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1st International Conference on Cryptography and Information Security, VCRIS 2024

作者： Bui-Huu, Dong Le-Nhat, Tan Nguyen-An, Khuong Ho Chi Minh University of Banking Faculty of Management Information Systems Department of Computer Science Ho Chi Minh City Viet Nam Fulbright Vietnam University Undergraduate Faculty Ho Chi Minh city Viet Nam Faculty of Computer Science and Engineering Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Ho Chi Minh City Viet Nam

ISBN: (纸本)9798331529949

Cashless payment has been emphasized in many countries, especially since the outbreak of the COVID-19 pandemic. Various payment methods have emerged, among which electronic wallets (e-wallets) have become a popular online payment method due to their convenience, flexibility, and time and cost savings for users. However, alongside the growth in the number and value of transactions, e-wallets face challenges from increasingly sophisticated and complex security threats, fraud, and forgery, originating both internally and externally, causing severe financial losses and diminishing customer trust. To ensure the safety of e-wallet, we propose an architecture developed based on the permissioned blockchain platform-Hyperledger Fabric, following a microservice model with high-performance APIs that can be easily integrated into e-wallet systems at an optimal cost without requiring complex infrastructure investments. Thanks to blockchain's immutable, transparent, and decentralized storage nature, this solution effectively monitors and supports real-time fraud detection and forgery prevention, ensuring the safety, security, and integrity of users' online payment transactions. Furthermore, this solution also provides users with immediate notifications via Email or SMS when an attack or fraud is detected on the e-wallet. © 2024 IEEE.

关键词： Blockchain

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Mitigating Crop Losses: AI-enabled Disease Detection in Tomato Plants 20

Mitigating Crop Losses: AI-enabled Disease Detection in Toma...

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

作者： Ashfaq, Hassan 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 University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

Fostering crop health is vital for global food security, underscoring the need for effective disease detection. This research introduces an innovative artificial intelligence (AI) model designed to enhance the detection and diagnosis of diseases in tomato plants, particularly focusing on Early Blight and Late Blight. Significantly, our model leverages cutting-edge image processing techniques to improve disease detection efficiency, outperforming traditional methods in terms of speed and accuracy. Our results demonstrate an impressive model accuracy of 92.58% on training data and 86.83% on validation data, showing the effectiveness of AI in diagnosing plant diseases. These high accuracy rates underline the potential of our model for timely disease classification, allowing for immediate and appropriate interventions. However, our research also identified a potential overfitting problem in the model's performance. To address this, we propose using regularization and data augmentation techniques to enhance the model's generalizability on unseen data. Additionally, we delve into inherent challenges that plague AI-based plant disease detection, such as the scarcity of diverse datasets and the difficulty of achieving broad generalizability across different plant species. In identifying potential solutions for these issues, our research lays the groundwork for the wider and more practical implementation of AI technologies in agriculture. © 2023 IEEE.

关键词： Crops

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Performance Evaluation of Popular Deep Neural Networks for Neural Machine Translation 20

Performance Evaluation of Popular Deep Neural Networks for N...

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

作者： Naeem, Muhammad Siddique, Abu Bakar Ali, Raja Hashim Arshad, Usama Abideen, Zain Ul Ali Khan, Talha Shah, Muhammad Huzaifa Zeeshan Ijaz, Ali Ali, Nisar 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 Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

The field of Neural Machine Translation (NMT) has shown impressive performance for quick and easy communication in various languages spoken all over the world. NMT helps us by improving communication between different languages. For this purpose, different sequential models are used such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Units (GRU). Analysis among these different models are important for making language translation better and choose the best model for the right job. This research investigates the performance of these models on two distinct language datasets, English-to-German and English-to-Urdu. Based on accuracy metrics, the findings reveal that GRU having test accuracy (88.22% ) outperforms RNN (87.21% ), and LSTM (85.70% )demonstrating the highest translation accuracy, followed by RNN and LSTM exhibiting comparatively lower accuracy levels. © 2023 IEEE.

关键词： Long short-term memory

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Biomedical Osteosarcoma Image Classification Using Elephant Herd Optimization and Deep Learning

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computers, Materials & Continua 2022年第12期73卷 6443-6459页

作者： Areej A.Malibari Jaber S.Alzahrani Marwa Obayya Noha Negm Mohammed Abdullah Al-Hagery Ahmed S.Salama Anwer Mustafa Hilal Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Science Mathematics and Computer Science DepartmentMenoufia UniversityEgypt Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Osteosarcoma is a type of malignant bone tumor that is reported across the *** advancements in Machine Learning(ML)and Deep Learning(DL)models enable the detection and classification of malignancies in biomedical *** this regard,the current study introduces a new Biomedical Osteosarcoma Image Classification using Elephant Herd Optimization and Deep Transfer Learning(BOIC-EHODTL)*** presented BOIC-EHODTL model examines the biomedical images to diagnose distinct kinds of *** the initial stage,Gabor Filter(GF)is applied as a pre-processing technique to get rid of the noise from *** addition,Adam optimizer with MixNet model is also employed as a feature extraction technique to generate feature ***,EHOalgorithm is utilized along with Adaptive Neuro-Fuzzy Classifier(ANFC)model for recognition and categorization of *** algorithm is utilized to fine-tune the parameters involved in ANFC model which in turn helps in accomplishing improved classification *** design of EHO with ANFC model for classification of osteosarcoma is the novelty of current *** order to demonstrate the improved performance of BOIC-EHODTL model,a comprehensive comparison was conducted between the proposed and existing models upon benchmark dataset and the results confirmed the better performance of BOIC-EHODTL model over recent methodologies.

关键词： Biomedical imaging osteosarcoma classification deep transfer learning parameter tuning fuzzy logic

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Correction to: Automated approach to predict cerebral stroke based on fuzzy inference and convolutional neural network (Multimedia Tools and Applications, (2024), 10.1007/s11042-024-19254-0)

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Multimedia Tools and Applications 2024年 1-2页

作者： Alrowais, Fadwa Jamjoom, Arwa A. Karamti, Hanen Umer, Muhammad Alsubai, Shtwai Abate, Andrea F. Ashraf, Imran Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science University of Salerno Fisciano84084 Italy Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

The original publication of this article contains an error in the affiliation of authors Fadwa Alrowais and Hanen Karamti. Incorrect: Department of Information systems, College of computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia Correct: Department of computer Sciences, College of computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia The original article has been corrected. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

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Energy-Saving VM in SDN-Driven Cloud Data Center with the Operational Vacation Policy 3

Energy-Saving VM in SDN-Driven Cloud Data Center with the Op...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Barik, Lalbihari Ghosh, Akash Pandey, Trilok Nath Gourisaria, Mahendra Kumar KIIT Deemed to Be University School of Computer Applications Bhubaneswar India King Abdul Aziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343632

The increasing use of the Internet of Things (IoT), which has resulted in an exponential growth in network traffic, has serious implications on energy consumption and network performance. To reduce power consumption in cloud data centers, a server vacation-based energy-efficient traffic management solution is recommended. The primary idea is that using software-defined network (SDN) technologies, cloud system virtual machines (VMs) can take vacations. The goal of the energy-saving optimization task is to reduce the number of virtual machines (VMs) in use while maintaining service quality standards. This research study focuses on impatient client behaviour, several vacations, limited buffer and queuing system with many servers. Impatient customers may be kept in the cloud system by implementing certain customer retention strategies. The steady-state probability for the proposed SDN-assisted cloud system have been determined using recurrences. To determine the efficiency of the proposed system, several performance studies are conducted. © 2023 IEEE.

关键词： Cloud data center Energy optimization Performance measurement Queueing model Software Defined Network Vacation policy

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PIVODL: Privacy-Preserving Vertical Federated Learning Over Distributed Labels

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 988-1001页

作者： Zhu, Hangyu Wang, Rui Jin, Yaochu Liang, Kaitai University of Surrey Department of Computer Science GuildfordGU27XH United Kingdom Delft University of Technology Department of Intelligent Systems Delft2628XE Netherlands Bielefeld University Nature Inspired Computing and Engineering Group Faculty of Technology BielefeldD-33619 Germany

Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing their private local data. Nonparametric models like gradient boosting decision trees (GBDTs) have been commonly used in FL for vertically partitioned data. However, all these studies assume that all the data labels are stored on only one client, which may be unrealistic for real-world applications. Therefore, in this article, we propose a secure vertical FL framework, named privacy-preserving vertical federated learning system over distributed labels (PIVODL), to train GBDTs with data labels distributed on multiple devices. Both homomorphic encryption and differential privacy are adopted to prevent label information from being leaked through transmitted gradients and leaf values. Our experimental results show that both information leakage and model performance degradation of the proposed PIVODL are negligible. Impact Statement - Federated learning is a distributed machine learning framework proposed for privacy preservation. Most federated learning algorithms work on horizontally partitioned data, with only a few exceptions considering vertically partitioned data that is widely seen in the real world. However, existing vertical federated learning makes an unrealistic assumption that data labels are distributed on only one device and no research has been reported so far that considers data labels distributed on multiple client devices. The PIVODL framework reported in this article allows us to build a secure vertical federated XGBoost system, in which the labels may distributed either on one device or on multiple devices, making it possible to apply federated learning to a wider range of real-world problems. © 2020 IEEE.

关键词： Decision trees

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Enhancing Automated Android Application Security through Hybrid Static and Dynamic Analysis Techniques

Enhancing Automated Android Application Security through Hyb...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： G.E. Manukulasooriya R.S.I. Munasingha Harinda Fernando H.K.I. Uthpali A.V.S.H. Yeshani Deemantha Siriwardana Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

The rapid growth of mobile applications has led to serious security challenges, resulting in vulnerabilities. Automation in security testing methods is becoming popular, with the Automated Vulnerability Detection methodology being one such method. This work introduces a hybrid approach for detecting vulnerabilities in Android applications, combining static and dynamic analysis. The approach is accessible to both general users and expert developers, allowing for the identification of ingrained vulnerabilities in the source code. The hybrid model’s effectiveness is assessed in various applications with different security flaws, providing a solid solution for automated vulnerability detection in Android applications, enhancing security and resilience in the mobile ecosystem.

关键词： Human computer interaction Solid modeling Source coding Solids Mobile applications Security Robots

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Online DVFS Scheduling Algorithm for Sporadic Tasks with Energy-Conscious in Hard Real-Time systems 16

Online DVFS Scheduling Algorithm for Sporadic Tasks with Ene...

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16th International Conference on computer engineering and systems, ICCES 2021

作者： El Sayed, Manal A. El Sayed, M. Saad Habashy, Shahira M. Aly, Rasha F. Helwan University Faculty of Engineering Department of Computer and Systems Cairo Egypt

ISBN: (纸本)9781665408677

Dynamic voltage frequency scaling (DVFS) has been an efficient technology in minimizing the energy consumption of real-time embedded systems. Many prior studies have attempted to balance performance and energy consumption through energy-efficient task scheduling using the DVFS approach. This paper presents a novel DVFS algorithm, named Energy Conscious-DVFS (EC-DVFS) that can be used with sporadic constrained-deadline hard real-time tasks implemented upon multicore platforms, where the scheduler is the Earliest Deadline First (EDF) algorithm. The EC-DVFS technique is an adaptive online speed determination mechanism that determines the safe speed for each released task firstly. That speed ensures all deadlines are achieved when the jobs are scheduled using EDF. Then, when the task is assigned to a core, the EC-DVFS adjusts the core speed online according to its actual workload by considering both current and future task load using runtime queues. We demonstrate the efficacy of the suggested approach through an extensive set of experiments. The experimental simulation results demonstrate that the suggested algorithm saves a significant amount of energy when the system is running across a variety of applications. © 2021 IEEE.

关键词： Scheduling

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Leveraging AI and NLP in Chatbot Development: An Experimental Study 20

Leveraging AI and NLP in Chatbot Development: An Experimenta...

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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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