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2023 international Conference on iT and industrial Technologies, iCiT 2023

作者： Shah, Muhammad Huzaifa Abu Bakar, Muhammad Ali, Raja Hashim Ul Abideen, Zain Arshad, Usama ijaz, Ali Zeeshan Ali, Nisar imad, Muhammad Nabi, Said Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

This study investigates the application of the Mutual information (Mi) feature selection technique to improve the accuracy of Machine Learning (ML) models on NSL-KDD datasets, building upon prior research. Six ML models, namely Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM) with different kernels (1st, 2nd, and 3rd), are implemented for classification purposes. The proposed DT model in this study shows higher accuracy than the DT model proposed in the original paper by ingre et al. for intrusion Detection System (iDS). Additionally, a multi-class classification model for NSL-KDD datasets is developed, considering both normalized and non-normalized features. interestingly, it is observed that the models trained without normalized features achieve higher accuracies compared to those trained with normalized features. Moreover, the study enhances the classification performance of the DT-based iDS using the Correlation based Feature Selection (CFS) technique for feature selection. The proposed iDS is evaluated both before and after feature selection for multi-class classification (normal and various attack types) and binary classification (normal and abnormal data). © 2023 iEEE.

关键词： Feature Selection

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Prediction of Hypertension Disease Using Machine Learning Techniques: Case Study from Jordan 25

Prediction of Hypertension Disease Using Machine Learning Te...

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25th international Arab Conference on information Technology, ACiT 2024

作者： Alazaidah, Raed Samara, Ghassan Katrawi, Anwar Hadi, Wael Al-Safarini, Maram Y. Al-Mamoori, Farah Elrashidi, Ali Alqaraleh, Muhyeeddin Alzoubi, Haneen Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of It Department of Computer Science Zarqa Jordan Petra University Faculty of It Information Security Department Amman Jordan Zarqa University Faculty of Pharmacy Department of Pharmaceutical Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of It Department of Software Engineering Zarqa Jordan Applied Science Private University Facultty of Information Technology Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Hypertension is one of the most common diseases in Jordan. it is one of the main reasons of death among Jordanian adult citizens. Worldwide, nearly 13% of all deaths are due to Hypertension with nearly 8 million deaths yearly. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that Random Forest, Random Committee, and MultilayerPerceptron are the best classification models to handle the task of the early prediction of Hypertension in Jordan. © 2024 iEEE.

关键词： Prediction models

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identifying COViD-19 through X-ray and CT scan images using Machine Learning 18

Identifying COVID-19 through X-ray and CT scan images using ...

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18th iEEE international Conference on Emerging Technologies, iCET 2023

作者： Shehzadi, Maham Arshad, Usama Abideen, Zainul Ali, Raja Hashim Khan, Ammad Aslam ijaz, Ali Zeeshan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Virtual University of Pakistan Dept. of Biological Sciences Rawalpindi Pakistan National University of Computer & Emerging Sciences Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350328172

in the current global scenario, marked by the COViD-19 pandemic, it has become imperative for healthcare systems around the globe to swiftly and accurately diagnose the disease. This is where cutting-edge approaches such as machine learning (ML) come into play, specifically, ML-based identification of COViD-19 from chest X-ray and CT-Scan imagery. Our research collective is devotedly working towards devising a robust, accurate ML model capable of deciphering these images to identify COViD-19 effectively. This involves the application of a variety of Ai algorithms and vast data resources. Our work is grounded on a large, annotated image dataset that aids in training and refining the model. Our findings indicate that ML methodologies can discern COViD-19 from chest X-ray and CT scan images with a mean accuracy of 90%. Such a tool holds promise in aiding medical practitioners in prompt and precise patient diagnosis, thereby improving patient prognosis. The most notable contribution of our study lies in the creation of an ML model that can accurately detect COViD-19 in chest X-ray and CT-Scan images, potentially aiding in early disease detection and management. © 2023 iEEE.

关键词： COViD-19

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Abstractive Text Summarization in Arabic-like Script using Multi-Encoder Architecture and Semantic Extraction Techniques

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iEEE Access 2025年 13卷 104977-104991页

作者： Fatima, Wajiha Rizvi, Syed Saqib Raza Ghazal, Taher M. Kharma, Qasem M. Ahmad, Munir Abbas, Sagheer Furqan, Muhammad Adnan, Khan Muhammad Capital University of Science & Technology Department of Computer Science Islamabad Pakistan Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Department of Networks and Cybersecurity Amman Jordan Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Software Engineering Department Amman Jordan National College of Business Administration and Economics Department of Computer Science Lahore Pakistan Korea University University College Seoul 02841 South Korea Prince Mohammad Bin Fahd University Computer Science Department Alkhobar Saudi Arabia Capital University of Science & Technology Department of Software Engineering Islamabad Pakistan Gachon University Faculty of Artificial Intelligence and Software Department of Software Seongnam-si 13120 South Korea

in the field of Natural Language Processing (NLP), the task of text summarization plays a vital role in understanding textual content and producing concise summaries. Text summarization approaches can be categorized as either extractive or abstractive, with the latter largely unexplored in the Arabic script languages. Previous research on abstractive summarization for the Urdu language has progressed in creating logical and succinct summaries but has failed to preserve the underlying semantics, often resulting in inconsistent and ambiguous outputs. This study focuses on designing and implementing an abstractive text summarization model for the Urdu language by achieving the semantic meaning of the original text. The proposed model improves readability and comprehension by generating concise and meaningful overviews of original content while introducing new words and expressions. The study implements a multi-layer transformer encoder as part of the mBART (Multilingual BART) model. The encoder consists of multiple stacked transformer layers. it utilizes self-attention mechanisms to process and understand the input sequences effectively, achieving a BERTScore of 90%, a BLEU score of 43%, and ROUGE scores of 80.3% for ROUGE-1, 74.3% for ROUGE-2, and 80.3% for ROUGE-L. These results demonstrate the efficacy of the model in producing coherent and high-quality abstractive summaries for the Urdu language. © 2013 iEEE.

关键词： Abstractive Text Summarization (ATS) Natural Language Processing (NLP) Semantic Meaning Text Generation Text Summarization (TS)

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Vibrating Particles System Algorithm for Solving Classification Problems

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computer Systems science & engineering 2022年第12期43卷 1189-1206页

作者： Mohammad Wedyan Omar Elshaweesh Enas Ramadan Ryan Alturki Department of Autonomous Systems Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan Department of Software Engineering Information Technology CollegeAL-Hussein bin Talal UniversityMa’an71111Jordan Department of Computer Science Prince Abdullah Bin Ghazi Faculty of Communication and Information TechnologyAl-Balqa Applied UniversityAl-Salt19117Jordan Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21961Saudi Arabia

Big data is a term that refers to a set of data that,due to its largeness or complexity,cannot be stored or processed with one of the usual tools or applications for data management,and it has become a prominent word in recent years for the massive development of *** immediately thereafter,the term“big data mining”emerged,i.e.,mining from big data even as an emerging and interconnected field of *** is an important stage in data mining since it helps people make better decisions in a variety of situations,including scientific endeavors,biomedical research,and industrial *** probabilistic neural network(PNN)is a commonly used and successful method for handling classification and pattern recognition *** this study,the authors proposed to combine the probabilistic neural network(PPN),which is one of the data mining techniques,with the vibrating particles system(VPS),which is one of the metaheuristic algorithms named“VPS-PNN”,to solve classi-fication problems more *** data set is eleven common benchmark medical datasets from the machine-learning library,the suggested method was *** suggested VPS-PNN mechanism outperforms the PNN,biogeography-based optimization,enhanced-water cycle algorithm(E-WCA)and the firefly algorithm(FA)in terms of convergence speed and classification accuracy.

关键词： Vibrating particles system(VPS) probabilistic neural network(PNN) classification problem data mining

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Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

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computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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Comparison of Conventional and Modified Ant Colony Approaches for Path Planning of Robot in an indoor Environment

Comparison of Conventional and Modified Ant Colony Approache...

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Disruptive Technologies (iCDT), international Conference on

作者： Sudeep Sharan Anh Tong Ngoc Minh Duc Thuan Nguyen Juan José Domínguez-Jiménez Peter Nauth Computer Science And Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany UCASE Software Engineering Group School of Engineering University of Cádiz Cádiz Spain

The optimal path for autonomous robots is highly needed, particularly in the industrial communities, manufacturing and social support industries. The meta-heuristic methods are strong and adaptable, and their employment in autonomous problems is taken into consideration. One of the methods used by the researchers is Ant Colony Optimization (ACO). However, this approach is less effective due to a few limitations. Thus, in order to mitigate its shortcomings and enhance the robustness of path planning, this paper aims to compare the different standard ACO algorithm with the new modified built algorithm, which is developed by taking the concept of ACO and extending it by condition-based rules, named as condition based-ant colony concept (CB-ACC), to solve path planning. The theoretical foundation of ACO with modification as another approach are used in this study to illustrate the ACO methods in the path planning, particularly in the static-complex environment. Simulation results show the comparison of the ACO approaches, including the Ant System (AS), the Ant Colony System (ACS) and the modified condition based-ant colony concept (CB-ACC), and how they are effective for static-complex environments compared to conventional ACO algorithms. Moreover, this paper presents the comparison results of the three approaches.

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Modeling the One Class of Radionuclide Migration Processes in Soils under Non-isothermal Conditions Using Perturbation Theory Methods 13

Modeling the One Class of Radionuclide Migration Processes i...

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13th international Conference on Advanced computer information Technologies, ACiT 2023

作者： Bomba, Andrii Ostapchuk, Oksana Vovk, Roman Kopnicky, Miroslav National University of Water and Environmental Engineering Department of Computer Science and Applied Mathematics Rivne Ukraine Ivano-Frankivsk National Technical University of Oil and Gas Department of Software Engineering Ivano-Frankivsk Ukraine Catholic University in Ružomberok Department of Catholic Theology Spišská Kapitula Slovakia

Based on the synthesis of complex analysis methods, perturbation theory, and characteristics, a new approach has been developed for accounting for osmosis and temperature in predicting the migration processes of radionuclides under non-isothermal conditions in quasi-ideal porous media (in curved regions) bounded by streamlines and equipotential lines. The solution to the corresponding degenerate problem was obtained based on the developed method of nonlinear inversion of boundary value problem solutions into quasi-conformal mappings. An algorithm for calculating a uniformly dynamic grid, streamlines, and speed of ideal filtration field was constructed. With the developed method of characteristics, formulas for the approximate solution of diffusion-convection mass transfer problems in perturbed osmosis and heat filtration fields were obtained. © 2023 iEEE.

关键词： Porous materials

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Comparative Analysis of Differential Privacy implementations on Synthetic Data

Comparative Analysis of Differential Privacy Implementations...

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iEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Huwida E. Said Qusay H. Mahmoud Mandeep Goyal Faiza Hashim Department of Computing and Applied Technolgy College of Technological Innovation Zayed University Dubai UAE Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada Department of Computer Science and Software Engineering College of Information Technology UAE University AI-Ain UAE

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Differential privacy offers a promising solution to balance data utility and user privacy. This paper compares two prominent differential privacy tools-PyDP and iBM's diffprivlib-that are applied to a synthetic dataset with medical attributes. We evaluate these tools based on their effectiveness in maintaining data privacy while preserving the statistical integrity of the data. Our results reveal that PyDP provides synthetic data that closely matches real-world data, making it appropriate for tasks requiring accuracy while striking a better balance between data utility and privacy. However, iBM's diffprivlib is more suitable for privacy-critical applications due to its stronger privacy guarantees, but at the cost of increased noise and decreased data utility. This paper contributes to the practical understanding of implementing differential privacy in machine learning and software applications and enhances the tools available for developers in sensitive data environments.

关键词： Differential privacy Costs Accuracy Conferences Noise Machine learning software Synthetic data

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MURDOC: Transforming Pixels into Perception for Camouflage Detection 43

MURDOC: Transforming Pixels into Perception for Camouflage D...

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43rd AiAA DATC/iEEE Digital Avionics Systems Conference, DASC 2024

作者： Hogue, Debra Kastl, Zak Karch, Joseph Nguyen, Don Schley, Lacey Lewis, Justin Connelly, Shane Weaver, Chris School of Computer Science The University of Oklahoma United States Us Air Force's 76th Software Engineering Group United States Oklahoma Aerospace and Defense Innovation Institute The University of Oklahoma United States The University of Oklahoma Department of Psychology Center for Applied Social Research United States

ISBN: (纸本)9798350349610

The MURDOC project introduces an application to enhance trustworthiness and explainability in computer vision models, focusing on camouflage detection. it aims to address the need for transparent and interpretable Ai systems in sensitive domains such as security and defense. MURDOC integrates advanced eXplainable Ai techniques including off-ramps for collecting interpretable insights, attention mechanisms for high-lighting relevant features, and a user-centric image pre-processing tool within the visualization interface. This paper offers an assessment of MURDOC's potential impact on trustworthiness and explainability in camouflaged object detection tasks. it discusses and assesses the potential effectiveness of off-ramps in gathering XAi output at various model stages, such as feature maps, attention mechanisms, and activation maps. Additionally, the paper investigates how user-driven image pre-processing mechanisms may enhance the trustworthiness of the model's predictions and decisions, allowing users to modify the input and observe prediction changes. As a work in progress, the development of MURDOC shows promise in bridging the gap for transparency and interpretability in camouflage detection. The paper also discusses the challenges, future directions, and potential applications of MURDOC's capabilities in diverse domains requiring transparent and trustworthy Ai systems. The source code for the version of MURDOC in this paper is available on GitHub at: https://***/Department-70/DASC-2024. © 2024 iEEE.

关键词： Camouflage

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