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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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Analysis of IoT Applications in Highly Precise Agriculture Farming

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Recent Patents on engineering 2025年第2期19卷 71-81页

作者： Reddy N, Latha Masila P, Gopinath School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India

Background: The IoT (Internet of Things) assigns to the capacity of Device-to-Machine (D2M) connections, which is a vital component in the development of the digital economy. IoT integration with a human being enables real-time decision-making in communication, collaboration, and technology analytics. Furthermore, environmental factors impacting plants, such as air humidity, temperature, air quality index, and soil wetness, are not frequently documented, emphasizing the development of a data monitoring system for future agricultural research and development. Methods: An IoT-based Intelligent Farming System is proposed. An innovative IoT-based intelligent farming system is developed that integrates real-time data monitoring, machine learning algorithms, and IoT technology to address the identified gaps and challenges. Results: In the face of climate change, extreme weather, and environmental constraints, increased food demand must be satisfied. Intelligent agriculture enabled by IoT technology can reduce waste and increase productivity for producers and farmers, from fertilizer use to tractor trips. Conclusion: In conclusion, this patent paper provides insightful and informative commentary on the progress made in technology within the agriculture industry and the challenges that still need to be overcome to achieve optimal outcomes. © 2025 Bentham Science Publishers.

关键词： IoT agriculture soil quality weather conditions machine learning environmental constraints

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Supportive particle swarm optimization with time-conscious scheduling (SPSO-TCS) algorithm in cloud computing for optimized load balancing

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 192-198页

作者： Menaka, M. Sendhil Kumar, K.S. School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India

Task scheduling for virtual machines (VMs) has shown to be essential for the effective development of cloud computing at the lowest cost and fastest turnaround time. A number of research gaps about job schedule optimization are included in the current paper. A thorough analysis of the data generated by this activity is essential to resolving the resource allocation mechanism of the cloud architecture. To fully utilize virtual machines with a similar weight distribution, a strategy-oriented mixed support and load balancing structure has been developed in this work. To minimize make-span time and accomplish initial load balancing, the SPSO-TCS technique combines Time-Conscious Scheduling with Supportive Particle Swarm Optimization. Finding the optimal make span time minimization for each virtual environment is the aim of this stage. Its main objective is to discover the sequence of activities with the least computation time and to reduce the time required to finish each operation. Utilizing the hybrid idea leads to a decrease in makespan and the use of the least amount of energy. © 2024

关键词： Virtual machine

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IoT based Predictive Modeling Techniques for Cancer Detection in Healthcare Systems

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Recent Patents on engineering 2025年第2期19卷 82-94页

作者： Ramya, T. Gopinath, M.P. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

Background: The main objective of the Internet of Things (IoT) has significantly influenced and altered technology, such as interconnection, interoperability, and sensor devices. To ensure seamless healthcare facilities, it's essential to use the benefits of ubiquitous IoT services to assist patients by monitoring vital signs and automating functions. In healthcare, the current state-of-the-art equipment cannot detect many cancers early, and almost all humans have lost their lives due to this lethal sickness. Hence, early diagnosis of cancer is a significant difficulty for medical experts and researchers. Methods: The method for identifying cancer, together with machine learning and IoT, yield reliable results. In the Proposed model FCM system, the SVM methodology is reviewed to classify either benign or malignant disease. In addition, we applied a recursive feature selection to identify characteristics from the cancer dataset to boost the classifier system's capabilities. Results: This method is being applied in conjunction with fuzzy cluster-based augmentation, and classification can employ continuous monitoring to forecast lung cancer to improve patient care. In the process of effective image segmentation, the fuzzy-clustering methodology is implemented, which is used for the goal of obtaining transition region data. Conclusion: The Otsu thresholding method is applied to help recover the transition region from a lung cancer image. Furthermore, morphological thinning on the right edge and the segmentation-improving pictures are employed to increase segmentation performance. In future work, we intend to design a prototype to ensure real-time analysis to provide enhanced results. Thus, this work may open doors to carry patent-based outcomes. © 2025 Bentham Science Publishers.

关键词： Lung cancer

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Automatic lung cancer detection using hybrid particle snake swarm optimization with optimized mask RCNN

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Multimedia Tools and Applications 2024年第31期83卷 76807-76831页

作者： Sudha, R. Maheswari, K. M. Uma Department of Computer Science and Engineering School of computing College of engineering and technology SRM Institute of Science and Technology Kattankulathur India Department of Computing Technologies School of Computing College of engineering and technology SRM Institute of Science and Technology Kattankulathur India

As a result of its aggressive nature and late identification at advanced stages, lung cancer is one of the leading causes of cancer-related deaths. Lung cancer early diagnosis is a serious and difficult challenge that is crucial to a person's survival. The first diagnosis of the malignant nodules is typically made using chest radiography (X-rays) and computed tomography (CT) scans;however, the potential presence of benign nodules results in incorrect conclusions. The early phases of both benign and malignant nodules exhibit striking similarities. In this paper, a novel deep learning-based model is proposed for the precise diagnosis of malignant nodules. The proposed approach consists of two stages namely, pre-processing and lung nodule detection. Initially, the Lung CT scan images are collected from the dataset. Then, to remove the noise present in the input image, we apply an adaptive median filter. Then, to enhance the image, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied. After pre-processing, the image is given to the optimized mask RCNN classifier to detect the malignant and benign nodules. To enhance the performance of the Mask RCNN classifier, the hyper-parameters are optimally selected using hybrid particle snake swarm optimization (PS2OA). The proposed PS2OA is a hybridization of particle swarm optimization (PSO) and snake swarm optimization (SSO). The performance of the proposed approach is analyzed based on different metrics and effectiveness compared with state-of-the-art works. The proposed approach attained the maximum accuracy of 97.67%. This work aimed at assisting radiologists to detect and diagnose small-size pulmonary nodules more accurately. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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An improved cylinder fitting method 3

An improved cylinder fitting method

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3rd International Conference on Electronic Information engineering and Data Processing, EIEDP 2024

作者： Zhang, Shuang Sun, Dongyu Chen, Ru Wang, Hua Hou, Daishuang Zhang, Wei School of Mechatronic Engineering Changchun Institute of Technology Changchun130012 China School of Mechatronic Engineering Changchun University of Science and Technology Changchun130022 China School of Mechatronic Engineering Changchun University of Technology Changchun130012 China

ISBN: (纸本)9781510680531

In order to solve the problem of cylinder fitting, this paper proposes a method to fit the initial value more accurately. Using the characteristics of the cylinder data collected by laser displacement sensor, the point cloud data is first fitted in plane, then the original data points are projected, and the section where the axis is located is found. Then, a point passing through the axis and the axis direction vector are found out. In this way, the method is used to find out Find the initial value of the cylinder. Then, the particle swarm optimization algorithm is used to iterate with the initial value, and then the 7 parameters of the cylinder are calculated accurately, and the cylinder equation of point cloud data is obtained. The method can obtain accurate data when the cylinder fitting is used to find the initial value, which makes it difficult to find the optimal particle swarm optimization. Compared with commercial software SA, the radius error is less than 0.05mm. The experiment shows that the accuracy of the method meets the requirements of the field. © 2024 SPIE.

关键词： Cylinders (shapes)

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A Study of Federated Learning with Internet of Things for Data Privacy and Security using Privacy Preserving Techniques

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Recent Patents on engineering 2024年第1期18卷 1-17页

作者： Shakeer, Shaik Mahamad Rajasekhara Babu, M. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore India

To address the privacy concerns that arise from centralizing model training on a large number of IoT devices, a revolutionary new distributed learning framework called federated learning has been developed. This setup of devices works together to train models without compromising the security of individual data streams. To solve machine learning problems, a federated learning architecture relies on many clients working together through a central aggregator. To protect each device's privacy, we only access the training data at random. Because of its focus on decentralized processing and model transmission, federated learning considerably reduces systemic privacy risks and costs in comparison to conventional centralized machine learning systems. All client data is encrypted before being stored in a local database. Each user's device collects data locally for training during a federated learning session, and then uploads the final model to a centralized server. To provide a comprehensive assessment and inspire more research, this paper explores into the inner workings of federated learning from five different vantage points: data partitioning, privacy approaches, machine learning models, communication architectures, and system heterogeneity. Then, suggestions for further research are given, and a list is compiled of the most recent problems in federated learning. We conclude with an examination of the present applications of federated Learning knowledge. © 2024 Bentham Science Publishers.

关键词： Privacy-preserving techniques

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An Improved Semantic Segmentation Network for Ultra-High Resolution Remote Sensing Images Based on DeepLabV3+ 3

An Improved Semantic Segmentation Network for Ultra-High Res...

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3rd International Conference on Electronic Information engineering and computer Science, EIECS 2023

作者： Zhang, Hua Jiang, Zhengang Yao, Xuekun Chen, Shifa Changchun University of Science and Technology School of Computer Science and Technology Changchun China Changchun Institute of Technology School of Computer Technology and Engineering Changchun China Changchun Institute of Technology Jilin Key Laboratory of Changbai History and Culture and Vr Technology Reconstruction Changchun China

ISBN: (纸本)9798350316674

With the continuous development of computer vision, semantic segmentation of remote sensing images has been known as a research hotspot in the field of remote sensing. In this paper, we propose an encoder-decoder structure to improve the DeepLabV3+ network based on ResNet for semantic segmentation of ultra-high resolution remote sensing images. We use ResNet18, ResNet101, and ResNet152 as the backbone of the network, respectively, and combine atrous spatial pyramid pooling (ASPP) to extract the semantic information of the remote sensing images at multiple scales and then reduce the feature maps to the input size by bilinear interpolation to realize the "end-to-end"pixel-level image classification and precise localization. We conducted experiments on the ultra-high resolution remote sensing image dataset ISRPS Vaihingen to verify the effectiveness of the proposed network. The experimental results show that the improved DeepLabV3+ network outperforms the original DeepLabV3+ network by 5.05%, 9.24%, 9.05%, and 7.62% in PA, MPA, MIoU, and FWIoU, respectively. © 2023 IEEE.

关键词： atrous spatial pyramid pooling DeepLabV3+ Remote sensing semantic segmentation ultra-high resolution

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Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 459-473页

作者： Ponnuviji, N.P. Nirmala, G. Kokila, M. L. Sworna Priyadharshini, S. Indra Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Thiruvallur India Department of Computer Science and Engineering RMD Engineering College Chennai India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nādu Kattankulathur Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing

关键词： Extraction

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Improving Object Detection in YOLOv8n with the C2f-f Module and Multi-Scale Fusion Reconstruction 6

Improving Object Detection in YOLOv8n with the C2f-f Module ...

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Li, Hui Wu, Aoyun Jiang, Zhiyu Liu, Fang Luo, Mingyue Changchun University of Technology School of Electrical and Electronic Engineering Changchun China Changchun Institute of Technology Changchun China

ISBN: (纸本)9798350316520

In the field of object detection, such as the YOLO series, the backbone network has been significantly optimized, but there needs some improvement for the space feature extraction. For example, when small objects are detected, there is a high probability of missed detection. In order to address this problem, a novel object detection module is introduced, and called C2f-f ***, the receptive fields of various features have been extended during the expansion convolution process to discover the differences between detailed target features and their relationships based on parameters. Secondly, when the necessary low-level feature information is extracted, also a majority of high-level information is preserved to ensure and mitigate the continuity of information flow, also prevent the gradient from disappearing. Thus, this helps to extract residual high-level information and improve the accuracy of the related model. Finally, a reconstruction for the backbone network based on a multi-scale fusion method with the principles of BiFPN is proposed to improve the network structure of YOLOv8n. Experimental validation on the COCO dataset demonstrates the effectiveness of the proposed method. More precise detection results for small object tasks are obtained, also the universality and innovation are exhibited via the new method than the existing *** new module exhibits more precise detection results for small object tasks, showcasing its universality and innovation across different models. © 2024 IEEE.

关键词： Object detection

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