Revolutionizing prostate cancer diagnosis: Unleashing the potential of an optimized deep belief network for accurate Gleason grading in histological images by S. Angel Latha Mary, S. Siva Subramanian, G. Priyanka, T. Vijayakumar, Suganthi Alagumalai

“…However, evaluating these sorts of images is difficult and time-consuming, requiring histopathological image recognition as the most reliable method for treating PC because of its distinct visual characteristics. …”
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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

“…<b>Methods:</b> We assembled one of the largest and most diverse CXR datasets to date, combining posteroanterior (PA) images from PadChest, NIH CXR, VinDr-CXR, and CheXpert. …”
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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

“…Abstract The complexity of traffic flow patterns significant challenges in predicting traffic green signal timings using conventional methods. Most of conventional methods relied on vehicle counts and speeds. …”
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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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Surface quality prediction and validation of Electro-Fenton chemical mechanical polishing for single crystal SiC based on neural network models by Jiacheng Geng, Jiayun Deng, Zilei Bai, Xiaoning Wen, Jiabin Lu, Qiusheng Yan, Xiaojing Yang

“…Based on SHAP value analysis, most influential process parameter affecting the MRR is the abrasive size, while the most critical factor impacting surface quality is the H₂O₂ concentration in the EF-CMP process. …”
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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

“…However, most studies that develop recognition models with kinematic data do not take into account any study of the significance that each kinematic variable plays in the recognition task, allowing for informed decisions at the time of training simpler models and choosing the sensor systems in deployment platforms. …”
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A Human-Centric, Uncertainty-Aware Event-Fused AI Network for Robust Face Recognition in Adverse Conditions by Akmalbek Abdusalomov, Sabina Umirzakova, Elbek Boymatov, Dilnoza Zaripova, Shukhrat Kamalov, Zavqiddin Temirov, Wonjun Jeong, Hyoungsun Choi, Taeg Keun Whangbo

“…A custom hybrid backbone that couples convolutional networks with transformers keeps the model nimble enough for edge devices. …”
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Machine learning-based estimation of crude oil-nitrogen interfacial tension by Safia Obaidur Rab, Subhash Chandra, Abhinav Kumar, Pinank Patel, Mohammed Al-Farouni, Soumya V. Menon, Bandar R. Alsehli, Mamata Chahar, Manmeet Singh, Mahmood Kiani

“…The sensitivity analysis indicated that pressure, temperature and crude oil API all negatively affect the IFT, with pressure being the most effective factor. The evaluation study proved that Random Forest is the most accurate developed intelligent model as it was characterized with acceptable R-squared (0.959), mean square error (1.65), average absolute relative error (6.85%) of unseen test datapoints as well as with correct trend prediction of IFT with regard to all input parameters of pressure, temperature and crude oil API. …”
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The use of pretrained neural networks for solving the problem of reverse searching of X-ray images of prohibited items and substances by A. K. Volkov, L. V. Mironova, S. E. Potapova

“…In this case, the class of the X-ray image element is the class most frequently encountered among the K nearest neighbors. …”
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Deep learning applications for human embryo assessment using time-lapse imaging: scoping review by Rawan AlSaad, Leen Abusarhan, Nour Odeh, Alaa Abd-alrazaq, Fadi Choucair, Rachida Zegour, Arfan Ahmed, Sarah Aziz, Javaid Sheikh

“…Convolutional neural networks (CNNs) were the predominant deep learning architecture used, accounting for 81% (n = 62) of the studies. …”
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Multi-Scale Long- and Short-Range Structure Aggregation Learning for Low-Illumination Remote Sensing Imagery Enhancement by Yu Cao, Yuyuan Tian, Xiuqin Su, Meilin Xie, Wei Hao, Haitao Wang, Fan Wang

“…Profiting from the surprising non-linear expressive capacity, deep convolutional neural networks have inspired lots of progress in low illumination (LI) remote sensing image enhancement. …”
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Multimodal Pain Recognition in Postoperative Patients: Machine Learning Approach by Ajan Subramanian, Rui Cao, Emad Kasaeyan Naeini, Seyed Amir Hossein Aqajari, Thomas D Hughes, Michael-David Calderon, Kai Zheng, Nikil Dutt, Pasi Liljeberg, Sanna Salanterä, Ariana M Nelson, Amir M Rahmani

“…RR models consistently outperformed other single modalities, particularly for lower pain intensities, while facial muscle activity (electromyogram) was most effective for distinguishing higher pain intensities. …”
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Refining Intra-Arterial Therapy Selection for Large Hepatocellular Carcinoma: A Deep Learning Approach Based on Covariate Interaction Analysis by An C, Li L, Luo Y, Zuo M, Liu W, Li C, Wu P

“…The model’s performance was further supported by its ability to stratify patients into subgroups most likely to benefit from TACE or HAIC.Conclusion: The DELICAITE model provides a precise and innovative approach to refine IAT schemes for large HCC, offering clinicians a reliable tool to select the most suitable treatment option and potentially improve patient survival outcomes.Keywords: hepatocellular carcinoma, intra-arterial therapies, deep learning, progressive disease, overall survival…”
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Research and Experiment on a Chickweed Identification Model Based on Improved YOLOv5s by Hong Yu, Jie Zhao, Xiaobo Xi, Yongbo Li, Ying Zhao

“…Currently, multi-layer deep convolutional networks are mostly used for field weed recognition to extract and identify target features. …”
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Leveraging sentiment analysis of food delivery services reviews using deep learning and word embedding by Dheya Mustafa, Safaa M. Khabour, Mousa Al-kfairy, Ahmed Shatnawi

“…Arabic is becoming one of the most extensively written languages on the World Wide Web, but because of its morphological and grammatical difficulty as well as the lack of openly accessible resources for Arabic SA, like as dictionaries and datasets, there has not been much research done on the language. …”
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RMVAD-YOLO: A Robust Multi-View Aircraft Detection Model for Imbalanced and Similar Classes by Keda Li, Xiangyue Zheng, Jingxin Bi, Gang Zhang, Yi Cui, Tao Lei

“…Second, we propose the Shared Convolutional Dynamic Alignment Detection Head (SCDADH), which enhances task interaction and collaboration by sharing convolutions between the classification and localization branches while simultaneously reducing the number of parameters, enhancing the model’s ability to deal with multi-scale targets. …”
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FPGA-oriented lightweight multi-modal free-space detection network by Feiyi Fang, Junzhu Mao, Wei Yu, Jianfeng Lu

“…Therefore most free-space detection algorithms are developed based on multiple sensors. …”
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