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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Nivaashini, M. Sathish Kumar, G. Uma Maheshwari, G. Soundariya, R.S. Kumar B, Arun Vijaya, G. Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Dr. Mahalingam College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Bannari Amman Institute of Technology Department of Computer Technology Tamil Nadu Sathyamangalam India Karpagam Academy of Higher Education Faculty of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350376135

The WHO's assessment indicates a rising prevalence of heart disease, exacerbated by challenges in detection and diagnosis in India due to population growth. Machine Learning (ML) is increasingly used for precise judgments in healthcare, aiming to accurately diagnose heart-related illnesses. This study proposes a Deep Learning (DL) ensemble method, combining ML classifiers to enhance accuracy. The ensemble model outperforms AdaBoost, XGBoost, Random Forest, Decision Tree, SVM, and Logistic Regression with 98.3% accuracy on a UCI dataset. Correlation-based Feature Selection (CFS) for relevant characteristic selection improves the model's accuracy, recall, precision, and f1-score. This research contributes to improved disease prediction and categorization in cardiovascular health, vital for effective healthcare interventions and outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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Breast Cancer Detection Using Ultrasound Images Based and Convolutional Neural Network

Breast Cancer Detection Using Ultrasound Images Based and Co...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Meher Langote Kunal Hiwase Radha Wande Abhay Tale Harsh Kalekar Swapnil Gundewar Faculty of Engineering and Technology Sawangi Artificial Intelligence and Data Science Maharashtra India Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Sawangi Maharashtra India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Breast cancer forms one of the most common types of cancer among women worldwide; therefore, it highlights the importance of having rapid and timely diagnosis methods. Here, we will survey the use of convolutional neural networks (CNNs) for mutually exclusive cancer detection in ultrasound images using detectors. A dataset of benign and malignant breast tumors visualized via ultrasound is taken as the base for the training and testing of the CNN model. The CNN architecture is arranged to select valuable features from the ultrasound pictures only, but not malignant lesions. The performance and the evaluation of the CNN model are done through metrics like accuracy, precision, recall, and F1 score. The findings confirm the efficiency of using the CNN here, as it shows the extent and solidity of reading textures with excellent precision and quick performance in breast cancer screening. This investigation is the advancement of computer-aided diagnostic tools for breast cancer screening. As medical practice sees these benefits, the efficiency of healthcare delivery and patient outcomes are bound to be optimized.

关键词： Training Surveys Visualization Ultrasonic imaging Accuracy Breast cancer Sensors Convolutional neural networks Reliability Testing

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Secure Channel Creation for Task Delivery in Vehicular Cloud Computing to Avoid Denial of Service (DoS) 3

Secure Channel Creation for Task Delivery in Vehicular Cloud...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Meenambika, A. Pavalarajan, S. Lavanya, N. Anu Jerom, B. Ajith Divya, S. Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Data Science PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9798331532420

Vehicular Cloud Computing (VCC) offers a promising platform for supporting various automotive applications and services. However, its distributed and dynamic nature makes it susceptible to Denial-of-Service (DoS) attacks, particularly during channel establishment. This research proposes a comprehensive approach to mitigate these risks by integrating a suite of security mechanisms. These include rate-limiting, resource management, traffic analysis, access control, authentication, encryption, intrusion detection, behavioral analysis, and blacklisting/whitelisting techniques. By incorporating these measures, the proposed approach enhances the security and reliability of VCC, ensuring the availability of critical automotive services and protecting vehicles from the impact of DoS attacks. © 2024 IEEE.

关键词： Cloud platforms

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Enhancing the Security of Big data based on the New AI & its Techniques 5

Enhancing the Security of Big Data based on the New AI & its...

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5th International Conference on Information Management and Machine intelligence, ICIMMI 2023

作者： Singh, Uday Pratap Kumari, Bersha Saxena, Vivek Goyal, Dinesh Department of Artificial Intelligence & Data Science Poornima Institute of Engineering and Technology Jaipur India Department of Computer Science & Engineering Poornima Institute of Engineering and Technology Jaipur India

ISBN: (纸本)9798400709418

Since that time i.e 1864 till today, research on different security level for the security of data which has been transferred or to be transfer within a network is on the boom day by day because with the advances in equipment innovations just as the quantity of gadgets associated day by day to the Internet, there are enormous data are gathered. Such data, whenever examined, will add significant qualities to the private just as governmental sectors. Simultaneously, artificial intelligences (AI) algorithms are demonstrated proficient in taking care of numerous unpredictable issues, particularly Deep Neural Networks (DNNs) on other Big data techniques. In this paper will have survey the efficiency of the system by applying different data value techniques and algorithms on the hardware tools. © 2023 ACM.

关键词： Deep neural networks

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Numerical solution hybridized by machine- leaning-based algorithm to provide an efficient method for analyzing thermomechanical shock behavior of the circumferentially-graded graphene-plates reinforced composite sandwich panel (sept, 10.1080/15376494.2023.2195417, 2023)

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年第29期31卷 12345-12348页

作者： Han, Shaoyong Ye, Qianqian Hakamy, A. Abd-Elnaiem, Alaa M. Changzhou Vocational Institute of Industry Technology Changzhou Jiangsu China Postdoctoral Scientific Research Workstation Bank of Zhengzhou Zhengzhou Henan China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou Zhejiang China Department of Physics Faculty of Applied Science Umm Al-Qura University Makkah Saudi Arabia Physics Department Faculty of Science Assiut University Assiut Egypt

The present research bridges the gap between a numerical solution with efficient machine-learning-based algorithms to provide a strong platform for predicting thermomechanical shock behavior of the sandwich cylindrical panels with polymer-made core and circumferentially-graded graphene-plates reinforced (CG-GPLR) nanocomposite face-sheets. The comprehensive declaration of the elasticity theory is considered to establish the governing equations of the system in three orthogonal directions. The well-known Lord-Shulman heat transfer theorem is implemented to present the time-variant nature of the system’s heat. The differential quadrature method (DQM) is used as a numerical approach to solve the spatial dependency of the governing differential equations. While the Laplace transform solves the time dependency of the governing differential equations. In order to decline the computational cost of predicting the time-variant analysis of the thermomechanical shock response of the system, an efficient machine-learning-based algorithm is employed in a way that it only requires the thermomechanical shock information of the selective set of points known as the training set. After completion of the training procedure, this machine-learning predictor can easily predict the thermoelastic response of all points included either as the train set or a new point. Validation of the applied solution is performed through a comparative process between the present results with those determined in the published literature. The results of this study are in excellent compatibility with those obtained in a solo numerical solution.

关键词： Coupled thermoelasticity analysis sandwich cylindrical shell optimized machine-learning regressor spatial and temporal stress distribution thermomechanical shock response

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OneScroll: Modernizing Supply Chain Business Networks with Digital technology

OneScroll: Modernizing Supply Chain Business Networks with D...

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2025 International Conference on Multi-Agent Systems for Collaborative intelligence, ICMSCI 2025

作者： Kanniga Parameshwari, M. Shobha Rani, P. Chokkalingam, S. Yogesh Kannan, S. Balaji Ganesh, K. Darunesh, R. R.M.K Engineering College Department of Information Technology India R.M.K Engineering College Department of Artificial Intelligence and Data Science India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

In today's fast-paced digital world, connecting distributors and retailers efficiently is crucial for business success in the supply chain market. Traditional methods of networking often involve a lot of travel and can miss potential opportunities due to logistical challenges. That's where 'OneScroll' comes in - a ground breaking mobile app designed to make these connections easier and more effective, similar to how social media works but specifically tailored for business networking. OneScroll stands out from platforms like LinkedIn and Instagram by focusing solely on business connections. It's a space where distributors and retailers can create detailed profiles, find potential business partners, and communicate seamlessly. This paper explores the development of OneScroll, highlighting its unique features and the technology behind it. In doing so, this promises to make business operations smoother and help businesses of various products to reach a wider audience in the evolving world. © 2025 IEEE.

关键词： Sales

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Early Detection of Coconut Palm Leaflet Diseases using Convolutional Neural Networks based Deep Learning Approach 4

Early Detection of Coconut Palm Leaflet Diseases using Convo...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Santhiya, S. Krishnasamy, Lalitha Sharmila, C. Jayadharshini, P. Dharshan, P.P. Haripreetha, S. Kongu Engineering College Department of Artificial Intelligence Erode India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode638052 India Srm Institute of Science and Technology Department of Cse Tiruchirappalli India Kongu Engineering College Erode India

ISBN: (纸本)9798331518981

In tropical regions, coconut palms are an essential part of the economy and daily life, providing food, oil, and many other products. However, their health and productivity can be affected by various diseases. Early and precise disease identification is essential for the management and prevention of many diseases. Leaflet diseases in coconut plants are those that impact individual leaflets, or leaf segments, on the fronds of coconut palms. Drying leaflets in coconut tree leaves can result from a variety of factors, including biotic (living organisms) and abiotic (environmental or non-living) impacts. In coconut trees, chlorosis, or the yellowing of the leaves, is a prevalent issue that can be caused by some factors. The CNN model is trained on a sizable set of high-resolution images of both healthy and diseased coconut leaves. Each disease's unique characteristics and patterns are automatically acquired with the aid of the deep learning architecture, enabling accurate categorization. Using an additional set of annotated photos of coconut tree leaves, the CNN model is assessed with remarkable accuracy in identifying illnesses. Comparing the system to traditional manual diagnosis procedures demonstrates the technology's efficacy and potential for early disease identification. Using this strategy raises the algorithm's accuracy to 95% © 2024 IEEE.

关键词： Plant diseases

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Enhancing Human Cobot Interaction with Mixed Reality: A Futuristic Review 2

Enhancing Human Cobot Interaction with Mixed Reality: A Futu...

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2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023

作者： Raffik, R. Rithish, Ramamoorthy Sathya Sushmita, V. Jyothi, Lakshmi N Gautham, S. Mechatronics Engineering Kumaraguru College of Technology Coimbatore India Artificial Intelligence and Data Science Kumaraguru College of Technology Coimbatore India

ISBN: (纸本)9798350306811

A literature review on the topic of cobots with MR technology would provide a comprehensive overview of existing research, studies, and scholarly articles related to the combination of cobots and MR in various fields. It would involve reviewing published works that investigate the application, benefits, challenges, and outcomes of integrating cobots with MR technology in manufacturing, healthcare, and other industries. Collaborative robots (cobots) have revolutionized manufacturing and other industries by working alongside humans to enhance productivity and efficiency. By conducting a literature review, researchers can identify gaps in current knowledge, understand the methodologies used in previous studies, and gain insights into the potential applications and limitations of cobots with MR technology. This review would serve as a foundation for further research and development in this field, helping researchers and practitioners make informed decisions and contribute to advancements in the use of cobots and MR. However, traditional programming and control interfaces for cobots have limitations that prevent their full potential from being realized. It is conceivable that cobots will develop into ever more useful and valuable resources across a range of industries, including Industry 5.0, as MR continues to advance. The user experience could be improved via cobot integration with MR. © 2023 IEEE.

关键词： Mixed reality

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Functional Verification of Extended Kalman Filter on FPGA for Railway Wheelset Parameters Estimation

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IEEE Access 2025年 13卷 96646-96659页

作者： Mal, Khakoo Memon, Tayab Din Kalwar, Imtiaz Hussain Junejo, Allah Rakhio Memon, Majid Hussain Memon, Tarique Rafique Memon, Sufyan Ali Chowdhry, Bhawani Shankar Sukkur IBA University Sukkur65200 Pakistan Mehran University of Engineering & Technology NCRA-Condition Monitoring Systems Lab Jamshoro76062 Pakistan Torrens University Center for Artificial Intelligence Research and Optimization Faculty of Design and Creative Technology Melbourne Australia Iqra University Faculty of Engineering Sciences and Technology Karachi75500 Pakistan Iqra University Department of Artificial Intelligence Karachi75500 Pakistan Department of Electronic Engineering Quaid-e-Awam University of Engineering Science and Technology Nawabshah67450 Pakistan Department of Defense Systems Engineering Sejong University Seoul Korea Republic of

The forces generated at the wheel-rail contact patch are important and crucial to the railway vehicle's operation because they control the dynamic behavior of the entire rolling stock. The transfer of the tractive force applied by the locomotive's traction and braking system requires a certain amount of adhesion force at the wheel-rail contact area. The wheel may skid while decelerating and slip when accelerating due to the maximal adhesion force at the wheel-rail interface may be less than the applied tractive force. A major difficulty is accurately estimating adhesion conditions and implementing them in real time because the contact forces between the wheel and rail are complicated and are highly non-linear. The fast-processing speed and hardware flexibility of field programmable gate arrays (FPGAs) make them ideal for the implementation of nonlinear condition monitoring systems. The goal of our work is to estimate various railway wheelset characteristics relating to wheel-rail contact forces under variable track-contact situations by designing and implementing the extended Kalman filter (EKF) model utilizing MATLAB and FPGA. The onboard estimation of wheel-rail interaction parameters is verified using the Xilinx® System-On-Chip (SoC) Zynq and Xilinx Spartan-3 FPGA devices, built-in with the National Instruments (NI) myRIO® development board and sbRIO® single-board controller. Two FPGA platforms are used to test the dataset generated by the MATLAB railway nonlinear wheelset model under different track circumstances, which include the vehicle's acceleration and deceleration modes. The 95% of the simulation results achieved. The proposed model's hardware synthesizes on FPGA are validated, and the area-performance is analyzed. Both FPGA devices take 52 cycles per sample and hence throughput becomes 769 kilo samples per second. Comparing the Xilinx System-On-Chip Zynq FPGA device to the Spartan-3, it is found that the latter offers a worse area-performance tradeoff fo

关键词： Extended Kalman filters

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A comparative analysis for deep-learning-based approaches for image forgery detection

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第1期8卷 1-10页

作者： Ch, Ravikumar Radha, Marepalli Mahendar, Maragoni Manasa, Pinnapureddy Department of Artificial Intelligence &Data Science Chaitanya Bharathi Institute of Technology Hyderabad 500075 India Department of Computer Science and Engineering CVR College of Engineering Hyderabad 501510 India Department of Computer Science and Engineering Neil Gogte Institute of Technology Hyderabad 500039 India

The detection of counterfeit photographs is critical in the digital age because of the widespread development of digital media and its significant impact on social networks. The legitimacy of digital content is being threatened by the growing sophistication of picture counterfeiting. With the help of pre-trained VGG-16 models and deep learning techniques that integrate Error Level Analysis (ELA) and Convolutional Neural Networks (CNNs), this study presents a fresh solution to this problem. The study thoroughly assesses and contrasts these models with a dataset that has been carefully chosen to bring the presented findings into perspective. To ensure a reliable evaluation of each model's performance 5000 experiments were carried out in total. With an accuracy rate of 99.87% and an accurate identification rate of 99% of hidden forgeries, the results demonstrate the exceptional effectiveness of the ELA-CNN model. However, despite its robustness, the VGG-16 model only achieves a significantly lower accuracy rate of 97.93% and a validation rate of 75.87%. This study clarifies the relevance of deep learning in the identification of image forgeries and highlights the practical ramifications of various models. Moreover, the research recognizes its constraints, especially for highly advanced counterfeits, and proposes possible paths for enhancing the accuracy and scope of detection algorithms. In the ever-changing world of digital media, the thorough comparative analysis provided in this study offers insightful information that can direct the creation of accurate forgery detection tools, protecting digital content integrity and reducing the effects of image manipulation. © (2023), (Society of Sytematic Innovation). All Rights Reserved.

关键词： Counterfeit images deep learning ELA-CNN Image forgery detection VGG-16 model

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