RGB-based machine vision for enhanced pig disease symptoms monitoring and health management: a review by Md Nasim Reza, Kyu-Ho Lee, Eliezel Habineza, Samsuzzaman, Hyunjin Kyoung, Young Kyoung Choi, Gookhwan Kim, Sun-Ok Chung

“…Machine vision and image processing algorithms enable for the real-time detection of subtle changes in pig appearance and behavior that may signify potential health issues. …”
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Methodology for Data Integration in 3D-HBIM Digital Models. Case Study: the Holy Chalice Chapel of Valencia Cathedral by Pablo Ariel Escudero, Concepción López González, Jorge Luis García Valldecabres

“…A key feature of the approach is the use of colorimetry-based classification, which detects significant changes in the RGB values of the points, thereby aiding in the identification of patterns and potential deterioration in construction materials.…”
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Application of artificial intelligence in insect pest identification - A review by Sourav Chakrabarty, Chandan Kumar Deb, Sudeep Marwaha, Md. Ashraful Haque, Deeba Kamil, Raju Bheemanahalli, Pathour Rajendra Shashank

“…AI-based detection methods use machine learning, deep learning algorithms, and computer vision techniques to automate and improve the identification of insects. …”
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3Mont: A multi-omics integrative tool for breast cancer subtype stratification. by Miray Unlu Yazici, J S Marron, Burcu Bakir-Gungor, Fei Zou, Malik Yousef

“…Our tool allows users to analyze the collective behavior of features in each pro-group (biological groups) utilizing ML algorithms. …”
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Effective deep learning aided vehicle classification approach using Seismic Data by Sherief Hashima, Mohamed H. Saad, Ahmad B. Ahmad, Takeshi Tsuji, Hamada Rizk

“…We propose a self-supervised contrastive learning approach for seismic signal classification, eliminating the need for labeled data for feature extraction and representation. Our method employs specialized data augmentation techniques to create positive and negative pairs, enhancing feature representation. …”
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Dynamic Principal Component Analysis with Nonoverlapping Moving Window and Its Applications to Epileptic EEG Classification by Shengkun Xie, Sridhar Krishnan

“…Along with this new technique, two detection methods based on extracted sparse features are applied to deal with signal classification. …”
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Automatic brake Driver Assistance System based on deep learning and fuzzy logic. by A R García-Escalante, R Q Fuentes-Aguilar, A Palma-Zubia, E Morales-Vargas

“…A key part of this automation includes tasks such as traffic light detection and automatic braking. While indoor experiments are prevalent due to computational demands and safety concerns, there is a pressing need for research and development of new features to achieve complete automation, addressing real-world implementation challenges by testing them in outdoor environments. …”
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CSL-SFNet for Cooperative Spectrum Sensing in Cognitive Satellite Network with GEO and LEO Satellites by Kai Yang, Shengbo Hu, Xin Zhang, Tingting Yan, Manqin Zhu

“…The simulation results demonstrate that the proposed algorithm can achieve a detection probability of 90% when the signal-to-noise ratio is −20 dB; it has a shorter running time and always outperforms the other CSS algorithms.…”
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Enhanced prediction of ventilator-associated pneumonia in patients with traumatic brain injury using advanced machine learning techniques by Negin Ashrafi, Armin Abdollahi, Kamiar Alaei, Maryam Pishgar

“…Overall, the results demonstrate that advanced ensemble learning, meticulous feature selection, and effective class imbalance handling can significantly enhance early detection in traumatic brain injury cases. …”
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Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques. by Hilman Nordin, Bushroa Abdul Razak, Norrima Mokhtar, Mohd Fadzil Jamaludin, Adeel Mehmood

“…Results indicate that both methods improve the accuracy and precision of mold defect detection compared to no classifier. However, the CART algorithm exhibits superior performance, increasing precision by 32% to 53% while maintaining high accuracy (96%) even with an imbalanced dataset. …”
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Facial recognition and analysis: A machine learning-based pathway to corporate mental health management by Zicheng Zhang, Tianshu Zhang, Jie Yang

“…Methods Utilizing the RetinaFace model for facial detection, the Dlib algorithm for feature extraction, and VGG16 for micro-expression classification, the system constructed a 10-dimensional emotion feature vector. …”
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A high precision method of segmenting complex postures in Caenorhabditis elegans and deep phenotyping to analyze lifespan by Bingyue Dong, Weiyang Chen

“…In addition, we propose a more accurate novel bending counting algorithm. In experiments, WormYOLO segmented images, followed by a feature point extraction algorithm to identify changes in worm skeleton positions, ultimately quantifying behavioral features with a counting algorithm. …”
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CNN Based Fault Classification and Predition of 33kw Solar PV System with IoT Based Smart Data Collection Setup by K. Punitha, G. Sivapriya, T. Jayachitra

“…Convolutional Neural Networks (CNNs) are a class of deep learning algorithms most commonly applied. They are particularly powerful for tasks involving data recognition, classification, and analysis due to their ability to automatically and adaptively learn spatial hierarchies of features. …”
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A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data by M. Priyadharshini, B. Deevena Raju, A. Faritha Banu, P. Jagdish Kumar, V. Murugesh, Oleg Rybin

“…It demonstrates that quantum algorithms can perform complex biological data suggesting more reliable and accurate drug sensitivity predictions. …”
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A Novel Joint Denoising Strategy for Coherent Doppler Wind Lidar Signals by Yuefeng Zhao, Wenkai Song, Nannan Hu, Xue Zhou, Jiankang Luo, Jinrun Huang, Qianqian Tao

“…This paper proposes a novel joint denoising algorithm based on SVD-ICEEMDAN-SCC-MF to remove noises in CDWL detection. …”
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Predicting future morphological changes of lesions from radiotracer uptake in 18F-FDG-PET images. by Ulas Bagci, Jianhua Yao, Kirsten Miller-Jaster, Xinjian Chen, Daniel J Mollura

“…The radiotracer accumulation in patients' scans was automatically detected and segmented by the proposed segmentation algorithm. …”
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Development and Validation of a Cost-Effective Machine Learning Model for Screening Potential Rheumatoid Arthritis in Primary Healthcare Clinics by Wu W, Hu X, Yan L, Li Z, Li B, Chen X, Lin Z, Zeng H, Li C, Mo Y, Wu Y, Wang Q

“…Guided by experienced rheumatologists, we built a comprehensive database with 26 clinical features. Using 10 classical machine learning algorithms, we developed screening models. …”
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Crop-Free-Ridge Navigation Line Recognition Based on the Lightweight Structure Improvement of YOLOv8 by Runyi Lv, Jianping Hu, Tengfei Zhang, Xinxin Chen, Wei Liu

“…In order to improve the intelligence level and operational efficiency of agricultural machinery and solve the problems of difficulties in recognizing navigation lines and a lack of real-time performance of transplanters in the crop-free ridge environment, we propose a crop-free-ridge navigation line recognition method based on an improved YOLOv8 segmentation algorithm. First, this method reduces the parameters and computational complexity of the model by replacing the YOLOv8 backbone network with MobileNetV4 and the feature extraction module C2f with ShuffleNetV2, thereby improving the real-time segmentation of crop-free ridges. …”
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FCB-YOLOv8s-Seg: A Malignant Weed Instance Segmentation Model for Targeted Spraying in Soybean Fields by Zishang Yang, Lele Wang, Chenxu Li, He Li

“…The detection results in different scenes show that the FCB-YOLOv8s-Seg model performs well in fine-grained feature segmentation in complex scenes. …”
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Autonomous vehicle surveillance through fuzzy C-means segmentation and DeepSORT on aerial images by Asifa Mehmood Qureshi, Moneerah Alotaibi, Sultan Refa Alotaibi, Dina Abdulaziz AlHammadi, Muhammad Asif Jamal, Ahmad Jalal, Bumshik Lee

“…After segmentation, we employed the YOLOv4 deep learning algorithm, which is efficient in detecting small-sized objects in vehicle detection. …”
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