The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution by Mohamed Khalid AlOmar, Faidhalrahman Khaleel, Abdulwahab Abdulrazaaq AlSaadi, Mohammed Majeed Hameed, Mohammed Abdulhakim AlSaadi, Nadhir Al-Ansari

“…In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. …”
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A Framework for Evaluating Geomagnetic Indices Forecasting Models by Armando Collado‐Villaverde, Pablo Muñoz, Consuelo Cid

“…To operationalize the evaluation framework, a comparative study is conducted between a baseline neural network model and a persistence model, showcasing the effectiveness of the new metric in evaluating forecasting performance during intense geomagnetic storms. …”
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Climate Regionalization of Asphalt Pavement Based on the K-Means Clustering Algorithm by Yanhai Yang, Baitong Qian, Qicheng Xu, Ye Yang

“…The pavement degradation in each climatic zone was related to the climate characteristics of the region. Probabilistic neural network (PNN) and support vector machine (SVM) climate regionalization predictive models were established with MATLAB. …”
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Fatigue Driving Prediction on Commercial Dangerous Goods Truck Using Location Data: The Relationship between Fatigue Driving and Driving Environment by Shifeng Niu, Guiqiang Li

“…From the six different categories of the predictor set, we obtain a set of 17 predictor variables to train logistic regression, neural network, and random forest classifiers. Then, we evaluate the predictive performance of the classifiers based on three indexes: accuracy, F1-measure, and area under the ROC curve (AUROC). …”
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Deep Learning-Based Home Energy Management Incorporating Vehicle-to-Home and Home-to-Vehicle Technologies for Renewable Integration by Marwan Mahmoud, Sami Ben Slama

“…Smart cities embody a transformative approach to modernizing urban infrastructure and harness the power of deep learning (DL) and Vehicle-to-Home (V2H) technology to redefine home energy management. Neural network-based Q-learning algorithms optimize the scheduling of household appliances and the management of energy storage systems, including batteries, to maximize energy efficiency. …”
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Integration in CNN and FIR filters for improved computational efficiency in signal processing by A. Sridevi, A. Sathiya

“…Importantly, the proposed multiplier based on the modification of Vedic Nikhilam yields the lowest power consumption (248.93 mW), the lowest delay (27.95 ns), and the lowest PDP (6.96 pJ), thus making it appropriate for usage in HPC related to signal processing and neural network computations. Moreover, the developed FIR filter for the CNN and the EEG signal datasets were employed to detect seizures and Alzheimer’s disease. …”
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Feature Extraction of Broken Glass Cracks in Road Traffic Accident Site Based on Deep Learning by Shuai Liang

“…This paper studies the feature extraction and middle-level expression of Convolutional Neural Network (CNN) convolutional layer glass broken and cracked at the scene of road traffic accident. …”
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Predicting the heat capacity of strontium-praseodymium oxysilicate SrPr4(SiO4)3O using machine learning, deep learning, and hybrid models by Amir Hossein Sheikhshoaei, Ali Khoshsima, Davood Zabihzadeh

“…In this study, the capability of five advanced machine learning models, including Random Forest (RF), Gradient Boosting (GBoost), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Decision Tree (DT) models, and three deep learning models, TabNet, Deep Belief Network (DBN), and Deep Neural Network (DNN) was investigated. Our analysis indicates that the Random Forest and Deep Belief Network models outperform all other competing models. …”
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Real-world pharmacovigilance analysis unveils the toxicity profile of amivantamab targeting EGFR exon 20 insertion mutations in non-small cell lung cancer by Jing Zhang, Wenjie Li

“…A comprehensive disproportionality analysis was performed, employing the reporting odds ratio (ROR), proportional reporting ratio (PRR), Empirical Bayes Geometric Mean (EBGM), and the Bayesian confidence propagation neural network to calculate information components (ICs), to identify statistically significant adverse events. …”
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Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches by Kamal Hossain Nahin, Jamal Hossain Nirob, Akil Ahmad Taki, Md Ashraful Haque, Narinderjit Sawaran SinghSingh, Liton Chandra Paul, Reem Ibrahim Alkanhel, Hanaa A. Abdallah, Abdelhamied A. Ateya, Ahmed A. Abd El-Latif

“…Leveraging a meta learner-based stacked generalization ensemble strategy, this study integrates classical machine learning techniques with an optimized multi-feature stacked ensemble to predict antenna properties with greater accuracy. Specifically, a neural network is applied as a base learner for predicting antenna parameters, resulting in increased predictive performance, achieving R², EVS, MSE, RMSE, and MAE values of 0.96, 0.998, 0.00842, 0.00453, and 0.00999, respectively. …”
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Novel transfer learning based bone fracture detection using radiographic images by Aneeza Alam, Ahmad Sami Al-Shamayleh, Nisrean Thalji, Ali Raza, Edgar Anibal Morales Barajas, Ernesto Bautista Thompson, Isabel de la Torre Diez, Imran Ashraf

“…The detection of bone fractures is crucial, and radiographic images are often relied on for accurate assessment. An efficient neural network method is essential for the early detection and timely treatment of fractures. …”
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AI-assisted Total Body Dermoscopic Evaluation of Changes in Melanocytic Nevi during Pregnancy: A Prospective, Comparative Study of 2,799 Nevi by Jelissa K. Peter, Fabrice Helfenstein, Sara E. Cerminara, Julia-Tatjana Maul, Mara L. Zehnder, Dagmar Jamiolkowski, Elisabeth Roider, Beda Mühleisen, Irene Hösli, Alexander A. Navarini, Lara V. Maul

“…We conducted the first prospective study to investigate dermoscopic changes in MN comparing pregnant with non-pregnant women on all body parts using a market-approved convolutional neural network (CNN). We included 25 pregnant and 25 non-pregnant women from Basel, Switzerland, who underwent standard skin cancer screenings and whose MN > 2 mm were digitally recorded and analysed by a CNN. …”
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Multi-Scale Bilateral Spatial Direction-Aware Network for Cropland Extraction Based on Remote Sensing Images by Weimin Hou, Yanxia Wang, Jia Su, Yanli Hou, Ming Zhang, Yan Shang

“…Compared to other neural network models, MBSDANet achieves better accuracy with a precision of 0.9481, an IoU of 0.8937, and an F1 score of 0.9438.…”
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Statistical Analysis of Medium‐Scale Traveling Ionospheric Disturbances Over Japan Based on Deep Learning Instance Segmentation by Peng Liu, Tatsuhiro Yokoyama, Weizheng Fu, Mamoru Yamamoto

“…Therefore, this research proposes a real‐time processing algorithm for dTEC maps based on Mask Region‐Convolutional Neural Network (R‐CNN) model of deep learning instance segmentation to detect wavelike perturbations intelligently with an accuracy of about 80% and a processing speed of about 8 fps. …”
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Detection of Viable but Nonculturable E. coli Induced by Low-Level Antimicrobials Using AI-Enabled Hyperspectral Microscopy by MeiLi Papa, Aarham Wasit, Justin Pecora, Teresa M. Bergholz, Jiyoon Yi

“…An EfficientNetV2-based convolutional neural network architecture was trained on these pseudo-RGB images, achieving 97.1% accuracy of VBNC classification (n = 200), outperforming the model trained on RGB images at 83.3%. …”
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Effect of Muscle Fatigue on Surface Electromyography-Based Hand Grasp Force Estimation by Jinfeng Wang, Muye Pang, Peixuan Yu, Biwei Tang, Kui Xiang, Zhaojie Ju

“…Specifically, the reduction in the maximal capacity to generate force is used as the metric of muscle fatigue in combination with a back-propagation neural network (BPNN) is adopted to build a sEMG-hand grasp force estimation model. …”
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Screening of multi deep learning-based de novo molecular generation models and their application for specific target molecular generation by Yishu Wang, Mengyao Guo, Xiaomin Chen, Dongmei Ai

“…Moreover, we propose an integrated end-to-end neural network learning framework based on one complete encoder-decoder architecture transformer model: Transfer Text-to-Text Transformer (T5), by learning the embedding vector representation space of conditional molecular properties to encode and guide the vector representation of SMILES sequences, resulting in the output of the final decoder block with a softmax output (maximum likelihood objective). …”
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Artificial intelligence for hemodynamic monitoring with a wearable electrocardiogram monitor by Daphne E. Schlesinger, Ridwan Alam, Roey Ringel, Eugene Pomerantsev, Srikanth Devireddy, Pinak Shah, Joseph Garasic, Collin M. Stultz

“…Methods We developed a deep neural network using single-lead electrocardiogram data to determine when the left atrial pressure is elevated. …”
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Employing the concept of stacking ensemble learning to generate deep dream images using multiple CNN variants by Lafta Alkhazraji, Ayad R. Abbas, Abeer S. Jamil, Zahraa Saddi Kadhim, Wissam Alkhazraji, Sabah Abdulazeez Jebur, Bassam Noori Shaker, Mohammed Abdallazez Mohammed, Mohanad A. Mohammed, Basim Mohammed Al-Araji, Abdulkareem Z. Mohmmed, Wasiq Khan, Bilal Khan, Abir Jaafar Hussain

“…For model development, a series of five pre-trained Convolutional Neural Network (CNN) architectures—VGG-19, Inception v3, VGG-16, Inception-ResNet-V2, and Xception were stacked in an ensemble learning approach to create Deep Dream images whereby the upper hidden layers of the architectures were activated, and the models were trained via the Adam optimizer. …”
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Soft computing paradigm for climate change adaptation and mitigation in Iran, Pakistan, and Turkey: A systematic review by Muhammad Talha, A. Pouyan Nejadhashemi, Kieron Moller

“…Although some articles utilized multiple techniques, classical ML methods appeared in approximately 37.3 % of the studies, neural network paradigms in about 57.5 %, and optimization or meta-heuristic algorithms in around 5.0 %. …”
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