Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images by Arezoo Borji, Taha-Hossein Hejazi, Abbas Seifi

“…In this paper, three deep-learning models, namely VGG16 and AlexNet, and a custom Convolutional Neural Network (CNN) with 8-fold cross-validation, have been used for classification. …”
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Onboard Processing of Hyperspectral Imagery: Deep Learning Advancements, Methodologies, Challenges, and Emerging Trends by Nafiseh Ghasemi, Jon Alvarez Justo, Marco Celesti, Laurent Despoisse, Jens Nieke

“…This article discusses the efficacy of different network architectures, highlighting the advantages of lightweight CNN models and 1D-CNNs for onboard processing. Moreover, the potential of hardware accelerators, particularly field programmable gate arrays, for enhancing processing efficiency is explored. …”
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Machine-learning crystal size distribution for volcanic stratigraphy correlation by Martin Jutzeler, Rebecca J. Carey, Yasin Dagasan, Andrew McNeill, Ray A. F. Cas

“…The workflow comprises three instance segmentation models for pre-processing the images, automated scale measurement and grain segmentation using Mask R-CNN. This avoids the laborious and time-consuming task of manual picking by image analysis, and allows for a rapid, unbiased and quantitative approach to determine crystal size distribution (CSD). …”
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Interpretable DWT-1DCNN-LSTM Network for Power Quality Disturbance Classification by Shuangquan Yang, Tao Shan, Xiaomei Yang

“…Experimental validation with simulated datasets demonstrates that the DWT-1DCNN-LSTM model achieves an accuracy of 99.27%, outperforming the DWT-1DCNN, 1DCNN-LSTM, LSTM, and CNN models by 1.59%, 1.13%, 1.44%, and 6.48%, respectively. …”
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Design, Multiperspective Investigations, and Performance Analysis of Multirotor Unmanned Aerial Vehicle for Precision Farming by Darshan Kumar Jayaram, Vijayanandh Raja, Beena Stanislaus Arputharaj, Hussein A. Z. AL-bonsrulah

“…Three AI systems were tested on different datasets to forecast plant stress by analyzing leaves due to technical constraints. CNN’s accuracy and computing speed make it ideal for precision farming. …”
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The Use of Machine Learning to Support the Diagnosis of Oral Alterations by Rosana Leal do Prado, Juliane Avansini Marsicano, Amanda Keren Frois, Jacques Duílio Brancher

“…Material and Methods: The study compares three convolutional neural network (CNN) architectures for classifying histological images: EfficientNet-B3, MobileNet-V2, and VGG16. …”
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Synthetic Network and Search Filter Algorithm in English Oral Duplicate Correction Map by Xiaojun Chen

“…On the basis of word vectors, the advantages and disadvantages of CNN, LSTM, and SVM models in this shared task are analyzed through experimental data. …”
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Enhancing security and efficiency in Mobile Ad Hoc Networks using a hybrid deep learning model for flooding attack detection by Pramodh Krishna D., E. Sandhya, Khaja Shareef Sk, Srihari Varma Mantena, Venkata Subbaiah Desanamukula, Ch Koteswararao, Srinivasa Rao Vemula, Maruthi Vemula

“…This study presents a novel hybrid deep learning approach integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to effectively detect and mitigate flooding attacks in MANETs. …”
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Diagnosis of approximal caries in children with convolutional neural networks based detection algorithms on radiographs: A pilot study by Zeynep Seyda Yavsan, Hediye Orhan, Enes Efe, Emrehan Yavsan

“…To create a convolutional neural network (CNN)-based diagnostic system for the prompt and efficient identification of approximal caries in pediatric patients aged 5–12 years. …”
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A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images by Jiang Xie, Jinzhu Wei, Huachan Shi, Zhe Lin, Jinsong Lu, Xueqing Zhang, Caifeng Wan

“…The code will be published on the https://github.com/jinzhuwei/BLTA-CNN .…”
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Motion Classification With Embroidery Bend Sensors Using Multiple Zigzag-Stitch for Loose-Fitting Garments by Kaisei Minami, Yasuhiro Akiyama, Takuya Umedachi

“…Moreover, motion classification was performed by training a one-dimensional convolutional neural network (1D-CNN) model with the sensor signals. The model successfully learned the differences in signal amplitude and frequency as distinguishing features of each activity, resulting in an average classification accuracy of 99.02% across the ten types of activities.…”
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Combined Oriented Data Augmentation Method for Brain MRI Images by Ahmeed Suliman Farhan, Muhammad Khalid, Umar Manzoor

“…The proposed method helps CNN models overcome overfitting and address class imbalance issues by combining Brain MRI images to generate new images. …”
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A semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI by M Nisha, T Kannan, K Sivasankari

“…Unlike the existing approaches such as UNet and Convolutional Neural Networks (CNN), the proposed algorithm generates an image that is similar to a real image by learning the distribution much more quickly by the semi-supervised iterative learning algorithm of the Deep Neuro-Fuzzy (DNF) technique. …”
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Identification of Civil Infrastructure Damage Using Ensemble Transfer Learning Model by A. Shamila Ebenezer, S. Deepa Kanmani, V. Sheela, K. Ramalakshmi, V. Chandran, M. G. Sumithra, B. Elakkiya, Bharani Murugesan

“…In this paper, an ensemble of three CNN models is proposed, and two are transfer learning-based models. …”
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A hybrid Framework for plant leaf disease detection and classification using convolutional neural networks and vision transformer by Sherihan Aboelenin, Foriaa Ahmed Elbasheer, Mohamed Meselhy Eltoukhy, Walaa M. El-Hady, Khalid M. Hosny

“…This proposed model leverages the strength of Convolutional Neural Networks (CNNs) and Vision Transformers (ViT), where an ensemble model, which consists of the well-known CNN architectures VGG16, Inception-V3, and DenseNet20, is used to extract robust global features. …”
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MPAR-RCNN: a multi-task network for multiple person detection with attribute recognition by S. Raghavendra, S. K. Abhilash, Venu Madhav Nookala, Jayashree Shetty, Praveen Gurunath Bharathi

“…Unlike the traditional Fast Region-based Convolutional Neural Network (R-CNN), which separately manages person detection and attribute classification with a dual-stage network, the MPAR-RCNN architecture optimizes both tasks within a single structure. …”
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EEG-RegNet: Regressive Emotion Recognition in Continuous VAD Space Using EEG Signals by Hyo Jin Jon, Longbin Jin, Hyuntaek Jung, Hyunseo Kim, Eun Yi Kim

“…The model leverages 2D convolutional neural networks (CNNs) for spatial feature extraction and a 1D CNN for temporal dynamics, providing robust spatiotemporal modeling. …”
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Developing an Intelligent System for Efficient Botnet Detection in IoT Environment by Ramesh Singh Rawat, Manoj Diwakar, Umang Garg, Prakash Srivastava

“…We obtained impressive results using these CNN, and LSTM RNN classifiers. We have also achieved a high attack detection rate.…”
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Subject independent evaluation of eyebrows as a stand‐alone biometric by Hoang (Mark) Nguyen, Ajita Rattani, Reza Derakhshani

“…Here, the evaluation of five deep learning models, lightCNN, ResNet, DenseNet, MobileNetV2, and SqueezeNet, for eyebrow‐based user authentication in a subject independent environment across different data sets, lighting conditions, resolutions, and facial expressions is done. …”
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Malaria Disease Prediction Based on Convolutional Neural Networks by Dhrgam AL Kafaf, Noor N. Thamir, Samara S. AL-Hadithy

“…The architectonics of the CNN model is meticulously devised, comprising of six blocks and three interconnected blocks, thereby rendering an efficient extraction of features and subsequent classification of the cells. …”
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