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检索条件"任意字段=10th International Conference on Computer Analysis of Images and Patterns"

共 4188 条记录，以下是41-50 订阅

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MobileNetV3 based Classification Model for Diabetic Retinopathy 10

MobileNetV3 based Classification Model for Diabetic Retinopa...

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10th international conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Prajapati, Manish Baliarsingh, Santos Kumar Hota, Jhalak Kiit Deemed to Be University School. of Computer Engineering Bhubaneswar751024 India

ISBN: (纸本)9781665490993

Diabetic retinopathy (DR) is a disease that affects the eyes and is caused by microvascular diabetic damage. Due to this disease, the blood vessels of the retina are compromised. Late detection of this disease increases the likelihood that the patient will lose his/her eyesight. With the rapid advancement of deep learning, it has been used in many fields, including analysis and classification of medical images. the aim of this study is to employ a recently developed convolutional neural network (CNN) classifier that can identify the retinopathy and develop a five class classification method which can categorize the retinopathy into five different stages: no DR, mild, moderate, severe and proliferative retinopathy. In this work, a MobileNetV3 training model is implemented using cross-validation training and testing with a set of 5590 images to identify the stages and patterns of diabetic retinopathy. MobileNetV3 model as a training substrate is used to transfer what has been learned to a pre-trained five class classification task which achieves the accuracy of 98.56%. In order to support the medical experts in assessing the influence of the retina at different stages, the corresponding thermal image is also provided. © 2023 IEEE.

关键词： Convolutional neural networks

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SentiMatch: Sentiment analysis of Multi-modal Social Media Posts 10

SentiMatch: Sentiment Analysis of Multi-modal Social Media P...

引用

10th international conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Yadav, Abhishek Jain, Nisarg Sharma, Lakshay Singh, Preety The Lnm Institute of Information Technology Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9781665490993

Social media users are increasingly using images and videos to communicate their feelings. However, researchers usually rely on textual data for sentiment analysis. In this paper, we focus on Twitter posts having both text and image data and analyze the match between their sentiments, when predicted independently. We also propose a method to predict the sentiment of a social media post as a collective entity from the individual sentiments of text and image. Comparing our proposed model to a naive approach where the sentiment of the text is assigned to the image also, we see an improvement of around 20% in prediction of sentiment of Twitter images associated with text. © 2023 IEEE.

关键词： Sentiment analysis

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Survival Prediction of Cirrhosis Patients Using Polynomial Features and Differential Evolution 10

Survival Prediction of Cirrhosis Patients Using Polynomial F...

引用

10th international conference on Biosignals, images and Instrumentation, ICBSII 2024

作者： Patil, Hemprasad Yashwant Bhagwan Patil, Priyanka Shanmugasundaram, Lackesh Balasubramaniam, Anishkaa Vellore Institute of Technology School of Electronics Engineering Tamil Nadu Vellore India Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798350350951

this research focuses on enhancing the accuracy of the Cirrhosis Patient Survival Prediction dataset from the UCI-MLR. Leveraging novel techniques such as feature engineering and hyperparameter tuning, the study employs a comprehensive dataset with attributes including drug administration, age, sex, bilirubin, cholesterol, and albumin levels, along with labels indicating the stage of liver cirrhosis. the chosen dataset, recognized for its reliability and widespread adoption in research, serves as a valuable benchmark for developing diagnostic models. the comparative analysis involves Gradient Boosting Classifier, Logistic Regression, K Nearest Neighbors Classifier, Decision Tree Classifier, Gaussian Naive Bayes Classifier, Linear Discriminant analysis, and Support Vector Classifier. Further, hyperparameter tuning through Grid Search and Differential Evolution methods results in substantial accuracy improvements, particularly with the latter yielding a remarkable 9% increase with respect to the control dataset with no alterations. this work showcases the potential of advanced methodologies in optimizing cirrhosis prediction models, emphasizing the importance of feature engineering and hyperparameter tuning in achieving significant performance enhancements. © 2024 IEEE.

关键词： Forecasting

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A Comparative Study of Machine Learning Techniques for the Detection of Alzheimer's Disease 10

A Comparative Study of Machine Learning Techniques for the D...

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10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and computer Engineering, UPCON 2023

作者： Yogesh Kumar, Kapil Kaur, Kanksha thakur, Amandeep Nazir, Nahida Dahiya, Omdev Agarwal, Sarthak School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350382471

the timely detection of Alzheimer's disease remains a significant challenge in the medical field, yet machine learning algorithms offer promising solutions. this study delves into the application of machine learning techniques, such as K-Nearest Neighbors (KNN) and Support Vector Machines (SVM), to discern Alzheimer's disease from MRI brain images. the findings demonstrate KNN's exceptional accuracy of 99.76% in identifying Alzheimer's, outperforming SVM at 98.98%. this research underscores the potential of KNN and SVM in aiding early Alzheimer's detection, potentially leading to more effective treatment strategies and improved patient outcomes. © 2023 IEEE.

关键词： Alzheimer's Disease KNN Machine learning patterns Precision SVM

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Paddy Leaf Disease Detection 10

Paddy Leaf Disease Detection

引用

10th international conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Bhaskar, J. Kumar, V. Nidheesh Naidu, L. Vineetha Reddy, R. Divya Bhargavi, T. Sumathi, D. School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9798350384369

India is the land of agriculture, and most of the population relies on agriculture for their livelihood. Paddy is the staple crop for the regional habitats. Paddy cultivation plays a crucial role in global food manufacturing, making the detection in advance and classification of paddy crop diseases crucial for ensuring crop yield and quality. Due to environmental reasons, the agricultural sector fails to inculcate modern technological advancements. To safeguard the crop and increase production and yield, it is the need of the hour to detect the obstacles that hinder production. Plant disease area Detection of disease in RGB images of plants might vary due to several factors, such as plant age, growth stage, and genetic diversity. Hence, this study focused on thermal images, which focus on temperature differences, which are more directly related to physiological changes caused by diseases and stress than the plant's visual appearance. the objective of this study is to present a comparative analysis of VGG16, ResNet-50, and MobileNetV2. Performance evaluation is conducted through various metrics. the results demonstrate the effectiveness of the MobilenET-v2 is found to accurately identifying and classifying paddy leaf diseases from thermal images. the comparative analysis reveals nuanced differences in performance among the models, shedding light on their respective strengths and weaknesses. © 2024 IEEE.

关键词： thermography (imaging)

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Comparative analysis of Decorrelation and Contrast Stretching for Multispectral Color Image Enhancement 10

Comparative Analysis of Decorrelation and Contrast Stretchin...

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10th international conference on Communication and Signal Processing, ICCSP 2024

作者： Ganesan, P. Leo Joseph, L.M.I. Sajiv, G. Chandrasekaran, B. Manigandan, E. Janakiraman, V. Vidya Jyothi Institute of Technology Dept. of Electronics and Communication Engineering Hyderabad India Sr University Dept. of ECE Telangana Warangal India Simats Saveetha School of Engineering Tamilnadu Chennai India Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350353068

the proposed work explains the comparative detailed study of multispectral image enhancement by means of contrast and decorrelation stretching. It is challenging to extract all necessary information from multispectral image which is a huge collection of information. Multispectral images are taken using different filters (sensors) that can recognize different spectral bands, giving us important details about the scene we're looking at. Most of the data is intensified inside a narrow portion of the accessible dynamic range. So the true color composite image looks monotonous. In addition, in multispectral images the visible bands are highly correlated with each other. this is the reason they looks so dull. So it is obligatory to decorrelate the bands and enhance the contrast of the input data. In this study, the efficiency of the contrast and decorrelation stretching transformation on test images is compared. the experimental outcomes undoubtedly demonstrate that decorrelation stretching outperformed the contrast stretching in this context. © 2024 IEEE.

关键词： Image enhancement

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2023 10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and computer Engineering, UPCON 2023

2023 10th IEEE Uttar Pradesh Section International Conferenc...

引用

10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and computer Engineering, UPCON 2023

ISBN: (纸本)9798350382471

the proceedings contain 329 papers. the topics discussed include: systematic cognitive computing framework application using medical information processing;comparative analysis of epileptic seizure prediction: exploring diverse pre-processing techniques and machine learning models;analyzing textual data for mental health assessment: natural language processing for depression and anxiety;assessing water desalination in the United Arab Emirates: an overview;selection of cutting fluid for sustainable design for manufacturing;ESDPCOS: effectiveness of combined CNN and handcrafted features for ovarian cyst detection in PCOS patients using ultrasound images;analyzing the effect of edge computing on real-time data processing and latency reduction;text recognition in images and converting recognized text to speech – image processing;and comparative study of proposed linear regression algorithm to Scikit-Learn algorithm.

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Classification of Electric Motors Faults Using Fourier-Based Features and Self-Organizing Maps 10

Classification of Electric Motors Faults Using Fourier-Based...

引用

10th international conference on Signal Processing and Intelligent Systems, ICSPIS 2024

作者： Aghaiee, Mostafa Vasighi, Mahdi Pahlevani, Peyman IASBS Department of Computer Science and Information Technology Zanjan Iran

ISBN: (纸本)9798331532543

this research has been carried out to improve the identification of faults in electric motors using data processing techniques. the acoustic and vibration data were divided into smaller overlapping segments to allow a more detailed analysis of frequency and time characteristics. Fast Fourier transform (FFT) and short-time Fourier transform (STFT) frequency analyzes were used to identify defects within one-second interval. A comparison with previous studies showed that this approach significantly increases the accuracy of defect identification. the self-organizing map model was used for complex data clustering and multidimensional analysis to identify hidden patterns, and it performed successfully. Also, the models of the nearest neighbor, support vector machine, decision tree, naive bays, and random forest showed high accuracy in separating the classes. the results show the effectiveness of FFT and STFT techniques in identifying frequency patterns, and better results can be achieved with proper pre-processing and accurate selection of frequency ranges and overlapping windows. this approach not only accurately detected various faults but also evaluated the severity of faults well, which has a high potential for use in predictive maintenance and monitoring systems. © 2024 IEEE.

关键词： Predictive maintenance

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EEG Signal analysis In Detecting Pornography Addiction 10

EEG Signal Analysis In Detecting Pornography Addiction

引用

10th international conference on Communication and Signal Processing, ICCSP 2024

作者： Pratama, Muhammad Gilang Setiawan, Noor Akhmad Ferdiana, Ridi Universitas Gadjah Mada Department of Electrical Engineering and Information Technology Yogyakarta Indonesia

ISBN: (纸本)9798350353068

the rapid advancements in digital technology greatly impact addictive behaviors, including pornography addiction. Pornography, originally serving as an outlet for sexual arousal, has resulted in compulsive sexual behavior and addiction to explicit content. this prolonged engagement can adversely affect individuals, affecting their physical and social well-being. In the field of health technology, a significant development is the emergence of a tool capable of monitoring brain activity: the Brain-computer Interface (BCI). this innovation enables communication between humans and external devices by interpreting brain activity patterns. Electroencephalography (EEG) is particularly notable among BCI methods due to its cost-effectiveness and high temporal resolution. By utilizing EEG-based BCI imagery and various signal processing techniques, it is anticipated to effectively differentiate individuals based on their pornography addiction status. the initial findings of this study demonstrate discernible differences in brainwave patterns between individuals with and without pornography addiction. Moving forward, our goal is to further explore the potential implications and consequences of these variations in brain signals associated with pornography addiction. © 2024 IEEE.

关键词： Electroencephalography

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Comparative analysis of Conventional and Modified Capsule Networks for Sperm Morphology Assessment 10

Comparative Analysis of Conventional and Modified Capsule Ne...

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10th international conference on Signal Processing and Intelligent Systems, ICSPIS 2024

作者： Mohammadi, Hossein Serbes, Gorkem Ilhan, Hamza Osman Yildiz Technical University Department of Computer Engineering Istanbul Turkey Yildiz Technical University Department of Biomedical Engineering Istanbul Turkey

ISBN: (纸本)9798331532543

the evaluation of sperm morphology is an essential factor in the diagnosis of male-related infertility. Manual sperm morphology assessments are labor intensive and can vary significantly between observers. Although automated sperm morphology analysis approaches have shown significant success in the last two decades, there is still a need for deep learning based solutions due to the challenges faced in new data sets such as the dramatic increase seen in number of sperm samples and the diversity of the labeled abnormality types. As a solution to these challenges, this research performs a comparative analysis of the conventional capsule network (CapsNet) and its modified version (FixCaps), using a recently introduced imbalanced dataset referred to as Hi-LabSpermMorpho which contains a total of 18,456 images labeled into 18 different classes. the classes include variants of abnormalities of the head, neck, and tail of the sperm, as well as the normal class. the evaluation focused on assessing the accuracy, computational efficiency, and the effects of different optimization strategies for both models. the results have shown that FixCaps achieved higher performance than CapsNet, with its improvements such as using large-kernel convolutions and the CBAM attention mechanism, which allow the model to capture more complex features and spatial relationships of the images in the dataset. FixCaps resulted in 52.67% classification accuracy while CapsNet performed only 37.89%. © 2024 IEEE.

关键词： Image classification

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