Machine Learning and Deep Learning for Crop Disease Diagnosis: Performance Analysis and Review by Habiba Njeri Ngugi, Andronicus A. Akinyelu, Absalom E. Ezugwu

“…Additionally, traditional ML models exhibited varied strengths; for instance, SVM performed better on balanced datasets, while RF excelled with imbalanced data. Preprocessing methods like K-means clustering, Fuzzy C-Means, and PCA, along with ensemble approaches, further improved model accuracy. …”
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Using Deep Learning Techniques to Enhance Blood Cell Detection in Patients with Leukemia by Mahwish Ilyas, Muhammad Bilal, Nadia Malik, Hikmat Ullah Khan, Muhammad Ramzan, Anam Naz

“…This supports early diagnosis and monitoring, which leads to more effective treatments and improved results for cancer patients. To accomplish this task, we use digital image processing techniques and then apply the convolutional neural network (CNN) deep learning algorithm to blood sample images. …”
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Machine learning for defect condition rating of wall wooden columns in ancient buildings by Yufeng Li, Wu Ouyang, Zhenbo Xin, Houjiang Zhang, Shuqi Sun, Dian Zhang, Wenbo Zhang

“…The RBF neural network model achieved the highest accuracy (94.57 %) on the feature fusion dataset, while Grey Wolf Optimizer (GWO) optimization further improved accuracy to 96.74 %. …”
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Research on the Application of Artificial Intelligence in Quantitative Investment: Implementation Scenarios, Practical Challenges, and Future Trends by Chen Boshan

“…Second, the research focuses on key AI applications in quantitative investment, including multi-factor model optimization, high-frequency market risk management, multimodal data integration, and algorithmic trading enhancement. …”
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Alpine Meadow Fractional Vegetation Cover Estimation Using UAV-Aided Sentinel-2 Imagery by Kai Du, Yi Shao, Naixin Yao, Hongyan Yu, Shaozhong Ma, Xufeng Mao, Litao Wang, Jianjun Wang

“…Subsequently, four machine learning models were employed for an accurate FVC inversion, using the estimated FVC values and UAV-derived reference FVC as inputs, following feature importance ranking and model parameter optimization. The results showed that: (1) Machine learning algorithms based on Sentinel-2 and UAV imagery effectively improved the accuracy of FVC estimation in alpine meadows. …”
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Comparison between Logistic Regression and K-Nearest Neighbour Techniques with Application on Thalassemia Patients in Mosul by Mohammed Al jbory, Hutheyfa Taha

“…It was also shown that the difference between distance calculation methods and the K value plays a major role in improving the classification results, as it was determined that the optimal value for K is 4, which led to improving the accuracy of predictions. …”
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AHA: Design and Evaluation of Compute-Intensive Hardware Accelerators for AMD-Xilinx Zynq SoCs Using HLS IP Flow by David Berrazueta-Mena, Byron Navas

“…We outline criteria for selecting algorithms to improve speed and resource efficiency in HLS design. …”
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Energy and Reserve Scheduling of Distribution Network in Presence of Electric Vehicle Aggregators: A Decentralized Approach by Atena Tazikeh Lemeski, Reza Ebrahimi, Alireza Zakariazadeh, Keyvan Firuzi

“…The results indicate that ignoring network conditions and constraints leads to impractical decisions whereas consideration of DSO requirements by the distributed algorithm will deliver a profitable schedule for AGGs while protecting their privacy and fulfilling DSO’s economical and technical objectives.…”
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Research on Status Assessment and Operation and Maintenance of Electric Vehicle DC Charging Stations Based on XGboost by Hualiang Fang, Jiaqi Liao, Shuo Huang, Maojie Zhang

“…The results of the case study demonstrate that the state evaluation and operation and maintenance strategy can significantly improve the reliability of the system and the overall benefits of operation and maintenance while meeting the required standards.…”
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A case study on the application of a data-driven (XGBoost) approach on the environmental and socio-economic perspectives of agricultural groundwater management by Sheng-Wei Wang, Yen-Yu Chen, Shu-Han Hsu, Yu-Hsuan Kao, Masaomi Kimura, Li-chiu Chang, Tzi-Wen Pan, Chuen-Fa Ni

“…This study develops a groundwater level prediction model using the extreme gradient boosting (XGB) algorithm, employing power consumption, precipitation, and groundwater level data as input features. …”
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Damage prediction of rear plate in Whipple shields based on machine learning method by Chenyang Wu, Xiangbiao Liao, Lvtan Chen, Xiaowei Chen

“…The results demonstrate that the training and prediction accuracies using the Random Forest (RF) algorithm significantly surpass those using Artificial Neural Networks (ANNs) and Support Vector Machine (SVM). …”
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Pavement pothole detection system based on deep learning and binocular vision by Tian Guan, Jianyuan Cai, Yu Wang, Wei Yang, Xiaobo Chang, Yi Han

“…Finally, vehicle experiments were conducted to verify the effectiveness of the algorithm in meeting detection requirements while considering long-range perception accuracy. …”
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Research on Anti-Interference Performance of Spiking Neural Network Under Network Connection Damage by Yongqiang Zhang, Haijie Pang, Jinlong Ma, Guilei Ma, Xiaoming Zhang, Menghua Man

“…Background: With the development of artificial intelligence, memristors have become an ideal choice to optimize new neural network architectures and improve computing efficiency and energy efficiency due to their combination of storage and computing power. …”
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Early Forest Fire Detection With UAV Image Fusion: A Novel Deep Learning Method Using Visible and Infrared Sensors by Kunlong Niu, Chongyang Wang, Jianhui Xu, Jianrong Liang, Xia Zhou, Kaixiang Wen, Minjian Lu, Chuanxun Yang

“…The results show that the improved registration method effectively aligns visible and infrared images, optimizing the fusion process and enhancing the use of multisource information. …”
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Can Knowledge Distillation Enable Seismic Interpolation, Super-Resolution, and Denoising Simultaneously? by Benwen Zhang, Linrong Wang

“…In seismic data processing, interpolation, super-resolution, and denoising are three key issues to improve data quality. While deep learning methods have successfully addressed one or two of these tasks, simultaneously resolving all three within a single network faces difficulties. …”
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Prediction of permeability and effective porosity values using ANN in Maleh field by Mohammed Essa Nassani, Ali Alaji

“…The ANN was tested on independent data and demonstrated exceptional performance, achieving 96% accuracy for effective porosity and 98% for permeability predictions in sandstone formations. This efficient algorithm eliminates the need for core sample analysis, reducing costs and time while improving prediction reliability, making it a valuable tool for subsurface characterization and resource exploration.…”
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Mission Sequence Model and Deep Reinforcement Learning-Based Replanning Method for Multi-Satellite Observation by Peiyan Li, Peixing Cui, Huiquan Wang

“…Both phases are formulated as Markov Decision Processes (MDPs) and optimized using the PPO algorithm. Extensive simulations demonstrate that our method significantly outperforms state-of-the-art approaches, achieving a 15.27% higher request insertion revenue rate and a 3.05% improvement in overall mission revenue rate, while maintaining a 1.17% lower modification rate and achieving faster computational speeds. …”
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Frailty in older adults patients: a prospective observational cohort study on subtype identification by Zhikai Yang, Chen Ji, Ting Wang, Wei He, Yuhao Wan, Min Zeng, Di Guo, Lingling Cui, Hua Wang

“…This study applied the K-means clustering algorithm to analyze 27 variables, determining the optimal cluster number using the Elbow method and Silhouette coefficient. …”
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Screening OSA in Chinese Smart Device Consumers: A Real-World Arrhythmia-Related Study by Chen Y, Zhang H, Li J, Xu P, Guo Y, Xie L

“…These findings support their utility for large-scale OSA screening and highlight cardiovascular risks management.Clinical Trial Registry Name: Mobile Health (mHealth) technology for improved screening, patient involvement and optimizing integrated care in atrial fibrillation.Registration Number: ChiCTR-OOC-17014138.Date of Registration: 2017– 12-26.Date of Last Refreshed On: 2018– 11-18.Keywords: obstructive sleep apnea, smart wearable device, arrhythmias, hypertension…”
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Resource allocation mechanism in the TWDM-PON and C-RAN joint architecture with hybrid energy supply by Ruyan WANG, Ningning XU

“…Aiming at the problems of low resource utilization rate,high energy consumption and poor user service quality in the existing virtualized Cloud Radio Access Network,an energy-aware virtualized resource allocation mechanism with hybrid energy supply was proposed.According to the energy sources and energy consumption of different network devices,energy arrival and energy consumption models were established.Furthermore,under the premise of guaranteeing the quality of user services,considering proportional fairness and energy consumption optimization,distributed algorithms based on asynchronous update were used to allocate resources and harvested energy for different types of virtual cloud radio access networks and user virtual base stations to effectively improve the energy efficiency of network.The simulation results show that the proposed resource allocation mechanism can reduce energy consumption while effectively reducing the latency and improving the throughput.…”
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