Application of Deep Learning in Forest Fire Prediction: A Systematic Review by Cesilia Mambile, Shubi Kaijage, Judith Leo

“…The study revealed that Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models are the most frequently used, with most datasets being publicly available. …”
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Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks by Magdalena Fafrowicz, Marcin Tutajewski, Igor Sieradzki, Jeremi K. Ochab, Jeremi K. Ochab, Anna Ceglarek-Sroka, Koryna Lewandowska, Tadeusz Marek, Barbara Sikora-Wachowicz, Igor T. Podolak, Paweł Oświęcimka, Paweł Oświęcimka, Paweł Oświęcimka

“…However, it's essential to match the type of learning method to the problem type, and extracting the information about the most important ROI connections might be challenging. …”
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Prediction of foreign currency exchange rates using an attention-based long short-term memory network by Shahram Ghahremani, Uyen Trang Nguyen

“…We conducted comprehensive experiments to evaluate and compare the performance of ALFA against several models used in previous work and against state-of-the-art deep learning models such as temporal convolutional networks (TCN) and Transformer. Experimental results show that ALFA outperforms the baseline models in most cases, across different currency pairs and feature sets, thanks to its attention mechanism that filters out irrelevant or redundant data to focus on important features. …”
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Fusion of Deep and Time–Frequency Local Features for Melanoma Skin Cancer Detection by Hamidreza Eghtesaddoust, Morteza Valizadeh, Mehdi Chehel Amirani

“…Skin cancer spreads quickly as the skin is the most vulnerable organ, and melanoma (MEL) is a fatal type of skin cancer. …”
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The Detection and Classification of Grape Leaf Diseases with an Improved Hybrid Model Based on Feature Engineering and AI by Fatih Atesoglu, Harun Bingol

“…Therefore, the early detection and classification of grape diseases with the latest artificial intelligence techniques and feature reduction techniques was carried out within the scope of this study. The most well-known convolutional neural network (CNN) architectures, texture-based Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) methods, Neighborhood Component Analysis (NCA), feature reduction methods, and machine learning (ML) techniques are the methods used in this article. …”
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Design and Analysis of an Expert System for the Detection and Recognition of Criminal Faces by Rishi Gupta, Amit Kumar Gupta, Deepak Panwar, Ashish Jain, Partha Chakraborty

“…An analysis of these strategies is also conducted and then put into practice to those that seem to be the most effective for the designed criminal recognition system. …”
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CNN Based Automatic Speech Recognition: A Comparative Study by Hilal Ilgaz, Beyza Akkoyun, Özlem Alpay, M. Ali Akcayol

“…The most advanced results have been obtained with speech recognition systems created using convolutional neural network (CNN) and recurrent neural networks (RNN). …”
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Advancements in the use of AI in the diagnosis and management of inflammatory bowel disease by Dalia Braverman-Jaiven, Luigi Manfredi

“…There are several datasets publicly available for endoscopy images and videos, but most of them are solely specialized in polyps. The use of DL algorithms to detect IBD is still in its inception, most studies are based on assessing the severity of UC. …”
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A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm by Zhixin Xia, Zhangqi Zheng, Lexin Bai, Xiaolei Yang, Yongshan Liu

“…At the same time, most of the algorithms only consider the general graph structure and do not fully consider the high-order information in the graph. …”
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Building Footprint Extraction from High Resolution Aerial Images Using Generative Adversarial Network (GAN) Architecture by Arnick Abdollahi, Biswajeet Pradhan, Shilpa Gite, Abdullah Alamri

“…Thus, we introduce an end-to-end convolutional neural network called Generative Adversarial Network (GAN) in this study to tackle these issues. …”
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Racism Detection by Analyzing Differential Opinions Through Sentiment Analysis of Tweets Using Stacked Ensemble GCR-NN Model by Ernesto Lee, Furqan Rustam, Patrick Bernard Washington, Fatima El Barakaz, Wajdi Aljedaani, Imran Ashraf

“…Owing to the superior performance of deep learning, a stacked ensemble deep learning model is assembled by combining gated recurrent unit (GRU), convolutional neural networks (CNN), and recurrent neural networks RNN, called, Gated Convolutional Recurrent- Neural Networks (GCR-NN). …”
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A Comprehensive Design of Hybrid Residual (2+1)-Dimensional CNN and Dense Networks With Multi-Modal Sensor for Fish Appetite Detection by Infall Syafalni, Agape D'Sky, Nana Sutisna, Trio Adiono

“…Fish is one of the most demanding protein sources in the food industry. …”
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Assessment of Bone Aging—A Comparison of Different Methods for Evaluating Bone Tissue by Paweł Kamiński, Aleksander Gali, Rafał Obuchowicz, Michał Strzelecki, Adam Piórkowski, Marcin Kociołek, Elżbieta Pociask, Joanna Kwiecień, Karolina Nurzyńska

“…The use of artificial intelligence in medical data analysis produces comparable outcomes; however, when dealing with a large number of descriptors, selecting the most optimal ones through statistical analysis enables the identification of the best solution quickly.…”
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Unlocking chickpea flour potential: AI-powered prediction for quality assessment and compositional characterisation by Ali Zia, Muhammad Husnain, Sally Buck, Jonathan Richetti, Elizabeth Hulm, Jean-Philippe Ral, Vivien Rolland, Xavier Sirault

“…Using a dataset comprising 136 chickpea varieties, the research compares the performance of several state-of-the-art deep learning models, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Graph Convolutional Networks (GCNs), and compares the most effective model, CNN, against the traditional Partial Least Squares Regression (PLSR) method. …”
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Multi‐Wound Classification: Exploring Image Enhancement and Deep Learning Techniques by Prince Odame, Maxwell Mawube Ahiamadzor, Nana Kwaku Baah Derkyi, Kofi Agyekum Boateng, Kelvin Sarfo‐Acheampong, Eric Tutu Tchao, Andrew Selasi Agbemenu, Henry Nunoo‐Mensah, Dorothy Araba Yakoba Agyapong, Jerry John Kponyo

“…The performance metrics showed similar results for the first two approaches, but FixCaps was the most proficient, with accuracy, precision, recall, and F‐score of 93.83%, 95.41%, 88.63%, and 90.93% respectively. …”
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Fusing satellite imagery and ground-based observations for PM2.5 air pollution modeling in Iran using a deep learning approach by Zohreh Sohrabi, Jamshid Maleki

“…Sensitivity analysis indicated that among 18 input parameters, population density, AOD, NO2, and rainfall had the most significant impact on PM2.5 concentrations.…”
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Advanced hybrid deep learning model for enhanced evaluation of osteosarcoma histopathology images by Arezoo Borji, Arezoo Borji, Arezoo Borji, Gernot Kronreif, Bernhard Angermayr, Bernhard Angermayr, Sepideh Hatamikia, Sepideh Hatamikia

“…This study focuses on osteosarcoma (OS), the most common bone cancer in children and adolescents, which affects the long bones of the arms and legs. …”
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Dynamic spatiotemporal graph network for traffic accident risk prediction by Pengcheng Zhang, Wen Yi, Yongze Song, Penggao Yan, Peng Wu, Ammar Shemery, Keith Hampson, Albert P. C. Chan

“…Our model uses channel-wise convolutional neural networks to detect spatial accident patterns across weekly, daily, and hourly time scales with automatic weight learning, simultaneously employing graph convolutional networks to process road network features, population feature while integrating external data like weather and dates. …”
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Local Auxiliary Spatial–Spectral Decoupling Transformer Network for Cross-Scene Hyperspectral Image Classification by Qiusheng Chen, Zhuoqun Fang, Zhaokui Li, Qian Du, Shizhuo Deng, Tong Jia, Dongyue Chen

“…The feature-level domain alignment based on deep learning techniques has greatly improved the performance of unsupervised domain adaptation (UDA) for hyperspectral image (HSI) classification. However, most of these methods leverage convolutional neural networks to capture local features, overlooking the comparable spatial global (SaG) and spectral global (SeG) information shared by both the source and target domains. …”
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AFDR-Det: Adaptive Feature Dual-Refinement Oriented Detector for Remote Sensing Object Detection by Jie Yang, Li Zhou, Yongfeng Ju

“…In remote sensing images, objects are distributed in arbitrary orientations, while convolutional features are inherently axis-aligned. This inevitably leads to spatial misalignment between the heuristically defined anchors and the convolutional features. …”
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