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Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

Software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： Software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

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computers, Materials & Continua 2023年第1期74卷 1905-1921页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb 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 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 System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and *** more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the ***,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole *** proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering *** is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor *** hybridmethod is primarily designed to combat active black-hole *** the LEACH clustering phase,however,can also detect passive black-hole *** effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested *** diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution ***,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

关键词： Black-hole attack mobile ad-hoc network optimization dipper throated optimization

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Comparative Analysis of Movie Recommendation systems Using Filtering Techniques on IMDB and Rotten Tomatoes 15

Comparative Analysis of Movie Recommendation Systems Using F...

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15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Majhi, Santosh Kumar Sahani, Preetipadma Pradhan, Rosy Acharya, Biswaranjan Grivokostopoulou, Foteini Kanavos, Andreas Gerogiannis, Vassilis C. Department of Computer Science and Information Technology Bilaspur India Veer Surendra Sai University of Technology Department of Computer Science and Engineering Odisha Burla India Veer Surendra Sai University of Technology Department of Electrical Engineering Odisha Burla India Marwadi University Department of Computer Engineering Ai Rajkot India Computer Technology Institute and Press 'Diophantus' Patras Greece Ionian University Department of Informatics Corfu Greece University of Thessaly Department of Digital Systems Larissa Greece

ISBN: (纸本)9798350368833

Recommendation systems are pivotal in industries like entertainment, where competitiveness is intense. This study enhances movie recommendations by utilizing rich datasets from IMDB and Rotten Tomatoes, incorporating movie ratings, user profiles, audience scores, and critics' reviews. These elements were subjected to sentiment analysis to refine the data quality. We explored three filtering techniques: content-based, which analyzes movie features;collaborative, which processes user behavior patterns;and hybrid, combining both to leverage their strengths. The effectiveness of these methods was assessed using Mean Average Precision at k (MAP-k) and Root Mean Square Error (RMSE), with our results showing notable improvements in recommendation accuracy. For instance, the hybrid method achieved a MAP ©k of 0.271 and an RMSE of 0.634, indicating enhanced precision and reliability of the recommendations. These findings suggest significant potential for improving user satisfaction and retention in digital platforms, demonstrating the benefit of integrating diverse data sources and analytical techniques in recommendation systems.

关键词： Collaborative filtering

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Enhancing Soft Skills in Autistic Children: The Next Generation of Mind Champ's Technological Approach

Enhancing Soft Skills in Autistic Children: The Next Generat...

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Advancements in Computing (ICAC), International Conference on

作者： Kalna Peiris Ruchira Nelaka Kalpani Manathunga Department of Computer Systems Engineering Department of Information Technology Department of Computer Science and Software Engineering

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Autism Spectrum Disorder (ASD) significantly impacts a child's ability to navigate social interactions, regulate emotions, and develop adaptive skills crucial for daily functioning. While various interventions exist to address cognitive and academic skills, the development of soft skills such as communication, emotional regulation, social interaction, and creativity remains an under explored area. This paper builds upon the foundational work of the Mind Champ platform, which originally targeted both learning and soft skills, by focusing exclusively on enhancing the soft skills development of autistic children. This enhanced version of the Mind Champ platform leverages advanced behavioral and emotional analysis techniques to offer a comprehensive technological solution. Through interactive activities centered on painting and music, the platform creates a structured, engaging, and supportive environment tailored to the unique needs of children with ASD. By prioritizing emotional engagement and creative expression, the platform empowers children to improve their social abilities, emotional regulation, and adaptability. The results from our continued research indicate that these technology-driven interventions contribute significantly to the holistic development of soft skills in autistic children, providing them with valuable tools to navigate social environments more effectively.

关键词： Autism Navigation Variable speed drives Motors Regulation Real-time systems Creativity Next generation networking Painting Resilience

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Decoding Android Malware with a Fraction of Features: An Attention-Enhanced MLP-SVM Approach 18th

Decoding Android Malware with a Fraction of Features: An ...

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18th International Conference on Network and System Security, NSS 2024

作者： Hakim, Safayat Bin Adil, Muhammad Acharya, Kamal Song, Houbing Herbert Department of Information Systems University of Maryland Baltimore County BaltimoreMD21250 United States Department of Computer Science and Engineering University at Buffalo BuffaloNY14260 United States

ISBN: (纸本)9789819635306

The escalating sophistication of Android malware poses significant challenges to traditional detection methods, necessitating innovative approaches that can efficiently identify and classify threats with high precision. This paper introduces a novel framework that synergistically integrates an attention-enhanced Multi-Layer Perceptron (MLP) with a Support Vector Machine (SVM) to make Android malware detection and classification more effective. By carefully analyzing a mere 47 features out of over 9,760 available in the comprehensive CCCS-CIC-AndMal-2020 dataset, our MLP-SVM model achieves an impressive accuracy over 99% in identifying malicious applications. The MLP, enhanced with an attention mechanism, focuses on the most discriminative features and further reduces the 47 features to only 14 components using Linear Discriminant Analysis (LDA). Despite this significant reduction in dimensionality, the SVM component, equipped with an RBF kernel, excels in mapping these components to a high-dimensional space, facilitating precise classification of malware into their respective families. Rigorous evaluations, encompassing accuracy, precision, recall, and F1-score metrics, confirm the superiority of our approach compared to existing state-of-the-art techniques. The proposed framework not only significantly reduces the computational complexity by leveraging a compact feature set but also exhibits resilience against the evolving Android malware landscape. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Android malware

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Joakinator: An Interface for Transforming Body Movement and Perception through Machine Learning and Sonification of Muscle-Tone and Force 23rd

Joakinator: An Interface for Transforming Body Movement and ...

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23rd International Conference on New Interfaces for Musical Expression, NIME 2023

作者： Díaz-Durán, Joaquín R. Vidal, Laia Turmo Tajadura-Jiménez, Ana DEI Interactive Systems Group Department of Computer Science and Engineering Universidad Carlos III de Madrid Madrid Spain

Joakinator is a wearable interactive interface that allows users to activate different media materials, such as sound, music, and video, through body gestures. The device, designed in the context of music and performing arts, integrates surface electromyogram, force sensors, and machine learning algorithms with tailored-made software for sonifying muscle-tone and force. This allows the body to reflect expressively the content of the media and the architecture of the device. Recently, we have started to investigate the potential of Joakinator to alter body perception in the context of BODYinTRANSIT, a European Research Council Project focused on the transformations of body perception through the use of interactive sound/haptics technology. At NIME-2023, we will showcase Joakinator and invite visitors to experience the device firsthand. Visitors will have the opportunity to try on the device, observe others using it, and reflect on its capabilities to transform body movement and perception through the sonification of muscle-tone and force. Overall, Joakinator is a technology that pushes the boundaries of bodycomputer interaction and opens new possibilities for human-computer interaction and expression. © 2023, International Conference on New Interfaces for Musical Expression. All rights reserved.

关键词： Machine Perception

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Improved Dijkstra Algorithm for Mobile Robot Path Planning and Obstacle Avoidance

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computers, Materials & Continua 2022年第9期72卷 5939-5954页

作者： Shaher Alshammrei Sahbi Boubaker Lioua Kolsi Department of Mechanical Engineering College of EngineeringUniversity of Ha’ilHa’il2440Saudi Arabia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Research Laboratory of Metrology and Energy Systems National Engineering SchoolEnergy Engineering DepartmentUniversity of MonastirMonastir5000Tunisia

Optimal path planning avoiding obstacles is among the most attractive applications of mobile robots(MRs)in both research and *** this paper,an optimal collision-free algorithm is designed and implemented practically based on an improved Dijkstra *** achieve this research objectives,first,the MR obstacle-free environment is modeled as a diagraph including nodes,edges and ***,Dijkstra algorithm is used offline to generate the shortest path driving the MR from a starting point to a target *** its movement,the robot should follow the previously obtained path and stop at each node to test if there is an obstacle between the current node and the immediately following *** this aim,the MR was equipped with an ultrasonic sensor used as obstacle *** an obstacle is found,the MR updates its diagraph by excluding the corresponding ***,Dijkstra algorithm runs on the modified *** procedure is repeated until reaching the target *** verify the efficiency of the proposed approach,a simulation was carried out on a hand-made MR and an environment including 9 nodes,19 edges and 2 *** obtained optimal path avoiding obstacles has been transferred into motion control and implemented practically using line tracking *** study has shown that the improved Dijkstra algorithm can efficiently solve optimal path planning in environments including obstacles and that STEAM-based MRs are efficient cost-effective tools to practically implement the designed algorithm.

关键词： Mobile robot(MR) STEAM path planning obstacle avoidance improved dijkstra algorithm

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Optimal Resource Allocation for NOMA Wireless Networks

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computers, Materials & Continua 2023年第2期74卷 3249-3261页

作者： Fahad R.Albogamy M.A.Aiyashi Fazirul Hisyam Hashim Imran Khan Bong Jun Choi Turabah University College Computer Sciences ProgramTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Jazan UniversityJazanP.O.Box 218Saudi Arabia Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra Malaysia(UPM)43400SerdangMalaysia Department of Electrical Engineering University of Engineering and Technology PeshawarP.O.Box 814Pakistan School of Computer Science and Engineering Soongsil UniversitySeoulKorea

The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user ***-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information,and multi-user detection algorithms,such as successive interference cancellation(SIC),are performed at the receiving end to demodulate the necessary user *** its basic signal waveform,like LTE baseline,could be based on orthogonal frequency division multiple access(OFDMA)or discrete Fourier transform(DFT)-spread OFDM,NOMA superimposes numerous users in the power *** contrast to the orthogonal transmission method,the nonorthogonal method can achieve higher spectrum ***,it will increase the complexity of its *** power allocation techniques will have a direct impact on the system’s *** a result,in order to boost the system capacity,an efficient power allocation mechanism must be *** research developed an efficient technique based on conjugate gradient to solve the problem of downlink power *** major goal is to maximize the users’maximum weighted sum *** suggested algorithm’s most notable feature is that it converges to the global optimal *** compared to existing methods,simulation results reveal that the suggested technique has a better power allocation capability.

关键词： Non-orthogonalmultiple access wireless networks power domain 5G networks

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GENERALIZED BAYES SURE FOR MULTIVARIATE NORMAL DISTRIBUTION UNDER BALANCED-QUADRATIC LOSS

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Journal of Mathematical sciences (United States) 2024年第2期280卷 262-274页

作者： Karamikabir, Hamid Afshari, Mahmoud Karamikabir, Nasrin Kiaei, Seyed Faridoddin Department of Statistics Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr Iran Department of Mathematics Hamedan Branch Islamic Azad University Hamedan Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

One of the most important subjects in statistics is the theory of estimation. In this paper, we consider the generalized Bayes shrinkage estimator of the mean vector for multivariate normal distribution with the unknown covariance matrix under balanced quadratic loss. Also, we find the minimax and admissible estimator of the mean vector based on the generalized Bayes estimator. We obtain Stein’s unbiased risk estimator (SURE) threshold with a generalized Bayes shrinkage estimator and we find the wavelet shrinkage generalized Bayes SURE. Finally, we present a simulation study to test the validity of the generalized Bayes SURE and the concrete compressive strength dataset is given to test the efficiency of this estimator in denoising. © 2024, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Balanced-quadratic loss Generalized bayes estimator Shrinkage estimaton Shrinkage wavelet SURE threshold

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The Power of Second Chance: Personalized Submodular Maximization with Two Candidates 17th

The Power of Second Chance: Personalized Submodular Maximiz...

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17th International Conference on Combinatorial Optimization and Applications, COCOA 2024

作者： Yuan, Jing Tang, Shaojie Department of Computer Science and Engineering University of North Texas Denton United States Center for AI Business Innovation Department of Management Science and Systems University at Buffalo Buffalo United States

ISBN: (纸本)9789819644476

Most of existing studies on submodular maximization focus on selecting a subset of items that maximizes a single submodular function. However, in many real-world scenarios, we might have multiple user-specific functions, each of which models the utility of a particular type of user. In these settings, our goal would be to choose a set of items that performs well across all the user-specific functions. One way to tackle this problem is to select a single subset that maximizes the sum of all of the user-specific functions. Although this aggregate approach is efficient in the sense that it avoids computation of sets for individual functions, it really misses the power of personalization - for it does not allow to choose different sets for different functions. In this paper, we introduce the problem of personalized submodular maximization with two candidate solutions. For any two candidate solutions, the utility of each user-specific function is defined as the better of these two candidates. Our objective is, therefore, to select the best set of two candidates that maximize the sum of utilities of all the user-specific functions. We have designed effective algorithms for this problem. We also discuss how our approach generalizes to multiple candidate solutions, increasing flexibility and personalization in our solution. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

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