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2025 International Conference on Advancement in Data science, E-learning and information system, ICADEIS 2025

作者： Melyani, Nasya Amirah Mustakim Angraini Afdal, M. Sultan Syarif Kasim State Islamic University of Riau Faculty of Science and Technology Department of Information Systems Pekanbaru Indonesia State Islamic University of Sultan Syarif Kasim Riau Faculty of Science and Technology Department of Information System Pekanbaru Indonesia

ISBN: (纸本)9798331513320

Stocks are financial instruments that provide ownership rights and profits to their holders in public companies, which attract investors because of the potential for high returns despite the risks. Because stock price volatility is difficult to predict, accurate analysis techniques are needed to help investors make the right decisions, especially in the banking sector such as Bank Rakyat Indonesia. This study predicts the stock price of Bank Rakyat Indonesia (BBRI) using deep learning techniques, namely Bidirectional Gated Recurrent Unit (Bi-GRU) and Stacked Gated Recurrent Unit (Stacked GRU), which are chosen because of their ability to capture complex time series data patterns. BBRI stock data from 2018 to 2024 and from 2023 to 2024 were collected, processed, and divided into training and testing data with a ratio of 80:20. The Bi-GRU and Stacked GRU algorithms were optimized with Nadam, Adamax, AdamW, and SGDM, and evaluated using MSE, RMSE, and MAPE. As a result, 7-year data outperform 2-year data with the best algorithm Bi-GRU with Nadam optimization, batch size 8, and learning rate 0.001 giving the best performance with MSE 7283.1848, RMSE 85.3416, MAE 65.0879, and MAPE 1.2421%. The best prediction shows a decrease in BBRI stock prices in November 2024 which indicates a decrease towards the end of the year, thus providing valuable information for companies and investors in anticipating market behavior and making the right investment decisions. © 2025 IEEE.

关键词： Time series

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Improving Security in IoT system Using a Blockchain and a Microservices technologyy 2

Improving Security in IoT System Using a Blockchain and a Mi...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Alshudukhi, Khulud Salem Khemakhem, Maher Ali Eassa, Fathy Elbouraey Jambi, Kamal M. King Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Jeddah21589 Saudi Arabia

ISBN: (纸本)9798350335644

In the Internet of Things (IoT), new inventions enhanced a demand for federation cloud layers through different smart organizations to exchange data and transaction. One problem with traditional security approaches is that they rely on a centralized entity. In this research paper, we present blockchain using microservices and smart contracts for federation cloud systems in an IoT scenario. Moreover, we demonstrate the validation system to allow transactions to be exchanged between heterogeneous blockchains used by various smart organizations. Finally, we present the proposed framework that enables interconnection and guarantees the security of sharing data for various IoT systems in a federated cloud. © 2023 IEEE.

关键词： Blockchain

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Performance Analysis of Existing SDN Load Balancing Controller Using Mininet 1

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International Conference on Advanced Engineering, technology and Applications, ICAETA 2024

作者： Baniya, Pashupati Nand, Parma Bhushan, Bharat Hameed, Alaa Ali Jamil, Akhtar Agrawal, Atul Department of Computer Science and Engineering School of Engineeringand Technology IEC College of Engineering and Technology Greater Noida India Department of Computer Science and Engineering School of Engineering and Technology ITS Engineering College Greater Noida India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Istanbul34396 Turkey Department of Computer Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

ISBN: (数字)9783031709241

ISBN: (纸本)9783031709234

Due to an increase in the load of network, load balancing service, i.e., a service that gives an equal volume of each task assignment to each of the servers in data centers, it is usually performed by the specialized hardware. Networks have to adjust due to the load and also as a result of dealing with faults and complexity which are provided in the implementation and configuration process. Network engineers or administrators must be aware of selecting controllers according to the network requirement, which can lead to performance issues, scalability challenges, reliability and fault tolerance and many more problems in the network. To tackle this problem, this research analyses four different controllers in an experiment: In-band, open flow, OVS, and remote controller, which are the key aspects. The paper shows how these controllers, which are detected through a comprehensive evaluation of network performance attributes (e.g., Throughput and Jitter), affect the overall system’s performance. This information provides beneficial information for the researchers and practitioners who wish to make informed choices whenever they select suitable controls for specific network needs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Remote control

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Motion Error Estimation and Coherent Integration for High-Speed Target With Airborne Bistatic Radar

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IEEE Transactions on Aerospace and Electronic systems 2024年第3期61卷 5699-5717页

作者： Li, Xiaolong Zhang, Zerui Yang, Fan Wang, Mingxing Yeo, Tat Soon Cui, Guolong University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China National University of Singapore Department of Electrical and Computer Engineering 119077 Singapore

With flexibility in maneuverability and remarkable adaptability, airborne bistatic radar system can obtain excellent detection performance for high-speed target by employing coherent integration. However, range migration (RM) and Doppler frequency migration (DFM) could become serious issues due to the relative motion characteristics of airborne platforms and high-speed target. Meanwhile, various unpredictable factors such as atmospheric turbulence and mechanical issues, etc., resulting in additional motion errors, would have further negative impacts on motion state and flight trajectory of airborne platforms. This phenomenon would serious consequence on coherent integration and target detection. Thus, we make contributions to tackle these limitations and enhance coherent integration and detection performance. First, we establish signal model with high-speed target in three-dimensional (3-D) space for airborne bistatic radar system, along with motion error model which simultaneously includes translational error and rotational error. Next, we articulate range history's mathematical expression and further derive echo signal model. We then propose an improved generalized Radon Fourier transform (IGRFT) method. More specifically, the purpose of IGRFT is achieving joint search for the parameters of the target motion and the parameters of motion error, to ensure high precision parameter estimation and high gain integration. However, the computational complexity surges due to the increasing of search dimensionality. To devise computationally feasible methods for practical applications, we split the high-dimensional maximization process into two disjoint problems by sequentially searching motion parameters and then motion error parameters, and this method is named GRT (generalized Radon transform)-IGRFT. Numerical simulations show that the proposed algorithms can correctly estimate parameters and achieve signal integration and target detection. Finally, we present performanc

关键词： Flight paths

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EUR/USD Forex Market Prices Prediction Using MLP 16

EUR/USD Forex Market Prices Prediction Using MLP

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16th International Conference on Signal Processing and Communication system, ICSPCS 2023

作者： Diab, Raed Nohra, Alain Atwi, Ahmad Haidar, Gaby Abou Achkar, Roger American University of Science and Technology Faculty of Engineering and Computer Science Department of Computer and Communications Engineering United States

ISBN: (纸本)9798350333510

Neural networks is one of the most developed concepts in artificial intelligence owing to their ability to solve complex computational tasks, and its efficiency in finding solutions. There is a wide range of applications that adopt this technique, one of which is in the financial investment domain. This paper presents an approach to predict foreign exchange market prices using a neural network. It considers MLP techniques using sequential models with dense layer methods. Tests were conducted on one of the forex major pair data sets, EURUSD. The work is divided into 5 parts: prediction for the next minute, hour, day, week, and month resulting in 5 different models. Accordingly, a best-case accuracy of 99% for MLP algorithms is demonstrated. © 2023 IEEE.

关键词： Investments

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ARiViT: attention-based residual-integrated vision transformer for noisy brain medical image classification

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European Physical Journal Plus 2024年第5期139卷 1-13页

作者： Hameed, Madiha Zameer, Aneela Khan, Saddam Hussain Raja, Muhammad Asif Zahoor Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences (PIEAS) Islamabad Pakistan Department of Computer System Engineering The University of Engineering and Applied Science (UEAS) Swat Pakistan Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan

Brain tumor detection in medical image processing presents a formidable challenge due to the complex behavior exhibited by these tumors. Their intricate nature arises from a variety of shapes and textures within brain tumor images, exacerbated by their diverse cellular origins. This complexity is further compounded by the presence of noisy images, which add an additional layer of difficulty. The overlapping image intensities in tumor and non-tumor areas pose a significant hurdle in extracting meaningful insights from raw data using predictive models. In response to these challenges, we introduce ARiViT, a novel framework based on residual learning. ARiViT merges vision transformers, convolutions, and adversarial learning to tackle the complexities of brain tumor detection. ARiViT’s core generator integrates RiT blocks, merging residual convolutions and transformers, with residual connections, channel compression, and strategic weight sharing for enhanced capabilities and computational efficiency. ARiViT demonstrates exceptional robustness in handling noisy images and adapts well to various modality setups. Our systematic approach involves dataset splitting into training, testing, and validation sets, maintaining distributions of 80:20 and subsequently 70:30, ensuring rigorous evaluation. Additionally, our data preprocessing strategies are tailored to handle noisy and low-quality images, as well as those with unusual tumor shapes. The GAN architecture embedded in ARiViT effectively manages the complexities inherent in such image variations, ensuring reliable tumor detection even in challenging scenarios. Through exhaustive comparative evaluations, ARiViT proves its superiority over existing CNN and transformer techniques, as evidenced by both qualitative and quantitative analyses. Our efforts culminate in remarkable achievements, including an F1-score of 98.09%, establishing a pioneering solution in the demanding field of brain tumor detection. © The Author(s), under

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Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

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computers, Materials & Continua 2022年第5期71卷 3407-3423页

作者： Shakra Mehak M.Usman Ashraf Rabia Zafar Ahmed M.Alghamdi Ahmed S.Alfakeeh Fawaz Alassery Habib Hamam Muhammad Shafiq Knowledge Unit of System and Technology University of Management&TechnologySialkot Campus51310Pakistan Department of Computer Science University of Management and TechnologySialkot Campus51310Pakistan Faculty of Computing and Technology University of Engineering&TechnologyNarowal51610Pakistan College of Computer Science and Engineering University of Jeddah21493Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Faculty of Engineering Moncton UniversityNBE1A3E9Canada Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Breast cancer(BC)is the most widely recognized cancer in women *** 2018,627,000 women had died of breast cancer(World Health Organization Report 2018).To diagnose BC,the evaluation of tumours is achieved by analysis of histological *** present,the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC *** contemplate three elements,*** count,*** formation,and *** atypia,which is a laborious process that witness’s variations in expert’s ***,some algorithms have been proposed for the detection of mitotic cells,but nuclear atypia in breast cancer histopathology has not received much *** atypia analysis is performed not only to grade BC but also to provide critical information in the discrimination of normal breast,non-invasive breast(usual ductal hyperplasia,atypical ductal hyperplasia)and pre-invasive breast(ductal carcinoma in situ)and invasive breast *** proposed a deep-stacked multi-layer autoencoder ensemble with a softmax layer for the feature extraction and classification *** classification results show the value of the multilayer autoencoder model in the evaluation of nuclear *** proposed method has indicated promising results,making them more fit in breast cancer grading.

关键词： Breast cancer nuclear atypia autoencoder

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A Fine-Tuned Large Language Model for Domain-Specific with Reinforcement Learning 3

A Fine-Tuned Large Language Model for Domain-Specific with R...

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3rd International Conference on Creative Communication and Innovative technology, ICCIT 2024

作者： Ismail, Amelia Ritahani Aminuddin, Amira Shazleen Nurul, Afiqa Zakaria, Noor Azura Fadaaq, Wafa Haussain Nasser International Islamic University Malaysia Kulliyyah of Information and Communication Technology Department of Computer Science Kuala Lumpur Malaysia Arab Open University Faculty of Computer Studies Riyadh Saudi Arabia

ISBN: (纸本)9798350367492

Large Language Models (LLMs) like GPT-3 and BERT have significantly shown advancement in natural language processing by providing robust tools for understanding and generating human languages. However, their broad but shallow knowledge across many domains often leads to less effective performance in domain-specific tasks, where detailed and special-ized knowledge is needed. To address this limitation, this paper investigates the effectiveness of fine-tuning LLMs for specific domains. The approach incorporates reinforcement learning to integrate user feedback, allowing the model to dynamically adjust and refine its responses. This will ensure the model adapts iteratively, improving communication and interaction with users. The fine-tuned model's performance is evaluated using two domain-specific datasets - medical and dental. Evaluation metrics such as Levenshtein distance and cosine similarity are used to assess the textual accuracy and semantic relevance of the fine-tuned model. The results from the dental the medical datasets indicate a low level of textual differences and strong semantic alignment, respectively. These suggest that the fine-tuned model effectively processes and preserves the integrity of domain-specific content with the potential of fine-tuning LLMs to enhance their applicability in specific domains. © 2024 IEEE.

关键词： Reinforcement learning

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Deep learning on medical image analysis

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CAAI Transactions on Intelligence technology 2025年第1期10卷 1-35页

作者： Jiaji Wang Shuihua Wang Yudong Zhang School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Biological Sciences Xi'an Jiaotong-Liverpool UniversitySuzhouJiangsuChina Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia School of Computer Science and Engineering Southeast UniversityNanjingJiangsuChina

Medical image analysis plays an irreplaceable role in diagnosing,treating,and monitoring various *** neural networks(CNNs)have become popular as they can extract intricate features and patterns from extensive *** paper covers the structure of CNN and its advances and explores the different types of transfer learning strategies as well as classic pre-trained *** paper also discusses how transfer learning has been applied to different areas within medical image *** comprehensive overview aims to assist researchers,clinicians,and policymakers by providing detailed insights,helping them make informed decisions about future research and policy initiatives to improve medical image analysis and patient outcomes.

关键词： CNN deep learning medical image transfer learning

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AI-Based Intelligent Model to Predict Epidemics Using Machine Learning Technique

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Intelligent Automation & Soft Computing 2023年第4期36卷 1095-1104页

作者： Liaqat Ali Saif E.A.Alnawayseh Mohammed Salahat Taher M.Ghazal Mohsen A.A.Tomh Beenu Mago College of Engineering and Technology University of Science and Technology of FujairahFujairahUAE Electrical Engineering Department Faculty of EngineeringMutah UniversityJordan School of Information Technology Skyline University CollegeUniversity City Sharjah1797UAE Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia43600Malaysia Faculty of Computer Science NCBA&ELahore54660Pakistan School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE

The immediate international spread of severe acute respiratory syn-drome revealed the potential threat of infectious diseases in a closely integrated and interdependent *** an outbreak occurs,each country must have a well-coordinated and preventative plan to address the *** and Communication Technologies have provided innovative approaches to dealing with numerous facets of daily *** intelligent devices and applica-tions have become a vital part of our everyday lives,smart gadgets have also led to several physical and psychological health problems in modern ***,we used an artiﬁcial intelligence AI-based system for disease prediction using an Artiﬁcial Neural Network(ANN).The ANN improved the regularization of the classiﬁcation model,hence increasing its *** unconstrained opti-mization model reduced the classiﬁer’s cost function to obtain the lowest possible *** verify the performance of the intelligent system,we compared the out-comes of the suggested scheme with the results of previously proposed *** proposed intelligent system achieved an accuracy of 0.89,and the miss rate 0.11 was higher than in previously proposed models.

关键词： Intelligent model epidemics artiﬁcial intelligence machine learning techniques

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