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A Hybrid Approach for Automated GUI Testing Using Quasi-Oppositional Genetic Sparrow Search Algorithm

A Hybrid Approach for Automated GUI Testing Using Quasi-Oppo...

2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Jayalakshmi, N. Sakthivel, K. Psna College of Engineering and Technology Department of Computer Applications Tamil Nadu Dindigul India K.S Rangasamy College of Technology Department of Computer Science and Business Systems Tamil Nadu Tiruchengode India

ISBN: (纸本)9798331543617

Testing of GUI is crucial for assessing software reliability, usability, and functionality;however, classical approaches are not sustainable in contemporary applications. It proposes the Quasi-Oppositional Genetic Sparrow Search Algorithm (OOGSSA) to auto-generate efficient GUI test cases. It combines genetic algorithms, sparrow search dynamics, and quasi-oppositional learning to optimize test suites, minimize redundant test cases, and improve the testing fault. The result obtained for the model OOGSSA is 95.8 % in test coverage, 85.3 % in redundancy, and 92.6 % in fault detection. Furthermore, test generation time was minimized to forty minutes and the overall testing time to seventy-five minutes, which is more efficient than conventional genetic or model-based testing. The outcomes presented above show that OOGSSA is highly efficient and effective in solving the issues of the current GUI testing and improving the overall quality and reliability of software. More enhancements to the algorithm will be done in future work as well as leveraging the algorithm to cover a wider GUI space including mobile and web. © 2024 IEEE.

关键词： Software reliability

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Inverted-circuit zero-noise extrapolation for quantum-gate error mitigation

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Physical Review A 2024年第4期110卷 042625页

作者： Kathrin F. Koenig Finn Reinecke Walter Hahn Thomas Wellens Tullastr. 72 79108 Freiburg Germany Department of Sustainable Systems Engineering Department of Computer Science

A common approach to deal with gate errors in modern quantum-computing hardware is zero-noise extrapolation. By artificially amplifying errors and extrapolating the expectation values obtained with different error strengths towards the zero-error (zero-noise) limit, the technique aims at rectifying errors in noisy quantum computing systems. For accurate extrapolation, it is essential to know the exact factors of the noise amplification. In this article, we propose a simple method for estimating the strength of errors occurring in a quantum circuit and demonstrate improved extrapolation results. The method determines the error strength for a circuit by appending to it the inverted circuit and measuring the probability of the initial state. The estimation of error strengths is easy to implement for arbitrary circuits and does not require previous characterization of noise properties. We compare this method with the conventional zero-noise extrapolation method and show that our method leads to a more accurate calculation of expectation values on current quantum hardware, showcasing its suitability for near-term quantum computing applications.

关键词： Quantum algorithms Quantum circuits Quantum computation Quantum information theory

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A Novel Machine Learning-Enhanced Swarm Intelligence Algorithm for Cost-Effective Cloud Load Balancing

A Novel Machine Learning-Enhanced Swarm Intelligence Algorit...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Satheesh, Nimmati Sakthivel, K. Psna College of Engineering and Technology Department of Computer Applications Tamil Nadu Dindigul India K.S Rangasamy College of Technology Department of Computer Science and Business Systems Tamil Nadu Tiruchengode India

ISBN: (纸本)9798331543617

Cloud computing refers to the instant accessibility of shared computer resources over the internet on-demand, thereby revolutionizing the way organizations deploy and manage their IT infrastructure. Efficient load balancing in a cloud environment scales up, utilizing resources with optimized cost-effectiveness. This paper introduces a new Machine Learning-guided Improved Swarm Intelligence Algorithm for reliable load balancing in cloud computing. Methodology It spans on three main elements. The first is the data center controller for the management of tasks. VM allocation uses an Improved Beetle Swarm Optimization Algorithm and one-dimensional convolutional probabilistic neural network is used for host load prediction. To propose an approach that looks to optimize the key metrics such as make span, average cost of VM, resource utilization, and value response time through iterative optimization process. The results of the experiments reflect the success of the proposed algorithm, because an improved performance of load balancing is obtained in comparison with those achieved using PSO, Grey Wolf Optimization, and Genetic Algorithm. The convergence diagram displays the iterative optimization process achieved by the proposed algorithm in obtaining a balance among various performance metrics. Overall, the methodology proposed in this work will provide promising facilitation in the increased efficiency, scalability, and cost-effectiveness of environments based on cloud computing with a reliable load balancing. © 2024 IEEE.

关键词： Genetic algorithms

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Fast Fourier Transform based Method for Accident Detection 10

Fast Fourier Transform based Method for Accident Detection

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10th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2022

作者： Khamis, Mohamed A. El-Mahdy, Ahmed Shata, Kholoud Gomaa, Walid Ejada Systems Ltd. Computer Science and Engineering Department Alexandria Egypt Faculty of Engineering Alexandria University Computer Science and Engineering Department Alexandria Egypt

ISBN: (纸本)9781665464635

Accidents fatality is generally dependent on the time an emergency service is dispatched to the accident scene. Decreasing this time requires fast and accurate accident detection and notification systems. Therefore, existing well-established systems rely on rugged devices, with specialised hardware and accurate sensors to allow for in-vehicle detection and notification. Smartphones have been considered as an alternative mainly due to their much lower cost. In this paper, we show that reliable accident detection can be achieved using main stream smartphone sensors (e.g., accelerometer and gyroscope). The method relies on detecting the accident pulse through using Fourier transform and a random forest classifier. The method also utilises a moving window to incorporate time;and is simple enough to allow for during-accident detection, not requiring the mobile to survive the accident. We have validated the model using the Ollie car-like robot micro accident-testbed and the gold standard in accident simulation, LS-DYNA, achieving a true positive rate of about 96% and true negative rate of 99%. © 2022 IEEE.

关键词： Smartphones

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Sailfish Optimization with Deep Learning Based Oral Cancer Classification Model

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computer systems science & engineering 2023年第4期45卷 753-767页

作者： Mesfer Al Duhayyim Areej A.Malibari Sami Dhahbi Mohamed K.Nour Isra Al-Turaiki Marwa Obayya Abdullah Mohamed Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh4545Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

Recently,computer aided diagnosis(CAD)model becomes an effective tool for decision making in healthcare *** advances in computer vision and artificial intelligence(AI)techniques have resulted in the effective design of CAD models,which enables to detection of the existence of diseases using various imaging *** cancer(OC)has commonly occurred in head and neck *** identification of OC enables to improve survival rate and reduce mortality ***,the design of CAD model for OC detection and classification becomes ***,this study introduces a novel computer Aided Diagnosis for OC using Sailfish Optimization with Fusion based Classification(CADOC-SFOFC)*** proposed CADOC-SFOFC model determines the existence of OC on the medical *** accomplish this,a fusion based feature extraction process is carried out by the use of VGGNet-16 and Residual Network(ResNet)***,feature vectors are fused and passed into the extreme learning machine(ELM)model for classification ***,SFO algorithm is utilized for effective parameter selection of the ELM model,consequently resulting in enhanced *** experimental analysis of the CADOC-SFOFC model was tested on Kaggle dataset and the results reported the betterment of the CADOC-SFOFC model over the compared methods with maximum accuracy of 98.11%.Therefore,the CADOC-SFOFC model has maximum potential as an inexpensive and non-invasive tool which supports screening process and enhances the detection efficiency.

关键词： Oral cancer computer aided diagnosis deep learning fusion model seagull optimization classification

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Position: Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized 41

Position: Scarce Resource Allocations That Rely On Machine L...

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41st International Conference on Machine Learning, ICML 2024

作者： Jain, Shomik Creel, Kathleen Wilson, Ashia Institute for Data Systems and Society MIT United States Department of Philosophy & Religion Khoury College of Computer Sciences Northeastern University United States Department of Electrical Engineering and Computer Science MIT United States

Contrary to traditional deterministic notions of algorithmic fairness, this paper argues that fairly allocating scarce resources using machine learning often requires randomness. We address why, when, and how to randomize by proposing stochastic procedures that more adequately account for all of the claims that individuals have to allocations of social goods or opportunities. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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Advancements in Fake News Detection: A Comprehensive Machine Learning Approach Across Varied Datasets

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SN computer science 2024年第5期5卷 1-14页

作者： Aslam, Adeel Abid, Fazeel Rasheed, Jawad Shabbir, Anza Murtaza, Manahil Alsubai, Shtwai Elkiran, Harun National College of Business Administration and Economics Lahore Pakistan Department of Information Systems University of Management and Technology Lahore Pakistan Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul Turkey Department of Computer Science College of Computer Engineering and Sciences in Al-Kharj Prince Sattam Bin Abdulaziz University Al-Kharj 11942 Saudi Arabia

Fake news has become a major social problem in the current period, controlled by modern technology and the unrestricted flow of information across digital platforms. The deliberate spread of inaccurate or misleading information jeopardizes the public's ability to make educated decisions and seriously threatens the credibility of news sources. This study thoroughly examines the intricate terrain of identifying false news, utilizing state-of-the-art tools and creative approaches to tackle this crucial problem at the nexus of information sharing and technology. The study uses advanced machine learning (ML) models comprising multinomial Naive Bayes (MNB), linear support vector classifiers (SVC), random forests (RF), logistic regression (LR), gradient boosting (GB), decision trees (DT), and to discern and identify instances of fake news. The research shows remarkable performance using publicly available datasets, achieving 94% accuracy on the first dataset and 84% on the second. These results underscore the model's efficacy in reliably detecting fake news, thereby contributing substantially to the ongoing discourse on countering misinformation in the digital age. The research not only delves into the technical intricacies of employing diverse ML models but also emphasizes the broader societal implications of mitigating the impact of fake news on public discourse. The findings highlight the pressing need for proactive measures in developing robust systems capable of effectively identifying and addressing the propagation of false information. As technology evolves, the insights derived from this research serve as a foundation for advancing strategies to uphold the integrity of information sources and safeguard the public's ability to make well-informed decisions in an increasingly digitalized world. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision making Digital platforms Social media Societal implications

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Deep Learning Algorithm for Brain Tumor Detection and Classification using MRI Images 1

Deep Learning Algorithm for Brain Tumor Detection and Classi...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Harshavardhan, A. Maheswari, N. Uma Prakash, M. Sammeta, Naresh VNR Vignana Jyothi Institute of Engineering and Technology Department of CSE Hyderabad India Panimalar Engineering College Department of Computer Science and Business Systems India Vellore Institute of Technology School of Computing Science and Engineering Vellore India R.M.K College of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350323795

Brain tumor classification is beneficial for identifying and diagnosing the tumor's specific location. According to the medical imaging system, early diagnosis and categorization of a tumor extend a person's life. Clinical specialists rely heavily on magnetic resonance imaging (MRI) among numerous imaging modalities since it provides contrast information on brain malignancies. The primary purpose of this project is to use a competent automated approach that improves tumour identification accuracy. Several segmentation strategies have been developed throughout the years to achieve and improve the categorization precision of brain tumours. Brain picture segmentation has long been recognised as a difficult and time-consuming aspect of medical image processing. This method for detecting brain tumors Brain pictures are classified using the Full Resolution Convolutional Network (FRCN) classification architecture after pre-processing and segmentation. This study presents a Full Resolution Convolutional Network (FRCN) with Support Vector Machine (SVM) approach for detecting tumors on MRI scans. The procedure is broken down into four steps. In the first phase, the anisotropic filter is utilized to pre-process raw MRI images, followed by segmentation using the Support vector machine (SVM) and skull classification. The singular value decomposition and primary component analysis operations are performed in the third step. Tumors are then detected and classified using the Full Resolution Convolutional Network (FRCN) approach. Simultaneously, the Support Vector Machine (SVM) technique is employed to improve the classification precision of the study model. The experimental results showed an amazing accuracy rate of nearly 100% in detecting both normal and diseased tissues from brain MR images, confirming the efficacy of the suggested technique. © 2023 IEEE.

关键词： Support vector machines

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Digital Learning Ecosystem: Online Concept for engineering Education 65

Digital Learning Ecosystem: Online Concept for Engineering E...

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65th IEEE Annual International Scientific Conference on Power and Electrical engineering of Riga Technical University, RTUCON 2024

作者： Zabasta, Anatolijs Kazymyr, Volodymyr Kunicina, Nadezhda Velihorskyi, Oleksandr Riga Technical University Faculty of Computer Science Information Technology and Energy Riga Latvia Chernihiv Polytechnic National University Information and Computer Systems Department Chernihiv Ukraine Chernihiv Polytechnic National University Radio Engineering and Embedded Systems Department Chernihiv Ukraine

ISBN: (纸本)9798350365771

This article examines the development of a Digital Learning Ecosystem (DLE) for engineering education, supported by online learning, within the framework of Erasmus+ international educational projects. We hypothesize that a DLE can address the challenges posed by rapid technological changes associated with Industry 4.0 and digital transformation, thus transforming engineering education to meet modern industry needs. Building on the earlier Erasmus+ CybPhys project, which established a Sharing Modelling and Simulation Environment (SMSE) for digitalizing online technical courses, the DIGITRANS project aims to enhance this architecture to include remote laboratories through the Sharing Remote Experiment Environment (SREE) platform. The project involves acquiring hardware and open-source software for SREE and establishing two physical remote laboratories. A study conducted in spring 2023, using the Technology Acceptance Model framework, indicated positive student perceptions of SMSE, with students believing it would enhance learning outcomes and expressing intentions to use the platform frequently. The efficacy of the DLE in addressing technological advancement challenges will be further evaluated through post-ante assessments and feedback from prospective employers and stakeholders, focusing on the implementation of industry recommendations and the correlation between pre- and post-implementation survey responses from industry and academic experts. © 2024 IEEE.

关键词： Open source software

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Conditioned fully convolutional denoising autoencoder for multi-target NILM

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Neural Computing and Applications 2025年第17期37卷 10491-10505页

作者： García, Diego Pérez, Daniel Papapetrou, Panagiotis Díaz, Ignacio Cuadrado, Abel A. Enguita, José M. Domínguez, Manuel Department of Electrical Engineering University of Oviedo Principado de Asturias Gijón33209 Spain SUPPRESS Research Group University of León León24007 Spain Department of Computer and Systems Science Stockholm University Kista SE-164 07 Stockholm Sweden

Energy management requires reliable tools to support decisions aimed at optimising consumption. Advances in data-driven models provide techniques like Non-Intrusive Load Monitoring (NILM), which estimates the energy demand of appliances from total consumption. Common single-target NILM approaches perform energy disaggregation by using separate learned models for each device. However, the use of single-target systems in real scenarios is computationally expensive and can obscure the interpretation of the resulting feedback. This study assesses a conditioned deep neural network built upon a Fully Convolutional Denoising AutoEncoder (FCNdAE) as multi-target NILM model. The network performs multiple disaggregations using a conditioning input that allows the specification of the target appliance. Experiments compare this approach with several single-target and multi-target models using public residential data from households and non-residential data from a hospital facility. Results show that the multi-target FCNdAE model enhances the disaggregation accuracy compared to previous models, particularly in non-residential data, and improves computational efficiency by reducing the number of trainable weights below 2 million and inference time below 0.25 s for several sequence lengths. Furthermore, the conditioning input helps the user to interpret the model and gain insight into its internal behaviour when predicting the energy demand of different appliances. © The Author(s) 2024.

关键词： Convolutional neural networks

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