Effective Breast Cancer Classification Using Deep MLP, Feature-Fused Autoencoder and Weight-Tuned Decision Tree by Nagham Rasheed Hameed Alsaedi, Mehmet Fatih Akay

“…This paper presents an advanced machine learning algorithm designed to improve classification accuracy in breast cancer diagnosis. …”
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Medical Device Failure Predictions Through AI-Driven Analysis of Multimodal Maintenance Records by Noorul Husna Abd Rahman, Khairunnisa Hasikin, Nasrul Anuar Abd Razak, Ayman Khallel Al-Ani, D. Jerline Sheebha Anni, Prabu Mohandas

“…This study proposes a data-driven machine-learning model for predicting medical device failure. …”
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An Ensemble Approach for Detection of Malicious URLs Using SOM and Tabu Search Optimization by Simar Preet Singh, Abhilash Maroju, Mohammad Kamrul Hasan, Karan Tejpal

“…This research presents a strong hybrid machine learning approach that combines accurate classification with effective feature extraction. …”
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Peer effects on rural household carbon emissions in China by Nana Yan

“…A systematic study was conducted using double/debiased machine learning method and Probit (Logit) models to investigate the relationship and mechanism between the same group effect and household carbon emissions. …”
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Adversarial Threats to Cloud IDS: Robust Defense With Adversarial Training and Feature Selection by Hariprasad Holla, Shashidhar Reddy Polepalli, Arun Ambika Sasikumar

“…The increasing adoption of cloud-based infrastructures necessitates robust cybersecurity measures, particularly in Intrusion Detection Systems (IDS). While Machine Learning (ML)-based IDS solutions improve attack detection, they remain highly vulnerable to adversarial attacks, where subtle perturbations deceive the model and evade detection. …”
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Impact of PM_2.5 Pollution on Solar Photovoltaic Power Generation in Hebei Province, China by Ankun Hu, Zexia Duan, Yichi Zhang, Zifan Huang, Tianbo Ji, Xuanhua Yin

“…To capture these complex aerosol–radiation–PV interactions, we developed and compared the following six machine learning models: Support Vector Regression, Random Forest, Decision Tree, K-Nearest Neighbors, AdaBoost, and Backpropagation Neural Network. …”
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Old Drugs, New Indications (Review) by I. I. Miroshnichenko, E. A. Valdman, I. I. Kuz'min

“…Computer design has become widespread, which or repurposing "in silico", where information about the drug is used: targets, chemical structures, metabolic pathways, side effects, followed by the construction of appropriate models. Machine learning (ML) algorithms: Bayes classifier, logistic regression, support vector machine, decision tree, random forest and others are successfully used in biochemical pharmaceutical, toxicological research. …”
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Uncertainty-Guided Prediction Horizon of Phase-Resolved Ocean Wave Forecasting Under Data Sparsity: Experimental and Numerical Evaluation by Yuksel Rudy Alkarem, Kimberly Huguenard, Richard W. Kimball, Stephan T. Grilli

“…This study investigates the performance of two machine learning models, time-series dense encoder (TiDE) and long short-term memory (LSTM), for forecasting phase-resolved ocean surface elevations under varying degrees of data degradation. …”
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Enhancing environmental sustainability and operational efficiency in a case study of limestone quarry in an arid climate by Hussein A. Saleem, Abebe Temesgen Ayalew

“…Automated environmental monitoring systems, incorporating IoT sensors and machine-learning algorithms, provide real-time data on air quality, dust levels, and noise pollution. …”
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Intellectual Rooms based on AmI and IoT technologies by Radhakrishnan Delhibabu, Pham Tuan Anh, Nataly Zhukova, Alexey Subbotin

“…The objective is to enhance patient care by using machine learning to process data from readily available devices.MethodsWe developed a method for creating Intellectual Rooms that utilize a complex model integrating medical domain knowledge with machine learning for image processing. …”
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Development of Residual Dense U-Net (RDU-Net)-Based Metal Artefacts Reduction Technique Using Spectral Photon Counting CT by Osama Khan, Briya Tariq, Nadine Francis, Nabil Maalej, Abderaouf Behouch, Amer Kashif, Asim Waris, Aamir Raja

“…We aim to develop an innovative machine learning-based technique called residual dense U-Net (RDU-Net), specifically for spectral photon-counting CT (SPCCT), to mitigate metal artefacts across all energy bins. …”
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Robust neural network filtering in the tasks of building intelligent interfaces by A. V. Vasiliev, A. O. Melnikov, S. A. Lesko

“…Mathematical signal processing techniques are used along with machine learning methods.Results. The overview of the literature on the topic of EMG signal processing is carried out. …”
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Moanna: Multi-Omics Autoencoder-Based Neural Network Algorithm for Predicting Breast Cancer Subtypes by Richard Lupat, Rashindrie Perera, Sherene Loi, Jason Li

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Assessment and Prediction of Typhoon-Related Direct Economic Loss in Fujian Province Based on LightGBM by Zhang Zhixia, Yang Jian, Chen Sixiao, Lin Sen

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Understanding the Borderline Brain: A Review of Neurobiological Findings in Borderline Personality Disorder (BPD) by Eleni Giannoulis, Christos Nousis, Ioanna-Jonida Sula, Maria-Evangelia Georgitsi, Ioannis Malogiannis

“…Transdiagnostic comparisons with PTSD and cocaine use disorder (CUD) suggest partial overlap in DMN dysregulation, though BPD-specific traits emerge in network topology. Machine learning models achieve a classification accuracy of 70–88% and may support the tracking of early treatment responses. …”
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Transformer-Based Amharic-to-English Machine Translation With Character Embedding and Combined Regularization Techniques by Surafiel Habib Asefa, Yaregal Assabie

“…It is also an under-resourced language, presenting significant challenges for natural language processing tasks like machine translation. The primary challenges include the scarcity of parallel data, which increases the risk of overfitting and limits the model’s ability to generalize effectively, and the complex morphology of Amharic, which further complicates learning patterns in translation tasks. …”
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Short-term and long-term inertia forecasting with low-inertia event prediction in IBR-integrated power systems using a deep learning approach by Santosh Diggikar, Arunkumar Patil, Katkar Siddhant Satyapal, Kunal Samad

“…However, most traditional and online estimation techniques provide reactive inertia assessments, limiting their effectiveness for proactive grid management. Moreover, existing machine learning (ML)-based models primarily focus on either short-term or long-term forecasting and are often trained on limited datasets, which undermines their robustness and generalisation capabilities. …”
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Discriminative graph regularized representation learning for recognition. by Jinshan Qi, Rui Xu

“…Feature extraction has been extensively studied in the machine learning field as it plays a critical role in the success of various practical applications. …”
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UFMTrack, an Under-Flow Migration Tracker enabling analysis of the entire multi-step immune cell extravasation cascade across the blood-brain barrier in microfluidic devices by Mykhailo Vladymyrov, Luca Marchetti, Sidar Aydin, Sasha GN Soldati, Adrien Mossu, Arindam Pal, Laurent Gueissaz, Akitaka Ariga, Britta Engelhardt

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Landscape Metrics as Ecological Indicators for PM₁₀ Prediction in European Cities by Seyedehmehrmanzar Sohrab, Nándor Csikós, Péter Szilassi

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