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10th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2024

作者： Aslam, Sheraz Michaelides, Michalis P. Herodotou, Herodotos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Cyprus

ISBN: (纸本)9789897587030

This study investigates the combined berth allocation problem (BAP) and quay crane allocation problem (QCAP) while considering a multi-quay setting. First, a mixed integer linear programming mathematical model is developed based on various constraints and real port settings. Then, the multi-quay combined BAP and QCAP is solved using both the exact method and a metaheuristic optimization method, namely, the cuckoo search algorithm (CSA). This analysis pertains to a one-week planning scenario, utilizing data from a real port. The results of the comparative analysis show that the proposed CSA can provide a near-optimal solution ( Copyright © 2024 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Integer programming

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Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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Credit card default prediction using ML and DL techniques

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 293-306页

作者： Wahab, Fazal Khan, Imran Sabada, Sneha Corvit Systems Peshawar Pakistan Department of Informatics University of Sussex United Kingdom Department of Engineering and Computer Science University of Hertfordshire United Kingdom

The banking sector is widely acknowledged for its intrinsic unpredictability and susceptibility to risk. Bank loans have emerged as one of the most recent services offered over the past several decades. Banks typically serve as intermediaries for loans, investments, short-term loans, and other types of credit. The usage of credit cards is experiencing a steady increase, thereby leading to a rise in the default rate that banks encounter. Although there has been much research investigating the efficacy of conventional Machine Learning (ML) models, there has been relatively less emphasis on Deep Learning (DL) techniques. The application of DL approaches to credit card default prediction has not been extensively researched despite their considerable potential in numerous fields. Moreover, the current literature frequently lacks particular information regarding the DL structures, hyperparameters, and optimization techniques employed. To predict credit card default, this study evaluates the efficacy of a DL model and compares it to other ML models, such as Decision Tree (DT) and Adaboost. The objective of this research is to identify the specific DL parameters that contribute to the observed enhancements in the accuracy of credit card default prediction. This research makes use of the UCI ML repository to access the credit card defaulted customer dataset. Subsequently, various techniques are employed to preprocess the unprocessed data and visually present the outcomes through the use of exploratory data analysis (EDA). Furthermore, the algorithms are hypertuned to evaluate the enhancement in prediction. We used standard evaluation metrics to evaluate all the models. The evaluation indicates that the AdaBoost and DT exhibit the highest accuracy rate of 82 % in predicting credit card default, surpassing the accuracy of the ANN model, which is 78 %. © 2024 The Authors

关键词： Prediction models

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Advanced CNN Architectures for Improved Garbage Image Classification: An In-depth Evaluation 9

Advanced CNN Architectures for Improved Garbage Image Classi...

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9th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2024

作者： Kanavos, Athanasios Papadimitriou, Orestis Vonitsanos, Gerasimos Maragoudakis, Manolis Mylonas, Phivos University of the Aegean Department of Information and Communication Systems Engineering Samos Greece University of Patras Computer Engineering and Informatics Department Patras Greece Ionian University Department of Informatics Corfu Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece

ISBN: (纸本)9798350342482

Convolutional Neural Networks (CNNs) have become instrumental in advancing image classification, particularly in the context of garbage image classification, a critical component for efficient waste management. This paper introduces a tailored CNN architecture that demonstrates enhanced accuracy in garbage classification tasks, even with constrained datasets. Our architecture incorporates multiple convolutional, max-pooling, and fully connected layers, with dropout regularization strategically applied to curb overfitting and improve model generalization across a varied waste image dataset. Comparative evaluations reveal that our model achieves a significant improvement in accuracy over existing CNN models. The results not only validate the robustness of our approach but also contribute valuable insights toward developing more precise and efficient systems for garbage image classification. © 2024 IEEE.

关键词： Convolutional neural networks

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Advancing Sentiment Analysis of IMDB Movie Reviews with a Hybrid Multinomial Naive Bayes and LSTM Approach 4th

Advancing Sentiment Analysis of IMDB Movie Reviews with a...

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4th International Conference on Novel and Intelligent Digital Systems, NiDS 2024

作者： Papadimitriou, Orestis Kanavos, Athanasios Vonitsanos, Gerasimos Maragoudakis, Manolis Mylonas, Phivos Department of Information and Communication Systems Engineering University of the Aegean Samos Greece Computer Engineering and Informatics Department University of Patras Patras Greece Department of Informatics Ionian University Corfu Greece Department of Informatics and Computer Engineering University of West Attica Athens Greece

ISBN: (纸本)9783031733437

Natural Language Processing (NLP) is increasingly pivotal in the natural sciences, with sentiment analysis emerging as a crucial application in the era of big data. Efficiently and accurately extracting meaningful insights from extensive textual data remains a significant challenge. This study explores sentiment analysis within the context of movie reviews, a domain where understanding nuanced viewer perceptions can influence industry outcomes. Traditional methods often struggle with the complexity of language, where identical phrases may convey different sentiments based on their context. To address these challenges, this research implements a hybrid approach, combining a Multinomial Naive Bayes Classifier and a Long Short-Term Memory (LSTM) model developed in TensorFlow. Our integrated methodology not only advances the robustness of sentiment analysis tools but also achieves a notable accuracy of 96.25%, underscoring its potential to enhance complex NLP tasks in real-world applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Natural language processing systems

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Enhancing Emotion Classification with a Hybrid BERT and CNN Architecture 19

Enhancing Emotion Classification with a Hybrid BERT and CNN ...

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19th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2024

作者： Papadimitriou, Orestis Kanavos, Athanasios Vonitsanos, Gerasimos Maragoudakis, Manolis Karkazis, Panagiotis Mylonas, Phivos University of the Aegean Department of Information and Communication Systems Engineering Samos Greece University of Patras Computer Engineering and Informatics Department Patras Greece Ionian University Department of Informatics Corfu Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece

ISBN: (纸本)9798331504502

In computational linguistics, effectively encoding and systematizing emotional expressions in language is a significant challenge. Existing machine learning (ML) models for text analysis often only recognize primary emotional states such as anger, happiness, and sadness, missing the more nuanced spectrum of human emotions. These models frequently overlook the complex semantic interplays and fail to capture the full diversity of emotional expressions, focusing instead on simplistic categorization. This paper proposes a robust ML framework that enhances emotion classification by discriminating among nuanced emotion categories - Anger, Joy, and Fear. Leveraging a sophisticated combination of Convolutional Neural Networks (CNNs) and the Bidirectional Encoder Representations from Transformers (BERT) architecture, our framework demonstrates exceptional accuracy in emotion detection. Evaluating a diverse dataset of text samples, each meticulously tagged with its expressed emotion, confirms the model's superior performance in recognizing and classifying a broad range of emotional states. © 2024 IEEE.

关键词： Convolutional neural networks

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xCViT:Improved Vision Transformer Network with Fusion of CNN and Xception for Skin Disease Recognition with Explainable AI

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computers, Materials & Continua 2025年第4期83卷 1367-1398页

作者： Armughan Ali Hooria Shahbaz Robertas Damaševicius Department of Electrical Engineering Wah Engineering CollegeUniversity of WahWah Cantt47040Pakistan Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Applied Informatics Vytautas Magnus UniversityKaunas44309Lithuania

Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)*** identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tumor *** study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis,with an architecture that integrates a Vision Transformer,a bespoke convolutional neural network(CNN),and an Xception *** were evaluated using two benchmark datasets,HAM10000 and Skin Cancer *** the HAM10000,the model achieves a precision of 95.46%,an accuracy of 96.74%,a recall of 96.27%,specificity of 96.00%and an F1-Score of 95.86%.It obtains an accuracy of 93.19%,a precision of 93.25%,a recall of 92.80%,a specificity of 92.89%and an F1-Score of 93.19%on the Skin Cancer ISIC *** findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin *** addition,the utilization of Explainable AI techniques,such as Grad-CAM visualizations,assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model.

关键词： Skin lesions vision transformer CNN Xception deep learning network fusion explainable AI Grad-CAM skin cancer detection

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A Personalized Fitness Application for Injury Rehabilitation Using Artificial Neural Networks 19

A Personalized Fitness Application for Injury Rehabilitation...

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19th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2024

作者： Kalofonos, Nikolaos Troussas, Christos Krouska, Akrivi Mylonas, Phivos Sgouropoulou, Cleo University of West Attica Department of Informatics and Computer Engineering Egaleo Greece

ISBN: (纸本)9798331504502

This paper presents a personalized fitness mobile application with the use of Artificial Intelligence (AI), namely Artificial Neural Networks (ANN), which contributes to injury rehabilitation. It automatically generates customized workout plans on a weekly basis, taking into consideration user-supplied data on issues such as injury type, fitness level, body mass index, and available equipment. The system continuously updates the workout plans with the help of feedback provided by the user to achieve the best recovery process. Built with React Native for the frontend and Django for the backend, this application guarantees access anytime, anywhere, with smooth data treatment. The paper further reflects on the general implications of using AI in e-fitness, stresses intuitive user interfaces, and furthers possibilities such technologies could undergo. © 2024 IEEE.

关键词： Electronic health record

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An Ethereum Based e-Voting System 16th

An Ethereum Based e-Voting System

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16th EAI International Conference on Wireless Internet, WiCON 2023

作者： Spanos, Achilleas Kantzavelou, Ioanna Department of Informatics and Computer Engineering University of West Attica Athens Greece

ISBN: (纸本)9783031580529

Developing an electronic voting system that would replace the old, traditional electing procedures has been a concern of many researchers for years. Blockchain technology could provide some guarantees for voting platforms, such as transparency, immutability, and confidentiality. In most research works, secure and reliable electronic voting systems are required to address known security, anonymity, and fraud issues. This paper presents a secure decentralized electronic voting system, named the EtherVote, which is based on the Ethereum Blockchain network focusing on eligible citizens’ identification. The EtherVote is a serverless e-voting model, thus improving security, privacy, and election costs. An effective method for voter registration and identification to enhance security is proposed. Among the main properties the EtherVote holds are storing encrypted votes, efficiency in handling elections with numerous participants, and simplicity. The system is tested and evaluated, vulnerabilities and possible attacks are exposed, and a discussion examines opportunities for enhancing the proposed e-voting system. © ICST Institute for computer Sciences, Social informatics and Telecommunications engineering 2024.

关键词： Smart contract

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Exploring Teachers’ Attitudes and Self-efficacy Towards AI Learning in Science Instruction 4th

Exploring Teachers’ Attitudes and Self-efficacy Towards AI ...

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4th International Conference on Novel and Intelligent Digital Systems, NiDS 2024

作者： Stryftoy, Stavroula Krouska, Akrivi Troussas, Christos Mylonas, Phivos Sgouropoulou, Cleo Department of Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (纸本)9783031733437

As teachers strive to adapt to the technologically evolving landscape of education, understanding the impact of Artificial Intelligence (AI) on pedagogical practices becomes increasingly crucial. This study explores the contribution of Artificial Intelligent Learning (AI Learning) model to teachers’ attitudes and self-efficacy in teaching science. As such, an educational intervention based on AI Learning was conducted with a sample of 13 primary school teachers. The teachers were asked to answer a Dimensions of Attitude toward Science (DAS) questionnaire designed for this study, before and after the intervention. Before the intervention, a significant percentage of teachers expressed disagreement about the importance of physics in primary education (53.8%) and had a negative attitude towards its introduction (46.2%). However, after the intervention, there was a substantial shift towards positive attitudes: 38.5% acknowledged the importance of physics and 38.5% expressed a positive intention towards its introduction. The intervention also positively impacted teachers’ anxiety levels, enjoyment of teaching physics, and perceptions of their knowledge adequacy to support students. Additionally, there was a noticeable positive change in teachers’ perceptions of their preparedness to provide help to students. The improvement of DAS score (mean 68.15 before intervention/mean 89.15 after intervention) indicates that the intervention led to a more positive attitude among teachers regarding the use of AI learning in teaching science. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Contrastive Learning

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