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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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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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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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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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An Energy Efficient Hybrid Communication Protocol for Large Area Wireless Sensor Networks 4

An Energy Efficient Hybrid Communication Protocol for Large ...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Karthigeyan, Shreyas Bhol, Sampark Jain, Akshat Yadav, Karthik Sinha, Akash Padma Priya, R. School of Computer Science and Information Systems Vellore Institute of Technology Vellore Campus Tamil Nadu Vellore632014 India School of Computer Science and Engineering Vellore Institute of Technology Vellore Campus Tamil Nadu Vellore632014 India Department of Computer Science and Engineering Maulana Azad National Institute of Technology Madhya Pradesh Bhopal462003 India

Energy conservation is an indispensable aspect of the protocols designed for Wireless Sensor Networks (WSNs). The communication protocols for WSN fall mainly under two categories: centralized and distributed. Centralized protocols are not feasible for real-world WSNs as the network can span across large areas where most of the nodes cannot directly communicate with the base station (BS). The routing issues in such networks can be addressed using multi-hop protocols. However, the process of BS guided clustering in such methods cannot be performed. Distributed clustering methods that minimize the intervention of BS in clustering can provide feasible solutions in such cases, but the energy expenditure is considerably high. This paper, hence, proposes a hybrid clustering based routing protocol for large WSN that employs certain aspects of centralized as well as that of distributed protocols. Experimental evaluation of the proposed protocol in terms of network lifetime gives promising results that clearly indicate the energy efficiency and consistency of the proposed work in different scenarios. The proposed work can prove beneficial in real-life scenarios where the deployed nodes are far away from the BS. © 2025 The Authors. Published by Elsevier B.V.

关键词： Hybrid power

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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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Wavelet-Based Hybrid Ensemble Forecasting for Accurate Web Traffic Prediction 1

Wavelet-Based Hybrid Ensemble Forecasting for Accurate Web T...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Manoj Kumar, M.V. Puneetha, B.H. Prashanth, B.S. Sneha, H.R. Patil, N.S. Ranjana, B.J. Nitte Meenakshi Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Bapuji Institute of Engineering and Technology Department of Computer Science and Business Systems Karnataka Davanagere577004 India Bapuji Institute of Engineering and Technology Department of Information Science and Engineering Karnataka Davanagere577004 India Karnataka Davanagere577004 India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

This paper presents a novel approach for accurate web traffic prediction on the websites using a Wavelet-Based Hybrid Ensemble Forecasting method. The proposed method decomposes the web traffic time series into time-shifted discrete wavelets, enabling individual forecasting on each decomposed series. Four models - SARIMA, ARIMA, LSTM, and KNN - were evaluated, and their forecasts were combined in a hybrid ensemble model. Evaluation metrics such as accuracy percentage, MAE, RMSE, and MAPE were employed. The hybrid model exhibited superior performance, achieving an accuracy of 99.39%, along with the lowest MAE and RMSE values. Combining forecasts from multiple models significantly improved accuracy and reduced error rates in web traffic prediction. © 2024 IEEE.

关键词： Prediction models

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A nano-scale design of a multiply-accumulate unit for digital signal processing based on quantum computing

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Optical and Quantum Electronics 2024年第1期56卷 1-25页

作者： Ahmadpour, Seyed-Sajad Navimipour, Nima Jafari Yalcin, Senay Bakhshayeshi Avval, Danial Ain, Noor Ul Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou 64002 Taiwan Department of Computer Engineering Nisantasi University Istanbul Turkey Department of Information Systems Engineering Sakarya University Sakarya Turkey Department of Business Administration Kadir Has University Istanbul Turkey

Digital signal processing (DSP) is used in computer processing to conduct different signal-processing tasks. The DSPs are used in the series numbers representing a continuous variable in a domain such as time, area, or frequency. The multiply-accumulate (MAC) unit is crucial in various DSP applications, including convolution, discrete cosine transform (DCT), Fourier Transform, etc. Thus, all DSPs contain a critical MAC unit in signal processing. The MAC unit conducts multiplication and accumulation operations for continuous and complicated DSP application processes. On the other hand, in the MAC structure, the stability of the circuit and the occupied area pose some significant challenges. However, high-performance quantum technology can easily overcome all the previous shortcomings. Hence, this paper suggests an efficient MAC for DSP applications using a Vedic multiplier, half adder, and accumulator based on quantum technology. All the proposed structures have used a single-layer layout without rotated cells. The suggested architecture is designed and validated based on the QCADesigner 2.0.3 tool. The findings revealed that all the developed circuits have a simple architecture with fewer quantum cells, optimal area, and low latency. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Multiply-accumulate (MAC) Nano-design Quantum dot cellular automata (QCA) Signal processing

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IoT-Enhanced Gesture Recognition System for Healthcare Applications

IoT-Enhanced Gesture Recognition System for Healthcare Appli...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Krithika, L.B. Nallakaruppan, M.K. Baza, Mohamed Badr, Mahmoud Deora, Yuvraj Singh School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India College of Charleston Deparment of Computer Science CharlestonSC United States Polytechnic Institute Department of Networks and Computer Security UticaNY12201 United States

ISBN: (纸本)9798350351408

In response to the prevalent language barriers in medical settings, this paper proposes an innovative solution leveraging gesture recognition technology. Through the integration of deep learning models and Internet of Things (IoT) tools, the system aims to facilitate seamless communication between patients and healthcare providers. Initially, a deep learning model is developed using a personalized dataset containing various medical gestures, allowing for the recognition of nuanced patient expressions such as 'I have Fever' or 'Shoulder pain.' Additionally, the system incorporates IoT sensors to monitor vital signs like body temperature and pulse in real-time, ensuring comprehensive patient assessment. With the capability to continuously evolve and adapt to new gestures, this solution not only enhances accessibility but also improves the quality of healthcare delivery, particularly in underserved communities. By bridging the gap between language barriers and medical communication, this innovative approach holds promise for fostering inclusive and effective healthcare practices worldwide. In addressing the challenges posed by language barriers in medical interactions, this paper presents a comprehensive solution that combines gesture recognition technology with deep learning models and IoT devices. By capturing and interpreting patient gestures, such as indicating specific symptoms or discomfort, the system facilitates clear communication between patients and doctors. Furthermore, the integration of IoT sensors enables continuous monitoring of vital signs, allowing for timely interventions and personalized care. With its ability to adapt and expand to accommodate new gestures and medical scenarios, this solution represents a significant advancement in healthcare accessibility and quality. By leveraging cutting-edge technology, this approach has the potential to revolutionize healthcare delivery, particularly in areas with limited access to linguistic resources or medi

关键词： Gesture recognition

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Wastewater treatment plant site selection using advanced decision tree machine learning and remote sensing techniques

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Environmental science and Pollution Research 2025年第1期32卷 234-247页

作者： Thangarasu, Thenmozhi Aldehim, Ghadah Alruwais, Nuha Kandasamy, Anguraj Department of Electronics and Communication Engineering Government College of Engineering Tamil Nadu Salem India Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science and Engineering College of Applied Studies and Community Services King Saud University Saudi Arabia P.O.Box 22459 Riyadh11495 Saudi Arabia Department of Electronics & Communication Engineering Sona College of Technology Tamil Nadu Salem India

Wastewater treatment plants in Coimbatore South are under pressure from rapid urbanization, inadequate infrastructure, and industrial pollution, leading to environmental and public health concerns. This study aimed to identify suitable locations for wastewater treatment plants using a combination of machine learning, remote sensing, and GIS-based multicriteria decision analysis (MCDA). Several datasets were analysed, with the analytical hierarchy process (AHP) assigning weights to factors such as slope (18.51) and elevation (31.39), which were found to be crucial in site selection. The study classified the suitability of sites into five categories, with the western region being the most favourable due to its low elevation (147 to 200 m), gentle slopes (0–3%), and substantial land availability (approximately 309.00 sq. km). Overall, the site suitability analysis revealed that 14.48% (110.2 sq. km) of the area falls within "Very High Favourable Zones," while 11.21% (85.3 sq. km) is categorized as "High Favourable Zones." Moderate and low favourable zones account for 9.63% (73.3 sq. km) and 24.04% (182.9 sq. km), respectively. The remaining 40.57% (308.7 sq. km) is considered "Very Low Favourable Zones." These results can guide urban planning decisions, highlighting the importance of factors such as land availability, population growth, wastewater volume, and flood vulnerability. Integrating GIS technology with decision-making processes enhances the strategic placement of urban utilities, ensuring long-term sustainability for Coimbatore South's wastewater management. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Site selection

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