Lithofacies and sandstone reservoir characterization for geothermal assessment through artificial intelligence by Zohaib Naseer, Muhsan Ehsan, Muhammad Ali, Muhammad Raiees Amjad, Muhammad Ali Umair Latif, Kamal Abdelrahman

“…Facies analysis was performed based on well data through machine learning, demonstrating that the major facies present are sandstone and shale in the target Formation. …”
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Predicting the impact of climate change on the re-emergence of malaria cases in China using LSTMSeq2Seq deep learning model: a modelling and prediction analysis study by Eric Kamana, Jijun Zhao, Di Bai

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Effect of social media-based education on self-care status, health literacy, and glycated hemoglobin in patients with type 2 diabetes by Ali Safdari, Nazi Nejat, Abdollah Abolfathi, Fatemeh Mehrabi, Fatemeh Rafiei

“…Nevertheless, the Mann–Whitney test did not indicate a significant statistical difference between the groups regarding the average HbA1c score before and after the intervention (p > 0.05).ConclusionThe findings of this study suggest that social networks provide a suitable platform for delivering self-care education to individuals with T2D. …”
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Forecasting Container Throughput of Singapore Port Considering Various Exogenous Variables Based on SARIMAX Models by Geun-Cheol Lee, June-Young Bang

“…Using monthly container throughput data of the Singapore port from 2010 to 2021, we develop a Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model. …”
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cpDNA-Gene-Sequence-Based Genetic Diversity, Population Structure, and Gene Flow Analysis of Ethiopian Lowland Bamboo (Bambusinea: Oxytenanthera abyssinica (A. Rich.) Munro) by Oumer Abdie Oumer, Kassahun Tesfaye, Tileye Feyissa, Dagnew Yibeyen, Jayaraman Durai, Muhammad Zeeshan Hyder

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FSS-ULivR: a clinically-inspired few-shot segmentation framework for liver imaging using unified representations and attention mechanisms by Ripon Kumar Debnath, Md. Abdur Rahman, Sami Azam, Yan Zhang, Mirjam Jonkman

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Evaluating Land use Mixed-ness on Street Level through Spatial Analyses and Gini Method by Hamid Motieyan, Mohammad Azmoodeh

“…The Gini index is a statistical index to measure the distribution of data among a community, which is often used to measure economic inequality, or in other words, how wealth is distributed among individuals in a society. …”
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Synergistic use of SAR satellites with deep learning model interpolation for investigating of active landslides in Cuenca, Ecuador by Mohammad Amin Khalili, Silvio Coda, Domenico Calcaterra, Diego Di Martire

“…In this study, we show the potential of a synergistic use of COSMO-SkyMed (CSK) and Sentinel-1A (S1A) synthetic aperture radar (SAR) data for comprehensively monitoring the Cuenca landslides. …”
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Research and application of mining AI video edge computing technology by ZHANG Liya, HAO Bonan, MA Zheng, YANG Zhifang

“…Currently, mining AI video systems mainly rely on ground servers for analysis and processing, which leads to issues such as high overall response latency, multi-system linkage delays, and high network bandwidth utilization. …”
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Multi-step Prediction of Monthly Sediment Concentration Based on WPT-ARO-DBN/WPT-EPO-DBN Model by GAO Xuemei, CUI Dongwen

“…Accurate multi-step sediment concentration prediction is of significance for regional soil erosion control,flood control and disaster reduction.To improve the multi-step prediction accuracy of sediment concentration and the prediction performance of the deep belief network (DBN),this paper proposes a multi-step prediction model of monthly sediment concentration by combining the artificial rabbit optimization (ARO) algorithm,eagle habitat optimization (EPO) algorithm,and DBN based on wavelet packet transform (WPT).The model is validated using time series data of monthly sediment concentration from Longtan Station in Yunnan Province.Firstly,WPT is employed to decompose the time series data of the monthly sediment concentration of the case in three layers,and eight more regular subsequence components are obtained.Secondly,the principles of ARO and EPO algorithms are introduced,and hyperparameters such as the neuron number in the hidden layer of DBN are optimized by ARO and EPO.Meanwhile,WPT-ARO-DBN and WPT-EPO-DBN prediction models are built,and WPT-PSO (particle swarm optimization)-DBN and WPT-DBN are constructed for comparative analysis.Finally,four models are adopted to predict each subsequence component,and the predicted values are superimposed to obtain the multi-step prediction results of the final monthly sediment concentration.The results are as follows.① WPT-ARO-DBN and WPT-EPO-DBN models have satisfactory prediction effects on the monthly sediment concentration of the case from one step ahead to four steps ahead.This yields sound prediction results for five steps ahead.The prediction effect for six steps ahead and seven steps ahead is average,and the prediction accuracy for eight steps ahead is poor and cannot meet the prediction accuracy requirements.② The multi-step prediction performance of WPT-ARO-DBN and WPT-EPO-DBN models is superior to WPT-PSO-DBN models and far superior to WPT-DBN models,with higher prediction accuracy,better generalization ability,and larger prediction step size.③ ARO and EPO can effectively optimize DBN hyperparameters,improve DBN prediction performance,and have better optimization effects than PSO.Additionally,WPT-ARO-DBN and WPT-EPO-DBN models can give full play to the advantages of WPT,new swarm intelligence algorithms and the DBN network and improve the multi-step prediction accuracy of monthly sediment concentration,and the prediction accuracy decreases with the increasing prediction steps.…”
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Statistical Modeling to Improve Time Series Forecasting Using Machine Learning, Time Series, and Hybrid Models: A Case Study of Bitcoin Price Forecasting by Moiz Qureshi, Hasnain Iftikhar, Paulo Canas Rodrigues, Mohd Ziaur Rehman, S. A. Atif Salar

“…Various hybrid models are then proposed utilizing these models, which are based on simple averaging of these models. The data-splitting technique, commonly used in comparative analysis, splits the data into training and testing data sets. …”
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Energy‐Efficient Hardware Implementation of Spiking‐Restricted Boltzmann Machines Using Pseudo‐Synaptic Sampling by Hyunwoo Kim, Suyeon Jang, Uicheol Shin, Masatoshi Ishii, Atsuya Okazaki, Megumi Ito, Akiyo Nomura, Kohji Hosokawa, Sungmin Lee, Matthew BrightSky, Sangbum Kim

“…In this study, a foundation is laid for maximizing the energy efficiency of spiking neural network processors by optimizing internal noise generation mechanisms.…”
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Comparing the Performance of Regression and Machine Learning Models in Predicting the Usable Area of Houses with Multi-Pitched Roofs by Leszek Dawid, Anna Marta Barańska, Paweł Baran

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Dynamic Routing in Urban Transport Logistics under Limited Traffic Information by Viktor Danchuk, Oleksandr Hutarevych, Serhii Taraban

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Optimal Forecast Combination for Japanese Tourism Demand by Yongmei Fang, Emmanuel Sirimal Silva, Bo Guan, Hossein Hassani, Saeed Heravi

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Leveraging an ensemble of EfficientNetV1 and EfficientNetV2 models for classification and interpretation of breast cancer histopathology images by Mahdi Azmoodeh-Kalati, Hasti Shabani, Mohammad Sadegh Maghareh, Zeynab Barzegar, Reza Lashgari

“…The advent of whole-slide scanners has revolutionized this process by enabling the use of Computer-Aided Detection (CAD) systems for automated analysis. In this study, we utilize state-of-the-art Convolutional Neural Networks (CNNs), specifically EfficientNetV1 and EfficientNetV2, for the binary classification of the BreakHis dataset—a collection of histopathological images categorized as benign or malignant breast tissues. …”
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Med-DGTN: Dynamic Graph Transformer with Adaptive Wavelet Fusion for multi-label medical image classification by Guanyu Zhang, Yan Li, Tingting Wang, Guokun Shi, Li Jin, Zongyun Gu, Zongyun Gu

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Image Generation and Lesion Segmentation of Brain Tumors and Stroke Based on GAN and 3D ResU-Net by Mingkang Sun, Xiang Li, Weiye Sun

“…For example, in T1<inline-formula> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula> Flair conversion, the generative multi-modal image analysis model based on perceptual loop consistency had an average peak signal-to-noise ratio of <inline-formula> <tex-math notation="LaTeX">$23.951~\pm ~2.735$ </tex-math></inline-formula>, an average structural similarity of <inline-formula> <tex-math notation="LaTeX">$0.873~\pm ~0.046$ </tex-math></inline-formula>, and an average root mean square error of <inline-formula> <tex-math notation="LaTeX">$16.998~\pm ~6.184$ </tex-math></inline-formula>. …”
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An integrated approach to flood risk assessment using multi-criteria decision analysis and geographic information system. A case study from a flood-prone region of Pakistan by Muhammad Ibrahim, Aidi Huo, Aidi Huo, Aidi Huo, Waheed Ullah, Safi Ullah, Zhao Xuantao

“…Data was collected from field surveys, questionnaires, and interviews, allowing for a detailed analysis of flood hazards. …”
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Explainable Deep Learning for COVID-19 Vaccine Sentiment in Arabic Tweets Using Multi-Self-Attention BiLSTM with XLNet by Asmaa Hashem Sweidan, Nashwa El-Bendary, Shereen A. Taie, Amira M. Idrees, Esraa Elhariri

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