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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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Field Demonstration of Blockchain-based Security for a Solar Farm

Field Demonstration of Blockchain-based Security for a Solar...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Ahn, BoHyun Nakka, Kalyan Handke, Nathanial R. Reyna, Trevor J. Kim, Taesic Texas A&m University-Kingsville Dept. of Electrical Engineering and Computer Science KingsvilleTX78363 United States Texas A&m University Dept. of Computer Science and Engineering College StationTX77843 United States University of Missouri-Columbia Dept. of Electrical Engineering and Computer Science ColumbiaMO65211 United States

ISBN: (纸本)9798350376067

This paper introduces a Blockchain (BC)-based security model for a solar farm, providing security functions such as firmware patching management, role-based access control, public key infrastructure, and man-in-the-middle attacks detection (MITM), malware file detection. In particular, this paper provides detailed field-testing methods at a solar fam to demonstrate the feasibility and effectiveness of cyber-attack detection methods. Practical cyberattacks targeting solar farms are designed and conducted. It is demonstrated that the proposed BC-based system proactively detects MITM attacks, firmware modification attack, and malware attack, while ensuring the continuous operation of the solar farm during attack events. © 2024 IEEE.

关键词： Cyber attacks

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An In-dept. Study on Handwritten Signature Authentication Using Unique Writer Traits 1

An In-Depth Study on Handwritten Signature Authentication Us...

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Vamsikrishna, M. Murari, Haritha Manikandan, M. Mishra, Nilamadhab Raghavendra Gupta, T. Patel, Rohit Aditya University Dept. of Computer Applications Kakinada India Spark Infotech Inc Sr Software Engineer PhoenixAZ United States VeL Tech. R Dr.S Dept. of Computer Science and Engineering Tamilnadu India VIT Bhopal University Dept. of Computer Science and Engineering Sehore India HITM Telenagana Hyderabad India Calorx Teachers' University Dept of CSE Gujrat India

ISBN: (纸本)9798350356816

Handwriting verification, a subset of signature verification, involves using handwritten text to verify or authorize someone's identification. Although simple handwriting can serve as a signature, it is often modified to suit the signer's preferences. The uniqueness of each person's handwritten signature makes it essential for accurate distinction from others. This study presents a handwritten signature verification system using a writer-dependent approach. While signature verification is widely used for identity verification and security, it is susceptible to forgeries. The writer-dependent approach considers the unique characteristics of an individual's signature, training the system to recognize them and making forgery replication more challenging. An extensive review of the literature on signature verification systems, focusing on the writer-dependent approach, was conducted. A system was then developed and implemented based on this approach, utilizing a dataset of signatures from 12 writers. Results indicate that the writer-dependent approach outperforms other methods in terms of accuracy and robustness. The system demonstrated high precision and recall rates, underscoring its effectiveness in detecting both genuine and forged signatures. This study highlights the writer-dependent approach's efficacy in enhancing the accuracy and security of signature verification systems, with implications for developing more reliable and efficient identity verification systems in today's digital age. © 2024 IEEE.

关键词： Authentication

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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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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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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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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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Harnessing LLMs for VQA: A Prompted Benchmark with Animate/Inanimate Keywords 15

Harnessing LLMs for VQA: A Prompted Benchmark with Animate/I...

引用

15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Lee, Chanwoo Lee, Hyunjeong Kim, Minsang Kim, Hyun Jang, Haneol Park, Cheoneum Seoul Woman's University Dept. of Software Convergence Seoul Korea Republic of Hanbat National University Dept. of Computer Engineering Daejeon Korea Republic of Korea University Dept. of Computer Science and Engineering Seoul Korea Republic of Electronics and Telecommunications Research Institute Superintelligence Creative Research Lab. Daejeon Korea Republic of

ISBN: (纸本)9798350364637

In the field of NLP, Large Language Models (LLMs) have recently achieved significant advancements, leading to the development of various benchmarks for their evaluation. Along-side NLP, Vision Language Models (VLMs) have also VLM have also significantly progressed, similar to LLMs. However, benchmarks for VLMs are still relatively underdeveloped compared to those for NLP, and their construction is often costly. In this work, we propose an automatically generated benchmark for evaluating VLMs based on LLMs and conduct a visual question answering task to assess this benchmark. The benchmark includes multiple-choice questions that not only distinguish between animate and inanimate objects but also generate these distinctions automatically, along with entity and object information within images. We evaluate the performance of open VLM using the generated multiple-choice questions, demonstrating the model's capabilities and the significance of the automatically generated benchmark. Finally, we discuss the necessity and future directions for benchmark research in this area. © 2024 IEEE.

关键词： Visual languages

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Securing Networks: Advanced Detection and Prevention of Address Resolution Protocol Cache Spoofing Attack 3

Securing Networks: Advanced Detection and Prevention of Addr...

引用

3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Sriharsha, V.V.S. Daule, Viraj Kisan Surya, R. Thangam, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508456

As the technology advances, an increase in the attacks to manipulate or extract data illegally has also risen. This also includes one such attack known as Address Resolution protocol cache spoofing attack. The Address Resolution protocol Spoofer conducts an in-dept. exploration into one of the most significant security vulnerabilities identified in local networks, namely Address resolution protocol cache poisoning. The study implements a system which uses a Python based tool which is able to simulate Address Resolution protocol spoofing attacks on the network traffic of two hosts through a Python library known as the Scapy library. This tool allows the execution of Man In The Middle attacks by forging Address Resolution protocol replies and changing the stream of traffic without setting off any alarm by the user. The tool also has a detection mechanism that alerts the users as soon as it detects attempted spoofing;which hence identifies both the offender and the defensive capabilities in Address Resolution protocol spoofing. Additionally, another layer of security is added by user-defined encryption and decryption of the message being delivered. The study has resulted in a secure network, protecting the data from ARP spoofing. © 2025 IEEE.

关键词： HTTP

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Real-Time Traffic Prediction And Optimization using Machine Learning 3

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

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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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