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Design and Implementation of Minkowski Feature Selection for Machine Learning Techniques

Design and Implementation of Minkowski Feature Selection for...

引用

information technology, Applied Mathematics and Statistics (ICITAMS), International Conference on

作者： Ekram Hakim Dhiah Al-Shammary Ahmed M. Mahdi Dep. Computer Science College of computer science and information technology University of Al-Qadisiyah Diwaniyah Iraq

ISBN: (数字)9798350335118

ISBN: (纸本)9798350335125

This paper proposes a design and implementation of a highly efficient feature selection based on Minkowski’s mathematical similarity for machine learning. Moreover, the development of the Minkowski feature selection can be general for intelligent datasets and machine learning. Intelligent optimizers and feature selection represent an advanced stage in machine learning and innovative computer applications by reducing the overall number of features to be included in the classification function process. This paper analyses Minkowski’s selection of best factors for EEG Bonn University data. Minkowski cancels the random movement and systematically limits the number of features and its point-to-point movement in order to promote exploration and reduce the probability of falling into stagnation. The main measures to analyze this research are Accuracy, precision, Recall, and F1score assessment. Moreover, the accuracy of Minkowski’s selection provided better classification results than some of the other approaches (100%)

关键词： Machine learning algorithms Limiting Machine learning computer applications Feature extraction Mathematical models Electroencephalography

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Clinical Informatics System to Understand Lung Cancer Using Transfer Learning-Based computer-Aided Application 7

Clinical Informatics System to Understand Lung Cancer Using ...

引用

7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Abugabah, Ahed Mehmood, Atif Shahid, Farah College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates Zhejiang Normal University Dep. of Computer Science and Technology Jinhua321002 China

ISBN: (纸本)9798350342154

Lung cancer is the second leading cause of death worldwide after cardiovascular disease. Due to late diagnosis, lung cancer patients have an extremely low survival rate when compared to other cancer patients. Hence, early lung cancer detection is critical for patients to undergo early therapies, boosting their chances of surviving or potentially becoming cancer-free. Over the past few years, researchers have developed several deep learning-based computer-aided diagnostic tools to detect lung cancer early. As a consequence, deep learning models are readily susceptible to over-fitting for fewer data samples and can be computationally time-consuming to pick hyper-parameters, which usually leads to a reduction in performance. Our proposed technique came up with a solution to this challenge of lung cancer classification tasks that used a layer-wise transfer learning strategy. In this work, three types of data samples such as Lung adenocarcinoma (Lung a), lung squamous cell carcinoma (Lung scc), and lung benign (Lung b) have been taken and implemented models, namely Group-I, II, III with frozen layers and baseline algorithms as VGG-19. To check the validity of the proposed models on lung samples and attain the best classification accuracy on Lung scc vs. Lung b 97.53%, also discriminate between Lung a vs. Lung b testing accuracy 92.59%. In contrast, the remaining Lung scc vs. Lung a attained 89.30%. Finally, a comparison to other studies demonstrating the suggested model superiority over state-of-the-art models in testing accuracy © 2023 IEEE.

关键词： Diseases

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An Enhanced Subject-Indep.ndent Approach for Hand-Based Micro Activities Recognition

An Enhanced Subject-Independent Approach for Hand-Based Micr...

引用

Omni-layer Intelligent Systems (COINS), International Conference on

作者： Florenc Demrozi Fadi Al Machot Dep. of Electrical Engineering and Computer Science University of Stavanger Norway Dep. of Data Science Norwegian University of Life Sciences Norway

ISBN: (数字)9798350349597

ISBN: (纸本)9798350349603

Human activity recognition (HAR) is crucial in various fields, including healthcare, assistive technologies, and human-computer interaction. Recognizing hand-based micro activities presents a unique challenge due to their complexity and variability. In this paper, we propose a novel HAR methodology to identify 24 distinct hand-based Activities of Daily Living (ADLs) using data from a wrist-weared inertial sensor. Our approach, tested on a dataset of 30 participants, employs a unique two- level segmentation and lightweight machine-learning model to handle the complex dynamics of micro-ADLs. The experimental outcomes, showcasing an overall accuracy of 84% and precision, recall, and F1-Scores above 83% underscore the method's efficacy and subject-indep.ndent reliability compared to the existing literature on micro-ADL recognition.

关键词： Human computer interaction Inertial sensors Tiny machine learning Medical services Assistive technologies Complexity theory Human activity recognition

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Comparative Analysis of ML Algorithms for Fraud Detection

Comparative Analysis of ML Algorithms for Fraud Detection

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Smart information Systems and Technologies (SIST), IEEE International Conference on

作者： Gulnara Abitova Miras Abalkanov Dep. of Eletrical Engineering and Computer Science Dep. of Computer Engineering State University of New York Astana IT University Binghamton NY USA Dep. of Eletrical Engineering and Computer Science Dep. of Computer Engineering State University of New York Astana IT University Astana Kazakhstan Department of Computer Engineeering Astana IT University Astana Kazakhstan

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

The challenge of credit card fraud presents a significant threat to both consumers and financial institutions alike, leading to substantial economic detriment. In response, this research applies various machine learning strategies to sift through transactional data for the identification of fraudulent activities. Specifically, we employ logistic regression, decision trees, and random forest algorithms to analyze a compiled dataset of genuine and deceptive transactions. This dataset undergoes segmentation into training and testing phases, allowing for a comprehensive assessment of each algorithm's effectiveness. The determination of accuracy scores across these phases serves as a metric for model evaluation. The findings lay down an essential groundwork for ongoing exploration into leveraging machine learning for the mitigation of credit card fraud. The integration of these analytical models promises to bolster the fraud detection capabilities of both financial entities and individual cardholders, significantly reducing the incidence of financial loss.

关键词： Training Logistic regression Machine learning algorithms Accuracy Sensitivity Credit cards Fraud

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Exploring Genetic Programming Models in computer-Aided Diagnosis of Skin Cancer Images 13

Exploring Genetic Programming Models in Computer-Aided Diagn...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Ain, Qurrat Ui Al-Sahaf, Harith Xue, Bing Zhang, Mengjie School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington New Zealand College of Engineering and Info. Technology AlShaab University Dep. of Cybersecurity Engineering Baghdad Iraq

ISBN: (纸本)9798350308365

Extracting important information from complex skin lesion images is vital to effectively distinguish between different types of skin cancer images. In addition to providing high classification performance, such computer-aided diagnostic methods are needed where the models are interpretable and can provide knowledge about the discriminative features in skin lesion images. This underlying information can significantly assist dermatologists in identifying a particular stage or type of cancer. With its flexible representation and global search abilities, Genetic Programming (GP) is an ideal learning al-gorithm to evolve interpretable models and identify important features with significant information to discriminate between skin cancer classes. This paper provides an in-dep.h analysis of a recent GP-based feature learning method where different well-developed feature descriptors are integrated into the learning algorithms to extract high-level features for skin cancer image classification. The study explores the effectiveness of utilizing feature learning for this complex task and designing program structure to suit the problem domain as it has shown promising results compared to commonly used feature descriptors and an existing GP-based feature learning method developed for general image classification. This study analyzes the GP-evolved models to identify the prominent features and most effective feature descriptors important for the classification of these skin cancer images. The evolved models are interpretable, they provide knowledge that can assist dermatologists in making diagnoses in real-time clinical situations by identifying prominent skin cancer characteristics captured by the feature descriptors and learned during the evolutionary process. © 2024 IEEE.

关键词： Genetic programming

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Preventing Overfitting on Noisy Labels Through Adaptive Checkpointing

Preventing Overfitting on Noisy Labels Through Adaptive Chec...

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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Aref Azizian Rouhollah Ahmadian Mehdi Ghatee Mostafa Shamsi Dep. of Math. & Computer Science Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798331532543

ISBN: (纸本)9798331532550

Overfitting and the presence of noisy labels are significant challenges in the training of machine learning models, particularly in complex datasets. This paper introduces a novel checkpointing method designed to mitigate overfitting while maintaining model calibration in the presence of noise. Our approach leverages the entropy of the two largest predicted probabilities from the model's output to determine when to save the model during training. This entropy-based criterion allows for effective checkpointing without necessitating a separate validation set. We evaluate our method across various datasets characterized by different noise levels and model architectures. The results demonstrate that our entropy-based checkpointing offers a more robust solution for managing model performance during training. This work contributes to the ongoing efforts to enhance model reliability in real-world applications where data imperfections are prevalent.

关键词： Checkpointing Training Adaptation models Prevention and mitigation Noise Machine learning Predictive models Robustness Noise measurement Overfitting

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Vision Transformer Model for Gastrointestinal Tract Diseases Classification from WCE Images

Vision Transformer Model for Gastrointestinal Tract Diseases...

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International Conference on Image and Signal Processing and their Applications (ISPA)

作者： Faiza Bella Ali Berrichi Abdelouahab Moussaoui Dep. of Computer Science LIMOSE University of M’Hamed Bougara Boumerdes Algeria Dep. of Computer Science University of Ferhat Abbes Setif I Setif Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Accurate disease classification utilizing endoscopic images indeed poses a significant challenge within the field of gastroenterology. This research introduces a methodology for assisting medical diagnostic procedures and detecting gastrointestinal (GI) tract diseases by categorizing features extracted from endoscopic images using Vision Transformer (ViT) models. We propose three ViT-inspired models for classifying GI tract diseases using colon images acquired through wireless capsule endoscopy (WCE). The highest achieved accuracy among our models is 97.83%. We conducted a comparative analysis with three pre-trained CNN (Convolutional Neural Network) models namely, Xception, DenseNet121, and MobileNet, alongside recent research papers to validate our findings.

关键词： Wireless communication Neural networks Gastroenterology Signal processing Transformers Feature extraction Gastrointestinal tract

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Internet of Things (IoT): survey of most important security risks 3

Internet of Things (IoT): survey of most important security ...

引用

3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2023

作者： Alaskri, Ahmed Salem Ahmed, Nuredin Ali Shaari, Hala Collage of Computer Technology Software Engineering Dep. Tripoli Libya Faculty of Engineering Computer Engineering Department Tripoli Libya Faculty of Information Web Technologies Department Tripoli Libya

ISBN: (纸本)9798350319897

The Internet of things (IoT) is getting more and more intrusive into our lives until the day comes when everything becomes connected to the Internet. Due to the limited resources and heterogeneous Internet of Things (IoT) devices, the traditional means of protection are useless and cannot be used to protect these devices. The most important security risks, their causes and the consequences of their occurrence have been listed, scheduled and categorized. The study concluded that there are real security risks that cannot be ignored, and they need to find innovative solutions to eliminate them or reduce their damage to a minimum. This paper showed the main risks addressed in previews research, and outlined the gaps in this field of technology, also producing a brief summary information about the most important solution to avoid many threats against the IoT field. © 2023 IEEE.

关键词： Internet of things

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Forward refutation for Gödel-Dummett Logics 37

Forward refutation for Gödel-Dummett Logics

引用

37th Italian Conference on Computational Logic, CILC 2022

作者： Fiorentini, Camillo Ferrari, Mauro Dep. of Computer Science Università degli Studi di Milano Italy Dep. of Theoretical and Applied Sciences Università degli Studi dell'Insubria Italy

We propose a refutation calculus to check the unprovability of a formula in Gödel-Dummett logics. From refutations we can directly extract countermodels for unprovable formulas, moreover the calculus is designed so to support a forward proof-search strategy that can be understood as a top-down construction of a model. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Calculations

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An Enhanced Method for Detecting Attack in Collaborative Recommender System 1

An Enhanced Method for Detecting Attack in Collaborative Rec...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Zayed, Reda A. Hefny, Hesham A. Ibrahim, Lamiaa F. Salman, Hesham A. Cairo University Faculty of Graduate Studies for Statistical Research Computer Scinces Dep. Cairo Egypt Everyone's Smart University College of Informatics United Arab Emirates Higher Institute for Computers & Information Technology ElShorouk Management Information System Dep. Cairo Egypt

ISBN: (纸本)9781665472753

In recent decades, the advent of digital information services by YouTube, Amazon, Netflix, and many other web services of this kind have made recommendation systems more and more ubiquitous in our lives. rice field. The system suggests and recommends articles to the user that may interest the user in online advertising (recommending and suggesting appropriate content to the user that matches the user's tastes and activities). Recommendation systems have become an integral part of our daily online journeys. The quality of predictions is degraded by the attackers by injection of fake profiles. therefore, the shilling attacks detection are necessary. thus, various shilling attacks detection techniques proposed. In this paper, we introduce an enhanced technique for detecting shilling attacks in collaborative recommender system using supervised learning techniques. The proposed method results show that getting better accuracy when we employee ensemble learning algorithm. © 2023 IEEE.

关键词： Recommender systems

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