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Feature Extraction and Dimensionality Reduction to Evaluate Decision Tree Ensembles for Diabetic Retinopathy Detection 4th

Feature Extraction and Dimensionality Reduction to Evaluate ...

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

作者： Lahmar, Chaymaa Idri, Ali Mohammed V Univ Rabat SPM ENSIAS Rabat Morocco Mohammed VI Polytech Univ Fac Med Sci Ben Guerir Morocco

ISBN: (纸本)9783031821554;9783031821561

Diabetic Retinopathy (DR) is an eye disease associated with chronic diabetes. It remains the primary cause of visual impairment and blindness among the global working-age population. Early detection of DR is crucial for ensuring timely diagnosis and effective treatment. this paper proposes a new homogeneous ensemble-based approach constructed using a set of hybrid architectures as base learners and two combination rules (weighted and hard voting) for referable DR detection, using fundus images from the Messidor-2, Kaggle DR, and APTOS datasets. the hybrid architectures are created using deep feature extraction techniques, dimensionality reduction techniques to reduce the size of the extracted features, and a decision tree algorithm (DT) for classification. the results showed the potential of the proposed new approach which achieved high accuracy values over the three datasets: 90.65%, 93.01%, and 83.32% using the APTOS, Kaggle DR, and Messidor-2 datasets respectively. therefore, we recommend using the proposed approach since it is impactful for referable DR classification, and it represents a promising tool to assist ophthalmologists in diagnosing DR.

关键词： Computer-Aided Diagnosis Diabetic Retinopathy machine learning Deep learning Ensemble learning Image Processing

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A data-Driven machine learning Approach for Human Gender Classification Using Explainable AI 4

A Data-Driven Machine Learning Approach for Human Gender Cla...

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4th international conference on Computer, Communication, Control and Information Technology, C3IT 2024

作者： Dipto, Shakib Mahmud Habib, Adria Binte Mim, Nadia Tasnim Eunus, Salman Ibne Orchi, Nabiha Tasnim University of Liberal Arts Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Brac University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350389128

Since gender recognition contains a wealth of information about the differences between male and female characteristics, it is crucial and essential for many applications in commercial domains, such as human-computer interaction applications and computer-aided physiological or psychological analysis. there are lots of scientific studies that contain different approaches to classify gender. Apart from those, we have included Explainable Artificial Intelligence to interpret the usability of the model. In this paper, we study a gender classification dataset to predict gender from tabular data of specific human body features such as hair length, forehead length, height, nose width, and length. We have used several machine learning (ML) algorithms such as - Gaussian Naive Bayes (GNB), Support Vector machine (SVM), Decision Tree (DT), and Stochastic Gradient Descent (SGD) to predict gender. After that, we performed a comparison study and implemented Lime on the best performing ML model to interpret the prediction of the model. the Gaussian Naive Bayes architecture with the dataset shows a reasonable accuracy of 98%, proving the usability of the model. © 2024 IEEE.

关键词： Contrastive learning

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the New Answer to Drug Discovery: Quantum machine learning in Preclinical Drug Development 4

The New Answer to Drug Discovery: Quantum Machine Learning i...

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4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Wong, Yew Kee Zhou, Yifan Liang, Yan Shing Qiu, Haichuan Wu, Yu Xi He, Bin Basis International School Guangzhou Guangzhou China

ISBN: (纸本)9798350324303

the Research & Development (R&D) phase of drug development Drug discovery and development (D&D) is a complex and costly endeavor, typically requiring six to nine years [1] and four hundred to fourteen hundred million USD [2] for the research and development (R&D) phase alone. To transform this process, we propose a novel concept that combines Quantum-based machine learning network (QML) and Quantum Computing Simulation (QS) to expedite the R&D phase to three to six months and reduce the cost to merely fifty to eighty thousand USD. Our approach takes as inputs the target protein/gene structure and the primary assay [3]. For Hit Generation [3], the QML network generates potential hits [4] based on the molecular structure of the target protein, while the QS filters molecules from the primary assay according to their reaction and binding efficacy with the target protein. then, for Lead Optimization [3], the resulting molecules from QML and QS are compared, and the ones that are common to both processes are subjected to dozens of molecular variations, while others are only modified slightly. Finally, all optimized molecules undergo multiple rounds of QS filtering with high standards for reaction efficacy and safety, producing a few dozen pre-clinical-trial-ready drugs. Our concept of QML and QS integration can also be applied to other fields, such as agricultural research, genetic editing, and aerospace engineering. Keywords: Quantum Computer, Hit to Lead, Lead Optimization Simulation, machine learning. © 2023 IEEE.

关键词： Molecules

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Drugs Recommender System Based on Side Effects and Opinions Analysis 4th

Drugs Recommender System Based on Side Effects and Opinions ...

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

作者： Ziani, Amel Adouane, Mohamed Amine Delladj, Mohamed Ayoub Brahimi, Fatima Zahra Merouani, Hayet Farida Chadli Benjedid Univ Dept Comp Sci El Tarf Algeria Badji Mokhtar Univ LRI Lab Annaba Algeria Benyoucef Benkhedda Univ Dept Comp Sci Algiers Algeria Badji Mokhtar Univ Dept Comp Sci Annaba Algeria

ISBN: (纸本)9783031821493;9783031821509

Currently, a large amount of clinical data spread across various websites makes it difficult for users to find useful information to improve their wellbeing. Furthermore, the overload of medical information has caused many difficulties for healthcare specialists to make decisions in favor of patients. the choice of the most suitable drug is important for patients and requires specialists who have a lot of information about all drugs as well as medical data of patients. the importance of drug choice is not just in the effectiveness of the latter in the treatment, but there is another factor, which is the side effects presented by the drug. A side effect can range from mild disturbances to life-threatening problems. therefore, this project's objective is to create a drug recommendation system that addresses the challenges of scattered clinical data and medical information overload. It aims to recommend effective drugs with minimal side effects, collecting patients' data to overcome data scarcity. the system targets cases where patients experience side effects, offering alternative drug options. Its significance lays in providing personalized drug recommendations for patients and building a comprehensive patient profile database. Ultimately, this system can enhance healthcare decision-making and improve patient well-being.

关键词： Health recommendation system machine learning opinion analysis datasets (*** and SIDER) construction of a profile-based dataset

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Real-Time Water Quality Monitoring via Impedance Spectroscopy and machine learning 12

Real-Time Water Quality Monitoring via Impedance Spectroscop...

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12th international conference on Agro-Geoinformatics (Agro-Geoinformatics)

作者： Aybar, Oguzhan Kara, Zeynep Yucel, Meric Ustundag, Burak Berk Istanbul Tech Univ Comp & Informat Dept Istanbul Turkiye Istanbul Tech Univ Natl Software Certificat Res Ctr Istanbul Turkiye

ISBN: (纸本)9798350380613;9798350380606

Water quality is crucial for plant growth, with factors like salinity levels significantly impacting crop health. Variations in water quality, especially from wells, necessitate regular monitoring. Existing methods are often costly, timeconsuming, or unsuitable for continuous monitoring. this study utilizes impedance spectroscopy for real-time water quality monitoring in irrigation systems and machine learning methods for prediction. the proposed method captures spectral features and employs a compact machine-learning model for efficient and accurate pattern recognition, outperforming traditional electric conductivity measurements. Experiments measured various spectral features from water with different concentrations of NaCl, MgSO4, and their mixtures, across 1kHz to 1MHz using an Analog Discovery 2 device. data from these experiments were used to estimate solute concentrations. machine learning methods, including Random Forest and Multilayer Perceptron, were employed to predict NaCl and MgSO4 concentrations in mixed samples. Results demonstrate significant improvements over existing methodologies, supporting continuous monitoring. With 55.4 ppm MAE for NaCl and 121.5 ppm MAE for MgSO4, the prediction results are promising for real-time water quality monitoring. Implementing farmer-specific devices could enhance agricultural automation by setting thresholds, warnings, and enabling automatic

关键词： Water quality IoT neural networks Impedance spectroscopy machine learning irrigation monitoring agricultural monitoring

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Enhancing Tomato Crop Health: Leveraging Modified InceptionResNetV2 for Disease Detection 4th

Enhancing Tomato Crop Health: Leveraging Modified InceptionR...

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

作者： Zubair, Fida Saleh, Moutaz Qatar Univ Comp Sci & Engn Dept Doha Qatar

ISBN: (纸本)9783031821523;9783031821530

the proposed research aims to address the critical need for robust disease detection in tomato crops in the Middle East, given the essential role of tomatoes in the region's food security. Recognizing the significance of plant disease detection becomes important for securing a stable supply of this essential crop. this research aims to employ transfer learning and A modified InceptionResNetV2 architecture for the purpose of disease detection. the study evaluates the efficiency of this model with two datasets: Plant Village and Noulam. the Proposed Model Surpasses Prior Works: Achieving 99.8% and 99.7% Accuracies on Respective datasets for Tomato Disease Detection. the primary objective of this research is to enhance the early detection of tomato diseases, contributing to the efficiency and sustainability of tomato cultivation.

关键词： Transfer learning InceptionResNetV2 tomato data augmentation Softmax Adam Optimizer

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Self-Supervised learning for Point Clouds through Multi-crop Mutual Prediction 4

Self-Supervised Learning for Point Clouds through Multi-crop...

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4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Zeng, Changyu Wang, Wei Xiao, Jimin Nguyen, Anh Yue, Yutao Institute of Deep Perception Technology JITRI Wuxi China Xi'An Jiaotong-Liverpool University School of Advanced Technology Suzhou China University of Liverpool Department of Computer Science Liverpool United Kingdom

ISBN: (纸本)9798350324303

As the demand for high-level autonomous driving continues to grow, efficient processing of large amount of point cloud data from LiDAR sensors has become a severe issue. Despite its great success, it is extremely difficult, if not impossible, to apply supervised learning in many real-world applications with natural scenes lacking annotations. In addition, removing label dependency and improving algorithm generalization ability are the current key research challenges in 3D deep learning. We propose a crop-based self-supervised learning framework for point clouds called Multi-Crop Mutual Prediction (MCMP). By cropping the original point clouds into different number of multiscale patches and making patches from the same object as close as possible through dynamic updates of online prototypes, the encoder is well able to maintain consistency between both local and global features. MCMP framework is model-agnostic so it is convenient to integrate it into encoders of any architectures. We pre-train three commonly used point cloud feature extraction networks on a single object-level dataset MondelNet40 and evaluate MCMP on three downstream tasks, namely, object classification, partial segmentation and semantic segmentation. Experimental results show that MCMP outperforms the previous crop-based self-supervised approaches in the object classification and semantic segmentation tasks and produces comparable results with the supervised benchmark methods. © 2023 IEEE.

关键词： Semantic Segmentation

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the Implementation of Non-negative Matrix Decomorization and Its Optimization Algorithm in Face recognition 4

The Implementation of Non-negative Matrix Decomorization and...

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4th international conference on Computer Vision and pattern Analysis, ICCPA 2024

作者： Wang, Youzuo Wuhan Guanggu Vocational College Wuhan China

ISBN: (纸本)9781510682528

With the popularization of mobile devices and the explosion of data in this era, more and more images, voice, text and other information need to use machine learning to improve the processing efficiency. In order to reduce the dimension of original processing, improve the learning rate and reduce the storage cost, researchers have studied a large number of data dimension reduction algorithms, which have made great achievements in the past hundred years, including principal component analysis, linear discriminant analysis, independent component analysis and so on. In the matrix decomposition of images, speech and text, the negative value obtained by decomposition often has no practical significance. therefore, Lee et al. proposed non-negative matrix decomposition in 1999, adding non-negative constraints in the decomposition process to decompose the base matrix and coefficient matrix. Generally, low-dimensional base matrix is replaced by the original data matrix to learn classification. Since non-negative matrix decomposition does not produce enough sparsity in the actual operation process and its nature is unsupervised learning cannot, some researchers propose non-negative matrix decomposition for adding sparsity and matrix decomposition for adding discriminative information, which can reduce the spatial storage and learning time more effectively. Due to the slow convergence rate, some researchers propose local decomposition and two-dimensional non-negative matrix decomposition, which effectively improve the convergence rate. In order to reduce the influence of each type on the same face portrait, the product of each matrix in the same angle can be used as the type limit item to the target function. With two constraint terms, more multi-angle data. In order to obtain face data in different dimensions, this paper also combines the multi-dimensional technology with Multi-view NMF technology, designs a multi-dimensional multi-view face recognition method, and provides a

关键词： Unsupervised learning

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Adaboost Optimized by Sinh Cosh Algorithm for Prediction of Software Defects 4th

Adaboost Optimized by Sinh Cosh Algorithm for Prediction of ...

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

作者： Villoth, Saramma John Villoth, John Philipose Zivkovic, Tamara Zivkovic, Miodrag Jovanovic, Luka Bacanin, Nebojsa Mani, Joseph Univ Technol & Appl Sci Nizwa Oman Singidunum Univ Belgrade Serbia Univ Belgrade Sch Elect Engn Belgrade Serbia Modern Coll Business & Sci Muscat Oman

ISBN: (纸本)9783031821523;9783031821530

Defect detection in software development represents a vital activity that relies on efficient and precise techniques to assure the quality of the software. this manuscript tries to explore the possibilities of the Adaboost model optimized by a modified sinh cosh metaheuristics algorithm for accurate and efficient detection of defects. As modern development projects are dynamic and with tight deadlines, the capability of Adaboost classifier to adopt intricate patterns and metrics in the software code may help in identifying problematic modules, thus allowing focused testing in limited available time. As software development industry recently recognized the significance of the proactive testing, this manuscript suggests an approach where machine learning model was effectively integrated in a framework that helps in identifying error-prone modules. the suggested optimized Adaboost classifier has shown very promising performance with respect to the precision and accuracy of identifying faulty modules based on the software metrics, making it a potentially crucial tool that could be applied in the modern software development practice.

关键词： Software testing defects identification Adaboost machine learning optimization methods swarm intelligence sinh cosh algorithm

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A MobileNetV2-CBAM-based Model for Forest Fire Classification Using UAV Imagery 4

A MobileNetV2-CBAM-based Model for Forest Fire Classificatio...

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4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Deng, Xinjie Khan, Burhan Lim, Chee Peng Liao, Ming-Yu Institute for Intelligent Systems Research and Innovation Deakin University Chongqing Jianzhu College Chongqing China Deakin University Institute for Intelligent Systems Research and Innovation Waurn PondsVIC Australia National Kaohsiung University of Science and Technology Department of International Business Taiwan

ISBN: (纸本)9798350324303

Forest fires are one of the worldwide severe natural disasters causing human and environmental losses. Early and accurate fire detection is critical to minimize damages caused by forest fires and reduce human risks in firefighting efforts. Recently, Unmanned Aerial Vehicles (UAVs) mounted with cameras and edge computing devices with Artificial Intelligence (AI)-based algorithms have been employed to tackle forest fire detection problems in real time. Considering that edge computing is limited in resources and power consumption, the developed AI algorithms need to be lightweight. this paper proposes lightweight deep learning (DL) algorithms based on MobileNetV2 and attention mechanism to detect forest fires using UAV imagery. Based on several experiments, the proposed model yields an accuracy rate of 86.14% and an F1-score of 0.8719, outperforming other related methods in forest fire detection and classification. © 2023 IEEE.

关键词： Antennas

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