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Systematic Literature Review for Optimization System with Advection-Diffusion-Reaction Non-Linear Equation in Water Quality

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2541-2554页

作者： Suryana, Ino Chaerani, Diah Abdullah, Atje Setiawan Prabuwono, Anton Satria Muslihin, Khoirunnisa Rohadatul Aisy Irmansyah, Athaya Zahrani Department of Computer Sciences Faculty of Mathematics and Natural Science Universitas Padjadjaran Sumedang45363 Indonesia Department of Mathematics Faculty of Mathematics and Natural Science Universitas Padjadjaran Sumedang45363 Indonesia Department of Computer Sciences Faculty of Mathematics and Natural Science Universitas Padjadjaran Sumedang45363 Indonesia Department of Information Technology Faculty of Computing and Information Technology in Rabigh King Abdul Aziz University Saudi Arabia Faculty of Mathematics and Natural Science Universitas Padjadjaran Indonesia

Water quality is an essential component of environmental health and sustainability, including various parameters such as chemical, physical, and biological attributes. These parameters determine the suitability of water for consumption, recreation, and ecosystem health. Clean and safe water availability is a crucial foundation for human well-being, ecological balance, and sustainable development, indicating the need to monitor and control water quality to meet human demands. Based on the importance of water quality, this research aimed to provide a comprehensive overview of previous investigations related to the optimization system of the advection-diffusionreaction (ADR) non-linear equation in water quality. Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines were conducted to ensure a structured article selection, data extraction, and analysis approach. This was followed by presenting a bibliometric analysis of selected data to support the study of research trends. The results showed that using ADR non-linear equation systems in modeling water quality optimization was a potential area requiring further exploration. Moreover, this research provided valuable information that could be used as a reference for further investigation of water quality optimization. A future research design is also presented. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Advection-diffusion-reaction Optimization PRISMA Sustainable Development Goals (SDGs) Systematic literature review Water quality

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Leveraging the Potential of Large Language Models

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Informatica (Slovenia) 2024年第8期48卷 1-16页

作者： Prasad, Shreya Gupta, Himank Ghosh, Arup Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Information Technology Graceland University LamoniIA United States

This study focuses on enhancing Natural Language Processing (NLP) in generative AI chatbots through the utilization of advanced pre-trained models. We assessed five distinct Large Language Models (LLMs): TRANSFORMER MODEL, FALCON 7B, LAMINI-FLAN-T5-783M, LLAMA-2-7B, and LLAMA-2-13B to identify the most effective one. Our findings revealed that the LLAMA Model excels in comprehending user queries and delivering precise responses during conversations. The article elucidates the methodology employed to evaluate and select various models for our chatbot. Through rigorous testing, we determined that the LLAMA-2-13B model exhibits enhanced response time and accuracy. Additionally, we employed tools such as Facebook Artificial Intelligence Similarity Search (FAISS) and experimented with user interfaces like Streamlit and Chainlit to enhance the chatbot's user-friendliness. The research underscores the significance of selecting the appropriate model for crafting efficient chatbots. Ultimately, the LLAMA-13B model emerged as the standout performer, showcasing superior performance. Benchmark assessments, including HellaSwag and WinoGrande, which gauge common sense reasoning, were employed to evaluate our chatbot's capabilities. The study concludes that LLAMA-based models hold significant promise for the development of innovative and user-friendly chatbots in the future. Povzetek: Študija se osredotoča na izboljšanje obdelave naravnega jezika (NLP) v generativnih AI klepetalnicah z uporabo naprednih vnaprej usposobljenih modelov. Analizirali so pet modelov: TRANSFORMER MODEL, FALCON 7B, LAMINI-FLAN-T5-783M, LLAMA-2-7B in LLAMA-2-13B. Model LLAMA-13B se je izkazal kot najboljši z vrhunskimi rezultati na testih HellaSwag in WinoGrande. Raziskava uporablja orodja, kot je FAISS, in uporabniške vmesnike, kot sta Streamlit in Chainlit za izboljšanje uporabniške izkušnje. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Machine learning

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Advancing skin cancer detection integrating a novel unsupervised classification and enhanced imaging techniques

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CAAI Transactions on Intelligence technology 2025年第2期10卷 474-493页

作者： MdAbdur Rahman Nur Mohammad Fahad Mohaimenul Azam Khan Raiaan Mirjam Jonkman Friso De Boer Sami Azam Department of Computer Science and Engineering United International UniversityDhakaBangladesh Faculty of Science and Technology Charles Darwin UniversityCasuarinaNorthern TerritoryAustralia

Skin cancer,a severe health threat,can spread rapidly if ***,early detection can lead to an advanced and efficient diagnosis,thus reducing *** classification techniques analyse extensive skin image datasets,identifying patterns and anomalies without prior labelling,facilitating early detection and effective diagnosis and potentially saving *** this study,the authors aim to explore the potential of unsupervised learning methods in classifying different types of skin lesions in dermatoscopic *** authors aim to bridge the gap in dermatological research by introducing innovative techniques that enhance image quality and improve feature *** achieve this,enhanced super-resolution generative adversarial networks(ESRGAN)was fine-tuned to strengthen the resolution of skin lesion images,making critical features more *** authors extracted histogram features to capture essential colour characteristics and used the Davies-Bouldin index and silhouette score to determine optimal ***-tuned k-means clustering with Euclidean distance in the histogram feature space achieved 87.77% and 90.5% test accuracies on the ISIC2019 and HAM10000 datasets,*** unsupervised approach effectively categorises skin lesions,indicating that unsupervised learning can significantly advance dermatology by enabling early detection and classification without extensive manual annotation.

关键词： histogram feature optimal cluster skin lesion unsupervised classification

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Transaction transmission model for blockchain channels based on non-cooperative games

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science China(information sciences) 2023年第1期66卷 97-114页

作者： Peiyun ZHANG Chenxi LI Mengchu ZHOU Wenjun HUANG Abdullah ABUSORRAH Omaimah O.BAMASAG Engineering Research Center of Digital Forensics of Ministry of Education School of Computer and SoftwareNanjing University of Information Science and Technology School of Computer and Information Anhui Normal University Department of Electrical and Computer Engineering New Jersey Institute of Technology Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering and K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.

关键词： Blockchain channel transaction transmission channel balance channel state channel transmission probability non-cooperative game

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A Computational Deep Fuzzy Network-Based Neuroimaging Analysis for Brain Hemorrhage Classification

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IEEE Journal of Biomedical and Health Informatics 2023年 PP卷 1-8页

作者： Malik, Payal Vidyarthi, Ankit Department of Computer Science Engineering and Information Technology Jaypee Institute of Information Technology Noida India

The boundaries and regions between individual classes in biomedical image classification are hazy and overlapping. These overlapping features make predicting the correct classification result for biomedical imaging data a difficult diagnostic task. Thus, in precise classification, it is frequently necessary to obtain all necessary information before making a decision. This paper presents a novel deep-layered design architecture based on Neuro-Fuzzy-Rough intuition to predict hemorrhages using fractured bone images and head CT scans. To deal with data uncertainty, the proposed architecture design employs a parallel pipeline with rough-fuzzy layers. In this case, the rough-fuzzy function functions as a membership function, incorporating the ability to process rough-fuzzy uncertainty information. It not only improves the deep model's overall learning process, but it also reduces feature dimensions. The proposed architecture design improves the model's learning and self-adaptation capabilities. In experiments, the proposed model performed well, with training and testing accuracies of 96.77% and 94.52%, respectively, in detecting hemorrhages using fractured head images. The comparative analysis shows that the model outperforms existing models by an average of 2.6$\pm$0.90% on various performance metrics. IEEE

关键词： Classification (of information)

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Performance Analysis of Boosting Algorithms on DDoS Attack Detection with Data Balancing Techniques 5

Performance Analysis of Boosting Algorithms on DDoS Attack D...

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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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Exploring image data association: A hybrid mining approach

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Multimedia Tools and Applications 2025年第9期84卷 5725-5740页

作者： Parashar, Nishtha Tiwari, Akhilesh Gupta, Rajendra Kumar Department of Computer Science and Engineering Madhav Institute of Technology & Science M.P. Gwalior India Department of Information Technology Madhav Institute of Technology & Science M.P. Gwalior India

In this paper, a new approach for mining image association rules is presented, which involves the fine-tuned CNN model, as well as the proposed FIAR and OFIAR algorithms. Initially, the image transactional database is generated using feature vectors obtained from the fine-tuned CNN architecture. The proposed FIAR algorithm is used to generate hash-indexed image association rules, which are further optimized using the proposed OFIAR algorithm. This methodology combines the strengths of the CNN model to extract histogram features from images, the FIAR algorithm to efficiently mine frequent image itemsets, and the OFIAR algorithm to optimize image association rules. The proposed methodology can be used to discover hidden relationships among images, leading to new insights in image processing and analysis. Efficient results were obtained with a minimum support of 0.50 and a minimum confidence of 0.50. Experiments were performed on the fruits image dataset consisting of 2618 images from six different classes, and the results show that image mining is feasible and can produce strong optimized image association rules that can be further used for classification purposes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Association rules

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Examining the impact of link failures and network performance on a 6to4, 6rd, CHANC and D4across6 tunneling-based networks using various routing protocols

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International Journal of information technology (Singapore) 2024年 1-19页

作者： Hamarsheh, Ala Faculty of Information Technology/Department of Computer Science Arab American University Jenin 00970 Palestine

The article discusses the importance of failure recovery in communication networks to ensure smooth and dependable service. It states that the performance of real-time applications has been negatively affected for years due to failed links, and thus it is crucial to address the issue to prevent collapse and cascading failures for both users and service providers. The paper then goes on to analyze the performance of a network by simulating the failure and recovery of network links for different time periods using the 6rd, CHANC, D4across6 and 6to4 tunneling network. Different routing protocols and tunneling techniques are employed to evaluate the performance of the network for the specific type of applications (i.e. video streaming and real-time voice). It also looks at different metrics, including data transfer rate, dropped traffic, and network convergence. Furthermore, the study delves into the examination of the route table properties of various routers and the IPv4 backbone when links between them experience repeated failures and recoveries. The purpose of this analysis is to anticipate network performance and to ensure network availability and accessibility of traffic through forecasting the network's ability to survive and recover from link failures. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： IPv4 tunneling IPv6 IPV6 transition mechanisms IPv6 tunneling OPNET network performance

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Optimized digital watermarking: Harnessing the synergies of Schur matrix factorization, DCT, and DWT for superior image ownership proofing

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Multimedia Tools and Applications 2025年第18期84卷 19817-19852页

作者： Al-Aiash, Issa Alquran, Rabee AlJamal, Mahmoud Alsarhan, Ayoub Aljaidi, Mohammad Al-Fraihat, Dimah Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa13133 Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa13110 Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman11622 Jordan

The contemporary context of abundant digital dissemination inherently gives rise to the need for media protection and the clear identification of ownership rights. This paper responded to this nagging issue of unauthorized media capture by developing a new digital watermarking approach uniting the Discrete Wavelet Transform DWT, Discrete Cosine Transform DCT, and Schur matrix factorization. The proposed watermark, which was a positive vector uniformly distributed, presented a high degree of robustness to several adversarial operating conditions, including histogram equalization, salt and pepper noise, ripple impact, smoothing attack, and cropping. The obtained results reveal the effectiveness of our approach. The correlation coefficient between watermarked and original images remains exceptionally high at 0.999998, ensuring minimal visual distortion. Mean Squared Error (MSE) graphs for both high-frequency (HH) and low-frequency (LL) domains demonstrate negligible changes after attacks, confirming the watermark's robustness. Notably, the watermark extraction process remains successful even after histogram equalization, as indicated by significantly lower MSE compared to random vectors. Moreover, the average processing times for different attacks are promising;Histogram Equalization and Salt and Pepper attacks each took 0.3 s, Ripple Attack required 0.28 s, Smoothing Attack completed in 0.29 s, and the Cropping Attack took 0.6 s. The robustness of our approach appears against the salt and pepper attack, which historically presented a vulnerability when employing Schur matrix factorization in image watermarking, indicating the novelty of and this contribution to improving media ownership verification. By combining disparate transformation methodologies, this technique offers a promising avenue for reinforcing the integrity and authenticity of digital media, thereby strengthening the foundations of intellectual property rights in the digital domain. © The Author(s), und

关键词： Discrete wavelet transforms

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An Adaptive Framework for Classification and Detection of Android Malware

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第21期18卷 59-73页

作者： Al Sharah, Ashraf Alrub, Yousef Abu Owida, Hamza Abu Elsoud, Esraa Abu Alshdaifat, Nawaf Khtatnaha, Hamzah Department of Electrical Engineering College of Engineering Technology Al-Balqa Applied University Amman Jordan Department of Computer Information Systems Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Medical Engineering Faculty of Engineering Al-Ahliyya Amman University Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Faculty of Information Technology Applied Science Private University Amman Jordan

The hardware and software of a computer are controlled by its operating system (OS), which performs essential tasks such as input and output processing, file and memory management, and the management of peripheral devices such as disk drives and printers. Application software refers to programs designed for specific purposes, these applications, often freely available and open source, contribute to the rising number of downloads. In the third quarter of 2022, combined downloads from the Apple App Store and Google Play Reached an estimated 35.3 billion. However, the prevalence of insecurity in these applications and technologies heightens the potential for cybercrimes. Protection against unauthorized intruders is crucial in identifying malicious applications. Machine learning (ML) serves as a promising avenue for detecting malware attacks, offering potential solutions to bolster cybersecurity measures. We propose a novel approach utilizing ML to enhance malware detection accuracy by segmenting datasets into distinct groups. Our research employs supervised ML techniques on the CICMaldroid2020 dataset, which includes comprehensive information such as intent actions, permissions, and sensitive APIs. The dataset was partitioned into four groups, each containing 150 features, and analyzed across four experiments to distinguish between attack and benign classes. Our proposed model demonstrated exceptional performance, with the random forest algorithm achieving an accuracy of 98.6% and a precision of 98.75%. These results highlight the effectiveness of our segmentation approach and its significant contribution to advancing malware detection in Android applications, offering a promising direction for future cybersecurity solutions. © 2024 by the authors of this article.

关键词： Android malware

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