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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Ramachandran, A. Ganesh Saravanan, S.K. Bhanumathi, M. Sangeetha, M. Fernandez, F. Mary Harin Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science Chennai India Saveetha School of Engineering Simats Department of Computers Science and Engineering Chennai India Department of Computer Science and Engineering Chennai India University of Technology and Applied Sciences Computer Engineering Department Nizwa Oman Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350388602

computer vision is defined in this study and one of the applications demonstrated is crop yield prediction in agricultural automation. Here we have emphasized on its use for computer vision for the agricultural domain. Using multispectral images that can be obtained from drones satellites and pairs with ground truths of average crop yield, weather conditions and parts of the soil treated with practices performed by an agronomist the proffered methodology would take advantage of the uniqueness of the concept to predict crop production. Further, elaborate machine learning methods such as line regression, decision trees, Random Forest, Support Vector Machine and CNN models are applied in order that the ultimate selection of the model would be contingent on the accuracy in relation to a crop yield. The DL based approaches as seen majority of this work and particularly the CNN exhibited improved results over regression models since there are more of classification than regression;In addition DL models are more appropriate for spatial relations and variations characteristic of crop data. Besides that, data fusion methods which map the accuracy of the images, weather and fields data develops a more accurate forecast as proven by the above crop modeling that involves multidimensional data. While transferring on fresh test data, it also demonstrates that the trained models have the capability to use the collected data in order to not only provide intelligent advices to the farmers on resource goodwill and crop productivity that will pave way for better results. Most importantly therefore for the hear of it the research show that with computer visions and machine learning the agricultural process can be reinvented this is how food security can be improved as the environment evolves. © 2024 IEEE.

关键词： Agricultural automation computer vision Crop yield prediction Deep learning Machine learning Multispectral imagery

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Mapping the Knowledge Structure and Research Evolution of Deep Learning

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第10期51卷 1404-1412页

作者： Yuan, Ke Zhao, Chenmeng Chen, Yayun Shen, Lin Tang, Qian Jia, Chunfu School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Spatial Information Processing Engineering Research Center Henan University Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China Yayun Chen is an undergraduate School of Computer and Information Engineering Henan University Kaifeng475004 China College of Geography and Environmental Science Henan University Kaifeng475004 China College of Cybersecurity Nankai University Tianjin 300350 China

Deep learning is a significant research field in today’s society, especially in the field of artificial intelligence. To gain a more comprehensive understanding of the current state of research in the field of deep learning and to identify areas of intense interest, this paper uses Citespace for research and learning, samples 1863 articles from the Web of science (WoS) core dataset, which spans the years 2017 to 2023, and adopts the methods of co-occurrence analysis, keyword burst analysis, and co-citation analysis. Information visualization is used to conduct bibliometric analysis, examining keywords, research institutions, authors, and countries. The analysis demonstrates a significant increase in the literature record from 2017 to 2022, followed by a decline from 2022 to 2023. The research on deep learning spans various fields, including computer science, engineering, and telecommunications, which are considered crucial areas of study. China, the USA, and England are leading the way in the field of deep learning research. These countries have established extensive cooperative relationships with many countries, with research institutions and authors playing a vital role in the cooperative network. The current theoretical research hotspots of deep learning mainly include "convolutional neural networks", "neural networks", "models", "classification", etc. The focus of research in deep learning has shifted from theoretical research to practical applications. It is now being applied in various areas, such as deep reinforcement learning, bearing fault, and semi-supervised learning. In the future, deep learning technology will be applied to more cutting-edge technologies. © (2024), (International Association of Engineers). All rights reserved.

关键词： Deep reinforcement learning

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Effect of granular shape on radial segregation in a two-dimensional drum

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Chinese Physics B 2024年第4期33卷 509-514页

作者：徐悦李然迟志鹏修文正孙其诚杨晖 School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Electrical and Electronic Engineering Shandong University of TechnologyZibo 255049China State Key Laboratory of Hydroscience and Engineering Tsinghua UniversityBeijing 100084China College of Medical Instruments Shanghai University of Medicine and Health SciencesShanghai 201318China

Granular segregation is widely observed in nature and *** research has focused on segregation caused by differences in the size and density of spherical ***,due to the fact that grains typically have different shapes,the focus is shifting towards shape *** this study,experiments are conducted by mixing cubic and spherical *** results indicate that spherical grains gather at the center and cubic grains are distributed around them,and the degree of segregation is *** experiments,a structured analysis of local regions is conducted to explain the inability to form stable segregation patterns with obviously different geometric ***,through simulations,the reasons for the central and peripheral distributions are explained by comparing velocities and the number of collisions of the grains in the flow layer.

关键词： granular materials cubic grains shape segregation segregation mechanism

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An Advanced Bald Eagle Search Algorithm for Image Enhancement

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computers, Materials & Continua 2025年第3期82卷 4485-4501页

作者： Pei Hu Yibo Han Jeng-Shyang Pan School of Computer and Software Nanyang Institute of TechnologyNanyang473004China College of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung413310Taiwan

Image enhancement utilizes intensity transformation functions to maximize the information content of enhanced *** paper approaches the topic as an optimization problem and uses the bald eagle search(BES)algorithm to achieve optimal *** our proposed model,gamma correction and Retinex address color cast issues and enhance image edges and *** final enhanced image is obtained through color *** BES algorithm seeks the optimal solution through the selection,search,and swooping ***,it is prone to getting stuck in local optima and converges *** overcome these limitations,we propose an improved BES algorithm(ABES)with enhanced population learning,position updates,and control *** is employed to optimize the core parameters of gamma correction and Retinex to improve image quality,and the maximization of information entropy is utilized as the objective *** benchmark images are collected to validate its *** results demonstrate that ABES outperforms the existing image enhancement methods,including the flower pollination algorithm,the chimp optimization algorithm,particle swarm optimization,and BES,in terms of information entropy,peak signal-to-noise ratio(PSNR),structural similarity index(SSIM),and patch-based contrast quality index(PCQI).ABES demonstrates superior performance both qualitatively and quantitatively,and it helps enhance prominent features and contrast in the images while maintaining the natural appearance of the original images.

关键词： Image enhancement gamma correction retinex bald eagle search algorithm

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Role of Machine Learning in the Prediction of Thyroid Disease

Role of Machine Learning in the Prediction of Thyroid Diseas...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kumari, Priyanka Kaur, Baljinder Rawat, Anil Kumar Rakhra, Manik Lovely Professional University School of Computer Science and Engineering Phagwara India Lovely Professional University School of Electronics & Communication Engineering Phagwara India

ISBN: (纸本)9798350378092

Numerous thyroid disorders impact people throughout the world. Numerous conditions can affect the thyroid gland, such as hyperthyroidism, hypothyroidism, and thyroid cancer. Thyroid illness patients may have excruciating symptoms. Effective classification and machine learning are highly useful in making timely thyroid illness diagnoses. This categorization will affect the patient's timely treatment. An endocrine ailment or condition known as hypothyroidism occurs when the thyroid gland does not generate enough thyroid hormone. Experts in the present system suspect hypothyroidism based on symptoms;it is a scientific investigation. The diagnosis of hypothyroidism can only come from a series of diagnostic tests. To ascertain their thyroid health, some groups need more precise tests. In India, 42 million individuals are estimated to have thyroid illness. The incidence of thyroid disorders has increased in recent years. Hyperactivity and hypothermia are two of the less common symptoms caused by thyroid gland disorders. Many people are diagnosed with thyroid diseases including hyperthyroidism and hypothyroidism each year. A shortage of T3 and T4 hormones can result in both hyperthyroidism and hypothyroidism. A strategic forecast of thyroid illness is required to ensure that the patient receives appropriate care at the appropriate time, save needless deaths, and minimize medical costs. Using state-of-the-art technology to enhance medical processes for the detection and prevention of thyroid issues is extremely desirable. The information from the UCI repository, Kaggle, and GitHub is used to categorize thyroid illnesses. similar to KNN, Logistic Regression, and Naïve Bayes, these algorithms also yielded results with up to 96% accuracy. © 2024 IEEE.

关键词： Logistic regression

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Research on Entity Recognition Method for Quality Standards of Concrete Prefabricated Components 1

Research on Entity Recognition Method for Quality Standards ...

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1st International Conference on Intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Zhang, Tingshu Tu, Ming Wang, Gefan Chen, Bingyong Zhao, Yishi Shang, Jianga School of Computer Science China University of Geosciences Wuhang China Intelligent Construction System Research Institute China Construction Third Engineering Buerau Group Co.L Wuhang China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education School of Computer Science China University of Geosciences Wuhang China

ISBN: (纸本)9781643685779

In recent years, the country has released a large number of standard documents related to prefabricated concrete components. Due to the dispersion and complexity of these standards, it is difficult for industry managers to implement them. Therefore, it is imperative to establish a knowledge graph in the field of quality standards for precast concrete components to achieve knowledge management. The construction of knowledge graph first requires named entity recognition, but due to the specificity of the construction industry, entity recognition of the text in this field still suffers from the problems of low recognition accuracy and high resource consumption. Aiming at the low recognition accuracy and high resource consumption in named entity recognition due to the specificity of the industry, this paper proposes a named entity recognition model ALBERT-BiLSTM-SA-CRF for the field of precast concrete component quality standard, which can capture the full text and local feature information more effectively through the combination of BiLSTM and self-attention mechanism, and accurately recognize entities in the standard. Meanwhile, using ALBERT as the word embedding layer can play the role of reducing resource consumption and improving processing efficiency. © 2025 The Authors.

关键词： Knowledge graph

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Smart IoT-Based Emotion Sensing Wearable: Revolutionizing Emotional Well-Being 2

Smart IoT-Based Emotion Sensing Wearable: Revolutionizing Em...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Kannapiran, Pritika Roy, Sritama School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9798350366570

In the fast-paced world of today, mental health has become a critical issue, with emotional stability being a key component of general health. Traditional methods of detecting and managing emotions often fall short due to their reliance on verbal and facial cues, leaving individuals with conditions such as Alexithymia or Autism Spectrum Disorder (ASD) at a disadvantage. This paper introduces a novel solution: the Smart IoT-Based Emotion Sensing Wearable, is designed to revolutionize emotional sensing and management. The primary goal of the research is to create a wearable gadget that can track vital signs and instantly identify different emotions. Using the Internet of Things (IoT), machine learning, and cloud computing, this wearable device bridges the gap for people with special needs and improves emotional understanding for all users by providing real-time emotion recognition and targeted help. © 2024 IEEE.

关键词： Emotion Recognition

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Weapon Detection Identification and Classification for DCNN and YOLO-V5 Techniques 5

Weapon Detection Identification and Classification for DCNN ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Ashok Kumar, J. Dhivya, G. Selvaraj, P. Karthikeyan, V. Ganesh Ramesh, S.P. Lakshmi, S. Jeya Saravanakumar, R. Karpagam Academy of Higher Education Department of Computer Applications Tamil Nadu Coimbatore India School of Computer Science and Engineering Department of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India School of Computing SASTRA Deemed to Be University Thanjavur Tamil Nadu Thirumalaisamudram India Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798331518097

Over 850,000 people die every year as a direct result of gun violence, yet civilians hold more than 85% of the world's weapons. Detecting weapons via manual surveillance has not been successful. It is critical to automate a system that detects weapons. A large number of convolution neural network CNN-based automated weapon identification systems have shown promising results. These methods are crucial for weapon identification but expensive. These devices produce a large amount of false negatives since monitoring videos are not high enough quality or visible enough to identify weapons. Finding the sweet spot between fast detection and a few false positives and negatives in weapon detection is the main goal of this research. In addition to defining an Area of Interest, the suggested structure uses YOLO. The models use previously processed images with backgrounds that have been blurred using a median filter. Metrics such as F1 scores, accuracy, recall, False Positive, and False Negative will be used to assess the suggested architecture. This investigation shows that YOLO-v5 can quickly identify and has a high recall rate. The deep convolution neural network DCNN required 0.17 ms for each frame, but this one only required 0.010 ms, which would indicate that security and weapon detection have good promise. Starting with a labeled collection of weapon and non-weapon photos is the initial stage in DCNN and YOLOv5 weapon detection. Use YOLOv5's ability to recognize multiple things within frames to train a real-time object localization and identification model. DCNNs categorize weapons and extract complex data effectively. DCNN classifies objects, whereas YOLOv5 detects, bounds, and tracks them. Optimize both models' hyperparameters and apply data augmentation for better performance. Install the integrated system on cutting-edge equipment for real-time weapon identification and classification. © 2024 IEEE.

关键词： Median filters

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Dual Attention Entropy Model for Efficient Neural Image Compression 21

Dual Attention Entropy Model for Efficient Neural Image Comp...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Tianwen, Wu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611-731 China

ISBN: (纸本)9798331519254

In the field of neural image compression, it is essential to develop an efficient entropy model which predicts the probability distribution of the quantized latent representation. Previous CNN based or transformer based entropy models achieve suboptimal estimation because CNN based model cannot capture long-range dependencies, while transformer based model suffers from excessive memory and computational cost. To address this issue, we propose a novel Entropy model based on Dual Attention aggregation (EDA), which takes into account both global and local dependencies effectively and efficiently. Specifically, a Dual Attention Aggregation (DAA) block is proposed. Within the DAA block, Deformable Attention Transformer (DAT) is proposed to capture global dependencies efficiently by using sparse key-value pairs to filter out large amounts of irrelevant contexts, and then Neighborhood Attention Transformer (NAT) is used for learning the complementary local dependencies. Experiments demonstrate that our model (EDA) delivers the state-of-the-art rate-distortion performance while being time-efficient and memory-friendly. The source code can be accessed at https://***/TNTWEN/EDA. © 2024 IEEE.

关键词： Image compression

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The propagation of Quasi-static region during granular impact

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Particuology 2023年第12期83卷 1-7页

作者： Moyu Yang Ran Li Yingnan Xiu Qiang Zeng Xiaoyan Ye Hui Yang School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Center for Particle-Laden Turbulence Key Laboratory of Mechanics on Disaster and Environment in Western China attached to the Ministry of Education of ChinaDepartment of Mechanics and Engineering ScienceSchool of Civil Engineering and MechanicsLanzhou UniversityLanzhou730000China

In this paper,through experiments and DEM simulations,it is found that there is a ring-shaped region named Quasi-static Region between the particles expanding outward and the particles collapsing inward during the impact ***-static Region is always generated from the impact point at the same time,and then spreads out at a uniform *** the propagation of Quasi-static Region,the velocity of the particles in the particle extend region varies linearly in space and *** the change rate is only related to the properties of the particle *** simulation results show that the particle flow in Quasi-static Region is elastic-inertial flow,while the particle flow expanding outward and collapsing inward is inertial flow.

关键词： Impact Quasi-staticregion Propagation Shearflow

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