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An efficient direct-forcing immersed boundary method for flow around a pair of spheres

International Journal for Numerical Methods in Fluids 2024年第12期96卷 1830-1863页

作者： Lo, Der Chang Lee, Katherine Shen, Pao-Lan Department of Maritime Information and Technology National Kaohsiung University of Science and Technology Kaohsiung City Taiwan PhD Program in Maritime Science and Technology College of Maritime National Kaohsiung University of Science and Technology Kaohsiung City Taiwan

The numerical study of flow around a pair of spheres and a square array of spheres is investigated by using a direct-forcing immersed boundary method. Using high resolution three-dimensional computations, we analyzed the flow around several configurations: a sphere, a pair of spheres in a tandem arrangement with center-to-center streamwise ratio L/D ranging from 1 to 6, and a square array with 9 spheres in a uniform arrangement. In the latter case, we explore the ratio of array diameter (DG) to sphere diameter (D) at 4, 5, 6 and 7. The center-to-center streamwise and transverse pitch is the same, varied from L/D = 1.5, 2, 2.5 to 3, and they were arranged in a square periodic array to allow uniform distribution within the array. Based on the effective direct-forcing immersed boundary projection method, the fractional time marching methodology is applied for solving four field variables involving three velocities and one pressure component. The pressure Poisson equation is advanced in space by using the fast Fourier transform (FFT) and a tridiagonal matrix algorithm (TDMA), effectively solving for the diagonally dominant tridiagonal matrix equations. A direct-forcing immersed boundary method is involved to treat the interfacial terms by adding the appropriate sources as force function at the boundary, separating the phases. Geometries featuring the stationary solid obstacles in the flow are embedded in the Cartesian grid with special discretizations near the embedded boundary using a discrete Dirac delta function to ensure the accuracy of the solution in the cut cells. An important characteristic of flow over the multiple spheres is devised by comparing with the drag and lift coefficients, as well as vortex shedding. © 2024 John Wiley & Sons Ltd.

关键词： Spheres

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Improvement of K-Means Clustering Through Center's Hybrid Initialization

Improvement of K-Means Clustering Through Center's Hybrid In...

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2024 Global Digital Health Knowledge Exchange and Empowerment Conference, gDigiHealth.KEE 2024

作者： Naji, Areen Subha, Muath Arab American University PhD in Information Technology Engineering Ramallah Palestine Arab American University Department of Computer Science PHD Coordinator Ramallah Palestine

ISBN: (纸本)9798331531331

This paper evaluates the effect of initial center selection on cluster quality and the rate of convergence in K-means clustering. In this paper, a new approach is proposed that uses random initialization and spatial distribution scores. The main objective is to enhance K-means clustering by creating a simple, novel, and efficient technique shredded for data classification and other high-level image processing tasks. Our approach relies on image color segmentation through the application of a transformation to the image's RGB color space, representing every pixel by three values of different components of color, divided into blocks, and then a calculation of the density of dots in every block. This value of density mark provided information about the distribution of color values around various parts of an image. It uses a tactical approach in selecting initial centers, namely Scored Centers Initialization, or SCI. It chooses the block with the highest score as the initial center block chosen randomly. Then, it selects subsequent centers based on the probability assessed by the Euclidean distance from the nearest one picked center and the density score of the block. It continuously does this until the desired number of centers is reached. The experimental results indicate that the clustering effect of the approach proposed was better than the traditional Random Centers Initialization method, thus proving the proposed strategy was effective. Based on the consideration of point-to-center distances and the spatial distribution score, it captured better data structures to get quicker convergence and higher quality in the cluster assignment. © 2024 IEEE.

关键词： K-means clustering

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Plug-and-Play with POA based Maximum a Posteriori Denoisers for Image 3

Plug-and-Play with POA based Maximum a Posteriori Denoisers ...

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3rd IEEE Mysore Sub Section International Conference, MysuruCon 2023

作者： Padthe, Adithya Kadakadiyavar, Sapna Thatikonda, Ramya Madhura, G.K. University of the Cumberlands Department of Information Technology United States Vemana Institute of Technology AI AndML Department Kormangala Bangalore India PhD in Information Technology at University of the Cumberlands United States Nitte Meenakshi Institute of Technology AI AndML Department Bengaluru India

ISBN: (纸本)9798350340358

Restoring damaged images has been an issue in the fields of image processing and basic computer vision for decades. Due to their improved performance, techniques based on discriminative convolutional neural networks (CNNs) have recently garnered a lot of interest. However, most of these frameworks only operate well on one type of picture restoration assignment, hence their performance on other types of image restoration is usually subpar. Research techniques leverage the fact that the Maximum a Posteriori (MAP) optimisation may be broken down into smaller sub-problems, such as a MAP denoising optimisation, to resolve this issue. We introduce the first full-stack method for MAP estimation in deep neural network image denoising. We prove that our approach will always result in a smaller MAP denoising goal, which can subsequently be employed in the general picture restoration optimisation technique known as the Puzzle Optimisation technique (POA). Our technique is theoretically analysed, and its quantitative efficacy is demonstrated through a number of tests. Our experiments validate the theoretical foundations of MAP and demonstrate that the suggested approach can reach 70x quicker presentation than the state-of-the-art. © 2023 IEEE.

关键词： Image reconstruction

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Diagnosis of Liver Tumor from CT Scan Images using Deep Segmentation Network with CMBOA based CNN 3

Diagnosis of Liver Tumor from CT Scan Images using Deep Segm...

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3rd IEEE Mysore Sub Section International Conference, MysuruCon 2023

作者： Thatikonda, Ramya Kadakadiyavar, Sapna Padthe, Adithya Madhura, G.K. PhD in Information Technology at University of the Cumberlands Software Engineer United States Vemana Institute of Technology Kormangala AI ML Department Bangalore India University of the Cumberlands Department of Information Technology United States Nitte Meenakshi Institute of Technology AI ML Department Bengaluru India

ISBN: (纸本)9798350340358

Accurate segmentation and categorization of liver tumours are crucial for the diagnosis and management of carcinoma or metastases. The liver tumour presents a challenging problem for precise and automated tumour segmentation and classification because of its blurry boundaries and large variety of potential forms, sizes, and placements. New AI models have emerged as computer technology has progressed. The NLP community has had such success with the transformer paradigm that the CV community has adopted it as well. Although established methods exist for categorising the liver, especially in clinical settings, they might be refined to be more accurate. Two deep learning-based models are used to do the segmentation and classification of the liver tumour in this study. As a first step, the input photos are median filtered and their histograms are equalised to prepare them for further processing. Then, Deep Segmentation Network (DSegNet) extracts the liver from the input pictures. The tumour is then categorised using an Optimised Convolutional Neural Network (OCNN) model, with the CNN's weight chosen using a Cat and Mouse Based Optimisation Algorithm (CMBOA). Two openly accessible datasets are used for the experimental study, with a focus on certain key metrics. The results demonstration that associated to pre-existing deep learning representations, the suggested model is about 98% more accurate in its classifications. © 2023 IEEE.

关键词： Convolutional neural networks

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The Impact of Blockchain and AI in the Healthcare 3

The Impact of Blockchain and AI in the Healthcare

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3rd IEEE International Conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Thatikonda, Ramya Kempanna, M. Tatikonda, Reshmi Bhuvanesh, A. University of the Cumberlands PhD in Information Technology Williamsburg United States Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India University of the Central Missouri Masters in Computer Information Systems Warrensburg United States PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350390728

Integrating blockchain and artificial intelligence (AI) in healthcare presents a transformative opportunity. Therefore, this can revolutionize patient care by improving data management and medical research. Decentralization and invulnerability granted by blockchain technology foster improved data consistency and interoperability. AI enhances blockchain by streamlining data examination, automatic tasks, and decision-making processes, resulting in more precise medical diagnoses and tailored therapy programs. The intersection of blockchain and AI is explored, along with their potential benefits and practical applications in healthcare. The document examines the hurdles encountered when integrating innovations, including scalability, interoperability, and regulatory adherence issues. Additionally, the study investigates moral questions surrounding data ownership and patient consent, thereby protecting confidentiality and trust. The synergy between blockchain, AI, and healthcare will lead to transformative advancements. It pledges to fortify data safety and expedite information sharing. Consequently, this will result in an improved, price-effective, and patient-focused healthcare environment. Recommendations were made for additional research and practical implementation in healthcare technology. The study should concentrate on creating scalable and compatible blockchain systems. A vital factor for extensive use is tackling regulatory and moral issues. Hybrid blockchain designs and federated learning strategies will bolster information privacy and utility. © 2024 IEEE.

关键词： Data consistency

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Analyzing recognition of EEG based human attention and emotion using Machine learning

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Materials Today: Proceedings 2022年 56卷 3349-3354页

作者： Shabbir Alam, Mohammad Zura A. Jalil, Siti Upreti, Kamal PhD Scholar Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Malaysia Engineering Department Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Malaysia Department of Information Technology Dr. Akhilesh Das Gupta Institute of Technology & Management New Delhi India

An emotionally recognised area of research has already been quite prominent. EEG brain signals have recently been used to recognise an individual's mental condition. Attention often plays a key role in human development, but needs more study. This article offers a noble method of acknowledgment of human attention by sophisticated machine learning algorithms. Scalp-EEG signalling is a cost-effective, single-swinged mechanism dependent on time. Many trials have shown possible support for emotional identification through brain EEG waves. This paper examines and suggests a modern technology for the identification of emotions through the application of new computer learning principles. Ablations experiments also demonstrate the clear and important benefit to the efficiency of our RGNN model from the adjacent matrix and two regularizers. Finally, neuronal researches reveal key brain regions and inter-channel relationships for EEG related emotional awareness. © 2021

关键词： Brain computer interface

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Improvement of K-Means Clustering Through Center's Hybrid Initialization

Improvement of K-Means Clustering Through Center's Hybrid In...

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Global Digital Health Knowledge Exchange & Empowerment Conference (gDigiHealth.KEE)

作者： Areen Naji Muath Subha Technology Engineering Arab American University PhD in Information Palestine Ramallah Department of Computer Science PHD Coordinator Arab American University Palestine Ramallah

ISBN: (数字)9798331531331

ISBN: (纸本)9798331531348

关键词： Image segmentation Graphical models Accuracy Image color analysis Clustering algorithms Euclidean distance Indexes Standards Distribution functions Convergence

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PrivFED: Protecting User Privacy in Federated Learning Systems Through Differential Privacy

PrivFED: Protecting User Privacy in Federated Learning Syste...

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International Conference on Electronics, Materials Engineering and Nano-technology (IEMENTech)

作者： Karthik Thumula Hariprasad Holla Chandu Gutti Arun Ambika Sasikumar Hemanth Gogineni Santa Clara Valley and Denver Chapter USA Executive PHD Information Technology University of the Cumberlands Williamsburg USA

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

This research examines the integration of Federated Learning (FL) with differential privacy (DP) to enhance privacy protection in distributed machine learning systems. We propose and implement a comprehensive privacy-preserving framework PRIVFED that addresses the growing challenges of data privacy while maintaining model utility. Through extensive experimentation using the MNIST dataset, we demonstrate that our approach achieves significant privacy improvements with minimal performance degradation. Our results show that moderate privacy protection (ε= 1.0) achieves 92.4% accuracy, while strong privacy protection (ε = 0.5) maintains 88.7% accuracy compared to the baseline of 98.2%. Furthermore, our system shows efficient scaling with increasing number of clients, introducing only 15.3% computational overhead. The proposed PRIVFED framework adapts dynamically to varying privacy requirements and data distributions, making it suitable for real-world applications across healthcare, financial services, and critical infrastructure sectors. This work establishes that machine learning systems can achieve both strong privacy guarantees and high model performance, providing practical guidelines for privacy-preserving distributed learning implementations in sensitive domains.

关键词： Privacy Differential privacy Accuracy Federated learning Distance learning Medical services Security Protection Financial services Guidelines

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Fog Computing based Privacy Preservation via Distributed Access Control System in Blockchain technology 1

Fog Computing based Privacy Preservation via Distributed Acc...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Thatikonda, Ramya Nandihal, Priya Priyanka, M.S. Mathapati, Mahantesh PhD in Information Technology University of the Cumberlands Williamsburg United States Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Dept. of Aiml Bengaluru India Dept.of Cse Bengaluru India

ISBN: (纸本)9798350316469

The proliferation of loT -enabled gadgets means that ever more information is being created and transmitted over the web. The release of sensitive loT data, however, is a privacy breach. Sensitive information generated by loT devices poses a serious security risk since it is transmitted across an unsecured network. The storage issue may be managed with the help of an external cloud. Cloud storage has the potential to compromise users' privacy. To prevent unauthorised access and maintain confidentiality during data transmission, current systems include symmetric encryption and access restrictions. Blockchain is an exciting distributed ledger technology with the potential to safeguard stored information against manipulation by unscrupulous actors. To protect information from Internet of Things devices, this article suggests using a blockchain-based decentralised access control system. Fog computing and the idea of an alliance chain provide the basis for the suggested approach. In order to encrypt and divide data at the network's edge, fog nodes are employed. Homomorphic encryption is used on the third-party cloud to combine data that has been partitioned and encrypted. The suggested system provides dynamic and granular access control for loT data, mitigating the risk of a single point of failure in access control. Results from experiments showed that this approach effectively protects the confidentiality of data collected from Internet of Things devices. © 2023 IEEE.

关键词： Blockchain Data Privacy Fog computing Homomorphic encryption Internet of Things Malicious Tampering

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Pragmatic Analysis of IoMT Network Modelling Techniques from a Statistical Perspective

Pragmatic Analysis of IoMT Network Modelling Techniques from...

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2022 International Conference on Electronics and Renewable Systems, ICEARS 2022

作者： Fuke, Radhika P. Mahajan, R.P. Sage University Department of Information Technology Phd Scholar Indore India Sage University Department of Computer Science Engineering and It Professor Indore India

ISBN: (纸本)9781665484251

Internet of Medical Things (IoMT) networks are high precision wireless interfaces that require design of accurate sensing, actuation, and processing devices. These devices include wireless electrocardiograph (ECG) sensors, electroencephalograph (EEG) sensors, wireless blood pressure monitoring devices, etc. Due to direct patient interface, these devices are required to have superior performance in terms of sensing accuracy, processing efficiency, and communication quality of service (QoS) parameters. A wide variety of models are proposed by IoMT researchers to perform this task, but each of these models vary in terms of network size, deployment complexity, cost of deployment, processing delay, etc. These models include machine learning routing techniques, blockchain based security methods, privacy preservation methods, high-precision sensor design methods, high-performance communication interfaces, etc. Due to such a wide variation in performance, IoMT network design requires continuous validation, which increases time-to-market, thereby increasing deployment cost. In order to reduce number of validations, a statistical survey of models for IoMT network design is discussed in this text. This discussion is focussed towards evaluating various characteristics, advantages, limitations, and future research scopes in existing models. readers would be able to identify best performing model(s) for a given IoMT application. It is followed by a statistical analysis of the reviewed IoMT network design models in terms of end-to-end delay, communication QoS, network security, scalability, computational complexity, cost of deployment, and application of use. This statistical performance evaluation will further assist readers to statistically compare the reviewed methods, and identify best performing model(s) their combinations for context-specific network deployments. Due to this, readers would be able to reduce network design validation delay, which will further assist in reduci

关键词： Scalability

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