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Advanced Simulation and Optimization Strategies in Thermo-Fluid Dynamics: A Deep Learning Approach to Enhancing Heat Transfer in Evaporative Cooling Systems

Advanced Simulation and Optimization Strategies in Thermo-Fl...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： B Rajalakshmi V Alekhya Sorabh Lakhanpal Irfan Khan Maha Chasib Munshid Ramya Maranan Department of Computer Science New Horizon College of Engineering Bangalore Institute of Aeronautical Engineering Dundigal Hyderabad Lovely Professional University Phagwara Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh Collage of Nursing National University of Science and Technology Dhi Qar Iraq Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This article describes a novel technique to study and improve evaporative cooling systems to move heat better. The system uses the GA, CNN, LSTM, and FVM algorithms. Each thermofluid dynamics study approach provides something unique. The CNN approach independently acquires spatial information from raw modeling data to study fluid movement. Temporal linkages in sequential data reveal heat’s movement over space and time via LSTM. GAN adds continuing antagonistic processes to the dataset, strengthening the model. GA adjusts GAN settings to improve data, whereas FVM simulates realistic fluid dynamics to improve heat transport. Ablation studies demonstrate each algorithm’s distinct contributions and importance. Performance comparisons reveal that the recommended framework outperforms others in accuracy, processing time, memory utilization, convergence rate, resilience, and adaptability. Bar charts, line charts, pie charts, and histograms show how the recommended method compares to others. The framework is precise, efficient, and versatile, making it ideal for improving thermo-fluid dynamics models in evaporative cooling systems. Researchers and engineers seeking to improve heat transfer in evaporative cooling systems can utilize it. The proposed architecture is full and adaptable to thermal-fluid utilization issues.

关键词： Heuristic algorithms Evaporative cooling Generative adversarial networks Data models Convolutional neural networks Long short term memory Optimization

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An integrated adaptive bilateral filter-based framework and attention residual U-net for detecting polycystic ovary syndrome

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Decision Analytics Journal 2024年 10卷

作者： Bedi, Pradeep Goyal, S.B. Rajawat, Anand Singh Kumar, Manoj Department of Computer Science & Engineering Galgotias University Greater Noida India Faculty of Information Technology City University Petaling Jaya Malaysia School of Computer Science & Engineering Sandip University Nashik India School of Computer Science University of Wollongong in Dubai Dubai Knowledge Park United Arab Emirates MEU Research Unit Middle East University Amman 11831 Jordan

Introduction: PCOS is a global hormonal disorder in women, increasing the risk of certain cancers. Early diagnosis and treatment can manage it. While metabolic markers are typical for detection, MRI and ultrasound physiological features are gaining interest. Ultrasound is especially promising for early-stage detection. AI and ML are enhancing this detection process. Methodology: The methodology focuses on developing a hybrid model for detecting PCOS, an early indicator of gynecological cancer in women. This model incorporates adaptive bilateral filter-based image pre-processing and a novel attention residual u-net (AResUNet). The adaptive bilateral filtering allows for efficient noise reduction, while AResUNet's architecture ensures adaptability across both 2D and multi-modal images. Findings: The findings of the study demonstrate a significant improvement in PCOS detection through the proposed hybrid model. It has been observed that the AResUNet model performs exceptionally well on both 2D and multi-modal images. Conclusion: The 2% improvement over current models reflects the innovation and potential applicability of this approach, providing a promising direction for further research and clinical practice in managing PCOS and other related health concerns. © 2023 The Authors

关键词： Adaptive bilateral filter Attention residual UNet Gynecological disorder Machine learning Polycystic ovary syndrome Segmentation

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Quantitative Evaluation to Detect Crack Depth in Beams Based on CNN-RNN-LSTM Approach

Quantitative Evaluation to Detect Crack Depth in Beams Based...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Mohammad Asif Gandhi A. N. Swaminathen Deepak T. Patil A Ravitheja R. Kamali Akshay Rajput Department of Mechanical Engineering Anjuman-I-Islam's Kalsekar Technical Campus Panvel Maharashtra India Adi Shankara Institute of Engineering and Technology Kerala India Bharati Vidyapeeth college of Architecture Navi Mumbai Maharashtra India Department of Civil Engineering SVR Engineering College Kurnool Andhra Pradesh India Department of Computer Science And Engineering Knowledge Institute of Technology Salem Tamilnadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

In this study, the proposed approach offers a novel hybrid image-based deep learning method for autonomous concrete fracture identification. Cracks are easier to spot and false positives are reduced with hybrid images that include visual and infrared thermography data. In specifically, a Visible and infrared camera as well as a line laser make up a hybrid imaging system for spatially scanning unmanned vehicles can efficiently assess large-scale concrete-made constructions like bridges and dams. Crack detection is a commonplace task in many industrial domains, however the current method of relying on expert judgment is typically laborious, time consuming, and inaccurate. The practical benefits of automated decision-making increase as the size of the target concrete construction grows. Cracks can be automatically identified and seen using the proposed method. Preprocessing, segmentation, feature extraction, and model training are the four components of the proposed method. It performs clever edge recognition, scaling, and greyscale conversion as part of its preparation. Improved K-Means segmentation is used for the separation. It employs GLCM for feature extraction, and CNN-RNN-LSTM for model training. The new method is compared to two established approaches, CNN-RNN and CNN-LSTM, and is found to be superior.

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TREC: transient redundancy elimination-based convolution 22

TREC: transient redundancy elimination-based convolution

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Jiawei Guan Feng Zhang Jiesong Liu Hsin-Hsuan Sung Ruofan Wu Xiaoyong Du Xipeng Shen Key Laboratory of Data Engineering and Knowledge Engineering (MOE) and School of Information Renmin University of China Computer Science Department North Carolina State University

ISBN: (纸本)9781713871088

The intensive computations in convolutional neural networks (CNNs) pose challenges for resource-constrained devices; eliminating redundant computations from convolution is essential. This paper gives a principled method to detect and avoid transient redundancy, a type of redundancy existing in input data or activation maps and hence changing across inferences. By introducing a new form of convolution (TREC), this new method makes transient redundancy detection and avoidance an inherent part of the CNN architecture, and the determination of the best configurations for redundancy elimination part of CNN backward propagation. We provide a rigorous proof of the robustness and convergence of TREC-equipped CNNs. TREC removes over 96% computations and achieves 3.51× average speedups on microcontrollers with minimal (about 0.7%) accuracy loss.

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Dct image compression implemented on raspberry pi to compress image captured by cmos image sensor 11th

Dct image compression implemented on raspberry pi to compres...

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11th National Technical Symposium on Unmanned System Technology, NUSYS 2019

作者： Mohsin, Ibrahim Saad Ahmad, Muhammad Imran Salman, Saad M. Al-Dabagh, Mustafa Zuhaer Nayef Isa, Mohd Nazrin Md Ahmad, Raja Abdullah Raja School of Computer and Communication Engineering Universiti Malaysia Perlis Kampus Pauh Putra Perlis02600 Malaysia Department of Mathematical and Computer Application College of Science Al-Nahrain University Baghdad Iraq School of Microelectronic Engineering Universiti Malaysia Perlis Kampus Pauh Putra Perlis02600 Malaysia Department of Computer Science College of Science Knowledge University Kurdistan Region Erbil Iraq

ISBN: (纸本)9789811552809

The purpose of compression is to reduce the amount of data at the same time maintain the quality of image and signal for the other purpose. Discrete Cosine Transform (DCT) is a family of image compression where the raw image is transformed to the other domain to produce smaller size of data. DCT transform has low computational complexity and fast processing algorithm. In this project, DCT transform will be implemented using PI camera and Raspberry Pi SBC development board running on an ARM based processor. The raspberry Pi board has an advantage of image processing implementation due to the existing software development tool offered a rich feature for image processing such as OPENCV. The result of applying DCT compression algorithm on images with six compression rate level which are 10, 20, 50, 100, 170 and 200. The best performance can be achieved with compression rate level 200. However, on reducing the quality level of compression rate, the error measurements start becoming worse until a point is reached, where the perceptual difference from the original image can be easily noted. © Springer Nature Singapore Pte Ltd 2021.

关键词： Image compression

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The Impact of Adversarial Attacks on Medical Imaging AI Systems

The Impact of Adversarial Attacks on Medical Imaging AI Syst...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Alok Jain K Sangeeta Swathi B Mohd. Sadim Shashi Prakash Dwivedi Ali Albawi Lovely Professional University Phagwara Institute of Aeronautical Engineering Dundigal Hyderabad Department of Information Science Engineering New Horizon College of Engineering Bangalore Department of Computer Science & Engineering IES Insititute of Technology and Management IES University Bhopal M. P. India Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

Bad actors threaten medical imaging AI systems' dependability and safety, affecting patient care and diagnosis accuracy. A novel defense against this expanding threat is adversarial defense via ensemble integration. Explainable Feature-Based Defense (XFBD), Adversarial Training with Transfer Learning (ATTL), and Robust Classifier Augmentation (RCA) are three innovative techniques that have never been combined. RCA enhances training with controlled aggression. This allows the model to distinguish authentic medical imaging data from altered sources. ATTL makes transfer-learning-taught models more resistant to topic-specific hostile approaches. XFBD simplifies the defensive process, helping us comprehend how the model picks and fights different methods. A comparison indicates that ADEI always outperforms tried-and-true approaches. ADEI outperforms typical approaches in accuracy, sensitivity, precision, stability, interpretability, private protection, and computing cost. Strong safeguards are needed as AI-powered healthcare research becomes increasingly popular. As a lighthouse, ADEI protects everything from attack. The math behind each section and how they work together to strengthen the system make it valuable. ADEI advances AI-assisted healthcare's future. Testing instruments must be accurate and dependable in this industry.

关键词： Training Sensitivity Computational modeling Transfer learning Stability analysis Security Artificial intelligence

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An investigation on feature and text extraction from images using image recognition in Android

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Materials Today: Proceedings 2022年 51卷 798-802页

作者： Brijeshkumar Y. Panchal Gaurang Chauhan Sandipkumar R. Panchal Urvashi M. Chaudhari Department of Computer Science and Engineering DEPSTAR CHARUSAT Charotar University of Science and Technology (CHARUSAT) CHARUSAT campus Changa 388421 India ITsouls Vadodara India Associate Professor & I/C Principal Knowledge Institute of Technology & Engineering Gujarat India Department of Computer Engineering Sardar Patel College of Engineering Bakrol-Anand Gujarat India

In today’s era, there is a huge rise in demand for the data and information available in a printed medium. To resume time, cost, and energy, we have been developing numerous technology. One of them, convert JPG, JPEG, or PDF softcopy into text, this process calls Text extraction. Identification of characters and detection of words and texts is again an upfront challenging task. This paper is focused on an investigation of feature and text extraction from images using image recognition in Android. In this era of Research and Technology, Image processing has become one of the most critical fields. Using a number of researchers' papers, we are going to study appropriate methods or algorithm, their accuracy, a key feature of each paper. All research papers going to study in the context of Android technology.

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CGMatch: A Different Perspective of Semi-supervised Learning

arXiv

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arXiv 2025年

作者： Cheng, Bo Lu, Jueqing Tian, Yuan Zhao, Haifeng Chang, Yi Du, Lan School of Artificial Intelligence Jilin University China International Center of Future Science Jilin University China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE China Faculty of Information Technology Monash University Australia Department of Computer Science Jinling Institute of Technology China

Semi-supervised learning (SSL) has garnered significant attention due to its ability to leverage limited labeled data and a large amount of unlabeled data to improve model generalization performance. Recent approaches achieve impressive successes by combining ideas from both consistency regularization and pseudo-labeling. However, these methods tend to underperform in the more realistic situations with relatively scarce labeled data. We argue that this issue arises because existing methods rely solely on the model’s confidence, making them challenging to accurately assess the model’s state and identify unlabeled examples contributing to the training phase when supervision information is limited, especially during the early stages of model training. In this paper, we propose a novel SSL model called CGMatch, which, for the first time, incorporates a new metric known as Count-Gap (CG). We demonstrate that CG is effective in discovering unlabeled examples beneficial for model training. Along with confidence, a commonly used metric in SSL, we propose a fine-grained dynamic selection (FDS) strategy. This strategy dynamically divides the unlabeled dataset into three subsets with different characteristics: easy-to-learn set, ambiguous set, and hard-to-learn set. By selective filtering subsets, and applying corresponding regularization with selected subsets, we mitigate the negative impact of incorrect pseudo-labels on model optimization and generalization. Extensive experimental results on several common SSL benchmarks indicate the effectiveness of CGMatch especially when the labeled data are particularly limited. Source code is available at https://***/BoCheng-96/CGMatch. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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CellMirror: Deciphering Cell Populations from Spatial Transcriptomics Data by Interpretable Contrastive Learning 1

CellMirror: Deciphering Cell Populations from Spatial Transc...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Xia, Junjie Cui, Jiangnan Huang, Zhenyu Zhang, Shuangquan Yao, Fang Zhang, Yijian Zuo, Chunman Institute of Artificial Intelligence Donghua University Department of Applied Mathematics Shanghai China College of Computer Science and Technology Jilin University Changchun China School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China Institute of Maternal and Child Medicine Southern Medical University Affiliated Shenzhen Maternity and Child Healthcare Hospital Shenzhen China Xinhua Hospital Affiliated Shanghai Jiao Tong University of Medicine Department of General Surgery Shanghai China Institute of Artificial Institute Donghua University Shanghai China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China

ISBN: (纸本)9798350358780

Spatial transcriptomics (ST) has enabled us elucidating tumor microenvironments, however, the technological limitations without single-cell resolution severely hinder its application. Here, we propose CellMirror, an interpretable contrastive learning model to decipher heterogeneous cell populations in ST data by single-cell RNA-sequencing (scRNA-seq) data. Specifically, CellMirror learns the disentangled shared (representing biological variations in both data) and salient features (specific to ST data) by two contrastive variational encoders, while constructing the relations between genes and features by a shared linear decoder. In various cancer samples, CellMirror outperforms other tools in learning common features for label transfer, and interpretation of features. Particularly, in breast cancer studies, CellMirror detects finer domains in ST data missed by other methods, and is robust to dissect cell populations in ST data using independent scRNA-seq data. These results demonstrate applications of CellMirror in interpreting the complex tumor structure in ST data by integrating scRNA-seq data. © 2023 IEEE.

关键词： Data integration

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Parameter aware utility proportional fairness scheduling technique in a communication network

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International Journal of Innovative Computing and Applications 2021年第2-3期12卷 98-107页

作者： Kumar, Neeraj Ahmad, Anwar Awasthi, Shashank Sharma, Kavita Department of Electronics and Communication Engineering Jamia Millia Islamia Jamia Nagar Okhla New DelhiDelhi110025 India Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management Plot No. 2 Knowledge Park III Greater Noida Dist. G.B. NagarUP201306 India

Conventionally, real-time (inelastic) users and non-real-time (elastic) users are scheduled separately. Further, some users may be left without getting any radio resources. In real scenario, such as in high network traffic, less network coverage, resource allocation for all types of users are needed. In the proposed technique, a utility function is defined for each elastic traffic and inelastic traffic type users. Then, it maximises the total utilities by network utility maximisation approach with considering scheduling parameters such as queue length, head of the line delay and experienced channel condition. Rate allocation based on queue awareness improves fairness among elastic traffic users, delay awareness improves QoS performance of inelastic traffic users and channel condition awareness improves throughput of all users. Further, the network utility maximisation approach provides minimum throughput to all users. © 2021 Inderscience Enterprises Ltd.

关键词： Queueing theory

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