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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Bhavani, Battu Durga Sudheer Kumar, Komuravelly Ajay, Shilpa Balassem, Zaid Ajzan Rajasekhar, Boddu Malla Reddy Engineering College for Women(RH) Department of It Hyderabad India Sr University Department of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Management Studies Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Ai&ml Hyderabad India

ISBN: (纸本)9798350375442

In recent years, there has been a significant rise in the number of credit card bank transactions as well as the frequency of fraud and card theft. However, as financial crime becomes more challenging task, so improved real-time detection methods is required. This research investigates the use of Machine Learning (ml) approach to identify fraudulent behavior in financial transactions, in real-time applications. Hence, the Aquila Optimization Algorithm with Random Forest (AOA-RF) is recommended in this research as a real-time solution for identifying financial transaction fraud. By using real-time processing, reduction of data, and ml flexibility are required when fraud tendencies arise to properly detect data patterns. By optimizing characteristics and model parameters, the combination of AOA and RF increases real-time financial transaction fraud detection. This hybrid strategy enhances forecast accuracy, decreases false positives, and dynamically adjusts fraud patterns in financial data. According to the results it illustrates that the proposed AOA-RF algorithm have achieved better performances in terms Accuracy of 99.97%, Sensitivity of 99.15% and specificity of 99.54% when compared to existing models such as eXtreme Gradient Boosting (XGBoost), Federated Learning (FL) and Stochastic Gradient Boosting (SGB). © 2024 IEEE.

关键词： aquila optimization algorithm data reduction financial transactions random forest real-time fraud detection

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A Powerful Algorithm for e-Commerce Credit Risk Analysis

A Powerful Algorithm for e-Commerce Credit Risk Analysis

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2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Dayanand Lal, N. Guruswamy, Sandhya Lalitha, Vuyyuru Awasthy, Mohan Pareek, Piyush Kumar Gitam University Bengaluru Campus Dept. of Cse Gst Bengaluru India School of Computing and Information Technology Reva University India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Bharati Vidyapeeth Peeth Deemed to Be University Department of Engineering and Technology Navi Mumbai India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350301595

It is critical to build a system and model for analysing the credit risk associated with e-commerce to decrease the chance of credit defaults occurring during online transactions. This approach examines a client's income, assets, negative transaction rate, reputation, and third-party credit evaluation findings to determine creditworthiness. The risk assessment index-based credit risk analysis model is built using the RB-XGBoost approach. The credit risk analysis algorithm's training process employs a data balancing technique to increase the system's capacity to forecast accurately. This method is designed to solve the problem of class imbalance in machine learning datasets. The training set is separated into groups at this point, and each group is compared to a specified threshold. The dataset is then made more representative by combining oversampling and undersampling. The system's capacity to analyse the credit risk presented by online shops is improved by balancing data subsets. Online retailers might utilise the proposed model and indexing approach to better assess their clients' creditworthiness and thereby reduce the number of consumers whose payments are late. By suggesting a unique way to address class imbalance in machine learning datasets, this study adds to the expanding body of work on credit risk assessment in online transactions. © 2023 IEEE.

关键词： Risk assessment

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Agglomerative Hierarchical Clustering based Method for Image Clustering using Restricted Boltzmann Machine 33

Agglomerative Hierarchical Clustering based Method for Image...

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33rd International Conference on computer Theory and Applications, ICCTA 2023

作者： Woubie, Abraham Solomon, Enoch Emiru, Eyael Solomon Silo AI Helsinki Finland Virginia State University Department of Computer Science RichmondVA United States University of Trento Department of Information Engineering and Computer Science Italy

ISBN: (纸本)9798350309577

In various verification systems, Restricted Boltzmann Machines (RBMs) have demonstrated their efficacy in both the front-end and back-end processes. In this study, we introduce the application of RBMs to the front-end for image clustering tasks. RBMs are trained to convert images into vector-based representations. In this work, we employ the conventional bottom-up Agglomerative Hierarchical Clustering (AHC) technique. To address the challenge of limited test face image data, we propose RBM adaptation to a global model. Initially, a universal RBM model is trained using all available training data. Subsequently, we train an adapted RBM model using the data from each test image. The visible-to-hidden weight matrices of these adapted models, along with the bias vectors, are concatenated and subjected to whitening to generate vector representations of face images, known as RBM vectors. These vectors effectively preserve class-specific information and are utilized in image clustering tasks. Our experimental results, conducted on two benchmark image datasets (MS-Celeb-1M and DeepFashion), demonstrate that our proposed approach surpasses well-known clustering algorithms such as k-means, spectral clustering, and approximate Rank-order. © 2023 IEEE.

关键词： Hierarchical clustering

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CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editions

CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Zhang, Hanchong Cao, Ruisheng Xu, Hongshen Chen, Lu Yu, Kai X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence SJTU AI Institute Shanghai Jiao Tong University Shanghai China Suzhou Laboratory Suzhou China

ISBN: (纸本)9798891761148

Recently, Large Language Models (LLMs) have been demonstrated to possess impressive capabilities in a variety of domains and tasks. We investigate the issue of prompt design in the multi-turn text-to-SQL task and attempt to enhance the LLMs’ reasoning capacity when generating SQL queries. In the conversational context, the current SQL query can be modified from the preceding SQL query with only a few operations due to the context dependency. We introduce our method called CoE-SQL1 which can prompt LLMs to generate the SQL query based on the previously generated SQL query with an edition chain. We also conduct extensive ablation studies to determine the optimal configuration of our approach. Our approach outperforms different in-context learning baselines stably and achieves state-of-the-art performances on two benchmarks SParC and CoSQL using LLMs, which is also competitive to the SOTA fine-tuned models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A novel deep learning model for early diabetes risk prediction using attention-enhanced deep belief networks with highly imbalanced data

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International Journal of Information Technology (Singapore) 2025年第4期17卷 1933-1955页

作者： Olabanjo, Olusola Wusu, Ashiribo Olabanjo, Olufemi Asokere, Mauton Afisi, Oseni Akinnuwesi, Boluwaji Center for Equitable AI and Machine Learning Systems (CEAMLS) Morgan State University Baltimore 21251 MD United States Department of Mathematics Morgan State University Baltimore MD United States Department of Computer Science Lagos State University Lagos Nigeria Department of Mathematics Lagos State University Lagos Nigeria Department of Cybersecurity University of Derby Derby United Kingdom Department of Philosophy Lagos State University Lagos Nigeria Department of Computer Science Faculty of Science and Engineering University of Eswatini Kwaluseni Swaziland

Diabetes mellitus is a prevalent chronic illness with severe complications that demand timely diagnosis. This study introduces an attention-enhanced Deep Belief Network (DBN) for early diabetes risk prediction, designed to address challenges associated with highly imbalanced datasets. Using a dataset from Sylhet Diabetes Hospital, which includes symptom and demographic information from patients, we applied an ensemble feature selection approach to identify critical predictors. To address the class imbalance, Generative Adversarial Networks (GANs) were used to generate synthetic data, ensuring the model’s robustness in identifying underrepresented cases. Additionally, a hybrid loss function combining cross-entropy and focal loss was implemented to improve classification, especially for hard-to-detect instances. Our results show that the attention-based DBN model, augmented with synthetic data from GANs and optimized with a hybrid loss function, achieves an AUC of 1.00, F1-score of 0.97, precision of 0.98, and recall of 0.95, outperforming several baseline models. This research offers a novel and effective approach for early diabetes detection, demonstrating potential for use as a clinical tool in preventive healthcare settings. © The Author(s) 2025.

关键词： Attention mechanism Deep belief networks (DBN) Diabetes risk Generative adversarial networks (GANs) Hybrid loss function Imbalanced data

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Li-Fi Enables Reliable Communication of VLT for Secured Data Exchange

Li-Fi Enables Reliable Communication of VLT for Secured Data...

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International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2021

作者： Gupta, N. Venkata Ramana Rajeshkumar, G. Selvi, S. Arun Mozhi Sengan, Sudhakar Subburaj, Arjun Priyadarsini, S. Rangasamy, Rajasekar Department of Computer Science and Engineering Prasad V. Potluri Siddhartha Institute of Technology Vijayawada Kanuru India Department Computer Science and Engineering Erode Sengunthar Engineering College Tamil Nadu Perundurai Erode India Department of Computer Science and Engineering DMI St. John The Baptist University Mangochi Malawi Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India Tranxit Technology Solutions Pvt. Ltd. Tamil Nadu Chennai India Department of Computer Science and Engineering P.S.R Engineering College Tamil Nadu Sivakasi India Department of Computer Science and Engineering-AI & ML School Engineering Malla Reddy University Hyderabad India

ISBN: (纸本)9789811900105

Li-Fi is a new method of sending and receiving secure data using light energy from one node to another. When data are transferred to the receiver node, the sender’s node light will blink continuously in the decade research. This may appear to be an uncomfortable remote control. Other than that, there are disadvantages to using a Wi-Fi network for the node because it has significant drawbacks such as bandwidth variances between nodes when using the communications system, such as some nodes achieving actual transmission speeds and others having low data rates. Li-Fi solves this issue by using light energy similar on both the sender and receiver sides, so the receiver node can transfer data at high speeds even when the device sending it is far away. The data transfer speed will be 9600 bits per second, which is fast enough to prevent lagging. The Li-Fi system has mainly been used in wireless nodes and IoT devices with embedded technology, a colour sensor and a microcontroller. The virtual node’s light source communicates with the destination nodes’ colour sensor, transforming the light into digital information. For the most part, these apps are found in IoT devices for user needs. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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A Deep Reinforcement Learning-Based Task Offloading Framework for Edge-Cloud Computing

A Deep Reinforcement Learning-Based Task Offloading Framewor...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Ponugoti Kalpana Muntather Almusawi Yerrolla Chanti V Sunil Kumar M Varaprasad Rao Department of Computer Science and Engineering AVN Institute of Engineering & Technology Telangana India The Islamic University Najaf Iraq School of Computer Science & Artificial Intelligence SR University Warangal India Department of AI and ML Nitte Meenakshi Institute of Technology Bengaluru India Department of Data Science CVR College of Engineering Hyderabad India

By introducing the processing and resources storage to the network edge, edge computing is a new, promising computing paradigm that drastically decreases network traffic and service latency. Many edge computing applications consist of interdependent tasks, wherein the results of one task are the inputs of another. The important and difficult problem of where to position each running task to optimise Quality-of-Service (QoS) is how to offload these tasks to edge of network. In this work, implemented a novel Deep Reinforcement Learning based Task Offloading (DRLTO) method utilized as the intelligent task offloading that uses a Directed Acyclic Graph (DAG) to represent the dependent tasks and off-policy reinforcement learning powered by a Sequence-to-Sequence (S2S) neural network. This research outcomes show that the DRLTO achieved less cloud processing time, and number of single terminal tasks, and percentage of failed task when compared to Deep reinforcement learning-based cloud-edge collaborative mobile computation offloading (DRL-CCMCO) and DeepEdge.

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DCBLCCG NET: A Fusion Approach For Enhanced Pepper Leaf Disease Identification With Hybrid Deep Learning

DCBLCCG NET: A Fusion Approach For Enhanced Pepper Leaf Dise...

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Shaik Salma Asiya Begum Hussainbi Shaik Hussain Syed Department of CSE(AI&ML) LakiReddy Bali Reddy College of Engineering Mylavaram NTR District AP India School of Computer Science and Engineering VIT-AP University Amaravati AP India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

The agricultural sector in developing countries like Ethiopia heavily relies on crop health and productivity. Pepper, a crucial crop for global food security, faces significant threats from various diseases, including blight leaf disease, gray leaf spot, common rust, fruit rot, and powdery mildew. The existence of 34 different pepper diseases has resulted in an average reduction of 33% in crop productivity. Traditional disease detection methods, such as visual inspection by farmers, are prone to errors and are time-consuming Previous research has largely employed image processing and deep learning for disease classification in pepper plants. This study introduces a novel approach by combining a Deep Convolutional Neural Network (DCNN) with a Bidirectional Long Short-Term Memory (BILSTM) network. While DCNN excels at extracting spatial features, BILSTM captures the sequential growth patterns of pepper plants. This hybrid model achieved $\mathbf{1 0 0 \%}$ accuracy during training and performed exceptionally well on a dataset of pepper disease images. Utilizing features from pretrained CNNs like VGG16 and AlexNet, the model attained a top classification accuracy of $\mathbf{9 9. 5 \%}$, with grayscale datasets and segmented images achieving $\mathbf{9 8. 9 \%}$ and $\mathbf{9 9. 3} \%$, respectively. This robust model is a powerful tool for rapid and accurate pepper disease identification, improving disease management and agricultural productivity.

关键词： Productivity Image segmentation Accuracy Microorganisms Transfer learning Bidirectional long short term memory Crops Gray-scale Feature extraction Diseases

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Personalized E-commerce Recommendation System using Marine Predator Algorithm with Gated Recurrent Unit

Personalized E-commerce Recommendation System using Marine P...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Kumar, Suvarna Sunil Ajay, Shilpa Kumar, Komuravelly Sudheer Obaid, Saif Sreevani, Voruganti Malla Reddy Engineering College for Women (Autonomous) Department of Information Technology Hyderabad India Nitte Meenakshi Institute of Technology Department of Management Studies Bengaluru India Sr University Department of Computer Science & Artificial Intelligence Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Hyderabad India

ISBN: (纸本)9798350375442

Through the constant advancement of Internet expertise and the crucial development of the network environments, e-commerce has grown vastly. With respect to resolve the issue of data overload and loss in choosing of e-commerce online shopping area. A personalized Recommendation System (RS) utilizing data filtering advancement has become most efficient. Hence, this research proposes the Marine Predator Algorithm with Gated Recurrent Unit (MPA-GRU) approach for the personalized e-commerce RS. The standard datasets like Amazon Product review and Retail Rocket are used to estimate effectiveness of the MPA-GRU approach. The pre-processing steps like stemming, lemmatization and tokenization are used to enhance the quality of the data. The Term Frequency-Inverse Document Frequency (TF-IDF) technique is used to extract relevant features and then these data are provided to the recommendation process. The experimental result represents that the proposed MPA-GRU approach attains the better accuracy of 96.21% and 95.29% on both Amazon Product review and Retail Rocket datasets as compared to the GRU approach. © 2024 IEEE.

关键词： e-commerce lemmatization personalized recommendation stemming tokenization

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Optimized Virtual Machine Scheduling for Green Cloud Computing Using mlP-LSTM with Sheep Flock Optimization 3

Optimized Virtual Machine Scheduling for Green Cloud Computi...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： D'Souza, Mithun Sreenivasa, B.L. Nagalavi, Deepa Ahmad Khan, Shamim Sathyanarayana, S. Dhaaraani, R. St. Joseph's University Department of Computer Science Bangalore India School of It Mangalore India Glocal University Glocal School of Science & Technology U.P India Nie First Grade College Department of Computer Science Mysore India K. Ramakrishnan College of Engineering Department of Ai & Ds Tamil Nadu Trichy India

ISBN: (纸本)9798331528201

With the growing demand for cloud computing services, it is important to efficiently manage resources using proper strategies in order to save energy consumption while retaining high-level performance. The innovative framework proposed here utilizes the mlP and LSTM models with a bio-inspired optimization algorithm in order to improve workload predictions and resource allocation for Virtual Machine (VM) scheduling. This work has an effective framework for energy-aware virtual machine scheduling in cloud computing environments using mlP-LSTM hybrid models optimized by the Sheep Flock Optimization algorithm, and comprehensive datasets it uses involve essential workload features, such as CPU and memory utilization. The proposed methodology utilizes the min-max normalization during data preprocessing, which ensures uniform scaling of all features. In the integrated mlP-LSTM model, an impressive accuracy of 98.7% was obtained for the task of prediction. Moreover, it was even better than other existing methodologies including Random Forest and Decision Tree. Using the predictions from the mlP-LSTM model, the SFO algorithm optimizes VM placement and migration, reduces energy consumption by a large margin, but at the same time maintains balanced resource distribution across all the physical servers. The results demonstrate the enhancement of both operational efficiency and energy usage, hence the sustainability of cloud computing. The implementation of the framework in Python gives it a realistic solution to situations that pose challenges to resource management in modern cloud infrastructures. This research validates the potential use of an advanced ml approach in conjunction with optimization techniques that can succeed in creating a green and efficient overall ecosystem for cloud computing. © 2024 IEEE.

关键词： Random forests

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