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2P3FL:A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

computer Modeling in Engineering & sciences 2024年第8期140卷 2035-2051页

作者： Sandeep Dasari Rajesh Kaluri School of Computer Science Engineering and Information Systems Vellore Institute of TechnologyVellore632014India

The increasing data pool in finance sectors forces machine learning(ML)to step into new *** data has significant financial implications and is *** users data from several organizations for various banking services may result in various intrusions and privacy *** a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global ***,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of *** address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global *** improves the privacy of the local *** analysis used the credit card and Canadian institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)***,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and *** experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.

关键词： Federated learning FedAvg FedProx flower framework privacy preservation financial sectors

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CRAQL: a novel clustering-based resource allocation using the Q-learning in fog environment

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International Journal of Cloud Computing 2024年第3期13卷 243-266页

作者： Ahlawat, Chanchal Krishnamurthi, Rajalakshmi Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India

Fog computing is an emerging paradigm that provides services near the end-user. The tremendous increase in IoT devices and big data leads to complexity in fog resource allocation. Inefficient resource allocation can lead to resource starvation and unable to complete the task assignment within a specific time. Hence, to enhance the efficiency of the fog resources, it is critical to perform proper resource allocation. This work targets to provide the solution to the resource allocation problem with a novel clustering-based resource allocation using the Q-learning (CRAQL) model. For this purpose, the problem is defined as a decision-making problem and formulated as Markov decision process (MDP). Next, to find the optimal resource, an enhanced optimal resource allocation (EORA) algorithm is proposed and detailed study is performed to analyse the impact of various performance parameters. Simulation results show the comparison of the EORA versus conventional Brute force method by varying the performance parameters such as learning rate and number of trials. The experimental results exhibit optimal solutions with significant improvement in learning rate at an average probability of 0.5 within limited epochs. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Reinforcement learning

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Artificial Intelligence and Natural Language Processing Inspired Chabot Technologies

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Recent Advances in computer science and Communications 2024年第1期17卷 11-20页

作者： Singh, Deepti Manju Jatain, Aman Netaji Subhash Institute of Technology New Delhi India Department of Computer Science and Engineering & Information Technology Jaypee Institute of Information Technology Noida India Department of Computer Science and Engineering Amity University Haryana India

Chatbots use artificial intelligence (AI) and natural language processing (NLP) algorithms to construct a clever system. By copying human connections in the most helpful way possi-ble, chatbots emulate individuals and serve as virtual assistants. They easily interface and respond to customers' requests. In the modern technical environment, these conversation agents or chatbots are considered the next-generation invention. Chatbot has become more popular in the business field right now as it can reduce customer service cost and handle multiple users at a time. There are many techniques used to involve such intelligent experts in daily business. A comprehensive analysis of the methods is needed to determine the viability of the different strategies. This paper tracks the progress of this invention and further clarifies the influence of chatbots on numerous businesses. Besides, a survey of the multiple chatbot methodologies suggested by various researchers is provid-ed. Along with the survey, a chatbot e-commerce customer service is designed to provide an efficient and accurate answer for any query based on the dataset of frequently asked questions. This chatbot can reduce customer service costs and can handle multiple customers at the same time. © 2024 Bentham science Publishers.

关键词： Machine learning

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Energy Cost Minimization Using String Matching Algorithm in Geo-Distributed Data Centers

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computers, Materials & Continua 2023年第6期75卷 6305-6322页

作者： Muhammad Imran Khan Khalil Syed Adeel Ali Shah Izaz Ahmad Khan Mohammad Hijji Muhammad Shiraz Qaisar Shaheen Department of Computer Science&Information Technology University of Engineering&TechnologyPeshawar25000Pakistan Department of Computer Science Bacha Khan UniversityCharsadda24541Pakistan Computer Science Department University of TabukTabuk47512Saudi Arabia Department of Computer Science Federal Urdu University of ArtsScience and Technology Islamabad44000Pakistan Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjabPakistan

Data centers are being distributed worldwide by cloud service providers(CSPs)to save energy costs through efficient workload alloca-tion *** CSPs are challenged by the significant rise in user demands due to their extensive energy consumption during workload *** research studies have examined distinct operating cost mitigation techniques for geo-distributed data centers(DCs).However,oper-ating cost savings during workload processing,which also considers string-matching techniques in geo-distributed DCs,remains *** this research,we propose a novel string matching-based geographical load balanc-ing(SMGLB)technique to mitigate the operating cost of the geo-distributed *** primary goal of this study is to use a string-matching algorithm(i.e.,Boyer Moore)to compare the contents of incoming workloads to those of documents that have already been processed in a data center.A successful match prevents the global load balancer from sending the user’s request to a data center for processing and displaying the results of the previously processed workload to the user to save *** the contrary,if no match can be discovered,the global load balancer will allocate the incoming workload to a specific DC for processing considering variable energy prices,the number of active servers,on-site green energy,and traces of incoming *** results of numerical evaluations show that the SMGLB can minimize the operating expenses of the geo-distributed data centers more than the existing workload distribution techniques.

关键词： String matching optimization geo-distributed data centers geographical load balancing green energy

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Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases

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computers, Materials & Continua 2023年第6期75卷 6269-6286页

作者： Wasif Akbar Adbul Mannan Qaisar Shaheen Mohammad Hijji Muhammad Anwar Muhammad Ayaz Department of Computer Science NFC Institute of Engineering and TechnologyMultanPakistan Faculty of Computer Science&Information Technology University of MalayaKuala LumpurMalaysia Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjabPakistan Faculty of Computers and Information Technology University of TabukTabuk47512Saudi Arabia Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahorePakistan Sensor Networks and Cellular Systems(SNCS)research center University of TabukTabuk47512Saudi Arabia

Machine Learning(ML)has changed clinical diagnostic procedures *** in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human *** studies focused on disease prediction but depending on multiple parameters,further investigations are required to upgrade the clinical ***-layered implementation of ML also called Deep Learning(DL)has unfolded new horizons in the field of clinical *** formulates reliable accuracy with big datasets but the reverse is the case with small *** paper proposed a novel method that deals with the issue of less data *** by the regression analysis,the proposed method classifies the data by going through three different *** the first stage,feature representation is converted into probabilities using multiple regression techniques,the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final *** experiments were carried out on the Cleveland heart disease *** results show significant improvement in classification *** is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future.

关键词： Machine learning heart disease cardiac disease deep regression regression learning

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An optimization driven deep belief network model for crop yield prediction in IoT based smart agriculture

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Multimedia Tools and Applications 2024年 1-23页

作者： Patel, Jaideep Chandel, Sonal Telang Sonker, Abhilash Department of Computer Science Engineering Sagar Institute of Research and Technology Bhopal India Department of Computer Science Engineering Indian Institute of Information Technology Bhopal India Department of Information Technology Madhav Institute of Technology & Science Gwalior India

Crop yield Prediction based on environmental, soil, water, and crop parameters has been an active area of research in agriculture. Many studies have shown that these parameters can have a significant impact on crop yields, and that accurate predictions of crop yields can be made by considering them together. Despite the progress made in crop yield prediction, several challenges still remain, such as the lack of high-quality and diverse data, the high cost of technology, and the lack of domain knowledge for certain crops and regions. Thus, Crop yield forecasting models can reasonably estimate yields, but better yield forecasting performance is still desirable. Thus, in order to enhance the accuracy of crop yield prediction in IoT, a novel optimization driven deep belief neural network model is developed in this study. IoT sensors can collect data on various environmental factors such as temperature, humidity, soil moisture, as well as crop parameters such as growth stage and leaf area index. These data can then be used to train a proposed deep learning-based model, namely an optimization driven deep belief neural network (DBNN) along with an effective crocodile optimization algorithm, to predict crop yields. To improve the performance of DBNN, adaptive crocodile optimization (ACO) algorithm is used. Using this algorithm, weight parameters of DBNN are chosen optimally. Results of the article depict that the proposed model achieve accuracy of 97.26%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Smart agriculture

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A multi-fused convolutional neural network model for fruit image classification

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International Journal of Cognitive Computing in Engineering 2024年第1期5卷 416-424页

作者： Sinha, Bam Bahadur Dhanalakshmi, R. Computer Science and Engineering National Institute of Technology Sikkim 737139 India Computer Science and Engineering Indian Institute of Information Technology Tiruchirappalli Tamil Nadu 620012 India

Fruit categorization presents a significant challenge due to the diverse range of fruit types and their similarities in color, shape, size, and structure. This challenge is addressed in this research by proposing a multi-fused CNN approach that integrates three state-of-the-art deep learning models, namely EfficientNet-B0, MobileNetV2, and ResNet50V2, into a unified pipeline. The primary objective is to enhance prediction accuracy and mitigate overfitting issues commonly encountered with conventional techniques. Utilizing the Fruit-360 dataset (both small and full versions), the proposed multi-fused CNN model demonstrates superior performance, achieving an impressive accuracy rate of 99.32% and 97.15% in classifying fruit types across 24 and 131 different categories, respectively. This substantial improvement underscores the potential of the proposed approach in reliably detecting fruit types. In future work, efforts will be directed towards optimizing the model for enhanced processing speed, facilitating real-time performance in practical applications, particularly in agricultural automation. Additionally, rigorous experiments on diverse real-world fruit images will be conducted to further validate the robustness of the proposed model. © 2024 The Authors

关键词： Fruits

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DNACDS:Cloud IoE big data security and accessing scheme based on DNA cryptography

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Frontiers of computer science 2024年第1期18卷 157-170页

作者： Ashish SINGH Abhinav KUMAR Suyel NAMASUDRA School of Computer Engineering KIIT Deemed to be UniversityBhubaneshwar 751024India Department of Computer Science and Engineering Indian Institute of Information Technology SuratSurat 394190India Department of Computer Science and Engineering National Institute of Technology AgartalaAgartala 799046India

The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the *** IoE-based cloud computing services are located at remote locations without the control of the data *** data owners mostly depend on the untrusted Cloud Service Provider(CSP)and do not know the implemented security *** lack of knowledge about security capabilities and control over data raises several security *** Acid(DNA)computing is a biological concept that can improve the security of IoE big *** IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol(StS KAP)and Feistel cipher *** paper proposed a DNA-based cryptographic scheme and access control model(DNACDS)to solve IoE big data security and access *** experimental results illustrated that DNACDS performs better than other DNA-based security *** theoretical security analysis of the DNACDS shows better resistance capabilities.

关键词： IoE based cloud computing DNA cryptography IoE big data security StS KAP feistel cipher IoE big data access

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Research Progress in Solar Flare Prediction Methods

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Research in Astronomy and Astrophysics 2025年第3期25卷 280-309页

作者： Ke Han Zhen Liu Xian-Yi Zhao Yi-Fei Li De-Quan Zheng Jie Wan School of Computer and Information Engineering Harbin University of Commerce Faculty of Computing Harbin Institute of Technology School of Energy Science and Engineering Harbin Institute of Technology

Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar flares in order to ensure the safety of human ***,the research focuses on two directions:first,identifying predictors with more physical information and higher prediction accuracy,and second,building flare prediction models that can effectively handle complex observational *** terms of flare observability and predictability,this paper analyses multiple dimensions of solar flare observability and evaluates the potential of observational parameters in *** flare prediction models,the paper focuses on data-driven models and physical models,with an emphasis on the advantages of deep learning techniques in dealing with complex and high-dimensional *** reviewing existing traditional machine learning,deep learning,and fusion methods,the key roles of these techniques in improving prediction accuracy and efficiency are *** prevailing challenges,this study discusses the main challenges currently faced in solar flare prediction,such as the complexity of flare samples,the multimodality of observational data,and the interpretability of *** conclusion summarizes these findings and proposes future research directions and potential technology advancement.

关键词： (Sun:) sunspots magnetohydrodynamics (MHD) Sun: activity Sun: flares Sun: magnetic fields

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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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