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2023 IET International conference on Engineering Technologies and applications, ICETA 2023

作者： Sun, Chi-Chia Setyawan, Novendra Tai, Wei-Ting Department of Electrical Engineering National Formosa University Taiwan

ISBN: (纸本)9781839539848

This study proposes an integrated roadside system that combines image enhancement for adverse weather conditions with artificial intelligence (AI) technology enforcement at intersections. It aims to address critical issues in traffic management, such as accidents and congestion. The AI optimizes road safety for users, reduces traffic violations, and employs improved methods to control vehicle speed and direction, thereby minimizing accidents and congestion. Furthermore, the system leverages Convolutional neural Network (CNN) based model called All in One Dehazing Network (AOD-Net) used to enhance detection performance under specific conditions, and the YOLOv7 technique accurately identifies vehicles. With these model, the intelligent roadside system can sensitively detect vehicle behavior on the road, better coordinate traffic flow, improve road efficiency, reduce traffic congestion, and decrease the occurrence of traffic accidents. Overall, this intelligent roadside system with AI-powered traffic signal lights is expected to play a vital role in urban traffic management, effectively reducing traffic accidents and congestion, enhancing road efficiency, and providing a safer and more convenient travel experience for the public. © The Institution of Engineering & Technology 2023.

关键词： Roadsides

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A Thorough Review on Deep Learning neural Network

A Thorough Review on Deep Learning Neural Network

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artificial Intelligence and Smart Communication (AISC), International conference on

作者： Payal Chhabra Dr. Sonali Goyal Department of C.S.E MMDU MM Engineering College Mullana-Ambala Haryana India

Deep neural Network Topologies are used by subfield of machine learning called "deep learning" that are similar but different to handle a variety of challenges in domains including bioinformatics, computer vision and, among others. Recent research on deep learning has grown significantly across several applications. Deep learning technique produces state-of-the-art results by using numerous layers of features or data representations. Deep learning is essentially the application of neural networks with multiple hidden layers of neurons. In particular, this review paper firstly aims to offer a more thorough overview of the most fundamental deep learning components, further shows how deep learning techniques outperformed well-known ML techniques and then outlines how to deploy and build deep learning model. Secondly, Convolutional neural networks (CNN), most common used deep learning networks, are then introduced, along with a description of how they have implemented with matrix representation. Thirdly, we concentrate on application domain of deep learning, with an emphasis on its use in object detection. Finally, Future study directions are provided after the conclusion of the publication to assist scholars in understanding the research gaps and findings.

关键词： Deep learning Knowledge engineering Network topology image processing Visual impairment Neurons Object detection

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Wheat Diseases Detection and Classification using Convolutional neural Network (CNN)

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND applications 2022年第11期13卷 719-726页

作者： Hossen, Md Helal Mohibullah, Md Muzammel, Chowdhury Shahriar Ahmed, Tasniya Acharjee, Shuvra Panna, Momotaz Begum Comilla Univ Dept CSE Cumilla Bangladesh Noakhali Sci & Technol Univ Inst Informat Technol Noakhali Bangladesh Jahangirnagar Univ Dept CSE Dhaka Bangladesh

Ever since the medieval era, the preponderance of our concentration has been concentrated upon agriculture, which is typically recognized to be one of the vital aspects of the economy in contemporary society. This focus on agriculture can be traced back to the advent of the industrial revolution. Wheat is still another type of grain that, in the same way as other types of harvests, satisfies the necessity for the essential nutrients that are required for our bodies to perform their functions correctly. On the other hand, the supply of this harvest is being limited by a variety of rather frequent ailments. This is making it difficult to meet demand. The vast majority of people who work in agriculture are illiterate, which hinders them from being able to take appropriate preventative measures whenever they are necessary to do so. As a direct consequence of this factor, there has been a reduction in the total amount of wheat that has been produced. It can be quite difficult to diagnose wheat illnesses in their early stages because there are so many various forms of environmental variables and other factors. This is because there are numerous distinct sorts of agricultural products, illiteracy of agricultural workers, and other factors. In the past, a variety of distinct models have been proposed as potential solutions for identifying illnesses in wheat harvests. This study demonstrates a two-dimensional CNN model that can identify and categorize diseases that affect wheat harvests. To identify significant aspects of the photos, the software employs models that have previously undergone training. The suggested method can then identify and categorize disease-affected wheat crops as distinct from healthy wheat crops by employing the major criteria described above. The reliability of the findings was assessed to be 98.84 percent after the collection of a total of 4800 images for this study. These images included eleven image classes of images depicting diseased crops and o

关键词： Wheat crop diseases artificial intelligence convolution neural networks image processing feature extraction

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Element Base of Pulse neural Network Units 2

Element Base of Pulse Neural Network Units

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2nd International conference on neural networks and Neurotechnologies, NeuroNT 2021

作者： Bureneva, Olga I. Saint-Petersburg Electrotechnical University 'Leti' Department of Computer Science and Engineering Saint Petersburg Russia

ISBN: (纸本)9781665445344

An important issue in modeling of information processing in biological systems is the coding of information by nerve impulses. Pulse streams are the form of information representation closest to the biological one. To build elements and units of pulse neural network it is necessary to implement a basic set of elements, which are able to perform various arithmetic and logical transformations in pulse format. The report presents a set of digital elements for pulse stream processing. The elements can be used in the design of neurocomputers, and technical systems based on neural networks, used for pattern recognition, image analysis and processing and other applications in the field of artificial intelligence. © 2021 IEEE.

关键词： Field programmable gate arrays (FPGA)

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A 3D Model Generation from a Single Snapshot in Outdoor Scene

A 3D Model Generation from a Single Snapshot in Outdoor Scen...

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IEEE Global conference on Consumer Electronics (GCCE)

作者： Luoxi Zhang Chun Xie Itaru Kitahara Doctoral Program in Empowerment Informatics University of Tsukuba Tsukuba Japan Center for Computational Science University of Tsukuba Tsukuba Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

While scene modeling is widely used in various fields such as VR/AR and 3D art asset creation, it usually requires multiple viewpoint images captured by calibrated cameras, which make difficult to realize practical applications. This paper proposes a method for 3D scene reconstruction from a single snapshot based on RGB-D information without camera parameters. Mask-RCNN are used to separate the foreground and background, and pretrained deep neural network is applied to infer the human mesh of the foreground region. image inpainting, monocular depth estimation using MiDaS, and point cloud processing are combined to generate the 3D background point cloud. Finally, the foreground and background are integrated to achieve modeling the 3D scene.

关键词： Point cloud compression Solid modeling Three-dimensional displays Computational modeling artificial neural networks Cameras Hardware image reconstruction Consumer electronics Load modeling

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An Enhanced Deep Learning Model for Classifying Human Emotions Based on Facial Expressions

An Enhanced Deep Learning Model for Classifying Human Emotio...

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International conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Chauhan Khushi Pramod Shanti Verma Department of Computer Applications L J University Ahmedabad

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

Human emotion detection (recognition or identification) is spread over an area of studies, and research on human emotions is continuously booming. One of the most important research fields is the human-computer relationship. Furthermore, in this interrelation, while humans are interacting with computers, the device (the computer) needs to start identifying root emotions. Identifying Human emotions is not everyone's cup of tea. Humans are extremely smart at covering their emotions. Therefore, recognizing emotion is challenging. Most human emotions are linked to facial expression. The goal of this paper is to classify individual emotions based on their facial expressions through image classification. Several deep learning algorithms such as Convolutional neural networks CNNs, artificial neural networks ANN, Fully connected neural network FCNN are used for image classification. Keras library which is part of CNN, ANN and FCNN is used for powerful image classification. The results of the study depicts that CNN model is best in terms of accuracy to classify all types of facial expressions.

关键词： Deep learning Computers Emotion recognition Market research Libraries Convolutional neural networks Reliability

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Exploring Data Augmentation and Stochastic Weight Averaging as an Alternative to Adversarial Training of Deep neural networks 26th

Exploring Data Augmentation and Stochastic Weight Averaging ...

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26th International conference on artificial Intelligence and applications, ICAI 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

作者： Termini, Anabetsy Mukherjee, Sumitra Nova Southeastern University Davie United States

ISBN: (纸本)9783031866227

Deep neural networks used for image classification are highly susceptible to adversarial attacks. The de facto method to increase adversarial robustness is to train neural networks with a mixture of adversarial images and unperturbed images, a method also known as adversarial training. However, this method leads to robust overfitting, where the network primarily learns to recognize one specific type of attack used to generate the images while remaining vulnerable to others after training. This study investigates whether combinations of state of the art data augmentation methods with Stochastic Weight Averaging improve adversarial robustness and diminish adversarial overfitting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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Histopathology image Classification for Breast Cancer Using Convolutional neural Network

Histopathology Image Classification for Breast Cancer Using ...

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Advances in Electrical Engineering and Computer applications( AEECA), 2020 IEEE International conference on

作者： Xinfei Gu Shenzhen University Shenzhen China

ISBN: (纸本)9798350355260

Breast cancer detection presents considerable challenges in terms of diagnostic accuracy and efficiency, particularly when relying on traditional manual examination techniques. As medical imaging data grows in scale and complexity, traditional machine learning methods struggle due to their dependence on manual feature extraction and limitations in modeling nonlinear relationships. Computer vision, a key area of artificial intelligence, plays a crucial role in automating image recognition tasks, and its application in medical imaging is becoming increasingly prominent. In this study, we propose a deep learning framework based on convolutional neural networks (CNN) for breast cancer histopathology image classification, a critical task within computer vision. By automatically learning hierarchical features from raw image data, our model bypasses the need for manual feature engineering, achieving an impressive classification accuracy of 94.26% and an F1 score of 0.97. The robustness and generalization ability of the model are especially evident in its performance on complex patterns found in invasive ductal carcinoma (IDC) images. Specifically, the CNNbased approach effectively captures distinct visual features such as edge details, complex textures, and differential staining, which are essential for precise classification in medical image recognition.

关键词： Training Accuracy Histopathology Computational modeling Biological system modeling Manuals Breast cancer Data models Robustness Convolutional neural networks

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Employing Satellite imagery on Investigation of Convolutional neural Network image processing and Poverty Prediction Using Keras Sequential Model 5

Employing Satellite Imagery on Investigation of Convolutiona...

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5th IEEE International conference on Cybernetics, Cognition and Machine Learning applications, ICCCMLA 2023

作者： Kumar, Voruganti Naresh Sonth, Mahesh V Mahvish, Arfa Koppula, Vijaya kumar Anuradha, B. Reddy, L Chandrasekhar CMR Technical Campus Department of CSE Telangana Hyderabad India CMR Technical Campus Department of ECE Telangana Hyderabad India CMR Technical Campus Department of IT Telangana Hyderabad India CMR College of Engineering & Technology Department of CSE Telangana Hyderabad India CMR College of Engineering & Technology Department of CSD Telangana Hyderabad India

ISBN: (纸本)9798350338287

The primary objective of the proposed model is that the most of the world's poorest individuals reside in areas where national domestic assessments are used to gather info on deficiency. It is difficult to acquire current and precise data because it takes a lot of assets to conduct these surveys. Due to advancements in computer vision and the widespread availability of abundant data sources such as satellite images captured during daylight and nocturnal lighting, a practical solution to the problem of data scarcity is now feasible. This study is going to expand on previous research by processing daytime satellite photos and nighttime lights employing machine learning techniques for the purpose predict, the distribution of poverty at the local level in countries utilizing new technology and modern data sources. Innovative and fascinating possibilities, such as the detailed classification of specific objects on a per-pixel basis, have become feasible due to the availability of aerial satellite data. This study demonstrates the efficacy of a convolutional neural network (CNN) in efficiently and accurately classifying individual pixels inside satellite imagery of a compact urban area. The broad segmentation is then refined by incorporating the expected detailed pixel classifications, enhancing the overall accuracy and speed of the classification process. Examined and assessed are the several architectural decisions made for the CNN architecture. The five different types of terrain, ground cover, roads, structures, and water are all physically categorized and assigned to the study area's land mass. The correctness of classification is compared with other per-pixel classification methods for contrast tests conducted on different terrain areas with a comparable number of categories. Convolutional neural networks (CNNs) have demonstrated their efficacy in effectively addressing the task of segmenting and detecting objects in remote sensing data. This is clear from the compl

关键词： Satellite imagery

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Face based Attendance and Behaviour Tracking

Face based Attendance and Behaviour Tracking

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Applied artificial Intelligence and Computing (ICAAIC), International conference on

作者： G. Vijayasekaran B. Rajalakshmi M Anindhitha B Bhavani S Bhavya Department Of Computer Science and Engineering Department New Horizon College of Engineering Bangalore India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

A high-accuracy system for multi-face detection, recognition, one of the busiest uses of image processing is face recognition, which is essential to the industry. The ability to recognize a human face is a current concern for authentication, particularly when it comes to student attendance. A highaccuracy system for multi-face detection, recognition, and behaviour tracking is challenging task in the modern industry sector. The technology uses sophisticated face identification algorithms and recognition models by utilizing datasets and deep learning. The GUI application for the same improves user interaction, ensuring strong performance for real-world applications. This project’s goal is to improve attendance accuracy, simultaneously recognizing several faces at once using MTCNN (Multi-task Cascaded Convolutional neural networks) algorithm and documenting the data. This study investigates how deep learning approaches can help with less time-consuming attendance tracking.

关键词： Industries Deep learning Accuracy image recognition Face recognition Organizations Multitasking

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