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27th International Conference on Soft Computing and Measurements, SCM 2024

作者： Korytov, Pavel V. Kholod, Ivan I. Gribetskiy, Yan Yu. Andreeva, Ekaterina A. Saint Petersburg Electrotechnical University 'LETI' Faculty of Computer Science and Technology Department of Computer Science and Engineering Saint-Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' Faculty of Computer Science and Technology Department of Information Systems Saint-Petersburg Russia

ISBN: (纸本)9798350363708

The task of identifying key skills from job vacancies descriptions is considered in the article. The source data consists of vacancy texts from the 'Jobs in Russia' portal, in which skill-related entities were manually annotated. Keyword extraction methods (YAKE, PositionRank and WikiNEuRal, KBIR-InSpec), and a named entity recognition model trained on the source data are compared. © 2024 IEEE.

关键词： Extraction

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Optimizing task scheduling in cloud environments: a hybrid golden search whale optimization algorithm approach

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Neural Computing and Applications 2025年 1-23页

作者： Acharya, Biswaranjan Panda, Sucheta Das, Satyabrata Majhi, Santosh Kumar Gerogiannis, Vassilis C. Kanavos, Andreas Department of Computer Science and Engineering Veer Surendra Sai University of Technology Odisha Burla India Bilaspur India Department of Digital Systems University of Thessaly Larissa Greece Department of Informatics Ionian University Corfu Greece

Managing fluctuating workloads and optimizing resource utilization in cloud environments pose significant challenges, particularly in fields requiring real-time data processing, such as healthcare. This paper introduces a novel hybrid metaheuristic technique, the Golden Search Whale Optimization Algorithm (GSWOA), specifically designed for scheduling independent dynamic biomedical data. GSWOA merges the strengths of Golden Search Optimization (GSO) and Whale Optimization Algorithm (WOA), optimizing numerical function optimization and achieving a balance between exploration and exploitation. The algorithm’s effectiveness was assessed using MATLAB by applying standard benchmark functions and further evaluated on a real-world biomedical dataset within the CloudSim environment. The performance evaluations demonstrate that GSWOA significantly outperforms existing metaheuristic and traditional scheduling techniques, achieving a 42.71% increase in resource utilization and a 14.17% reduction in makespan compared to conventional methods. These results highlight GSWOA’s potential to enhance scheduling efficiency substantially in cloud computing infrastructures, suggesting it is a powerful tool for complex task allocations. Future research will explore the scalability of GSWOA and its applicability across other data-intensive sectors. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Scheduling algorithms

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Remote Sensing–Based UAV Imaging in Heat Pattern Analysis Impact on Climate Change Detection Using Fuzzy Stacked Lasso Elastic-Net Model

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Remote Sensing in Earth systems sciences 2024年第4期7卷 699-708页

作者： Sailaja, M. Kumar, M. Prema Jyothi, B. Swarna Vanguri, G. L. Narasamba Manjula, S. Priya, D. Divya Department of Computer Science and Engineering Ravindra College of Engineering for Women Andhra Pradesh Kurnool India Dept. of ECE Shri Vishnu Engineering College for Women (A) Andhra Pradesh Bhimavaram 534202 India Department of Computer Science & amp Engineering Madanapalle Institute of Technology & amp Science Andhra Pradesh Madanapalle India Information Technology Aditya College of Engineering & amp Technology(A) Andhra Pradesh Surampalem 533437 India Department of Computer Science and Business Systems Nehru Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Urban heat islands raise surface temperatures, which has an effect on city dwellers’ health and welfare. Urbanisation-related changes to the land surface, which are especially notable right after sunset, have an impact on radiative forcing. Recently, there has been a surge in the usage of unmanned aerial vehicle (UAV) technology, particularly for precision agriculture and plant phenotyping. This is due to the declining cost and increased accessibility of both UAVs and thermal imaging sensors. This interruption affects subsequent operations like target item categorisation and water quality parameter inversion. The goal of this study was to evaluate the potential for combining machine learning (ML) with unmanned aerial vehicle (UAV)–based imaging technology. In this research, the novel technique in UAV image–based temperature pattern detection of various regions of earth and their sea level rise analysis using image classification by machine learning model. Here, the input is collected as UAV images which have been processed for noise removal and normalisation. These image features have been extracted using convolutional active contour–based kernel component analysis and classified using region-based fuzzy stacked lasso elastic-net model. The classified output shows temperature analysis with sea level rise based on image analysis. The experimental analysis has been carried out for various UAV image dataset in terms of detection accuracy, RMSE, F-measure, mean average precision (MAP), and recall. Proposed technique F-measure of 89%, detection accuracy of 96%, MAP of 94%, recall of 92%, and RMSE of 60%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional active contour Heat pattern detection Kernel component analysis Lasso elastic-net model Remote sensing

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Metaheuristic Optimization of Time Series Models for Predicting Networks Traffic

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computers, Materials & Continua 2023年第4期75卷 427-442页

作者： Reem Alkanhel El-Sayed M.El-kenawy D.L.Elsheweikh Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia

Traffic prediction of wireless networks attracted many researchersand practitioners during the past decades. However, wireless traffic frequentlyexhibits strong nonlinearities and complicated patterns, which makes it challengingto be predicted accurately. Many of the existing approaches forpredicting wireless network traffic are unable to produce accurate predictionsbecause they lack the ability to describe the dynamic spatial-temporalcorrelations of wireless network traffic data. In this paper, we proposed anovel meta-heuristic optimization approach based on fitness grey wolf anddipper throated optimization algorithms for boosting the prediction accuracyof traffic volume. The proposed algorithm is employed to optimize the hyperparametersof long short-term memory (LSTM) network as an efficient timeseries modeling approach which is widely used in sequence prediction *** prove the superiority of the proposed algorithm, four other optimizationalgorithms were employed to optimize LSTM, and the results were *** evaluation results confirmed the effectiveness of the proposed approachin predicting the traffic of wireless networks accurately. On the other hand,a statistical analysis is performed to emphasize the stability of the proposedapproach.

关键词： Network traffic soft computing LSTM metaheuristic optimization

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IoT-Supported Indoor Navigation System Using Machine Learning Technique 12th

IoT-Supported Indoor Navigation System Using Machine Learnin...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Christy Jeba Malar, A. Deva Priya, M. Bhuvana, S. Vidhya, K. Sudharsan, S. Suhita, S. Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819788354

Indoor Navigation System (INS) supports seamless movement of objects within confined spaces in smart environments. In this paper, a novel INS that relies on ESP32-based Received Signal Strength Indication (RSSI) measurements for precise estimation of distance using K-Nearest Neighbour (K-NN) algorithm is proposed. The system architecture includes three ESP32 nodes functioning as clients, and ESP8266 combined with ESP32 acting as a server. To overcome the limitations of Global Positioning System (GPS) within an indoor environment, Machine Learning (ML)-based prediction models are employed to estimate distance between nodes and predict user locations along with the distance between source and destination. This approach not only addresses the challenges posed by GPS in indoor settings, but also shows the potential of ESP32 devices and predictive algorithms for enhancing navigation accuracy and reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

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Improved Beluga Whale Optimizer-Based Cluster Head Selection in FANET 3

Improved Beluga Whale Optimizer-Based Cluster Head Selection...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Venkatasubramanian, S. Saranathan College of Engineering Department of Computer Science and Business Systems Trichy India

ISBN: (纸本)9798350317022

Important issues have arisen for the reliability of communications in FANETs as of the high-speed mobility of unmanned aerial vehicles (UAVs). One of the more recent Meta-heuristic (MH) methods, makes use of the Beluga Whale optimizer (BWO) to optimize network parameters in FANET. However, BWO may get stuck in local optima and converge too quickly because of its lack of diversity. This paper provides two steps to improve the original BWO procedure. Opposition-based learning (OBL), also known as OBWO's first stage, helps to speed up the search procedure and improve the learning procedure so that a higher quality generation of candidate keys for the basic BWO can be selected. In addition, a powerful cluster maintenance algorithm for continually updating UAV clusters is proposed. The simulation findings show that the projected OBWO scheme outperforms state-of-the-art clustering techniques by a wide margin in terms of packet delivery ratio, network lifetime to cluster head altering ratio, and energy usage. © 2023 IEEE.

关键词： Beluga Whale Optimization Cluster Head Selection Energy Ratio Flying ad hoc network Opposition-Based Learning Packet delivery ratio

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Framework for Effective Utilization of Distributed Scrum in Software Projects

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computer systems science & engineering 2023年第1期44卷 407-422页

作者： Basit Shahzad Wardah Naeem Awan Fazal-e-Amin Ahsanullah Abro Muhammad Shoaib Sultan Alyahya Department of Software Engineering National University of Modern LanguagesIslamabad44000Pakistan Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science Sukkur IBA UniversitySindh65200Pakistan Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

There is an emerging interest in using agile methodologies in Global Software Development(GSD)to get the mutual benefits of both *** is currently admired by many development teams as an agile most known meth-odology and considered adequate for collocated *** the same time,stake-holders in GSD are dispersed by geographical,temporal,and socio-cultural *** to the controversial nature of Scrum and GSD,many significant challenges arise that might restrict the use of Scrum in *** conducted a Sys-tematic Literature Review(SLR)by following Kitchenham guidelines to identify the challenges that limit the use of Scrum in GSD and to explore the mitigation strategies adopted by practitioners to resolve the *** validate our reviewfindings,we conducted an industrial survey of 305 *** results of our study are consolidated into a research *** framework represents current best practices and recommendations to mitigate the identified distributed scrum challenges and is validated byfive experts of distributed *** of the expert review were found supportive,reflecting that the framework will help the stakeholders deliver sustainable products by effectively mitigating the identified challenges.

关键词： Global software development distributed Scrum sustainable development challenges mitigation strategies

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A Comprehensive Survey on Summarization Techniques

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SN computer science 2023年第5期4卷 560页

作者： Uppalapati, Padma Jyothi Dabbiru, Madhavi Rao, K. Venkata Research Scholar Computer Science and Systems Engineering Department AU-TDR Hub Andhra University Andhra Pradesh Visakhapatnam 530003 India Computer Science and Engineering Department Dr.Lankapalli Bullayya College of Engineering Andhra Pradesh Visakhapatnam 530013 India Computer Science and Systems Engineering Department Andhra University Andhra Pradesh Visakhapatnam 530003 India Computer Science and Engineering Department Vishnu Institute of Technology Andhra Pradesh Bhimavaram 534202 India

Summarization is the method of extracting valuable information or a summary out of a significant amount of data using a variety of computation techniques. The summarization task has been studied for a long time. Researchers are still paying attention to this task, as text can be extracted from a variety of formats, including text, audio, and multimedia, and because it presents a number of special challenges that require deep learning techniques, including temporal ordering, compression ratio, redundancy, cohesion, and coreference. In addition, the study discusses numerous cutting-edge datasets that can be used for text summarizing as well as various assessment metrics that are used to this issue. The paper’s primary goal is to summarize the various deep learning techniques currently being utilized and addresses the challenges of summarization in the education sector. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Abstractive text summarization (ATS) Corpora Deep learning Machine learning Natural language processing (NLP) Summary evaluation metrics

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Topology, Size, and Shape Optimization in Civil engineering Structures: A Review

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computer Modeling in engineering & sciences 2025年第2期142卷 933-971页

作者： Ahmed Manguri Hogr Hassan Najmadeen Saeed Robert Jankowski Faculty of Civil and Environmental Engineering Gdansk University of TechnologyGdansk80-223Poland Department of Computer Science ModelingElectronics and Systems EngineeringUniversity of CalabriaCosenza87036Italy Civil Engineering Department University of RaparinRania46012Iraq Civil Engineering Department Tishk International UniversityErbil44001Iraq

The optimization of civil engineering structures is critical for enhancing structural performance and material efficiency in engineering *** optimization approaches seek to determine the optimal design,by considering material performance,cost,and structural *** design approaches aim to reduce the built environment’s energy use and carbon *** comprehensive review examines optimization techniques,including size,shape,topology,and multi-objective approaches,by integrating these *** trends and advancements that contribute to developing more efficient,cost-effective,and reliable structural designs were *** review also discusses emerging technologies,such as machine learning applications with different optimization *** of truss,frame,tensegrity,reinforced concrete,origami,pantographic,and adaptive structures are covered and *** techniques are explained,including metaheuristics,genetic algorithm,particle swarm,ant-colony,harmony search algorithm,and their applications with mentioned structure *** and non-linear structures,including geometric and material nonlinearity,are *** role of optimization in active structures,structural design,seismic design,form-finding,and structural control is taken into account,and the most recent techniques and advancements are mentioned.

关键词： Structural optimization topology optimization size optimization shape optimization multi-objective optimization

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Extracting Facial Features to Detect Deepfake Videos Using Machine Learning

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International Journal of Advanced computer science and Applications 2025年第4期16卷 834-842页

作者： Aslam, Ayesha Mir, Jamaluddin Zaman, Gohar Rahman, Atta Salam, Asiya Abdus Ali, Farhan Alhiyafi, Jamal Bakry, Aghiad Gul, Mustafa Jamal Gollapalli, Mohammed Mahmud, Maqsood Department of Computer Science Abbottabad University of Science and Technology Havelian Pakistan Department of Computer Science College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam31441 Saudi Arabia Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam31441 Saudi Arabia College of Electronics and Information Engineering Shenzhen University Shenzhen China Department of Computer Science Kettering University FlintMI United States Department of Business Administration University of York York YO10 5DD Heslington United Kingdom Department of Information Technology & Engineering Sydney Met SydneyNSW2000 Australia School of Computing Ulster University Northern Ireland Belfast United Kingdom

Generative adversarial networks (GANs) have gained popularity for their ability to synthesize images from random inputs in deep learning models. One of the notable applications of this technology is the creation of realistic videos known as deepfakes, which have been misused on social media platforms. The difficulty lies in distinguishing these fake videos from real ones with the naked eye, leading to significant concerns. This study proposes a supervised machine learning approach to effectively differentiate between real and counterfeit videos by detecting visual artifacts. To achieve this, two facial features are extracted: eye blinking and nose position, utilizing landmark detection techniques. Both features were trained on supervised machine learning classifiers and evaluated using the publicly available UADFV and Celeb-DF deepfake datasets. The experiments successfully demonstrate that the proposed method achieves a promising and superior performance, with an area under the curve (AUC) of 97% for deepfake detection in contrast to state-of-the-art methods investigating the same datasets. © (2025), (science and Information Organization). All rights reserved.

关键词： Support vector machines

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