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Automatic Detection Model of Food Microbial Contamination Combined with image processing and pattern recognition

Automatic Detection Model of Food Microbial Contamination Co...

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Telecommunications and Power Electronics (TELEPE), international conference on

作者： Wei Wang Yanchun Ruan Mei Lin Ruichen Bu College of Cuisine Science and Technology Jiangsu Vocational College of Tourism Yangzhou Jiangsu China College of Food Science and Engineering Yangzhou University Yangzhou Jiangsu China

ISBN: (数字)9798350369212

ISBN: (纸本)9798350380002

Microbial contamination of food is one of the important factors affecting food safety, and it is of great significance to detect microbial contamination of food quickly and accurately for ensuring public health. The purpose of this study is to develop an automatic detection model of food microbial contamination combining image processing and pattern recognition, so as to improve the detection efficiency and accuracy of food microbial contamination. First of all, the paper constructs an image data set containing different categories of food microorganisms, and preprocesses the image data, including denoising, enhancement and other operations to improve the image quality. Then, using deep learning technology and pre-trained convolutional neural network (CNN) to extract features, the high-level abstract features of the image are obtained. Then, a new deep feature fusion classifier (DFFC) is constructed by combining the traditional machine learning method support vector machine (SVM) with deep learning features, which is used for automatic detection of food microbial contamination. In the experiment, the DFFC model is evaluated in detail and compared with the model using CNN features or traditional machine learning methods alone. The experimental results show that DFFC model is superior to other models in Accuracy, Recall, Precision and F1 Score, and achieves 92% Accuracy, 89% Recall, 94% Precision and 91 % F1 score. This shows that DFFC model can effectively identify food microbial contamination, and has high classification accuracy and robustness. The DFFC model proposed in this study provides a reliable solution for the automatic detection of food microbial contamination, and is expected to play an important role in food safety monitoring and management.

关键词： Deep learning Support vector machines Accuracy Microorganisms Feature extraction Robustness pattern recognition Convolutional neural networks Contamination Monitoring

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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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Cultural heritage image classification using transfer learning for feature extraction: a comparison 1

Cultural heritage image classification using transfer learni...

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1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2022

作者： Babić, Radmila Janković Mathematical Institute of the Serbian Academy of Sciences and Arts Kneza Mihaila 36 Belgrade Serbia

image classification in the domain of cultural heritage becomes extremely important with the development of digitisation practices. This study aims to analyze how classification performance on the small dataset representing cultural heritage changes depending on the feature extraction method. The dataset comprised of 150 images belonging to three classes: (i) archaeological sites, (ii) frescoes, and (iii) monasteries. Five transfer learning architectures were used to extract the features from images, while classification was per-formed using four traditional machine learning algorithms, mainly Random forest, Naïve Bayes, Decision tree, and Multilayer perceptron classifier. The results suggest that Random forest and Multilayer perceptron are the most suitable algorithms for classification of cultural heritage images, especially when used in combination with the DenseNet121 pre-trained architecture. Naïve Bayes also performed well, with a maximum accuracy of 100% obtained when features are extracted using EfficientNetB0. However, the Decision tree algorithm reached only moderate performance. © 2022 Copyright for this paper by its authors.

关键词： image classification

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image processing Techniques for Detecting steel Defects with MATLAB and machine learning

Image Processing Techniques for Detecting Steel Defects with...

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Advanced Computing and Emerging Technologies (ACET), international conference on

作者： Dankan Gowda V P. Arockia Mary S. V. Ramanan N M G Kumar Pullela SVVSR Kumar Ibrahim Abdullah Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India Department of Information Technology V.S.B. Engineering College Karur Tamilnadu India Department of Electronics and Communication Engineering PPG Institute of Technology Coimbatore Tamil Nadu India Department of Electrical and Electronics Engineering Mohan Babu University Sree Vidyanikethan Engineering College Tirupathi Andhra Pradesh India Department of Computer Science & Engineering Aditya College of Engineering and Technology Surampalem Andhra Pradesh India Department of Computer Science Al-Turath University College Baghdad Iraq

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

This paper focuses on the detection of surface defects in the steel manufacturing industry since these defects inhibit the quality of the manufactured products and the efficiency of the manufacturing processes. Conventional inspection techniques require a extensive amount of work to be done at the inspection sites and perhaps associated with several kinds of errors. The current research work describes an automated system for determining the defects in the steel using image processing and integrating it with computational intelligence in MATLAB. The proposed method consists of pre-processing where the images are enhanced, feature extraction where characteristics of the defects are obtained and classification utilizing the machine learning models. The efficiency of the proposed approach is proved using experiments on the dataset of steel images and a substantial increase in the precision of the defects’ recognition compared to other techniques. Thus, the’ findings of the apply demonstrate that the use of image processing and machine learning can offer a strong system for real time defect identification in steel production.

关键词： Manufacturing industries Manufacturing processes Process control machine learning Production Inspection Real-time systems steel MATLAB Surface treatment

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Crop Disease Detection using CNN Architecture VGG16

Crop Disease Detection using CNN Architecture VGG16

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Advances in Computing, Communication and Networking (ICAC2N), international conference on

作者： Sandeep Raj Surinder Kaur Neetu Singh Monica Gupta Department of Artificial Intelligence & Machine Learning Dronacharya College of Engineering Farrukh Nagar Haryana Department of Information Technology Bharati Vidyapeeth’s College of Engineering New Delhi Department of Electronics and Communication Engineering Bharati Vidyapeeth’s College of Engineering New Delhi

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

The Agriculture plays a major part in any nation for economy through the production of various crops and it’s an essential source of income in India. The plant disease is one of the leading task of agriculture. When the infections occur in crops result in reduced quantity and quality of production of crops which will result in economic loss. Therefore, plague needs to be identified as quickly as possible for the healthy production of crops and plants. Various methods and solutions were proposed for crop disease detection by the researchers using image processing, machine learning, and deep learning. The deep learning plays an essential part in the field of image processing using convolution neural networks and their architecture. In this experiment, we have examined the CNN, Transfer learning VGG16 model on the plant-village dataset which was available on the Kaggle. The dataset(records) includes images of various potato, tomato and pepper crops of 15 different classes. The detection was done using the leaf samples. Our goal is to detect the disease in plants with accurate without taking so much time so that people can take care of their plants and apply the appropriate treatment. In future this VGG16 can be used to identify specific objects, animals, plants, and more in a picture. to identify, recognition and classification of image, that will use in many applications. Medical, Banking, Transportations well as Agriculture.

关键词： Deep learning Plant diseases Transfer learning Neural networks Crops Transportation Production Computer architecture Agriculture Object recognition

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Medical image Analysis Using Soft Computing Feature Selection and Classification of Skin Cancer 1

引用

international conference on Modern Electronics Devices and Communication Systems, MEDCOM 2021

作者： Saraswat, Birendra Kumar Srivastava, Shipra Singh, Samender Takuli, Arun Kumar Saxena, Aditya Department of Computer Science and Engineering G.L.A. University Mathura India Department of Information Technology Greater Noida Institute of Technology Greater Noida India Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science and Engineering Hi-Tech Institute of Engineering and Technology Ghaziabad India

ISBN: (数字)9789811963834

ISBN: (纸本)9789811963827

Adaptive resonance theory ARTI neural network (unsupervised learning) is being implemented for the classification of feature values with the objective of carrying out a comparative study with the performance of other technique including multi-layer feed forward network transform (supervised learning). A neural network is applied for the detection/identification of melanoma skin cancer. The images were collected from melanoma database is first processed and some distinguishable features were extracted by a MATLAB GUI. Two classes of the images were used: (a) benign, (b) malignant. A collection of interpreted binary input data, which is digitized from analog data after normalization is, then clustered adaptively with neural network ART-1. The goal of the project is to classify the feature dataset using supervised and unsupervised neural networks. To collect the feature set 1 implemented MATLAB image processing toolbox to create my own MATLAB functions. I created my own ARTI codes in MATLAB 701 (AKTI) to analysis which features are giving better efficiency using different vigilance parameter. Along with these supervised and unsupervised classification and pattern recognition, I am also trying to classify my own data-set of extracted features on Weka machine learning benchmarking software which is nothing but implements machine learning algorithms in JAVA. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Supervised learning

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You only Look Once(YOL O) Object Detection with COCO using machine learning 1

You only Look Once(YOL O) Object Detection with COCO using M...

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1st IEEE international Interdisciplinary Humanitarian conference for Sustainability, IIHC 2022

作者： Jaison, B. Anjali Jha, G. Jeevitha, J. Devi Priya, C. RMK Engineering College Computer Science and Engineering Chennai India

ISBN: (纸本)9781665456876

Object Detection is the most important mission in computer imaginative and prescient within the latest times. It is widely used in Autonomous driving, wildlife protection, security system, etc. The orthodox processing objects of the image have various shortcomings in order to achieve the requirements of the latest current improved high level computer vision needs. With the recent advancement in deep neural network and various different image processing and machine learning algorithms, now things are quite comfortable to go with object detection, classification and many more. In this paper, Convolution Neural Network (CNN) is employed in YOLO (You Only Look Once) algorithm to detect Objects in real-time. Object Detection includes various approaches inclusive of fast R-CNN, Retina-Net, and Single-shot Multi Box Detector (SSD). YOLO is used in real-time image processing at a larger extend because of its great performance with superior speed and accuracy over the aforementioned object detection techniques. In this project,COCO model is used with YOYO algorithm in order to achieve the requirements of latest object detection of the advancedworld. © 2022 IEEE.

关键词： Object detection

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Detecting Fake Faces In Smart Cities Security Surveillance Using image recognition And Convolutional Neural Networks 1

Detecting Fake Faces In Smart Cities Security Surveillance U...

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1st international conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Daya Sagar, K.V. Kamesh, D.B.K. Srinivasa Rao, T. Krishna, Chinta Venkata Murali Department of Electronics and Computer Engineering Koneru Lakshmaiah Education Foundation Vadeswaram Andhra Pradesh Guntur522502 India Dept.of CSE Mallareddy Engineering for Woen Hyderabad India Koneru Lakshmaiah Education Foundation India Department of C.S.E. NRI Institute of Technology India

ISBN: (纸本)9781607685395

Smart cities are planned to have millions of Internet-connected sensors and devices. Sensors can create a huge amount of data in a range of applications. In modern urban environments, quality of life in a Smart City is heavily dependent on the safety of its residents. For a long time, public safety has been a major source of anxiety. For everyone, stopping a breach of private space security has become a priority. Traditional security systems raise an alarm whenever they detect a breach of safety. It is possible to find a breach of an advanced model by using image processing and a deep analysis of convolutional neural networks to classify images. Because of the ability to reduce complicated aspects from photographs using exact algorithms for facial and body detection. The results of specific machine learning, such as deep learning techniques are outstanding. The processing time of the proposed system is reduced, and true rate of face recognition is 72.7% under varying distance from 2m to *** paper aims to show that when used together the security sector, the two can achieve more than might have been previously assumed models. © The Electrochemical Society

关键词： Face recognition

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Efficient Transfer learning for Visual Tasks via Continuous Optimization of Prompts 21st

Efficient Transfer Learning for Visual Tasks via Continuous ...

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21st international conference on image Analysis and processing (ICIAP)

作者： Conder, Jonathan Jefferson, Josephine Pages, Nathan Jawed, Khurram Nejati, Alireza Sagar, Mark Soul Machines Auckland New Zealand

ISBN: (纸本)9783031064272;9783031064265

Traditional methods for adapting pre-trained vision models to downstream tasks involve fine-tuning some or all of the model's parameters. There are a number of trade-offs with this approach. When too many parameters are fine-tuned, the model may lose the benefits associated with pre-training, such as the ability to generalize to out-of-distribution data. But, if instead too few parameters are fine-tuned, the model may be unable to adapt effectively for the tasks downstream. In this paper, we propose Visual Prompt Tuning (VPT) as an alternative to fine-tuning for Transformer-based vision models. Our method is closely related to, and inspired by, prefix-tuning of language models [22]. We find that, by adding additional parameters to a pre-trained model, VPT offers similar performance to fine-tuning the final layer. In addition, for low-data settings and for specialized tasks, such as traffic sign recognition, satellite photo recognition and handwriting classification, the performance of Transformer-based vision models is improved with the use of VPT.

关键词： Computer vision Few-shot Fine-tuning Prompt engineering Prefix-tuning CLIP Transformers Vision transformers

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