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An Enhanced Automatic Arabic Essay Scoring System Based on Machine Learning Algorithms

computers, Materials & Continua 2023年第10期77卷 1227-1249页

作者： Nourmeen Lotfy Abdulaziz Shehab Mohammed Elhoseny Ahmed Abu-Elfetouh Department of Information Systems Faculty of Computers and Information ScienceMansoura UniversityMansoura35516Egypt Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia College of Computing and Informatics University of SharjahSharjahUnited Arab Emirates

Despite the extensive effort to improve intelligent educational tools for smart learning environments,automatic Arabic essay scoring remains a big research *** nature of the writing style of the Arabic language makes the problem even more *** study designs,implements,and evaluates an automatic Arabic essay scoring *** proposed system starts with pre-processing the student answer and model answer dataset using data cleaning and natural language processing ***,it comprises two main components:the grading engine and the adaptive fusion *** grading engine employs string-based and corpus-based similarity algorithms *** that,the adaptive fusion engine aims to prepare students’scores to be delivered to different feature selection algorithms,such as Recursive Feature Elimination and ***,some machine learning algorithms such as Decision Tree,Random Forest,Adaboost,Lasso,Bagging,and K-Nearest Neighbor are employed to improve the suggested system’s *** experimental results in the grading engine showed that Extracting DIStributionally similar words using the CO-occurrences similarity measure achieved the best correlation ***,in the adaptive fusion engine,the Random Forest algorithm outperforms all other machine learning algorithms using the(80%–20%)splitting method on the original *** achieves 91.30%,94.20%,0.023,0.106,and 0.153 in terms of Pearson’s Correlation Coefficient,Willmot’s Index of Agreement,Mean Square Error,Mean Absolute Error,and Root Mean Square Error metrics,respectively.

关键词： Arabic corpus-based similarity correlation machine learning string-based similarity text similarity

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UltraSegNet:A Hybrid Deep Learning Framework for Enhanced Breast Cancer Segmentation and Classification on Ultrasound Images

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computers, Materials & Continua 2025年第5期83卷 3303-3333页

作者： Suhaila Abuowaida Hamza Abu Owida Deema Mohammed Alsekait Nawaf Alshdaifat Diaa Salama Abd Elminaam Mohammad Alshinwan Department of Computer Science Faculty of Prince Al-Hussein Bin Abdallah II for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Medical Engineering Department Faculty of EngineeringAl-Ahliyya Amman UniversityAmman19328Jordan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Faculty of Computers Science Misr International UniversityCairo11800Egypt Jadara Research Center Jadara UniversityIrbid21110Jordan

Segmenting a breast ultrasound image is still challenging due to the presence of speckle noise,dependency on the operator,and the variation of image *** paper presents the UltraSegNet architecture that addresses these challenges through three key technical innovations:This work adds three things:(1)a changed ResNet-50 backbone with sequential 3×3 convolutions to keep fine anatomical details that are needed for finding lesion boundaries;(2)a computationally efficient regional attention mechanism that works on high-resolution features without using a transformer’s extra memory;and(3)an adaptive feature fusion strategy that changes local and global featuresbasedonhowthe image isbeing *** evaluation on two distinct datasets demonstrates UltraSegNet’s superior performance:On the BUSI dataset,it obtains a precision of 0.915,a recall of 0.908,and an F1 score of *** the UDAIT dataset,it achieves robust performance across the board,with a precision of 0.901 and recall of ***,these improvements are achieved at clinically feasible computation times,taking 235 ms per image on standard GPU ***,UltraSegNet does amazingly well on difficult small lesions(less than 10 mm),achieving a detection accuracy of *** is a huge improvement over traditional methods that have a hard time with small-scale features,as standard models can only achieve 0.63–0.71 *** improvement in small lesion detection is particularly crucial for early-stage breast cancer *** from this work demonstrate that UltraSegNet can be practically deployable in clinical workflows to improve breast cancer screening accuracy.

关键词： Breast cancer ultrasound image segmentation classification deep learning

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Detection of Left Ventricular Cavity from Cardiac MRI Images Using Faster R-CNN

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computers, Materials & Continua 2023年第1期74卷 1819-1835页

作者： Zakarya Farea Shaaf Muhammad Mahadi Abdul Jamil Radzi Ambar Ahmed Abdu Alattab Anwar Ali Yahya Yousef Asiri Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn MalaysiaParit RajaBatu Pahat86400JohorMalaysia Department of Computer Science College of Science and ArtsSharurahNajran UniversityNajran61441Saudi Arabia Department of Computer Science Faculty of Computer Science and Info.SystemsThamar UniversityDhamar87246Yemen Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The automatic localization of the left ventricle(LV)in short-axis magnetic resonance(MR)images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest(ROI).The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or ***,this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different ***,this study proposed a region-based convolutional network(Faster R-CNN)for the LV localization from short-axis cardiac MRI images using a region proposal network(RPN)integrated with deep feature classification and *** was trained using images with corresponding bounding boxes(labels)around the LV,and various experiments were applied to select the appropriate layers and set the suitable *** experimental findings showthat the proposed modelwas adequate,with accuracy,precision,recall,and F1 score values of 0.91,0.94,0.95,and 0.95,*** model also allows the cropping of the detected area of LV,which is vital in reducing the computational cost and time during segmentation and classification ***,itwould be an ideal model and clinically applicable for diagnosing cardiac diseases.

关键词： Cardiac short-axis MRI images automatic left ventricle localization deep learning models faster R-CNN

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An Overview chatGPT in Higher Education in Indonesia and Malaysia

An Overview chatGPT in Higher Education in Indonesia and Mal...

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2023 International Conference on Informatics, Multimedia, Cyber and information systems, ICIMCIS 2023

作者： Arista, Artika Shuib, Liyana Ismail, Maizatul Akmar Universitas Pembangunan Nasional Veteran Jakarta Information Systems Faculty of Computer Science Jakarta Indonesia Faculty of Computer Science and Information Technology Universiti Malaya Department of Information Systems Kuala Lumpur Malaysia

ISBN: (纸本)9798350307306

ChatGPT is a useful tool for teaching and learning in the context of education. Despite ChatGPT's growing popularity across several fields, little is known about how it is accepted and used, particularly in the context of higher education. ChatGPT holds a lot of potential to be a helpful tool for both students and lecturers. However, in higher education, the introduction of ChatGPT has raised serious questions about academic integrity. Thus, this research focuses on the overview of chatGPT in Higher Education in Indonesia and Malaysia. This study is conducted using the literature review method. According to this research result, this tool can help students and instructors with academic writing, language instruction, and learning in class in Higher Education in Indonesia. Higher Education in Malaysia is also raising optimism that this technology will speed up the transformation of higher education. Even though there are positive and negative effects of ChatGPT. Future research will need to conduct more case studies and interviews to obtain an in-depth comprehension of the application of ChatGPT in learning especially in higher education. © 2023 IEEE.

关键词： Students

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A Glimpse of chatGPT: An Introduction of Features, Challenges, and Threads in Higher Education

A Glimpse of chatGPT: An Introduction of Features, Challenge...

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2023 International Conference on Informatics, Multimedia, Cyber and information systems, ICIMCIS 2023

作者： Arista, Artika Shuib, Liyana Ismail, Maizatul Akmar Universitas Pembangunan Nasional Veteran Jakarta Information Systems Faculty of Computer Science Jakarta Indonesia Universiti Malaya Faculty of Computer Science and Information Technology Department of Information Systems Kuala Lumpur Malaysia

ISBN: (纸本)9798350307306

When used in conjunction with education, ChatGPT is a helpful tool for teaching and understanding fundamental concepts. Students who struggle to find study partners or lack the time to attend tutoring sessions may discover that reviewing and studying with the chatGPT are helpful. Students' access to material and completion of assignments can be accelerated using this technology in research and education. More than one-third of university students who participated in a poll said that they used the ChatGPT for writing assessments. Professors have referred to these technologies as a "threat"and a "plague on education,"and ChatGPT has been prohibited from several educational institutions. Thus, this research focuses on the introduction of Features, Challenges, and Threads chatGPT in Higher Education. This study is conducted using the literature review method. According to this research result, this tool can boost student engagement by providing immersive, dynamic, and customized learning environments. However, there are some related issues that need to be considered such as integrity, accuracy and reliability, information bias, and privacy issues. We can therefore infer that there may be benefits as well as drawbacks to employing AI in education that need to be taken into account. © 2023 IEEE.

关键词： Students

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FAIRlead: A Conceptual Framework for a Model Driven Software Development Approach in the Field of FAIR Data Management 16

FAIRlead: A Conceptual Framework for a Model Driven Software...

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16th International Conference on Knowledge Management and information systems, KMIS 2024 as part of 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2024

作者： Schmidt, Andreas Koubaa, Mohamed Anis Liu, Nan Schmurr, Philipp Stucky, Karl-Uwe Süß, Wolfgang Karlsruhe Germany Department of Computer Science and Business Information Systems Karlsruhe University of Applied Sciences Karlsruhe Germany

ISBN: (纸本)9789897587160

The publication of scientific results together with the underlying experiments is an important source of further research. In 2016, the "FAIR Guiding Principles for scientific data management and stewardship" were published, in which the authors postulate a series of guidelines for improving the (F)indability, (A)ccessibility, (I)nteroperability and (R)eusability of digital information (FAIR). The point (I)nteroperability deals with the prerequisites for the reusability of digital objects. The central point here is the need to have a common understanding of the meaning of digital objects. This understanding is provided by formal languages of knowledge representation (ontologies), which describe the actual data. These descriptions of data are also known as metadata. As part of our current work at the Institute for Automation and applied computer science (IAI) at KIT, we are implementing novel concepts and technologies for the sustainable handling of research data using high-quality metadata. As part of this work, we plan to develop a software tool that can be used to enrich data with suitable metadata and thus automate the process of making research results available. A key requirement is that the tool must be independent of the underlying domain. In order to be able to deal with data from any domain, we have opted for a model-driven approach in which an ontology, and possibly other platform-specific information, are input for a software generator, which then generates an (interactive) tool for specifying the metadata and linking it to the data itself. The generated tool includes the complete software stack, starting with a user interface, programmatic APIs for connecting additional application logic, and a persistence component. How these individual layers are realized is not specified, but defined by the mapping rules of the software generator, which also opens up the possibility of generating and evaluating different variants of the software. © 2024 by SCITEPRESS

关键词： Ontology

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Artificial Fish Swarm for Multi Protein Sequences Alignment in Bioinformatics

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computers, Materials & Continua 2022年第9期72卷 6091-6106页

作者： Medhat A.Tawfeek Saad Alanazi A.A.Abd El-Aziz Department of Computer Science College of Computer and Information SciencesJouf UniversitySaudi Arabia(KSA Department of Computer Science Faculty of Computers and InformationMenoufia UniversityEgypt Department of Information Systems College of Computer and Information SciencesJouf UniversitySaudi Arabia(KSA) Department of Information Systems and Technology Faculty of Graduates Studies and ResearchCairo UniversityEgypt

The alignment operation between many protein sequences or DNAsequences related to the scientific bioinformatics application is very *** is a trade-off in the objectives in the existing techniques of MultipleSequence Alignment (MSA). The techniques that concern with speed ignoreaccuracy, whereas techniques that concern with accuracy ignore speed. Theterm alignment means to get the similarity in different sequences with highaccuracy. The more growing number of sequences leads to a very complexand complicated problem. Because of the emergence;rapid development;anddependence on gene sequencing, sequence alignment has become importantin every biological relationship analysis process. Calculating the numberof similar amino acids is the primary method for proving that there is arelationship between two sequences. The time is a main issue in any alignmenttechnique. In this paper, a more effective MSA method for handling themassive multiple protein sequences alignment maintaining the highest accuracy with less time consumption is proposed. The proposed method dependson Artificial Fish Swarm (AFS) algorithm that can break down the mostchallenges of MSA problems. The AFS is exploited to obtain high accuracyin adequate time. ASF has been increasing popularly in various applicationssuch as artificial intelligence, computer vision, machine learning, and dataintensive application. It basically mimics the behavior of fish trying to getthe food in nature. The proposed mechanisms of AFS that is like preying,swarming, following, moving, and leaping help in increasing the accuracy andconcerning the speed by decreasing execution time. The sense organs that aidthe artificial fishes to collect information and vision from the environmenthelp in concerning the accuracy. These features of the proposed AFS make thealignment operation more efficient and are suitable especially for large-scaledata. The implementation and experimental results put the proposed AFS as afirst choice in th

关键词： Multiple sequence alignment swarm intelligence artificial fish swarm protein sequences

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Interpretable Arabic Hate Speech Detection using Large Language Model 2

Interpretable Arabic Hate Speech Detection using Large Langu...

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Alwateer, Majed Gad, Ibrahim Elmarhomy, Mahmoud Elmarhomy, Ghada Hashim, Hasan Almaliki, Malik Atlam, El-Sayed College of Computer Science and Engineering Taibah University Department of Computer Science Saudi Arabia Tanta University Faculty of Science Computer Science Department Tanta31527 Egypt College of Applied Taibah University Computer Science and Information Saudi Arabia College of Computer Science and Engineering Taibah University Department of Information Systems Saudi Arabia

ISBN: (纸本)9798331506995

Social media platforms serve as significant spaces for users to have conversations, discussions and express their opinions. However, anonymity provided to users on these platforms allows the spread of hate speech and other offensive material. Due to the wide-ranging nature of these platforms, there is a critical need to automatically detect and report occurrences of hate speech. There are various detection methods, but many of them operate as black boxes, lacking interpretability and explainability by design. To address the lack of interpretability, this study explores the development of an interpretable framework to detect hate speech in Arabic using large language models (LLMs). The proposed approach combines advanced natural language processing techniques with interpretable machine learning methods to enhance understanding of model decisions. The experimental results demonstrate that the model achieves high accuracy while maintaining interpretability, enabling users to understand the reasoning behind the detections. The proposed method achieves an accuracy of 0.846%, with a precision of 0.843% and a recall of 0.846%, outperforming existing Arabic hate speech detection models. These results show the effectiveness of combining LLM with interpretability for this critical task, providing a reliable and transparent solution for automated moderation of harmful content. © 2025 IEEE.

关键词： Arabic Hate Speech Detection Large Language Mode XAI

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Enhanced Neutrosophic Set and Machine Learning Approach for Kidney Disease Prediction

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Neutrosophic Sets and systems 2025年 80卷 465-477页

作者： Al-Doori, Humam M. Alkellezli, Tareef S. Abdelhafeez, Ahmed Eassaa, Mohamed Sawah, Mohamed S. El-Douh, Ahmed A. Cybersecurity Sciences Department College of Science Ashur University Baghdad Iraq Cybersecurity Engineering Department College of Engineering Ashur University Baghdad Iraq Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan Cybersecurity Technology Engineering Department College of Engineering Technology Ashur University Baghdad Iraq

Kidney disease (KD) is a gradually increasing global health concern. It is a chronic illness linked to higher rates of morbidity and mortality, a higher risk of cardiovascular disease and numerous other illnesses, and expensive medical expenses. The machine learning (ML) models are applied for KD prediction with higher accuracy and precision. The KD dataset has uncertainty and vague information, so, we used the neutrosophic set (NS) to deal with vague and uncertainty information in the KD dataset. The KD dataset is converted into the N-KD dataset with three membership functions: truth, indeterminacy, and falsity. Three ML models are used in this study such as logistic regression (LR), support vector machine (SVM), and k-nearest neighbor (KNN). These ML models are applied to the N-KD dataset. The results show the LR has higher accuracy and precision on the N-KD dataset than the original KD dataset. © 2025, University of New Mexico. All rights reserved.

关键词： Logistic regression

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Double Pepper: Mediated Human-Robot Interaction and Its Influence on Human Perception 10

Double Pepper: Mediated Human-Robot Interaction and Its Infl...

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10th IEEE International Conference on Behavioural and Social Computing, BESC 2023

作者： Keller, Lisa Muller, Nicholas H. Technical University of Applied Sciences Würzburg-Schweinfurt Faculty of Computer Science and Business Information Systems Würzburg Germany

ISBN: (纸本)9798350395884

Remote communication solutions have grown in our globalized world. Combining this area with robotics, telepresence robots enable people to feel more present at distant locations and allow the operator to move freely. Seemingly, there is no difference in perceived human affinity whether a person interacts with another person directly or through a telepresence robot, as a preliminary study demonstrates. In contrast to the preliminary study focusing on affinity towards a human interaction partner, the study in this paper deploys a humanoid robot as interaction partner. Hence, this work aims to explore if human affinity towards a humanoid robot varies if people interact with it directly or through a telepresence robot (so called mediated human-robot interaction). A Double 2 robot was used as telepresence robot and a Pepper as humanoid robot interlocutor. To measure participants' mood, the Multidimensional Mood State Questionnaire was applied before and after interacting with Pepper. Also, human affinity was measured through a questionnaire. The results indicate that there is no significant difference regarding mood and perceived human affinity whether someone interacts with a humanoid robot in person or through a telepresence robot. However, participants were significantly more awake after interacting in person. Concluding, the field of mediated human-robot interaction can enable new successful use cases e.g. deploying a humanoid robot in a telepresence robot tour as a guide instead of a person. Thereby, neither attendees nor a human guide have to be onsite which is beneficial in health hazard crises such as the corona pandemic. © 2023 IEEE.

关键词： Human robot interaction

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