Landfill Site Suitability Assessment Based on GIS and Multicriteria Analysis: A Case Study of Kirkuk City by Hana Kareem Shekho, Muntadher Aidi Shareef

“…The Analytic Hierarchy Process (AHP) was utilized for multi-criteria decision analysis of possible landfill sites, linear regression was employed for population projection, and a Convolutional Neural Network (CNN) was utilized for Normalized Difference Vegetation Index (NDVI)/ Normalized Difference Built-up Index (NDBI) prediction. …”
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A deep learning-based multi-view approach to automatic 3D landmarking and deformity assessment of lower limb by Reyhaneh Rostamian, Masoud Shariat Panahi, Morad Karimpour, Hadi G. Kashani, Amirhossein Abi

“…The average coordinate error (difference between automatically and manually determined coordinates) of the landmarks was 2.05 ± 1.36 mm on test data, whereas the average angular error (difference between automatically and manually calculated angles in three and two dimensions) on the same dataset was 0.53 ± 0.66° and 0.74 ± 0.87°, respectively. …”
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Efficient and Effective NDVI Time-Series Reconstruction by Combining Deep Learning and Tensor Completion by Ang Li, Menghui Jiang, Dong Chu, Xiaobin Guan, Jie Li, Huanfeng Shen

“…Reconstruction of normalized difference vegetation index (NDVI) time series plays an imperative part in the inference of vegetation dynamics. …”
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Hyperspectral Signatures for Detecting the Concrete Hydration Process Using Neural Networks by Shiming Li, Alfred Strauss, Damjan Grba, Maximilian Granzner, Benjamin Täubling-Fruleux, Thomas Zimmermann

“…This means that inadequate curing conditions lead to a loss of concrete quality and negative consequences in structural engineering. In addition, different state-of-the-art (SOTA) curing surface treatments and hydration periods have a significant effect on durability. …”
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Audio-Based Engine Fault Diagnosis with Wavelet, Markov Blanket, ROCKET, and Optimized Machine Learning Classifiers by Bernardo Luis Tuleski, Cristina Keiko Yamaguchi, Stefano Frizzo Stefenon, Leandro dos Santos Coelho, Viviana Cocco Mariani

“…Developing appropriate fault-labeled datasets can be challenging due to nonlinearity variations and divergence in feature distribution among different engine kinds or operating scenarios. To solve this task, this study experimentally measures audio emission signals from compression ignition engines in different vehicles, simulating injector failures, intake hose failures, and absence of failures. …”
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Comparative Analysis of Hybrid Attention and Progressive Layering Through a Comprehensive Evaluation of ARU-Net and PLU-Net in Brain Tumour Segmentation by Noman Ahmed Siddiqui, Muhammad Tahir Qadri, Muhammad Ovais Akhter, Tahira Anwar, Fahad Farooq

“…The preprocessing steps are quite different as ARU-Net uses simplified Z-score normalisation and resizing, and PLU-Net involves a full pipeline, involving skull stripping, bias field correction, and two normalisations. …”
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Dual-Branch Deep Learning with Dynamic Stage Detection for CT Tube Life Prediction by Zhu Chen, Yuedan Liu, Zhibin Qin, Haojie Li, Siyuan Xie, Litian Fan, Qilin Liu, Jin Huang

“…The D-TCN branch captures multi-scale temporal features through multi-layer dilated convolutions, effectively handling non-linear changes in the degradation phase. …”
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Unbalancing Datasets to Enhance CNN Models Learnability: A Class-Wise Metrics-Based Closed-Loop Strategy Proposal by Somayeh Shahrabadi, Victor Alves, Emanuel Peres, Raul Morais Dos Santos, Telmo Adao

“…Using these datasets, 72 models with varying configurations – including different convolutional neural network architectures, initial learning rates, and optimizers – were initially trained and then evaluated against imagery test sets. …”
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A Malware Detection Method Based on Genetic Algorithm Optimized CNN-SENet Network by Zheng Yang, Hua Zhu, Zhao Li, Gang Wang, Meng Su

“…To this end, this paper proposes a malware detection method based on genetic algorithm optimization of the CNN-SENet network, which firstly introduces the SENet attention mechanism into the convolutional neural network to enhance the spatial feature extraction capability of the model; then, the application programming interface (API) sequences corresponding to different software behaviors are processed by segmentation and de-duplication, which in turn leads to the sequence feature extraction through the CNN-SENet model; finally, genetic algorithm is used to optimize the hyperparameters of CNN-SENet network to reduce the computational overhead of CNN and to achieve the recognition and classification of different malware at the output layer. …”
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TraitBertGCN: Personality Trait Prediction Using BertGCN with Data Fusion Technique by Muhammad Waqas, Fengli Zhang, Asif Ali Laghari, Ahmad Almadhor, Filip Petrinec, Asif Iqbal, Mian Muhammad Yasir Khalil

“…Abstract Personality prediction via different techniques is an established and trending topic in psychology. …”
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Spatio-Temporal Prediction of Surface Remote Sensing Data in Equatorial Pacific Ocean Based on Multi-Element Fusion Network by Tianliang Xu, Zhiquan Zhou, Chenxu Wang, Yingchun Li, Tian Rong

“…In this paper, we propose a multi-element fusion network model based on convolutional long short-term memory (ConvLSTM) and an attention mechanism to predict the SST and analyze the effects of different elemental inputs on the model’s prediction performance using the prediction results. …”
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LCC-Net: Swin transformer-CNN hybrid for enhanced land cover classification in natural disaster monitoring by P. Shailaja, Pala Mahesh Kumar, Nalla Nikhitha, Kunta Neeraj Kumar Reddy, Enthala Mukesh Reddy, Goli Ganesh Reddy, Vadde Indu

“…The core of LCC-Net employs the Swin Transformer Convolutional Neural Network (ST-CNN), which leverages self-attention mechanisms to capture intricate spatial features and temporal dynamics. …”
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Software Defect Prediction Based on Effective Fusion of Multiple Features by Chaozheng Zhang, Junhua Wu

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Deep Learning Applications in Medical Image Processing by Ayşe Gül Eker, Nevcihan Duru

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A Fault Diagnosis Method for Wind Turbines Based on Zero-Shot Learning by PAN Meiqi, HE Xing

“…In engineering practice, wind turbine fault diagnosis encounters situations where the fault category in the training data is different from the actual one. To diagnose unknown wind turbine faults, it is necessary to transfer the fault feature information learned during training to the unknown fault category. …”
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From Image to Sequence: Exploring Vision Transformers for Optical Coherence Tomography Classification by Amirali Arbab, Aref Habibi, Hossein Rabbani, Mahnoosh Tajmirriahi

“…Current methods for OCT image classification encounter specific challenges, such as the inherent complexity of retinal structures and considerable variability across different OCT datasets. Methods: This paper introduces a novel hybrid model that integrates the strengths of convolutional neural networks (CNNs) and vision transformer (ViT) to overcome these obstacles. …”
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Improved Technique in Arabic Handwriting Recognition by Ammar A. Al-Hamadani, Maad Kamal Al-Anni, Gamil R. S. Qaid, Najran Nasser Hamood

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Recent Advances in Fault Diagnosis Methods for Electrical Motors- A Comprehensive Review with Emphasis on Deep Learning by Jawad Faiz, F. Parvin

“…Additionally, it examines different datasets and features used in these methods, highlighting their advantages and limitations. …”
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Lamb wave ultrasonic testing technology for diagnosis and prediction of hidden cracks in metal structures by Dawen Yao, Peiwen Meng, Shuqi Li, Jinggang Xu

“…The system has a detection success rate of more than 88% for cracks of different sizes and types, and the error is controlled within 0.1 mm; the crack expansion prediction accuracy rate is 87.5%, which effectively predicts the development trend of cracks. …”
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Identification of Nanoparticle Agglomeration in Polymer Plates Reinforced with Carbon Nanotubes by Use of Active Thermography and Deep Learning-Based Image Processing by Mazharul Islam, Raihan Sarker

“…The method proposed in this paper utilizes active infrared thermography along with deep convolutional neural networks (DCNN). Five nanocomposite specimens were prepared with artificially generated zones of agglomeration at the centers. …”
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