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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Geetha Jenifel, M. Anita Jasmine, r. Umanandhini, d. Srm Institute of Science and Technology Data Science and Business Systems KTR Campus Chengalpattu India Vel Tech Rangarajan Dr. Shagunthala R&d Institute of Science and Technology India

ISBN: (纸本)9798350370249

Cryptocurrency markets, exemplified by the notable volatility in Bitcoin prices, have become pivotal arenas for financial exploration and investment. In response to this, our research undertakes a comprehensive comparison of diverse machine-learning models to predict Bitcoin prices. The models scrutinized include Linear regression, ridge regression, decision Tree, random Forest, Support Vector Machine (SVM), K-nearest neighbors (KNN), and Neural Networks. This study evaluates and contrasts these models based on performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and r -squared. This work encompasses the collection and preprocessing of historical Bitcoin price data, the engineering of pertinent features, and the division of the dataset into training and test sets. Each machine learning model is meticulously trained on the training set, allowing for the tuning of hyperparameters to optimize predictive capabilities. Subsequently, the models' performance is systematically assessed on the test set, providing valuable insights into their accuracy and reliability. The scope of this research is delimited by a specific timeframe, focusing on historical Bitcoin price data up to 15/11/2023. By addressing these objectives, this research aspires to guide investors, researchers, and analysts in navigating the intricate landscape of cryptocurrency investment decisions. The findings contribute to an enhanced understanding of the nuanced strengths and limitations inherent in different machine learning models when applied to the volatile context of Bitcoin price prediction. Ultimately, this research aims to facilitate more informed and strategic decision-making processes in the cryptocurrency market. © 2024 IEEE.

关键词： Adversarial machine learning

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Control Strategy for Cooperative driving Automation in Traffic Incidents

Control Strategy for Cooperative Driving Automation in Traff...

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2023 International Conference on Control, Automation and diagnosis, ICCAd 2023

作者： Kwag, Sujin Kang, Byeongju Jang, Hyeonsoo Park, Junyeop Hwang, Yunhyoung Korea Automotive Technology Institute Department of Big Data and SW Platform R&D Gyeonggi-do Seongnam-si Korea Republic of

ISBN: (纸本)9798350347074

With the advancement on automated vehicle technology, the highly automated driving is expected to work in complicated traffic situations such as urban intersections and traffic incidents. However, the traffic incidents still remain a comprehensively challenging situation especially in the mixed traffic. To resolve this problem, a sophisticated control strategy is required to support the automated driving in these unexpected traffic situations. From this background, this study mainly focuses on developing a generalized control strategy to manage the automated vehicles driving in traffic incident situations. First, the representative scenarios are analyzed, and a generalized control strategy is elaborated based on the framework of cooperative driving automation. The proposed control strategy can be extended to more complicated situations, and it is expected to extend the operational design domain of automated vehicles. © 2023 IEEE.

关键词： Autonomous vehicles

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Cellulose Elementary Fibrils as deagglomerated Binder for High-Mass-Loading Lithium Battery Electrodes

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Nano-Micro Letters 2025年第5期17卷 281-296页

作者： Young-Kuk Hong Jung-Hui Kim Nag-Young Kim Kyeong-Seok Oh Hong-I Kim Seokhyeon ryu Yumi Ko Ji-Young Kim Kwon-Hyung Lee Sang-Young Lee Department of Chemical and Biomolecular Engineering Yonsei University50 Yonsei-RoSeodaemun-GuSeoul 03722Republic of Korea Advanced Analysis and Data Center Korea Institute of Science and Technology(KIST)Seoul 02792Republic of Korea Ulsan Advanced Energy Technology R&D Center Korea Institute of Energy Research(KIER)Ulsan 44776Republic of Korea Department of Battery Engineering Yonsei University50 Yonsei-RoSeodaemun-GuSeoul 03722Republic of Korea

Amidst the ever-growing interest in high-mass-loading Li battery electrodes,a persistent challenge has been the insufficient continuity of their ion/electron conduction ***,we propose cellulose elementary fibrils(CEFs)as a class of deagglomerated binder for high-mass-loading *** from natural wood,CEF represents the most fundamental unit of cellulose with nanoscale *** preparation of the CEFs involves the modulation of intermolecular hydrogen bonding by the treatment with a proton acceptor and a hydrotropic *** elementary deagglomeration of the cellulose fibers increases surface area and anionic charge density,thus promoting uniform dispersion with carbon conductive additives and suppressing interfacial side reactions at ***,a homogeneous redox reaction is achieved throughout the *** resulting CEF-based cathode(overlithiated layered oxide(OLO)is chosen as a benchmark electrode active material)exhibits a high areal-mass-loading(50 mg cm^(-2),equivalent to an areal capacity of 12.5 mAh cm^(-2))and a high specific energy density(445.4 Wh kg–1)of a cell,which far exceeds those of previously reported OLO *** study highlights the viability of the deagglomerated binder in enabling sustainable high-mass-loading electrodes that are difficult to achieve with conventional synthetic polymer binders.

关键词： Cellulose elementary fibrils deagglomeration Electrode binders Lithium batteries High-mass-loading

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Advanced Wallis: A domain adaption method based on remote sensing image homogenization technique

Advanced Wallis: A domain adaption method based on remote se...

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2023 International Conference on Machine Vision, Image Processing and Imaging technology, MVIPIT 2023

作者： Wang, Hui Zhou, Mengdie Tang, Panpan Zhao, Bo Nanhu Laboratory & Advanced Institute of Big Data Research Center of Big Data Technology Jiaxing Beijing China KQ GEO Technologies Co. Ltd Remote Sensing Platform R&D Center Beijing China

ISBN: (纸本)9798350306545

domain adaption has been a hot topic in artificial intelligence (AI) -driven understanding tasks. Simultaneously, as a critical technology in image preprocessing, the homogenization method plays an irreplaceable role in remote sensing interpretation. This paper proposes a domain adaptation method based on the Wallis homogenization algorithm to improve the reasoning performance of deep models, which is named advanced Wallis. Its core idea is to reduce the variability of intra-class features by reducing the hue difference between the source and target domains. The method was tested on three publicly available datasets and yielded an average improvement of 2.3% in Fl scores. A convenient implementation of the method is provided to make sure it does not consume additional computational resources and storage space. © 2023 IEEE.

关键词： Homogenization method

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Post-Quantum Encryption for Mathematical Model Using Quantum Machine Learning Approach 1

Post-Quantum Encryption for Mathematical Model Using Quantum...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Kavitha, J. Gayatri, V. rajani, r. Sharmila, L. Harilakshmi, V.S. Misba, M. School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Geethanjali Institute of Science and Technology Department of CSE Andhra Pradesh India Department of Artifical Intelligence and Data Science Nagercoil India

ISBN: (纸本)9798331518943

Because of the increasing emphasis put on the aviation industry in as a main industry of the global transportation industry, a number of organizations depend on different flight companies to get in touch with parts of the world which may be an effective measure to control the disease from spreading to other areas of the world. However, severe conditions can actually influence airline services in a given area of operation most significantly, flight delays. In this case, service encounters took place between the airline as a large organizational sector and the individual traveling as customers. The working for this issue is to accurately expect such flight delays though enables enhanced performers while in travel in order to be fit for destinations. disability which will be occasioned by the existence of the aforementioned impediment. Quantum machine learning is used to examine the measures that were used in building models to analyze probability of flights. Problems associated with late and early arrival and;delays due to unfavorable weather conditions. In the initial the process of project management, in which at the planned stage of the work, we study the feasibility of integration of Support Vector Machine with Python-based linear regression. © 2024 IEEE.

关键词： Project management

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Polygenic risk scores adaptation for Height in a Vietnamese population 14

Polygenic risk scores adaptation for Height in a Vietnamese ...

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14th International Conference on Knowledge and Systems Engineering, KSE 2022

作者： Tran, Trang T. H Tran, Mai H Nguyen, duong T Pham, Tien M. Vu, Giang M. Vo, Nam S Nguyen, Nam N. Vu, Quang T. Vingroup Big Data Institute R&d Department GeneStory Hanoi Viet Nam Institute of Genome Research Vietnam Academy of Science and Technology R&d Department GeneStory Hanoi Viet Nam

ISBN: (数字)9781665452816

ISBN: (纸本)9781665452809

Genome-wide association studies (GWAS) with millions of genetic markers have proven to be useful for precision medicine applications as means of advanced calculation to provide a Polygenic risk score (PrS). However, the potential for interpretation and application of existing PrS models has limited transferability across ancestry groups due to the historical bias of GWAS toward European ancestry. Here we propose an adapted workflow to fine-tune the baseline PrS model to the dataset of target ancestry. We use the dataset of Vietnamese whole genomes from the 1KVG project and build a PrS model of height prediction for the Vietnamese population. Our best-fit model achieved an increase in r2 of 0.152 (according to 29.8%) compared to the null model, which only consists of the metadata. © 2022 IEEE.

关键词： Knowledge engineering Adaptation models Precision medicine Sociology Genomics Europe Predictive models

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Prediction of gender from structural MrI images using Multiscale ShuffleNet Extreme Learning Machine 3

Prediction of gender from structural MRI images using Multis...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Stanley, Berakhah.F. Jeen retna Kumar, r. Gnanaprakash, V. Bini Palas, P. Patturose, J Gold Beaulah Joel devadass daniel, d.J. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India SRM Easwari Engineering College Department of Electronics and Communication Engineering Chennai India St. Joseph's Institute of Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350372120

In recent years, the application of machine learning techniques to medical imaging data has shown promising results in various clinical tasks. One such task is the prediction of gender from structural MrI (magnetic resonance imaging) images, which holds potential implications for personalized medicine and understanding neurobiological differences between genders. In this study, we propose a novel approach utilizing Multiscale ShuffleNet Extreme Learning Machine (MSEL), a fusion of Multiscale feature extraction and ShuffleNet architecture with Extreme Learning Machine classifier. We demonstrate the effectiveness of our method on a large dataset of structural MrI images, employing state-of-the-art preprocessing techniques and feature extraction methods. Our results indicate significant accuracy and robustness in gender prediction, outperforming existing methodologies. Furthermore, we conduct comprehensive analyses to investigate the contribution of different components in our proposed framework, shedding light on the underlying mechanisms of gender-related brain structural differences. Overall, our study presents a promising avenue for utilizing advanced machine learning techniques in neuroimaging research, with potential applications in clinical diagnostics and personalized healthcare. © 2024 IEEE.

关键词： Magnetic resonance imaging

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rEAL-TIME GLUCOSE MONITOrING SYSTEM ANd dIETArY rECOMMENdATIONS FOr dIABETES MELLITUS USING FOG COMPUTING

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Telecommunications and radio Engineering (English translation of Elektrosvyaz and radiotekhnika) 2025年第5期84卷 55-67页

作者： Sathish, N. Elangovan, d. Nagalakshmi, r. Suresh, G. Chitra, devi d. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science Kings Engineering College Sriperumbudur India Department of Computer Science And Engineering S.A. Engineering College Chennai India

This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1d) and Type 2 (T2d). The loss of beta cells brought on by an autoimmune reaction causes the pancreas to be unable to produce enough insulin, which leads to Type 1 diabetes. A deficiency of insulin could arise as the illness worsens. The most frequent cause is a combination of excessive body weight and inadequate exercise. Currently, 90–95% of patients have Type 2 diabetes mellitus (T2dM). Age-specific normal blood sugar ranges exist and knowing them is crucial for determining a target blood sugar level that is both healthy and appropriate. The main takeaway is that diet regimens ought to be tailored to the patient’s requirements and should account for their capacity for change. Carbohydrates are the nutrients that have the biggest impact on the rise in blood glucose. In this paper, we have proposed a novel method for glucose monitoring and diet recommendations for diabetic patients using fog computing. A paradigm known as fog computing makes use of the benefits that come from using both cloud and edge devices to support modern computing systems by offering high-quality services, cutting latency, facilitating mobility, supporting multi-tenancy, and many other features. © 2025 by Begell House, Inc.

关键词： Fog computing

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Investigation on Nail denting or Crumbling from Nail Psoriasis Patient Ailment data using the Machine Learning Algorithm and data Mining Techniques 8

Investigation on Nail Denting or Crumbling from Nail Psorias...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Janani, K. Santhoshkumar, S.P. Vikram, d. Priya, S. Vijayaraj, M. Priyanga, M. Manipal Institute of Technology Department of Data Science and Computer Applications Manipal India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Avadi Department of Computer Science and Engineering Chennai India Rathinam Technical Campus Department of Artificial Intelligence and Data Science Coimbatore India Rathinam Technical Campus Department of Information Technology Coimbatore India

ISBN: (纸本)9798350367904

Nail denting or crumbling may result from nail psoriasis. A nail condition known as psoriatic onychodystrophy or psoriatic nails. Psoriasis sufferers frequently experience it;reported occurrences range from 10% to 78%. Psoriatic nails are more common in the elderly and in people with psoriatic arthritis. By examining the nails on a human hand, numerous illnesses can be found early in the diagnosing process. The nail color of an individual can help in the diagnosis of specific medical disorders. In this case, the recommended method results in the decision-making process for sickness diagnosis. The system is fed by human nail art. The technique recognizes nail features unique to a given condition by analyzing images of nails. The suggested technique makes advantage of the various distinctive characteristics of human nails to detect illness by changing the colour of the nails. The first training set data, which is derived from an image of a patient's nails with a particular ailment, is handled by the Weka tool. To get the desired results, the feature discoveries of the picture are compared to the training dataset. The distortion of the nail unit is known as nail disease. Nail units have their own disease class due to their own indications, symptoms, causes, and consequences, which may or may not be related to other medical disorders. Nail problems are still difficult and ambiguous to identify. A novel machine learning method for recognising and classifying nail problems from photographs is presented in this study. This framework combines CNN models with data mining techniques to extract characteristics. This research was also contrasted with certain other province algorithms (Support vector, ANN, K - nearest neighbors, and rF) evaluated on datasets and showed positive results. The accuracy numbers show how well the model can distinguish nail conditions. This ground-breaking discovery lays the groundwork for precise diagnosis in dermatology, medical image processing, an

关键词： Medical image processing

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real Time Crop Health Monitoring Through Convolution Neural Networks 4

Real Time Crop Health Monitoring Through Convolution Neural ...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Sridevi, Bommagani Padma, Nanda Manjusha, Puram Tirunagari, Sharanya Hariharan, Shanmugasundaram Famila, S. Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics & Communication Engineering Tamil Nadu Chennai602001 India

ISBN: (纸本)9798331529635

The agriculture sector, also known as the primary sector in India, is currently facing significant challenges due to the increasing number of pests and insects. These pests can cause serious damage to crop fields, negatively affect crop health, reduce yields, and lead to substantial economic losses for farmers. Early detection of crop diseases can help minimize yield losses by 20-40%, depending on the specific crop type and its various growth stages. The paper discusses the important use of deep learning algorithms in agriculture, specifically focusing on developing an effective approach for the automatic detection of crop diseases through advanced image analysis techniques. The proposed Convolutional Neural Network (CNN) model architecture consists of multiple layers that extract meaningful features from images using convolution and pooling operations. After the feature extraction process, additional layers are used for accurately classifying the images into different disease categories. This model is trained using the Adam optimizer, which is known for its efficiency in handling large datasets and improving the overall training speed. It employs sparse categorical cross-entropy loss over 50 epochs, with a batch size of 32, to effectively optimize the learning process. during the training phase, the model's accuracy is continuously monitored to assess its performance and make any necessary adjustments to enhance its effectiveness. One of the key innovations of this approach is the ability to perform real-time crop disease detection using a camera. This advancement eliminates the need for manual picture collection from the fields, making the entire process faster and much more efficient. By enabling farmers to quickly identify diseases, this cutting-edge technology can significantly help improve crop management practices and increase overall agricultural productivity. Ultimately, the goal is to enhance food security and reduce economic losses in the agriculture sect

关键词： Convolutional neural networks

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