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9th International Conference on computer Science and engineering, UBMK 2024

作者： Dedetürk, Beyhan Adanur Dedetürk, Bilge Kagan Abdullah Gul University Department of Computer Engineering Kayseri Turkey Erciyes University Department of Software Engineering Kayseri Turkey

ISBN: (纸本)9798350365887

One type of insurance that people purchase to cover unexpected medical expenses is health insurance. In exchange for premium payments, the insurance company may cover a portion of the insured person's medical expenses, such as prescription medications, hospital stays, and doctor visits. This method makes access to healthcare easier and less expensive. Health insurance systems do, however, have a number of issues, including fair insurance premium calculation, automation, data verification, privacy and security, and cost effectiveness. These issues are starting to be addressed by blockchain technology, particularly with the help of smart contracts. Using a comparison analysis between Ethereum and Optimism smart contracts, this paper demonstrates the performance of health insurance. Simulation of these BC technologies was carried out both on the Sepolia testnet and using Alchemy. Tools and metrics provided to monitor the performance of Alchemy applications, detect errors, and analyze user interactions were used in the measurements. While Ethereum's well-established ecosystem offers robust support for smart contracts, Optimism distinguishes itself as a scalable substitute that delivers quicker transaction speeds and more affordable options. According to the analysis results, the advantages and disadvantages of Ethereum and Optimism are highlighted when it comes to health insurance. © 2024 IEEE.

关键词： Ethereum

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Ethical Aspects of ChatGPT in software engineering Research

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 254-267页

作者： Akbar, Muhammad Azeem Khan, Arif Ali Liang, Peng Lappeenranta-Lahti University of Technology Software Engineering Department Lappeenranta15210 Finland University of Oulu M3S Empirical Software Engineering Research Unit Oulu90014 Finland Wuhan University School of Computer Science Wuhan430072 China

ChatGPT can improve software engineering (SE) research practices by offering efficient, accessible information analysis, and synthesis based on natural language interactions. However, ChatGPT could bring ethical challenges, encompassing plagiarism, privacy, data security, and the risk of generating biased or potentially detrimental data. This research aims to fill the given gap by elaborating on the key elements: motivators, demotivators, and ethical principles of using ChatGPT in SE research. To achieve this objective, we conducted a literature survey, identified the mentioned elements, and presented their relationships by developing a taxonomy. Furthermore, the identified literature-based elements (motivators, demotivators, and ethical principles) were empirically evaluated by conducting a comprehensive questionnaire-based survey involving SE researchers. In addition, we employed an interpretive structure modeling approach to analyze the relationships between the ethical principles of using ChatGPT in SE research and develop a level-based decision model. We further conducted a cross-impact matrix multiplication applied to classification analysis to create a cluster-based decision model. These models aim to help SE researchers devise effective strategies for ethically integrating ChatGPT into SE research by following the identified principles by adopting the motivators and addressing the demotivators. The findings of this study will establish a benchmark for incorporating ChatGPT services in SE research with an emphasis on ethical considerations. © 2020 IEEE.

关键词： software engineering

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Review on Image Processing for MRI-Focused Brain Tumor Identification Using Machine Learning 7

Review on Image Processing for MRI-Focused Brain Tumor Ident...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Agha, Dur-E-Shawar Qazi, Farheen Rasheed, Rohail Ali Khan, M. Sadiq Naseem, Muhammad Mubeen, Tauseef Sir Syed University of Engineering & Technology Software Engineering Department Karachi Pakistan Computer Science Department Karachi Pakistan

ISBN: (纸本)9798331534271

Today, image processing plays a significant role in early disease detection, allowing doctors to treat conditions like breast, lung, and brain malignancies. A brain tumor is a serious condition that can be fatal. Brain tumor diagnosis is the most important task for early therapy, and automatic visual classification might be extremely important. It is essential to classify brain cancers in order to assess the tumor's and determine the best course of treatment for each class. To find brain tumors, numerous imaging modalities are used. However, MRI is frequently utilized since it produces superior images and uses no ionizing radiation. This research aims to provide a review of different Machine Learning (ML) techniques for detection and classification of brain tumor. Also, different feature extraction techniques are analyzed and their benefits are discussed. This review is conducted by different research papers which helps new researchers to start immediately a literature review and knowing different machine learning techniques their advantages and disadvantages to classify brain tumor through MRI. Also, they know the accuracy of each algorithm which leads them to directly target the problem not implementing the existing techniques. © 2024 IEEE.

关键词： Lung cancer

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Test-Driven Development (TDD) in Small software Development Teams: Advantages and Challenges 5

Test-Driven Development (TDD) in Small Software Development ...

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5th International Conference on Advancements in Computational Sciences, ICACS 2024

作者： Ramzan, Hafiz Arslan Ramzan, Sadia Kalsum, Tehmina Institute of Computer and Software Engineering Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan National University of Modern Languages Department of Computer Science Multan Pakistan University of Engineering and Technology Department of Software Engineering Taxila Pakistan

ISBN: (纸本)9798350394764

In the context of small software development teams, this research article gives a thorough investigation of the adoption of test-driven development (TDD) approaches. It aims to highlight the benefits that TDD offers, such as improved code quality through modularization and proactive defect spotting which results in effective debugging and development processes. It also discusses the complex issues that arise when TDD is implemented in smaller teams, such as the learning curve and resource constraints. This study significantly advances the understanding of how TDD can be used to optimize software development techniques in organizations or software houses having small development teams. It also explores the possible advantages and challenges. © 2024 IEEE.

关键词： Modular construction

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Machine Learning Based Phishing Attacks Detection Using Multiple Datasets

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第5期17卷 71-83页

作者： Aljammal, Ashraf H. taamneh, Salah Qawasmeh, Ahmad Salameh, Hani Bani Department of Computer Science and Applications The Hashemite University Zarqa Jordan Department of Software Engineering The Hashemite University Zarqa Jordan

Nowadays, individuals and organizations are increasingly targeted by phishing attacks, so an accurate phishing detection system is required. Therefore, many phishing detection techniques have been proposed as well as phishing datasets have been collected. In this paper, three datasets have been used to train and test machine learning classifiers. The datasets have been archived by Phish-Tank and UCI Machine Learning Repository. Furthermore, Information Gain algorithm have been used for features reduction and selection purpose. In addition, six machine learning classifiers have been evaluated, namely NaiveBayes, ANN, DecisionStump, KNN, J48 and RandomForest. However, the classifiers have been trained and tested over the three datasets in two stages. The first stage is using all features included in each dataset while the second stage using selected features by IG algorithm. At the first stage RandomForest classifier has shown the best performance over Dataset-1 and Dataset-2, while J48 has shown the best performance over Dataset-3. On the other hand, after features selection, the RandomForest classifier was the superior among the other five classifiers over Dataset-1 and Dataset-2 with accuracy of 98% and 93.66% respectively. While ANN classifier has shown the best performance with accuracy of 88.92% over Dataset-3. Because of the few number of instances as well as features in Dataset-3 comparing to the other two dataset;the performance of the classifiers has been affected © 2023, International Journal of Interactive Mobile *** Rights Reserved.

关键词： Network security

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Medical Image Segmentation with U-Net for Breast Cancer and Lump Type Prediction

Medical Image Segmentation with U-Net for Breast Cancer and ...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Aygün, Elif Nur Kaya, Mehmet Celal Bayar University Department of Software Engineering Manisa Turkey Firat University Department of Computer Engineering Elaziǧ Turkey

ISBN: (纸本)9798350369106

Today, it is known that, like other types of cancer, breast cancer cases are increasing every year. It is an indisputable fact that early diagnosis and correct diagnosis, which medical literature pays particular attention to, have positive effects on patient health in this field. Therefore, it is a great necessity to identify breast cancer cases correctly and not to confuse malignant masses with benign masses. At this stage, a study was carried out to support the physician's decision, and segmentation was carried out on images containing potential breast cancer with the U-Net model. In addition, a two-class classification model is proposed to determine whether ultrasound images containing masses are benign or malignant. While the detection rate of the current model for benign masses is 99.9%, it reaches 86.6% for malignant masses. In addition, various data augmentation techniques were used hybridly to increase malignant mass images containing fewer samples, and the overall prediction success was increased to 91.13%. © 2024 IEEE.

关键词： Lung cancer

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Network-Wide Energy Efficiency Maximization in UAV-Aided IoT Networks: Quasi-Distributed Deep Reinforcement Learning Approach

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IEEE Internet of Things Journal 2025年第11期12卷 15404-15414页

作者： Lee, Seungmin Ban, Tae-Won Lee, Howon Newratek Inc Department of Wi-Fi MAC Standards and Software Engineering Seoul06175 Korea Republic of Gyeongsang National University Department of Intelligent Communication Engineering Jinju52828 Korea Republic of Ajou University Department of Electrical and Computer Engineering Gyeonggi Suwon16499 Korea Republic of

In unmanned aerial vehicle (UAV)-aided Internet of Things (IoT) networks, providing seamless and reliable wireless connectivity to ground devices (GDs) is difficult owing to the short battery lifetimes of UAVs. Hence, we consider a deep reinforcement learning (DRL)-based UAV base station (UAV-BS) control method to maximize the network-wide energy efficiency of UAV-aided IoT networks featuring continuously moving GDs. First, we introduce two centralized DRL approaches;round-robin deep Q-learning (RR-DQL) and selective-k deep Q-learning (SK-DQL), where all UAV-BSs are controlled by a ground control station that collects the status information of UAV-BSs and determines their actions. However, significant signaling overhead and undesired processing latency can occur in these centralized approaches. Hence, we herein propose a quasi-distributed DQL-based UAV-BS control (QD-DQL) method that determines the actions of each agent based on its local information. By performing intensive simulations, we verify the algorithmic robustness and performance excellence of the proposed QD-DQL method based on comparison with several benchmark methods (i.e., RR-DQL, SK-DQL, multi-agent Q-learning, and exhaustive search method) while considering the mobility of GDs and the increase in the number of UAV-BSs. © 2014 IEEE.

关键词： Deep reinforcement learning

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Prediction of the Most Common Symptoms in Psychological Illnesses with Language Representation Models 10

Prediction of the Most Common Symptoms in Psychological Illn...

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Aygun, Irfan Kaya, Mehmet Celal Bayar University Department of Software Engineering Manisa Turkey Firat University Department of Computer Engineering Elaziǧ Turkey

ISBN: (纸本)9798350363104

It is a known fact as a result of researches that psychological disorders are seen more frequently in society day by day and early diagnosis of these disorders is very important. To detect psychological disorders, it is an important achievement to identify the symptoms in the sentences of potential patients. In the present study, the most frequently used symptoms in the sentences of past psychiatric patients were investigated. The deep learning supported BERT model was used to analyze the texts and the Named Entity Recognition (NER) method was used for symptom detection. Thus, a model is proposed that enables the detection of symptoms even when they are expressed in different ways. The success of the proposed model in detecting the symptoms is between 83.6 and 86.2% and the most common symptoms are shortness of breath, loss of attention and loss of appetite. © 2024 IEEE.

关键词： Deep learning

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Brain magnetic resonance image (MRI) segmentation using multimodal optimization

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Multimedia Tools and Applications 2024年 1-50页

作者： Akan, Taymaz Oskouei, Amin Golzari Alp, Sait Bhuiyan, Mohammad Alfrad Nobel Department of Medicine Louisiana State University Health Sciences Center ShreveportLA71103 United States Software Engineering Department Istanbul Topkapi University Istanbul34020 Turkey Faculty of Information Technology and Computer Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Software Engineering İstinye University İstanbul Turkey Department of Artificial Intelligence Engineering Trabzon University Trabzon61335 Turkey

One of the highly focused areas in the medical science community is segmenting tumors from brain magnetic resonance imaging (MRI). The diagnosis of malignant tumors at an early stage is necessary to provide treatment for patients. The patient’s prognosis will improve if it is detected early. Medical experts use a manual method of segmentation when making a diagnosis of brain tumors. This study proposes a new approach to simplify and automate this process. In recent research, multi-level segmentation has been widely used in medical image analysis, and the effectiveness and precision of the segmentation method are directly tied to the number of segments used. However, choosing the appropriate number of segments is often left up to the user and is challenging for many segmentation algorithms. The proposed method is a modified version of the 3D Histogram-based segmentation method, which can automatically determine an appropriate number of segments. The general algorithm contains three main steps: The first step is to use a Gaussian filter to smooth the 3D RGB histogram of an image. This eliminates unreliable and non-dominating histogram peaks that are too close together. Next, a multimodal particle swarm optimization method identifies the histogram’s peaks. In the end, pixels are placed in the cluster that best fits their characteristics based on the non-Euclidean distance. The proposed algorithm has been applied to a Cancer Imaging Archive (TCIA) and brain MRI Images for brain Tumor detection dataset. The results of the proposed method are compared with those of three clustering methods: FCM, FCM_FWCW, and FCM_FW. In the comparative analysis of the three algorithms across various MRI slices. Our algorithm consistently demonstrates superior performance. It achieves the top mean rank in all three metrics, indicating its robustness and effectiveness in clustering. The proposed method is effective in experiments, proving its capacity to find the proper clusters. © The Auth

关键词： Image segmentation

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Streamlining plant disease diagnosis with convolutional neural networks and edge devices

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Neural Computing and Applications 2024年第29期36卷 18445-18477页

作者： Ahamed, Md. Faysal Salam, Abdus Nahiduzzaman, Md. Abdullah-Al-Wadud, M. Islam, S. M. Riazul Department of Computer Science and Engineering Rajshahi University of Engineering & ampTechnology Rajshahi6204 Bangladesh Department of Electrical and Computer Engineering Rajshahi University of Engineering & ampTechnology Rajshahi6204 Bangladesh Department of Electrical Engineering Qatar University Doha Qatar Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Natural and Computing Sciences University of Aberdeen AberdeenAB24 3FX United Kingdom

In addressing labor-intensive process of manual plant disease detection, this article introduces an innovative solution—the lightweight parallel depthwise separable convolutional neural network (PDSCNN) coupled with a hybrid ridge regression extreme learning machine classifier. This combined approach automates plant disease classification, significantly expediting the learning process and reducing computational complexity. The PDSCNN model excels in feature capture, boasting a mere 9 layers and 0.053 million parameters, resulting in exceptional computational efficiency. On top of that, the classification performances of traditional ELM increased by introducing a hybrid ridge ELM model. Its accelerated operations, reduced memory usage, and optimized parallel processing render it lightweight and ideal for edge devices. The proposed framework exhibits remarkable efficacy, showcasing a precision of 95.76%, a recall of 89.29%, an F1-score of 91.25%, an accuracy of 94.22%, and an AUC of 99.72%. Moreover, training and testing times clock in at just 0.2314 and 0.0259 s, respectively. A comparative analysis against state-of-the-art transfer learning methods underscores its real-time analytical prowess. To demonstrate real-world applicability, we successfully validated our model on the Jetson Nano hardware, making it suitable for resource-constrained settings. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Computational efficiency

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