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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Najjar, Hazem Al-Rousan, Nadia University Of Petra Faculty Of Information Technology Department Of Computer Science Amman Jordan German Jordanian University Faculty Of Electrical Engineering And Information Systems Department Of Computer Engineering Jordan

ISBN: (纸本)9798350394962

we introduced image encryption algorithms with high sensitivity, such that even a single alteration in a plain-text image would result in a complete transformation of the ciphered image. The first algorithm employed pixel values in a substitution approach, whereas the second algorithm utilized pixel values to generate chaotic function keys. Additionally, we employed a Rossler chaotic function with pixel data to generate chaotic values for the shuffling and substitution processes. After subjecting these algorithms to rigorous testing using standard metrics, our experiments revealed the efficiency of the algorithms in image encryption. Notably, even a minute change in the original image resulted in a vastly different ciphered image. These proposed algorithms underscored the effectiveness of integrating chaotic functions with pixel information, significantly enhancing image security. © 2024 IEEE.

关键词： Cryptography

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Robust Human Interaction Recognition Using Extended Kalman Filter

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computers, Materials & Continua 2024年第11期81卷 2987-3002页

作者： Tanvir Fatima Naik Bukht Abdulwahab Alazeb Naif Al Mudawi Bayan Alabdullah Khaled Alnowaiser Ahmad Jalal Hui Liu Faculty of Computing&Artificial Intelligence Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

In the field of computer vision and pattern recognition,knowledge based on images of human activity has gained popularity as a research *** recognition is the process of determining human behavior based on an *** implemented an Extended Kalman filter to create an activity recognition system *** proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the *** minimize noise,we use Gaussian *** of silhouette using the statistical *** use Binary Robust Invariant Scalable Keypoints(BRISK)and SIFT for feature *** next step is to perform feature discrimination using Gray *** that,the features are input into the Extended Kalman filter and classified into relevant human activities according to their definitive *** experimental procedure uses the SUB-Interaction and HMDB51 datasets to a 0.88%and 0.86%recognition rate.

关键词： Pattern recognition geometric features activity recognition full-body texture

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Advanced Feature Extraction and Machine Learning Techniques for Classifying Steam Game Feedback

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第1期19卷 107-125页

作者： Al Tawil, Arar Fathi, Hanaa Alzoubi, Sharaf Shaban, Amneh Almazaydeh, Laiali H. Faculty of Information Technology Applied Science Private University Amman Jordan College of Computer Science and Informatics Amman Arab University Amman Jordan College of Engineering AbuDhabi University Abu Dhabi United Arab Emirates

The gaming industry produces vast amounts of user-generated feedback, making it challenging for developers to efficiently analyze and respond to real-time reviews. This study addresses the problem of classifying large-scale unstructured user feedback from Steam reviews. In this paper an approach that integrates traditional machine learning models and deep learning models is proposed. XGBoost is used to manage feature-rich datasets, reducing overfitting. Long-short-term memory (LSTM) and Bi-directional LSTM are used to enhance the accuracy and robustness of classification. Feature extraction techniques, such as sentiment analysis and topic modeling, are employed to enrich the dataset and improve model performance. The experimental results show that the XGBoost model achieved the highest performance with an accuracy of 0.9499 and a ROC-AUC score of 0.6113, demonstrating superior performance in distinguishing between positive and negative feedback. In comparison, deep learning models such as LSTM and Bi-directional LSTM showed lower ROC-AUC scores, indicating less effectiveness in handling the classification task. This approach offers game developers a reliable and scalable solution for classifying user sentiment, leading to better game improvements based on user reviews. © 2025 by the authors of this article.

关键词： Contrastive Learning

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An Efficient Technique to Prevent Data Misuse with Matrix Cipher Encryption Algorithms

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computers, Materials & Continua 2023年第2期74卷 4059-4079页

作者： Muhammad Nadeem Ali Arshad Saman Riaz Syeda Wajiha Zahra Ashit Kumar Dutta Moteeb Al Moteri Sultan Almotairi Department of Computing Abasyn UniversityIslamabadPakistan Department of Computer Science National University of TechnologyIslamabadPakistan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Kingdom of Saudi Arabia Department of Management Information Systems Business Administration CollegeKing Saud UniversityRiyadh11451Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

Many symmetric and asymmetric encryption algorithms have been developed in cloud computing to transmit data in a secure *** cryptography is a data encryption mechanism that consists of different steps and prevents the attacker from misusing the *** paper has developed an efficient algorithm to protect the data from invaders and secure the data from *** this algorithm is applied to the cloud network,the attacker will not be able to access the *** encrypt the data,the values of the bytes have been obtained by converting the plain text to ASCII.A key has been generated using the Non-Deterministic Bit Generator(NRBG)mechanism,and the key is XNORed with plain text bits,and then Bit toggling has been *** that,an efficient matrix cipher encryption algorithm has been developed,and this algorithm has been applied to this *** capability of this algorithm is that with its help,a key has been obtained from the plain text,and only by using this key can the data be decrypted in the first steps.A plain text key will never be used for another plain *** data has been secured by implementing different mechanisms in both stages,and after that,a ciphertext has been *** the end of the article,the latest technique will be compared with different *** will be a discussion on how the present technique is better than all the other techniques;then,the conclusion will be drawn based on comparative analysis.

关键词： Symmetric cryptography ciphertext encryption decryption cloud security matrix cipher

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Traffic information Extraction And Estimation In Developing Countries 9

Traffic Information Extraction And Estimation In Developing ...

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9th International Conference on Applying New Technology in Green Buildings, ATiGB 2024

作者： Minh, Quang Tran Phan, Trong Nhan Bao, Thu Le Thi Faculty of Computer Science and Engineering Department of Information Systems Ho Chi Minh city Viet Nam

ISBN: (纸本)9798331505042

This study aims to investigate and propose solutions to traffic information extraction used for state estimation based on traffic data collected from crowd-source. Concretely, a framework to traffic data collection such as voice and image/videos and the AI techniques to analyze such data to estimate traffic state will be proposed. This traffic information such as congested locations, reason caused such a traffic condition, routing suggestion will then be disseminated to commuters, which helps to eliminate traffic congestions. We have conducted evaluations using our system prototype and the results show the effectiveness of the proposed solutions revealing that the proposed approaches can be applied to real-world applications, especially in developing countries. © 2024 IEEE.

关键词： Data collection

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Wind Power Prediction Based on Machine Learning and Deep Learning Models

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computers, Materials & Continua 2023年第1期74卷 715-732页

作者： Zahraa Tarek Mahmoud Y.Shams Ahmed M.Elshewey El-Sayed M.El-kenawy Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Mohamed A.El-dosuky Faculty of Computers and Information Computer Science DepartmentMansoura UniversityMansoura35561Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Faculty of Computers and Information Computer Science DepartmentSuez UniversitySuezEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Arab East CollegesRiyadh13544Saudi Arabia

Wind power is one of the sustainable ways to generate renewable *** recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar *** achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base *** regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)*** addition,data cleaning and data preprocessing were performed to the *** dataset used in this study includes 4 features and 50530 *** accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM *** evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.

关键词： Prediction of wind power data preprocessing performance evaluation

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Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

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computers, Materials & Continua 2024年第6期79卷 3855-3875页

作者： Maie Aboghazalah Passent El-kafrawy Abdelmoty M.Ahmed Rasha Elnemr Belgacem Bouallegue Ayman El-sayed Math and Computer Science Department Faculty of ScienceMenoufia UniversityShebin El-komEgypt College of Engineering Computer Science DepartmentEffat UniversityJeddahKingdom of Saudi Arabia Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityShebin El-komEgypt Climate Change Information Center and Expert Systems Agriculture Research CenterGizaEgypt

Heart monitoring improves life ***(ECGs or EKGs)detect heart *** learning algorithms can create a few ECG diagnosis processing *** first method uses raw ECG and time-series *** second method classifies the ECG by patient *** third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer *** ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and *** using all three approaches have not been examined till *** researchers found that Machine Learning(ML)techniques can improve ECG *** study will compare popular machine learning techniques to evaluate ECG *** algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization *** plus prior knowledge has the highest accuracy(99%)of the four ML *** characteristics failed to identify signals without chaos *** 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.

关键词： ECG extraction ECG leads time series prior knowledge and arrhythmia chaos theory QRS complex analysis machine learning ECG classification

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Enhancing Anomaly Detection in Agriculture by Considering the Impact of Concept Drift: An Exploratory Study Using Digital Replica 23

Enhancing Anomaly Detection in Agriculture by Considering th...

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5th World Symposium on Software Engineering, WSSE 2023

作者： Huo, Dongyang Malik, Asad Waqar Ravana, Sri Devi Ur Rahman, Anis Ahmedy, Ismail Department of Information Systems Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Islamabad Pakistan Department of Computer Systems and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798400708053

The advent of precision agriculture is disrupting traditional farming practices, leading to a significant increase in the data flow. However, current anomaly detection methods overlook the impact of concept drift. The phenomenon of concept drift refers to the changes in the underlying distribution of the data over time, which can result in unexpected and misleading results. As such, it is essential to consider the impact of theconcept drift in the context of anomaly detection in precision agriculture. This study uses a digital replica of an agricultural system equipped with intelligent agents to examine the system dynamics and the temporal variability of end-to-end data flow in precision agriculture. It highlights the need for robust methods that can effectively address the challenges posed by concept drift and systems’ dynamism enabling accurate and reliable anomaly detection in the IoT farming system. © 2023 Copyright held by the owner/author(s).

关键词： Precision agriculture

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Addressing the Libyan Arabic Dialect Identification: A Comparative Study of Ensemble Classification Methods 4

Addressing the Libyan Arabic Dialect Identification: A Compa...

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4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Ghumeid, Najah A. Essgaer, Mansour Sebha University Faculty of Sciences Computer Science Department Sebha Libya Sebha University Faculty of Information Technology Information Systems Department Sebha Libya

ISBN: (纸本)9798350372632

Dialect identification presents a challenging task within the realm of Natural Language Processing. The growing utilization of Arabic dialects in written form, particularly on social media platforms, has created new demands for accurate identification of these dialects. In multilingual datasets, distinguishing the Libyan Arabic dialect from neighboring Arabic dialects has emerged as a crucial goal. To address this, we conducted experiments using different lengths of word n-gram features to determine the most suitable combination of word n-grams and term frequency-inverse document frequency. Our aim was to capture contextual information effectively while mitigating linguistic similarities between Arabic dialects of neighboring countries. The QCRI Arabic Dialects Identification dataset was employed, and we evaluated the performance of three ensemble tree-based classification methods: Random Forest, Gradient Boost Machine, and eXtreme Gradient Boosting. The evaluation, based on the f-measure metric, yielded respective scores of 68%, 37%, and 52% for Random Forest, Gradient Boost Machine, and eXtreme Gradient Boosting. These results underscore the effectiveness of our approach in identifying the Libyan Arabic dialect and highlight the potential of ensemble tree-based methods in Arabic dialect identification tasks. © 2024 IEEE.

关键词： Computational linguistics

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Towards Leveraging Explainable Artificial Intelligent (XAI) in Requirements Engineering (RE) to Identify Aspect (Crosscutting Concern): A Systematic Literature Review (SLR) and Bibliometric Analysis 11

Towards Leveraging Explainable Artificial Intelligent (XAI) ...

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11th International Conference on information Technology, ICIT 2023

作者： Magableh, Aws A. Yarmouk University Faculty of Computer Science and Information Technology Department of Information Systems Irbid Jordan

ISBN: (纸本)9798350320060

The software development life cycle contains many phases, among the most notable ones is requirements engineering (RE), it is considered a very important activity because poorly implemented RE steps can result in poor software quality which might lead to expensive maintenance costs and so many other undesirable issues. Thus, owing to the importance of RE in general and aspect (crosscutting concerns) identification in particular, researchers have proposed many approaches that can help to produce a clear and complete set of requirements document. On the other hand, artificial intelligence methods have a proven record of handling complicated text. Yet there is insufficient understanding of how AI and explainable AI can be employed and incorporated to be utilized in RE. Nevertheless, join forces to produce research for beneficial outcomes in this theme. The present study is a comprehensive SLR that reviewed the current state-of-the-art of AI/XAL in RE and aspect orientation. We reviewed the research from 2007 till 2022, PRISMA methodology is utilized to obtain a final set of (n =23) articles from four major scientific databases (Scopus, and Web of science). Clear and new taxonomies were invented during the review discussion. Additionally, to enhance the contribution of this research, a bibliometric comprehensive analysis was conducted on the basis of annual scientific production, country scientific production, and co-occurrence. As a result, it is deemed a noteworthy topic of extensive exploration especially after we identify critical research gaps with their corresponding solutions and generic thoughts to serve as a guide for future directions. © 2023 IEEE.

关键词： Requirements engineering

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