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5th International Conference on Advanced information Technologies, ICAIT 2024

作者： Naing, Soe Kalayar Thwel, Tin Thein University of Information Technology Faculty of Computer Science Yangon Myanmar University of Computer Studies Yangon Faculty of Information Science Yangon Myanmar

ISBN: (纸本)9798331510046

Today, a lot of people use the Internet of Things (IoT) in organizations, businesses, and daily life. Attackers have taken advantage of this potential for IoT devices to expose the integrity and security of user data. Especially, the security of the internet and user data is seriously threatened by distributed denial of service (DDoS) attacks. A DDoS attack is a cyberattack, that intends to break down or shut legal traffic from leading to its destination. There are various Machine Learning is employed in this work as classification methods for DDoS attack detection to develop an automated system that can recognize an attack immediately. This research intends a lightweight boosting classification approach to detect DDoS attacks of IoT based on the resampling techniques to minimize the data imbalance. It compares the performance of the proposed approach with AdaBoost, CatBoost, XGBoost, and LightGBM of boosting models based on time, ROC ACU, accuracy, MAE, RMSE, and F1-score. The experimental results demonstrated that the model with 4.079 seconds of the shortest runtime, highest accuracy, and low error rate is XGBoost using the SMOTE technique on the CICDDOS2019 dataset and KDDCUP99 dataset. XGBoost illustrates excellent time efficiency between other models, and it is a pleasant option for scenarios where rapid processing is critical. The accuracy of XGBoost illustrates highest than other models with 0.9998 with Smote and 0.9900 with borderline smote by using CICDDOS2019 dataset as performance. Additionally, it illustrates 0.699 accuracy with smote and 0.9699 of accuracy with borderline smote by using KDDCUP99 dataset. This research proposes an XGBoost classification approach for an effective IoT DDoS attack detection model using SMOTE and borderline SMOTE for balanced resampling. © 2024 IEEE.

关键词： Denial-of-service attack

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Robust Deep Convolutional Solutions for Identifying Biotic Crop Stress in Wild Environments

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 497-508页

作者： Pal, Chiranjit Mukherjee, Imon Chatterji, Sanjay Pratihar, Sanjoy Mitra, Pabitra Chakrabarti, Partha Pratim Indian Institute of Information Technology Kalyani Department of Computer Science and Engineering Kalyani741235 India Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur721302 India

In the realm of agricultural automation, the precise identification of crop stress holds immense significance for enhancing crop productivity. Existing methods primarily focus on controlled environments, which may not accurately reflect field conditions. Field-based leaf image analysis poses challenges due to varying image quality and sunlight intensity. Moreover, the complexity of crop stress images, with their random lesion distribution, diverse symptoms, and complex backgrounds, further complicates the analysis. To overcome these limitations, a lightweight hybrid convolutional neural network has been developed. This system integrates the powerful three-deep blocks model with an autoencoder running in parallel to highlight regions of crop stress effectively. To support this approach, we have introduced the Indian Rice Disease Dataset (IRDD) with labeled images. The proposed system reports an average true positive rate (TPR) of 0.8766 and an average positive predicted value of 0.8720 on IRDD, which are higher than other state-of-the-art crop disease detection models. The system is validated on benchmark datasets, yielding significant results: TPR of 0.9870 (rice), 0.9985 (tomato), and 0.8559 (corn). Furthermore, the proposed model outperforms recent state-of-the-art works on the benchmark PlantDoc dataset, showing its effectiveness in generalizing plant disease identification tasks. Finally, an ablation study has been carried out to explore the importance of the two parallel branches. Overall, this study acts as a bridge between advanced science and practical application, showcasing how interdisciplinary automation could revolutionize crop disease identification, improve agricultural efficiency, and reshape broader industrial practices. © 2024 IEEE.

关键词： Convolutional neural networks

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AFARM: Anxiety-Free Autonomous Routing Model for Electric Vehicles with Dynamic Route Preferences

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第8期18卷 67-86页

作者： Quttoum, Ahmad Nahar Alsarhan, Ayoub Moh’d, Abidalrahman Aljaidi, Mohammad Samara, Ghassan Alshammari, Muteb Department of Computer Engineering The Hashemite University Zarqa Jordan Department of Information Technology The Hashemite University Zarqa Jordan Department of Math and Computer Science Eastern Illinois University CharlestonIL United States Department of Computer Science Zarqa University Zarqa Jordan Department of Information Technology Northern Border University Arar Saudi Arabia

Energy and environmental concerns have fostered the era of electric vehicles (EVs) to take over and be welcomed more than ever. Fuel-powered vehicles are still predominant;however, this trend appears to be changing sooner than we might expect. Countries in Europe, Asia, and many states in America have already made the decision to transition to a fully EV industry in the next few years. This looks promising;however, drivers still have concerns about the battery mileage of such vehicles and the anxiety that such driving experiences! Indeed, driving with the probability of having insufficient battery charge that may be involved in guaranteeing the delivery to the trip destination imposes a level of anxiety on the vehicle drivers. Therefore, for an alternative to traditional fuel-powered vehicles to be convincing, there needs to be sufficient coverage of charging stations to serve cities in the same way that fuel stations serve traditional vehicles. The current navigation models select routes based solely on distance and traffic metrics, without taking into account the coverage of fuel service stations that these routes may offer. This assumption is made under the belief that all routes are adequately covered. This might be true for fuel-powered vehicles, but not for EVs. Hence, in this work, we are presenting AFARM, a routing model that enables a smart navigation system specifically designed for EVs. This model routes the EVs via paths that are lined with charging stations that align with the EV’s current charge requirements. Different from the other models proposed in the literature, AFARM is autonomous in the sense that it determines navigation paths for each vehicle based on its make, model, and current battery status. Moreover, it employs Dijkstra’s algorithm to accommodate varying least-cost navigation preferences, ranging from shortest-distance routes to routes with the shortest trip time and routes with maximum residual battery capacities as well. According to t

关键词： Navigation systems

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Sensitive information leakage analysis of database code by abstract interpretation

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International Journal of Security and Networks 2023年第2期18卷 91-105页

作者： Jana, Angshuman Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India

An information system stores outside data in the backend database to process them efficiently and protects sensitive data from illegitimate flow or unauthorised users. However, most information systems are made in such a way that the sensitive information stored in a database may be leaked explicitly or implicitly during data processing along with the control structure of the program to the output channels. Therefore, sensitive data leakage is one of the crucial security threat. In this paper, the main objective is to detect the illegitimate flow of confidential information in an information system. We propose a framework to detect sensitive information leakage through the data-flow paths of an information system. In particular, to compute the precise set of data-flow paths, we use the non-relational abstract property of the interval domain and the relational abstract property of the polyhedra domain that enables the framework to produce efficient security analysis results. © 2023 Inderscience Enterprises Ltd.

关键词： Formal methods

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Securing Autonomous UAV Cluster With Blockchain-Based Threshold Key Management system Utilizing Crypto-Asset and Multisignature

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5765-5778页

作者： Kharjana, Mebanjop Sahana, Subhas Chandra Saha, Goutam North-Eastern Hill University Department of Information Technology Shillong793022 India National Institute of Technology Department of Computer Science and Engineering Durgapur713209 India

Unmanned aerial vehicles deployed in remote locations rely on self-governed key management for their protection. However, conventional key management depends on a centralized ground-based station or single vehicle. Such a system is vulnerable to compromised certificate authority problems and single-points-of-failure. This paper proposed to resolve these vulnerabilities using a blockchain-based threshold key management system. The proposed system utilized blockchain’s concepts of crypto-asset and multisignature. Keys are defined as crypto-assets to improve their management in the blockchain network. Multisignature facilitates collaboration during key management based on a threshold value. The threshold value is also configurable to meet systems’ security and performance requirements. The proposed system secured the process of re-enforcement, sub-clustering, re-merging, and inter-cluster migration. Security analysis revealed that the proposed system complied with most key management security guidelines. The custom signature module used to authenticate intra-cluster communication was also verified as safe. Threats to the cluster were identified, assessed for risk, and mitigated accordingly. Performance analysis found that both AODV and DSDV routing protocols offer consistent performance but DSDV prevailed during the worst-case network scenario. The paper finally identified research gaps, including the requirement for an optimized mechanism for collecting consent signatures. © 2002-2012 IEEE.

关键词： Blockchains Autonomous Aerial Vehicles Security Authentication Drones Public Key Mobile Computing Smart Contracts Collaboration Global Positioning system Autonomous UAV Cluster Threshold Key Management system Blockchain Crypto Asset Multisignature Unmanned Aerial Vehicles Autonomous Unmanned Aerial Vehicles Key Management system Unmanned Aerial Vehicles Cluster Worst Case Scenario Performance Requirements Security Analysis Security Requirements Certificate Authority Energy Consumption Application Programming Interface Secret Key Directed Acyclic Graph Public Key Denial Of Service Elliptic Curve Best Case Scenario Secret Sharing Smart Contracts Public Key Infrastructure Key Operations Unmanned Aerial Vehicles Networks Distributed Ledger technology Signature Scheme Digital Signature Blockchain Platform Random Oracle Model Bitcoin

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Multi Label Classification Based on Ensemble Learning: Investigative Study 25

Multi Label Classification Based on Ensemble Learning: Inves...

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25th International Arab Conference on information technology, ACIT 2024

作者： Nasir, Ola Alazaidah, Raed Hadi, Wael Samara, Ghassan Al Daoud, Essam Owaida, Suhaila Abu Elrashidi, Ali Elsoud, Esraa Abu Alzoubi, Haneen Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Petra University Faculty of Information Technology Department of Cyber Security Amman Jordan Zarqa University Faculty of Information Technolog Department of Computer Science Zarqa Jordan Al-al-Bayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Applied Science Private University Faculty of It Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

In the big data technology nowadays, activities associated with Multi-Label Classification (MLC) pose big and complex challenges, receiving terrific interest in diverse fields. Existing MLC algorithms suffer from low overall performance. This paper considers four well-known Problem Transformation Methods (PTMs). Two of these PTMs are based on the frequency of the labels (MFL and LFL) while the last two PTMs are based on the positive correlations among labels. These PTMs have been implemented and compared against four multi label datasets of various sizes to identify the best one that fits the nature of MLC datasets. Moreover, six popular ensemble learning models have been carried out and compared on the four datasets with respect to several evaluation metrics such as Accuracy, Hamming loss, and Exact Match and using SMO, DecisionStump, Random Forest, and Random Commission classifiers. The results revealed that MFL accomplished the quality performance amongst the four PTMs, and Stacking SVM is the best ensemble learning technique. © 2024 IEEE.

关键词： Contrastive Learning

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Predictive Analytics in Mobile Education: Evaluating Logistic Regression, Random Forest, and Gradient Boosting for Course Completion Forecasting

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第5期19卷 210-232页

作者： Hayadi, B. Herawan Hariguna, Taqwa Information Technology Education Faculty of Teacher Training and Education Bina Bangsa University Banten 42124 Indonesia Department of Information System and Magister Computer Sciences Universitas Amikom Purwokerto Purwokerto Utara 53127 Indonesia

This study aimed to compare the effectiveness of three predictive algorithms—logistic regression, random forest, and GBM—in predicting course completion using user engagement data from online learning platforms. By analyzing engagement metrics such as session duration, session frequency, and quiz scores, the study sought to identify the most effective model for forecasting course completion, providing insights into which aspects of student behavior were most predictive of success. Logistic regression emerged as the best overall performer, achieving the highest accuracy (52.13%) and F1-Score (56.17%), indicating its balanced approach to predicting course completion and non-completion. Random forest and gradient boosting machines (GBM) showed strengths in specific areas;random forest maintained a good balance between precision and recall, while GBM excelled in recall, identifying students likely to complete courses but with lower precision, leading to more false positives. The findings have practical implications for educational technology, particularly in designing personalized learning paths and targeted interventions to support at-risk students. The study also acknowledged limitations, including the dataset’s focus on engagement metrics without demographic context and the potential for model-specific biases. Future research should explore additional predictive features, larger datasets, and more advanced algorithms to enhance the robustness and applicability of predictive models in real-time educational settings. © 2025 by the authors of this article.

关键词： Prediction models

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Karnauph classifier for predicting breast cancer based on morphological features

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International Journal of information technology (Singapore) 2024年第1期16卷 353-359页

作者： Zabian, Arwa Ibrahim, Ahmed Zohair Faculty of Science and Information Technology Department of Computer Science Jadara University Irbid Jordan Department of Computer Sciences Riyadh College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia

Breast cancer is the primary cause of mortality in women in the world, using artificial intelligence in predicting, detecting and early diagnosing of breast cancer can reduce the mortality rate. In this study, we propose a new approach to find the best morphological features that give the best prediction accuracy about the type of cancer benign or malignant. Our proposed algorithm Karnauph algorithm is a hybrid mathematical model that combines between features from the supervised learning and some other features from the unsupervised learning to obtain an easy and robust algorithm in term of prediction and computation. We apply our proposed algorithm on breast cancer dataset and we compare its performance in term of prediction accuracy with that of Naive Bayes, decision tree and Adaboost algorithms. The results show that our algorithm performance is similar to all of these algorithms when they are implemented on the same dataset and it gives better performance than other algorithms when evaluated on unseen data. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Breast cancer Hybrid algorithm information gain Morphological features Prediction accuracy

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Diabetic Retinopathy Detection: A Hybrid Intelligent Approach

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computers, Materials & Continua 2024年第9期80卷 4561-4576页

作者： Atta Rahman Mustafa Youldash Ghaida Alshammari Abrar Sebiany Joury Alzayat Manar Alsayed Mona Alqahtani Noor Aljishi Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia

Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy(DR).Early detection and treatment are crucial to prevent complete blindness or partial vision *** detection methods,which involve ophthalmologists examining retinal fundus images,are subjective,expensive,and ***,this study employs artificial intelligence(AI)technology to perform faster and more accurate binary classifications and determine the presence of *** this regard,we employed three promising machine learning models namely,support vector machine(SVM),k-nearest neighbors(KNN),and Histogram Gradient Boosting(HGB),after carefully selecting features using transfer learning on the fundus images of the Asia Pacific Tele-Ophthalmology Society(APTOS)(a standard dataset),which includes 3662 images and originally categorized DR into five levels,now simplified to a binary format:No DR and DR(Classes 1-4).The results demonstrate that the SVM model outperformed the other approaches in the literature with the same dataset,achieving an excellent accuracy of 96.9%,compared to 95.6%for both the KNN and HGB *** approach is evaluated by medical health professionals and offers a valuable pathway for the early detection of DR and can be successfully employed as a clinical decision support system.

关键词： Diabetic retinopathy transfer learning machine learning fundus images binary classification APTOS

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Mitigating Bias and Assessment Inconsistencies with BERT-Based Automated Short Answer Grading for the Indonesian Language

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第3期52卷 533-545页

作者： Amalia, Amalia Lydia, Maya Silvi Muchtar, Muhammad Anggia Manik, Fuzy Yustika Sinu Gunawan, Dani Department of Computer Science Universitas Sumatera Utara Medan Indonesia Department of Information Technology Universitas Sumatera Utara Medan Indonesia

Automating the grading of short answers in Indonesian presents unique challenges, primarily due to the inherent variability in student responses and the limited linguistic resources available for fine-tuning models. This study aims to develop an automated grading system for short answers by employing a modified BERT model tailored explicitly for the Indonesian language. Our methodology involves translating the Stanford Question Answering Dataset (SQuAD 2.0) and augmenting it with domain-specific content from lecture materials, resulting in an additional 1,000 question-answer pairs. Furthermore, we optimize the model using the Optuna library for hyperparameter tuning. Experimental results show a minimum loss of 1.81 for the model, with optimized hyperparameters including a learning rate of 3.36 × 10-5, a training batch size of 8 per device, and two training epochs. The model achieves an F1-score of 69%, demonstrating a satisfactory level of accuracy comparable to or slightly exceeding typical performances on SQuAD 2.0 in English, which averages around 66%. Evaluations were conducted across various scenarios, including short-answer, long-context, and long-answer assessments. The results revealed that short answers achieved the highest cosine similarity and a perfect QWK score of 1. In contrast, long-context and long-answer scenarios yielded a QWK of 0.5, indicating a need for further improvement;however, the model shows promise for long answers with additional enhancements. This study demonstrates BERT’s potential to enhance equity and precision in grading Indonesian short answers, despite limitations in response completeness. © (2025), (International Association of Engineers). All rights reserved.

关键词： Linguistics

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