Predicting determinants of unimproved water supply in Ethiopia using machine learning analysis of EDHS-2019 data by Jember Azanaw, Mihret Melese, Eshetu Abera Worede

“…To examine the significance of features in tree-based models, permutation importance and SHAP values were utilized. …”
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An intelligent framework for skin cancer detection and classification using fusion of Squeeze-Excitation-DenseNet with Metaheuristic-driven ensemble deep learning models by J. D. Dorathi Jayaseeli, J Briskilal, C. Fancy, V. Vaitheeshwaran, R. S. M. Lakshmi Patibandla, Khasim Syed, Anil Kumar Swain

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Exploring the relationship between sepsis and Golgi apparatus dysfunction: bioinformatics insights and diagnostic marker discovery by Wanli Ma, Xinyi Liu, Ran Yu, Jiannan Song, Lina Hou, Ying Guo, Hongwei Wu, Dandan Feng, Qi Zhou, Haibo Li

“…To further evaluate immune microenvironmental features, unsupervised clustering was applied to identify immunological subgroups. …”
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Comparative Evaluation of Multimodal Large Language Models for No-Reference Image Quality Assessment with Authentic Distortions: A Study of OpenAI and Claude.AI Models by Domonkos Varga

“…Our results demonstrate that these LLMs outperform traditional methods based on hand-crafted features. However, more advanced deep learning models, especially those based on deep convolutional networks, surpass LLMs in performance. …”
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Evaluation of a Peak-Free Chemometric Laser-Induced Breakdown Spectroscopy Method for Direct Rapid Cancer Detection via Trace Metal Biomarkers in Tissue by Otieno Emily Akinyi, Angeyo Hudson Kalambuka, Alix Dehayem-Kamadjeu

“…In this work, a peak-free chemometric LIBS method based on a single-shot (for rapidity and nondestructiveness) and an artificial neural network multivariate calibration strategy with spectral feature selection was evaluated for its utility for direct trace quantitative analysis of copper (Cu), iron (Fe), manganese (Mg), magnesium (Mg), and zinc (Zn) in model soft body tissue. …”
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Flow-based Text Style Transfer Model by Zihan ZHANG, Jinqiao DAI, Pin YANG

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Limits of Solar Flare Forecasting Models and New Deep Learning Approach by G. Francisco, M. Berretti, S. Chierichini, R. Mugatwala, J. Fernandes, T. Barata, D. Del Moro

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NeuroMorse: a temporally structured dataset for neuromorphic computing by Ben Walters, Yeshwanth Bethi, Taylor Kergan, Binh Nguyen, Amirali Amirsoleimani, Jason K Eshraghian, Saeed Afshar, Mostafa Rahimi Azghadi

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RT-DETR-Pro: An Enhanced RT-DETR Model for Visual Detection of Photovoltaic Module Defects by Yunliang Chen, Mingtao Shan, Wei Zhou, Bing Ma, Xun Mei

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Enhanced intrusion detection model based on principal component analysis and variable ensemble machine learning algorithm by Ayuba John, Ismail Fauzi Bin Isnin, Syed Hamid Hussain Madni, Farkhana Binti Muchtar

“…As a result, because it is a linear transformation, it has challenges capturing non-linear relationships between feature properties in the network traffic datasets. …”
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A Deep Learning Based Analysis of the Big Five Personality Traits from Handwriting Samples Using Image Processing by Ahmed Elngar, Nikita Jain, Divyanshu Sharma, Himani Negi, Anuj Trehan, Akash Srivastava

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Prediction of microbe-drug associations using a CNN-Bernoulli random forest model by Zihao Song, Qingnuo Li, Jincheng Zhao, Qinggang Bu, Zekang Bian, Jia Qu

“…A convolutional neural network (CNN) is then used to reduce the dimensionality of all feature vectors, including those in the training set. …”
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Deep learning-based debris flow hazard detection and recognition system: a case study by Fei Wu, Jianlin Zhang, Dunlong Liu, Andreas Maier, Vincent Christlein

“…It consists of a video feature extraction network using a 3D convolutional neural network (CNN), a debris flow hazard detection network using a multi-layer perceptron (MLP), and a debris flow hazard recognition network for verification employing another CNN. …”
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A Novel Multistep Wavelet Convolutional Transfer Diagnostic Framework for Cross-Machine Bearing Fault Diagnosis by Lujia Zhao, Yuling He, Hai Zheng, Derui Dai

“…Firstly, a multistep time shift wavelet convolutional network (MTSWCN) based on the multiscale technique and wavelet transform is proposed to explore the diversity information regarding original vibration data and enhance the feature expression ability. …”
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Tertiary Review on Explainable Artificial Intelligence: Where Do We Stand? by Frank van Mourik, Annemarie Jutte, Stijn E. Berendse, Faiza A. Bukhsh, Faizan Ahmed

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Software defect prediction based on stacked sparse denoising autoencoders and enhanced extreme learning machine by Nana Zhang, Shi Ying, Kun Zhu, Dandan Zhu

“…To address these two issues, a novel defect prediction model called SSEPG based on Stacked Sparse Denoising AutoEncoders (SSDAE) and Extreme Learning Maching (ELM) optimised by Particle Swarm Optimisation (PSO) and another complementary Gravitational Search Algorithm (GSA) are proposed in this paper, which has two main merits: (1) employ a novel deep neural network – SSDAE to extract new combined features, which can effectively learn the robust deep semantic feature representation. (2) integrate strong exploitation capacity of PSO with strong exploration capability of GSA to optimise the input weights and hidden layer biases of ELM, and utilise the superior discriminability of the enhanced ELM to predict the defective modules. …”
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A novel approach to intrusion detection system using hybrid flower pollination and cheetah optimization algorithm by Deepshikha Kumari, Prashant Pranav, Abhinav Sinha, Sandip Dutta

“…A novel hybrid IDS model integrating the Flower Pollination Algorithm (FPA), Cheetah Optimization Algorithm (COA), and Artificial Neural Networks (ANN) is proposed to enhance detection accuracy, reduce false positives, and optimize feature selection, anomaly detection, and rule adaptation. …”
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AutoLDT: a lightweight spatio-temporal decoupling transformer framework with AutoML method for time series classification by Peng Wang, Ke Wang, Yafei Song, Xiaodan Wang

“…TS-separable linear self-attention mechanism and convolutional feedforward network achieve feature extraction in a lightweight way by decoupling temporal and spatial features of time series. …”
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Attack resilient IoT security framework using multi head attention based representation learning with improved white shark optimization algorithm by Jawhara Aljabri

“…Therefore, identifying numerous anomalies or cyberattacks in a network and constructing an effectual intrusion detection system (IDS) becomes more significant. …”
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Research on cloud computing users’ public safety trust model based on scorecard-random forest by Shengli ZHOU, Canghong JIN, Lifa WU, Zheng HONG

“…Traditional cloud computing trust models mainly focused on the calculation of the trust of users’ behavior.In the process of classification and evaluation,there were some problems such as ignorance of content security and lack of trust division verification.Aiming to solve these problems,cloud computing users’ public safety trust model based on scorecard-random forest was proposed.Firstly,the text was processed using Word2Vec in the data preprocessing stage.The convolution neural network (CNN) was used to extract the sentence features for user content tag classification.Then,scorecard method was used to filter the strong correlation index.Meanwhile,in order to establish the users’ public safety trust evaluation model in cloud computing,a random forest method was applied.Experimental results show that the proposed users’ public safety trust evaluation model outperforms the general trust evaluation model.The proposed model can effectively distinguish malicious users from normal users,and it can improve the efficiency of the cloud computing users management.…”
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