Optimized Ensemble Deep Learning for Real-Time Intrusion Detection on Resource-Constrained Raspberry Pi Devices by Muhammad Bisri Musthafa, Samsul Huda, Tuy Tan Nguyen, Yuta Kodera, Yasuyuki Nogami

“…The rapid growth of Internet of Things (IoT) networks has increased security risks, making it essential to have effective Intrusion Detection Systems (IDS) for real-time threat detection. …”
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LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection by Jyoti Parashar, Rituraj Jain, Mahesh K. Singh, Ashwani Kumar, Premananda Sahu, Kamal Upreti

“…Using a large dataset of Low Dose CT (LDCT) scans, the system is built with fine-tuned pre-trained Convolutional Neural Networks (CNNs) such that feature extraction is reliable though minimal reducing radiation exposure. …”
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Triplet Multi-Kernel CNN for Detection of Pulmonary Diseases From Lung Sound Signals by Pumin Duangmanee, Khomdet Phapatanaburi, Wongsathon Pathonsuwan, Talit Jumphoo, Atcharawan Rattanasak, Khwanjit Orkweha, Patikorn Anchuen, Monthippa Uthansakul, Peerapong Uthansakul

“…Recent studies have demonstrated the notable success of Convolutional Neural Networks (CNNs) to detect respiratory diseases from Lung Sound (LS) signals. …”
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CUNet-CLSTM: A Novel Fusion of CUNet and CLSTM for Superior Liver Cancer Detection in CT Scans by K. Vijayaprabakaran, Padmanaban Ramalingam, Rajakumar Ramalingam, A. Ilavendhan, R. Vedhapriyavadhana

“…The anticipated CUNet-CLSTM model evaluated on two benchmark datasets: LiTS and 3DICARDB. …”
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A novel deep learning framework with artificial protozoa optimization-based adaptive environmental response for wind power prediction by Sangkeum Lee, Mohammad H. Almomani, Saleh Ali Alomari, Kashif Saleem, Aseel Smerat, Vaclav Snasel, Amir H. Gandomi, Laith Abualigah

“…To address these, this study proposes a novel hybrid deep learning framework, IAPO-LSTM, which combines Convolutional Neural Networks (CNNs) for spatial feature extraction and Gated Recurrent Units (GRUs) for temporal sequence modeling. …”
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Inductive and Transfer Learning‐Based Hybrid Model Techniques for Accurate and Automated Diagnosis of Neurological Diseases by Saroj Kumar Pandey, Yogesh Kumar Rathore, Sunakshi Mehra, Anurag Sinha, Tarun Raj Kumar, Ankit Kumar, Rekh Ram Janghel, Ayodele Lasisi, Quadri Noorulhasan Naveed, Md. Sazid Reza

“…Methods NeuroDL utilizes convolutional neural networks (CNNs) trained on two publicly available, annotated MRI datasets. …”
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Revolutionizing Mental Health Sentiment Analysis With BERT-Fuse: A Hybrid Deep Learning Model by Md. Mithun Hossain, Sanjara, Md. Shakil Hossain, Sudipto Chaki, Md. Saifur Rahman, A. B. M. Shawkat Ali

“…To address these challenges, we propose BERT-Fuse, a hybrid deep learning model combining traditional feature extraction techniques (TF-IDF and Bag-of-Words) with advanced neural architectures, including Bidirectional Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Transformer blocks, and Convolutional Neural Networks (CNN). …”
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Predicting ICU mortality in heart failure patients based on blood tests and vital signs by Yeao Wang, Jianke Rong, Zhili Wei, Xiaoyu Bai, YunDan Deng

“…We utilized a variety of machine learning algorithms for modeling purposes, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Trees, Random Forests, Gradient Boosting Machines (GBM), XGBoost, and Neural Networks. The performance of the model was assessed through cross-validation and evaluated using the F1-score.ConclusionThrough feature selection, 15 key variables were ultimately identified. …”
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Drone hyperspectral imaging and artificial intelligence for monitoring moss and lichen in Antarctica by Juan Sandino, Johan Barthelemy, Ashray Doshi, Krystal Randall, Sharon A. Robinson, Barbara Bollard, Felipe Gonzalez

“…Data collected during a 2023 summer expedition to Antarctic Specially Protected Area 135, East Antarctica, were used to evaluate 12 configurations derived from five ML models, including gradient boosting (XGBoost, CatBoost) and convolutional neural networks (CNNs) (G2C-Conv2D, G2C-Conv3D, and UNet), tested with full and light input feature sets. …”
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Deep learning-based automatic diagnosis of rice leaf diseases using ensemble CNN models by Prameetha Pai, S. Amutha, Seema Patil, T. Shobha, Mustafa Basthikodi, B. M. Ahamed Shafeeq, Ananth Prabhu Gurpur

“…These models were selected based on diverse architectural principles to ensure complementary feature extraction capabilities. An ensemble model, integrating these four high-performing networks via a simple average fusion strategy, was subsequently developed, significantly reducing misclassification rates and providing robust, scalable diagnostic capabilities suitable for deployment in real-world agricultural settings. …”
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Benchmarking Hook and Bait Urdu news dataset for domain-agnostic and multilingual fake news detection using large language models by Sheetal Harris, Jinshuo Liu, Hassan Jalil Hadi, Naveed Ahmad, Mohammed Ali Alshara

“…Abstract Fake News (FN) prevalence on Online Social Networks (OSNs) and online websites is a worldwide issue. …”
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Measurement of spatial heterogeneity in street restorative perceptions and street refinement design by Yalun Lei, Qingqing Li, Jingwen Tian, Jia Hu, Jixiang Jiang

“…Abstract Restorative perception of streets is an essential metric for evaluating urban environments and serves as a key indicator of pedestrians’ perspectives on street refinement design. …”
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Identifying determinants of under-5 mortality in Bangladesh: A machine learning approach with BDHS 2022 data. by Shayla Naznin, Md Jamal Uddin, Ahmad Kabir

“…<h4>Methods</h4>Multiple machine learning (ML) algorithms were applied to data from the 2022 Bangladesh Demographic Health Survey, including Random Forest, Decision Tree, K-Nearest Neighbors, Logistic Regression, Support Vector Machine, XGBoost, LightGBM and Neural Networks. Feature selection was performed using the Boruta algorithm and model performance was evaluated by comparing accuracy, precision, recall, F1 score, MCC, Cohen's Kappa and AUROC.…”
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Method to Use Transport Microsimulation Models to Create Synthetic Distributed Acoustic Sensing Datasets by Ignacio Robles-Urquijo, Juan Benavente, Javier Blanco García, Pelayo Diego Gonzalez, Alayn Loayssa, Mikel Sagues, Luis Rodriguez-Cobo, Adolfo Cobo

“…The methodology is tested using simulations of real road scenarios, featuring a fiber-optic cable buried along the westbound shoulder with sections deviating from the roadside. …”
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Development and Optimization of Grape Skin Extract-Loaded Gelatin–Alginate Hydrogels: Assessment of Antioxidant and Antimicrobial Properties by Jovana Bradic, Anica Petrovic, Aleksandar Kocovic, Vukasin Ugrinovic, Suzana Popovic, Andrija Ciric, Zoran Markovic, Edina Avdovic

“…The antioxidant capacity of the hydrogels was evaluated using DPPH, ABTS, and FRAP assays. <b>Results:</b> Our results showed that higher gelatin and CaCl₂ concentrations produced denser crosslinked networks, leading to reduced swelling and increased stiffness. …”
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Sleeping sound with autism spectrum disorder (ASD): study protocol for an efficacy randomised controlled trial of a tailored brief behavioural sleep intervention for ASD by Harriet Hiscock, Cathrine Mihalopoulos, Nicole Papadopoulos, Emma Sciberras, Jane McGillivray, Lidia Engel, Matthew Fuller-Tyszkiewicz, Susannah T Bellows, Deborah Marks, Patricia Howlin, Nicole Rinehart

“…Cost-effectiveness of the intervention is also evaluated.Ethics and dissemination Findings from this study will be published in peer-reviewed journals and disseminated at national and international conferences, local networks and online.Trial registration number ISRCTN14077107 (ISRCTN registry dated on 3 March 2017).…”
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Social Determinants Influencing Internet‐Based Service Adoption Among Female Family Caregivers in Bangladesh: A Sociodemographic and Technological Analysis by Mohammad Ishtiaque Rahman, Jahangir Alam, Khadija Khanom, Forhan Bin Emdad

“…SHAP analysis revealed that social networks, service reliability, and cost‐effectiveness were the most influential factors driving adoption. …”
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SHM System for Composite Material Based on Lamb Waves and Using Machine Learning on Hardware by Gracieth Cavalcanti Batista, Carl-Mikael Zetterling, Johnny Öberg, Osamu Saotome

“…This robust system is validated through experiments and demonstrates its potential for real-time applications in aerospace composite structures, addressing challenges related to material complexity, outliers, and scalable hardware implementation for larger sensor networks.…”
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Machine learning based prediction of geotechnical parameters affecting slope stability in open-pit iron ore mines in high precipitation zone by John Gladious, Partha Sarathi Paul, Manas Mukhopadhyay

“…To enhance model robustness and generalizability, synthetic data was generated using Generative Adversarial Networks (GANs), augmenting the original dataset and ensuring a diverse range of conditions. …”
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A Hybrid Machine Learning Approach for High-Accuracy Energy Consumption Prediction Using Indoor Environmental Quality Sensors by Bibars Amangeldy, Nurdaulet Tasmurzayev, Timur Imankulov, Baglan Imanbek, Waldemar Wójcik, Yedil Nurakhov

“…Environmental variables including carbon dioxide concentration (CO₂), particulate matter (PM_2.5), total volatile organic compounds (TVOCs), noise levels, humidity, and temperature were recorded over a four-month period. We evaluated two ensemble configurations combining support vector regression (SVR) with either Random Forest or LightGBM as base learners and Ridge regression as a meta-learner, alongside single-model baselines such as SVR and artificial neural networks (ANN). …”
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