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International Conference on Inventive Computation Technologies (ICICT)

作者： K. Rama Krishna Reddy V. Sravya Reddy V. Sriram Sathvik N. Muthukumaran Shaik Mahaboob Subhani Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

In the field of diagnosing health there has been a shift, in focus from simply identifying bone fractures to a broader evaluation of skeletal fragility. Skeletal fragility involves not detecting fractures but also assessing the health and resilience of the skeletal system, which is crucial for preventing future fractures. While an automated network (DNN) model has achieved impressive accuracy of 92.44% in distinguishing between healthy and fractured bones iti s important to consider additional factors when measuring skeletal fragility. These supplementary factors include bone density, composition, and comprehensive markers of bone health like vitamin D levels. To create a framework for assessing skeletal fragility and not just bone fractures it is necessary to expand the model’s scope by incorporating these essential additional factors and indicators. This holistic approach has the potential to provide a nuanced perspective, on bone health and the likelihood of future fractures. Ultimately this can significantly improve care by facilitating measures.

关键词： Deep learning Image processing Noise Surgery Manuals Bones Data augmentation

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A PRIORI AND A POSTERIORI ERROR ESTIMATES FOR DISCONTINUOUS GALERKIN TIME-DISCRETE METHODS VIA MAXIMAL REGULARITY

arXiv

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arXiv 2024年

作者： Akrivis, Georgios Larsson, Stig Department of Computer Science and Engineering University of Ioannina Ioannina451 10 Greece Institute of Applied and Computational Mathematics FORTH Crete Heraklion700 13 Greece Department of Mathematical Sciences Chalmers University of Technology University of Göteborg Göteborg Sweden

The maximal regularity property of discontinuous Galerkin methods for linear parabolic equations is used together with variational techniques to establish a priori and a posteriori error estimates of optimal order under optimal regularity assumptions. The analysis is set in the maximal regularity framework of UMD Banach spaces. Similar results were proved in an earlier work, based on the consistency analysis of Radau IIA methods. The present error analysis, which is based on variational techniques, is of independent interest, but the main motivation is that it extends to nonlinear parabolic equations;in contrast to the earlier work. Both autonomous and nonautonomous linear equations are *** Codes 65M12, 65M15 © 2024, CC BY.

关键词： Galerkin methods

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Fractional-order Modified Windkessel Model of the Human Arterial Vascular System

Fractional-order Modified Windkessel Model of the Human Arte...

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2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023

作者： Bahloul, Mohamed A. Aboelkassem, Yasser Belkhatir, Zehor Laleg-Kirati, Taous-Meriem Alfaisal University College of Engineering Electrical Engineering Department Riyadh11533 Saudi Arabia University of Michigan-Flint College of Innovation and Technology MI48502 United States University of Michigan Michigan Institute for Data Science Ann ArborMI48109 United States University of Southampton Digital Health & Biomedical Engineering Group School of Electronics and Computer Science Southampton United Kingdom Computer Electrical and Mathematical Sciences and Engineering Makkah Saudi Arabia National Institute for Research in Digital Science and Technology Paris-Saclay France

ISBN: (纸本)9798350300260

This paper explores fractional-order derivatives to model the apparent arterial compliance dynamics in human vascular aging subjects. The proposed model employs fractional-order capacitor (FOC) elements that combine complex and frequency-dependence characteristics of proximal and distal arterial compliances. The FOC modeling approach accounted for both resistive and capacitive properties. The mathematical derivation has been combined into a global arterial lumped parameter representation forming a novel fractional-order modified arterial Windkessel representation. The model is then validated using aortic pressure and flow rate data acquired from human subjects at different ages. The results show that the FOC model is an accurate and flexible approach that can estimate arterial compliance dynamics and central blood pressure distribution while maintaining a low model complexity. © 2023 IEEE.

关键词： aortic input impedance Arterial Wind-kessel Fractional calculus fractional-order capacitor vascular compliance

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Data-driven Error Estimation: Upper Bounding Multiple Errors without Class Complexity as Input

arXiv

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arXiv 2024年

作者： Krishnamurthy, Sanath Kumar Lyubarskaja, Anna Brunskill, Emma Athey, Susan Meta Central Applied Science United States Institute for Computational and Mathematical Engineering Stanford University United States Department of Computer Science Stanford University United States Stanford University Graduate School of Business United States Stanford Institute for Human-Centered Artificial Intelligence United States

Constructing confidence intervals that are simultaneously valid across a class of estimates is central for tasks such as multiple mean estimation, bounding generalization error in machine learning, and adaptive experimental design. We frame this as an "error estimation problem," where the goal is to determine a high-probability upper bound on the maximum error for a class of estimates. We propose an entirely data-driven approach that derives such bounds for both finite and infinite class settings, naturally adapting to a potentially unknown correlation structure of random errors. Notably, our method does not require class complexity as an input, overcoming a major limitation of existing approaches such as union bounding and bounds based on Talagrand’s inequality. In this paper, we present our simple yet general solution and demonstrate its flexibility through applications ranging from constructing multiple simultaneously valid confidence intervals to optimizing exploration in contextual bandit algorithms. © 2024, CC BY-NC-ND.

关键词： Random errors

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computer-Aided Design for Skin Disease Identification and Categorization Using Deep Learning

Computer-Aided Design for Skin Disease Identification and Ca...

引用

International Conference on Image Information Processing

作者： K. Varalakshmi A. Revathi Department of Computer Science and Engineering SRM Institute of Science and TechnologyKattangulathur Chennai Tamilnadu India Department of Computational Intelligence SRM Institute of Science and TechnologyKattangulathur Chennai Tamilnadu India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

Slin diseases are disorders that affect the tissues of the human skin. The common skin disease includes eczema, psoriasis, acne, moles and various fungal infections. Skin disorders contribute 1.79% of the global burden of disease across the world. The identification and categorization of skin diseases are done through advanced methodologies which include deep learning techniques with computer-aided designs. This helps the medical professional to accurately diagnose and categorize skin diseases. The initial step of skin disease identification involves image processing and feature extraction through the Convolutional Neural Network (CNN).This helps in analyzing the skin images and classifying the distinct patterns and anomalies which forms the essential for the identification of particular skin conditions. This helps in accurately recognizing various skin diseases like melanoma, psoriasis and eczema through the evaluation of large amounts of datasets. The categorization of skin disease is obtained through RNN and LSTM. This is done by analyzing the temporal characteristics and symptom progression. These skin disease exhibits unique patterns and minute changes in appearance over time. The accurate identification of skin disease progression is obtained through modelling sequential data using LSTM and RNN. This provides an accurate solution for disease progression with personalized treatment recommendations. They help the dermatologist by providing automatic decision-making techniques. Thus the integration of CNN, RNN and LSTM helps to achieve higher efficiency and accuracy in skin disease diagnosis and monitoring. This helps to obtain improved potential to reduce diagnostic errors and enhance patient outcomes.

关键词： Deep learning Solid modeling Design automation Dermatology Eczema Skin Convolutional neural networks

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Micromagnetics Simulation of Highly Efficient Oscillation of Magnetization in a Ferromagnetic Local Areas via Spin Waves Induced by Spin-Orbit Torque

Micromagnetics Simulation of Highly Efficient Oscillation of...

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2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023

作者： Koda, Tetsunori Muroga, Sho Endo, Yasushi Oshima College National Institute of Technology Department of Electronic Mechanical Engineering Suo-Oshima742-2193 Japan Akita University Graduate School of Engineering Science Department of Mathematical Science and Electrical-Electronic-Computer Engineering Akita010-8502 Japan Department of Electrical Engineering Graduate School of Engineering Tohoku University Sendai980-8579 Japan Tohoku University Center for Spintronics Research Network Sendai980-8577 Japan Sendai980-8577 Japan

ISBN: (纸本)9798350338362

We study numerical simulation of highly efficient oscillation of magnetization in a ferromagnetic local region using the spin-orbit torque. It is found that in the presence of multiple SOT regions, the precession motion can be excited at a lower current density through the interaction. This interaction is caused by spin waves, and as a result, phase control between the precession motions is possible through the gap between the areas. Our new finding indicates that the interaction between the wave source and the spin wave is very complex in devices that utilize the interference effects of spin waves. © 2023 IEEE.

关键词： Spin waves

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H-BlockEnc: A Pioneering Blockchain Integrated Cryptographic Security Model for IoT-Healthcare Systems

H-BlockEnc: A Pioneering Blockchain Integrated Cryptographic...

引用

Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： Jackulin Mahariba A Saravanan T R Prabaharan G Sangeetha Yempally Kiruthika M Velliangiri Sarveshwaran Department of Computational Intelligence SRM Institute of Science and Technology Chennai TamilNadu India Department of Computer Science and Engineering B V Raju Institute of Technology Narasapur Hyderabad india Department of Electronics and Communication Engineering B V Raju Institute of Technology Narasapur Hyderabad India

Recent research studies have demonstrated that secured medical data sharing greatly enhances people's quality of life through improving medical treatment and care. The challenges associated with the safety of medical data make it even more difficult for the whole healthcare system to be interoperable. When it comes to striking a balance within the field of healthcare, blockchain technology has begun to emerge as a significant option. It is hard to find well-established solutions, nonetheless, due to the ongoing development of innovative Blockchain technology and the transformation of healthcare facilities. The main purpose of this paper is to develop the most straightforward and efficient security paradigm known as, Healthcare - Blockchain paired with homomorphic encryption (H-BlockEnc) framework for IoT-healthcare systems. Here, cutting-edge blockchain technology has been used to quickly and cheaply secure the safety and confidentiality of medical data. In addition, for increased security and data privacy, the homomorphic encryption standard is also utilized for cryptographic activities. The Beetle Swarm Optimization (BSO) technique is used throughout this process to generate keys as efficiently as possible. Based on processing time, energy consumption, block count, and loss rate, the performance and security level of the suggested H-BlockEnc model are verified and contrasted.

关键词：

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PyTOPress: Python code for topology optimization with design-dependent pressure loads

arXiv

引用

arXiv 2024年

作者： Saxena, Shivajay Sarkar, Swagatam Islam Kumar, Prabhat Department of Computer Science and Engineering National Institute of Technology Chhattisgarh Raipur492010 India Department of Mechanical and Aerospace Engineering Indian Institute of Technology Telangana Hyderabad502285 India Department of Computational Engineering Indian Institute of Technology Telangana Hyderabad502285 India Department of Engineering Science Indian Institute of Technology Telangana Hyderabad502285 India

Python is a low-cost and open-source substitute for the MATLAB programming language. This paper presents "PyTOPress", a compact Python code meant for pedagogical purposes for topology optimization for structures subjected to design-dependent fluidic pressure loads. PyTOPress, based on the "TOPress" MATLAB code [1], is built using the NumPy and SciPy libraries. The applied pressure load is modeled using the Darcy law with the conceptualized drainage term. From the obtained pressure field, the constant nodal loads are found. The employed method makes it easier to compute the load sensitivity using the adjoint-variable method at a low cost. The topology optimization problems are solved herein by minimizing the compliance of the structure with a constraint on material volume. The method of moving asymptotes is employed to update the design variables. The effectiveness and success of PyTOPress code are demonstrated by optimizing a few design-dependent pressure loadbearing problems. The code is freely available at https://***/PrabhatIn/PyTOPress. Copyright © 2024, The Authors. All rights reserved.

关键词： MATLAB

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A Deep Convolutional Kernel Neural Network based Approach for Stock Market Prediction using Social Media Data 7

A Deep Convolutional Kernel Neural Network based Approach fo...

引用

7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Agarwal, Varsha Ravi Kumar, P. Shankar, Shiva Praveena, S. Dubey, Vivek Chauhan, Amit ATLAS SkillTech University ISME Maharashtra Mumbai India K.Narayanapura Department of Media Studies Karnataka Bangalore India Computational Intelligence Srmist Kattankulathur Tamil Nadu Chengalpattu India Department of Electronics and Communication Engineering Telangana Hyderabad India Computer Science and Engineering Bharat Institute of Engineering and Technology Telangana Hyderabad India Department of Life Sciences Karnataka Bengaluru India

ISBN: (纸本)9798350397253

Several economists and social scientists have held a longstanding fascination with the practice of stock market prediction. As the stock market is essentially uncontrollable chaos, many experts believe that trying to predict it is futile. Due to the complexity of the numerous factors, accurate stock price predictions are notoriously difficult to achieve. While the market behaves more like a scale than a voting machine over the long run, its behavior may be predicted with some certainty. Information from Twitter is used into the algorithm. In this proposed method, a convolutional extreme learning machine model with kernel support was introduced (CKELM). To improve feature extraction and data classification, the CKELM model builds on the KELM's hidden layer by adding convolutional and subsampling layers. The convolutional layer and the subsampling layer do not employ the gradient technique to fine-tune their parameters because some designs worked well with random weights. When compared to popular models like CNN and KELM, The proposed model fares quite well, with an accuracy of around 98.3 percent. © 2023 IEEE.

关键词： Convolutional neural networks

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Sky Sentinel: Pioneering Real-Time Facial Recognition in Intelligent Drones 4

Sky Sentinel: Pioneering Real-Time Facial Recognition in Int...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Visalatchi, L. Anjelin, Praveena Eswaran, Malathi Vijayalakshmi, K. Sathish Kumar, L. Inamdar, Chinmay Dr.Umayal Ramanathan College for Women Department of It Tamil Nadu Karaikudi India B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Cse India Kongu Engineering College Perundurai Tamil Nadu Erode India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Chennai603203 India Bhopal University Scse VIT Madhya Pradesh India Vit Bhopal University Department of Computer Science and Engineering Madhya Pradesh India

ISBN: (纸本)9798350300888

The goal of this article is to provide a method that makes use of a face recognition-powered unmanned aerial vehicle (UAV). The goal of this approach is to make it easier for law enforcement to identify suspects, track down missing persons, verify identities, and carry out age-based monitoring. A facial recognition system is an electronic tool that uses AI to recognise people by their faces. Identity verification often makes use of facial recognition technologies. The facial features of a picture are analysed in this procedure. This research details the steps required to design, build, and test an unmanned aerial vehicle (UAV) integrated with a camera and face recognition software. The UAV is used as a remote control for a robot equipped with Bluetooth technology. The suggested method proves it can adapt to different drone orientations. Drone camera angles in all 37 directions have an accuracy of 99.3 percent, proving this point. Recovery activities, rescue missions, occurrence inquiries, offender assessment, and crowd surveillance are just a few examples of how specialised software may aid law enforcement in their investigative work. © 2023 IEEE.

关键词： Drones

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