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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Paracha, Abdul Wahab Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Ali Khan, Talha Ijaz, Ali Zeeshan Ali, Nisar Siddique, Abu Bakar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

In the current era of chatbots, this research delves into the advancements in AI chatbots, drawing on artificial intelligence (AI) and natural language processing (NLP) techniques to mimic human-like conversations. A particular focus is given to the potential of chatbots in facilitating multitasking dialogues, offering emotional support, and addressing complex subject matter, all the while respecting user privacy and trust. The implemented chatbot model is trained on a neural network, using Keras and TensorFlow libraries. This model's performance indicates a considerable dependence on the dataset's size, with larger datasets leading to better outcomes by providing more extensive language usage and context examples. Additionally, we also analyze the effect of varying architectures and hyperparameters on chatbot performance. The significance of localizing chatbots to adapt to different languages and cultures is also highlighted. While promising, the study identifies areas of improvement, suggesting future research directions in enhancing language capture techniques, expanding training datasets, and integrating emotional intelligence within chatbot systems. © 2023 IEEE.

关键词： Natural language processing systems

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A Security Enhanced Chaotic-Map-Based Authentication Protocol for Internet of Drones

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IEEE Internet of Things Journal 2024年第12期11卷 22301-22309页

作者： Mahmood, Khalid Ghaffar, Zahid Farooq, Muhammad Yahya, Khalid Das, Ashok Kumar Chaudhry, Shehzad Ashraf National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan National Yunlin University Of Science And Technology Graduate School Of Engineering Science And Technology Douliu64002 Taiwan Abu Dhabi University Department Of Mathematics And Statistics College Of Arts And Sciences Abu Dhabi United Arab Emirates Istanbul Gelisim University Department Of Electrical And Electronics Engineering Istanbul34310 Turkey Sabratah University Department Of Electrical Engineering Faculty Of Engineering Regdalen Libya Center For Security Theory And Algorithmic Research Hyderabad500 032 India Abu Dhabi University Department Of Computer Science And Information Technology College Of Engineering Abu Dhabi United Arab Emirates Nisantasi University Faculty Of Engineering And Architecture Department Of Software Engineering Istanbul34398 Turkey

The Internet of Drones (IoD) extends the capabilities of unmanned aerial vehicles, enabling them to participate in a connected network. In IoD infrastructure, drones communicate not only among themselves but also with users and a control center. This interconnected communication framework holds promise for various applications, from collaborative decision-making to real-time data exchange. However, the expansion of communication in IoD also introduces new challenges, particularly in terms of security, privacy and authentication. Unfortunately, the current authentication protocols are inadequate in offering robust security features against various attacks in the IoD environment. To address these security issues and limitations, we proposed a secure protocol for the IoD environment using chaotic maps and hash functions. In addition, we also employed a physically unclonable function in the development of the proposed protocol. We assess the security of the protocol through both informal and formal security analysis. The formal security analysis is conducted through a widely used random or real (RoR) model. The informal analysis shows the rigorous security features against various attacks, such as masquerading, anonymity violation, and physical cloning attacks. Moreover, we compare the performance of the devised protocol with similar existing protocols across important performance parameters, such as communication overhead, computation overhead, and security features. The devised protocol provides a 67.86% and 17.80% reduction in computation and communication overheads, respectively, as compared to related protocols. The analysis demonstrates the proposed protocol's capacity to support secure communication in the IoD environment and satisfy desirable security attributes. © 2014 IEEE.

关键词： Drones

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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Taqieddin, Eyad The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan Jordan University of Science & Technology Dept. of Software Engineering Irbid21110 Jordan School of Computing and Informatics Dept. of Cyber Security Al-Hussein Technical University Amman Jordan

ISBN: (纸本)9798350335590

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier. © 2023 IEEE.

关键词： Sentiment analysis

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The Simulation Model of Multipath Data Transmission in Wireless Networks with Interference

The Simulation Model of Multipath Data Transmission in Wirel...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Evgenia A. Abramova Ngo Minh Duc Nguyen Van Thuan Faculty of Software Engineering and Computer Technology University ITMO Saint Peterburg Russia Faculty of Secure Information Technologies University ITMO Saint Peterburg Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

Load balancing methods in networks aim to distribute traffic evenly between nodes to prevent overloading of individual routes and improve overall network performance. For instance, machine learning-based routing allows you to analyze network behavior and predict optimal routes for data transmission, which is especially effective in complex dynamic networks. Another option is Pareto Optimization, which is used to consider multiple criteria, such as minimizing latency and energy consumption, simultaneously. The reliability of networks, especially wireless ones, is becoming an issue that requires solutions that can adapt to constantly changing conditions, creating stable communication systems. The article describes and tests a simulation model of a wireless network in which requests are sent from sources through towers to two different recipients simultaneously along several paths. The use of intermediate switching nodes allows using the AOMDV protocol to build different data transmission routes. The simulation results for various modifications of the model allow us to evaluate the advantages of using multipath routing.

关键词： Wireless networks Simulation Poles and towers Interference Switches Pareto optimization Routing Routing protocols Data communication Optimization

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Robust and Reliable Liveness Detection Models for Facial Recognition Systems 20

Robust and Reliable Liveness Detection Models for Facial Rec...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Anjum, Haris Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Khan, Talha Ali Ijaz, Ali Zeeshan Siddique, Abu Bakar Imad, Muhammad 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

ISBN: (纸本)9798350395785

Facial Liveness Detection is instrumental in combating fraudulent practices and identity theft by differentiating genuine faces from forgeries. Given that facial recognition is now an integral part of many sectors like banking and law enforcement, liveness detection has become a vital aspect for maintaining the trustworthiness of these applications. Unfortunately, the utility of prevalent facial liveness detection models is constrained by their lack of generalizability to novel, unseen data, which presents a substantial impediment to practical applications. To mitigate this issue, our research endeavored to construct a reliable and precise facial liveness detection model that can effectively navigate this challenge and ensure consistent performance across diverse datasets. The devised model exhibited excellent accuracy, which attests to its robustness. This research underscores the advantages of employing deep learning methods such as Convolutional Neural Networks (CNNs) as they significantly enhance the model's accuracy. We intend for our findings to drive further research and progress in this critical field, by laying the groundwork for future work that integrates multiple liveness detection methods and collects more varied training data to develop an even more resilient and efficient system. © 2023 IEEE.

关键词： Feature extraction

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Automation of Monitoring and Optimization of Docker Containers Using Artificial Intelligence

Automation of Monitoring and Optimization of Docker Containe...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Sava Stanišić Vanja Luković Branislav Belotić Department of Computer and Software Engineering Faculty of Technical Sciences Čačak Republic of Serbia Department of Information Technologies Faculty of Technical Sciences Čačak Republic of Serbia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

The increasing adoption of Docker containers in modern software development requires effective monitoring and optimization to ensure high performance and efficient resource utilization. This paper provides an overview of the various tools and technologies that leverage artificial intelligence (AI) to automate the monitoring and optimization of Docker containers. The authors explore the capabilities of AI-driven tools such as machine learning-based anomaly detection, predictive analytics, and automated resource management. By examining these tools, the authors highlight their benefits in enhancing container performance, scalability, and availability. Through comparative analysis, the strengths and limitations of each tool and their impact on container management are discussed. The findings underscore the potential of AI to revolutionize container monitoring and optimization, leading to more efficient and robust IT infrastructure.

关键词： Quantum computing Scalability Containers Real-time systems Resource management Security Artificial intelligence Predictive analytics Optimization Monitoring

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An Intelligent Thyroid Diagnosis System Utilizing Multiple Ensemble and Explainable Algorithms with Medical Supported Attributes

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2840-2855页

作者： Sutradhar, Ananda Al Rafi, Mustahsin Ghosh, Pronab Shamrat, F M Javed Mehedi Moniruzzaman, Md. Ahmed, Kawsar Azad, A.K.M. Bui, Francis M. Chen, Li Moni, Mohammad Ali Daffodil International University Department of Computer Science and Engineering Dhaka1216 Bangladesh Lakehead University Department of Computer Science Thunder BayONP7B 5E1 Canada University of Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia Lakehead University Department of Software Engineering Thunder BayONP7B 5E1 Canada University of Saskatchewan Department of Electrical and Computer Engineering SaskatoonSK S7N 5A9 Canada Imam Mohammad Ibn Saud Islamic University Faculty of Science Department of Mathematics and Statistics Riyadh13318 Saudi Arabia Charles Stuart University AI and Cyber Futures Institute BathurstNSW2795 Australia

The widespread impact of thyroid disease and its diagnosis is a challenging task for healthcare experts. The conventional technique for predicting such a vital disease is complex and time-consuming. A data-driven approach may offer predictive solutions, but it relies on all relevant attributes, which are computationally expensive. Hence, we propose a novel machine learning (ML) based disease prediction system that could potentially predict it by considering three crucial steps. First, to reduce the dimension of the dataset, three feature selection techniques were employed, including feature importance (FIS), information gain selections (IGS), and least absolute shrinkage and selection operator (LAS). Moreover, recommended medical references were considered while developing a feature set having the identical attributes as high-risk factors (HRF). Second, the models, including the three stage hybrid classifier (3SHC) and the three stage hybrid artificial neural network (3SHANN), are used as classifiers on the training data set. Third, a local interpretable model-agnostic explanations (LIME) to the 3SHC with the HRF samples was applied to individually explain the predictions. Then, the overall behaviors of both gender and age categories were explored with the help of a partial dependence plot (PDP). Finally, the proposed system is validated with extensive experiments, where the 3SHC achieves an accuracy (ACC) of 99.29%, which can play a crucial role in preventing thyroid disease and alleviating stress in the healthcare sector. © 2020 IEEE.

关键词： Neural networks

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CD-FL:Cataract Images Based Disease Detection Using Federated Learning

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computer Systems Science & engineering 2023年第11期47卷 1733-1750页

作者： Arfat Ahmad Khan Shtwai Alsubai Chitapong Wechtaisong Ahmad Almadhor Natalia Kryvinska Abdullah Al Hejaili Uzma Ghulam Mohammad Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Information Systems Department Faculty of ManagementComenius University in BratislavaOdbojárovBratislava440Slovakia Faculty of Computers&Information Technology Computer Science DepartmentUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan

A cataract is one of the most significant eye problems worldwide that does not immediately impair vision and progressively worsens over *** cataract prediction based on various imaging technologies has been addressed recently,such as smartphone apps used for remote health monitoring and eye *** recent years,advances in diagnosis,prediction,and clinical decision support using Artificial Intelligence(AI)in medicine and ophthalmology have been *** to privacy concerns,a lack of data makes applying artificial intelligence models in the medical field *** address this issue,a federated learning framework named CDFL based on a VGG16 deep neural network model is proposed in this *** study collects data from the Ocular Disease Intelligent Recognition(ODIR)database containing 5,000 patient *** significant features are extracted and normalized using the min-max normalization *** the federated learning-based technique,the VGG16 model is trained on the dataset individually after receiving model updates from two *** transferring the attributes to the global model,the suggested method trains the local *** global model subsequently improves the technique after integrating the new *** client analyses the results in three rounds to decrease the over-fitting *** experimental result shows the effectiveness of the federated learning-based technique on a Deep Neural Network(DNN),reaching a 95.28%accuracy while also providing privacy to the patient’s *** experiment demonstrated that the suggested federated learning model outperforms other traditional methods,achieving client 1 accuracy of 95.0%and client 2 accuracy of 96.0%.

关键词： Privacy-preserving cataract disease federated learning fundus images healthcare smartphone applications machine learning

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Detection of Ophthalmic Disorders Through Deep Feature Fusion

Detection of Ophthalmic Disorders Through Deep Feature Fusio...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Hussain, Muhammad Toheed Faraz, Muhammad Aziz, Sumair Iqtidar, Khushbakht Khan, Muhammad Umar Montes, Gabriel Axel University of Engg. and Tech. Taxila Department of Electronics Engineering Taxila Pakistan University of Canberra Faculty of Science and Technology CanberraACT Australia National University of Sciences and Tech. Dept. of Computer and Software Engineering Islamabad Pakistan University of Newcastle CallaghanNSW Australia

ISBN: (纸本)9798350349719

Given the rising prevalence of ophthalmic disorders, this study aims to meet the urgent need for an automated technique capable of diagnosing and treating these conditions. Our approach combines deep learning and machine learning techniques to develop a diagnostic system that can detect and classify seven distinct ophthalmic disorders. Manual diagnosis of ophthalmic disorders is labor-intensive and slow, worsened by a shortage of medical experts. In this paper, we propose a medical diagnostic system that utilizes deep features extracted from a ResNet-50 CNN, which has been trained on retinal images enhanced through contrast-limited adaptive histogram equalization (CLAHE). These deep features serve as input for the machine learning model. To address the class imbalance problem, we employed ADASYN to generate synthetic features for all classes. An Ensemble subspace KNN classifier, trained on these deep features, achieved a remarkable mean accuracy of 99.18% compared to other classifiers evaluated in this study. The proposed technique has the potential to assist medical experts in achieving timely and accurate diagnoses of ophthalmic diseases. © 2024 IEEE.

关键词： Ophthalmology

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Virtual Laboratory Learning Experience in engineering: An Extended Technology Acceptance Model (TAM) 3

Virtual Laboratory Learning Experience in Engineering: An Ex...

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3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Ahmed Alnagrat, Ahmed Jamah Ahmed, Khamiss M. S. Alkhallas, Muhannad Ismail Almakhzoom, Osama Abulaqasim Ibrahim Syed Idrus, Syed Zulkarnain Che Ismail, Rizalafande Higher Institute of Science and Technology Wadi Al-Shati Department of Computer Science and Information Technology Wadi al-Shati Fezzan Libya Sebha University Faculty of Information Technology Department of Computer Science Sebha Libya Faculty of Engineering Technology in Zuwarah Department of Software Engineering Zuwarah Libya Department of Communication Perlis Kangar01000 Malaysia Arau02600 Malaysia

ISBN: (纸本)9798350319897

This research presents an enhanced version of the Technology Acceptance Model (TAM), which integrates usability and learning objectives to evaluate the effect of adopting virtual laboratories on student performance. Data from 97 participants, gathered via a survey, was used to examine the correlation between perceived usefulness (PU), perceived ease of use (PEOU), and intention to use (IU) concerning their experiences and outcomes with a simulation-based virtual laboratory. A custom questionnaire was designed to assess TAM components, namely PU, PEOU, and IU, specifically in the context of virtual laboratory learning environments. Findings indicate that the integration of specific laboratory learning objectives fosters the creation of a more credible and authentic simulation tool. This, in turn, optimizes the learning process and enhances students' learning outcomes. The refined TAM model provides valuable insights into the determinants of user acceptance of virtual laboratories and offers practical guidance for educators and instructional designers to construct more effective and efficient virtual laboratory environments in educational contexts. © 2023 IEEE.

关键词： Virtual reality

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