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Using Pyramid Vision Transformer for Assisting Glaucoma Diagnosis with Small Datasets 21

Using Pyramid Vision Transformer for Assisting Glaucoma Diag...

引用

21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Wu, Qinhao Yao, Shuaiyu Ouyang, Wendi Huang, Jingzhe Kang, Qi Eindhoven University of Technology Dept. of Mathematics and Computer Science Eindhoven Netherlands Coll. of Electronic and Information Engineering Tongji University Shanghai China Coll. of Economics and Management Nanjing University of Aeronautics and Astronautics Nanjing China King's College London Dept. of Mathematics London United Kingdom

ISBN: (纸本)9798350365221

Glaucoma is one of the leading causes of blindness worldwide. Patients with open-angle glaucoma have difficulty realizing the graduate vision loss. However, the optic nerve head's morphological change can manifest in the retinal image. Therefore, many studies offer computer-aided glaucoma diagnosis based on retinal image analysis. With the trend of deep learning, there have been several attempts to utilise deep-learning-based glaucoma classification in practice. However, the limited size of the dataset is a problem when adapting deep-learning models to glaucoma classification. Therefore, we test three vision transformer models to handle this problem and demonstrate their performance in glaucoma classification. Also, based on their performance, we give suggestions for their usage in clinical practice. In this paper, we fine-tuned three transformer models for glaucoma classification using two datasets, RIM-ONE-DL and Drishti-GS. Based on our experiments, the pyramid vision transformer (PVT) model achieves the best performance, with good scores in terms of accuracy and sensitivity. This suggests that the PVT can catch the missing detail and can be used to support the glaucoma diagnosis. © 2024 IEEE.

关键词： Ophthalmology

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Pear Classification using Machine Learning 10

Pear Classification using Machine Learning

引用

10th IEEE Conference on Systems, Process and Control, ICSPC 2022

作者： Pratondo, Agus Novianty, Astri Telkom University School of Applied Sciences Dept. of Multimedia Engineering Bandung Indonesia Telkom University School of Electrical Engineering Dept. of Computer Engineering Bandung Indonesia

ISBN: (纸本)9781665470988

Pears are a popular fruit with nutrients and fibre that are good for the human body. Several types of pears on the market have shapes, colours, and sizes that distinguish one another. However, some types are quite challenging to determine, for example, pear of Century, Sweat, and Xiangli. This study aims to create a classifier using machine learning to identify pear images. Several traditional machine learning algorithms are employed: k-nearest neighbour, support vector machine, decision tree, and random forest. Subsequently, the best result is compared to the results produced by deep learning architecture, specifically, the Inception-v3. Experimental results show that the classifier built using the Inception-v3 generates the best performance by obtaining accuracy scores of 94.00%. This result indicates that the model is quite promising for classifying the three pears in practical use. © 2022 IEEE.

关键词： Decision trees

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Implementing a Robust and Scalable Detect-and-Avoid Algorithm for UAV Navigation in ROS 15

Implementing a Robust and Scalable Detect-and-Avoid Algorith...

引用

15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Meimetis, Dimitrios Daramouskas, Ioannis Lappas, Vaios Perikos, Isidoros Kapoulas, Vaggelis Paraskevas, Michael Nkua Department of Aerospace Science & Technology Athens Greece Computer Technology Institute and Press 'Diophantus' Patras Greece UoP Applied Mechanics Lab Patras Greece UoP Computer Engineering and Informatics Department Patras Greece Computer Technology Institute and Press 'Diophantus' University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350368833

Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions that drones must execute efficiently is navigation in real-world environments. This paper presents a decentralized approach for enabling unmanned aerial vehicles (UAVs) to navigate safely in unknown environments and avoid obstacles. Leveraging the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, implemented in the Robot Operating System (ROS), our method facilitates conflict detection and resolution in 2D environments. Through simulations using ROS, Gazebo, and Iris drones, we validate the effectiveness of our approach in scenarios with initial trajectory conflicts. Our work addresses the pressing need for UAVs to autonomously plan and execute safe flights, laying the groundwork for enhanced UAV capabilities in various real-world applications. The simulation results demonstrate the efficiency and robustness of our approach. © 2024 IEEE.

关键词： Drones

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Investigation of Credit Card Fraud detection under the lens of Comparative Machine Learning models

Investigation of Credit Card Fraud detection under the lens ...

引用

2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2022

作者： Tahsin, Mohammad Sadman Firoz, Neda Chowdhury, Shah Abul Hasnat Wasei, S.M.A. Karim, Musaddiq Al Ara, Mst Yeasmin Brac University Department of Computer Science and Engineering Dhaka Bangladesh Tomsk State University Institute of Applied Mathematics and Computer Science Tomsk Russia American International University-Bangladesh Dept. of Electrical and Electronics Engineering Dhaka Bangladesh American International University-Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (数字)9781665451932

ISBN: (纸本)9781665451932

Fraudulent use of credit cards is common and may result in significant financial losses. Using Trojans or Phishing, criminals may get the credit card numbers of innocent victims for their criminal acts. As a result, it is vital to have fraud detection technology that can catch a thief in the act of purchasing using a stolen credit card. One solution is to utilize machine learning algorithms to construct normal/fraud behavior characteristics from all historical transaction data, including legitimate and fraudulent ones, and then use these features to identify whether a transaction is fraudulent or not. In this paper, SVC, Multinomial Naïve Bayes, K-Neighbors Classifier, Logistic Regression, Random Forest, Bernoulli Naïve Bayes, and SVM algorithms were used for credit card fraud detection. The detailed results of all seven algorithms were studied. Then a comparison between these seven algorithms with their precise accuracy, ROC curve, train-test, validation scores, and the learning curve was discussed. Logistic Regression, Random Forest, Bernoulli Naïve Bayes, and SVM showed 100% accuracy in training dataset. While Random Forest algorithm outdid every other algorithm by scoring 100% for both train and test data, the result of the remaining algorithms for the train and test data were also satisfactorily ranging from 75.3% to 99.6%. Logistic Regression, Random Forest, Bernoulli Naïve Bayes, and SVM all 4 scored 100% in both train and test data, and Random Forest scored 100% leaving behind all the algorithms. Logistic Regression, Bernoulli Naïve Bayes and SVM scored 99.6% in test. The best-performing algorithm was selected according to its performance and discussed its AUC score and confusion matrix. © 2022 IEEE.

关键词： Crime

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A Sequential Pattern Mining Approach for Situation-Aware Human Activity Projection

A Sequential Pattern Mining Approach for Situation-Aware Hum...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Giuseppe D'Aniello Roberto Falcone Matteo Gaeta Zia Ur Rehman Giancarlo Fortino Dept. Information and Electrical Engineering and Applied Mathematics University of Salerno Fisciano SA Italy Dept. Computer Engineering Modeling Electronics and Systems University of Calabria Rende Cs Italy

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Human activity prediction has become increasingly prevalent in a plethora of time-critical applications. To realize accurate identification and prediction of human behaviour, we propose a situation-aware wearable computing system. A wearable computing system has the capability to perceive, comprehend and project situations by analyzing the human behavioral patterns in different environments. In particular, this work proposes a situation-aware human activity prediction (SA-HAP) approach based on sequential pattern mining that aims to anticipate future activities and tailor its responses according to situations by analyzing frequent sequential patterns and their correlations to understand how these situations are interrelated. The approach not only improves prediction accuracy but also provide the foundation for a more informed decision-making process, as the projected situations can be explained using the identified behavioral patterns. The approach is compared with other traditional techniques for activity prediction (LSTM and HMM), achieving better performance on the Extrasensory dataset.

关键词： Accuracy Correlation Decision making Hidden Markov models Medical services Predictive models Time factors Wearable devices Long short term memory Cybernetics

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Classification of Aglaonema Using Machine Learning 14

Classification of Aglaonema Using Machine Learning

引用

14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

作者： Pratondo, Agus Novianty, Astri School of Applied Sciences Telkom University Dept. of Multimedia Engineering Bandung Indonesia School of Electrical Engineering Telkom University Dept. of Computer Engineering Bandung Indonesia

ISBN: (纸本)9781665487719

Recently, Aglaonema has become an ornamental plant with high economic value. Various Aglaonema variants are available in the market with various prices offered, e.g., Lipstick, Suksom, Red cochin, Widuri, and Pride of Sumatra (pos). People often need help distinguishing the variants to prevent being deceived when buying the plants. A classifier to distinguish several local Aglaonema variants with high similarities is proposed to solve this problem. A collection of images from each Aglaonema variant are used to train the classifier. Both traditional classification algorithms, such as the k-nearest neighbours and support vector machines, and modern algorithms, specifically the Inception-v3, were employed. Classifier evaluation is performed by applying 5-folds cross-validation. Experimental results show that the best level of accuracy is obtained from the Inception-v3 algorithm by achieving an accuracy rate of 83.02%. This result indicates that the classification of Aglaonema is relatively hard, but feasible to continue for improvements. © 2022 IEEE.

关键词： Support vector machines

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Classification of Longan Edibility using Machine Learning 1

Classification of Longan Edibility using Machine Learning

引用

1st International Conference on Smart Technology, applied Informatics, and engineering, APICS 2022

作者： Pratondo, Agus Novianty, Astri Telkom University Dept. of Multimedia Engineering School of Applied Sciences Bandung Indonesia Telkom University Dept. of Computer Engineering School of Electrical Engineering Bandung Indonesia

ISBN: (数字)9781665489850

ISBN: (纸本)9781665489850

Longan fruit (Dimocarpus longan) originating from Southeast Asia, is a fruit that is commonly consumed in the region. The fruit is susceptible to damage during the distribution process. The selection of fruit that is suitable for consumption and that which is not is still made manually. This study aims to build a model using machine learning that can automatically classify longan edibility. Two classification algorithms are used, namely the k-nearest neighbor and the support vector machine. The model is trained with a number of images consisting of two classes, namely, fit (accepted) and unfit (rejected) to eat. The experimental results show that the algorithm's accuracy for k- nearest neighbors and the support vector machine are 93% and 98%, respectively. These results indicate that the classification of longan using machine learning is quite promising. © 2022 IEEE.

关键词： Support vector machines

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Joint Sensing and Communication with Graphene FETs Targeting Terahertz Band

Joint Sensing and Communication with Graphene FETs Targeting...

引用

International Symposium on Communication Systems, Networks and Digital Signal Processing, CNSDSP

作者： Monica La Mura Patrizia Lamberti Alessandro Stuart Savoia Roberto Alesii Dajana Cassioli Vincenzo Tucci Dept. of Industrial Electronic and Mechanical Engineering Roma Tre University Rome Italy Dept. of Information and Electrical Engineering and Applied Mathematics University of Salerno Fisciano Italy Dept. of Information Engineering Computer Science and Mathematics University of L'Aquila L'Aquila Italy

ISBN: (数字)9798350348743

ISBN: (纸本)9798350348750

The upcoming 6G paradigm for wireless communication networks promises enhanced data rates, reduced latency, and pervasive connectivity. In this scenario, intelligent and adaptive communication protocols and innovative smart sensing devices can be efficiently integrated to offer innovative systems for different applications. In the future, graphene technology might greatly contribute to the evolving landscape of sensing combined with high-frequency communication. This paper explores possible architectures for radiofrequency (RF) electronics for joint sensing and modulation based on graphene field-effect transistors (GFETs). Current challenges and limitations of graphene technology are also discussed.

关键词： Radio frequency Wireless communication 6G mobile communication Technological innovation Protocols Graphene Sensor phenomena and characterization

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Comparison of Wood Classification using Machine Learning 10

Comparison of Wood Classification using Machine Learning

引用

10th IEEE Conference on Systems, Process and Control, ICSPC 2022

作者： Pratondo, Agus Novianty, Astri Telkom University School of Applied Sciences Dept. of Multimedia Engineering Bandung Indonesia Telkom University Dept. of Computer Engineering School of Electrical Engineering Bandung Indonesia

ISBN: (纸本)9781665470988

Wood makes an essential contribution to human life and is used for various needs such as building materials, furniture materials, restorative materials, and so on. The economic value of each wood often varies significantly. Unfortunately, some woods have relatively high similarities and are difficult to distinguish for beginners. In this research, several models were built to detect several types of wood that have a fairly high similarity, namely: jeungjing wood (Paraserianthes falcataria), puspa wood (Schima wallichii) and suren wood (Toona sureni). Several classification algorithms are used to build classifiers, namely: the k-nearest neighbours, support vector machine, decision tree, random forest, and Inception-v3. The experimental results show that the accuracy level for the k-nearest neighbours, support vector machine, decision tree, random forest, and Inception-v3 are 82.00%, 90.00%, 69.33%, 90.67%, and 90,33%, respectively. The model produced by the Inception-v3 gives quite promising results and is suitable for practical use. © 2022 IEEE.

关键词： Wood

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Enhancing Printed Circuit Board Defect Detection through Ensemble Learning 1

Enhancing Printed Circuit Board Defect Detection through Ens...

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1st IEEE International Workshop on Future Intelligent Technologies for Young Researchers, FITYR 2024

作者： Law, Ka Nam Canaan Yu, Mingshuo Zhang, Lianglei Zhang, Yiyi Xu, Peng Gao, Jerry Liu, Jun San José State University Department of Applied Data Science San José United States Virginia Tech Dept. of ISE Blacksburg United States San José State University Dept. of Computer Engineering San José United States

ISBN: (纸本)9798331539757

The quality control of printed circuit boards (PCBs) is paramount in advancing electronic device technology. While numerous machine learning methodologies have been utilized to augment defect detection efficiency and accuracy, previous studies have predominantly focused on optimizing individual models for specific defect types, often overlooking the potential synergies between different approaches. This paper introduces a comprehensive inspection framework leveraging an ensemble learning strategy to address this gap. Initially, we utilize four distinct PCB defect detection models utilizing state-of-The-Art methods: EfficientDet, MobileNet SSDv2, Faster RCNN, and YOLOv5. Each method is capable of identifying PCB defects independently. Subsequently, we integrate these models into an ensemble learning framework to enhance detection performance. A comparative analysis reveals that our ensemble learning framework significantly outperforms individual methods, achieving a 95% accuracy in detecting diverse PCB defects. These findings underscore the efficacy of our proposed ensemble learning framework in enhancing PCB quality control processes. © 2024 IEEE.

关键词： Contrastive Learning

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