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4th International Multidisciplinary Information Technology and engineering Conference, IMITEC 2024

作者： Ovono, Gerald Zuva, Travos Otunniyi, Temidayo Faculty of Applied and Computer Sciences Department of Computer South Africa VaalUniversity of Technology Faculty of Engineering and Technology Department of Process Control and Computer Systems South Africa

ISBN: (纸本)9798350387988

The exponential growth of academic publications has made scholarly research recommender systems indispensable tools for researchers. These systems rely on diverse evaluation metrics to assess their effectiveness and relevance. This paper provides a comprehensive review and analysis of these metrics, categorizing them into accuracy, ranking, diversity, user-centric, coverage, temporal, and computational metrics. By employing a mixed-methods approach, combining systematic literature review and empirical analysis, we identify the prevalence and application of these metrics across various scholarly domains such as literature, conferences, and reviewers. Our findings highlight the widespread use of traditional metrics like precision, recall, and Fl-score, while emphasizing the growing importance of novel metrics such as unexpectedness and serendipity, especially in literature and conference recommendations. We also underscore the domain-specific nature of metric selection, with user-centric evaluations more prominent in literature recommendations, and ranking-focused metrics dominant in conference and reviewer recommendations. Furthermore, the study reveals a significant gap in the use of temporal and computational metrics, suggesting key areas for future research. This review offers valuable insights for researchers and practitioners, guiding them in metric selection and highlighting potential directions for enhancing the effectiveness, adaptability, and user satisfaction of scholarly recommender systems. © 2024 IEEE.

关键词： Algorithms Evaluation Metrics Novelty Recommender systems Scholarly Research Serendipity

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Towards Green AI by Reducing Training Effort of Recurrent Neural Networks Using Hyper-Parameter Optimization with Dynamic Stopping Criteria 22

Towards Green AI by Reducing Training Effort of Recurrent Ne...

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22nd IEEE International Symposium on Intelligent systems and Informatics, SISY 2024

作者： Podgorelec, Vili Fister, Iztok Vrbancic, Grega University of Maribor Intelligent Systems Laboratory Faculty of Electrical Engineering and Computer Science Slovenia

ISBN: (纸本)9798350385601

Neural networks have become a leading model in modern machine learning, able to model even the most complex data. For them to be properly trained, however, a lot of computational resources are required. With the carbon footprint of ever-growing adoption of neural networks in mind, an approach to reduce the required training resources would be very welcome. We designed a new training effort reduction method based on the calculation of area under the normalized loss curve and assessed it on the electricity consumption forecasting problem with the recurrent neural networks. The results show that the proposed method was able to considerably reduce the amount of computational resources, while maintaining the predictive performance, and thus contributing towards the Green AI. © 2024 IEEE.

关键词： Green AI hyper-parameter optimization machine learning neural networks resource optimization

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Bidirectional attentional mechanism for Arabic chatbot

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International Journal of Information Technology (Singapore) 2024年第5期16卷 3109-3120页

作者： Youness, Farida Elshenawy, Ayman Makour, Mohamed Ashraf Systems and Computer Engineering Department Faculty of Engineering Al-Azhar University Cairo Egypt Software Engineering and Information Technology Department Faculty of Engineering and Technology Egyptian Chinees University Cairo Egypt

A chatbot is an intelligent agent that developed based on Natural language processing (NLP) to interact with people in a natural language. The development of multiple deep NLP models has allowed for the creation of a vector representation of words and sentences that captures meaning in context. In this paper, a generative Arabic chatbot is trained with the Sequence to Sequence (Seq2Seq) model based on encoder-decoder architecture. It utilizes both the bi-directional encoder types of Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM). The chatbot was trained using preprocessed dataset of 12 k conversations that were translated from English to Arabic. An attention layer was provided to the RNNs to focus on the important data between the input and output sequences. The obtained results showed that the proposed chatbot performed well with long sentences and elicited positive responses. In addition to that BiLSTM with attention performs better than BiGRU without attention. To assess the model, the Bilingual Evaluation Understudy (BLEU) Score is employed. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Arabic chatbot Attention mechanism Deep learning RNN

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A Language Framework for Measuring Semantic and Syntactic Similarity for Arabic Texts

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SN computer Science 2024年第4期5卷 351页

作者： Ismail, Shimaa Alsammak, AbdelWahab Elshishtawy, Tarek Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Benha 13511 Egypt Computer Systems Department Faculty of Engineering Shoubra Benha University Benha Egypt

A language framework for determining the similarity of two snipped texts is proposed. The edit distance concept is employed as a frame algorithm to capture syntactic and semantic similarities. In the proposed work, syntax level distances between lemma-form words are calculated, while partial edit costs are allowed to embed semantic similarity measurements. Many knowledge resources have been used, such as words’ synonyms, negation rules, and word semantic spaces. A researchable Arabic thesaurus dictionary is built in two forms, surface form and lemma form. Semantic word spaces are generated from one of the word embedding models, which represents the words in vector spaces. The algorithm is enhanced to overcome problems with different word orders between sentences by a word permutation technique that elects the best alignment of the snipped text words to yield the best matching score. The algorithm also studied the effect of negation words on textual similarity. The proposed approach was implemented to find the similarity between Arabic language texts. Results are compared with other state-of-the-art algorithms using two benchmark datasets. The experimental results show that the proposed approach achieves a higher Pearson correlation coefficient compared to other works. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Arabic text similarity Lexical similarity Negation effect Permutation feature Semantic similarity Word embedding

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Competing with autonomous model vehicles:a software stack for driving in smart city environments

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Autonomous Intelligent systems 2024年第1期4卷 166-178页

作者： Julius Bächle Jakob Häringer Noah Köhler Kadir-KaanÖzer Markus Enzweiler Reiner Marchthaler Institute for Intelligent Systems Faculty of Computer Science and EngineeringEsslingen University of Applied SciencesFlandernstraße 101Esslingen am Neckar73732Germany

This article introduces an open-source software stack designed for autonomous 1:10 scale model *** developed for the Bosch Future Mobility Challenge(BFMC)student competition,this versatile software stack is applicable to a variety of autonomous driving *** stack comprises perception,planning,and control modules,each essential for precise and reliable scene understanding in complex environments such as a miniature smart city in the context of *** the limited computing power of model vehicles and the necessity for low-latency real-time applications,the stack is implemented in C++,employs YOLO Version 5 s for environmental perception,and leverages the state-of-the-art Robot Operating System(ROS)for inter-process *** believe that this article and the accompanying open-source software will be a valuable resource for future teams participating in autonomous driving student *** work can serve as a foundational tool for novice teams and a reference for more experienced *** code and data are publicly available on GitHub.

关键词： Autonomous model vehicle Software architecture Embedded real-time systems Bosch Future Mobility Challenge Autonomous driving

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Achieving a Zero False Negative Rate Unsupervised Detection on Controller Area Network (CAN) Bus with LSTM-Based Autoencoder 3

Achieving a Zero False Negative Rate Unsupervised Detection ...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Afify, Karim Kamal Abbas, Hazem M. Abdullah, Bassem A. Computer and Systems Engineering Ain Shams University Faculty of Engineering Cairo Egypt

ISBN: (纸本)9798350306231

The increasing complexity and connectivity of modern automotive systems have introduced new challenges in ensuring the security and reliability of Controller Area Network (CAN) bus communication. Cyber-attacks on CAN bus networks can lead to severe consequences, including vehicle malfunctions and compromise of passenger safety. As a result, there is a pressing need for efficient and effective anomaly detection mechanisms to identify and mitigate potential security breaches. TThis paper presents a practical anomaly detection approach that harnesses the capabilities of autoencoders and Long Short-Term Memory (LSTM) neural networks to fortify CAN bus cybersecurity with a focus on real-world implementation. The proposed method leverages the autoencoder's ability to learn the normal behavior of the CAN bus traffic and identify deviations from it, while the LSTM architecture provides temporal sequence modeling to capture dynamic patterns in the data. © 2023 IEEE.

关键词： Long short-term memory

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Multilingual Model Fine-tuning for Sentiment Analysis 11

Multilingual Model Fine-tuning for Sentiment Analysis

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11th IEEE International Conference on Intelligent Computing and Information systems, ICICIS 2023

作者： Elrefai, Mohamed L. Khalil, Mahmoud I. Abbas, Hazem M. Ain Shams University Computer and Systems Engineering Faculty of Engineering Cairo Egypt

ISBN: (纸本)9798350322101

Multilingual language models have decreased the barrier between languages, as it will be helpful overcoming many problems, such as sentiment analysis because the importance of this task is to make good decisions and customize products. Obtaining information from one language can help other languages generalize and understand a task more effectively. In this paper, we propose a general method for sentiment analysis of data that includes data from many languages, which enables all applications to use sentiment analysis results in a language-blind or language-independent manner. We performed experiments on two language combinations (English and Arabic) for sentence-level sentiment classification and found that the model with the final setup after adding translations from one language to another and fine-tuning the multilingual language model for Twitter, was the best setup, achieving for two languages and 71.2% and 68.1% f1-score for English and Arabic, respectively. © 2023 IEEE.

关键词： Sentiment analysis

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Enhancing Authorship Verification using Sentence-Transformers 24

Enhancing Authorship Verification using Sentence-Transformer...

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24th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF-WN 2023

作者： Ibrahim, Momen Akram, Ahmed Radwan, Mohammed Ayman, Rana Abd-El-Hameed, Mustafa El-Makky, Nagwa Torki, Marwan Computer and Systems Engineering Department Faculty of Engineering Alexandria University Alexandria Egypt

Authorship verification is a growing field of research that aims to determine whether two texts were written by the same author or different authors. In this paper, we describe our system for the PAN@CLEF 2023 Authorship Verification challenge [1] which requires solving the task on a cross-Discourse Types and open-set collection of essays (written discourse), emails (written discourse), Interviews (spoken discourse), and Speech transcriptions (spoken discourse). We use Sentence-Transformers which is a popular framework for generating sentence embedding based on the idea of using pre-trained language models, such as BERT (Bidirectional Encoder Representations from Transformers) to capture the semantic features of different authors’ documents. As a result of studying multiple sentence-transformers, we selected all-MiniLM-L12-v2 model and achieved the first rank in PAN 23 with an overall score of 62.3% on the testing set. © 2023 Copyright for this paper by its authors.

关键词： Semantics

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Comprehensive multimodal approach for Parkinson’s disease classification using artificial intelligence: insights and model explainability

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Soft Computing 2025年第3期29卷 1845-1877页

作者： Balaha, Hossam Magdy Hassan, Asmaa El-Sayed Ahmed, Rawan Ayman Balaha, Magdy Hassan Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Computer Science and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Mathematics and Engineering Physics Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Obstetrics and Gynecology Faculty of Medicine Tanta University Tanta Egypt

Parkinson’s disease (PD) is a debilitating neurodegenerative disorder affecting millions worldwide. Early detection is vital for effective management, yet remains challenging. In this study, we investigated four distinct datasets for PD detection. Through comprehensive experimentation employing ensemble methods and feature selection, we achieved high classification accuracies across the datasets. For the Oxford Parkinson’s Disease Detection Dataset, an accuracy of 95.67%, precision of 97.59%, recall of 84.5%, specificity of 99.32%, and F1-score of 90.57% were achieved. For the Alzheimer Parkinson Diseases 3 Class Dataset, the "Stacking" approach surpasses individual models, reaching an accuracy of 99.85%, precision of 99.81%, recall of 99.81%, specificity of 99.86%, and F1 of 99.81%. For the NewHandPD dataset, Regarding the Spiral category, The "Base-P32-384" model surpasses others with an accuracy of 97.35%, precision of 96.50%, recall of 98.57%, and F1-score of 97.53%. The collective "Stacking" approach proves highly effective regarding the Circle category, achieving 100% across all performance metrics. Regarding the Meander category, the "Base-P16-224" model achieves an accuracy of 97.35%, precision of 99.26%, recall of 95.71%, specificity of 99.19%, and F1 of 97.45%. The Mobile Device Voice Recordings at King’s College London (MDVR-KCL) dataset contains two datasets. Regarding the "SpontaneousDialogue" dataset, accuracy, BAC, precision, recall, specificity, and F1-score were computed, resulting in values of 94.03%, 92.83%, 90.78%, 100.0%, and 85.67%, respectively. Regarding the "ReadText" dataset, accuracy, BAC, precision, recall, specificity, and F1-score were computed, resulting in values of 91.89%, 90.62%, 87.5%, 100.0%, and 81.25%, respectively. Our findings highlight the efficacy of leveraging diverse data sources and advanced machine learning techniques to enhance PD detection accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germa

关键词： Neurodegenerative diseases

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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