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Test Suite Minimization Through PSO 11

Test Suite Minimization Through PSO

引用

11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Ahuja, Neeru Bhatia, Pradeep Kumar Guru Jambheshwar University of Science and Technology Dept. of Computer Science and Engineering Haryana Hisar125001 India

ISBN: (纸本)9798350350357

Regression testing is a critical stage that ensures the software changes are not affecting existing functionality. Ideally, conducting regression tests after each software change is essential. However, the exhaustive repetition of full tests for every alteration proves time-consuming, expensive, and demands significant resources. Test suite minimization serves as a solution by eliminating redundant test cases. This research presents an advanced approach to eliminate redundant test cases effectively. The proposed method focuses on identifying a concise yet effective set of test cases that align with requirements and efficiently uncover faults. This streamlined approach not only reduces testing expenses but also contributes to a more efficient testing process. The study demonstrates its effectiveness in minimizing the test suite by employing the Particle Swarm Optimization (PSO) algorithm with varied inertia weight strategies. Results from simulations indicate the success of this approach in reducing the test suite size while maintaining or even improving code coverage. Comparative analysis against Genetic Algorithms (GA) and Artificial Bee Colony (ABC) methods underscores the superior performance of PSO, specifically with tailored inertia weight strategies. This research significantly contributes to optimizing software testing methodologies, ultimately enhancing fault detection efficiency in software development. © 2024 IEEE.

关键词： Software testing

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Enhanced Predictive Modeling Techniques for Early Detection of COPD Utilizing 1D Convolutional Neural Networks 15

Enhanced Predictive Modeling Techniques for Early Detection ...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Jha, Sneha Sahu, Prakash Kumar, Santosh CSVTU Dept. of Data Science Chhattisgarh India IIIT-Naya Computer Science & Engineering Raipur India

ISBN: (纸本)9798350370249

Artificial intelligence (AI) breakthroughs have created new opportunities in the field of medical diagnostics, especially for the early identification of respiratory conditions like Chronic Obstructive Pulmonary Disease (COPD), the most prevalent respiratory condition, is characterized by breathing difficulties, mucus (sputum) coughing, and wheezing. Early verification is critical for efficient administration. A one-dimensional convolutional neural network (1D CNN) model for COPD detection, optimized using Adam and RMSprop algorithms is proposed for this purpose. Utilizing the ICBHI 2017 dataset, the performance of each optimizer based on training and validation accuracy is evaluated. The Adam optimizer achieved a training accuracy of 9 4% and a validation accuracy of 9 0%. In comparison, the RMSprop optimizer yielded a training accuracy of 9 2% and a validation accuracy of 8 8%. These results demonstrated that the Adam optimizer surpassed RMSprop in terms of accuracy. It showcased the potential of the proposed 1D CNN model as a reliable diagnostic tool, contributing to the advancement of respiratory health analytics. © 2024 IEEE.

关键词： Convolutional neural networks

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Design of Digitized Visual Search Task for Assessment of Aging Effects 2

Design of Digitized Visual Search Task for Assessment of Agi...

引用

2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Navalyal, Geeta Navalyal, Apoorva Naik, Sayali Dept. of Computer Science & Engineering Uttarakhand Dehradun India University of Florida Dept. of Electrical & Computer Engineering GainesvilleFL United States Grant Thornton Karnataka Bengaluru India

ISBN: (纸本)9798350372847

Mild cognitive impairments (MCI) develop in adults due to the normal aging process and may sometimes progress to a more serious cognitive decline stage resulting in Dementia. If the cognitive decline is identified at the MCI stage, it can be delayed or prevented from progressing by different interventions. A typical indicator of cognitive impairments that might be caused due to an injury, aging, or other factors is found to be a decreased search efficiency in visual search tasks. Here we propose a model to assess this efficiency by designing a digitized, easy-to-use visual search task capable of incorporating multiple variables in the visual stimulus, affecting the search capabilities. The variables under consideration include the number of distracting elements, target size, target distance from the center of the stimulus area, and habituation effects. This designed stimulus is tested on participants from different age groups. © 2024 IEEE.

关键词： aging cognitive impairments Visual search task

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An Extensive Review of Intelligent Deep Learning Based Anomaly Detection and Classification of 3D MRI Brain Imaging Data 3

An Extensive Review of Intelligent Deep Learning Based Anoma...

引用

3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Hima Bindu, J. Uma Dev, M. Vignan's Foundation for Science Dept. of Computer Science and Engineering Andhra Pradesh Guntur522213 India

ISBN: (纸本)9798350396157

MRI - Magnetic Resonance Imaging is one of the frequently employed imaging modalities for brain anomaly detection. MRI produces a massive quantity of data, which includes a consecutive set of scans taken at several time instants. Since the existence of brain anomalies that exists examined on every MR sequence, manual brain anomaly detection necessitates anatomical knowledge, costly, laborious, and inaccurate due to human error. Automatic brain anomaly segmentation from 3D Magnetic Resonance Image (MRI) is essential to perform proper diagnosis, monitoring, and treatment planning of the disease. However, owing to the structural difficulties such as hazy boundaries with uneven shapes, precise 3D brain tumor demarcation is interesting. With the recent developments of Deep Learning (DL) models, this research study reviews the recent brain anomaly detection and classification models based on DL approach with 3D MRI data. The existing techniques related to brain tumor segmentation, classification, and validations are reviewed. Every reviewed technique is investigated based on the aim, underlying technique used, dataset, and evaluation parameters. Besides, a comparison table is provided by summarizing the reviewed techniques under several aspects. Finally, a quick analysis of the evaluated techniques' results is conducted to gauge their effectiveness. © 2024 IEEE.

关键词： Brain

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Efficiency Improvement Mechanism Analysis of Sidewall Passivation GaN based Micro-LEDs by Atomic Layer Deposition

Efficiency Improvement Mechanism Analysis of Sidewall Passiv...

引用

International Symposium, Seminar, and Exhibition, Display Week 2024

作者： Zhanghu, Mengyuan Liu, Yibo Liu, Zhaojun Dept. of Electric and Electronic Engineering Southern University of Science and Technology Shenzhen China Dept. of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Sidewall passivation using atomic layer deposition (ALD) can efficiently improve external quantum efficiency (EQE). Such treatment increases EQE, a combination of internal quantum efficiency (IQE) and light extraction efficiency (LEE), primarily by curtailing surface recombination according to the ABC model. Determined by the ratio of radiative recombination rate to total recombination rate, the IQE of an LED holds a significant correlation with the diode's ideality factor in the Shockley model. In this paper, we analysis the mechanism of ALD sidewall passivation enhancing device performance in detail from the aspects of electrical performance improvement and optical performance improvement. © 2024, John Wiley and Sons Inc. All rights reserved.

关键词： Gallium nitride

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Measuring Motor Planning Using an Affordable Sound-Based Virtual Reality Setup for Accessible Perception-Action Studies

Measuring Motor Planning Using an Affordable Sound-Based Vir...

引用

2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Barumerli, Roberto Privitera, Alessandro Giuseppe Carollo, Alessandro Fontana, Giovanni Patarini, Eros Zaninelli, Alberto Cesari, Paola Geronazzo, Michele Dept. of Neuroscience Biomedicine and Movement Italy Dept. Mathematics Computer Science and Physics Italy Dept. of Engineering and Management Italy

ISBN: (纸本)9798331514846

The recent availability of consumer head-mounted displays (HMDs) for virtual reality (VR) provides an immersive and affordable platform to study sound perception and action outside laboratory settings. While traditional perception-action studies rely on complex systems like motion capture, HMDs integrate dynamic sound delivery and real-time movement tracking. In this study, we adapted the experimental protocol from Geronazzo et al. (2023) to test the feasibility of measuring user behaviour with a consumer-grade HMD. Instead of muscular activity measured from surface electromyography, we analysed reaction times using kinematic data from the HMD's onboard sensors and controllers. Data from ten participants showed that spatial properties of looming sounds influence auditory perception-action loops, aligning with findings from the original study. Our results demonstrate that reaction time is a scalable and meaningful measure for studying space perception through sound, providing a possible alternative to muscular activity measurements and showcasing the potential of consumer-grade VR for accessible and robust experimental research despite current limitations. © 2025 IEEE.

关键词： Helmet mounted displays

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Real-Time Traffic Prediction And Optimization using Machine Learning 3

Real-Time Traffic Prediction And Optimization using Machine ...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vineetha, Kundrapu Tharun Reddy, M. Jaffer Ali, Md. Prathima, Narisetty Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The Real-Time Traffic Prediction and Optimization System may be described as enriched, spacious, algorithm-oriented, and designed to help contribute to the anti-symptomatic deprievement of increasingly congested urban traffic streams. The system with the help of integrating real time data of numerous sources like GPS tracker, traffic sensor as well as weather API, the system using advanced machine learning algorithm in order to predict traffic accurately and optimize routes based on it. This predictive capability then greatly improves the flow of vehicles, the time taken, and the occurrence of choke points. The structure of the system is fully based in the cloud, which makes it easy to scale and very compatible with other systems to amalgamate a vast array of data inputs with high dependability. Fully customizable and intuitive control panel gives live view of traffic, offers actionable stats, and instant control mechanisms to traffic operators to make correct decisions quickly. Congestion in urban areas has an influence on time taken to cover a certain distance, fuel consumption and emission to the atmosphere. On the basis of the above analysis, this work extends a Real-Time Traffic Prediction and Optimization System (RTPOPS) using machine learning approach to predict the traffic density and to further optimize traffic signals and routes. The used system demonstrated successful management and reduction of vehicle density by 30%, and increasing speed limits by 18% and proved that Intelligent Urban Mobility has the potential to be achieved. © 2025 IEEE.

关键词： Traffic congestion

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Enhanced Image Captioning Using CNN and BLIP Models

Enhanced Image Captioning Using CNN and BLIP Models

引用

2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Tejesh, Vambara Supriya, M. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331509828

This research focuses on generating image captions using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. As deep learning advances, the availability of large datasets and increased computing power make it more feasible to build models capable of creating captions for images. This paper uses CNN and RNN models in Python to achieve this. Image captioning combines image recognition and Natural Language Processing (NLP) to interpret image context and express it in English, drawing on core computer vision principles. This study reviews important concepts in image captioning, including the applications of Keras, NumPy, and Jupyter notebook for development. This paper also explores the use of the Flickr dataset and CNN for image classification. The BLEU score of the proposed models is found to be close to 60%. On further enhancement to the model, it could be achieved to get a better score. © 2025 IEEE.

关键词： Convolutional neural networks

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Heart disease Classification Utilising Machine Learning Techniques 3

Heart disease Classification Utilising Machine Learning Tech...

引用

3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Dhondiyal, Shiv Ashish Verma, Himanshu Kumar, Rohit Sawan, Vikash Uniyal, Ishita Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Haridwar University Roorkee Dept. of Computer Science and Engineering India GLA University Department of Computer Engineering & Applications Mathura India Era Hill University Department of Electrical Engineering Graphic India

ISBN: (纸本)9798350354171

Heart diseases comprise a extensive Various heart conditions and are commonly referred to as cardio vascular diseases. These encompass issues with the rhythm of the heart, blood vessel diseases, and congenital cardiac defects. It has been the world's biggest cause of death for the last few decades. Finding a reliable and accurate way to automate the process of early disease diagnosis and efficient disease management is urgently needed. Data science facilitates the medical sciences in analysing vast quantities of data. Researchers utilize various Data Mining and Machine Learning methods to evaluate extensive datasets in order to accurately forecast heart illness. This study examines the supervised learning models of Random Forest, K-NN, and SVM to provide a comparative analysis of their efficiency. It is discovered that, when compared to other algorithms, SVM offers the highest accuracy at 0.994. © 2024 IEEE.

关键词： Random forests

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Topic Modeling Using Latent Dirichlet Allocation Method Based On Child Anecdotal Record Data 4

Topic Modeling Using Latent Dirichlet Allocation Method Base...

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4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Fernandis, Rasio Swanjaya, Daniel Ramadhani, Risky Aswi Kasih, Patmi Sahertian, Julian Universitas Nusantara Pgri Kediri Faculty of Engineering and Computer Science Dept. Informatics Engineering Kediri Indonesia

ISBN: (纸本)9798350376111

Kindergarten education has a very important role in shaping the foundation of child development. For parents, see reports on the achievement of children's development at school through the results of each semester's assessment. The process of preparing teacher assessments uses 4 types of assessments, one of which is anecdotal record assessment. However, teachers experience problems in analyzing the topics in children's anecdotal notes so that the results become less clear. In addition, it is still done manually so it takes a long time to analyze. Assessment of children's anecdotal notes is an assessment in the form of a record of events that occur to children while at school. Anecdotal notes are in the form of text data, therefore this research aims to find out the topics in children's anecdotal notes data by utilizing topic modeling using the Latent Dirichlet Allocation (LDA) method. The children's anecdotal notes data is used as one dataset with a total of 276 collected directly from YBPK Sidorejo Kindergarten, after which data preprocessing is carried out in the form of case folding, remove punctuation, tokenizing, stopwords, and stemming. In the LDA topic modeling research using the Gensim library, 5 experiments were carried out by determining the number of different iterations, and displaying a range of 2-10 topics for each experiment. Analysis and evaluation using coherence score to determine the optimal number of topics by looking at the highest coherence score value in the experiment conducted. The analysis results in 3 topics with a coherence score of 0.4704 and 40 iterations or passes. © 2024 IEEE.

关键词： anecdotal record coherence score kindergarten Latent Dirichlet Allocation topic modeling

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