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

作者： El-Tohamy, Ahmed Maghawry, Huda Amin Badr, Nagwa Ain Shams University Faculty of Computer and Information Sciences Information Systems Department Cairo Egypt

ISBN: (纸本)9798350322101

Viruses are an important group of pathogens that can cause a wide range of diseases in humans and other organisms. Accurate and efficient classification of viruses is crucial for the development of diagnostic tools and therapies, as well as for understanding the evolutionary relationships and diversity of these viruses. There has been a surge of interest in the application of machine learning and deep learning approaches for DNA viral classification in recent years. These methods can effectively handle the large and complex datasets that are often encountered in this context. A deep learning architecture is proposed in this study for the classification of DNA virus into its respective family. The model was trained on a newly collected dataset that covers a wide variety of viral family genomes. The architecture utilizes Convolutional Neural Networks (CNN) as a feature extraction layer and Extreme Learning Machines (ELMs) as a classification layer, using k-mer encoding. The use of CNN as a feature extraction layer and ELMs for classification allows for the effective and efficient classification of DNA virus into their diverse virus families. The proposed architecture was tested on a dataset obtained in FASTA format, as provided by the National Center for Biotechnology information (NCBI). The dataset consists of the major 10 virus families. The architecture achieved an accuracy of 94.54% on the test set, with a confusion matrix showing strong performance across all 10 virus families. A comparative analysis was also performed with other k-mer based machine learning algorithms and the proposed architecture achieved the highest accuracy amongst them. © 2023 IEEE.

关键词： Deep neural networks

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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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Delving into the Efficiency of AI Technologies within the Financial Sector: A Systematic Literature Review 16th

Delving into the Efficiency of AI Technologies within the Fi...

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16th International Conference on Application of Fuzzy systems, Soft Computing and Artificial Intelligence Tools, ICAFS 2023

作者： Cavus, Nadire Ubah, Adaeze Sancar, Nuriye Department of Computer Information Systems Near East University Nicosia99138 Cyprus Computer Information Systems Research and Technology Centre Near East University Nicosia99138 Cyprus Department of Mathematics Near East University Nicosia99138 Cyprus

ISBN: (纸本)9783031762826

In the global world, standard methods and some other technologies are used to make decisions, analyze risks, find fraud, and help customers. But the use of artificial intelligence (AI) in other fields such as education, automation, medicine, etc. has shown to be successful. But the research that’s already been done shows that there hasn’t been enough research done to show how AI tools can be used in the finance industry. For this reason, the goal of this study is to investigate how AI can be used in the banking industry. The PRISMA dataflow was used as a guide to do a thorough literature review for this study. The data for this in-depth literature study came from the scientific databases Science Direct, IEEE Xplore, EBSCO, SCOPUS, and Web of Science. With the help of specific keywords to the aim of the study, the search was narrowed to include only studies from 2019 to 2023. The most important results of the study showed that using AI technologies in banking and finance has improved decision-making, risk assessment, fraud discovery, customer service, investment advice, and personalized financial services. Consequently, deep learning methods and artificial intelligence have also shown promise in reducing cognitive and emotional errors in the financial sector. This could help banks make more money and make their customers happier. We think that the systematic literature review results will help the top managers in the finance sector figure out how to use and integrate AI tools to make the finance sector more sustainable and competitive. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decentralized finance

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A typology of disinformation intentionality and impact

A typology of disinformation intentionality and impact

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作者： French, Aaron Storey, Veda C. Wallace, Linda Department of Information Systems and Security Kennesaw State University KennesawGA United States Computer Information Systems J. Mack Robinson College of Business Georgia State University AtlantaGA United States Accounting and Information Systems Virginia Tech BlacksburgVA United States

In contemporary society, the increased reliance on social media as a vital news source has facilitated the spread of disinformation that has potential polarising effects. Disinformation, false information deliberately crafted to deceive recipients, has escalated to the extent that it is now acknowledged as a significant cybersecurity concern. To proactively tackle this issue, and minimise the risk of negative outcomes associated with disinformation, this research presents a typology of disinformation intentionality and impact (DII) to understand the intentionality and impact of disinformation threats. The typology draws upon information manipulation theory and risk management principles to evaluate the potential impact of disinformation campaigns with respect to their virality and polarising impact. The intentionality of disinformation spread is related to its believability among susceptible consumers, who are likely to propagate the disinformation to others if they assess it to be believable. Based on the dimensions of intentionality and impact, the DII typology can be used to categorise disinformation threats and identify strategies to mitigate its risk. To illustrate its utility for evaluating the risk posted by disinformation campaigns, the DII typology is applied to a case study. We propose risk mitigation strategies as well as recommendations for addressing disinformation campaigns spread through social media platforms. © 2023 John Wiley & Sons Ltd.

关键词： Polarization

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An efficient multi-objective task scheduling in edge computing using adaptive honey badger optimisation

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International Journal of Web Engineering and Technology 2024年第2期19卷 110-126页

作者： Nagalakshmi, Bantupalli Subramanian, Sumathy School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India

Task scheduling, which is important in cloud computing, is one of the most challenging issues in this area. Hence, an efficient and reliable task scheduling approach is needed to produce more efficient resource employment. So, a multi-objective-based task scheduling for edge computing is suggested in this study. This paper develops the adaptive honey badger optimisation algorithm (AHBA) to accomplish this goal. The lack of population, the original honey badger algorithm (HBO) has the issue of becoming trapped in local optima. To maintain population variety and improve convergence towards the ideal solution, HBO is combined with the opposition-based learning technique (OBL). Based on makespan, cost, energy consumption, and resource usage, the multi-objective function is created. According to simulation results, the proposed approach has a lot of potential in this field. Java and cloud Simulator are used to implement the suggested model. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Energy utilization

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Text recuperated using ontology with stable marriage optimization technique and text visualization using AR

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Multimedia Tools and Applications 2024年第41期83卷 88559-88586页

作者： Ouali, Imene Ben Halima, Mohamed Masmoudi, Nesrine Ayadi, Manel Almuqren, Latifa Wali, Ali University of Sfax Sfax Tunisia Management Information System Department Yanbu Taibah University Yanbu46421 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia

Written text stands as a cornerstone of communication in our daily lives. However, it is not uncommon for letters to be marred by obscurities, blurriness, erasures, or obstructions, which can lead to misinterpretation and convey unintended meanings. In this study, we present a comprehensive solution to rectify this challenge, comprising three pivotal phases. In the initial phase, we employ an advanced Deep Learning-based text detection and recognition method, specifically utilizing the Text-Block technique to pinpoint textual blocks. In the subsequent phase, we employ a robust combination of database and ontology to reconstruct unclear words. The final phase involves transforming the recovered word into a vivid 3D object through Augmented Reality, leveraging the Vuforia engine. This visualization technique aids visually impaired individuals with inaccurate word comprehension. To validate our approach, we rigorously compared our text detection and recognition methods against prevailing state-of-the-art techniques, achieving unmatched precision. Furthermore, we administered a comprehensive questionnaire to a cohort of visually impaired participants, evaluating the solution against key metrics such as user experience, satisfaction, efficiency, and effectiveness. The results from this survey unequivocally demonstrate the superior quality and efficacy of our proposed methodology. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Ontology

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Text Augmentation-Based Model for Emotion Recognition Using Transformers

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computers, Materials & Continua 2023年第9期76卷 3523-3547页

作者： Fida Mohammad Mukhtaj Khan Safdar Nawaz Khan Marwat Naveed Jan Neelam Gohar Muhammad Bilal Amal Al-Rasheed Department of Computer Science The University of HaripurHaripur22620Pakistan Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and Technology PeshawarPeshawar25120Pakistan Department of Electronics Engineering Technology University of TechnologyNowshera24100Pakistan Department of Computer Science Shaheed Benazir BhuttoWomen UniversityPeshawar25000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

关键词： Emotion recognition in conversation graph-based network text augmentation-basedmodel multimodal emotion lines dataset bidirectional encoder representation for transformer

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Applications of Machine Learning in Detecting Unethical Sources of Raw Materials in Supply Chains in the Cosmetic Industry 4

Applications of Machine Learning in Detecting Unethical Sour...

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4th International Conference on Sustainable Expert systems, ICSES 2024

作者： Sanghavi, Khush Mehta, Heet Pawade, Geet Konde, Malhar Singh, Shreyansh Seetha, R. School of Computer Science Engineering and Information Systems Vit University Tamil Nadu Vellore632014 India

ISBN: (纸本)9798331540364

The cosmetic industry maintains a global and complex supply chain network, where products composed of multiple raw ingredients are transported to numerous countries. Due to consumer pressure and regulatory requirements, ethical sourcing issues have attracted the attention of global consumers and businesses. This study proposes a machine-learning (ML)-oriented approach for identifying unethical sources in the cosmetic supply chain. We develop an ML-based framework by applying supervised learning, unsupervised learning and natural language processing (NLP), which includes text classification algorithms using a recommendation system to create training modules tailored to specific respondents. The proposed framework identifies unethical sourcing at any stage within the supply chain, thereby enhancing transparency, traceability, sustainability and business ethics as well as gaining the trust of consumers. While existing models have been successful in identifying certain unethical practices, they often face challenges such as limited scalability, high false positive rates, and the need for extensive labeled data. This study aims to address these limitations by developing a more robust and scalable machine learning framework. The results show that our ML models effectively detect potential unethical practices at early stages before harming reputations and adopt outlier detection in real-time monitoring or early warning systems with minimum configuration effort due to frequent changes in violation patterns or control mechanisms faced by practitioners. This study demonstrates that ML can aid governments and industries in implementing sustainable practices through stronger governance of business activities along their supply chains. © 2024 IEEE.

关键词： Self-supervised learning

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A novel U-net model for brain tumor segmentation from MRI images

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Alexandria Engineering Journal 2025年 126卷 220-230页

作者： Obayya, Marwa Alshuhail, Asma Mahmood, Khalid Alanazi, Meshari H. Alqahtani, Mohammed Aljehane, Nojood O. Almansour, Hamad Al-Hagery, Mohammed Abdullah Department of Biomedical Engineering College of Engineering Princess Nourah bint Abdulrahman University PO Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal university Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Applied Collage Najran University Najran Saudi Arabia Department of Computer Science College of Computer Qassim University Saudi Arabia

Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated with traditional segmentation methods. Convolutional neural networks (CNNs) and U-Net architectures have demonstrated their efficiency and effectiveness in segmenting brain tumors from MRI images using deep learning techniques. The paper presents an improved U-Net-based segmentation algorithm that integrates nested skip paths to improve encoder-decoder feature fusion. The performance of segmentation was optimized by utilizing a variety of activation functions and loss functions, including Dice Loss and Intersection over Union (IoU). A high level of accuracy was demonstrated in the proposed model when it was evaluated using the LGG Segmentation Dataset. The proposed approach for segmenting medical images has been shown to be both robust and efficient in a comparative analysis. © 2025 The Authors

关键词： Convolutional neural networks

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A Comparison of Deep Learning and Machine Learning Methods for Water Quality Prediction 3

A Comparison of Deep Learning and Machine Learning Methods f...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Srinivasa Gupta, N. Valarmathi, B. Hiwanj, Ishaan Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798331527549

Predicting water quality is essential to preserving human health and environmental sustainability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accuracies of 62%, 72%, 83%, 69%, 63%, 66%, 71%, 63%, and 64%, respectively, the current techniques utilized were Logistic Regression, Decision Trees, Random Forest Regressor, Extreme Gradient Boosting, Naive Bayes, K-nearest neighbors, Support Vector Machine, AdaBoost, and Bagging [9]. This study addresses these challenges by leveraging Adaptive Synthetic Sampling (ADASYN) to balance the dataset and evaluating model performance on datasets of 5,000 and 10,000 entries per class. A robust dataset obtained from Kaggle was used, with five models – Long Short-Term Memory (LSTM), Feed Forward Neural Network (FFNN), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Random Forest – evaluated and compared. The proposed methods demonstrate significant improvements in accuracy, with XGBoost achieving the highest accuracy of 95.53%, followed by Random Forest at 93.98%. This work underscores the importance of advanced machine learning techniques in addressing the limitations of traditional methods, enhancing accuracy, scalability, and adaptability in water quality prediction. These findings contribute to advancing environmental monitoring and management practices with reliable, data-driven insights. © 2025 IEEE.

关键词： Support vector machines

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