Efficient and low complex architecture for detection and classification of Brain Tumor using RCNN with Two Channel CNN by Nivea Kesav, M.G. Jibukumar

“…The Brain Tumor is one of the most serious scenarios associated with the brain where a cluster of abnormal cells grows in an uncontrolled fashion. …”
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Enhancing prostate cancer segmentation in bpMRI: Integrating zonal awareness into attention-guided U-Net by Chao Wei, Zheng Liu, Yibo Zhang, Lianhui Fan

“…Purpose Prostate cancer (PCa) is the second most common cancer in males worldwide, requiring improvements in diagnostic imaging to identify and treat it at an early stage. …”
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Sea-Ice Surface Types Characterization and SAR Volume Backscattering Properties in Response to Sea States Interactions Using Structural Feature Fusion by Iman Heidarpour Shahrezaei, Hyun-Cheol Kim

“…To accomplish this, a numerical approach for modeling random polar media (RPM) is proposed, benefiting from transform-domain fusion method decompositions in conjunction with a convolutional neural network to jointly reconstruct a reference MIZ model in response to wind waves. …”
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A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information by Dengfeng Zhao, Yudong Zhong, Zhijun Fu, Junjian Hou, Mingyuan Zhao

“…Through a detailed analysis of the features of random forests, support vector machines, convolutional neural networks, and recurrent neural networks used to build driving behavior recognition models, the following findings are obtained: the driving behavior model constructed by traditional machine learning model is relatively mature but it is greatly affected by feature extraction, data scale, and model structure, which affects the accuracy of the final driving behavior recognition. …”
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Technical note: Impact of tissue section thickness on accuracy of cell classification with a deep learning network by Ida Skovgaard Christiansen, Rasmus Hartvig, Thomas Hartvig Lindkær Jensen

“…Random forest analysis generally identified variations in nuclear granularity as the most important features in distinguishing cells. …”
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Symbol Detection and Channel Estimation for Space Optical Communications Using Neural Network and Autoencoder by Abdelrahman Elfikky, Zouheir Rezki

“…Additionally, with no fading and for both perfect and imperfect CSI with different code rates and fading channels, the proposed AE-based detection outperforms both benchmark learning frameworks and most popular convolutional codes.…”
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Study on Quality Assessment Methods for Enhanced Resolution Graph-Based Reconstructed Images in 3D Capacitance Tomography by Robert Banasiak, Mateusz Bujnowicz, Anna Fabijańska

“…However, given the recent advancements in Graph Convolutional Neural Networks (GCNs) for improving ECT image reconstruction, reliable Quality Assessment methods are essential for comparing the performance of different GCN models. …”
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Optimal Feature Selection and Classification for Parkinson’s Disease Using Deep Learning and Dynamic Bag of Features Optimization by Aarti, Swathi Gowroju, Mst Ismat Ara Begum, A. S. M. Sanwar Hosen

“…The brain cells involved in dopamine generation handle adaptation and control, and smooth movement. Convolutional Neural Networks are used to extract distinctive visual characteristics from numerous graphomotor sample representations generated by both PD and control participants. …”
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Road Damage Detection Using YOLOv7 with Cluster Weighted Distance-IoU NMS by Rudy Rachman, Nanik Suciati, Shintami Chusnul Hidayati

“…Potholes are one of the most common types of road damage. Previous research that used images as input for pothole detection used the Faster Regional Convolutional Neural Network (R-CNN) method. …”
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Machine Learning-Potato Leaf Disease Detection App (MR-PoLoD) by Ahmad Fauzi, Annisya E Chandra, Sofyah Imammah, Malvin Zapata, Marza I Marzuki, Soni Prayogi

“…This application uses the CNN (Convolutional Neural Network) Machine Learning Algorithm because currently, CNN is recognized as the most efficient and effective model in pattern and image recognition tasks. …”
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Short-term Power Load Forecasting for a 33/11 KV Sub-Station by Utilizing Attention-Based Hybrid Deep Learning Architectures by Mukkamala R.

“…This evaluation included the following models: Autoregressive Integrated Moving Average (ARIMA), Multi-Layer Perceptron (MLP), Ran-dom Forest (RF), Gradient Boosting (GB), Long Short-Term Memory (LSTM) networks with Atten-tion mechanisms, Double Attention mechanisms, LSTM-Convolutional Neural Network (CNN) Attention, Recurrent Neural Networks (RNN) with Input Attention, Bidirectional LSTM (BiLSTM) with Attention, and CNN-BiLSTM Attention mechanism. …”
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Deep learning based rapid X-ray fluorescence signal extraction and image reconstruction for preclinical benchtop X-ray fluorescence computed tomography applications by Amrit Kaphle, Sandun Jayarathna, Sang Hyun Cho

“…Here we propose a novel end-to-end deep learning (DL) framework that integrates a one-dimensional convolutional neural network (1D CNN) for rapid XRF signal extraction with a U-Net model for XFCT image reconstruction. …”
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How to detect occluded crosswalks in overview images? Comparing three methods in a heavily occluded area by Yuanyuan Zhang, Joseph Luttrell, IV, Chaoyang Zhang

“…Deep learning models based on convolutional neural networks (CNNs) with the VGG16 architecture were trained using 16 815 images to automatically detect crosswalks from both aerial and street view images. …”
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TinySurveillance: An Extra Low-Power Event-Based Surveillance Method for UAVs by Arash Farahdel, Alimul H. Khan, Hossein Keshmiri, Khan A. Wahid

“…The server colorizes the grayscale images using a convolutional neural network trained by the colored images. …”
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An effectiveness of deep learning with fox optimizer-based feature selection model for securing cyberattack detection in IoT environments by Mimouna Abdullah Alkhonaini

“…Furthermore, the FOFSDL-SCD model utilizes the Fox optimizer algorithm (FOA) method for the feature selection process to select the most significant features from the dataset. Moreover, the temporal convolutional network (TCN) model is employed for classification. …”
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CELM: An Ensemble Deep Learning Model for Early Cardiomegaly Diagnosis in Chest Radiography by Erdem Yanar, Fırat Hardalaç, Kubilay Ayturan

“…This study investigates the application of deep learning techniques for the automated diagnosis of cardiomegaly from chest X-ray (CXR) images, utilizing both convolutional neural networks (CNNs) and Vision Transformers (ViTs). …”
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Milling Machine Fault Diagnosis Using Acoustic Emission and Hybrid Deep Learning with Feature Optimization by Muhammad Umar, Muhammad Farooq Siddique, Niamat Ullah, Jong-Myon Kim

“…The genetic algorithm (GA) is used to optimize feature selection and ensure the selection of the most relevant features to further improve the model’s performance. …”
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PVD-GSTPS: design of an efficient parallel vehicle detection based green signal time prediction system by Nikhil Nigam, Dhirendra Pratap Singh, Jaytrilok Choudhary, Surendra Solanki

“…Recent advancements in Convolutional Neural Networks (CNNs) have enabled better capturing of patterns in traffic data. …”
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Passive Sensing for Mental Health Monitoring Using Machine Learning With Wearables and Smartphones: Scoping Review by ShiYing Shen, Wenhao Qi, Jianwen Zeng, Sixie Li, Xin Liu, Xiaohong Zhu, Chaoqun Dong, Bin Wang, Yankai Shi, Jiani Yao, Bingsheng Wang, Xiajing Lou, Simin Gu, Pan Li, Jinghua Wang, Guowei Jiang, Shihua Cao

“…Deep learning models (eg, convolutional neural networks and long short-term memory) showed high accuracy, while traditional ML (eg, random forest) remained prevalent due to better interpretability. …”
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Laparoscopic Suture Gestures Recognition via Machine Learning: A Method for Validation of Kinematic Features Selection by Juan M. Herrera-Lopez, Alvaro Galan-Cuenca, Antonio J. Reina, Isabel Garcia-Morales, Victor F. Munoz

“…Research in this task has utilized both image data, mainly using Deep Learning and Convolutional Neural Networks, and kinematic data extracted from the surgeons’ instruments, processing kinematic sequences with Markov models, Recurrent Neural Networks and even unsupervised learning techniques. …”
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