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Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

computers, Materials & Continua 2023年第1期74卷 2303-2316页

作者： K.Kalyani Velmurugan Subbiah Parvathy Hikmat A.M.Abdeljaber T.Satyanarayana Murthy Srijana Acharya Gyanendra Prasad Joshi Sung Won Kim PG and Research Department of Computer Science Marudupandiyar College(Affiliated to Bharathidasan UniversityTiruchirappalli)Thanjavur613403India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmmanJordan Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelangana500075India Department of Convergence Science Kongju National UniversityGongju32588Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced *** successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial *** stockholders focused on the prediction of return rate and risk rate of financial ***,an automatic return rate bitcoin prediction model becomes essential for BC financial *** newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive *** study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial *** presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial ***,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification *** application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return *** experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many *** experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

关键词： Financial products blockchain return rate prediction model machine learning parameter optimization

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Wormhole Attack Detection Technques in MANET 2

Wormhole Attack Detection Technques in MANET

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

作者： Bashir, Muhammad Tahir, Sidra Almufareh, Maram Fahaad Hamid, Bushra Qamar, Farkhanda PMAS ARID Agriculture University Rawalpindi Punjab Pakistan Jouf University College of Computer and Information Sciences Department of Information Systems AL-Jouf Sakakah Saudi Arabia Applied Science Private University Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

MANET is a wireless medium, with no infrastructure, for nodes to communicate within a network. Movement of nodes is a lot more in this network than other networks due to lack of infrastructure (Wireless nature). In a mobile ad hoc network, each node functions as a router and finds the best path forwarding a packet via origin to destination. This network is important in places where building a framework is impossible, like in a military setting. A mobile ad-hoc network (MANET) is a temporary network created by unrestricted nodes that can move around and communicate without a centralized network. In a black hole attack, several or even more attacker node use a tunnel-like private channel to find a wormhole attack. Wormhole tunnel will then start collecting the data packets and transferring them to another location. The proposed work encouraged secure and effective routing in MANET. At that point, we selected 20 important attributes to create a dataset that was labelled with the aid of a unique node address. Apply two well-known machine learning classifiers that categorise data into two groups, specifically normal and malicious, to test sample data in this manner. For feature selection and SVM (Support Vector Machine) classification, respectively, we employ genetic algorithms. The demonstration of the technology was assessed on many quantifiable characteristics and compared to contemporary techniques. © 2023 IEEE.

关键词： Mobile ad hoc networks

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Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data 8

Deep Transfer Hashing for Adaptive Learning on Federated Str...

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8th International Workshop and Tutorial on Interactive Adaptive Learning, IAL@ECML-PKDD 2024

作者： Röder, Manuel Schleif, Frank-Michael Faculty of Computer Science and Business Information Systems TUAS Würzburg-Schweinfurt Würzburg Germany Faculty of Technology Bielefeld University Bielefeld Germany Center for Artificial Intelligence and Robotics Würzburg Germany

This extended abstract explores the integration of federated learning with deep transfer hashing for distributed prediction tasks, emphasizing resource-efficient client training from evolving data streams. Federated learning allows multiple clients to collaboratively train a shared model while maintaining data privacy - by incorporating deep transfer hashing, high-dimensional data can be converted into compact hash codes, reducing data transmission size and network loads. The proposed framework utilizes transfer learning, pre-training deep neural networks on a central server, and fine-tuning on clients to enhance model accuracy and adaptability. A selective hash code sharing mechanism using a privacy-preserving global memory bank further supports client fine-tuning. This approach addresses challenges in previous research by improving computational efficiency and scalability. Practical applications include Car2X event predictions, where a shared model is collectively trained to recognize traffic patterns, aiding in tasks such as traffic density assessment and accident detection. The research aims to develop a robust framework that combines federated learning, deep transfer hashing and transfer learning for efficient and secure downstream task execution. © 2024 Copyright for this paper by its authors.

关键词： Federated learning

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Multi-Criteria Decision-Making Model to Achieve Sustainable Developmental Goals in Industry 4.0 for Smart City Infrastructure

HighTech and Innovation Journal

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HighTech and Innovation Journal 2024年第4期5卷 1135-1153页

作者： Akila, D. Pal, Souvik Sarkar, Bikramjit Jayalaksshmi, S. Muthaiyah, Saravanan Anbananthen, Kalaiarasi Sonai Muthu Department of Computer Applications Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Management Information Systems Saveethas College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Computer Science and Engineering JIS College of Engineering Kalyani India Department of Information Technology Vels Institute of Science Technology and Advanced Studies India School of Business and Technology International Medical University Kuala Lumpur57000 Malaysia Faculty of Information Science and Technology Multimedia University Melaka75450 Malaysia

Due to a shortage of funding and other market challenges, Small and Medium-sized Enterprises (SMEs) face difficulties in adopting new technologies. Numerous technological obstacles negatively impact the long-term commercial achievement of SMEs. The deployment of Industry 4.0hopes to resolve these technological challenges. A sustainable city is a complex structure where economic, societal, and ecological components interact and compete. There is a scarcity of l methodologies for measuring interactions in this complex structure. Industry 4.0 aims to obtain higher performance effectiveness, profitability, and automation. The main goal is to develop a reliable method of evaluating small and medium-sized enterprises (SMEs) adopting Industry 4.0 technologies, particularly concerning smart city applications. This paper aims to determine the influence of Industry 4.0 in fostering economic efficiency and sustainability amongst these SMEs. The study introduces a multi-criteria decision-making (SC-MCDM) system designed to test an SME’s achievement of their targeted sustainable developmental goals. A technique for computing the interaction between various standards, i.e., (static interactions and dynamical pattern resemblance), as well as the weightage of variables of every indicator generated by the connection, is included within SC-MCDM. Furthermore, applying the suggested technique is validated by assessing the sustainable development goals of twelve Chinese cities within the Triple Bottom Line (TBL) paradigm. From a geographic-temporal viewpoint, spatial variations in city sustainability reveal regional sustainable inequalities. Indicator scores suggest that the most significant factors for most communities are the lack of research spending, falling financing in stationary assets, shortage of financial development, and inadequate shared transit. Furthermore, the growth of tertiary industries, improvement of energy performance, expansion of green areas, and reduction of poll

关键词： Sustainable city

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Automated Machine Learning Enabled Cybersecurity Threat Detection in Internet of Things Environment

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computer systems science & Engineering 2023年第4期45卷 687-700页

作者： Fadwa Alrowais Sami Althahabi Saud S.Alotaibi Abdullah Mohamed Manar Ahmed Hamza Radwa Marzouk Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Recently,Internet of Things(IoT)devices produces massive quantity of data from distinct sources that get transmitted over public *** becomes a challenging issue in the IoT environment where the existence of cyber threats needs to be *** development of automated tools for cyber threat detection and classification using machine learning(ML)and artificial intelligence(AI)tools become essential to accomplish security in the IoT *** is needed to minimize security issues related to IoT gadgets ***,this article introduces a new Mayfly optimization(MFO)with regularized extreme learning machine(RELM)model,named MFO-RELM for Cybersecurity Threat Detection and classification in IoT *** presented MFORELM technique accomplishes the effectual identification of cybersecurity threats that exist in the IoT *** accomplishing this,the MFO-RELM model pre-processes the actual IoT data into a meaningful *** addition,the RELM model receives the pre-processed data and carries out the classification *** order to boost the performance of the RELM model,the MFO algorithm has been employed to *** performance validation of the MFO-RELM model is tested using standard datasets and the results highlighted the better outcomes of the MFO-RELM model under distinct aspects.

关键词： Cybersecurity threats classification internet of things machine learning parameter optimization

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Offloading Decision Making in Mobile Edge Computing: A Survey 2

Offloading Decision Making in Mobile Edge Computing: A Surve...

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2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022

作者： Abu-Taleb, Najlaa Abdulrahman Hasan Abdulrazzak, Fuad Zahary, Ammar Thabit Al-Mqdashi, Abdulmajid M. Thamar University Faculty of Computer Science and Information Systems Thamar Yemen University of Science and Technology Faculty of Computing and IT Sana'a Yemen

ISBN: (数字)9781665461818

ISBN: (纸本)9781665461818

Mobile Edge Computing (MEC) is considered as a promising solution to augment the computational capabilities of mobile devices. Because of frequently changing locations, mobile users often see dynamically changing network conditions which have a great impact on the perceived application performance. Mobile users can offload computation-intensive tasks to edge clouds collocated with base stations. Task offloading in MEC is a solution to handle such computational-intensive applications. However, making high-quality offloading decisions at runtime is difficult in mobile environments. This paper presents a survey for offloading decisions in MEC. It shows the most critical issues and challenges that affect offloading decisions. In addition, the survey discusses the existing offloading decision-making approaches and models which have been categorized according to their schemes' optimization. Furthermore, a literature review of offloading decisions for various studies is investigated and analyzed from the perspectives of objectives, infrastructures, and optimization approach. Finally, the survey highlights the open research challenges and trends in the field of offloading decisions in MEC. © 2022 IEEE.

关键词： Mobile edge computing

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Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images

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Intelligent Automation & Soft Computing 2023年第4期36卷 127-143页

作者： Abdullah A.Asiri Ahmad Shaf Tariq Ali Muhammad Aamir Ali Usman Muhammad Irfan Hassan A.Alshamrani Khlood M.Mehdar Osama M.Alshehri Samar M.Alqhtani Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Department of Clinical Laboratory Sciences Collage of Applied Medical ScienceNajran UniversityNajranSaudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients *** in this technol-ogy-based arena,brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original *** artiﬁcially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented *** over-come the highlighted issue,a Generative Adversarial Network(GAN)deep learn-ing technique in which two neural networks compete to become more accurate in creating artiﬁcially realistic data for MRI *** GAN network contains mainly two parts known as generator and ***,a generator is the convolutional neural network,and a discriminator is the deconvolutional neural *** this research,the publicly accessible Contrast-Enhanced Mag-netic Resonance Imaging(CE-MRI)dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been *** proposed method is simple and achieved an accuracy of 96%.We compare our technique results with the existing results,indicating that our proposed technique outper-forms the best results associated with the existing methods.

关键词： GAN network CE-MRI images convolutional neural network brain tumor classiﬁcation

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Minimizing energy consumption in fixed node networks using a novel neutrosophic model: comparative analysis with standard and existing algorithms

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International Journal of information Technology (Singapore) 2025年 1-15页

作者： Ghimire, Sangam Bhurtel, Nirjal Jha, Sudan Ahmad, Sultan Abdeljaber, Hikmat A. M. Nazeer, Jabeen Department of Computer Science and Engineering Kathmandu University 28 kilo Bagmati Dhulikhel 45210 Nepal Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-kharj 11942 Saudi Arabia School of Computer Science and Engineering Lovely Professional University Punjab Phagwara 144411 India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Lebanon

The growing development and utilization of networked systems has led to more concern regarding the energy efficiency of these systems. In this paper, we present a novel approach to minimizing energy consumption in fixed node network for which we propose a new Neutrosophic model. This model leverages the unique strengths of Neutrosophic logic to enhance the efficiency by minimizing the energy consumption. The simulation of results is performed using Matplotlib (Python) with implementation parameters such as number of nodes (n) and additive white Gaussian noise (AWGN) (η). Additionally, we show a comparative analysis between the proposed model and other prevalent approaches used in minimizing energy consumption in networked systems. The comparative results shows improvement in energy efficiency, showcasing the potential of the proposed model as an alternative for optimizing energy consumption in networked systems. This work highlights the effectiveness of the proposed model and opens new avenues for future advancements in energy-efficient networked system using Neutrosophic approach. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Comparative analysis Energy-consumption optimization Networked systems Neutrosophic model Sensitivity analysis

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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