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SSRN 2023年

作者： Erbey, Ali Barışçı, Necaattin Department of Computer Programming Distance Education Vocational School Usak University Usak64200 Turkey Department of Information Systems Informatics Institute Gazi University Ankara Turkey Department of Computer Engineering Faculty of Technology Gazi University Ankara06500 Turkey

Lip reading is a technique that aims to understand spoken words by analyzing people's lip movements. Deep learning algorithms are used as a powerful tool for detecting and recognizing lip movements more accurately. In this study, we present a lip-reading architecture for the Turkish language. The architecture is trained to recognize Turkish words using 3DCNN (3D Convolutional Neural Network) architecture and two-stream deep learning methods. The model was trained on video data representing lip movements and learned to classify words accurately. The Turkish lip reading system has been trained and tested on a large Turkish lip reading dataset. In the experiments, an accuracy of 87.12% was achieved with the proposed model, indicating successful results in the Turkish language. The proposed model demonstrates high accuracy rates in recognizing Turkish words, showcasing its effectiveness. This paper introduces a novel architecture, a lip-reading system that employs deep learning techniques, providing a robust tool for successful Turkish lipreading applications. © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Spectral Analysis of Neural Network Long-Term Predictions

Spectral Analysis of Neural Network Long-Term Predictions

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2021 International Conference on High technology for Sustainable Development, HiTech 2021

作者： Koparanov, Kiril Trifonov, Rumen Minkovska, Daniela Georgiev, Krasin Technical University of Sofia Department of Programming and Computer Technologies Sofia Bulgaria Technical University of Sofia Information Technology in Industry Sofia Bulgaria Technical University of Sofia Department of Aeronautics Sofia Bulgaria

ISBN: (纸本)9781665448734

Long-term predictions of time series is a task with multiple applications. Exploiting neural networks for such problem has not been studied enough for financial purposes. An empirical study of specific cases with real financial multivariate time series and data augmentation was performed. The results are compared for different neural network models and two datasets using standard and customized spectral evaluation metrics. © 2021 IEEE.

关键词： Spectrum analysis

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A Horizontal Federated Learning Approach to IoT Malware Traffic Detection: An Empirical Evaluation with N-BaIoT Dataset 26

A Horizontal Federated Learning Approach to IoT Malware Traf...

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26th International Conference on Advanced Communications technology, ICACT 2024

作者： Do, Phuc Hao Le, Tran Duc Vishnevsky, Vladimir Berezkin, Aleksandr Kirichek, Ruslan The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint-Petersburg Russia Danang Architecture University Da Nang Viet Nam University of Telecommunications Department of Communication Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State Russia University of Science and Technology The University of Danang Da Nang Viet Nam The University of Danang University of Science and Technology Information Technology Faculty Danang Viet Nam V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia Institute of Control Sciences of Russian Academy of Sciences Russia Bonch Bruevich Saint Petersburg State University of Telecommunications Department of Programming Engineering and Computer Science Russia

ISBN: (纸本)9791188428120

The increasing prevalence of botnet attacks in IoT networks has led to the development of deep learning techniques for their detection. However, conventional centralized deep learning models pose challenges in simultaneously ensuring user data privacy and detecting botnet attacks. To address this issue, this study evaluates the efficacy of Federated Learning (FL) in detecting IoT malware traffic while preserving user privacy. The study employs N-BaIoT, a dataset of real-world IoT network traffic infected by malware, and compares the effectiveness of FL models using Convolutional Neural Network, Long Short-Term Memory, and Gated Recurrent Unit models with a centralized approach. The results indicate that FL can achieve high performance in detecting abnormal traffic in IoT networks, with the CNN model yielding the best results among the three models evaluated. The study recommends the use of FL for IoT malware traffic detection due to its ability to preserve data privacy. © 2024 Global IT Research Institute - GIRI.

关键词： Malware

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Performance Evaluation of Metaheuristic Optimization Techniques in Insulation Problem 8

Performance Evaluation of Metaheuristic Optimization Techniq...

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8th International Conference on Information technology Trends, ITT 2022

作者： Gunal, Ozen Akpinar, Mustafa Akpinar, Kevser Ovaz Turgutlu Vocational School Manisa Celal Bayar University Department of Computer Programming Manisa Turkey Sakarya University Department of Software Engineering Sakarya Turkey School of Engineering Architecture and Interior Design Amity University Dubai Dubai United Arab Emirates Rochester Institute of Technology Department of Computing Security Dubai United Arab Emirates

ISBN: (数字)9781665487429

ISBN: (纸本)9781665487412

Selection of insulation material and determination of its thickness are the two most important parameters that prevent heat loss. Too much thickness complicates the price and use. The low coefficient of thermal conductivity of the insulation material reduces heat loss and increases the cost. One of the most important factors when determining the material thickness and conductivity coefficient is the price. In this study, two cases were examined under certain conditions, independent of the price. In the first case, the insulation material is determined, whereas the material thickness is determined in the second case. Six different current metaheuristic algorithms were used to calculate the two cases. It has been observed that harris hawks optimization (HHO), salp swarm optimization (SSO), and whale optimization (WO) algorithms determine the global optimum point without any error in determining the targets of the problem while the processing times were short. Overall, it has been seen that all six algorithms can create solutions to heat transfer problems with very low errors. © 2022 IEEE.

关键词： Optimization

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Data poisoning: issues, challenges, and needs

Data poisoning: issues, challenges, and needs

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7th IET Smart Cities Symposium (SCS 2023)

作者： Mohammad Aljanabi Alaa Hamza Omran Maad M Mijwil Mostafa Abotaleb El-Sayed M. El-kenawy Sahar Yousif Mohammed Abdelhameed Ibrahim Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science & Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence.

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The prediction of students' Academic performances with a classification Model Built using Data Mining Techniques 6

The prediction of students' Academic performances with a cla...

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6th Smart Cities Symposium, SCS 2022

作者： Alkattan, Hussein Abotaleb, Mostafa Subhi, Alhumaima Ali Adelaja, Oluwaseun A. Kadi, Ammar Ibrahim Al-Mahdawi, Hassan K. Department of System Programming South Ural State University Chelyabinsk454080 Russia Electronic and Computer Center University of Diyala BaqubahMJJ2+R9G Iraq Information Communication and Technology Department Lagos State University Lagos102101 Nigeria Department of Food and Biotechnology South Ural State University Chelyabinsk454080 Russia

ISBN: (纸本)9781839538544

In the purview of most educational sectors today, numerous reviews regarding data mining have been the primary focus, with goals of discovering vast knowledge patterns for students' data. This paper focuses on building model that predicted the performance of computer science students in some courses they were examined through classification technique. The model built for the purpose of classification via decision tree classifier which was adopted in WEKA and KNIME analytics platform. The workflow model basically connected nodes to generate the decision trees while J4.8 Decision Tree Classifier programmable outputs were generated for the courses to obtain the confusion matrix and other instances. Bar Charts were plotted to display the students' performance in the courses. The mean absolute errors, RMS error and relative absolute error all read 0% while the kappa value obtained from the analysis ranged between 0.991 - 1.00 which perfectly agrees for most statistical values. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Decision trees

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Interference Mitigation Using Uplink Power Control in 5G Relay-Based Heterogeneous Networks 2

Interference Mitigation Using Uplink Power Control in 5G Rel...

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2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022

作者： Sultan, Jamil Jabbar, Waheb A. Al-Thobhani, Nashwan S. Al-Hetar, Abdulaziz Saif, Mobarak Sana'a Community College Telecommunication Engineering Technology Deptartment Yemen University of Modern Sceinces Computer Network Eng Dept Sana'a Yemen Birmingham City University School of Engineering and Built Environment BirminghamB4 7XG United Kingdom University of Modern Sciences Faculty of Engineering Sana'a Yemen School of Engineering Sana'a Yemen Sana'a Community College Computer Application Programming Dept Sana'a Yemen

ISBN: (数字)9781665461818

ISBN: (纸本)9781665461818

Heterogeneous network (HetNet) is an attractive solution for future cellular networks with high data rate and coverage requirements. In HetNets, small cells such as micro cells, pico cells, femto cells and relay node (RN) are added to the network of macro cells in the same region. A large number of low power RNs produces new cell edges with significant intra-cell and inter-cell interferences. In the uplink (UL) scenarios of time-division based HetNets with RN, the user equipment (UE) desired signal may be interfered by the transmissions of the co-channel UEs during the first time slot and by the transmissions of the co-channel UEs or RNs during the second time slot. The interference caused by the RNs may significantly degrade the UE signal. UL transmission power control (PC) is essential for mitigating interference and, as a result, enhancing the cell edge and overall system performance. This research proposes a PC algorithm in order to mitigate the UL interference in 5g relay-based HetNets. This research also investigates the UL performance of HetNets when PC is applied at the RNs. Simulation results indicate that UL PC at the RNs greatly reduces average interference and improves average UL signal-to-interference-plus-noise ratio (SINR) and average UL end-to-end throughput compared to the situation in which UL PC is not implemented. © 2022 IEEE.

关键词： Heterogeneous networks

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The Modified Picture Fuzzy Weighted Geometric Operator for Decisionmaking Processes and its Applications to Ambient Air Pollution

SSRN

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SSRN 2023年

作者： Jaikumar, R.V. Raman, Sundareswaran Pal, Madhumangal Department of Mathematics St. Joseph’s Institute of Technology OMR Chennai India Department of Mathematics Sri Sivasubramaniya Nadar College of Engineering Chennai India Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University Midnapore India

The aggregation operator is a popular tool for dealing with multi-criteria decision-making (MCDM) problems. An MCDM technique based on the modified picture fuzzy weighted geometric operator (MPFWGO) is proposed in this article. First, the drawbacks of various existing picture fuzzyweighted geometric aggregation operators (PFWGOs) that lead to imprecise outcomes in some exceptional instances are investigated. Second, an MPFWGO is proposed to address the shortcomings of existing PFWGOs. Furthermore, the suggested MPFWGO's properties such as idempotency, boundedness, monotonicity, and commutativity are explored. Numerical examples demonstrate that our proposed operator and corresponding MCDM approach are efficient and reasonable enough to prevent ambiguous results in exceptional instances. Finally, we applied the proposed method to rank the World Health Organization (WHO) regions for air pollution control. © 2023, The Authors. All rights reserved.

关键词： Decision making

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A novel reduction method for type-2 uncertain normal critical values and its applications on 4D profit transportation problem involving damageable and substitute items

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International Journal of Applied and Computational Mathematics 2021年第4期7卷 1-30页

作者： Sahoo, Palash Jana, Dipak Kumar Pramanik, Sutapa Panigrahi, Goutam Department of Mathematics National Institute of Technology Durgapur Durgapur 713209 West Bengal India School of Applied Sciences & Humanities Haldia Institute of Technology Haldia Purba Midnapur 721657 West Bengal India Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University Midnapore 721102 West Bengal India

In this paper, we developed and investigated some four-dimensional profit maximization transportation problems considering damageablity and substitutability, where the parameters are of a type-2 normal uncertain variable in nature. Here, unit transportation cost, distances of the paths, fixed charges, rate of breakability, availability, demands, capacities of conveyances, the unit selling price, unit purchasing cost, unit procurement cost are regarded as type-2 normal uncertain variables. Two models are formulated based on two separate criteria of the substitution items. We use a critical value-based reduction method to reduce type-2 normal uncertain variables into type-1 normal uncertain variables and then apply some properties of uncertainty theory to convert the profit transportation parameters into deterministic form. The two deterministic models are solved using the optimization software named LINGO -18.0. A real-life numerical example and optimal results are presented here to show the application of the proposed model and the proposed reduction method. © 2021, The Author(s), under exclusive licence to Springer Nature India Private Limited.

关键词： Critical value Four dimensional profit transportation problem Reduction method Type-2 normal uncertain variables

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Evaluating Keyphrase Extraction Methods for Clustering Influenza-Related Scientific Papers

Evaluating Keyphrase Extraction Methods for Clustering Influ...

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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Majid Forghani Artyom Firstkov Mohammad. M. AlyanNezhadi Kazem Forghani Mathematical Programming Lab. Krasovskii Institute of Mathematics & Mechanics Yekaterinburg Russia Department of Science University of Science and Technology of Mazandaran Behshahr Iran Computer Science Department Iran University of Science and Technology Tehran Iran

ISBN: (纸本)9781665476249

The number of annual scientific publications is growing year by year, which has led to the accumulation and formation of large databases. This increases the complexity of the search for relevant articles. Modern search engines leverage keyphrases to improve the performance of search results. Keyphrases (or keywords) are a set of single or multi-word expressions which provide a very compact summary of contents and describe the overall topic of a document. Implementation of keyphrase extraction can be varied for specific-domain databases. This motivated us to evaluate these methods on a database of influenza scientific literature. In this work, we considered 9 well known methods for extracting keyphrases. Our preliminary results show txhat graph-based methods which employ topics outperform others using our database. We also determined that influenza-related papers can be grouped into three general topics: public health & medical care; molecular biology & immunology; and phylogenetic & epidemiological studies.

关键词： Databases Immunology Biological system modeling Influenza Search engines Phylogeny Molecular biology

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