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10th IEEE Smart World Congress, SWC 2024

作者： Wang, Wei Luo, Yuewen Wang, Xin Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China

ISBN: (纸本)9798331520861

Accurate segmentation of skin lesion areas is significant for the diagnosis and analysis of skin diseases. Due to the irregular and blurred boundaries between healthy skin and lesion areas and sometimes the interference of hair and pigmentation deposits, it is still a challenging task to accomplish accurate segmentation. To solve the above problems, a hybrid attention network (SISR-HANet) based on stage interaction and scale reconstruction is proposed. The network contains three main modules: The Multi-Scale Global Perception (MSGP) module perceives global features through multi-scale receptive fields, thus compensating for the lack of global contextual information. The Multi-Stage Spatial Reconstruction (MSSR) module utilizes the cross-mixing of features from different stages to adaptively generate feature masks corresponding to each stage for reconstructing the features in each stage, thus eliminating the semantic differences between feature maps at different scales. The Channel-Optimized Inter-Group Aggregation (COGA) module helps the model recover more detailed features during the up-sampling process by optimizing and regrouping the channel information. To evaluate the effectiveness of SISR-HANet, we conducted experimental validation on the public datasets ISIC2017, ISIC2018, and PH2. The experimental results show that the DSC scores of these datasets are improved by 1.58%, 1.53%, and 1.34%, respectively, compared with the suboptimal networks. © 2024 IEEE.

关键词： Image reconstruction

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A Secure and Efficient Information Authentication Scheme for E-Healthcare System

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computers, Materials & Continua 2023年第9期76卷 3877-3896页

作者： Naveed Khan Jianbiao Zhang Ghulam Ali Mallah Shehzad Ashraf Chaudhry Faculty of Information Technology Beijing University of TechnologyBeijing100124China Department of Computer Science Shah Abdul Latif UniversityKhairpur66111Pakistan Department of Computer Engineering Faculty of Engineering ArchitectureNisantasi UniversityIstanbul34398Turkey

The mobile cellular network provides internet connectivity for heterogeneous Internet of Things(IoT)*** cellular network consists of several towers installed at appropriate locations within a smart *** cellular towers can be utilized for various tasks,such as e-healthcare systems,smart city surveillance,traffic monitoring,infrastructure surveillance,or sidewalk *** is a primary concern in data broadcasting,particularly authentication,because the strength of a cellular network’s signal is much higher frequency than the associated one,and their frequencies can sometimes be aligned,posing a significant *** a result,that requires attention,and without information authentication,such a barrier cannot be ***,we design a secure and efficient information authentication scheme for IoT-enabled devices tomitigate the flaws in the e-healthcare *** proposed protocol security shall check formally using the Real-or-Random(ROR)model,simulated using ProVerif2.03,and informally using pragmatic *** comparison,the performance phenomenon shall tackle by the already result available in the MIRACL cryptographic lab.

关键词： IoT-enable device e-healthcare authentication edge computing

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Real-Time Bitcoin Cost Identification to Improve Efficiency Using Lasso Regression in Comparison with Decision Tree

Real-Time Bitcoin Cost Identification to Improve Efficiency ...

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2024 IEEE Wireless Antenna and Microwave Symposium, WAMS 2024

作者： Pavankumar, Gajula Velmurugan, J. Padmakala, S. Saveetha School of Engineering SIMATS Department of Computer Science and Engineering Saveetha University Chennai India

ISBN: (纸本)9798350358926

Enhancing the Bitcoin cost prediction to improve accuracy using a decision tree in comparison with Lasso regression. For trend confirmations, we often utilize the financial phrases support and resistance to the prediction. Materials and Methods: The price of bitcoin is forecasted using a straightforward prediction technique called Lasso regression. The effectiveness of each model's performance on the task was evaluated, and the findings were examined. The goal of testing so many different models was to examine how the fundamental hypotheses of each of them varied. We used Lasso regression together with the best parameters to weigh the input variables because it is a reliable technique. Lasso regression is a Model based on a sample T-test with a value of p is 0.000 (Independent sample t-test p © 2024 IEEE.

关键词： Forecasting

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CNN-Based Detection of DDoS Attacks in Multi-Cloud Environments

CNN-Based Detection of DDoS Attacks in Multi-Cloud Environme...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Verma, Rashmi Jailia, Manisha Kumar, Munish Kaliraman, Bhawna Banasthali Vidyapith Department Of Computer Science And Engineering Rajasthan Tonk India Koneru Lakshmaiah Education Foundation Department Of Computer Science And Engineering Vaddeswaram India Dpg Institute Of Technology And Management Department Of Computer Science And Engineering Gurugram India

ISBN: (纸本)9798350366846

With the increasing reliance on cloud computing services, ensuring the security of cloud-based infrastructures has become paramount. This paper proposes a novel approach utilizing Convolutional Neural Networks (CNNs) to detect Distributed Denial of Service (DDoS) attacks in a multi-cloud environment. The utilization of CNNs, known for their proficiency in feature extraction from structured and unstructured data, offers an innovative solution to the dynamic and evolving nature of DDoS attacks. The datasets utilized in this study include NSL-KDD. As the global frequency of cyberattacks rises, the digital landscape faces significant threats impacting both individual online presence and corporate entities. This paper employs deep learning techniques to enhance security against DDoS attacks, utilizing the inherent ability of deep learning to extract intricate patterns from vast datasets. This makes it a potent tool for constructing effective detection and mitigation systems for the DDoS threat. The research presents a comprehensive approach for detecting DDoS attacks by leveraging CNNs (Convolutional Neural Networks) and advanced data preprocessing methods, focusing on the widely recognized NSL-KDD dataset. The research findings reveal that the proposed CNN-based approach consistently outperforms, achieving an impressive accuracy score of 97.46%. These results underscore the promising potential of the proposed methodology in significantly improving the accuracy and effectiveness of intrusion detection systems. In a parallel exploration, another research paper introduces an alternative approach by offering a CNN model on the same dataset, achieving an accuracy outcome of 96.61%. While this alternative approach demonstrates commendable performance, the findings highlight the superior accuracy achieved by the proposed CNN methodology in the context of DDoS attack detection. © 2024 IEEE.

关键词： Denial-of-service attack

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Detecting Schizophrenia from Healthy control using Machine Learning and EEG Signals 4

Detecting Schizophrenia from Healthy control using Machine L...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Tamilarasi, K. Pavitra, S. Kumar, Anuj School of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India

ISBN: (纸本)9798350385793

A brain condition called schizophrenia has a significant impact on people's thinking, behaviors, and lives on a global state. The prevalence of the condition rises along with the population, and those who suffer from schizophrenia lose their capacity for thought and behavior with time. All these issues arise because it is challenging to identify schizophrenia patients. As a result, proper care and quality patient treatment require an accurate and quick diagnosis procedure. This article aims to improve the accuracy of. detection by using EEG signals and Machine Learning algorithms. It is tested using the Resting state EEG dataset, which demonstrates a high level of capability in distinguishing between schizophrenia patients and healthy controls. The EEG data is converted to different forms, where the given machine learning techniques extract the EEG signals to identify patients with schizophrenia and healthy control. We are utilizing the 28-file public dataset in this study. In the file, there are 14 healthy individuals as well as 14 SCZ sufferers. In accordance with the findings, (1) the decision tree successfully distinguished SCZ patients from healthy individuals using EEG signals with an accuracy of 89.12%, and (2) logistic regression was also used to successfully identify the affected patient using EEG signals with an accuracy of 69.78%. (3) The SCZ patient was correctly identified by SVM with an accuracy of 65.51 %. Our research demonstrates the potential for machine learning to play a significant role in aiding in the diagnosis of schizophrenia. © 2024 IEEE.

关键词： Logistic regression

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RTPD-YOLO: Reconciling YoLo-V8 Model for Real-Time Potholes Detection

RTPD-YOLO: Reconciling YoLo-V8 Model for Real-Time Potholes ...

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2024 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2024

作者： Balakrishna, Sivadi Solanki, Vijender Kumar Department of Advanced Computer Science & Engineering Vignan’s Foundation for Science Technology & Research Guntur A.P Vadlamudi India Department of Computer Science & Engineering Stanley College of Engineering and Technology for Women Telangana Hyderabad India

ISBN: (纸本)9798350389180

Indian road privilege systems employ low-quality building materials, making pothole detection and avoidance difficult. Preventing accidents requires quickly identifying and fixing potholes. Roadside potholes may damage cars and endanger drivers. This article introduces a new method for realtime pothole identification called RTPD-YOLOv8. It can identify several forms of potholes, including cracks, oil stains, patches, and stones. To begin processing, pothole videos are first transformed into picture frames. The Ultratics are applied to these frames before processing to lessen distortions. The next step is to use the pre-processed photos to detect the pothole and identify its region using the Sobel edge detector and YOLOv8. Thereafter. The proposed RTPD-YOLO will detect the potholes efficiently. The Simulation results show that Accuracy, Precision, Recall, and F1-score were used to measure the performance of the RTPD-YOLO approaches. A total Accuracy of 99.72% is attained by the RTPD-YOLO. Also, the RTPD-YOLO model gets a 98.86% precision, 94.64 percent recall, and 91.35% F1-score. When compared to other models, like Faster RCNN, SSD, and mask R CNN, the RTPD-YOLO network achieves a higher Accuracy range. © 2024 IEEE.

关键词： Edge detection

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Revolutionizing Urban Waste Management: Cutting-Edge Image Processing for Precise Solid Waste Classification 5

Revolutionizing Urban Waste Management: Cutting-Edge Image P...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Rama Krishna, S. Patan, Muzeeb Khan Sudarsan, J.S. Padmapriya, R. Bhagavathiperumal, S. Balaram, Allam Department of Computer Science & Engineering Hyderabad502329 India College of Engineering and Technology University of Technology and Applied Sciences Department of EEE Muscat Oman India Sathyabama Institute of Science and Technology Department of Civil Engineering Chennai India Sri Krishna College of Engineering and Technology Coimbatore India MLR Institute of Technology Department of Computer Science and Engineering Hyderabad500043 India

ISBN: (纸本)9798331530013

Due to rapid urbanization and population growth, urban waste management has become one of the major challenges faced by cities around the world. Solid waste classification and sorting are necessary for intensifying recycling, safeguarding environment and manage additional landfilling of materials that are reusable or recyclable, and are getting in landfill of solid waste in certain extent. In this study, an original ensemble model using the VGG19 and EfficientNet architectures is proposed to classify solid waste in urban area. This proposed model was applied on a dataset consists of 2,527 number of images in to six classes to increase the accuracy of classification as well as to maximize the efficiency of waste. It preprocesses the incoming data using various techniques such as image resizing, normalization and augmentation to make the dataset more diverse and robust. The ensemble mechanism performed better combining the detailed feature extraction skills of VGG19 and versatile and scalable architecture of EfficientNet. On test set, it outperformed individual models and the accuracy for the proposed model was 98.75% which closely followed, previously obtain the best result. © 2024 IEEE.

关键词： Population statistics

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A Comparative Analysis of Different Machine Learning Techniques For Medical Insurance Premium Prediction 1

A Comparative Analysis of Different Machine Learning Techniq...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Dash, Satyabrata Panigrahi, Bhawani Sankar Sanikommu, Vijay Venkata Bhaskar Reddy Madhavi, B.K. Sahoo, Susanta Kumar Department of Computer Science & Engineering Vishakhapatnam India Vardhman College of Engineeing Department of Information Technology Telangana Hyderabad India Ramachandra College of Engineering Department of Computer Science & engineering Andhra Pradesh Eluru India Indira Gandhi Institute of Technology Department of Computer Science Engineering & Applications Odisha Sarang India

ISBN: (纸本)9798350361780

Predicting health insurance premiums is a crucial task for both insurance companies and policyholders. This paper explores the use of regression approaches to predict health insurance premiums. The study uses a dataset of insurance premiums and related variables, including age, gender, BMI, and location to determine the factors that have the biggest effects on insurance rates. The study found that age and BMI were the main predictors of insurance rates, with gender and location having a relatively little influence. To create predictive models, the study combined various regression approaches, including logistic regression, Random Forest Regression, Decision Tree Regression, Gradient Boosting Regression, and linear regression. The predictive models were highly accurate, with a mean absolute error of less than 5% across all examined samples. The findings have significant implications for both insurers and customers, as accurate premium pricing is essential for insurers to maintain their financial health and offer customers coverage at reasonable prices. Accurate premium forecasts help policyholders plan their spending wisely for insurance costs and make informed decisions regarding their healthcare coverage. © 2024 IEEE.

关键词： Health insurance

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A Systematic Study for Understanding the Security Risks in 5G Core Network 16

A Systematic Study for Understanding the Security Risks in 5...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Dixit, Unnati Vittal, Shwetha Antony Franklin, A. Indian Institute of Technology Hyderabad Department of Computer Science and Engineering India

ISBN: (纸本)9798350383119

The widespread adoption of 5G networks has revolutionized communication, providing great connectivity and services across various verticals. Specifically, on the 5G Core (5GC) this has been made possible by leveraging network slicing and Network Function Virtualization (NFV) where multiple Network Functions (NFs) are involved to serve different user requests on the control plane and a variety of service traffic on the data plane. However, as 5G networks embrace zero trust and multi-tenant architectures, this rapid technological advancement has also exposed the 5GC to various security risks such as Distributed Denial of Service (DDoS), Man in The Middle (MITM), cross-slice disruptions, and side-channel attacks. In this regard, this paper presents a comprehensive approach to enhance the security of 5GC through a systematic study of dependency graphs, Betweenness Centrality (BC), and attack graphs. Potential attack scenarios are carefully analyzed by modeling the 3GPP-defined 5GC, performing vulnerability assessments, building attack graphs, analyzing them, and presenting the key inferences from the study. © 2024 IEEE.

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Parameter Optimization Design of GA-LADRC Electro-Hydraulic Position Servo Controller 3rd

Parameter Optimization Design of GA-LADRC Electro-Hydraulic ...

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3rd International Conference on Information Control, Electrical engineering, and Rail Transit, ICEERT 2023

作者： Zhang, Xinglan Hao, Yuke Ouyang, Qi Department of Computer Science and Engineering Chongqing University of Technology Chongqing400044 China

ISBN: (纸本)9789819788903

To address the problem that the parameters of the self-disturbance rejection control with piston displacement feedback are hardly adjustable and easily fall into local optimum, an improved genetic algorithm is proposed to optimize the parameters of the self-disturbance suppression controller in the electro-hydraulic servo position control system. The method first establishes the second-order closed-loop state space equations based on the valve-controlled electro-hydraulic cylinder mechanism model. The self-disturbance suppression controller is improved by introducing parameter perturbations and external disturbances, and an extended state observer is established by using a combination of linear and nonlinear fast-tracking differentiators as well as an improved sliding mode control law. In the process of optimizing the parameters of the self-disturbance rejection controller, the dynamic characteristics of the controller and the input constraints of the system are taken as the optimization objectives. The simulation experiment results indicate that the proposed method can effectively improve the control accuracy, stability, and anti-interference performance of the displacement feedback-based active disturbance suppression controller. The study provides a reference for further improving the control performance of electro-hydraulic servo systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Disturbance rejection

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