Solar Energy Forecasting Framework Using Prophet Based Machine Learning Model: An Opportunity to Explore Solar Energy Potential in Muscat Oman by Mazhar Baloch, Mohamed Shaik Honnurvali, Adnan Kabbani, Touqeer Ahmed, Sohaib Tahir Chauhdary, Muhammad Salman Saeed

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Artificial Intelligence in the Non-Invasive Detection of Melanoma by Banu İsmail Mendi, Kivanc Kose, Lauren Fleshner, Richard Adam, Bijan Safai, Banu Farabi, Mehmet Fatih Atak

“…We specifically focus on AI application in clinical imaging, dermoscopic evaluation, algorithms that can distinguish melanoma from non-melanoma skin cancers, and in vivo skin imaging devices.…”
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IPO: An Improved Parrot Optimizer for Global Optimization and Multilayer Perceptron Classification Problems by Fang Li, Congteng Dai, Abdelazim G. Hussien, Rong Zheng

“…The Parrot Optimizer (PO) is a new optimization algorithm based on the behaviors of trained Pyrrhura Molinae parrots. …”
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Enhanced Dung Beetle Optimizer-Optimized KELM for Pile Bearing Capacity Prediction by Bohang Chen, Mingwei Hai, Gaojian Di, Bin Zhou, Qi Zhang, Miao Wang, Yanxiu Guo

“…Initially, experimental data on pile bearing capacity was gathered from the existing literature and subsequently normalized to facilitate effective integration into the model training process. A detailed introduction of the multi-strategy improved beetle optimization algorithm (IDBO) is provided, with its superior performance validated through 23 benchmark functions. …”
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Enhancing patient rehabilitation outcomes: artificial intelligence-driven predictive modeling for home discharge in neurological and orthopedic conditions by Leonardo Buscarini, Paola Romano, Elena Sofia Cocco, Carlo Damiani, Sanaz Pournajaf, Marco Franceschini, Francesco Infarinato

“…This process involved variables recoding, scaling, and the evaluation of different dataset balancing methods to optimize model performance. …”
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Cutting-edge approaches to specific energy prediction in TBM disc cutters: Integrating COSSA-RF model with three interpretative techniques by Jian Zhou, Zijian Liu, Chuanqi Li, Kun Du, Haiqing Yang

“…The evaluation results indicate that the COSSA-RF models demonstrate superior performance compared to other four machine learning models. …”
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Application of deep learning convolutional neural networks to identify gastric squamous cell carcinoma in mice by Yuke Ren, Yuke Ren, Shuangxing Li, Di Zhang, Yongtian Zhao, Yanwei Yang, Guitao Huo, Xiaobing Zhou, Xingchao Geng, Zhi Lin, Zhe Qu

“…Tumor prediction images (algorithm results shown as overlays) derived from the slide images were compared, and the performance of the constructed models was evaluated using Precision, Recall, and F1-score.ResultsThe Precision, Recall, and F1-scores of DenseNet, U-Net, and DeepLabv3 + algorithms were all above 90%. …”
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A novel deep learning-based 1D-CNN-optimized GRU approach for heart disease prediction by Jini Mol G., Ajith Bosco Raj T.

“…The proposed model’s overall accuracy is 99%. This is completely evaluated against other deep learning algorithms.…”
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Fast evaluation on the fatigue level of copper contact wire based on laser induced breakdown spectroscopy and supervised machine learning for high speed railway by Wenfu Wei, Langyu Xia, Zefeng Yang, Huan Zhang, Like Pan, Jian Wu, Guangning Wu

“…Herein, the authors propose a strategy using nanosecond pulsed laser induced breakdown spectroscopy (LIBS) combined with machine learning technique to realise a fast evaluation on the fatigue level of copper contact line. …”
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Dry sliding tribological characteristics evaluation and prediction of TiB2-CDA/Al6061 hybrid composites exercising machine learning methods by Amit Aherwar, Anamika Ahirwar, Vimal Kumar Pathak

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Ensuring information security in the production network of an industrial enterprise by A. R. Aydynan, D. G. Kirsanov

“…Methods for improving information security are proposed, including regular training, audits, network segmentation, and the use of specialized protection systems.Result. …”
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Prediction and Monitoring Model of Concrete Dam Deformation Based on WOA-RFR by FENG Yu, WU Yunxing, GU Wenjing, PANG Qiong, GU Yanchang, CHEN Siyu

“…The random forest model belonging to the machine learning algorithm has many advantages such as strong generalization ability and fast training speed, and it has a strong mapping capability for nonlinear features. …”
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Hybrid extreme learning machine for real-time rate of penetration prediction by Abdelhamid Kenioua, Omar Djebili, Ammar Touati Brahim

“…The models were trained and validated using a dataset of 13,262 data points from an Algerian field, with different statistical metrics used for evaluation. …”
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Parameter sensitivity analysis for diesel spray penetration prediction based on GA-BP neural network by Yifei Zhang, Gengxin Zhang, Dawei Wu, Qian Wang, Ebrahim Nadimi, Penghua Shi, Hongming Xu

“…The GA-BP neural network was selected for its ability to optimize neural network weights and thresholds, thereby improving model convergence and avoiding local minima, which are common challenges in complex, non-linear problems such as spray prediction. The model was trained using experimental data from diesel injector spray tests, and its accuracy was evaluated through parametric sensitivity analysis, examining the influence of various input factors. …”
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Toward accurate and scalable rainfall estimation using surveillance camera data and a hybrid deep-learning framework by Fiallos-Salguero Manuel, Soon-Thiam Khu, Jingyu Guan, Mingna Wang

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Immunogenic cell death genes in single-cell and transcriptome analyses perspectives from a prognostic model of cervical cancer by Li Ning, Li Ning, Xiu Li, Xiu Li, Yating Xu, Yating Xu, Yu Si, Yu Si, Hongting Zhao, Qinling Ren, Qinling Ren

“…Immunogenic cell death-related features (ICDRs) were constructed using multiple machine algorithms, and ICDRs were evaluated in training and validation sets to provide quantitative tools for predicting prognosis in clinical practice. …”
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Oxidative stress-related genes in uveal melanoma: the role of CALM1 in modulating oxidative stress and apoptosis and its prognostic significance by Yue Wu, Xiaoyan Cai, Menghan Hu, Runyan Cao, Yong Wang

“…Protein–protein interaction (PPI) networks were constructed to identify hub genes, and machine learning algorithms were utilized to screen for diagnostic genes, employing methods such as least absolute shrinkage and selection operator (LASSO) regression, random forest, support vector machine (SVM), gradient boosting machine (GBM), neural network algorithm (NNET), and eXtreme gradient boosting (XGBoost). …”
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Mobile application based on KDD to predict high-crime areas and promote sustainability in citizen security in a district of Lima-Perú by Hugo Vega-Huerta, Javier Vilca Velasquez, Nicolas Anicama Espinoza, Gisella Luisa Elena Maquen-Niño, Luis Guerra-Grados, Jorge Pantoja-Collantes, Oscar Benito-Pacheco, Juan Carlos Lázaro-Guillermo, Adegundo Camara-Figueroa, Javier Cabrera-Díaz, Rubén Gil-Calvo, Frida López-Córdova

“…Using historical data of criminal incidents and reports registered by users in the application, models capable of predicting the occurrence of crimes in real time are trained. The data mining process follows the KDD methodology, which includes the stages of selection, preprocessing, transformation, data mining, evaluation and knowledge consolidation. …”
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Finite Element and Machine Learning-Based Prediction of Buckling Strength in Additively Manufactured Lattice Stiffened Panels by Saiaf Bin Rayhan, Md Mazedur Rahman, Jakiya Sultana, Szabolcs Szávai, Gyula Varga

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A SWIN-based vision transformer for high-fidelity and high-speed imaging experiments at light sources by Songyuan Tang, Tekin Bicer, Kamel Fezzaa, Samuel Clark

“…These high data acquisition rates are often prone to noisy measurements, or in the case of slower (but less noisy) rates, the loss of scientifically significant phenomena.MethodsWe develop a Shifted Window (SWIN)-based vision transformer to reconstruct high-resolution x-ray image sequences with high fidelity and at a high frame rate and evaluate the underlying algorithmic framework on a high-performance computing (HPC) system. …”
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