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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Ramveer Singh Pooja Ambuj Kumar Agarwal Rohaila Naaz Raj Kumar Richa Vijay Greater Noida Institute of Professional Studies Greater Noida School of Engineering and Technology CT University Ludhiana Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Faculty of Engineering and Computing Sciences Teerthanker Mahaveer University Moradabad Department of Computer Applications Manav Rachna International Institute of Research and Studies (MRIIRS) Faridabad

ISBN: (纸本)9781665487351

In Today's World, Blockchain is a promising Technology in all areas; things have also been drastically changed after COVID-19; challenges surfaced for implementing blockchain technology in the context of its computational complexity and security. After invention of it in 2008, cryptocurrency applications, i.e., Bitcoin it also getting introduced in Different applications. One of the Major Building blocks of Blockchain is Cryptography techniques. This paper discusses Cryptographic primitives used in different phases of Blockchain implementation and the challenges of implementing these cryptographic primitives. Bitcoin uses Cryptographic primitives to generate public-private key pairs and mining, while Ethereum uses Elliptic curve cryptography to create public-private keys and digital signatures. This paper divided uses of cryptographic primitives into three categories used in Blockchains, i.e., key generation Hashes, signature generation hashes, and proof hashes.

关键词： Data privacy Technological innovation Data integrity Bitcoin Proof of Work Market research Elliptic curve cryptography

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An efficient optimized deep learning model for the risk prediction of arterial stiffness in diabetes patients

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Biomedical Signal Processing and Control 2025年 110卷

作者： Mohana Priya, A. Rajesh Kanna, P. Vanithamani, S. Sathish Kumar, G. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Bannari Amman Institute of Technology Tamil Nadu Erode India Department of Master of Computer Applications M.Kumarasamy College of Engineering Tamil Nadu Karur India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Tamil Nadu Coimbatore India

Arterial Stiffness (AS) is a potential biomarker for assessing cardiovascular disease (CVD) risk in patients with diabetes. It is commonly evaluated using Pulse Wave Velocity (PWV) where increased arterial stiffness is a major contributor to CVD-related events. However, the relationship between cardiovascular risk and diabetes remains unclear and machine learning based clinical prediction models for AS detection are still underdeveloped. Monitoring AS can provide early insights into abnormalities in diabetic patients enabling timely intervention. This study proposes an optimized deep learning model to assess AS risk in diabetic patients to improve CVD risk prediction. The model utilizes brachial-ankle pulse wave velocity (baPWV) and Fasting Blood Glucose (FBG) as diagnostic parameters. First, critical features from patient medical records are extracted using the Least Absolute Shrinkage and Selection Operator (LASSO) method followed by Principal Component Analysis (PCA) to remove outliers using Mahalanobis distance and reconstruction error. Next, a feature selection process is applied using the Gaussian LASSO Cat Optimization Algorithm (GLCOA). Finally, the Deep Kronecker Neural Network (DKNN) optimized with the Frog Leap Algorithm (FLA) referred to as DKNN-FL. In addition to this, SMOTE augmentation and cross validation are applied to avoid overfitting. The statistical test ANOVA shows the proposed work gives a better performance on a discovery dataset from the Dryad digital repository. From the comparative analysis, DKNN-FL with GLCOA gives Area under the Receiver Operating Characteristic Curve (AUROC) of 0.989 and an accuracy of 98.7 %. The proposed model is applied to another diabetic's prediction dataset and achieves an accuracy of 95.8 %. © 2025 Elsevier Ltd

关键词： Cardiology

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Hybrid clustering algorithm for an efficient brain tumor segmentation

Hybrid clustering algorithm for an efficient brain tumor seg...

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International Conference on Newer trends and Innovations in Mechanical Engineering, ICONTIME 2020

作者： Maheswari, K. Balamurugan, A. Malathi, P. Ramkumar, S. Department of Computer Applications School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu626 126 India Department of Physics Government Arts and Science College Avinashi Tirupur India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

This work describes the data mining methods, techniques and algorithms used for implementation. It is an emerging field of IT industry and research. There are many other fields such as Artificial Intelligence, Machine Learning, Deep Learning, Virtualization, Visualization, Parallel Computing and Image Processing. The human internal Brain can be seen or visualized by the Magnetic Resonance Imaging scan or computerized Tomography scan. The MRI image is scanned and will be taken as input for processing. The MRI scan is more advantageous and more comfortable than CT scan for diagnosis. MRI scan provides detailed picture of organs. It does not affect the human health and body condition. It doesn't use any radiation. It is purely based on the magnetic field and radio waves. LIPC technique makes the training samples from the patients and arranges them into different group of classes used to construct different dictionaries. Image segmentation is a technique of dividing an image into different multiple portions, which is used to spot out objects and boundaries in images. There are many image segmentation techniques applicable for image processing. No acceptable method is available for solving all kinds of segmentation problem. Every method has merits and demerits. So, choosing good method is the challenging task. The hybrid clustering method is proposed in this work. The k-means algorithm and fuzzy c-means algorithm is proposed for brain tumor segmentation. The algorithm is implemented in synthetic and real time dataset. From the experimental results, this method provides better results in the form of accuracy. © 2020 Elsevier Ltd. All rights reserved.

关键词： computerized tomography

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Portable noise level detector

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AIP Conference Proceedings 2024年第1期3171卷

作者： Abishek Asokaraja Syamala Vishnu Sreekumar Amrita Varshini Shiju Gayathri Narayanan Prabha Geena Prasad Department of Mechanical Engineering Amrita Vishwa Vidyapeetham Amritapuri Kollam India Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri Kollam India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kollam India

This study is a pioneering initiative focused on developing a sophisticated device for precise noise measurement in specific locations. It delves into the design, implementation, and evaluation phases of the project, emphasizing its dedication to ensuring reliability and accuracy. Commencing with a detailed analysis of existing noise measurement techniques, the project identifies key requirements and addresses challenges to develop a versatile device with wide-ranging applications. Beyond its technical dimensions, the project makes significant contributions to mobile sensing and environmental monitoring, offering potential benefits in evaluating noise pollution, occupational health and safety, and aiding in urban planning. This brief abstract encapsulates the core of the project, highlighting its commitment to technological advancement and societal impact in fostering healthier and more sustainable urban environments.

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Machine Learning Based Prediction of Soil pH 5

Machine Learning Based Prediction of Soil pH

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5th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Sunori, Sandeep Kumar Kumar, Santosh Anandapriya, B. Nesamani, S. Leena Maurya, Sudhanshu Singh, Manoj Kumar Graphic Era Hill University Department of ECE Bhimtal Campus India Dr. C. V. Raman University Bihar India Patrician College of Arts and Science Chennai India Dr. M.G.R.Educational and Research Institute Department of Computer Applications Maduravoyal Chennai India

ISBN: (纸本)9781665435246

The significance of pH value of soil in assessing the soil fertility has been the motivation behind writing this research paper. As the pH of the soil is strongly dependent on the extent of minerals present in it, its pH can be estimated using the mineral data. The soil data belonging to the Kumaun part of Uttarakhand is used to design the prediction models. A machine learning techniaue SVM (support vector machine) has been implemented in MATLAB version R2021a. Three different prediction models with linear, quadratic and cubic kernels are established, and their prediction accuracy is compared. © 2021 IEEE.

关键词： Support vector machines

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A Novel Application of K-means Cluster Prediction Model for Diabetes Early Identification using Dimensionality Reduction Techniques

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Open Bioinformatics Journal 2023年 16卷

作者： Vamshi, Krishna B. Raguru, Jaya K. Bhuvaneswari, A.P. Gururaj, H.L. Ravi, Vinayakumar Almeshari, Meshari Alzamil, Yasser Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India School of Computer Science and Applications Reva University Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Diagnostic Radiology College of Applied Medical Sciences University of Ha'il Ha'il Saudi Arabia

Purpose: Diabetes is a condition where the body cannot utilize insulin properly. Maintenance of the levels of insulin in the body is mandatory, otherwise it will lead to several disorders of kidney failure, heart attack, nervous weakness, blindness, etc. Among the 10 majority diseases, diabetes is occupying the second role by covering 34.2 million individuals as for the National Diabetes Statistics report. According to the World Health Organization, diabetes is playing the 7th role in cause of death. Thus early identification of diabetes can overcome these severe damages. Methods: Accurate predictions require a lot of data, which is introducing the curse of dimensionality. In the present research, PIMA Indians diabetes data set is considered and different classification models viz., K-means clustering with logistic regression, SVM (Support Vector Machine), Random Forest, etc. are implemented in predicting the accuracy of diabetes. Results: The accuracies for diabetes prediction are ranging from 0.9875 to 1.0. KCPM (K-means cluster prediction model) and has shown an increase in accuracy of 0.67% for the combined K-means clustering and different classification algorithms. In KCPM, firstly, the data is clustered using k-means into patients with and without diabetes, and then the clustered results are compared with the target variable and then filtered, followed by applying the different supervised classification algorithms for predicting the disease. Conclusion: The results show that KCPM predicts diabetes with a higher accuracy of 0.67% compared with other existing methods. By KCPM-based automated diabetes analysis system, early prediction of the disease may protect patients from facing severe disorders in life. © 2023 The Author(s). Published by Bentham science Publisher.

关键词： Accuracy Classification Classifiers Clustering Curse of dimensionality Diabetes Prediction

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The Controlling OF Viability of LM Controller in ULR System

The Controlling OF Viability of LM Controller in ULR System

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Mukesh Kamreed Udham Singh Kadim A. Jabbar M Sathish Kumar Laith Hussein Tareq Hafdhi Abdtawfeeq Abdul Hassan Majli Jaafar Ali Saad Alwan department of computer applications hindustan institute of technology and science school of computing graphic era hill university dehradun uttarakhand graphic era deemed to be university dehradun uttarakhand college of technical engineering national university of science and technology dhi qar iraq department of computers techniques engineering college of technical engineering the islamic university najaf iraq al-farahidi university baghdad iraq medical instrument department al-kunooze university college basra iraq business administration department kut university college al kutwasit iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

The Leap Motion Controller is an affordable marker-less optical sensor that uses a range of characteristics to follow the movements of the human hand. Individuals with disabilities are the focus of upper limb rehabilitation treatment, which uses continuous motion exercises to promote healing. However, patients’ motivation may be impacted by the repeated nature of these activities, which might be viewed as boring or demoralising. Serious games have been included into therapy to improve motivation and enjoyment in order to address this. This study looks at the feasibility, repeatability, and accuracy of using a Leap Motion Controller (LMC) in conjunction with a serious game to aid with upper limb rehabilitation. The results of experimental feasibility tests, which recreate upper limb motions detected by the LMC using an industrial robot, show good tracking accuracy. On the other hand, noted restrictions concerning quantifiable workplace are addressed..

关键词： Accuracy Tracking Employment Medical treatment Industrial robots Control systems Serious games

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Frozen 1-RSB structure of the symmetric Ising perceptron

arXiv

引用

arXiv 2021年

作者： Perkins, Will Xu, Changji Department of Mathematics Statistics and Computer Science University of Illinois at Chicago United States Center of Mathematical Sciences and Applications Harvard University United States

We prove, under an assumption on the critical points of a real-valued function, that the symmetric Ising perceptron exhibits the ‘frozen 1-RSB’ structure conjectured by Krauth and Mézard in the physics literature;that is, typical solutions of the model lie in clusters of vanishing entropy density. Moreover, we prove this in a very strong form conjectured by Huang, Wong, and Kabashima: a typical solution of the model is isolated with high probability and the Hamming distance to all other solutions is linear in the dimension. The frozen 1-RSB scenario is part of a recent and intriguing explanation of the performance of learning algorithms by Baldassi, Ingrosso, Lucibello, Saglietti, and Zecchina. We prove this structural result by comparing the symmetric Ising perceptron model to a planted model and proving a comparison result between the two models. Our main technical tool towards this comparison is an inductive argument for the concentration of the logarithm of number of solutions in the model. Copyright © 2021, The Authors. All rights reserved.

关键词： Hamming distance

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The simplest fuzzy variational formulation and its generalization

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AIP Conference Proceedings 2024年第1期3097卷

作者： Sinan Hatif Fadhel S. Fadhel Department of Mathematics College of Computer Science and Mathematics Tikrit University Tikrit Iraq Department of Mathematics College of Education for Pure Science (Ibn Al-Haytham) University of Baghdad Baghdad Iraq Department of Mathematics and Computer Applications College of Science Al-Nahrain University Jadriya Baghdad Iraq

In the present paper, we employ the first variation method to derive the fuzzy necessary condition, as a Euler-Lagrange equation for extremizing both the simplest and the generalized fuzzy variational problems. The latter class of problems includes those with higher derivatives, subject to the generalized Hukuhara differentiability. To illustrate the validity of the newly obtained Euler-Lagrange equation, two examples are considered in which the first example is considered with trapezoidal fuzzy numbers as the endpoints conditions while the second example is considered with triangular fuzzy numbers.

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Assessing Cybersecurity Awareness among The Hashemite University Students In Terms Of computer Usage

Assessing Cybersecurity Awareness among The Hashemite Univer...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Ashraf H. Aljammal Ahmad Qawasmeh Salah Taamneh Fadi Wedyan Mamoon Obiedat Hani Bani Salameh Department of Computer Science and Applications Prince Al Hussein bin Abdullah II Faculty of Information Technology The Hashemite University Zarqa Jordan Department of Engineering Computing and Mathematical Sciences Lewis University Romeoville Illinois USA Department of Information Technology Prince Al Hussein bin Abdullah II Faculty of Information Technology The Hashemite University Zarqa Jordan Department of Software Engineering Prince Al Hussein bin Abdullah II Faculty of Information Technology The Hashemite University Zarqa Jordan

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

Internet usage among information technology users has expanded substantially, as have cybercrimes with potentially disastrous repercussions. Therefore, internet users have to employ the available security measures and policies during internet usage. Nowadays, University students make up a sizable portion of internet users. This study assesses the cybersecurity awareness among The Hashemite University students In terms of computer Usage. A questionnaire survey is used to assess students’ cybersecurity knowledge while using their PCs and connecting to the internet, concentrating on their behaviors and use of security measures.

关键词： Surveys Malware Internet computer security Information technology computer crime Artificial intelligence

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