Machine learning and knowledge extraction最新文献

{"title":"Social Intelligence Mining: Unlocking Insights from X","authors":"Hossein Hassani, N. Komendantova, Elena Rovenskaya, M. R. Yeganegi","doi":"10.3390/make5040093","DOIUrl":"https://doi.org/10.3390/make5040093","url":null,"abstract":"Social trend mining, situated at the confluence of data science and social research, provides a novel lens through which to examine societal dynamics and emerging trends. This paper explores the intricate landscape of social trend mining, with a specific emphasis on discerning leading and lagging trends. Within this context, our study employs social trend mining techniques to scrutinize X (formerly Twitter) data pertaining to risk management, earthquakes, and disasters. A comprehensive comprehension of how individuals perceive the significance of these pivotal facets within disaster risk management is essential for shaping policies that garner public acceptance. This paper sheds light on the intricacies of public sentiment and provides valuable insights for policymakers and researchers alike.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"10 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138980651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Solving Partially Observable 3D-Visual Tasks with Visual Radial Basis Function Network and Proximal Policy Optimization","authors":"Julien Hautot, Céline Teulière, Nourddine Azzaoui","doi":"10.3390/make5040091","DOIUrl":"https://doi.org/10.3390/make5040091","url":null,"abstract":"Visual Reinforcement Learning (RL) has been largely investigated in recent decades. Existing approaches are often composed of multiple networks requiring massive computational power to solve partially observable tasks from high-dimensional data such as images. Using State Representation Learning (SRL) has been shown to improve the performance of visual RL by reducing the high-dimensional data into compact representation, but still often relies on deep networks and on the environment. In contrast, we propose a lighter, more generic method to extract sparse and localized features from raw images without training. We achieve this using a Visual Radial Basis Function Network (VRBFN), which offers significant practical advantages, including efficient and accurate training with minimal complexity due to its two linear layers. For real-world applications, its scalability and resilience to noise are essential, as real sensors are subject to change and noise. Unlike CNNs, which may require extensive retraining, this network might only need minor fine-tuning. We test the efficiency of the VRBFN representation to solve different RL tasks using Proximal Policy Optimization (PPO). We present a large study and comparison of our extraction methods with five classical visual RL and SRL approaches on five different first-person partially observable scenarios. We show that this approach presents appealing features such as sparsity and robustness to noise and that the obtained results when training RL agents are better than other tested methods on four of the five proposed scenarios.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":" 39","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138619199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian Network Structural Learning Using Adaptive Genetic Algorithm with Varying Population Size","authors":"Rafael Rodrigues Mendes Ribeiro, Carlos Dias Maciel","doi":"10.3390/make5040090","DOIUrl":"https://doi.org/10.3390/make5040090","url":null,"abstract":"A Bayesian network (BN) is a probabilistic graphical model that can model complex and nonlinear relationships. Its structural learning from data is an NP-hard problem because of its search-space size. One method to perform structural learning is a search and score approach, which uses a search algorithm and structural score. A study comparing 15 algorithms showed that hill climbing (HC) and tabu search (TABU) performed the best overall on the tests. This work performs a deeper analysis of the application of the adaptive genetic algorithm with varying population size (AGAVaPS) on the BN structural learning problem, which a preliminary test showed that it had the potential to perform well on. AGAVaPS is a genetic algorithm that uses the concept of life, where each solution is in the population for a number of iterations. Each individual also has its own mutation rate, and there is a small probability of undergoing mutation twice. Parameter analysis of AGAVaPS in BN structural leaning was performed. Also, AGAVaPS was compared to HC and TABU for six literature datasets considering F1 score, structural Hamming distance (SHD), balanced scoring function (BSF), Bayesian information criterion (BIC), and execution time. HC and TABU performed basically the same for all the tests made. AGAVaPS performed better than the other algorithms for F1 score, SHD, and BIC, showing that it can perform well and is a good choice for BN structural learning.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"6 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138624618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Android Malware Classification Based on Fuzzy Hashing Visualization","authors":"Horacio Rodriguez-Bazan, Grigori Sidorov, P. J. Escamilla-Ambrosio","doi":"10.3390/make5040088","DOIUrl":"https://doi.org/10.3390/make5040088","url":null,"abstract":"The proliferation of Android-based devices has brought about an unprecedented surge in mobile application usage, making the Android ecosystem a prime target for cybercriminals. In this paper, a new method for Android malware classification is proposed. The method implements a convolutional neural network for malware classification using images. The research presents a novel approach to transforming the Android Application Package (APK) into a grayscale image. The image creation utilizes natural language processing techniques for text cleaning, extraction, and fuzzy hashing to represent the decompiled code from the APK in a set of hashes after preprocessing, where the image is composed of n fuzzy hashes that represent an APK. The method was tested on an Android malware dataset with 15,493 samples of five malware types. The proposed method showed an increase in accuracy compared to others in the literature, achieving up to 98.24% in the classification task.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139221019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FCIoU: A Targeted Approach for Improving Minority Class Detection in Semantic Segmentation Systems","authors":"Jonathan Plangger, Mohamed Atia, H. Chaoui","doi":"10.3390/make5040085","DOIUrl":"https://doi.org/10.3390/make5040085","url":null,"abstract":"In this paper, we present a comparative study of modern semantic segmentation loss functions and their resultant impact when applied with state-of-the-art off-road datasets. Class imbalance, inherent in these datasets, presents a significant challenge to off-road terrain semantic segmentation systems. With numerous environment classes being extremely sparse and underrepresented, model training becomes inefficient and struggles to comprehend the infrequent minority classes. As a solution to this problem, loss functions have been configured to take class imbalance into account and counteract this issue. To this end, we present a novel loss function, Focal Class-based Intersection over Union (FCIoU), which directly targets performance imbalance through the optimization of class-based Intersection over Union (IoU). The new loss function results in a general increase in class-based performance when compared to state-of-the-art targeted loss functions.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"216 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139242762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Active Learning in the Detection of Anomalies in Cryptocurrency Transactions","authors":"Leandro L. Cunha, Miguel A. Brito, Domingos F. Oliveira, Ana P. Martins","doi":"10.3390/make5040084","DOIUrl":"https://doi.org/10.3390/make5040084","url":null,"abstract":"The cryptocurrency market has grown significantly, and this quick growth has given rise to scams. It is necessary to put fraud detection mechanisms in place. The challenge of inadequate labeling is addressed in this work, which is a barrier to the training of high-performance supervised classifiers. It aims to lessen the necessity for laborious and time-consuming manual labeling. Some unlabeled data points have labels that are more pertinent and informative for the supervised model to learn from. The viability of utilizing unsupervised anomaly detection algorithms and active learning strategies to build an iterative process of acquiring labeled transactions in a cold start scenario, where there are no initial-labeled transactions, is being investigated. Investigating anomaly detection capabilities for a subset of data that maximizes supervised models’ learning potential is the goal. The anomaly detection algorithms under performed, according to the results. The findings underscore the need that anomaly detection algorithms be reserved for situations involving cold starts. As a result, using active learning techniques would produce better outcomes and supervised machine learning model performance.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139245344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proximal Policy Optimization-Based Reinforcement Learning and Hybrid Approaches to Explore the Cross Array Task Optimal Solution","authors":"Samuel Corecco, Giorgia Adorni, L. Gambardella","doi":"10.3390/make5040082","DOIUrl":"https://doi.org/10.3390/make5040082","url":null,"abstract":"In an era characterised by rapid technological advancement, the application of algorithmic approaches to address complex problems has become crucial across various disciplines. Within the realm of education, there is growing recognition of the pivotal role played by computational thinking (CT). This skill set has emerged as indispensable in our ever-evolving digital landscape, accompanied by an equal need for effective methods to assess and measure these skills. This research places its focus on the Cross Array Task (CAT), an educational activity designed within the Swiss educational system to assess students’ algorithmic skills. Its primary objective is to evaluate pupils’ ability to deconstruct complex problems into manageable steps and systematically formulate sequential strategies. The CAT has proven its effectiveness as an educational tool in tracking and monitoring the development of CT skills throughout compulsory education. Additionally, this task presents an enthralling avenue for algorithmic research, owing to its inherent complexity and the necessity to scrutinise the intricate interplay between different strategies and the structural aspects of this activity. This task, deeply rooted in logical reasoning and intricate problem solving, often poses a substantial challenge for human solvers striving for optimal solutions. Consequently, the exploration of computational power to unearth optimal solutions or uncover less intuitive strategies presents a captivating and promising endeavour. This paper explores two distinct algorithmic approaches to the CAT problem. The first approach combines clustering, random search, and move selection to find optimal solutions. The second approach employs reinforcement learning techniques focusing on the Proximal Policy Optimization (PPO) model. The findings of this research hold the potential to deepen our understanding of how machines can effectively tackle complex challenges like the CAT problem but also have broad implications, particularly in educational contexts, where these approaches can be seamlessly integrated into existing tools as a tutoring mechanism, offering assistance to students encountering difficulties. This can ultimately enhance students’ CT and problem-solving abilities, leading to an enriched educational experience.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"41 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139258498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS","authors":"Juan R. Terven, Diana-Margarita Córdova-Esparza, J. Romero-González","doi":"10.3390/make5040083","DOIUrl":"https://doi.org/10.3390/make5040083","url":null,"abstract":"YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. We present a comprehensive analysis of YOLO’s evolution, examining the innovations and contributions in each iteration from the original YOLO up to YOLOv8, YOLO-NAS, and YOLO with transformers. We start by describing the standard metrics and postprocessing; then, we discuss the major changes in network architecture and training tricks for each model. Finally, we summarize the essential lessons from YOLO’s development and provide a perspective on its future, highlighting potential research directions to enhance real-time object detection systems.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"65 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139259115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human Pose Estimation Using Deep Learning: A Systematic Literature Review","authors":"Esraa Samkari, Muhammad Arif, Manal Alghamdi, Mohammed A. Al Ghamdi","doi":"10.3390/make5040081","DOIUrl":"https://doi.org/10.3390/make5040081","url":null,"abstract":"Human Pose Estimation (HPE) is the task that aims to predict the location of human joints from images and videos. This task is used in many applications, such as sports analysis and surveillance systems. Recently, several studies have embraced deep learning to enhance the performance of HPE tasks. However, building an efficient HPE model is difficult; many challenges, like crowded scenes and occlusion, must be handled. This paper followed a systematic procedure to review different HPE models comprehensively. About 100 articles published since 2014 on HPE using deep learning were selected using several selection criteria. Both image and video data types of methods were investigated. Furthermore, both single and multiple HPE methods were reviewed. In addition, the available datasets, different loss functions used in HPE, and pretrained feature extraction models were all covered. Our analysis revealed that Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are the most used in HPE. Moreover, occlusion and crowd scenes remain the main problems affecting models’ performance. Therefore, the paper presented various solutions to address these issues. Finally, this paper highlighted the potential opportunities for future work in this task.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"53 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136346415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconstruction-Based Adversarial Attack Detection in Vision-Based Autonomous Driving Systems","authors":"Manzoor Hussain, Jang-Eui Hong","doi":"10.3390/make5040080","DOIUrl":"https://doi.org/10.3390/make5040080","url":null,"abstract":"The perception system is a safety-critical component that directly impacts the overall safety of autonomous driving systems (ADSs). It is imperative to ensure the robustness of the deep-learning model used in the perception system. However, studies have shown that these models are highly vulnerable to the adversarial perturbation of input data. The existing works mainly focused on studying the impact of these adversarial attacks on classification rather than regression models. Therefore, this paper first introduces two generalized methods for perturbation-based attacks: (1) We used naturally occurring noises to create perturbations in the input data. (2) We introduce a modified square, HopSkipJump, and decision-based/boundary attack to attack the regression models used in ADSs. Then, we propose a deep-autoencoder-based adversarial attack detector. In addition to offline evaluation metrics (e.g., F1 score and precision, etc.), we introduce an online evaluation framework to evaluate the robustness of the model under attack. The framework considers the reconstruction loss of the deep autoencoder that validates the robustness of the models under attack in an end-to-end fashion at runtime. Our experimental results showed that the proposed adversarial attack detector could detect square, HopSkipJump, and decision-based/boundary attacks with a true positive rate (TPR) of 93%.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"41 167","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135540006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}