{"results":[{"id":"doaj_10.1016/j.iswcr.2025.09.004","title":"Global patterns of gully occurrence and their sensitivity to environmental changes","authors":[{"name":"Yixian Chen"},{"name":"Sofie De Geeter"},{"name":"Jean Poesen"},{"name":"Francis Matthews"},{"name":"Benjamin Campforts"},{"name":"Pasquale Borrelli"},{"name":"Panos Panagos"},{"name":"Matthias Vanmaercke"}],"abstract":"Gully formation is a significant driver of soil erosion and land degradation worldwide and often leads to important downstream impacts. Nonetheless, our understanding of the global patterns and the factors controlling this process remains limited. Here, we present the first global assessment of gully density's spatial patterns. Using mapped observations from over 17,000 representative study sites worldwide, we trained random forest models that simulate both the susceptibility to gullying at a 1 km2 resolution and the corresponding gully head density (GHD). Through an interpretable machine learning framework, we demonstrate that global GHD patterns result from a combination of environmental factors with non-linear interactions, leading to significant regional variations in the dominant factors controlling GHD. We distinguish between gully hotspots driven primarily by natural factors such as topography, geomorphology, tectonics, pedology or climate and those where land use and land cover play a dominant role. Based on these insights, we identified critical global areas of gully erosion, i.e., hotspots where gully occurrence is likely highly sensitive to anthropogenic drivers. These include the Chinese Loess Plateau, the Ethiopian Highlands, and large parts of the Mediterranean and Sahel regions. Also desert regions are often characterized by high GHDs. However, in these cases, their occurrence is mainly driven by natural factors. The insights we provide are valuable to inform land management and targeted erosion mitigation strategies.","source":"DOAJ","year":2026,"language":"","subjects":["Engineering (General). Civil engineering (General)"],"doi":"10.1016/j.iswcr.2025.09.004","url":"http://www.sciencedirect.com/science/article/pii/S209563392500111X","is_open_access":true,"published_at":"","score":70},{"id":"doaj_10.46481/jnsps.2025.2273","title":"A feature selection and scoring scheme for dimensionality reduction in a machine learning task","authors":[{"name":"PHILEMON UTEN EMMOH"},{"name":"christopher  ifeanyi Eke"},{"name":"Timothy Moses"}],"abstract":"\nSelection of important features is very vital in machine learning tasks involving high-dimensional dataset with large features. It helps in reducing the dimensionality of a dataset and improving model performance. Most of the feature selection techniques have restriction in the kind of dataset to be used. This study proposed a feature selection technique that is based on statistical lift measure to select important features from a dataset. The proposed technique is a generic approach that can be used in any binary classification dataset. The technique successfully determined the most important feature subset and outperformed the existing techniques. The proposed technique was tested on lungs cancer dataset and happiness classification dataset. The effectiveness of the proposed technique in selecting important features subset was evaluated and compared with other existing techniques, namely Chi-Square, Pearson Correlation and Information Gain. Both the proposed and the existing techniques were evaluated on five machine learning models using four standard evaluation metrics such as accuracy, precision, recall and F1-score. The experimental results of the proposed technique on lung cancer dataset shows that logistic regression, decision tree, adaboost, gradient boost and random forest produced a predictive accuracy of 0.919%, 0.935%, 0.919%, 0.935% and 0.935% respectively, and that of happiness classification dataset produced a predictive accuracy of 0.758%, 0.689%, 0.724%, 0.655% and 0.689% on random forest, k-nearest neighbor, decision tree, gradient boost and cat boost respectively, which outperformed the existing techniques.\n","source":"DOAJ","year":2025,"language":"","subjects":["Physics"],"doi":"10.46481/jnsps.2025.2273","url":"https://journal.nsps.org.ng/index.php/jnsps/article/view/2273","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.1016/j.eng.2024.10.021","title":"LearningEMS: A Unified Framework and Open-Source Benchmark for Learning-Based Energy Management of Electric Vehicles","authors":[{"name":"Yong Wang"},{"name":"Hongwen He"},{"name":"Yuankai Wu"},{"name":"Pei Wang"},{"name":"Haoyu Wang"},{"name":"Renzong Lian"},{"name":"Jingda Wu"},{"name":"Qin Li"},{"name":"Xiangfei Meng"},{"name":"Yingjuan Tang"},{"name":"Fengchun Sun"},{"name":"Amir Khajepour"}],"abstract":"An effective energy management strategy (EMS) is essential to optimize the energy efficiency of electric vehicles (EVs). With the advent of advanced machine learning techniques, the focus on developing sophisticated EMS for EVs is increasing. Here, we introduce LearningEMS: a unified framework and open-source benchmark designed to facilitate rapid development and assessment of EMS. LearningEMS is distinguished by its ability to support a variety of EV configurations, including hybrid EVs, fuel cell EVs, and plug-in EVs, offering a general platform for the development of EMS. The framework enables detailed comparisons of several EMS algorithms, encompassing imitation learning, deep reinforcement learning (RL), offline RL, model predictive control, and dynamic programming. We rigorously evaluated these algorithms across multiple perspectives: energy efficiency, consistency, adaptability, and practicability. Furthermore, we discuss state, reward, and action settings for RL in EV energy management, introduce a policy extraction and reconstruction method for learning-based EMS deployment, and conduct hardware-in-the-loop experiments. In summary, we offer a unified and comprehensive framework that comes with three distinct EV platforms, over 10  000 km of EMS policy data set, ten state-of-the-art algorithms, and over 160 benchmark tasks, along with three learning libraries. Its flexible design allows easy expansion for additional tasks and applications. The open-source algorithms, models, data sets, and deployment processes foster additional research and innovation in EV and broader engineering domains.","source":"DOAJ","year":2025,"language":"","subjects":["Engineering (General). Civil engineering (General)"],"doi":"10.1016/j.eng.2024.10.021","url":"http://www.sciencedirect.com/science/article/pii/S2095809924007136","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.21608/jesaun.2024.326709.1374","title":"Evaluation of the Applications of using Global free Digital Elevation Models and GNSS-RTK data for Agricultural purposes in Egypt using Machine Learning","authors":[{"name":"Ashraf abdallah"},{"name":"Bara\u0026#039; Al-MISTAREHI"},{"name":"Amir SHTAYAT"}],"abstract":"Agriculture is a vital component of Egypt's economy; therefore, using Digital Elevation Models (DEMs) in agricultural planning in Egypt has significant benefits regarding water management, site appropriateness assessment, flood risk mitigation, and infrastructure construction. It is also essential for planners to make more informed decisions, optimize resource allocation, and support sustainable farming practices. This research paper investigates the accuracy of obtaining DEM data from four free global models (STRM30, ALOS30, COP30, and TanDEM-X90). The global DEM data has been compared to an actual GNSS-RTK DEM data surveyed onsite for two agricultural block areas in Aswan, the southern Government of Egypt. The two blocks are a part of a national project. For Block I and II, the RMSE of the Model STRM30 was 2.92 m and 3.59 m, respectively, indicating a poorer solution. Regarding accuracy, the ALOS30 model ranks third, reporting an RMSE of 2.58 m for block II and 3.30 m for block I. COP30 has an RMSE value of 1.06 m for blocks I and II and.91 m overall. TanDEM-X90 is the most accurate model in this investigation; block I provided an RMSE of 0.90 m with an SD of 0.58 m (SD95% = 0.38 m). After removing the anomalies, the model's stated RMSE for block II was 0.34 m, with an SD value of 0.62 m and 1.03 m. According to the classification using machine learning algorithms, with an accuracy of 84.7% for block I and 85% for block II, TanDEM-X90 is the best solution.","source":"DOAJ","year":2025,"language":"","subjects":["Engineering (General). Civil engineering (General)"],"doi":"10.21608/jesaun.2024.326709.1374","url":"https://jesaun.journals.ekb.eg/article_393447_9546e355affffebb2c5da930aaeba9a0.pdf","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.3390/s25247634","title":"EEG Sensor-Based Computational Model for Personality and Neurocognitive Health Analysis Under Social Stress","authors":[{"name":"Majid Riaz"},{"name":"Pedro Guerra"},{"name":"Raffaele Gravina"}],"abstract":"This paper introduces an innovative EEG sensor-based computational framework that establishes a pioneering nexus between personality trait quantification and neural dynamics, leveraging biosignal processing of brainwave activity to elucidate their intrinsic influence on cognitive health and oscillatory brain rhythms. By employing electroencephalography (EEG) recordings from 21 participants undergoing the Trier Social Stress Test (TSST), we propose a machine learning (ML)-driven methodology to decode the Big Five personality traits—Extraversion (Ex), Agreeableness (A), Neuroticism (N), Conscientiousness (C), and Openness (O)—using classification algorithms such as support vector machine (SVM) and multilayer perceptron (MLP) applied to 64-electrode EEG sensor data. A novel multiphase neurocognitive analysis across the TSST stages (baseline, mental arithmetic, job interview, and recovery) systematically evaluates the bidirectional relationship between personality traits and stress-induced neural responses. The proposed framework reveals significant negative correlations between frontal–temporal theta–beta ratio (TBR) and self-reported Extraversion, Conscientiousness, and Openness, indicating faster stress recovery and higher cognitive resilience in individuals with elevated trait scores. The binary classification model achieves high accuracy (88.1% Ex, 94.7% A, 84.2% N, 81.5% C, and 93.4% O), surpassing the current benchmarks in personality neuroscience. These findings empirically validate the close alignment between personality constructs and neural oscillatory patterns, highlighting the potential of EEG-based sensing and machine-learning analytics for personalized mental-health monitoring and human-centric AI systems attuned to individual neurocognitive profiles.","source":"DOAJ","year":2025,"language":"","subjects":["Chemical technology"],"doi":"10.3390/s25247634","url":"https://www.mdpi.com/1424-8220/25/24/7634","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.3390/info17010009","title":"Evaluating Model Resilience to Data Poisoning Attacks: A Comparative Study","authors":[{"name":"Ifiok Udoidiok"},{"name":"Fuhao Li"},{"name":"Jielun Zhang"}],"abstract":"Machine learning (ML) has become a cornerstone of critical applications, but its vulnerability to data poisoning attacks threatens system reliability and trustworthiness. Prior studies have begun to investigate the impact of data poisoning and proposed various defense or evaluation methods; however, most efforts remain limited to quantifying performance degradation, with little systematic comparison of internal behaviors across model architectures under attack and insufficient attention to interpretability for revealing model vulnerabilities. To tackle this issue, we build a reproducible evaluation pipeline and emphasize the importance of integrating robustness with interpretability in the design of secure and trustworthy ML systems. To be specific, we propose a unified poisoning evaluation framework that systematically compares traditional ML models, deep neural networks, and large language models under three representative attack strategies including label flipping, random corruption, and adversarial insertion, at escalating severity levels of 30%, 50%, and 75%, and integrate LIME-based explanations to trace the evolution of model reasoning. Experimental results demonstrate that traditional models collapse rapidly under label noise, whereas Bayesian LSTM hybrids and large language models maintain stronger resilience. Further interpretability analysis uncovers attribution failure patterns, such as over-reliance on neutral tokens or misinterpretation of adversarial cues, providing insights beyond accuracy metrics.","source":"DOAJ","year":2025,"language":"","subjects":["Information technology"],"doi":"10.3390/info17010009","url":"https://www.mdpi.com/2078-2489/17/1/9","is_open_access":true,"published_at":"","score":69},{"id":"arxiv_2509.08759","title":"Fourier Learning Machines: Nonharmonic Fourier-Based Neural Networks for Scientific Machine Learning","authors":[{"name":"Mominul Rubel"},{"name":"Adam Meyers"},{"name":"Gabriel Nicolosi"}],"abstract":"We introduce the Fourier Learning Machine (FLM), a neural network (NN) architecture designed to represent a multidimensional nonharmonic Fourier series. The FLM uses a simple feedforward structure with cosine activation functions to learn the frequencies, amplitudes, and phase shifts of the series as trainable parameters. This design allows the model to create a problem-specific spectral basis adaptable to both periodic and nonperiodic functions. Unlike previous Fourier-inspired NN models, the FLM is the first architecture able to represent a multidimensional Fourier series with a complete set of basis functions in separable form, doing so by using a standard Multilayer Perceptron-like architecture. A one-to-one correspondence between the Fourier coefficients and amplitudes and phase-shifts is demonstrated, allowing for the translation between a full, separable basis form and the cosine phase-shifted one. Additionally, we evaluate the performance of FLMs on several scientific computing problems, including benchmark Partial Differential Equations (PDEs) and a family of Optimal Control Problems (OCPs). Computational experiments show that the performance of FLMs is comparable, and often superior, to that of established architectures like SIREN and vanilla feedforward NNs.","source":"arXiv","year":2025,"language":"en","subjects":["cs.LG","math.OC"],"url":"https://arxiv.org/abs/2509.08759","pdf_url":"https://arxiv.org/pdf/2509.08759","is_open_access":true,"published_at":"2025-09-10T16:49:20Z","score":69},{"id":"doaj_10.1038/s41598-024-73898-4","title":"Machine learning-based diagnostic prediction of minimal change disease: model development study","authors":[{"name":"Ryunosuke Noda"},{"name":"Daisuke Ichikawa"},{"name":"Yugo Shibagaki"}],"abstract":"Abstract Minimal change disease (MCD) is a common cause of nephrotic syndrome. Due to its rapid progression, early detection is essential; however, definitive diagnosis requires invasive kidney biopsy. This study aims to develop non-invasive predictive models for diagnosing MCD by machine learning. We retrospectively collected data on demographic characteristics, blood tests, and urine tests from patients with nephrotic syndrome who underwent kidney biopsy. We applied four machine learning algorithms—TabPFN, LightGBM, Random Forest, and Artificial Neural Network—and logistic regression. We compared their performance using stratified 5-repeated 5-fold cross-validation for the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Variable importance was evaluated using the SHapley Additive exPlanations (SHAP) method. A total of 248 patients were included, with 82 cases (33%) were diagnosed with MCD. TabPFN demonstrated the best performance with an AUROC of 0.915 (95% CI 0.896–0.932) and an AUPRC of 0.840 (95% CI 0.807–0.872). The SHAP methods identified C3, total cholesterol, and urine red blood cells as key predictors for TabPFN, consistent with previous reports. Machine learning models could be valuable non-invasive diagnostic tools for MCD.","source":"DOAJ","year":2024,"language":"","subjects":["Medicine","Science"],"doi":"10.1038/s41598-024-73898-4","url":"https://doi.org/10.1038/s41598-024-73898-4","is_open_access":true,"published_at":"","score":68},{"id":"doaj_10.1186/s12888-024-06253-6","title":"The voice of depression: speech features as biomarkers for major depressive disorder","authors":[{"name":"Felix Menne"},{"name":"Felix Dörr"},{"name":"Julia Schräder"},{"name":"Johannes Tröger"},{"name":"Ute Habel"},{"name":"Alexandra König"},{"name":"Lisa Wagels"}],"abstract":"Abstract Background Psychiatry faces a challenge due to the lack of objective biomarkers, as current assessments are based on subjective evaluations. Automated speech analysis shows promise in detecting symptom severity in depressed patients. This project aimed to identify discriminating speech features between patients with major depressive disorder (MDD) and healthy controls (HCs) by examining associations with symptom severity measures. Methods Forty-four MDD patients from the Psychiatry Department, University Hospital Aachen, Germany and fifty-two HCs were recruited. Participants described positive and negative life events, which were recorded for analysis. The Beck Depression Inventory (BDI-II) and the Hamilton Rating Scale for Depression gauged depression severity. Transcribed audio recordings underwent feature extraction, including acoustics, speech rate, and content. Machine learning models including speech features and neuropsychological assessments, were used to differentiate between the MDD patients and HCs. Results Acoustic variables such as pitch and loudness differed significantly between the MDD patients and HCs (effect sizes 𝜼2 between 0.183 and 0.3, p \u003c 0.001). Furthermore, variables pertaining to temporality, lexical richness, and speech sentiment displayed moderate to high effect sizes (𝜼2 between 0.062 and 0.143, p \u003c 0.02). A support vector machine (SVM) model based on 10 acoustic features showed a high performance (AUC = 0.93) in differentiating between HCs and patients with MDD, comparable to an SVM based on the BDI-II (AUC = 0.99, p = 0.01). Conclusions This study identified robust speech features associated with MDD. A machine learning model based on speech features yielded similar results to an established pen-and-paper depression assessment. In the future, these findings may shape voice-based biomarkers, enhancing clinical diagnosis and MDD monitoring.","source":"DOAJ","year":2024,"language":"","subjects":["Psychiatry"],"doi":"10.1186/s12888-024-06253-6","url":"https://doi.org/10.1186/s12888-024-06253-6","is_open_access":true,"published_at":"","score":68},{"id":"doaj_10.1109/ACCESS.2024.3406249","title":"LatentColorization: Latent Diffusion-Based Speaker Video Colorization","authors":[{"name":"Rory Ward"},{"name":"Dan Bigioi"},{"name":"Shubhajit Basak"},{"name":"John G. Breslin"},{"name":"Peter Corcoran"}],"abstract":"While current research predominantly focuses on image-based colorization, the domain of video-based colorization remains relatively unexplored. Many existing video colorization techniques operate frame-by-frame, often overlooking the critical aspect of temporal coherence between successive frames. This approach can result in inconsistencies across frames, leading to undesirable effects like flickering or abrupt color transitions between frames. To address these challenges, we combine the generative capabilities of a fine-tuned latent diffusion model with an autoregressive conditioning mechanism to ensure temporal consistency in automatic speaker video colorization. We demonstrate strong improvements on established quality metrics compared to existing methods, namely, PSNR, SSIM, FID, FVD, NIQE and BRISQUE. Specifically, we achieve an 18% improvement in performance when FVD is employed as the evaluation metric. Furthermore, we performed a subjective study, where users preferred LatentColorization to the existing state-of-the-art DeOldify 80% of the time. Our dataset combines conventional datasets and videos from television/movies. A short demonstration of our results can be seen in some example videos available at \u003curi\u003ehttps://youtu.be/vDbzsZdFuxM\u003c/uri\u003e.","source":"DOAJ","year":2024,"language":"","subjects":["Electrical engineering. Electronics. Nuclear engineering"],"doi":"10.1109/ACCESS.2024.3406249","url":"https://ieeexplore.ieee.org/document/10539953/","is_open_access":true,"published_at":"","score":68},{"id":"doaj_10.1109/ACCESS.2024.3484007","title":"Exploring the Impact of Alternatives of Object-Oriented Cohesion Measures on Machine Learning-Based Predictions of Inheritance Reusability","authors":[{"name":"Jehad Al Dallal"},{"name":"Bader Alkhazi"}],"abstract":"The cohesion of an object-oriented class refers to the relatedness of its methods and attributes. Constructors, destructors, and access methods are special types of methods featuring unique characteristics that can artificially affect class cohesion quantification. Methods within a class can also directly or transitively invoke each other, representing another cohesion aspect not considered by most existing cohesion measures. The impact of considering special methods (SPs) and transitive relations (TRs) in cohesion measurement on the abilities of the measures to predict inheritance reusability has yet to be investigated. In this paper, we empirically explored this effect. We applied a statistical technique to test the significance of the cohesion value changes across seven scenarios of ignoring or considering SPs and TRs. In addition, we applied a machine learning-based technique to build inheritance reusability prediction models using each of the considered measures and scenarios, evaluated the classification performance of the prediction models, and statistically compared the inheritance reusability prediction results. The results show that for most of the considered measures, the ignorance/consideration of SPs and TRs changed the cohesion values and the corresponding prediction significantly. Based on the study findings, when building inheritance reusability prediction models, software engineers are advised to 1) combine cohesion with other quality factors; 2) exclude the TRs from cohesion quantification; and 3) decide whether to consider or ignore SPs in cohesion quantification based on the selected measure(s) to be used in the prediction model, as this decision differs from one measure to another.","source":"DOAJ","year":2024,"language":"","subjects":["Electrical engineering. Electronics. Nuclear engineering"],"doi":"10.1109/ACCESS.2024.3484007","url":"https://ieeexplore.ieee.org/document/10723299/","is_open_access":true,"published_at":"","score":68},{"id":"doaj_10.34010/komputika.v13i2.12609","title":"Perbandingan Algoritma Decision Tree dan K-Nearest Neighbor untuk Klasifikasi Serangan Jaringan IoT","authors":[{"name":"Zishwa Muhammad Jauhar Nafis"},{"name":"Rahmatun Nazilla"},{"name":"Rega Nugraha"},{"name":"Shofwatul ’Uyun Shofwatul ’Uyun"}],"abstract":"\nSeiring dengan perkembangan jumlah penggunaan Internet of Things yang terus meningkat dan meluas. Ancaman keamanan pada jaringan IoT juga meningkat. Terdapat beberapa teknik yang diterapkan untuk mengatasi ancaman keamanan ini. Salah satunya adalah teknik untuk mengklasifikasi suatu aktivitas yang termasuk dalam serangan atau bukan beserta jenis serangannya. Machine learning dapat dimanfaatkan untuk proses pengklasifikasian ini. Diantara algoritma machine learning yang dapat digunakan untuk penelitian ini adalah pendekatan algoritma Decision Tree dan K-Nearest Neighbor. Penelitian ini bertujuan untuk mendapatkan hasil klasifikasi terbaik untuk mendeteksi jenis serangan jaringan IoT baik dalam klasifikasi  biner maupun klasifikasi multikleas. Dalam penelitian ini memanfaatkan Dataset Edge-IIoTset Cyber Security Dataset of IoT \u0026 IIoT. Hasil nilai evaluasi yang didapatkan menunjukkan bahwa performa algoritma Decision Tree lebih baik dibandingkan dengan Algoritma KNN. Dengan selisih nilai presisi, recall, F1-score, dan akurasi secara berurutan adalah 0.15, 0.18, 0.17 dan 0.08 dalam klasifikasi biner. Sedangkan dalam klasifikasi multikelas mendapatkan nilai selisih antar kedua algoritma sebesar 0.26, 0.20, 0.22, dan 0.23 secara berurutan untuk presisi, recall, F1-score, dan akurasi.\n","source":"DOAJ","year":2024,"language":"","subjects":["Technology"],"doi":"10.34010/komputika.v13i2.12609","url":"https://ojs.unikom.ac.id/index.php/komputika/article/view/12609","is_open_access":true,"published_at":"","score":68},{"id":"doaj_10.1002/cnr2.2045","title":"Developments and future prospects of personalized medicine in head and neck squamous cell carcinoma diagnoses and treatments","authors":[{"name":"Shalindu Malshan Jayawickrama"},{"name":"Piyumi Madhushani Ranaweera"},{"name":"Ratupaskatiye Gedara Gunaratnege Roshan Pradeep"},{"name":"Yovanthi Anurangi Jayasinghe"},{"name":"Kalpani Senevirathna"},{"name":"Abdul Jabbar Hilmi"},{"name":"Rajapakse Mudiyanselage Gamini Rajapakse"},{"name":"Kehinde Kazeem Kanmodi"},{"name":"Ruwan Duminda Jayasinghe"}],"abstract":"Abstract Background Precision healthcare has entered a new era because of the developments in personalized medicine, especially in the diagnosis and treatment of head and neck squamous cell carcinoma (HNSCC). This paper explores the dynamic landscape of personalized medicine as applied to HNSCC, encompassing both current developments and future prospects. Recent Findings The integration of personalized medicine strategies into HNSCC diagnosis is driven by the utilization of genetic data and biomarkers. Epigenetic biomarkers, which reflect modifications to DNA that can influence gene expression, have emerged as valuable indicators for early detection and risk assessment. Treatment approaches within the personalized medicine framework are equally promising. Immunotherapy, gene silencing, and editing techniques, including RNA interference and CRISPR/Cas9, offer innovative means to modulate gene expression and correct genetic aberrations driving HNSCC. The integration of stem cell research with personalized medicine presents opportunities for tailored regenerative approaches. The synergy between personalized medicine and technological advancements is exemplified by artificial intelligence (AI) and machine learning (ML) applications. These tools empower clinicians to analyze vast datasets, predict patient responses, and optimize treatment strategies with unprecedented accuracy. Conclusion The developments and prospects of personalized medicine in HNSCC diagnosis and treatment offer a transformative approach to managing this complex malignancy. By harnessing genetic insights, biomarkers, immunotherapy, gene editing, stem cell therapies, and advanced technologies like AI and ML, personalized medicine holds the key to enhancing patient outcomes and ushering in a new era of precision oncology.","source":"DOAJ","year":2024,"language":"","subjects":["Neoplasms. Tumors. Oncology. Including cancer and carcinogens"],"doi":"10.1002/cnr2.2045","url":"https://doi.org/10.1002/cnr2.2045","is_open_access":true,"published_at":"","score":68},{"id":"arxiv_2404.12511","title":"Generalizing Machine Learning Evaluation through the Integration of Shannon Entropy and Rough Set Theory","authors":[{"name":"Olga Cherednichenko"},{"name":"Dmytro Chernyshov"},{"name":"Dmytro Sytnikov"},{"name":"Polina Sytnikova"}],"abstract":"This research paper delves into the innovative integration of Shannon entropy and rough set theory, presenting a novel approach to generalize the evaluation approach in machine learning. The conventional application of entropy, primarily focused on information uncertainty, is extended through its combination with rough set theory to offer a deeper insight into data's intrinsic structure and the interpretability of machine learning models. We introduce a comprehensive framework that synergizes the granularity of rough set theory with the uncertainty quantification of Shannon entropy, applied across a spectrum of machine learning algorithms. Our methodology is rigorously tested on various datasets, showcasing its capability to not only assess predictive performance but also to illuminate the underlying data complexity and model robustness. The results underscore the utility of this integrated approach in enhancing the evaluation landscape of machine learning, offering a multi-faceted perspective that balances accuracy with a profound understanding of data attributes and model dynamics. This paper contributes a groundbreaking perspective to machine learning evaluation, proposing a method that encapsulates a holistic view of model performance, thereby facilitating more informed decision-making in model selection and application.","source":"arXiv","year":2024,"language":"en","subjects":["cs.LG"],"url":"https://arxiv.org/abs/2404.12511","pdf_url":"https://arxiv.org/pdf/2404.12511","is_open_access":true,"published_at":"2024-04-18T21:22:42Z","score":68},{"id":"arxiv_2402.01393","title":"ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Data","authors":[{"name":"Carmen Martin-Turrero"},{"name":"Maxence Bouvier"},{"name":"Manuel Breitenstein"},{"name":"Pietro Zanuttigh"},{"name":"Vincent Parret"}],"abstract":"We seek to enable classic processing of continuous ultra-sparse spatiotemporal data generated by event-based sensors with dense machine learning models. We propose a novel hybrid pipeline composed of asynchronous sensing and synchronous processing that combines several ideas: (1) an embedding based on PointNet models -- the ALERT module -- that can continuously integrate new and dismiss old events thanks to a leakage mechanism, (2) a flexible readout of the embedded data that allows to feed any downstream model with always up-to-date features at any sampling rate, (3) exploiting the input sparsity in a patch-based approach inspired by Vision Transformer to optimize the efficiency of the method. These embeddings are then processed by a transformer model trained for object and gesture recognition. Using this approach, we achieve performances at the state-of-the-art with a lower latency than competitors. We also demonstrate that our asynchronous model can operate at any desired sampling rate.","source":"arXiv","year":2024,"language":"en","subjects":["cs.CV","cs.LG","cs.NE"],"url":"https://arxiv.org/abs/2402.01393","pdf_url":"https://arxiv.org/pdf/2402.01393","is_open_access":true,"published_at":"2024-02-02T13:17:19Z","score":68},{"id":"doaj_10.1049/rpg2.12731","title":"Static voltage stability margin prediction considering new energy uncertainty based on graph attention networks and long short‐term memory networks","authors":[{"name":"Tong Liu"},{"name":"Xueping Gu"},{"name":"Shaoyan Li"},{"name":"Yansong Bai"},{"name":"Tieqiang Wang"},{"name":"Xiaodong Yang"}],"abstract":"Abstract The existing static voltage stability margin evaluation methods cannot meet the actual demand of current power grid well in terms of calculation speed and accuracy. Thus, this paper proposes a static voltage stability margin prediction method based on a graph attention network (GAT) and a long short‐term memory network (LSTM) to predict the static voltage stability margin of a power system accurately, fast, and effectively, considering new energy uncertainty. First, an innovative machine learning framework named the GAT‐LSTM is designed to extract highly representative power grid operation features considering the spatial‐temporal correlation of the power grid operation. Then, a static voltage stability margin prediction method based on the GAT‐LSTM is developed. Particularly, considering the influence of new energy power uncertainty, two loss functions of certainty and uncertainty are used in the proposed method to predict the voltage stability margin and voltage fluctuation range. Finally, the IEEE39‐bus power system and a practical power system are employed to verify the proposed method. The results show that the computational speed of the proposed method is greatly improved compared to the traditional methods not based on machine learning; the computation results are more accurate and reliable than the existing machine learning methods. Compared with the existing methods, the proposed method has higher scalability and applicability.","source":"DOAJ","year":2023,"language":"","subjects":["Renewable energy sources"],"doi":"10.1049/rpg2.12731","url":"https://doi.org/10.1049/rpg2.12731","is_open_access":true,"published_at":"","score":67},{"id":"doaj_10.3390/en16114403","title":"Cloud-Based Artificial Intelligence Framework for Battery Management System","authors":[{"name":"Dapai Shi"},{"name":"Jingyuan Zhao"},{"name":"Chika Eze"},{"name":"Zhenghong Wang"},{"name":"Junbin Wang"},{"name":"Yubo Lian"},{"name":"Andrew F. Burke"}],"abstract":"As the popularity of electric vehicles (EVs) and smart grids continues to rise, so does the demand for batteries. Within the landscape of battery-powered energy storage systems, the battery management system (BMS) is crucial. It provides key functions such as battery state estimation (including state of charge, state of health, battery safety, and thermal management) as well as cell balancing. Its primary role is to ensure safe battery operation. However, due to the limited memory and computational capacity of onboard chips, achieving this goal is challenging, as both theory and practical evidence suggest. Given the immense amount of battery data produced over its operational life, the scientific community is increasingly turning to cloud computing for data storage and analysis. This cloud-based digital solution presents a more flexible and efficient alternative to traditional methods that often require significant hardware investments. The integration of machine learning is becoming an essential tool for extracting patterns and insights from vast amounts of observational data. As a result, the future points towards the development of a cloud-based artificial intelligence (AI)-enhanced BMS. This will notably improve the predictive and modeling capacity for long-range connections across various timescales, by combining the strength of physical process models with the versatility of machine learning techniques.","source":"DOAJ","year":2023,"language":"","subjects":["Technology"],"doi":"10.3390/en16114403","url":"https://www.mdpi.com/1996-1073/16/11/4403","is_open_access":true,"published_at":"","score":67},{"id":"doaj_10.1109/ACCESS.2023.3271748","title":"Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A Survey","authors":[{"name":"Michela Prunella"},{"name":"Roberto Maria Scardigno"},{"name":"Domenico Buongiorno"},{"name":"Antonio Brunetti"},{"name":"Nicola Longo"},{"name":"Raffaele Carli"},{"name":"Mariagrazia Dotoli"},{"name":"Vitoantonio Bevilacqua"}],"abstract":"Automatic vision-based inspection systems have played a key role in product quality assessment for decades through the segmentation, detection, and classification of defects. Historically, machine learning frameworks, based on hand-crafted feature extraction, selection, and validation, counted on a combined approach of parameterized image processing algorithms and explicated human knowledge. The outstanding performance of deep learning (DL) for vision systems, in automatically discovering a feature representation suitable for the corresponding task, has exponentially increased the number of scientific articles and commercial products aiming at industrial quality assessment. In such a context, this article reviews more than 220 relevant articles from the related literature published until February 2023, covering the recent consolidation and advances in the field of fully-automatic DL-based surface defects inspection systems, deployed in various industrial applications. The analyzed papers have been classified according to a bi-dimensional taxonomy, that considers both the specific defect recognition task and the employed learning paradigm. The dependency on large and high-quality labeled datasets and the different neural architectures employed to achieve an overall perception of both well-visible and subtle defects, through the supervision of fine or/and coarse data annotations have been assessed. The results of our analysis highlight a growing research interest in defect representation power enrichment, especially by transferring pre-trained layers to an optimized network and by explaining the network decisions to suggest trustworthy retention or rejection of the products being evaluated.","source":"DOAJ","year":2023,"language":"","subjects":["Electrical engineering. Electronics. Nuclear engineering"],"doi":"10.1109/ACCESS.2023.3271748","url":"https://ieeexplore.ieee.org/document/10113226/","is_open_access":true,"published_at":"","score":67},{"id":"arxiv_2306.04338","title":"Changing Data Sources in the Age of Machine Learning for Official Statistics","authors":[{"name":"Cedric De Boom"},{"name":"Michael Reusens"}],"abstract":"Data science has become increasingly essential for the production of official statistics, as it enables the automated collection, processing, and analysis of large amounts of data. With such data science practices in place, it enables more timely, more insightful and more flexible reporting. However, the quality and integrity of data-science-driven statistics rely on the accuracy and reliability of the data sources and the machine learning techniques that support them. In particular, changes in data sources are inevitable to occur and pose significant risks that are crucial to address in the context of machine learning for official statistics.   This paper gives an overview of the main risks, liabilities, and uncertainties associated with changing data sources in the context of machine learning for official statistics. We provide a checklist of the most prevalent origins and causes of changing data sources; not only on a technical level but also regarding ownership, ethics, regulation, and public perception. Next, we highlight the repercussions of changing data sources on statistical reporting. These include technical effects such as concept drift, bias, availability, validity, accuracy and completeness, but also the neutrality and potential discontinuation of the statistical offering. We offer a few important precautionary measures, such as enhancing robustness in both data sourcing and statistical techniques, and thorough monitoring. In doing so, machine learning-based official statistics can maintain integrity, reliability, consistency, and relevance in policy-making, decision-making, and public discourse.","source":"arXiv","year":2023,"language":"en","subjects":["stat.ML","cs.LG"],"url":"https://arxiv.org/abs/2306.04338","pdf_url":"https://arxiv.org/pdf/2306.04338","is_open_access":true,"published_at":"2023-06-07T11:08:12Z","score":67},{"id":"arxiv_2302.08893","title":"Active learning for data streams: a survey","authors":[{"name":"Davide Cacciarelli"},{"name":"Murat Kulahci"}],"abstract":"Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world applications where data is only available in an unlabeled form. Annotating each observation can be time-consuming and costly, making it difficult to obtain large amounts of labeled data. To overcome this issue, many active learning strategies have been proposed in the last decades, aiming to select the most informative observations for labeling in order to improve the performance of machine learning models. These approaches can be broadly divided into two categories: static pool-based and stream-based active learning. Pool-based active learning involves selecting a subset of observations from a closed pool of unlabeled data, and it has been the focus of many surveys and literature reviews. However, the growing availability of data streams has led to an increase in the number of approaches that focus on online active learning, which involves continuously selecting and labeling observations as they arrive in a stream. This work aims to provide an overview of the most recently proposed approaches for selecting the most informative observations from data streams in real time. We review the various techniques that have been proposed and discuss their strengths and limitations, as well as the challenges and opportunities that exist in this area of research.","source":"arXiv","year":2023,"language":"en","subjects":["stat.ML","cs.LG","stat.ME"],"doi":"10.1007/s10994-023-06454-2","url":"https://arxiv.org/abs/2302.08893","pdf_url":"https://arxiv.org/pdf/2302.08893","is_open_access":true,"published_at":"2023-02-17T14:24:13Z","score":67}],"total":174934,"page":1,"page_size":20,"sources":["DOAJ","arXiv"],"query":"machine learning"}