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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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Construction of Security Risk Prediction Model for Wireless Transmission of Multi Axis NC Machining data 4th

Construction of Security Risk Prediction Model for Wireless ...

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4th international conference on machine learning for Cyber Security, ML4CS 2022

作者： Zhao, Guoqiang Zhang, Meitao Wu, Yingying Shandong Vocational College of Science and Technology Weifang261053 China Weichai Heavy Machinery Co. Ltd. Weifang261108 China Weichai Power Co. Ltd. Weifang261061 China

ISBN: (纸本)9783031200953

In multi axis NC machining, there is a certain security risk in wireless data transmission. A security risk prediction model for wireless data transmission in multi axis NC machining is designed. through frequent pattern mining, multi axis NC machining data wireless transmission security risk data mining is implemented. Based on machine learning, the security risk prediction model of data wireless transmission is constructed. the model is mainly divided into four parts: data collection and processing module, grey synthesis and Markov prediction module, Bayesian network and evidence theory module and risk quantification calculation module. AHP method is used to analyze the weight of model indexes. Test the prediction performance of the model. the test results show that the accuracy of risk prediction of the model in different scenarios is high, higher than 88%, and the prediction time is short, less than 6 s, which proves the prediction performance of the model. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

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Ensemble learning and Time-Domain Feature mining based Appliance Power Consumption pattern recognition 22

Ensemble Learning and Time-Domain Feature Mining based Appli...

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22nd international learning and Technology conference, L and T 2025

作者： Qaisar, Saeed Mian Effat University Electrical and Computer Engineering Department Jeddah21478 Saudi Arabia

ISBN: (纸本)9798331532215

As technology has advanced, using smart meters instead of conventional ones has changed. these smart meters are essential parts of intelligent grids, offering significant advantages to many stakeholders regarding social, environmental, and economic constraints. A detailed method of fine-grained collection and analysis of the metering data is necessary to benefit the different smart grid stakeholders. this results in collecting, transmitting, and processing a large amount of data with an exponentially raised dimension. It seriously constrains the system processing resources, latency, data management, transmission effectiveness, and analysis response time. In this regard, a new hybridization of ensemble learning and time-domain feature extraction is suggested for the automatic classification of appliances through the processing of their power consumption data. Time-domain feature mining makes up for the previously noted deficiency and can result in a considerable real-time data dimension reduction without sacrificing important information. data in compressed form can therefore be processed, examined, stored, and transmitted with efficiency. It offers a significant reduction in the post cloud-based classification latency along with computational and transmission effectiveness. the classification is carried out using the robust ensemble learning (EL) classifiers. the performance of considered EL classifiers is evaluated for the case of a real multi-class and multi-device smart meter dataset. the devised solution achieves a highest average classification accuracy of 88.33% for a 10-class problem while securing a compression gain of 32.73. © 2025 IEEE.

关键词： Smart power grids

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Deep learning Based Intrusion Detection for IoT Networks 4

Deep Learning Based Intrusion Detection for IoT Networks

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4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Ananthi, P. Nirmaladevi, K. Mothika, G. Kongu Engineering College Department of Computer Technology-PG Tamilnadu Erode638060 India Madanapalle Institute of Technology & Science Department of Computer Applications India Kongu Engineering College Erode India

ISBN: (纸本)9798331529635

Intrusion detection is crucial for securing IoT networks amid the rapid proliferation of devices, posing significant security challenges. this study offers a unique intrusion detection model for IoT deep learning methods used in networks include Convolutional Neural Networks (CNN), TabNet, and the Transient Search Optimization (TSO) method. the project starts by developing and evaluating distinct intrusion detection components. CNN, specializing in feature extraction, analyzes network traffic for potential intrusions. TabNet, known for interpretability and effectiveness with structured data, captures intricate relationships. the novel TSO algorithm improves the system's ability to detect temporary anomalies. Each component's performance is assessed individually, highlighting unique strengths. TabNet excels in structured IoT data pattern identification, surpassing CNN. the TSO algorithm strengthens temporary anomaly recognition, enhancing IoT network security. An ensemble technique combines CNN and TabNet predictions, reducing false positives and negatives to elevate overall detection accuracy. the ensemble, using both CNN and TabNet, achieves an impressive accuracy rate exceeding 99%, demonstrating significant gains in intrusion detection performance. © 2024 IEEE.

关键词： Network intrusion

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Deep learning Methods of Dynamic Sign Language Translation: A Rapid Umbrella Review 4

Deep Learning Methods of Dynamic Sign Language Translation: ...

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4th IEEE international conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Bopape, Lebogang Puree Tsoeu, Mohohlo Samuel Durban University of Technology Department of Electronic and Computer Engineering South Africa Durban University of Technology Department of Electronic and Computer Engineering Durban South Africa

ISBN: (纸本)9798350395914

Countries globally are recognizing sign languages, which are used by more than 5% of national and global populations as official languages. In Africa, South Africa was the fourth country to officialize sign language in 2023. this trend combined with advances in computer vision and machine learning has resulted in an increase of scholarly literature on the translation between spoken and signed languages to bridge the language and communication divide. this has been accompanied by a high volume of published literature reviews on the subject, most of them distinguished by time and geographical contexts. Despite numerous published review papers on the subject, to date, no formal or umbrella reviews of these reviews have been performed to consolidate the similarities, contrasts, gaps, and opportunities reported in these reviews. In this paper, we conduct a first-of-its-kind, rapid systematic umbrella review of 11 literature reviews about machine translation and recognition of dynamic sign languages using machine learning. three lenses namely, the evolution of machine learning and computer vision technologies and resulting applications to sign language translation;geographical contexts and associated applications;and the unique linguistic context of sign languages are used. We found that most reported literature has solved the sign language translation and recognition problem from a purely data-driven perspective, without due attention to specific linguistic complexities of the languages. Consequently, we found that most literature has not paid attention to proper corpora design, model design, and meaningful performance evaluation besides the generic and inherited measures of performance from spoken language processing and computer vision literature. © 2024 IEEE.

关键词： Adversarial machine learning

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4th international conference on Smart Systems: Innovations in Computing, SSIC 2023

4th International Conference on Smart Systems: Innovations i...

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4th international conference on Smart Systems: Innovations in Computing, SSIC 2023

ISBN: (纸本)9789819736898

the proceedings contain 60 papers. the special focus in this conference is on Smart Systems: Innovations in Computing. the topics include: An Algorithmic Framework for Clustering Cab Pickup Geo-points for Cab Recommender System (CRS);simulation and Analysis of Routing Protocols Using Real-Time data in Vehicular Ad Hoc Networks;research Trends and Key themes in the Intersection of Renewable Energy and Smart Homes;smart Attendance System Using Face recognition;data Augmentation to Improve Fake Account Detection Using ANN;end-to-End Portfolio Optimization Using Transformer Encoder Model for Long Positions Strategy;phonetic Redundancy Avoidance Technique;towards Sustainable Power: A Quantitative Analysis of Environmental Factors and Energy Demand in Different Indian States;customer Segmentation Using K-means Clustering;web Miner: Automated Web Crawling and database System with Puppeteer and ***;machine learning-Based Cardiovascular Disease Prediction;Efficient Identification of Waste in Water Bodies with YOLO V7-Object Detection;bumbleBee: the Multi-purpose Voice Assistant;efficientNet Deep learning Model for Computer-Aided Polyps Classification from Colonoscopy Images;improved Gastric Cancer Diagnosis with machine learning Technique: Addressing Imbalanced data Distribution;an Effective and Secure Privacy Diagnosis Made Online System for the E-Healthcare Ecosystem Based on Federated learning;efficientViT: An Efficient Vision Transformer for Fire and Smoke Image Classification;Tomato Disease Classification Using CNN;artificial Intelligence Application for Electronic Governance to Achieve Sustainability: A Case of Selected Segments of Indian Economy;analyzing Sentiments of Global Workforce Towards Hybrid Work Culture in the New Normal;deep learning Techniques for Real-Time Object Detection and Classification;Task Scheduling Cost Evaluation over Cloud with Using GA, ACO, PSO.

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Predicting Indian Master's and PhD Enrollment using data mining & machine learning Approaches 4

Predicting Indian Master's and PhD Enrollment using Data Min...

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4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Moharana, Bhimasen Singh, Vinay Kumar Sarkar, Tiyas Hassan, Abass Pandey, Vikas Kumar School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350387490

Every year, after acquiring their undergraduate degrees, a large number of Indian students plan to continue their education overseas. Selecting a university to pursue further education may be a difficult undertaking for students. Students who have ordinary or lower academic credentials-that is, those with undergraduate assessments, English proficiency test results, work experience, and research experiences-find it difficult to select colleges that fit their needs. this research examines actual, distinct data from Indian students who were admitted to various universities throughout the world to pursue postgraduate education. Lastly, using RF (random forest) and DT (decision tree) methodologies, we have created prediction models that, depending on students' prior academic achievement, may identify suitable colleges for them in certain classes. this study examines two distinct models: one being Master [M S] scholars & additional being PhD (Doctoral degree) students. Eight classes for PhD candidates and nine classes for MS pupils make up the educational institutions where the students were admitted, accordance through latest Quacquarelli Symonds (Q S) World Rankings of University's. the two distinct machine learning methods have been examined regarding parameter (Accuracy, Precision, Recall, & Fl-Score). Based on numerical results, the algorithms DT & RF have a similar precision as 89% for data from PhD students and 86% for data from MS students. © 2024 IEEE.

关键词： Random forests

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4th international conference on Advances in Computational Science and Engineering, ICACSE 2023

4th International Conference on Advances in Computational Sc...

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4th international conference on Advances in Computational Science and Engineering, ICACSE 2023

ISBN: (纸本)9789819729760

the proceedings contain 52 papers. the special focus in this conference is on Advances in Computational Science and Engineering. the topics include: Sensitivity Analysis on Absorber Column as Hydrogen Gas Purification Unit Using Aspen Plus;use that Pinky on the ‘A’ Key: A Finger-Key Identification Module for a Touch Typing Trainer;Accelerating Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Using Vincenty’s Inverse Method on CUDA;procedural Modeling for Sustainable Urban Development and Planning: A Blender Plugin for 3D Modeling of Philippine Cities;estimating Orangutans Population Size in Sabah Rainforest in Malaysia Using Spatial Modelling;multilayer Gaussian Feature Extraction Algorithm for Sky Image Classification;face Expression recognition: A Survey on Hyperparameter Optimization;Enhancing Low Light Image Classification Using MADPIP Approach;a Robust Multiple Adaptive Derivative Face recognition System on Pose and Illumination;performance Comparison of Convolutional Neural Network Deep learning Architectures for Remote Sensing Image Segmentation;revolutionizing Human–Computer Interaction: Unraveling the Power of Deep learning Convolutional Neural Networks in Face recognition;Multiple Adaptive Derivative Passive Image Processing Approach to Unsharp Masking Restoration for CNN on Low Light Images;data-Driven Insights for Strengthening Information Security Awareness in Higher Education Institutions;Determining Motivational Factors for Retention and Course Completion Among Filipino MOOC Learners: A thematic Analysis;development of Kansei-Based Visualization pattern for E-learning Website;augmented Reality in Education: Transformative Innovations and Immersive learning Experiences;classification of Game Mechanics: A Brief Review;challenges and Issues in Team Gamer Loyalty for Massively Multiplayer Online Game;optimizing Cloud-Based Educational Services for Enhanced learning in Higher Education Institutions;biomarker Identification for Lung Canc

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Explanation-based Graph Neural Networks for Graph Classification 26

Explanation-based Graph Neural Networks for Graph Classifica...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Seo, Sangwoo Jung, Seungjun Kim, Changick Korea Adv Inst Sci & Technol KAIST Sch Elect Engn 291 Daehak Ro Daejeon 34141 South Korea

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Graph Neural Network models can be used to quickly analyze interactions between multiple data expressed in a graph structure, with high accuracy. Previous studies accurately extract subgraphs which have a significant influence on the whole graph, providing accurate explanations for predictions of GNN. We noted that explanation components could help improve classification performance as unique representations of each class. therefore, we suggest the GNN performance can be further improved by using explanation components. In this paper, we propose an Explanation-Based Graph Neural Networks (EBGNN) that utilizes contrastive learning at the instance level, by applying explanation components. In EBGNN, the explanation components ensure similarity for instances within the same class, and promote separability for instances in different classes. Finally, we conducted an evaluation on five benchmark datasets (MUTAG, IMDB-BINARY, PROTEINS, NCI1, and DD). Our experiment showed a significant increase in graph classification performance compared to state-of-the-art methods.

关键词： Proteins Computer vision Analytical models machine learning Benchmark testing Graph neural networks data models

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Fine-Tuning Pre-trained Model with Prototype learning for OOD Detection 4th

Fine-Tuning Pre-trained Model with Prototype Learning for ...

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Liu, Jia-Yi Deng, Zhi-Dong Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9789819787012

Out-of-distribution (OOD) detection primarily learns the in-distribution (ID) representation to distinguish OOD samples from ID classes. Recent methods often fine-tune a pre-trained model with classification objective (cross-entropy, CE) to learn ID representation, disregarding the discrimination between ID and unknown OOD. In this work, we propose to fine-tune pre-trained models in the framework of open-set recognition, which trains a multi-class classifier with ID samples while considering OOD rejection according to distances to ID classes. We adopt prototype learning, which aims to discriminate the ID classes while guiding compact distributions for ID classes in the feature space. the compact ID distributions favor classification and OOD detection using distances, including Euclidean distance and Mahalanobis distance. We evaluated the method on various backbone architectures including ResNet, ViTs and BEiT, and the results show that the prototype learning method on pre-trained models outperforms classification-based method and produces state-of-the-art performance in OOD detection. © the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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