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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Amutha, M. Arul, U. Radha Devi, G. Manikandan, J. Uma, R. Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha Nagar Thandalam Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India Sri Sairam Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Hindusthan College of Engineering and Technology Department of Mechanical Engineering Taminadu Coimbatore India Malla Reddy College of Engineering Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

The Web of Things (IoT) enables people to achieve higher levels of automation through means of building systems, sensors and interconnected tools, and the Internet. In intensive care units, silence checking is an important and fundamental activity, as even a slight delay in choosing a treatment for a patient can lead to endless helplessness or, in some cases, death. Organized sensors (worn on frames or provided on living plant elements) provide rich statistics by means and means for physical and intellectual development. Initially, IoT's possibilities and issues can be assigned when acquiring this creative and attachment as a management restore method. Most ICU units are equipped with a variety of sensors to estimate prosperity boundaries, but it is recommended that you constantly display the screen. Pain is primarily based on the main framework, which is mainly available, mainly based on the main framework, and begins to understand the emergency and speak of the administration's personnel. At danger, she can help the initial aggressive and fast treatment. Recovery management in this format reduces confidence in important experts for keeping more time to reduce confidence in human mixes and choose as real ideas. © 2022 IEEE.

关键词： Intensive care units

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Evaluation of CatBoost for Diabetes Prevention in Comparison to XGBoost: To avert mortality by developing an AI model capable of predicting the onset of diabetes

Evaluation of CatBoost for Diabetes Prevention in Comparison...

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Electronic Systems and Intelligent Computing (ICESIC), International Conference on

作者： G. Gifta Jerith S. Jagadeesh P. Ponmathi Jeba Kiruba C Mahesh S. Anantha Babu S. Samsudeen Shaffi Dept. of CSE (Artificial Intelligence and Machine Learning) School of Engineering Malla Reddy University Hyderabad India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Saveetha Engineering College Chennai India Department of Information Technology Panimalar Engineering College Chennai India Department of Computer Science and Engineering GITAM School of Technology GITAM University Bangalore India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

Diabetes is a metabolic condition defined by consistently elevated blood glucose levels. Hyperglycemia is characterized by heightened hunger, thirst, and urination. Numerous consequences may arise from untreated diabetes. Diabetic ketoacidosis, hyperosmolarity, hyperglycemia, and even mortality may occur as acute consequences. Heart disease, stroke, chronic renal failure, foot ulcers, and eye impairment are some of the worst long-term consequences. A total of 463 million individuals were found to have diabetes in 2019, according to the worldwide diabetes organisation. Assuming predictions come true, that number will soar to 700 million by 2045, up 578 million by 2030. It has modern medical applications, allowing for the rapid and precise diagnosis of illness. Thus, we may use machine learning to avert mortality by developing an AI model capable of predicting the onset of diabetes; to determine which algorithm is most suited for this task, we will compare the CatBoost and XGBoost methods. In the end, the research compared two diabetes prediction models, XGBoost and CatBoost, using a variety of health indicators from the dataset. Using a real-world dataset obtained from Kaggle, our suggested system is trained and built using Python. Performance assessment criteria such as precision, recall, F1score and accuracy measurements are used to thoroughly analyse our methods. In comparison to XGBoost's 88.33% accuracy rate, 85.25 percent precision, 85.63 percent recall, and an F1 Score of 86.50. CatBoost's 91.08% percent accuracy, 85.25 percent precision, 85.63 percent recall, and 87.38 F1 Score. CatBoost marks it as the most successful ensemble approach.

关键词： Deep learning Heart Accuracy machine learning algorithms Prevention and mitigation Mortality Medical services Predictive models Prediction algorithms Diabetes

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IoT and machine learning Technology based Smart Shopping System 3

IoT and Machine Learning Technology based Smart Shopping Sys...

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3rd International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Arul, U. Prasath, A. Arul Mishra, Suman Shirisha, J. Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India CMR Engineering College Department of Electronics and Communication Engineering Telangana Hyderabad India Department of Electronics and Communication Engineering Telangana Hyderabad India

ISBN: (纸本)9781665462754

In today's environment, traditional shopping is a time-consuming activity. Customers must wait in huge lines at the pay counter in traditional shopping. As a result, both the client and the clerk waste a lot of time and energy. To combat this interaction, we're pushing a fantastic shopping framework built on an Android app. The user must first download and install the software on his or her Android phone in this framework. He then scans the item's QR code and enters the required amount. After that, the object is put onto the car. Customers buy the items and put them into the trucks in this manner. The client can add or remove items from the vehicle. The customer will be given the recommended items as well as the most convenient method of obtaining them. When the items are purchased, the bill is generated. This bill might be sent to the client's mobile device via internet banking, allowing the consumer to make a quick payment and leave the business sooner. © 2022 IEEE.

关键词： machine learning

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Metaverse for Wireless Systems: Architecture, Advances, Standardization, and Open Challenges

arXiv

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arXiv 2023年

作者： Khan, Latif U. Guizani, Mohsen Niyato, Dusit Al-Fuqaha, Ala Debbah, Mérouane The Department of Machine Learning Mohamed Bin Zayed University of Artificial Intelligence United Arab Emirates The School of Computer Science and Engineering Nanyang Technological University Singapore The College of Science and Engineering Hamad Bin Khalifa University Qatar The Technology Innovation Institute United Arab Emirates

The growing landscape of emerging wireless applications is a key driver toward the development of novel wireless system designs. Such a design can be based on the metaverse that uses a virtual model of the physical world systems along with other schemes/technologies (e.g., optimization theory, machine learning, and blockchain). A metaverse using a virtual model performs proactive intelligent analytics prior to a user request for efficient management of the wireless system resources. Additionally, a metaverse will enable self-sustainability to operate wireless systems with the least possible intervention from network operators. Although the metaverse can offer many benefits, it faces some challenges as well. Therefore, in this tutorial, we discuss the role of a metaverse in enabling wireless applications. We present an overview, key enablers, design aspects (i.e., metaverse for wireless and wireless for metaverse), and high-level architecture of metaverse-based wireless systems. Then, we discuss network management, reliability, and security of the metaverse-based system. Finally, we outline open challenges and present possible solutions. Copyright © 2023, The Authors. All rights reserved.

关键词： Augmented reality

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Rescaled Hinge Loss Function based Convolutional Neural Network for Identification of Art Forgery by Image Analysis

Rescaled Hinge Loss Function based Convolutional Neural Netw...

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Software, Systems and Information Technology (SSITCON), International Conference on

作者： K. Sundravadivelu S Karthika Ramy Riad Al-Fatlawy Jothi. R Monisha Mandapati Venkata Rama Sundari Department of Computer Science School of Information Technology Madurai Kamaraj University Madurai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Science and Engineering (DS) Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

The rapid growth of Information Technology (IT) as well as computer Science becomes an important evolution in marketable painting applications. The systematic authentication of paintings embraces the implication into the monarchy of art gathering. Performing the Convolutional Neural Network (CNN) approach for the identification of the reliable as well as forged painting arts according to the colour images is a feasible but trivial selection. However, this is challenging due to variations in artistic styles, textures, and subtle details which ambiguous forgery detection. Hence, this research proposes the Rescaled Hinge Loss Function based CNN (RHLF-CNN) approach for the identification of art forgery. The RHLF improves the model’s capability to discriminate between genuine and forged artworks, leads to more precise classification. The high-definition and hyperspectral image dataset of Chinese characters is utilized to estimate the effectiveness of the RHLF-CNN approach. The Local Binary Pattern (LBP) approach is used for the feature extraction process. The Chi-square approach is utilized for extracting the relevant features from the extraction process. The experimental results demonstrates that the proposed RHLF-CNN approach attains the better accuracy of 93.16% as compared to the existing approaches like Pulse-Coupled Neural Network (PCNN).

关键词： Adaptation models Art Authentication Fasteners Feature extraction Forgery Convolutional neural networks Information technology Painting Hyperspectral imaging

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An Optimized Elliptical Curve Cryptography based Crypto-Watermarking Model in Smart Healthcare Application

An Optimized Elliptical Curve Cryptography based Crypto-Wate...

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Data Science and Information System (ICDSIS), IEEE International Conference on

作者： G N Keshava Murthy Manjula G Harshitha P Achyutha Prasad N Department of Electronics and Instrumentation Engineering Siddaganga Institute of Technology Tumakuru Karnataka India Department of CSE BGS College of Engineering and Technology Bangalore India Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Telemedicine uses current communication and information technology to provide clinical treatment remotely. Given the ubiquitous usage of DWT and DCT are used for watermarking, ECC-FFA is used for encryption. The only cryptographic algorithms used in the current approaches for watermarking are AES, DES, and RSA, and the DCT and DWT are typically used separately. But because we integrated the ECC-FFA cryptographic method with the DWT and DCT watermarking technologies, the recommended solution is more effective than earlier ones.. A strong and lossless patient medical information exchange system employing crypto-watermarking is suggested in order to secure medical data. For the watermarking approximate, the discrete wavelet alter (DWT) and discrete cosine alter (DCT) are combined, and the elliptical curve cryptography (ECC) algorithm is utilized. At the same time, metaheuristic algorithms may be used to solve the optimum key creation issue in cryptography, which is viewed as an optimisation challenge. As a stem, the master nucleus of this masterpiece is on the development of cryptographic encryption for picture security based on metaheuristic optimization algorithms. According to this perspective, this study creates the ECC-FFA model, an effective produce fruit fly optimisation (FFO) technique with ECC for picture protection. The suggested model uses the ECC technique to carry out the encryption and decryption procedure. Various medical photos were used in the experiment, and the peak signal to noise ratio was used to assess how well the suggested method worked.

关键词： Telemedicine Wireless networks Metaheuristics Watermarking Medical services Elliptic curve cryptography Discrete wavelet transforms

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Hyper Spectral Spatial Image Segmentation Using machine learning Algorithms

Hyper Spectral Spatial Image Segmentation Using Machine Lear...

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Intelligent Technologies for Sustainable Electric and Communications Systems (iTech SECOM), International Conference on

作者： Saravanan G Suresh A Jagannathan J Thanga Mariappan L Najeem Dheen Abdul Majeeth Nithish Kumar Department of Computer Science and Engineering Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamilnadu India School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamilnadu India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Tamilnadu India

In recent years, there has been a notable surge in the development of methods dedicated to classifying hyper spectral image samples. However, the inherent high dimensionality of hyper spectral data presents significant challenges for traditional supervised classification algorithms. Prior studies have attempted to address these challenges by incorporating active learning methods for hyper spectral data segmentation. Nevertheless, a crucial drawback of active learning lies in its dependence on labeled data samples, leading to less improved classification results for unlabeled data samples. In response to this limitation, our work introduces Supervised Active learning (SAL) techniques that harness both spectral and spatial information. The key innovation of our research lies in the introduction of a novel hyper spectral image segmentation approach based on the k-dependence Bayesian (KDB) model. We estimate the maximizer of the posterior marginal using Symmetric Iterative Proportional Fitting (SIPF) within this KDB model. By implementing the KDB model for hyper spectral image segmentation, our goal is to enhance the classification accuracy of our SAL classifiers. The SAL framework integrates shape prior probability estimation, class density results from Improved Multi-Level Logistic (IMLL) methods, and the maximization of the posterior marginal estimated through Symmetric Iterative Proportional Fitting (SIPF). We evaluate the accuracy of our proposed MPM-SIPF-SAL system using the real ROSIS Hyper Spectral Dataset, demonstrating that our system outperforms existing methods in terms of classification accuracy.

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Empirical Evaluation of a Cybersecurity Powered Human Body Data Communication over Wireless Body Area Network 1

Empirical Evaluation of a Cybersecurity Powered Human Body D...

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1st International Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Mabel Rani, A. Jaya Ramachandran, Lalitha Sangeethapriya, S. Samuthira Pandi, V. Maniraj, K. Anni Princy, B. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of CSE Tamil Nadu Chennai India R.M.K. Engineering College Department of Mathematics Tamil Nadu Chennai India S.A. Engineering College Department of English H&S Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India MLR Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Panimalar Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India

ISBN: (纸本)9798350365016

All contemporary, user-friendly electronics necessitate technologies that allow for close-range communication between humans and other devices. In this paper, we show how to build and deploy a Human Area Network, which allows us to communicate with one another face-to-face. It is now crucial for mobile terminals to communicate with terminals embedded in the environment. The usage of wires is cumbersome since they often become tangled and are a pain to maintain when communicating between nearby devices. Data rates are lowered in congested settings, such exposition grounds, when extremely weak radio signals are utilized for communication due to packet collision. Additionally, unauthorized signal interception poses a security concern. In order to solve these kinds of challenges, we employ the human body as a means of communication. Cybersecurity Powered Human Data Communication (CPHDC) is a new communication logic introduced in this paper that uses the Red Tacton device to achieve threat- and error-free communication. To test how well it works, it is cross-validated with Frequential Band based Data Communication (FBDC), a conventional communication scheme. Using wearable devices for identification and data transmission to the master device in real time, the suggested model offers a safe and efficient communication system. With its integrated CPU and insulating material (copper) that enable intra-body communication through galvanic coupling, the suggested model is easily worn on the wrist or any other area of the body. Therefore, in comparison to existing wireless technologies, the suggested human body approach improves the security of sent signals. © 2024 IEEE.

关键词： Cybersecurity

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Relaxed Rotational Equivariance via G-Biases in Vision

arXiv

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arXiv 2024年

作者： Wu, Zhiqiang Liu, Yingjie Sun, Licheng Yang, Jian Dong, Hanlin Lin, Shing-Ho J. Tang, Xuan Mi, Jinpeng Jin, Bo Wei, Xian Software Engineering Institute East China Normal University China School of Geospatial Information Information Engineering University China School of Artificial Intelligence University of Chinese Academy of Sciences China School of Communication and Electronic Engineering East China Normal University China Institute of Machine Intelligence University of Shanghai for Science and Technology China School of Computer Science and Technology Tongji University China

Group Equivariant Convolution (GConv) can capture rotational equivariance from original data. It assumes uniform and strict rotational equivariance across all features as the transformations under the specific group. However, the presentation or distribution of real-world data rarely conforms to strict rotational equivariance, commonly referred to as Rotational Symmetry-Breaking (RSB) in the system or dataset, making GConv unable to adapt effectively to this phenomenon. Motivated by this, we propose a simple but highly effective method to address this problem, which utilizes a set of learnable biases called G-Biases under the group order to break strict group constraints and then achieve a Relaxed Rotational Equivariant Convolution (RREConv). To validate the efficiency of RREConv, we conduct extensive ablation experiments on the discrete rotational group Cn. Experiments demonstrate that the proposed RREConv-based methods achieve excellent performance compared to existing GConv-based methods in both classification and 2D object detection tasks on the natural image datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

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Skin Lesion Classification For Cancer Detection Using Convolutional Neural Network

Skin Lesion Classification For Cancer Detection Using Convol...

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Research Methodologies in Knowledge Management, artificial intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： M. A. Mukunthan Devaki Sai Sanuth Mohan Manikanta. V Dheeraj. K Dept. Of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Dept. Of Computer Science and Engineering With Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Dept. Of Computer Science and Engineering With Artificial Intelligence and Data Science Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331598488

ISBN: (纸本)9798331598495

Skin cancer is a major global health issue with millions of annual cases diagnosed. In 2023, nearly 100,000 new cases of melanoma, which is the most deadly, are estimated in the United States. For improved survival rates and appropriate treatment, early detection is critical. Conversely, conventional diagnostic techniques are long-drawn-out and at times variabe which often triggers to diagnostic-introducing delays. We propose for the first vanishing point, to enhance their VII. A CNN model is trained on a wide dataset of dermoscopic images to classify a skin lesion as cancerous or non-cancerous. Performs analysis is robust using measures of accuracy, sensitivity, specificity, and AUC The outcomes reveal that the CNN-based algorithm achieves a high level of precision exceeding previous techniques and allowing for a rapid and accurate diagnosis. This technique could become a gamechanger in skin cancer detection with early intervention and treatment and ultimately improved patient outcomes and decreased costs to the healthcare industry and society.

关键词： Training Accuracy Computational modeling Transfer learning Medical services Skin Robustness Lesions Convolutional neural networks artificial intelligence

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