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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data science and systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Wang, Xin Ul Hasan, Wordh Li, Juan Hendawi, Rasha Du, Yang Xie, Bo Tao, Cui North Dakota State University Department of Computer Science Fargo United States Valley City State University Department of Computer Systems and Software Engineering Valley City United States School of Nursing The University of Texas at Austin Austin United States School of Biomedical Informatics University of Texas Health Science Center at Houston Houston United States

ISBN: (纸本)9798350330014

The proliferation of digital health technologies has led to an abundance of personal health data. However, querying and retrieving specific health-related information from disparate sources can be challenging and inconvenient, particularly for older individuals unfamiliar with technology. While ChatGPT offers a conversational interface, it lacks domain-specific knowledge, including personalized health information. To address this limitation, we present a novel approach that combines a knowledge graph and GPT to enable personalized health queries. Our solution utilizes a personal knowledge graph as a comprehensive knowledge source and fine-tunes GPT to provide accurate responses. We have implemented a voice assistant mobile app incorporating this knowledge graph-assisted GPT model and conducted initial feasibility testing. © 2023 IEEE.

关键词： Query processing

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software Defect Prediction Using Ensemble Technique

Software Defect Prediction Using Ensemble Technique

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International Conference on Information Management and Technology (ICIMTech)

作者： Pandu Wicaksono Panji Arisaputra Rezky Yunanda Willy Kristian Almuzhidul Mujhid Software Engineering Department School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

software defect prediction is the methodical process of identifying code segments that are likely to have problems. This is done by analyzing software metrics and using categorization algorithms. This work introduces an alternate approach that utilizes ensemble learning techniques to improve the effectiveness of fault detection models. The models utilized in this study are CodeGPT and CodeBERT, both of which are transformer-based deep learning models that can extract code features from source code. The models are trained using the PROMISE dataset, which consists of Java projects that have been labeled with defects. The test results show that using ensemble learning methods can improve the accuracy, precision, recall, and F1 score of a single model by around 1-3%. This study contributes to the progress of software dependability and quality by utilizing advanced software fault prediction algorithms.

关键词： Codes Source coding software algorithms Predictive models Prediction algorithms Feature extraction Transformers software Ensemble learning software measurement

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Automated Classification of Medical Images Using Convolutional Neural Networks 15

Automated Classification of Medical Images Using Convolution...

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

作者： Pillai, N. Muthuvairavan Manimala, S. Rongali, Aruna Sri Gugnani, Anchit Kumar, Senthil A. Sriramakrishnan, G.V. Computer Science and Business Systems R.M.D Engineering College Thiruvallur district Kavarapettai India Computer science and engineering University SJCE JSS Science and technology University Karnataka Mysore India Sr. Software Engineer Department Information Technology Emphasis/Information Technology Ph.D University The country University of The Cumberlands United States Physiotherapy Amity University Rajasthan Jaipur India Artificial Intelligent department Shri Vishnu Engineering College for Women AP Bhimavaram India Tamilnadu Chennai India

ISBN: (纸本)9798350370249

Medical imagery has been increasingly used in diagnosing and treating various diseases, leading to advances in healthcare. While humans usually lead the qualitative interpretation of visual features, Automated Classification of Medical Images Using Convolution Neural Networks (CNNs) has gained traction for accurate image classification. CNNs are a type of deep learning algorithm that attempts to mimic the structure of the human brain and are ideal for image classification due to their convolution layers that can detect patterns within the image. These convolution layers enable the networks to learn the characteristics of the data through a series of convolution and pooling operations. Additionally, they can rapidly identify relationships and patterns within the dataset and extract features from the input image, thus allowing a more accurate classification. With the increasing availability of medical imaging data, advancements in the field of ML/AI has enabled the use of CNNs for automated medical image classification, helping clinicians to diagnose diseases accurately with less time and effort. ©2024 IEEE.

关键词： Convolutional neural networks

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A Light Field Image Quality Assessment Method Based on Stereo Vision

A Light Field Image Quality Assessment Method Based on Stere...

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2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Fu, Wei Shen, Xingfa Zhou, Wenhui Zhdanov, Andrei D. Geng, Chuntong Itmo Joint Institute Hangzhou Dianzi University Department of Computer Science Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Itmo University Faculty of Software Engineering and Computer Systems St.Petersburg Russia

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

Light field imaging is an important achievement in visual information exploration in recent years, which can capture more abundant visual information from the real world. However, most existing light field image quality assessment (LF-IQA) indicators rely heavily on feature extraction based on high complexity statistics in quality evaluation tasks, which is not comprehensive in future modern applications or power-limited devices. To solve this problem, the research puts forward a light field image quality evaluation method based on stereo vision. By introducing a brand-new light field image coding method and training the neural network, the purpose of image quality evaluation is finally achieved. Some experiments show that our model achieves good results in open source data sets. © 2022 IEEE.

关键词： Stereo vision

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Xtractor: A Two-Step Tweet Extractor for Sentiment Analysis

Xtractor: A Two-Step Tweet Extractor for Sentiment Analysis

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International Conference on Advancements in Computational sciences (ICACS)

作者： Muhammad Bux Alvi Naeem Ahmed Mahoto Mukhtiar Ali Unar Muhammad Akram Shaikh Computer Systems Engineering The Islamia University of Bahawalpur Bahawalpur Pakistan Software Engineering Department Mehran UET Jamshoro Jamshoro Pakistan Computer Systems Engineering Department Mehran UET Jamshoro Jamshoro Pakistan PASTIC Pakistan Science Foundation Islamabad Pakistan

Twitter (now X) has been gaining popularity with each passing day since its inception in 2006. People have been using Twitter as an instant repository to collect data and gain insight into folks’ minds on trending issues. Although Twitter allows access to its data through streaming and rest APIs, extracting the required data is difficult. The data (tweet) returned by Twitter is in .json format, having at least 26 fields. Each field is bundled in the dictionary form data structure. tweet metadata such as tweet “likes” and “retweets” increase the volume and complexity of a tweet, making data extraction in a cleaner format more difficult. This work aims to develop an effective two-step tweet extractor (Xtractor: an online-offline keyword-based Twitter data extractor). In the first step, Xtractor collects publicly available topical tweets using keywords, hashtags, or their list on the fly, parses all the tweet fields, and filters them to retain potential field contents without additional payload. The second step of the Xtractor evicts partially matching tweets using the regular expression method to acquire the targeted domain-specific tweets. Using the proposed two-step Twitter data extraction method, datasets concerning the people of Pakistan have been collected, which can be leveraged to get insight for decision-making, specially in the context of sentiment analysis. We found this method efficient, capacitive, and productive.

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Exploratory Data Analysis and Predicting Indian Crime Against Women Using Machine Learning Approaches

Exploratory Data Analysis and Predicting Indian Crime Agains...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： N. Srinivasa Gupta B. Valarmathi K. Santhi T.Chellatamilan Sameeksha Nanda Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Crime forecasting is a critical endeavor aimed at preventing future criminal actions, specifically the rising rate of violence against women in India. This study proposes the model to predict crimes committed against women using data from the National Crime Records Bureau's (NCRB) monthly crime report. The dataset encompasses district and state-wise crime activities from 2001 to 2014. The proposed work objectives are identifying factors influencing these crimes and developing predictive models using machine learning (ML). The study employs Exploratory Data Analysis (EDA), to enhance the predictive power of the models. The suggested study uses XGBoost, Random Forest (RF), and Neural Network (NN). The neural network algorithm outperforms the Random Forest and XGBoost techniques among these three. The Random Forest algorithm has the worst performance. After comparing the accuracies of various methods, such as XGBoost (92%), and Random Forest (89.84%) classifiers using imbalanced datasets (without employing Synthetic Minority Oversampling Technique (SMOTE) technique), it was found that the neural network algorithm (95.81%) delivers the highest accuracy. This study looks at model accuracies and finds that, after applying the SMOTE algorithm, neural network algorithms performed best, with an accuracy of 96.76%, outperforming the accuracies of the remaining models, which included the Random Forest (93.08%) and XG-Boost (95%).

关键词： Radio frequency Technological innovation Accuracy Machine learning algorithms Data analysis Predictive models Prediction algorithms Data models Classification algorithms Random forests

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Energy Optimised Security against Wormhole Attack in IoT-Based Wireless Sensor Networks

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computers, Materials & Continua 2021年第8期68卷 1967-1981页

作者： Hafsa Shahid Humaira Ashraf Hafsa Javed Mamoona Humayun Nz Jhanjhi Mohammed A.AlZain Department of Computer Science and Software Engineering International Islamic University IslamabadIslamabadPakistan Department of Information Systems College of Computer and Information ScienceJouf UniversityAl-JoufSaudi Arabia School of Computer Science and Engineering(SCE) Taylor’s UniversitySelangorMalaysia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

An IoT-based wireless sensor network(WSN)comprises many small sensors to collect the data and share it with the central *** sensors are battery-driven and resource-restrained devices that consume most of the energy in sensing or collecting the data and transmitting *** data sharing,security is an important concern in such networks as they are prone to many threats,of which the deadliest is the wormhole *** attacks are launched without acquiring the vital information of the network and they highly compromise the communication,security,and performance of the *** the IoT-based network environment,its mitigation becomes more challenging because of the low resource availability in the sensing *** have performed an extensive literature study of the existing techniques against the wormhole attack and categorised them according to their *** analysis of literature has motivated our *** this paper,we developed the ESWI technique for detecting the wormhole attack while improving the performance and *** algorithm has been designed to be simple and less complicated to avoid the overheads and the drainage of energy in its *** simulation results of our technique show competitive results for the detection rate and packet delivery *** also gives an increased throughput,a decreased end-to-end delay,and a much-reduced consumption of energy.

关键词： IoT Internet of Things energy wormhole WSN wireless sensor networks

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Contemporary software Modernization: Strategies, Driving Forces, and Research Opportunities

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ACM Transactions on software engineering and Methodology 2025年第5期34卷

作者： Assunção, Wesley K.G. Marchezan, Luciano Arkoh, Lawrence Egyed, Alexander Ramler, Rudolf Department of Computer Science North Carolina State University RaleighNC United States Institute of Software Systems Engineering Johannes Kepler University Linz Linz Austria Software Competence Center Hagenberg GmbH Hagenberg Austria

software modernization is a common activity in software engineering, since technologies advance, requirements change, and business models evolve. Differently from conventional software evolution (e.g., adding new features, enhancing performance, or adapting to new requirements), software modernization involves re-engineering entire legacy systems (e.g., changing the technology stack, migrating to a new architecture style, or programming paradigms). Given the pervasive nature of software today, modernizing legacy systems is paramount to provide customers with competitive and innovative products and services, while keeping companies profitable. Despite the prevalent discussion of software modernization in gray literature, and the many papers in the literature, there is no work presenting a "big picture" of contemporary software modernization, describing challenges, and providing a well-defined research agenda. The goal of this work is to describe the state of the art in software modernization in the past 10 years. We collect the state of the art by performing a rapid review (searching five digital libraries), identifying potential 3,460 studies, leading to a final set of 126. We analyzed these studies to understand which strategies are employed, the driving forces that lead organizations to modernize their systems, and the challenges that need to be addressed. The results show that studies in the last 10 years have explored eight strategies for modernizing legacy systems, namely cloudification, architecture redesign, moving to a new programming language, targeting reuse optimization, software modernization for new hardware integration, practices to leverage automation, database modernization, and digital transformation. Modernization is triggered by 14 driving forces, with the most common ones being reducing operational costs, improving performance and scalability, and reducing complexity. In © 2025 Copyright held by the owner/author(s). Publication rights licensed to

关键词： Legacy systems

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Dilated Neural Networks for Improving Microaneurysm Detection in Fundus Images

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IEEE Access 2025年 13卷 104037-104046页

作者： Norah Asiri Abdel Monim Artoli Muna Al-Razgan Taha Alfakih Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

Diabetic retinopathy (DR) is a microvascular complication that can lead to blindness if left untreated. Early detection of microaneurysms (MAs) is crucial for effective DR diagnosis. However, detecting MAs is challenging due to their small size, low contrast, and the presence of noise and other artifacts. This paper presents a novel deep learning approach for MA detection using a dilated residual network (DRN) with an Region of interest (ROI) layer. Unlike traditional models such as VGG16 and ResNet, our DRN model achieves a higher input resolution $(28\times 28)$ for ROI pooling, significantly improving MA detection accuracy. Extensive experiments confirm the effectiveness of our approach, achieving superior performance compared to state-of-the-art methods on the e-ophtha-MA, DiaretDB1-MA, and ROCh datasets. Notably, our method achieves the best FROC (free-response receiver operating characteristic) score of 0.6038 on the e-ophtha-MA dataset.

关键词： Feature extraction Retina Deep learning Residual neural networks Diabetic retinopathy Hemorrhaging Training Noise Accuracy Shape

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Heuristic Search for Nonlinear Substitutions for Cryptographic Applications

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data engineering and Communications Technologies 2023年 180卷 288-298页

作者： Kuznetsov, Oleksandr Frontoni, Emanuele Kandiy, Sergey Smirnov, Oleksii Ulianovska, Yuliia Kobylianska, Olena Department of Political Sciences Communication and International Relations University of Macerata Via Crescimbeni 30/32 Macerata62100 Italy Department of Information and Communication Systems Security Faculty of Comupter Science V. N. Karazin Kharkiv National University 4 Svobody Sq Kharkiv61022 Ukraine Department of Information Engineering Marche Polytechnic University Via Brecce Bianche 12 Ancona60131 Italy Cybersecurity & Software Academic Department Central Ukrainian National Technical University 8 University Avenue Kropyvnytskyi25006 Ukraine Department of Computer Science and Software Engineering University of Customs and Finance Vernadskogo Street 2/4 Dnipro49000 Ukraine

Heuristic algorithms are used to solve complex computational problems quickly in various computer applications. Such algorithms use heuristic functions that rank the search alternatives instead of a full enumeration of possible variants. The algorithm selects, at each iteration, an alternative with the best value for the heuristics. In this paper, we investigate the complex computational problem of finding highly nonlinear substitutions (S-boxes) in the space of 8-bit permutations. The generation of S-boxes is an important field of research, since nonlinear substitutions are widely used in various cryptographic applications. For instance, S-boxes in symmetric ciphers are responsible for cryptographic strength to linear, differential, algebraic, and other types of cryptanalysis. We propose new heuristics in the form of a cost function, calculated with the Walsh-Hadamard transform. We use the Hill Climbing Algorithm to find highly nonlinear substitutions. Our experiments demonstrate that the new heuristics give good results – it takes about 80,000 iterations of the algorithm to generate 8-bit S-boxes with a nonlinearity of 104. For the optimized parameters, the number of iterations of the algorithm is comparable to the best known results, which confirms the significance and value of the research. © The Author(s).

关键词： Cost functions

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