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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Artificial bee colony optimized random forest model for prediction of fly ash concrete compressive strength by Manish Bali, Ved Prakash Mishra, Anuradha Yenkikar

“…The proposed ABC-RF model achieved an R2 of 0.95 and outperformed several state-of-the-art deep learning models. The model also identified that a 30 % fly ash replacement yields optimal compressive strength, a finding further supported by SEM analysis, which showed dense C-S-H matrix formation at this level. …”
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A state-of-the-art review of diffusion model applications for microscopic image and micro-alike image analysis by Yan Liu, Tao Jiang, Rui Li, Lingling Yuan, Marcin Grzegorzek, Chen Li, Xiaoyan Li

“…The generated images serve as a powerful tool for data augmentation when training deep learning models. Diffusion model also excels in microscopic image segmentation. …”
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Enhanced estimation of reference evapotranspiration using hybrid deep learning models and remote sensing variables by Tze Ying Fong, Yuk Feng Huang, Ren Jie Chin, Chai Hoon Koo

“…The proposed hybrid deep learning models, combined model of convolutional neural network (CNN) with LSTM and GRU, respectively, achieved higher accuracy compared to individual models. …”
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Canned Apple Fruit Freshness Detection Using Hybrid Convolutional Neural Network and Transfer Learning by Mudasar Iqbal, Syed Tahseen Haider, Rana Saud Shoukat, Saif Ur Rehman, Khalid Mahmood, Santos Gracia Villar, Luis Alonso Dzul Lopez, Imran Ashraf

“…The experimental results indicate the superior performance of the proposed approach with a 98% accuracy on the original dataset, which is better than other deep learning models. The model secures a 97% accuracy on the augmented dataset. …”
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COSMIC-2 RFI Prediction Model Based on CNN-BiLSTM-Attention for Interference Detection and Location by Cheng-Long Song, Rui-Min Jin, Chao Han, Dan-Dan Wang, Ya-Ping Guo, Xiang Cui, Xiao-Ni Wang, Pei-Rui Bai, Wei-Min Zhen

“…The experimental results show that compared with the traditional band-pass filtering inter-correlation method and other deep learning models, the model in this paper has a root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R²) of 1.0185, 1.8567, and 0.9693, respectively, in RFI prediction, which demonstrates a higher RFI detection accuracy and a wide range of rough localization capabilities, showing significant competitiveness. …”
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DTC-m6Am: A Framework for Recognizing N6,2′-O-dimethyladenosine Sites in Unbalanced Classification Patterns Based on DenseNet and Attention Mechanisms by Hui Huang, Fenglin Zhou, Jianhua Jia, Huachun Zhang

“…Methods: Our proposed DTC-m6Am model first represents RNA sequences by One-Hot coding to capture base-based features and provide structured inputs for subsequent deep learning models. The model then combines densely connected convolutional networks (DenseNet) and temporal convolutional network (TCN). …”
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Transfer learning with YOLOV8 for real-time recognition system of American Sign Language Alphabet by Bader Alsharif, Easa Alalwany, Mohammad Ilyas

“…This study specifically addresses the recognition of ASL alphabet gestures using computer vision through Mediapipe for hand movement tracking and YOLOv8 for training the deep learning model. The model achieved notable performance metrics: precision of 98%, recall rate of 98%, F1 score of 99%, mean Average Precision (mAP) of 98%, and mAP50-95 of 93%, underscoring its exceptional accuracy and sturdy capabilities.…”
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Enhancing Explainability in Predictive Maintenance : Investigating the Impact of Data Preprocessing Techniques on XAI Effectiveness by Mouhamadou Lamine NDAO, Genane YOUNESS, Ndèye NIANG, Gilbert SAPORTA

“…In predictive maintenance, the complexity of the data often requires the use of Deep Learning models. These models, called “black boxes”, have proved their worth in predicting the Remaining Useful Life (RUL) of industrial machines. …”
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Multimodal deep learning-based radiomics for meningioma consistency prediction: integrating T1 and T2 MRI in a multi-center study by Huanjie Lin, Yubiao Yue, Li Xie, Bingbing Chen, Weifeng Li, Fan Yang, Qinrong Zhang, Huai Chen

“…Three models—a radiomics model (Rad_Model), a deep learning model (DL_Model), and a combined model (DLR_Model)—were developed. …”
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Gas permeability, diffusivity, and solubility in polymers: Simulation-experiment data fusion and multi-task machine learning by Brandon K. Phan, Kuan-Hsuan Shen, Rishi Gurnani, Huan Tran, Ryan Lively, Rampi Ramprasad

“…By amalgamating high throughput generated simulation data with available experimental data for gas permeability, diffusivity, and solubility for various gases, we construct multi-task deep learning models. These models can simultaneously predict all three properties for all gases under consideration, with markedly enhanced predictive accuracy, particularly compared to traditional models reliant solely on experimental data for a singular property. …”
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Automatic detection of manipulated Bangla news: A new knowledge-driven approach by Aysha Akther, Kazi Masudul Alam, Rameswar Debnath

“…However, in Bangla, existing endeavors on fake news detection generally relied on linguistic style analysis and latent representation-based machine learning and deep learning models. These models primarily rely on manually labeled annotations. …”
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Breast cancer image classification by using HCNN and LeNet5 by Pramoda Patro, Shaik Honey Fathima, R. Harikishore, Aditya Kumar Sahu

“…Finally, classification is performed using a hybrid deep learning model. This model combines a Convolutional Neural Network (CNN) with an Enhanced Recurrent Neural Network (ERNN), leveraging the strengths of both architectures. …”
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Breast cancer survival prediction using an automated mitosis detection pipeline by Nikolas Stathonikos, Marc Aubreville, Sjoerd deVries, Frauke Wilm, Christof A Bertram, Mitko Veta, Paul J vanDiest

“…Considering the potential of AI to improve reproducibility of MC between pathologists, we undertook the next validation step by evaluating the prognostic value of a fully automatic method to detect and count mitoses on whole slide images using a deep learning model. The model was developed in the context of the Mitosis Domain Generalization Challenge 2021 (MIDOG21) grand challenge and was expanded by a novel automatic area selector method to find the optimal mitotic hotspot and calculate the MC per 2 mm2. …”
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Soft-Label Supervised Meta-Model with Adversarial Samples for Uncertainty Quantification by Kyle Lucke, Aleksandar Vakanski, Min Xian

“…Despite the recent success of deep-learning models, traditional models are overconfident and poorly calibrated. …”
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Can deep learning technology really recognize Mpox? A positive response from the EfficientNet model by Xiaoqian Zhao, Xiaoqian Zhao, Long Lyu, Li Zhang, Li Zhang

“…To assist healthcare managers and medical professionals in efficiently and accurately identifying Mpox cases from similar conditions, this study proposes a lightweight deep learning model. The model uses EfficientNet as the backbone network and employs transfer learning techniques to transfer the pre-trained EfficientNet parameters, originally trained on the ImageNet dataset, into this model. …”
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Enhancing feature learning of hyperspectral imaging using shallow autoencoder by adding parallel paths encoding by Bibi Noor Asmat, Hafiz Syed Muhammad Bilal, M. Irfan Uddin, Faten Khalid Karim, Samih M. Mostafa, José Varela-Aldás

“…However, this abundance leads to redundant information, posing a computational challenge for deep learning models. Thus, models must effectively extract indicative features. …”
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Auto Evaluation for Essay Assessment Using a 1D Convolutional Neural Network by Novalanza Grecea Pasaribu, Gelar Budiman, Indrarini Dyah Irawati

“…We present a novel approach utilizing a One-dimensional Convolutional Neural Network (1D CNN) deep learning model. This model is specifically designed to analyze image-based student answer sheets, automatically classifying them according to the scores allocated for each question. …”
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River total dissolved gas prediction using a hybrid greedy-stepwise feature selection and bidirectional long short-term memory model by Khabat Khosravi, Salim Heddam, Changhyun Jun, Sayed M. Bateni, Dongkyun Kim, Essam Heggy

“…Hourly data on water temperature, barometric pressure, dam spill, sensor depth, and discharge serve as input variables for deep-learning models. Several models are developed and tested, including long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and an alternating model tree (AMT) hybridized with iterative absolute error regression (IAER) and iterative classifier optimizer (ICO) algorithms. …”
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Identification and grading method of HCAs in gas pipelines based on multisource data fusion by Haoran TANG, Junnan XIONG, Zhiwei YONG, Wenjie CHEN, Aoru LIU, Qisheng WANG, Huiwen XIAO, Rongkang WANG

“…Methods Based on high-resolution orthoimages captured by unmanned aerial vehicles (UAVs), a Squeeze-and-Excitation U network (SEU-Net) was developed as a deep learning model. This model improves the extraction of building edges to outline buildings around pipelines from the orthoimages. …”
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