Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography by Ruiting Jia, Baozhi Liu, Mohsin Ali

“…The diagnostic accuracy was 90.58%, with an overall positive predictive value of 89% and an overall negative predictive value of 86%. The algorithm effectively handled the CT images at the preprocessing stage, and the deep learning model performed well in detecting and classifying nodules. …”
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A Deep Pedestrian Tracking SSD-Based Model in the Sudden Emergency or Violent Environment by Zhihong Li, Yang Dong, Yanjie Wen, Han Xu, Jiahao Wu

“…The experimental results show that the tracking accuracy of mAP is higher than 95% both in normal and abnormal data sets and higher than that of the traditional detection algorithm. …”
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Using Partial Differential Equation Face Recognition Model to Evaluate Students’ Attention in a College Chinese Classroom by Xia Miao, Ziyao Yu, Ming Liu

“…In the evaluation of students’ concentration in class, this paper firstly uses the face detection algorithm in face recognition technology to detect the face and intercept the expression data, and calculates the rise rate. …”
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Predictive analytics in gamified education: A hybrid model for identifying at-risk students by Devanshu Sawarkar, Latika Pinjarkar, Pratham Agrawal, Devansh Motghare

“…By integrating logistic regression, decision trees, and random forests, we construct a robust ensemble model that leverages the strengths of each algorithm for precise risk assessment. The model analyzes key indicators such as academic performance, participation levels, and task completion rates using data derived from a gamified learning platform. …”
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Robust Extrinsic Calibration for LiDAR-Camera Systems via Depth and Height Complementary Supervision Network by Chen Yaqing, Wang Huaming

“…However, conventional calibration methods are laborious and require dedicated data collection. Although recent research has shown the potential of learning-based solutions for end-to-end use, existing methods tend to focus on feature matching rather than geometric constraints, leading to inefficiency and instability in traffic scenarios with various disturbances. …”
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G-ROBOT: An Intelligent Greenhouse Seedling Height Inspection Robot by Baoqi Wang, Yang Ding, Chunyan Wang, Deyu Li, Hongchang Wang, Zhilong Bie, Yuan Huang, Shengyong Xu

“…Based on STM32 and Raspberry Pi as the core, the robot is equipped with various sensors to build a reliable control system for intelligent navigation for inspection tasks as well as acquisition of high-quality images and environmental information data of seedling crops. The developed growth point detection algorithm based on the EfficientNet deep learning network can efficiently measure the heights of seedlings and the application of the host software and cloud server makes it easy to monitor and control the robot and store and manage various data. …”
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Artificial intelligence enhanced electrochemical immunoassay for staphylococcal enterotoxin B by Yuliang Zhao, Tingting Sun, Huawei Zhang, Chao Lian, Zhongpeng Zhao, Yongqiang Jiang, Huiqi Duan, Yuhao Ren, Xuyang Sun, Zhikun Zhan, Mingyue Qu, Shaolong Chen

“…This enhancement significantly improves both the accuracy and interpretability of SEB measurement data. Lastly, a multivariate linear regression algorithm is employed to effectively train and fit the extracted feature data. …”
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Grey wolf optimization technique with U-shaped and capsule networks-A novel framework for glaucoma diagnosis by Govindharaj I, Ramesh T, Poongodai A, Senthilkumar K. P, Udayasankaran P, Ravichandran S

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Aerial remote sensing system to control pathogens and diseases in broccoli crops with the use of artificial vision by Darwin Laura, Elsa Pilar Urrutia, Franklin Salazar, Jeanette Ureña, Rodrigo Moreno, Gustavo Machado, Maria Cazorla-Logroño, Santiago Altamirano

“…The system's autonomous drone efficiently covered large crop areas, providing precise geolocation data for targeted interventions. The collected data, stored in a centralized database, facilitated continuous improvement of the detection algorithm, ensuring reliable pathogen control and maintaining high production quality.…”
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Robust Tracking Method for Small and Weak Multiple Targets Under Dynamic Interference Based on Q-IMM-MHT by Ziqian Yang, Hongbin Nie, Yuxuan Liu, Chunjiang Bian

“…Furthermore, the algorithm utilizes Support Vector Machines (SVMs) for anomaly detection and trajectory recovery, thereby enhancing the accuracy of data association and the overall robustness of the system. …”
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Condition Monitoring of Chain Sprocket Drive System Based on IoT Device and Convolutional Neural Network by Sang Kwon Lee, Jiseon Back, Kanghyun An, Sunwon Kim, Changho Lee, Pungil Kim

“…A convolution neural network (CNN) was employed to extract deep features embedded in the images, which are closely related to fault types. To update the learning parameters of the CNN, the RMSprop learning algorithm was applied, and the CNN was trained using 500 image samples. …”
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Multi-label semantic segmentation of magnetic resonance images of the prostate gland by Mark Locherer, Christopher Bonenberger, Wolfgang Ertel, Boris Hadaschik, Kristina Stumm, Markus Schneider, Jan Philipp Radtke

“…Medical doctors are assisted by computer-aided detection and diagnosis systems systems and methods derived from artificial intelligence deep learning-based systems have to be trained on existing data. …”
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Field Evaluation of an Autonomous Mobile Robot for Navigation and Mapping in Forest by Diego Tiozzo Fasiolo, Lorenzo Scalera, Eleonora Maset, Alessandro Gasparetto

“…The system integrates light detection and ranging data and images using a framework based on simultaneous localization and mapping (SLAM) and a deep learning model for trunk segmentation and tree keypoint detection. …”
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OTFS-ISAC system channel estimation based on GAN-LSTM network in urban rail train-infrastructure scenario by YANG Qian, SU Hongsheng, TAO Wanglin, LIU Dawei

“…Simulation results show that the proposed method is superior to traditional channel estimation methods in normalized mean square error and bit error rate, and provides necessary data basis for ISAC signal detection and recovery.…”
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Deep Unified Model For Face Recognition Based on Convolution Neural Network and Edge Computing by Muhammad Zeeshan Khan, Saad Harous, Saleet Ul Hassan, Muhammad Usman Ghani Khan, Razi Iqbal, Shahid Mumtaz

“…An effective and efficient paradigm is needed to deal with the bulk amount of data produced by the Internet of Things (IoT). Deep learning and edge computing are the emerging technologies, which are used for efficient processing of huge amount of data with distinct accuracy. …”
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Prediction of the Calorific Value and Moisture Content of <i>Caragana korshinskii</i> Fuel Using Hyperspectral Imaging Technology and Various Stoichiometric Methods by Xuehong De, Haoming Li, Jianchao Zhang, Nanding Li, Huimeng Wan, Yanhua Ma

“…For water content and calorific value, three quantitative detection models, partial least squares regression (PLSR), random forest regression (RFR), and extreme learning machine (ELM), respectively, were established, and Monte Carlo cross-validation (MCCV) was chosen to remove outliers from the raw spectral data to improve the model accuracy. …”
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Intelligent Analysis of Flow Field in Cleaning Chamber for Combine Harvester Based on YOLOv8 and Reasoning Mechanism by Qinglin Li, Ruihai Wan, Zhaoyue Wu, Yuting Yan, Xihan Zhang

“…YOLOv8, one of the deep learning object detection algorithms, was selected to identify key-point data from the flow field in a cleaning chamber. …”
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UruDendro, a public dataset of 64 cross-section images and manual annual ring delineations of Pinus taeda L. by Henry Marichal, Diego Passarella, Christine Lucas, Ludmila Profumo, Verónica Casaravilla, María Noel Rocha Galli, Serrana Ambite, Gregory Randall

“…This dataset can be used to develop and test automatic tree ring detection algorithms. The dataset presented here was used to develop the Cross-Section Tree-Ring Detection (CS-TRD) method, an open-source automated ring-detection algorithm for cross-sectioned images. …”
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Using Masked Image Modelling Transformer Architecture for Laparoscopic Surgical Tool Classification and Localization by Hisham ElMoaqet, Rami Janini, Mutaz Ryalat, Ghaith Al-Refai, Tamer Abdulbaki Alshirbaji, Nour Aldeen Jalal, Thomas Neumuth, Knut Moeller, Nassir Navab

“…The proposed approach uses a self-supervised learning algorithm for surgical tool classification followed by a weakly supervised algorithm for surgical tool localization, eliminating the need for explicit localization annotation. …”
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Prediction acrylamide contents in fried dough twist based on the application of artificial neural network by Xinyu Wu, Haiyang Yan, Yue Cao, Yuan Yuan

“…The weight and threshold values in the BP-ANN network prediction model were optimized with a multi-population genetic algorithm and the test data were set to obtain an optimized BP neural network predicting model. …”
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