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International Conference on Computing and Networking Technology (ICCNT)

作者： Piyush Sharma Kiranpreet Kaur Aaryan Gandotra Pranay Patial Radhika Sehaj Gaba Department of CSE Chandigarh University Mohali India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In an age where the Internet is dominated by visual content, the generation of animated captions has become a must. It has always been an interesting study for researchers in the Department of Artificial Intelligence. Enabling the machine to describe images with the same skillful accuracy as the human has important applications in various fields such as robotic vision, manufacturing, and beyond This project integrates recurrent neural networks with is a topic of contextual parallelism dedicated to extracting features from images Natural Language processing Computer vision and integrating them seamlessly, this research provides insights a it goes further on this interdisciplinary topic. Additionally, annotations for the sample images are created and performed a comparative analysis of different feature extraction and encoder patterns to determine which model provided the highest accuracy and delivered the desired results.

关键词： Visualization Accuracy Recurrent neural networks image databases Computational modeling Parallel processing Feature extraction Web sites Multimedia communication Object recognition

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machine Learning Algorithms for Signal and image processing

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2023年

作者： edited by Deepika Ghai Lovely Professional University IN Suman Lata Tripathi Lovely Professional University IN Sobhit Saxena Lovely Professional University IN Manash Chanda Meghnad Saha Institute of Technology IN Mamoun Alazab Charles Darwin University AS. Ghai Deepika editor. Tripathi Suman Lata editor. Saxena Sobhit editor. Chanda Manash editor. Alazab Mamoun 1980-editor.

ISBN: (数字)9781119861843;9781119861836

ISBN: (纸本)9781119861829

machine Learning Algorithms for Signal and image processing Enables readers to understand the fundamental concepts of machine and deep learning techniques with interactive, real-life applications within signal and image processing machine Learning Algorithms for Signal and image processing aids the reader in designing and developing real-world applications using advances in machine learning to aid and enhance speech signal processing, image processing, computer vision, biomedical signal processing, adaptive filtering, and text processing. It includes signal processing techniques applied for pre-processing, feature extraction, source separation, or data decompositions to achieve machine learning tasks. Written by well-qualified authors and contributed to by a team of experts within the field, the work covers a wide range of important topics, such as:

Speech recognition, image reconstruction, object classification and detection, and text processing
Healthcare monitoring, biomedical systems, and green energy
How various machine and deep learning techniques can improve accuracy, precision rate recall rate, and processing time
Real applications and examples, including smart sign language recognition, fake news detection in social media, structural damage prediction, and epileptic seizure detection

Professionals within the field of signal and image processing seeking to adapt their work further will find immense value in this easy-to-understand yet extremely comprehensive reference work. It is also a worthy resource for students and researchers in related fields who are looking to thoroughly understand the historical and recent developments that have been made in the field.

关键词： Signal processing.image processing.machine learning

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Feature extraction and multi-period classification of sea mushroom based on image information

Feature extraction and multi-period classification of sea mu...

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2022 International Conference on Artificial Intelligence, Information processing and Cloud Computing, AIIPCC 2022

作者： Zhong, Yang Du, Wanhe Yang, Shuzhen Yu, Tao Zheng, Xueping School of Mechatronic Engineering and Automation Shanghai University Shanghai China College of Engineering Shanghai Polytechnic University Shanghai China Shanghai Rongmei Agricultural Technology Co LTD Shanghai China

ISBN: (纸本)9781665462877

The phenotype of edible fungus basically relies on visual observation and empirical judgment at present, and there is still a lack of reports on the phenotypic techniques and applications for cultivation of seafood mushroom. Accurate characterization of different growth stages in the reproductive culture step of sea mushroom and rapid judgment of its growth stage is the foundation for its industrial cultivation to intelligent cultivation. In this work, a method of multi-period growth feature extraction and period classification of sea mushroom based on machine vision technology is proposed. According to the characteristics of the image itself, the method extracts 28 features of color, shape, and texture from the acquired data set. Subsequently, the KNN (K-nearest Neighbor) is utilized to classify the images represented by the selected features. The results indicated that the multi-period classification accuracy of the growth period of the mushroom image based on the comprehensive representation of the three types of features was 85%, which could better distinguish the growth period through the image, and made guidelines for the regulation of the cultivation environment. © 2022 IEEE.

关键词： Classification (of information)

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1st International Conference on Artificial Intelligence and its applications, ICAIA 2023

1st International Conference on Artificial Intelligence and ...

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1st International Conference on Artificial Intelligence and its applications, ICAIA 2023

ISBN: (纸本)9783031843938

The proceedings contain 37 papers. The special focus in this conference is on Artificial Intelligence and its applications. The topics include: The Hybrid Cardiac Risk Assessment and Prediction Model Using Convolutional Neural Networks;deep Learning applications for Malaria Detection and Diagnosis: A Review;Environmental Considerations in the Ethics of AI Adoption in Healthcare: Striving for Sustainable and Responsible Practices;harnessing the Power of Cognitive Computing: Assessing Point-of-Care Decision Support Tools in Oncology Practice;Critical Analysis in Use of AI in Health Care Management;classification and Prediction of Spinal Tuberculosis Disease Using Optimization of Convolution Neural Network Using Spatial and Temporal Constraints;artificial Intelligence for Remote Healthcare in Underserved Areas: Enhancing Access and Quality of Healthcare Delivery;machine Learning Based Skin Cancer Detection and Recognitions Techniques in IoT Environment;validation of a Chronic Kidney Disease Prediction System Using machine Learning Techniques;real-Time Feedback Detection Using Emotion Detection and Facial Recognition;revolutionizing vision Tasks: Unlocking Potential Through Patch-Based Approaches;automated Knee Implant Identification from 2D Templates Using image processing and Artificial Intelligence – An Experimental Approach;crop Analysis and Classification Based on Phenotype Using Ensemble Learning;Classification and Identification with Health Benefit Assessment and Nutrient Profile of Brewed Tea Utilizing Computer vision with ML and DL and Sensory Approaches;enhancing Industrial Automation Flexibility Through Neural Network-Empowered machine vision applications.

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1st International Conference on Artificial Intelligence and its applications, ICAIA 2023

1st International Conference on Artificial Intelligence and ...

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1st International Conference on Artificial Intelligence and its applications, ICAIA 2023

ISBN: (纸本)9783031843969

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基于机器视觉的隧道裂缝检测方法研究

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重庆大学学报 2024年第12期47卷 83-91页

作者：张振海季坤党建武兰州交通大学自动化与电气工程学院兰州730070 兰州交通大学甘肃省人工智能与图形图像处理工程研究中心兰州730070

裂缝检测是结构安全性评估的重要途径,基于阈值分割的传统裂缝检测方法在光照不均匀、背景噪声污染严重的隧道环境下具有分割精度低,难以提取完整裂缝的缺点。针对该问题,提出一种基于机器视觉的隧道裂缝检测方法。首先,对采集到的隧道... 详细信息

裂缝检测是结构安全性评估的重要途径,基于阈值分割的传统裂缝检测方法在光照不均匀、背景噪声污染严重的隧道环境下具有分割精度低,难以提取完整裂缝的缺点。针对该问题,提出一种基于机器视觉的隧道裂缝检测方法。首先,对采集到的隧道图像进行频域滤波与空域差分,以增强图像纹理特征;将经上述步骤分割后的图像通过设置面积参数Tv、饱和度参数Ts与特殊参数T′v、T′s提取背景噪声并删除,避免误检现象发生,使算法能够检测出完整的隧道裂缝图像;最后,结合本文应用场景的无突变性与发展规律性,设计轻量化裂缝连接算法连接上述步骤中断裂的裂缝,避免漏检现象的发生。实验结果表明,所提方法能在复杂隧道环境中有效提取完整裂缝,使隧道裂缝图像识别的精确率达到94%,召回率98%,尺寸精度92%,检测精度能够满足实际工程需求。

关键词：隧道裂缝机器视觉成分分析图像分割

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Attention-based Skin Cancer Classification Through Hyperspectral Imaging 25

Attention-based Skin Cancer Classification Through Hyperspec...

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25th Euromicro Conference on Digital System Design (DSD)

作者： La Salvia, Marco Torti, Emanuele Gazzoni, Marco Marenzi, Elisa Leon, Raquel Ortega, Samuel Fabelo, Himar Callico, Gustavo M. Leporati, Francesco Univ Pavia Dept Elect Comp & Biomed Engn Pavia Italy Univ Las Palmas Gran Canaria Inst Appl Microelect IUMA Las Palmas Gran Canaria Spain

ISBN: (纸本)9781665474047

In recent years, hyperspectral imaging has been employed in several medical applications, targeting automatic diagnosis of different diseases. These images showed good performance in identifying different types of cancers. Among the methods used for classification, machine learning and deep learning techniques emerged as the most suitable algorithms to handle these data. In this paper, we propose a novel hyperspectral image classification architecture exploiting vision Transformers. We validated the method on a real hyperspectral dataset containing 76 skin cancer images. Obtained results clearly highlight that the vision Transforms are a suitable architecture for this task. Measured results outperform the state-of-the-art both in terms of false negative rates and of processing times. Finally, the attention mechanism is evaluated for the first time on medical hyperspectral images.

关键词： vision Transformers medical hyperspectral imaging skin cancer deep learning

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Contextual Information based Network with High-Frequency Feature Fusion for High Frame Rate and Ultra-Low Delay Small-Scale Object Detection 17

Contextual Information based Network with High-Frequency Fea...

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17th International Conference on machine vision applications (MVA)

作者： Huang, Dongmei Zhang, Jihan Hu, Tingting Fuchikami, Ryuji Ikenaga, Takashi Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan Panasonic Corp Fukuoka 8128531 Japan

ISBN: (纸本)9784901122207

High frame rate and ultra-low delay small-scale object detection plays an important role in factory automation for its timely and accurate reaction. Although many CNN based detection methods have been proposed to improve the accuracy of small object detection for the low resolution and large gap between the object and the background, it is difficult to achieve a trade-off between accuracy and speed. For the pursuit of ultra-low delay processing by utilizing FPGA, this paper proposes: (A) IoU and distance based loss function, (B) Contextual information with high temporal correlation based parallel detection, (C) High frequency feature fusion for enhancing low-bit networks. The proposed methods achieve 45.3 % mAP for test sequences, which is only 0.7 % mAP lower compared with the general method. Meanwhile, the size of the model has been compressed to 1.94 % of the original size and reaches a speed of 278 fps on FPGA and 15 fps on GPU.

关键词： Correlation machine vision Graphics processing units Object detection Feature extraction Delays High frequency

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Reducing Complexity: A Comparative Analysis of Dimensionality-Reduction Techniques 4

Reducing Complexity: A Comparative Analysis of Dimensionalit...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Ramachandran, Raji Yamunakrishnan Govind, G. Devkrishna, M.K. Anil, Abhiram A. Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350305258

machine Learning (ML) is a modern fast-growing technology. It has extensive applications in disciplines like computer vision, bioinformatics, the medical field, finance, fraud detection, and so on. As we've seen, real-world data are used to train machine learning models, which can be used to process the data for purposes like prediction, classification, image processing, etc. The number of attributes in a dataset is known as its dimension. Real-world data sets have a lot of variables, which increases their dimension. It becomes increasingly challenging to envision and work on the training set as the number of variables increases. Sometimes the correlation between most of these factors makes them unnecessary. The benefits of using dimensionality are that when it comes to data compression and gaining less data space, dimensionality reduction is a great instrument. It drastically cuts down on computation time and it makes it simple to remove redundant characteristics from datasets. This project aims to provide a comparison of different dimensionality reduction algorithms. Dimensionality reduction uses a variety of techniques that yield varied outcomes. To compare and analyze the same, we have chosen four algorithms: The linear discriminant analysis algorithm (LDA), the CUR matrix decomposition algorithm, the singular value decomposition algorithm (SVD), and the principal component analysis algorithm (PCA). We are assessing the performance of these algorithms using the classifiers: Decision Tree classifier, Gaussian Naïve Bayes classifier, and the Random-Forest classifier, based on the parameter's accuracy, recall, precision, and F-measure. © 2023 IEEE.

关键词： CUR matrix decomposition algorithm Decision Tree classifier Dimensionality reduction Gaussian Naïve Bayes classifier linear discriminant analysis algorithm (LDA) Principal component analysis algorithm (PCA) Random-forest classifier Singular value decomposition algorithm (SVD)

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Precision diagnosis of tomato diseases for sustainable agriculture through deep learning approach with hybrid data augmentation

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CURRENT PLANT BIOLOGY 2025年 41卷

作者： Joshi, Kamaldeep Hooda, Sahil Sharma, Archana Sonah, Humira Deshmukh, Rupesh Tuteja, Narendra Gill, Sarvajeet Singh Gill, Ritu Maharshi Dayanand Univ Univ Inst Engn & Technol UIET Dept Comp Sci & Engn Rohtak 124001 India Maharshi Dayanand Univ Ctr Biotechnol Rothak 124001 Haryana India Cent Univ Haryana Dept Biotechnol Mahendergarh 123029 Haryana India Int Ctr Genet Engn & Biotechnol ICGEB Plant Mol Biol Grp New Delhi 110067 India

Tomato is a key crop in global agriculture, yet it faces yield and quality challenges due to various diseases. Traditional disease identification methods are slow and require expertise, limiting their practicality in large-scale farming. Integrating automated disease detection with precision agriculture provides a timely, accurate diagnosis, promoting sustainable practices. However, the scarcity of real-world data hampers effectiveness. To address this issue, data augmentation techniques simulate variations in farm images, enriching datasets for improved detection of diseases. This investigation aims to identify seven different tomato diseases, such as bacterial spot, early blight, late blight, and others, while also detecting healthy plant leaves. Unlike previous studies that relied on the controlled PlantVillage dataset, this study utilizes the real-world PlantDoc dataset. The study addresses different challenges faced throughout the model development process, like data scarcity and imbalances. A hybrid data augmentation technique is introduced to increase the dataset size from 737 images to 6696 images, which improves the accuracy and robustness of the computer vision model. The study employs the YOLOv8n deep convolutional neural network, achieving 96.5 % mAP, 97 % precision, 93.8 % recall, and 95 % F1 score. The results demonstrate a significant improvement in disease detection, addressing challenges from inadequate datasets and advancing AI-driven precision agriculture. The proposed YOLOv8n model has the potential to be applied beyond its current scope by training it on datasets of other crops. The model can learn and generalize the unique image features associated with various crop types, expanding its utility in agricultural applications. This flexibility allows the model to detect and classify plant characteristics, diseases, or pests across different crops, enabling its use in diverse agricultural environments. As a result, the YOLOv8n model could serve as

关键词： Automated disease identification Hybrid data augmentation image processing machine learning in agriculture Precision agriculture Tomato disease detection Yolov8n

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