Semantic Segmentation with Multispectral Satellite Images of Waterfowl Habitat by Mateo Gannod, Nicholas Masto, Collins Owusu, Cory Highway, Katherine Brown, Abigail Blake-Bradshaw, Jamie Feddersen, Heath Hagy, Douglas Talbert, Bradley Cohen

“…Advances in multispectral imagery and deep learning algorithms may enable continuous and autonomous detection of these habitat features. …”
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Large-Scale Monitoring of Potatoes Late Blight Using Multi-Source Time-Series Data and Google Earth Engine by Zelong Chi, Hong Chen, Sheng Chang, Zhao-Liang Li, Lingling Ma, Tongle Hu, Kaipeng Xu, Zhenjie Zhao

“…Notably, the blue band data (458–523 nm) were critical during the month of May. These features are related to vegetation health and soil moisture are critical for early detection. …”
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Malware prediction technique based on program gene by Da XIAO, Bohan LIU, Baojiang CUI, Xiaochen WANG, Suoxing ZHANG

“…With the development of Internet technology,malicious programs have risen explosively.In the face of executable files without source,the current mainstream malware detection uses feature detection based on similarity,with lack of analysis of malicious sources.To resolve this status,the definition of program gene was raised,a generic method of extracting program gene was designed,and a malicious program prediction method was proposed based on program gene.Utilizing machine learning and deep-learning algorithms,the forecasting system has good prediction ability,with the accuracy rate of 99.3% in the deep-learning model,which validates the role of program gene theory in the field of malicious program analysis.…”
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An unsupervised underwater image enhancement method based on generative adversarial networks with edge extraction by Yanfei Jia, Ziyang Wang, Liquan Zhao

“…Obtaining such paired datasets in natural conditions is challenging, leading to performance issues in these algorithms. To address this issue, we propose an unsupervised generative adversarial network with edge detection for enhancing underwater images without needing paired data. …”
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Smart Grid Security: Proactive Prediction of Advanced Persistent Threats by Motahareh Dehghan, Erfan Khosravain

“…The feature importance analysis reveals that traffic-related features such as packet size variance and connection duration are crucial in identifying Advanced Persistent Threats. …”
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Aircraft Sensor Fault Diagnosis Based on GraphSage and Attention Mechanism by Zhongzhi Li, Jinyi Ma, Rong Fan, Yunmei Zhao, Jianliang Ai, Yiqun Dong

“…Experiments demonstrate that the proposed method outperforms baseline approaches, achieving better detection performance and faster computational speed. …”
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SHARPNESS IMPROVEMENT OF MAGNETIC RESONANCE IMAGES USING A GUIDED-SUBSUMED UNSHARP MASK FILTER by Manar AL-ABAJI, Zohair AL-AMEEN

“…Sometimes, MRI images are obtained blurry due to various inevitable constraints related to the imaging equipment, which affects the detection of important features in the image. Several sharpening methods were introduced, but not all were successful in this task, as artifacts may be introduced, contrast may be changed, and high complexity may be involved. …”
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Enhancing semi‐supervised contrastive learning through saliency map for diabetic retinopathy grading by Jiacheng Zhang, Rong Jin, Wenqiang Liu

“…Moreover, the performance of these algorithms is hampered by the scarcity of large‐scale, high‐quality annotated data. …”
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High-Quality Multispectral Image Reconstruction for the Spectral Camera Based on Ghost Imaging via Sparsity Constraints Using CoT-Unet by Tao Hu, Jianxia Chen, Shu Wang, Jianrong Wu, Ziyan Chen, Zhifu Tian, Ruipeng Ma, Di Wu

“…To solve the problem of poor quality in ghost imaging via sparsity constraints (GISC) multispectral image reconstruction with correlation operations and compressed sensing algorithms under low sampling rate detection conditions, we propose an end-to-end deep-learning-based method. …”
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Kernel machine tests of association using extrinsic and intrinsic cluster evaluation metrics. by Alexandria M Jensen, Peter DeWitt, Brianne M Bettcher, Julia Wrobel, Katerina Kechris, Debashis Ghosh

“…The literature on analysis of these community detection algorithms has focused on comparing them within the same subject. …”
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Predictive analysis of heart disease using quantum-assisted machine learning by Mehroush Banday, Sherin Zafar, Parul Agarwal, M. Afshar Alam, Siddhartha Sankar Biswas, Imran Hussain, K. M. Abubeker

“…The proposed research is developed with a hybrid approach that combines different machine learning algorithms, such as KNN + RF, DT + RF, LR + RF, and Adaboost + RF, for diagnosing coronary illness with higher accuracy through feature selection. …”
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Computed tomography enterography radiomics and machine learning for identification of Crohn’s disease by Qiao Shi, Yajing Hao, Huixian Liu, Xiaoling Liu, Weiqiang Yan, Jun Mao, Bihong T. Chen

“…This study aims to develop a non-invasive method for detecting bowel lesions associated with Crohn’s disease using CT enterography radiomics and machine learning algorithms. …”
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Cervical cancer prediction using machine learning models based on routine blood analysis by Jie Su, Hui Lu, Ruihuan Zhang, Na Cui, Chao Chen, Qin Si, Biao Song

“…Using least absolute shrinkage and selection operator (LASSO) and the random forest method (RF) method, 15 key routine blood features were ultimtely selected from an initial set of 23 features for model training. …”
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Development and application of an early prediction model for risk of bloodstream infection based on real-world study by Xiefei Hu, Shenshen Zhi, Yang Li, Yuming Cheng, Haiping Fan, Haorong Li, Zihao Meng, Jiaxin Xie, Shu Tang, Wei Li

“…The occurrence rate of BSI, distribution of pathogens, and microbial primary reporting time were analyzed within the training set. During the feature selection stage, univariate regression and ML algorithms were applied. …”
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Development of prediction models for screening depression and anxiety using smartphone and wearable-based digital phenotyping: protocol for the Smartphone and Wearable Assessment f... by Sujin Kim, Ah Young Kim, Yu-Bin Shin, Seonmin Kim, Min-Sup Shin, Jinhwa Choi, Kyung Lyun Lee, Jisu Lee, Sangwon Byun, Heon-Jeong Lee, Chul-Hyun Cho

“…Introduction Depression and anxiety are highly prevalent mental health conditions that significantly affect quality of life and cause societal burdens. However, their detection and diagnosis rates remain low owing to the limitations of the current screening methods. …”
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Design of a home-based elderly service robot using ROS by Zhao Zebei, Feng Peilin, Xie Xiaorui, Zhang Jing

“…By employing facial recognition based on Haar features and LBPH algorithm, voice interaction using Alpaca-2 model, and fall detection based on ZED2i stereo camera, it provides more comprehensive and intelligent care services for the elderly. …”
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Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry by PNV Srinivasa Rao, PVY Jayasree

“…The study focuses on the optimization of predictive maintenance as a service on the industrial Internet of Things by machine learning algorithms. The main contribution of the study is the use of optimization techniques for feature selection and RNN-LSTM for improved accuracy.   …”
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Analysis of multiple faults in induction motor using machine learning techniques by Puja Pohakar, Ravi Gandhi, Surender Hans, Gulshan Sharma, Pitshou N. Bokoro

“…Based on key operating parameters like voltage, current, and speed, this article describes how machine learning (ML) algorithms like Random Forest (RF), K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), Support Vector Machines (SVM), and Extreme Gradient Boosting with Feature Interaction (XGBoost + FIS) are used to detect different motor faults. …”
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THE CURRENT STATE OF ARTIFICIAL INTELLIGENCE IN RADIOLOGY – A REVIEW OF THE BASIC CONCEPTS, APPLICATIONS, AND CHALLENGES by Mariana Yordanova

“…CNNs excel in tasks like lesion detection and disease classification, aiding radiologists in diagnosing conditions more accurately. …”
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Explainable Machine Learning Models for Colorectal Cancer Prediction Using Clinical Laboratory Data by Rui Li MS, Xiaoyan Hao MS, Yanjun Diao MD, Liu Yang MS, Jiayun Liu MD

“…Incorporating stool miR-92a detection into the model further improved diagnostic performance. …”
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