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Next-Generation Computational Models for Enhanced Interpretation of Biospeckle Images Leveraging Deep Learning and Fourier Transform Techniques in Biomedical Applications

Next-Generation Computational Models for Enhanced Interpreta...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： R Gowtham Raj S Sreekanth Ginni Nijhawan Shubhi Gupta Saurabh Sharma T Mounika Deparment of Mechanical Engineering New Horizon College of Engineering Bangalore India Computer Science and Engineering(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Plot No. 11 Knowledge Park II Greater Lloyd Law College Noida Uttar Pradesh India Lovely Professional University Phagwara India Amity School of Engineering & Technology Amity University Greater Noida India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This study looks at advanced computational techniques that enhance the understanding of biospeckle images in biological contexts. Our models use Fourier transform and deep learning techniques to achieve improved specificity, recall, accuracy, and precision. The Fourier transform may provide a more accurate description of frequency domain data as biological processes evolve over time. Deep learning algorithms recognise patterns in biospeckle images, facilitating analysis, memory, and response. The analysis research delineates the several components that influence the outcome and provides evidence of their respective roles. This research shows how helpful the models are and offers suggestions on how to make them better. Non-invasive biomedical testing uses these computer models to illustrate the temporal evolution of biological cells. Our techniques improve the interpretation of biospeckle images, potentially revolutionising non-invasive biomedical imaging. Future researchers may evaluate these models using different datasets and testing conditions. This guarantees their adaptability and power in biological contexts.

关键词： Deep learning Adaptation models Fourier transforms Accuracy Evolution (biology) Computational modeling Biological system modeling Frequency-domain analysis Biomedical imaging Testing

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An Effective Emotion Recognition Method Using Facial and Speech Features

An Effective Emotion Recognition Method Using Facial and Spe...

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Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), International Conference on

作者： K. Muthumanickam P.C.Senthil Mahesh P. Vijayalakshmi Department of Information Technology Kongunadu College of Engineering and Technology (Autonomous) Thottiyam Tamilnadu India Department of Computer Science and Engineering Excel Engineering College Komarapalayam Tamilnadu India Department of Computer Science and Engineering Knowledge Institute of Technology Salem Tamilnadu India

In face-to-face conversations, facial expressions help humans communicate nonverbally. Since the early 1990s, academics' focus on automatic facial expression detection, which is essential for human-machine interfaces, has grown. Traditional machine learning techniques can demand a difficult feature extraction procedure and yield subpar results. Using current developments in deep learning, we present efficient deep Convolutional Neural Networks (CNNs) that can automatically and accurately evaluate the semantic information contained in faces and speech without manually defining feature descriptors. Additionally, in order to develop an effective CNNs based emotion recognition system with a high degree of classification capability, we employ various loss functions and training techniques. The experimental results demonstrate that, the proposed method outperforms existing solutions in terms of performance and makes it ideal for real-time systems.

关键词： Training Emotion recognition Face recognition Semantics Speech recognition Oral communication Feature extraction

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Energy-efficient routing to prevent void holes in heterogeneous 5G wireless sensor network using game theory

Energy-efficient routing to prevent void holes in heterogene...

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Smart Computing and Application (ICSCA), International Conference on

作者： Abdul Mateen Abdul Ahad Salam Zia Ibraheem Shayea Sawsan Ali Knowledge Unit of Systems and Technology (KUST) University of Management and Technology Sialkot Pakistan Istanbul Technical University of Turkey Department of Computer Science University of Sialkot Sialkot Pakistan Department of Electronics and Communication Engineering Istanbul Technical University Department of Computer Science and Engineering University of Ha'il Ha'il Saudi Arabia

Heterogenous 5G wireless Sensor Networks face several constraints throughout the routing process. Reduced costs, minimal energy use, and reliable data transmission between nodes are the primary problems of a heterogenous 5G wireless sensors network. During the transmission process of nodes, vacant holes use the most energy. The two most problematic aspects of heterogenous 5G WSNs are the location mentioned above mistake and the battery drain on individual nodes. However, the network's performance suffers significantly due to data packet loss and excessive energy consumption. Thus, many routing protocols incorporate various energy-saving techniques. In contrast, a routing strategy is suggested in this research to prevent void holes and maximize network stability. In addition, the scalability and efficacy of the proposed protocol are demonstrated by comparing it with state-of-the-art protocols. This study also introduces the idea of location error to increase the network's efficiency. In addition, a scalability study of the suggested systems has been conducted. Extensive simulations were also conducted to evaluate the effectiveness of the proposed technique. The findings confirm that the recommended routing strategy outperforms the competing schemes.

关键词： Wireless sensor networks 5G mobile communication Scalability Packet loss Routing Stability analysis Routing protocols

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Fostering Sustainable Development through Distributed Intelligence Applications

Fostering Sustainable Development through Distributed Intell...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Csl Vijaya Durga Shruti Agrawal R J Anandhi Navdeep Singh Ravi Kalra Myasar Mundher Adnan Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Science & Engineering IES College of Technology Bhopal Ma. P India Department of Information Science Engineering New Horizon College of Engineering Bangalore Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh The Islamic university Najaf Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

SDODI, which stands for Sustainable Development Optimization using Distributed Intelligence, advances sustainable development. SDODI is the first to examine environmental, social, and economic challenges together using EcoOpt, InclusNet, and ResilienceHub. Environmental optimization program EcoOpt increases the total effect while ensuring resource efficiency and environmentally friendly behavior. InclusNet uses math to include everyone and make communities more active, varied, and accessible. The ResilienceHub algorithm prioritizes financial stability, flexibility, and economic power. Sustainable initiatives are typically divided by traditional approaches. SDODI utilizes information to better allocate resources, engage the community, and strengthen the economy. SDODI is unique because it adapts to changing situations, giving it a comprehensive sustainable growth solution. SDODI is new because it supports and improves sustainability ideals.

关键词： Industries Technological innovation Leadership Transforms Sustainable development Socioeconomics Protection

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Robustifying Vision Transformer for Image Forgery Localization with Multi-Exit Architectures

SSRN

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SSRN 2023年

作者： Shi, Zenan Chen, Haipeng Dong, Zhang College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology 999077 Hong Kong

The proliferation of image manipulation tools has led to an increase in the number of manipulated images being disseminated online, posing risks like the propagation of fake news and telecom fraud. Thus, there is an increasing demand for precise, generic, and robust methods for detecting and locating manipulated images. In this paper, we propose a simple and clean model, named MEAFormer, for image forgery localization that does not heavily rely on pre-training models. MEAFormer comprises three main components: encoder network, neck network, and decoder network. Specifically, the transformer-based encoder network extracts hierarchical feature representations from the input image, providing rich contextual information in each layer. The neck network, incorporating our proposed cross-layer feature aggregation (CFA), aggregates these hierarchical features. To achieve better spatial feature co-occurrence, instead of using noise or edge artifacts, we introduce a multi-scale graph reasoning (MGR) module within the decoder network via bipartite graphs over the encoder and decoder features in a multi-scale fashion. The cross-level enhancement (CLE) further performs adjacent-level feature fusion to amplify the region-of-interests in aggregated manipulation features. Finally, the multi-exit architecture (MEA) guides the model to learn fine-grained features and segment out the manipulated region. Extensive experiments across diverse and challenging datasets conclusively establish MEAFormer’s superiority over existing state-of-the-art methods, excelling in accuracy, generalization, and robustness. © 2023, The Authors. All rights reserved.

关键词： Network architecture

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Degradation modeling of degradable copolymers for biomimetic scaffolds

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Friction 2020年第3期8卷 594-603页

作者： Taohong ZHANG Yue GAO Lingling ZHU Qingfeng ZENG Ming ZHOU Computer Department School of Computer&Communication EngineeringUniversity of Science and Technology Beijing(USTB)Beijing 100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing 100083China State Key Laboratory of Tribology School of Mechanical EngineeringTsinghua UniversityBeijing 100084China

Biomimetic scaffolds provide a suitable growth environment for tissue engineering and demonstrate good potential for application in biomedical ***-sized copolymerized biomimetic scaffolds degrade differently,and the degradation rate is affected by the copolymerization *** study of the degradation property is the foundational research necessary for realizing individualized biomimetic scaffold *** degradation performance of polyesters with different copolymerization ratios has been widely reported;however,the modeling of this performance has been rarely *** this research,the degradation of copolymers was studied with multi-scale modeling,in which the copolymers were dispersed in a cellular manner,the chain break time was simulated,and the chain selection was based on the Monte Carlo(MC)*** probability model of the copolymer's chain break position was established as a//roulette,/model,whose probability values were estimated by the calculation of the potential energy difference at different chain break positions by molecular dynamics that determined the position of chain shear,thereby fully realizing the simulation of the chain micro-break *** diffusion of the oligomers was then calculated using the macro diffusion equation,and the degradation process of the copolymer was simulated by three-scale coupling *** calculation results were in good agreement with the experimental data,demonstrating the effectiveness of the proposed method.

关键词： copolymer for biomimetic scaffolds copolymerization ratio degradation modeling multi-scale model probability model for chain break location

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Modelling Wind Turbine Power Curves based on Frank’s copula

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2023年 21卷 511-515页

作者： Garcia-Vaca, M.A. Sierra-García, J.E. Santos, M. Pandit, R. Computer Science Faculty Complutense University of Madrid Madrid28040 Spain Department of Electromechanical Engineering University of Burgos Spain Institute of Knowledge Technology Complutense University of Madrid Spain Cranfield University United Kingdom

In the study of wind turbines, one of the most relevant and useful indicators is the power curve. It has been shown to be of paramount importance in evaluating turbine performance and therefore reducing operation and maintenance (O&M) costs. Various techniques can be applied to model and obtain the shape of this curve, which relates the electrical power generated by a turbine to the wind speed. Statistical copulas are used in this paper, a tool used in other fields such as econometrics, and whose potential lies in its ability to capture the complex dependency between the variables involved. In particular, the Frank copula is applied to obtain a probabilistic model of the power curve of a wind turbine. This model is compared with the Gaussian Mixture Model, a technique widely used to obtain parametric probabilistic models. As a result of this comparison, it is observed that the Frank copula model fits the power curve of the wind turbine with greater precision and reliability, which would allow its use for prediction and fault detection. © 2023, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Wind turbines

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Relation-Aware Image Captioning for Explainable Visual Question Answering

Relation-Aware Image Captioning for Explainable Visual Quest...

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International Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Ching-Shan Tseng Ying-Jia Lin Hung-Yu Kao Intelligent Knowledge Management Lab Department of Computer Science and Information Engineering National Cheng Kung Univiserty Tainan Taiwan

Recent studies leveraging object detection models for Visual Question Answering (VQA) ignore the correlations or interactions between multiple objects. In addition, the previous VQA models are black boxes for human beings, which means it is difficult to explain why a model returns correct or wrong answers. To solve the problems above, we propose a new model structure with image captioning for the VQA task. Our model constructs a relation graph according to the relative positions between region pairs and then produces relation-aware visual features with a relation encoder. To make the predictions explainable, we introduce an image captioning module and conduct a multi-task training process. In the meantime, the generated captions are injected into the predictor to assist cross-modal understanding. Experiments show that our model can generate meaningful answers and explanations according to the questions and images. Besides, the relation encoder and the caption-attended predictor lead to improvement for different types of questions.

关键词： Training Visualization Semantics Object detection Learning (artificial intelligence) Predictive models Multitasking

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Comparison between Machine Learning Classifier Based On Face Recognition

Comparison between Machine Learning Classifier Based On Face...

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Renewable Energy and Sustainable E-Mobility Conference (RESEM), IEEE

作者： Ibrahim Mahmood Rashid Al-Bakri Muhammad Imran Ahmad Mohd Nazrin Md Isa Mustafa Zuhaer Nayef Al-Dabagh Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Collage of Agriculture Telafer University Telafer Iraq Department of Computer Science Knowledge University Erbil Iraq

With face recognition, machine learning is one of the computer sciences fields that is getting bigger the quickest. The goal of this study is to give a basic overview of machine learning and the algorithmic paradigms it provides. The study gives a detailed explanation of the basic ideas behind machine learning and the math that turns these ideas into methodologies that can be used in the real world, and discusses and compares the performance of various face recognition methods. Machine learning, a field of AI, has emerged as an important part of the digitizing approaches that have attracted a lot of interest. The purpose of this work is to provide a high-level overview of several of the most widely utilized and commonly used algorithmic techniques for machine learning currently available. The goal of this work is to help readers make educated decisions about the best algorithm for machine learning they should employ for a given task by highlighting the benefits and drawbacks of each method from an implementation point of view.

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Elucidating Spatial Complex Structures from Mass Spectrometry Imaging with Deep Multimodal Model 1

Elucidating Spatial Complex Structures from Mass Spectrometr...

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

作者： Cui, Jiangnan Xia, Junjie Li, Xuan Wang, Yan Qiu, Fufang Xu, Yaochen Xu, Keren Zuo, Chunman School of Computer Science and Technology Institute of Artificial Intelligence Donghua University Shanghai China College of Computer Science and Technology Jilin University Changchun China Huashan Hospital Department of Neurosurgery Shanghai China Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Cas Key Laboratory of Systems Biology Shanghai China Institute of Artificial Intelligence Donghua University Shanghai China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China

ISBN: (纸本)9798350358780

Spatial mass spectrometry imaging (SMSI) technology has enabled the characterization of biomolecule patterns within the tissue microenvironment, yet its analyses suffer from substantial noise. The lack of effective methods for exploiting multi-view features (e.g., spatial location) has hindered their elucidation ability for tissue heterogeneity. Here, we propose MSG, a multimodal modal that integrates histology, spatial location, and MSI data, in deciphering tissue structure by graph neural network. Specifically, MSG learns low-dimensional representations by aggregating histomorphological features from neighboring tiles via graph attention autoencoder. MSG outperforms other methods in identifying spatial domains and denoising data. The subsequent domain-guided differential expression analysis of the denoised data enables the identification of ions with significantly enriched patterns within the recognized domains. MSG exhibits its versatility in handling SMSI data from various platforms. © 2023 IEEE.

关键词： Data integration

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