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7th International Conference on Soft Computing: Theories and applications, SoCTA 2023

作者： Mathur, Abhishek Shrivastava, Shailendra Ku. Department of Information Technology Samrat Ashok Technological Institute Vidisha India Samrat Ashok Technological Institute Vidisha India

ISBN: (纸本)9789819720880

Crop diseases pose a severe risk to global food supplies, but accurate and timely diagnosis is hampered by a lack of resources in many regions. As the number of people with access to smartphones continues to rise throughout the world, and as recent advancements in computer vision allowed by deep learning pave the way, we may soon be able to use them to aid in the detection of diseases. Acquiring pictures, preprocessing, segmenting, and extracting features are all part of the image processing pipeline for disease detection in plants. In this review study, we take a look back at some of the most popular methods for classifying plant diseases: "Convolutional Neural Network (CNN)", "Support Vector machine (SVM)", "K-Nearest Neighbors", and "Artificial Neural Network (ANN)". The results of the study show that the convolutional neural network method outperforms the more conventional techniques in terms of accuracy. This review study discusses the use of DL models to depict different plant diseases and gives a detailed description of their operation. Furthermore, several research gaps are noted from which better clarity may be obtained in regard with early detection of plant diseases. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial neural network Convolution neural network Deep learning image processing Plant disease detection Recurrent neural network

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A machine Learning based Facial Expression and Emotion Recognition for Human Computer Interaction through Fuzzy Logic System 6

A Machine Learning based Facial Expression and Emotion Recog...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Vinutha, K. Niranjan, Manoj Kumar Makhijani, Jagdish Natarajan, B. Nirmala, V. Vijaya Lakshmi, T.R. Department of Information Science and Engineering BMS Institute of Technology and Management Karnataka Bengaluru India Department of Computer Applications Rustamji Institute of Technology Madhya Pradesh Gwalior India Department of Computer Science and Engineering Rustamji Institute of Technology Madhya Pradesh Gwalior India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India Department of Science and Humanities Faculty of Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Mahatma Gandhi Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350398496

Facial recognition is in use for the past decade there are many applications that needs facial expression to learn the human behaviour and emotions for certain activities. Facial recognition is in a development phase where many service providers use this feature to find the expression of the people on using their BlogSpot or website or reading any news article. This recognition of facial expression is highly possible with the help of machine learning technology. This research study has developed a facial expression recognizing algorithm using Python programming language with the help of Keras software package. This algorithm is purely based on machine learning approach that enables the programmer to process the facial image and convert it into data that is helpful in prediction of facial expression using the fuzzy logic technique. The fuzzy logic technique is a prediction method that helps programmer to predict the intermediate data by providing the initial and ending conditions. For enabling the facial recognition to process any system or a mobile device the algorithm needs permission to access the camera, once the onto the access is permitted the algorithm retrieves the image from the vision sensor and with the help of image processing technology of the machine learning algorithm the program the program converts the data from the vision sensor into required facial expression and emotional content. © 2023 IEEE.

关键词： Emotion Recognition

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Deepfake image and Video Detection Using Deep Learning Algorithms

Deepfake Image and Video Detection Using Deep Learning Algor...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Srinivasan, Srivanth Nischitha, P. Chavan, Akshita Mohana Rv College of Engineering® Bengaluru India Rv College of Engineering® Bengaluru India

ISBN: (纸本)9798331509828

Deep learning (DL) algorithms are swiftly finding applications in computer vision and natural language processing. Nonetheless, they can also be employed for creating convincing deepfakes, which are challenging to distinguish from reality. The advancements in image and video technology and tools, especially on social media platforms, potentially lead to misuse for malicious purposes like blackmail or defamation. To tackle this issue, several group of researchers tried upon spreading or creating awareness on real or fake data. The proposed approach involves combining Deepfake generation using GANs and Autoencoders with a Deepfake detection method. The aim of this initiative is exclusively to combat disinformation and online fraud for the welfare of the general population. Deepfakes, products of AI, have become increasingly realistic, rendering it nearly difficult to distinguish the content. Auto-encoders with sufficient time can achieve about 92 % accuracy. As the generator improves, the discriminator performance worsens as it struggles to differentiate real or fake data. A perfect generator results in 50% accuracy. With advancements in computational capacity and data availability, the proposed DDM (Deepfake Detection Model) has achieved greater accuracy rate of up to 92.3%. © 2025 IEEE.

关键词： Adversarial machine learning

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A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation

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machine vision AND applications 2024年第4期35卷 67-67页

作者： Sarker, Sushmita Sarker, Prithul Stone, Gunner Gorman, Ryan Tavakkoli, Alireza Bebis, George Sattarvand, Javad Univ Nevada Dept Comp Sci & Engn Reno NV 89557 USA Univ Nevada Dept Min & Met Engn Reno NV USA

Point cloud analysis has a wide range of applications in many areas such as computer vision, robotic manipulation, and autonomous driving. While deep learning has achieved remarkable success on image-based tasks, there are many unique challenges faced by deep neural networks in processing massive, unordered, irregular and noisy 3D points. To stimulate future research, this paper analyzes recent progress in deep learning methods employed for point cloud processing and presents challenges and potential directions to advance this field. It serves as a comprehensive review on two major tasks in 3D point cloud processing-namely, 3D shape classification and semantic segmentation.

关键词： 3D classification Computer vision Point cloud Semantic segmentation

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Development of software interface for AI-driven weed control in robotic vehicles, with time-based evaluation in indoor and field settings

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SMART AGRICULTURAL TECHNOLOGY 2024年 9卷

作者： Sunil, G. C. Upadhyay, Arjun Sun, Xin North Dakota State Univ Dept Agr & Biosyst Engn Fargo ND 58108 USA

Herbicide blanket application is widely practiced by farmers to chemically control weeds in the field, thereby enhancing productivity and improving crop quality. However, their repetitive use also has caused several negative issues on the environment and human health. To overcome these negative issues precision site-specific weed control can be used to reduce the herbicide application by targeted application on only weed areas. Hence, a robotic platform utilizing NVIDIA Jetson embedded device as control and processing unit was developed and tested on lab and field conditions for weed control. The average time taken for data acquisition and artificial intelligence-based computer vision tasks was tested under both lab and field conditions. Moreover, a grid map creation algorithm using YOLOv4 deep learning algorithm was evaluated to control the nozzles of the robotic platform. Integrating all these components together enabled real-time weed management approaches, enhancing the precision and efficiency of herbicide application Independent paired t-tests reveal that there is no significant difference in computational time between lab and field testing. Conversely, independent paired t-test revels that there is significant difference in image size between lab and field testing. The reduction of herbicide application based on grid map was obtained 79 to 80 %, which does not include YOLOv4 algorithm failure and system synchronization failure. The study suggests the importance of field testing for real-time applications of the robotic platform by using deep learning computer vision methods for weed control.

关键词： Artificial intelligence Computer vision machine vision Robotic Weed detection

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image Labeling Using Convolutional Neural Network

Image Labeling Using Convolutional Neural Network

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Vasantha Kumari, N. Garimella, Mounika Bhan, Madhu Ramaiah Institute of Technology Dept. of Mca Bengaluru India Ramaiah Institute of Technology Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350300826

Due to the rapid rise in the identification of digital materials, automatic image classification has emerged as the most difficult topic of computer vision. In comparison to human vision, automatic visual understanding and analysis by a system is challenging. The findings of many experiments that were done to fix problems of automatic picture classification with the current categorization method were simple picture primitives. However, those methods don't accurately classify photos. In this research work, a system has been created that makes use of deep learning to deliver the necessary results in the area of computer vision. Our technology automatically categorizes photographs using Convolutional Neural Network, a popular machine learning method. Our algorithm classifies grayscale photos using the Digit of MNIST dataset as a benchmark. The dataset that is employed for the training of the system contains grayscale images, which demand higher processing resources for image categorization. By employing Convolution Neural Network to train the photos, we were able to attain higher accuracy results in the experimental section, demonstrating that our model is highly accurate at classifying images. © 2023 IEEE.

关键词： image classification

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Spiking Neural Network in Computer vision: Techniques, Tools and Trends 4th

Spiking Neural Network in Computer Vision: Techniques, Tool...

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4th International Conference on Advanced Computational and Communication Paradigms, ICACCP 2023

作者： Agarwal, Rohit Ghosal, Palash Murmu, Narayan Nandi, Debashis National Institute of Technology Durgapur713209 India Sikkim Manipal Institute of Technology Sikkim Majitar737136 India

ISBN: (纸本)9789819942831

Artificial Neural Networks (ANN) have become one of the most powerful machine learning tools that cover a wide range of applications such as surveillance, video and image recognition, medical image analysis, control systems, robotics, bioinformatics, etc. These wide applications have encouraged researchers to search for more realistic neuron models that really mimic human biological neurons. Recent neurological research explored a set of new characteristics of biological neurons which led to generating the third-generation neural networks called Spiking Neural Networks (SNN). SNNs are more faithful to drawing biological properties which makes them energy-efficient and fast with higher processing capability. Many SNN models along with various learning algorithms have been proposed in the last decade to explore fast and efficient machine learning applications. In this paper, we have reviewed recent advancements in SNN architectures, their learning algorithms, applications, advantages, limitations and future scopes. We have also presented a brief comparison among some well-cited state-of-the-art works in terms of performance, advantages and limitations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Learning algorithms

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Deep Learning-Based Depth Map Defect Removal for Industrial applications 10

Deep Learning-Based Depth Map Defect Removal for Industrial ...

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Conference on Optical Metrology and Inspection for Industrial applications X

作者： Voronin, V. Gapon, N. Zhdanova, M. Tokareva, O. Khamidullin, I. Semenishchev, E. Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia Don State Tech Univ Rostov Na Donu Russia

ISBN: (纸本)9781510667877;9781510667884

A system for determining the distance from the robot to the scene is useful for object tracking, and 3-D reconstructions may be desired for many manufacturing and robotic tasks. While the robot is processing materials, such as welding parts, milling, drilling, etc., fragments of materials fall on the camera installed on the robot, introducing unnecessary information when building a depth map, as well as the emergence of new lost areas, which leads to incorrect determination of the size of objects. There is a problem comprising a decrease in the accuracy of planning the movement trajectory caused by wrong sections on the depth map because of erroneous distance determination to objects. We present an approach combining defect detection and depth reconstruction algorithms. The first step for image defect detection is based on a convolutional auto-encoder (U-Net). The second step is a depth map reconstruction using a spatial reconstruction based on a geometric model with contour and texture analysis. We apply contour restoration and texture synthesis for image reconstruction. A method is proposed for restoring the boundaries of objects in an image based on constructing a composite curve by cubic splines. Our technique outperforms the state-of-the-art methods quantitatively in reconstruction accuracy on the RGB-D benchmark for evaluating manufacturing vision systems.

关键词： depth map defect detection image reconstruction manufacturing deep learning U-Net

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Beyond images: data visualization through headline analysis in historical newspaper with computer vision

Beyond images: data visualization through headline analysis ...

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2023 International Workshop on Signal processing and machine Learning, WSPML 2023

作者： Yip, Michael Kin-Fu Lum, Vincent Wai-Yip Digital Initiatives The Chinese University of Hong Kong Library Hong Kong

ISBN: (纸本)9781510671928

This paper introduces an innovative method that combines Computer vision and Deep Learning to extract headlines from a historical newspaper. Through the illustrations from historical newspapers, one of our goals is to use these extracted headlines to support digital humanities. The research goes beyond traditional image analysis by exploring how new digital technologies can facilitate the understanding of newspaper content by visualizing through time and place. The experimental results reveal that our recommended approaches, which involve Optical Character Recognition (OCR) with scraping and Deep Learning Object Detection models, can successfully obtain the required information for more advanced analytics. Due to the distinctive historical and humanities values, we chose "The Hongkong News" from the Hong Kong Early Tabloid Newspaper collection to illustrate the efficacy of our methodology. In addition, we constructed several visualization applications to demonstrate the viability of our suggested approaches. © 2023 SPIE. All rights reserved.

关键词： Geographic information systems

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Efficient Hardware Architectures for Accelerating Deep Neural Networks: Survey

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IEEE ACCESS 2022年 10卷 131788-131828页

作者： Dhilleswararao, Pudi Boppu, Srinivas Manikandan, M. Sabarimalai Cenkeramaddi, Linga Reddy Indian Inst Technol Bhubaneswar Sch Elect Sci Bhubaneswar 752050 India Indian Inst Technol Bhubaneswar Sch Elect Sci Bhubaneswar 678557 India Univ Agder Dept ICT N-4879 Grimstad Norway

In the modern-day era of technology, a paradigm shift has been witnessed in the areas involving applications of Artificial Intelligence (AI), machine Learning (ML), and Deep Learning (DL). Specifically, Deep Neural Networks (DNNs) have emerged as a popular field of interest in most AI applications such as computer vision, image and video processing, robotics, etc. In the context of developed digital technologies and the availability of authentic data and data handling infrastructure, DNNs have been a credible choice for solving more complex real-life problems. The performance and accuracy of a DNN is a way better than human intelligence in certain situations. However, it is noteworthy that the DNN is computationally too cumbersome in terms of the resources and time to handle these computations. Furthermore, general-purpose architectures like CPUs have issues in handling such computationally intensive algorithms. Therefore, a lot of interest and efforts have been invested by the research fraternity in specialized hardware architectures such as Graphics processing Unit (GPU), Field Programmable Gate Array (FPGA), Application Specific Integrated Circuit (ASIC), and Coarse Grained Reconfigurable Array (CGRA) in the context of effective implementation of computationally intensive algorithms. This paper brings forward the various research works on the development and deployment of DNNs using the aforementioned specialized hardware architectures and embedded AI accelerators. The review discusses the detailed description of the specialized hardware-based accelerators used in the training and/or inference of DNN. A comparative study based on factors like power, area, and throughput, is also made on the various accelerators discussed. Finally, future research and development directions, such as future trends in DNN implementation on specialized hardware accelerators, are discussed. This review article is intended to guide hardware architects to accelerate and improve the effe

关键词： machine learning field programmable gate array (FPGA) deep neural networks (DNN) deep learning (DL) application specific integrated circuits (ASIC) artificial intelligence (AI) central processing unit (CPU) graphics processing unit (GPU) hardware accelerators

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