorithms 7

Feature Selection for Classification on High- Dimensional Da...

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7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Pradeep Kumar, D. Bj, Sowmya Kanavalli, Anitha Deeptashree, D. Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India Ramaiah Institute of Technology Department of Artificial Intelligence and Data Science Bangalore India

ISBN: (纸本)9798350343144

Feature selection plays a crucial role in classification by identifying relevant features. Bio-inspired algorithms, such as genetic algorithms, particle swarm optimization, and ant colony optimization, have gained popularity due to their ability to mimic natural processes and explore high-dimensional feature spaces efficiently. The analysis includes evaluating classification accuracy, and computational efficiency as performance metrics. The experimental setup involves dataset selection, choosing a classification algorithm, implementing bio-inspired feature selection, evaluation, parameter tuning, and comparative analysis. The findings provide valuable insights into the effectiveness and efficiency of bio-inspired algorithms for feature selection in classification. The project focuses on reducing high- dimensional datasets to low-dimensional ones using feature selection through classification with swarm optimization algorithms. It aims to validate the effectiveness of the Jellyfish Search algorithm (JSA), Horse Herd Optimization algorithm (HOA), and Binary Bat algorithm (BBA) on real-world datasets. The objectives include evaluating the algorithms' performance, conducting a comprehensive performance analysis, execution of classifiers like RF, DT, KNN, Voting Classifier and SVM, obtaining and tuning the results, and publishing the findings. The research outcomes contribute to the knowledge of feature selection for classification and provide guidance in selecting appropriate algorithms for specific classification tasks. © 2023 IEEE.

关键词： Classification (of information)

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Hybrid System with Modified Cuckoo Search to Design Airfoil

Hybrid System with Modified Cuckoo Search to Design Airfoil

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2023 International Conference on data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Thinakaran, K. Anand, M. Kumar, T.Rajesh Atham, Saira Banu Subramanian, K.M. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science India Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Chennai Kattankulathur India Saveetha Institute of Medical and Technical Science Saveetha School of Engineering India Presidency University School of Cse & Is Department of Computer Science and Engineering Bangaluru India Shadan College of Engineering and Technology Department of Computer Science Engineering Hyderabad India

ISBN: (纸本)9798350348910

The proposed models can design the airfoil by Cuckoo search with Levenberg-Marquardt. The Neural Network framework has impediments due to over-fitting. This paper proposed a modified cuckoo search. here the aerodynamic coefficient as an input to produce output the airfoil coordinates. The generated airfoil is compared to know its performance metrics. The Cuckoo search with the Feedforward Neural Network model yields the lowest prediction error. © 2023 IEEE.

关键词： Airfoils

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