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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Hanbay, Kazim Uzen, Huseyin Ozdemir, Taha Burak Ercelik, Cetin İnönü University Faculty of Engineering Department of Software Engineering Malatya Turkey Bingöl University Faculty of Engineering Department of Computer Engineering Bingöl Turkey İnönü University Faculty of Engineering Department of Computer Engineering Malatya Turkey

ISBN: (纸本)9798331531492

Synthetic aperture radar (SAR) images are used extensively in agricultural applications, coastal boundary detection and object recognition. This imaging technology provides desired results in many challenging applications due to its ability to provide images with appropriate resolution in harsh weather and climate conditions. In this study, an image database was created to detect ships from SAR images. The images were preprocessed in accordance with the literature and made suitable for ship detection methods. The noise in the images was reduced with a deep learning-based architecture. Using this database, image processing and machine learning methods were used to develop ship detection methods. © 2024 IEEE.

关键词： Deep learning

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A Package Level Regression Test Selection Approach for Java software Systems

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IEEE Access 2025年 13卷 87848-87861页

作者： Al-Refai, Mohammed Hammad, Mahmoud M. Jordan University of Science and Technology Faculty of Computer and Information Technology Computer Science Department Irbid22110 Jordan Faculty of Engineering and Information Technology Ajman United Arab Emirates Jordan University of Science and Technology Faculty of Computer and Information Technology Software Engineering Department Irbid22110 Jordan

Regression testing of software systems is an important and critical activity yet expensive and resource-intensive. An approach to enhance its efficiency is Regression Test Selection (RTS), which selectively re-executes a subset of relevant tests that are impacted by code modifications. Previous studies on static and dynamic RTS for Java software have shown that selecting tests at the class level is more effective than using finer granularities like methods or statements. Nevertheless, RTS at the package level, which is a coarser granularity than class level, has not been thoroughly investigated or evaluated for Java projects. To address this gap, we propose PKRTS, a static package-level RTS approach that utilizes the structural dependencies of the software system under test to construct a package-level dependency graph. PKRTS analyzes dependencies in the graph and identifies relevant tests that can reach modified packages, i.e., packages containing altered classes. In contrast to conventional static RTS techniques, PKRTS implicitly considers dynamic dependencies, such as Java reflection and virtual method calls, among classes belonging to the same package by treating all those classes as a single cohesive node in the dependency graph. We evaluated PKRTS on 885 revisions of 9 open-source Java projects, with its performance compared to Ekstazi, a state-of-the-art dynamic class-level approach, and STARTS, a state-of-the-art static class-level approach. We used Ekstazi as the baseline to measure the safety and precision violations of PKRTS and STARTS. The results indicated that PKRTS outperformed static class-level RTS in terms of safety violation, which measures the extent to which relevant test cases are missed. PKRTS showed an average safety violation of 2.29% compared to 5.94% safety violation of STARTS. Despite this, PKRTS demonstrated lower precision violation and lower reduction in test suite size than class-level RTS, as it selects higher number of irrelevant te

关键词： Java programming language

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Machine Learning Methods of Symptom Extraction from Medical Notes

Machine Learning Methods of Symptom Extraction from Medical ...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Galai, Olesia Kurdiumov, Dmitrii Rusak, Alena ITMO University Faculty of Software Engineering and Computer Systems Saint-Petersburg Russia

ISBN: (纸本)9798350395044

Medical notes contain valuable information about patient conditions, treatments, and progress. Extracting symptoms from these unstructured notes is crucial for clinical research, population health analysis, and decision support systems. Traditional manual methods are time-consuming, but recent advances in natural language processing (NLP) and machine learning offer automated solutions. This article presents a novel approach that combines NLP techniques, such as conditional random fields (CRF) and transformer-based architectures. The proposed method demonstrates effective symptom extraction from medical notes, overcoming challenges such as varied terminologies and linguistic nuances. The study utilizes a dataset of Russian medical records, transforming it into a tabular format for training and employing unique tokenization algorithms for different models. Among the evaluated models, RuBERT achieved the highest accuracy of 91%, indicating its strong performance on the test dataset. SBERT exhibited the highest precision and F1 score, suggesting its effectiveness in accurately identifying specific sequence labels. © 2024 IEEE.

关键词： Clinical research

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A Fast Algorithm for Computing the Deficiency Number of a Mahjong Hand

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Chinese Journal of Electronics 2024年第6期33卷 1383-1398页

作者： Xueqing YAN Yongming LI Sanjiang LI School of Computer Science Shaanxi Normal University School of Mathematics and Statistics Shaanxi Normal University Centre for Quantum Software and Information Faculty of Engineering and Information TechnologyUniversity of Technology Sydney

The tile-based multiplayer game Mahjong is widely played in Asia and has also become increasingly popular worldwide. Face-to-face or online, each player begins with a hand of 13 tiles and players draw and discard tiles in turn until they complete a winning hand. An important notion in Mahjong is the deficiency number(*** number in Japanese Mahjong) of a hand, which estimates how many tile changes are necessary to complete the hand into a winning hand. The deficiency number plays an essential role in major decision-making tasks such as selecting a tile to discard. This paper proposes a fast algorithm for computing the deficiency number of a Mahjong hand. Compared with the baseline algorithm, the new algorithm is usually 100 times faster and, more importantly,respects the agent's knowledge about available tiles. The algorithm can be used as a basic procedure in all Mahjong variants by both rule-based and machine learning-based Mahjong AI.

关键词： Machine learning algorithms Knowledge based systems Decision making Asia Games

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MangoLeafXNet: An Explainable Deep Learning Model for Accurate Mango Leaf Disease Classification

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IEEE Access 2025年 13卷 93977-94008页

作者： Rayed, Md Eshmam Jim, Jamin Rahman Islam, Md Juniadul Mridha, M.F. Kabir, Md Mohsin Hossen, Md. Jakir American International University-Bangladesh Department of Computer Science and Engineering Dhaka1229 Bangladesh Mälardalens University Division of Computer Science and Software Engineering Västerås722 20 Sweden Multimedia University Faculty of Engineering and Technology Department of Robotics and Automation Melaka Malaysia

Addressing the global challenge of ensuring a consistent and abundant supply of fresh fruit, particularly in the context of fruit crops, is hindered by the prevalence of plant diseases. These diseases directly impact the quality of fruits, leading to a decline in overall agricultural production. Mango leaf diseases pose significant threats to global mango production, necessitating accurate and efficient classification techniques for timely disease management. Our study focuses on introducing MangoLeafXNet, a customized Convolutional Neural Network (CNN) architecture specifically tailored for the classification of mango leaf diseases, along with a healthy class. Our proposed model comprises six layers optimized to capture intricate disease patterns, demonstrating superior performance compared with prevalent pre-trained models. The model is trained and evaluated on three publicly available datasets: MangoLeafBD (4000 images across 8 classes), MangoPest (16 pest classes including healthy leaves), and MLDID (3000 high-resolution images across 5 classes). Our model demonstrated exceptional classification performance, attaining 99.8% accuracy, 99.62% recall, 99.5% precision, and an F1-score of 99.56%. Further validation on the MangoPest dataset and the Mango Leaf Disease Identification Dataset (MLDID) resulted in accuracies of 96.31% and 96.33%, respectively, confirming the robustness and adaptability of MangoLeafXNet across different datasets. Additionally, we incorporate Explainable AI techniques, including GRAD-CAM, Saliency Map, and LIME to enhance the interpretability of our model. We deployed Gradio web interface to create an interactive interface that allows users to upload images of mango leaves and get real-time classification and validation results along with confidence scores. This contribution not only advances the state-of-the-art in mango leaf disease classification but also offers promising prospects for real-time disease diagnosis and precision agriculture

关键词： Fruits

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Evaluation of the Influence of Priorities in Multichannel Data Transmission Systems

Evaluation of the Influence of Priorities in Multichannel Da...

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2023 International Russian Smart Industry Conference, SmartIndustryCon 2023

作者： Lygdenov, Sodnom ITMO University Faculty of Software Engineering and Computer Engineering Saint-Petersburg Russia

ISBN: (纸本)9781665464291

Subject of research. Evaluation of the impact of priority data transmission in multi-channel systems of high load, presented as a queuing system with prioritization of applications. Method. The impact of priorities on the efficiency of data transmission in multichannel systems was assessed using simulation methods and mathematical statistics. Main results. A numerical method for calculating time indicators based on a simulation model of a multichannel data transmission system with a non-uniform load is proposed. Simulation experiments have been carried out to evaluate the impact of priority data transmission at various coefficients of variation in the intensity of requests on time indicators in a multichannel system. The areas of efficiency of using priorities in highly loaded multichannel data transmission systems are shown. Practical significance. The presented results can be used in the design of high-load multichannel computer data transmission systems. © 2023 IEEE.

关键词： Efficiency

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A study on dynamic group signature scheme with threshold traceability for blockchain

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High-Confidence Computing 2024年第2期4卷 73-81页

作者： Hyo-jin Song Teahoon Kim Yong-Woon Hwang Daehee Seo Im-Yeong Lee Department of Software Convergence Soonchunhyang UniversityAsan 31538Republic of Korea Faculty of Artificial Intelligence and Data Engineering Sangmyung UniversitySeoul 03016Republic of Korea Department of Computer Software Engineering Soonchunhyang UniversityAsan 31538Republic of Korea

Blockchain technology provides transparency and reliability by sharing transactions and maintaining the same information through consensus among all ***,single-signature applications in transactions can lead to user identification issues due to the reuse of public *** address this issue,group signatures can be used,where the same group public key is used to verify signatures from group members to provide anonymity to ***,in dynamic groups where membership may change,an attack can occur where a user who has left the group can disguise themselves as a group member by leaking a partial *** problem cannot be traced back to the partial key *** this paper,we propose assigning different partial keys to group members to trace partial key leakers and partially alleviate the damage caused by partial key *** schemes have shown that arbitrary tracing issues occurred when a single administrator had exclusive key generation and tracing *** paper proposes a group signature scheme that solves the synchronization problem by involving a threshold number of TMs while preventing arbitrary tracing by distributing authority among multiple TMs.

关键词： Blockchain Group signature Privacy Anonymity Traceability

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Xgboost-Based Multi-Steps Cybersecurity Attacks Detection Model 3

Xgboost-Based Multi-Steps Cybersecurity Attacks Detection Mo...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Ba Alawi, Abdulfattah E. Bozkurt, Ferhat Baturalp, Faruk Software Engineering Taiz University Faculty of Engineering and It Taiz Yemen Computer Engineering Ataturk University Faculty of Engineering Erzurum Turkey

ISBN: (纸本)9798350305333

With the rapid growth of software and networks, the rate of cyber-attacks has increased rapidly. As a result, the demand for a dependable and suitable Intrusion Detection System (IDS) solution for safeguarding devices and networks has become essential. Nevertheless, in order to accurately detect the activities of new kinds of crimes, particularly tasking-step incidents, an effective IDS requires an accurate and up-to-date dataset. In this study, MSCAD is used which comes with multi-step attack tasking: the initial attack is for password-cracking type, and the subsequent one is a volume-driven Distributed Denial of Service (DDoS) attack. The dataset used (MSCAD) contains five types of internet attacks including Port Scan Traffic, App-based DDoS, Volume-based DDoS, Web Crawling, and Password Cracking (Brute Force). Nine algorithms including Gaussian Naive Bayes, Bournuli Naive Bayes, Decision Tree, K-Nearest Neighbors, Catboost, XGB, and Random Forest were employed as classifiers. The accuracy rate reached over 99.9% in terms of accuracy and AUC-ROC. © 2023 IEEE.

关键词： Intrusion detection

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CNN-Based Colon Cancer Recognition Model 3

CNN-Based Colon Cancer Recognition Model

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Ba Alawi, Abdulfattah E. Bozkurt, Ferhat Taiz University Software Engineering Faculty of Engineering and It Taiz Yemen Ataturk University Computer Engineering Faculty of Engineering Erzurum Turkey

ISBN: (纸本)9798350305333

Digital pathology is being used extensively for the diagnosis of tumors. Disappointingly, the existing approaches are still constrained whenever confronted with a resolution, a size of images, and a lack of extensively cleaned datasets. Further, the recognition accuracy mostly does not reach high scores. In terms of Deep Learning (DL) approaches' capacity to handle extensive applications, such an approach appears to be an absorbing solution for both categorization and tissue segmentation in histopathology images. The present study concentrates on the application of deep learning models in the classification of the context of histopathology data and the recognition of colon cancer. In this, cutting-edge Fully Convolutional Network (CNN) models such as DenseNet121, EfficientNetB7, EfficientNetB1, EfficientNetB2, and DenseNet201 have been evaluated for the recognition of Colon Cancer. The assessment of the algorithms used for the proposed CNN-based colon cancer detection model ensures reliable classification findings with EfficientNetB2 attaining up to 99.9994% in terms of accuracy. © 2023 IEEE.

关键词： Diagnosis

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A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

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International Journal of computers and Applications 2024年第8期46卷 666-685页

作者： Benedek Szabo, Aron Katona, Jozsef Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Instrumentation and Automation Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science Kecskemét Hungary

The integration of machine learning (ML) into mobile applications presents unique challenges, particularly in resource-constrained environments such as iOS devices. Skin lesion classification is a critical task in dermatology, where accurate and efficient diagnostic tools can significantly aid in early detection of malignant lesions. This study aims to implement a machine learning-based iOS mobile application and develop a binary classification model for skin lesion images to determine whether a lesion is malignant. The research utilized Create ML to develop a convolutional neural network optimized for iOS, employing a transfer learning approach. A logistic regression model was cascaded with the convolutional neural network N to enhance classification accuracy. The model's performance was assessed through various validation metrics, ensuring its robustness and efficiency within the constraints of mobile hardware. A curated dataset from the International Skin Imaging Collaboration archive was used for training and testing. The model achieved an accuracy of 92%, a precision of 90%, a recall of 93%, and an F1-score of 91% in classifying skin lesions. These metrics validate the model's efficacy in identifying malignant lesions. Data curation involved collecting, labeling, and preparing a dataset from publicly available sources, ensuring the inclusion of diagnostically relevant features. The final model was integrated into an iOS application using Core ML and Vision frameworks. The developed application demonstrates reliable performance in classifying skin lesions with high accuracy on iOS devices. The inclusion of comprehensive performance metrics justifies the efficacy of the proposed approach. Future work will explore enhancements in model architecture and object detection capabilities to further improve diagnostic precision and application usability. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image recognition

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