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Artificial intelligence-Based on Automatic Detection of Diabetic Eye Diseases : A Systematic Review 4th

Artificial Intelligence-Based on Automatic Detection of Diab...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Ba, Mandicou Mbaye, Amadou Diop, Idy Bah, Alassane Cheikh Anta Diop Univ UCAD Med Imaging & Bioinformat Lab LIMBI Polytech Higher Sch ESP Dakar Senegal

ISBN: (纸本)9783031821554;9783031821561

The diagnosis of eye diseases, especially those related to diabetes, has long posed enormous challenges for ophthalmologists in developing countries. In Africa, the main difficulty stems from the limited number of technologies and/or equipment available. Nowadays, with the advancement of technology and the proliferation of artificial intelligence models, the detection and analysis of eye diseases are becoming increasingly easier. It is clear that existing prediction systems can diagnose eye disorders such as glaucoma, cataracts, diabetic retinopathy, etc., but sometimes with very low accuracy. Manual diagnosis of fundus images by ophthalmologists also constitutes a slow, expensive, tedious task and may even be prone to errors. However, despite this, it is worth noting that some doctors still continue to practice this method. This paper highlights the crucial role of artificial intelligence systems, particularly those based on machine learning or deep learning, in the early detection of diabetes-related eye disorders in Africa. In a continent where the prevalence of diabetes is increasing, but resources are limited, these technologies offer significant potential to improve access to ocular healthcare and reduce the workload of healthcare professionals. This also underscores the importance of promoting research in the field of artificial intelligence in ophthalmology, especially in the African context.

关键词： Artificial intelligence machine Learning Deep Learning Diabetic Retinopathy Glaucoma Cataracts

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3rd international conference on Innovations in Computational intelligence and Computer Vision, ICICV 2022

3rd International Conference on Innovations in Computational...

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3rd international conference on Innovations in Computational intelligence and Computer Vision, ICICV 2022

ISBN: (纸本)9789819926015

The proceedings contain 57 papers. The special focus in this conference is on Innovations in Computational intelligence and Computer Vision. The topics include: Attention Deficit Hyperactivity Disorder Prediction Using Resting-State Networks;Neuroinformatics Deep Learning Synthesizer Based on Impulse Control Disorder Using LSTM Cells;Wasserstein GANs-Enabled Spectral Normalization on Credit Card Fraud Detection;Classification of Bipolar Disorder Using Deep Learning Models on fMRI Data;Predicting Schizophrenia from fMRI Using Deep Learning;patrolling Robot with Facial Detection;a Graph-Based Relook Beyond Metadata for Music Recommendation;an Interpretability Assisted Empirical Study of Affective Traits in Visual Content of Disinformation;multi-scale Fusion-Based Object Detection Network for Advance Driver Assistance Systems;secured Face recognition System Based on Blockchain with machine Learning;Vectorization of Python Programs Using Recursive LSTM Autoencoders;phenology Detection for Croplands Using Sentinel-2 and Computer Vision Techniques;deep Learning-Based Safety Assurance of Construction Workers: Real-Time Safety Kit Detection;Automatic Detection and Classification of Melanoma Using the Combination of CNN and SVM;CyINSAT: Cyclone Dataset from Indian National Satellite for Forecasting;classification of Ocular Diseases: A Vision Transformer-Based Approach;Analyzing Performance of Masked R-CNN Under the Influence of Distortions;COVID-19 Detection in Chest X-Ray Images Using Non-iterative Deterministic Learning Classifier;multimodal Classification via Visual and Lingual Feature Layer Fusion;a Review on Rural Women’s Entrepreneurship Using machine Learning Models;classifying Paintings/Artworks Using Deep Learning Techniques;handling Class Imbalance Problem Using Feature Selection Techniques: A Review;an Intelligent Human Pose Recommendation System Using Feature Fusion Technique;ensemble machine Learning Algorithms for Predicting Cardiovascular Disease;AI-Based Open

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machine Learning Model for Glass type Prediction 3

Machine Learning Model for Glass type Prediction

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Logeswaran, K. Savitha, S. Suresh, P. Vimaladevi, M. Saliniyan, P. Sanjay, A. Yeswanth, A. Kongu Engineering College Department of Artificial Intelligence Tamilnadu Erode638060 India K.S.R College of Engineering Departmen of CSE Tamilnadu Tiruchengode637215 India Vellore Institute of Technology School of Computer Science and Engineering Department of Database Systems Tamilnadu Vellore632014 India

ISBN: (纸本)9798350354171

Successful operations in industries such as recycling, manufacturing and quality control depend on identifying and classifying different types of glass. Traditional methods, including manual analysis and traditional image processing methods, often suffer from errors and inconsistencies due to human factors and variations. This paper introduces a new automatic glass type identification system that uses advanced learning techniques, specifically K-Nearest Neighbors (KNN), CNN,RNN. By applying these algorithms, it is aimed to increase the accuracy and performance of the system's glass type classification. Next, we delve into the design and implementation of machine learning models by discussing data collection techniques, feature extraction technology, and training methods. A comparative analysis of the performance of KNN, RNN, Naive Bayes, Logistic Regression, Random Forest, Feed Forward Network, RNN, ANN, CNN. is performed and their accuracy, performance, and robustness are evaluated in different situations. It shows the most accurate and reliable glass type classification among traditional methods. This study also explores the benefits of using such systems in the manufacturing industry and shows how work efficiency can be increased and the number of errors reduced. © 2024 IEEE.

关键词： Logistic regression

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Spam recognition Model Based on TextCNN 3

Spam Recognition Model Based on TextCNN

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3rd international conference on pattern recognition and machine Learning, PRML 2022

作者： Yixuan, Gao Yuanyuan, Zhang Ye, Cheng Fuzhou University Maynooth International Engineering College Fuzhou China Fuzhou University College of Computer and Data Science Fuzhou China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

With the advancement of modern Internet technology, email is widely used in people's daily lives and has become one of the common communication tools. However, at the same time, email-based spam has also been widely spread, flooding people's lives and not only wasting public resources but also infringing on people's legitimate rights and interests. Based on this, we propose a Spam recognition model based on TextCNN by combining Convolutional Neural Network and Natural Language Processing, where natural language processing techniques are used to pre-process the text in the spam corpus to obtain text features, which are then imported into the constructed Convolutional Neural Network analysis model. A CNN spam recognition model is then designed and analyzed hierarchically using the TensorFlow framework. After extracting the features from the email dataset, the model was fed into the convolutional neural network again for several iterations of training. The results indicated that the model achieved an accuracy of 97.7% in spam recognition, which is an exceptionally significant improvement in accuracy and stability compared to the SVM model used previously, demonstrating good filtering performance. © 2022 IEEE.

关键词： Convolutional neural networks

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Application Research of Computer Artificial intelligence Technology in Intelligent Home System 3

Application Research of Computer Artificial Intelligence Tec...

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2023 IEEE 3rd international conference on Data Science and Computer Application, ICDSCA 2023

作者： He, Shuiying Shanghai Institute of Commerce & Foreign Languages Shanghai201399 China

ISBN: (纸本)9798350341546

This paper constructs an intelligent home monitoring system based on NI wireless sensor network. The system consists of multiple sensors to collect temperature and humidity, smoke, theft and other environmental data. The NI wireless sensor network node sends the data to the upper PC in the way of wireless network. LabVIEW software is used to judge and control the data on PC. The system has obtained a good man-machine interface. Among them, the software design process of the system is introduced in detail. The intelligent home monitoring system has the characteristics of NI wireless sensor network - low power consumption, good real-time performance, easy to expand, remote monitoring and so on. © 2023 IEEE.

关键词： Artificial intelligence computer labVIEW remote monitoring sensor node smart home

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Analysis of Computer Image Processing Technology based on Intelligent Optimization Algorithm 3

Analysis of Computer Image Processing Technology based on In...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Li, Hui Shanxi Provincial Eye Hospital Shanxi Taiyuan030002 China

ISBN: (纸本)9798350319125

With the continuous development of computer technology, computer image processing technology has become an indispensable part of modern society. Computer image processing technology mainly includes image acquisition, image enhancement, image compression, image recognition and so on. In this process, intelligent optimization algorithm plays a very important role. This paper will introduce the application of intelligent optimization algorithm in computer image processing technology in detail. © 2023 IEEE.

关键词： artificial intelligence computer technology Image processing technology Intelligent optimization algorithm

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Shop Signboards Detection Using the ShoS Dataset 3rd

Shop Signboards Detection Using the ShoS Dataset

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3rd international conference on pattern recognition and Artificial intelligence, ICPRAI 2022

作者： Almuhajri, Mrouj Suen, Ching Y. Centre for Pattern Recognition and Machine Intelligence Concordia University MontrealQCH3G 2W1 Canada Department of Computer Science Saudi Electronic University Riyadh Saudi Arabia

ISBN: (纸本)9783031092817

This work was driven by the possibility of enabling beneficial applications for humans and municipal agencies utilizing deep learning techniques to detect shop signboards. Data is the foundation of successful AI-based applications. Despite the abundance of data, providing datasets for such applications is a challenge as they have to be refined and characterized accurately for better performance. This paper describes the process of collecting the ShoS dataset from Google Street Views for the detection of the signboards of shops on streets. A total of 10k storefront signboards were captured and fully annotated within 7500 images. The ShoS datasets were tested by running different baseline methodology using one-stage detectors YOLO and SSD. Out of the tested methods and variances, the YOLOv3 yields the highest performance reaching 94.23% (mAP@0.5). © 2022, Springer Nature Switzerland AG.

关键词： Object detection

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Using Artificial intelligence to Track and Predict Student Performance in Degree Programmes 3

Using Artificial Intelligence to Track and Predict Student P...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Ravi Kiran, G. Nassa, Vinay Kumar Banappagoudar, Sudharani B Lourens, Melanie Sidana, Neeru Tiwari, Tripti School of Science Sr University Telangana Warangal India India School of Nursing Sciences Itm University MP Gwalior India Durban University of Technology Faculty of Management Sciences South Africa Amity School of Economics Amity University India Institute of Management and Research Department of Management Studies Delhi India

ISBN: (纸本)9798350343274

Predicting learners' prospective performance depending on their current educational data is critical for implementing appropriate educational measures for guaranteeing learners' timely and acceptable graduation. Though there is a large body of research on predicting students' performance when resolving issues or preparing for classes employing statistical methods, predicting students' performance when finishing degrees is a new area of research. Techniques enable machines to arrive at judgements for humans in artificial intelligence (AI). This technology improves the consumer encounter in a variety of manners. Numerous research in the realm of education were undertaken to address the issue of learner focus and performance employing different machine Learning (ML) methods. We demonstrate that the suggested technique outperforms standard methods in extended simulations on an undergraduate learner database gathered over a 3-year period at UCLA. © 2023 IEEE.

关键词： machine learning

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YouTube Video Summarizer in Regional Language 3

YouTube Video Summarizer in Regional Language

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3rd international conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Jadhav, Ranjana Damre, Prajwal Hire, Atharva Gosavi, Priti Deshmukh, Sanskruti Vishwakarma Institiute of Technology Department of Multidisciplinary Engineering Pune India Vishwakarma Institiute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350396157

The idea and features of the proposed You Tube video summarizer tool created especially for regional languages is presented in this research study. Generally, it is a challenging task for viewers, who prefer regional languages to access and understand videos that are primarily available in a different language in YouTube. The proposed You Tube video summarizer in regional language attempts to overcome this challenge by offering a service to summarize videos in regional languages, making them easier for viewers who might not understand the original language of the video to access and comprehend. The developed program analyses and extracts important information from the video's audio or subtitles using natural language processing (NLP) and machine learning algorithms. This YouTube video summarizer in local languages has a wide range of potential uses. It can improve non-English speakers' access to information, improve video browsing, and promote effective content consumption in localized language groups. © 2024 IEEE.

关键词： machine learning

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Design of Automatic Game System Based on Artificial intelligence for Three Pieces of Chess 3

Design of Automatic Game System Based on Artificial Intellig...

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3rd international conference on Artificial intelligence, Internet of Things and Cloud Computing Technology, AIoTC 2024

作者： Li, Ziwen Lin, Dun Deng, Kaitao Li, Weijia Yang, Jiale Zhang, Lei College of Electronic Information Engineering Zhuhai College of Science and Technology Guangdong Zhuhai China

ISBN: (纸本)9798331530280

In this paper, we design an automatic three pieces game device based on STM32F407ZGT6, aiming at realizing the accurate human-computer interaction game function. The device utilizes an air pump and X, Y gantry mechanical structure and integrates YOLOv5 deep learning model. Equipped with Emm42_V5.0 closed-loop stepper motors and MG996R servo motors, the device can precisely control the placement of the pieces in 3D space. By connecting to MaixCAM to realize piece position detection and confirmation, ensuring accurate execution of the game. the introduction of the YOLOv5 model significantly improves the accuracy and speed of piece recognition, enabling the system to respond to user actions and game changes in a timely manner. Provides a friendly user interface with physical key input. Users can notify the system to start the timer by pressing the designated button after completing a round of piece placement. Meanwhile, feedback to the user after each round of the game is provided through an indicator light, which facilitates real-time monitoring of the progress of the game. © 2024 IEEE.

关键词： Stepping motors

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