Accurate estimation of permeability reduction resulted from low salinity water flooding in clay-rich sandstones by Xiaojuan Zhang, Muntadher Abed Hussein, Tarak Vora, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Farzona Alimova, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Ahmad Khalid

“…The results show that random forest and ensemble learning algorithms delivered the highest predictive accuracy, evidenced by the most substantial coefficient of determination (R2) and minimal error metrics. …”
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Optimized AI and IoT-Driven Framework for Intelligent Water Resource Management by Mahmoud Badee Rokaya Mahmoud, Dalia Ismaeil Ibrahim Hemdan, Samah Hazzaa Alajmani, Raneem Yousif Alyami, Ghada Elmarhomy, Hassan Hashim, El-Sayed Atlam

“…The architecture combines the ensemble-learning algorithms (XGBoost, LightGBM), hybrid AIs (XGBoost + Autoencoder), and metaheuristic feature selection (GA, PSO, SA) for making intelligent decisions. …”
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Radio signal recognition based on image deep learning by Xin ZHOU, Xiaoxin HE, Changwen ZHENG

“…A technical idea was innovatively proposed that uses image deep learning to solve the problem of radio signal recognition.First,the radio signal was transformed into a two-dimensional picture,and the radio signal recognition problem was transformed into the object detection problem in the field of image recognition.Then,the advanced achievements about image recognition were used to improve the intelligence and ability of radio signal recognition in complex electromagnetic environment.Based on the proposed idea,a novel radio signal recognition algorithm named RadioImageDet was proposed.The experimental results show that the algorithm can effectively identify the waveform types and time/frequency coordinates of radio signals.After training and testing on the self-collected data set with 12 types and 4 740 samples,the accuracy reaches 86.04% and the mAP value reaches 77.72,while the detection time is only 33 ms on the medium configured desktop computer.…”
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Formation of the pediatric electroretinogram database parameters for the development of doctor’s decisionmaking algorithm by A. E. Zhdanov, A. Yu. Dolganov, V. N. Kazaykin, V. I. Borisov, V. O. Ponomarev, L. G. Dorosinsky, A. V. Lizunov, E. Luchian, X. Bao

“…This article describes the formation of the parameters of pediatric electroretinogram database parameters for the development of doctor’s decision-making algorithm. The signal parameters were obtained by extracting the parameters from the wavelet scalogram of the electroretinogram signal using digital image processing and machine learning methods.…”
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An Industrial System for Inspecting Product Quality Based on Machine Vision and Deep Learning by Xuan-Thuan Nguyen, Thi-Thoa Mac, Quang-Dinh Nguyen, Huy-Anh Bui

“…By leveraging the power of machine learning algorithms, computer vision systems can automatically detect and classify defects in industrial products with high precision and efficiency. …”
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Leakage Detection in Subway Tunnels Using 3D Point Cloud Data: Integrating Intensity and Geometric Features with XGBoost Classifier by Anyin Zhang, Junjun Huang, Zexin Sun, Juju Duan, Yuanai Zhang, Yueqian Shen

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Deep Learning Method with Domain-Task Adaptation and Client-Specific Fine-Tuning YOLO11 Model for Counting Greenhouse Tomatoes by Igor Glukhikh, Dmitry Glukhikh, Anna Gubina, Tatiana Chernysheva

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Utilization of Deep Learning YOLO V9 for Identification and Classification of Toraja Buffalo Breeds by Abdul Rachman Manga', Herawati Herawati, Purnawansyah Purnawansyah

“…Future research is recommended to explore broader datasets, compare alternative object detection algorithms, and develop an integrated application for practical field use.…”
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High-dimensional outlier detection based on deep belief network and linear one-class SVM by Haoqi LI, Na YING, Chunsheng GUO, Jinhua WANG

“…Aiming at the difficulties in high-dimensional outlier detection at present,an algorithm of high-dimensional outlier detection based on deep belief network and linear one-class SVM was proposed.The algorithm firstly used the deep belief network which had a good performance in the feature extraction to realize the dimensionality reduction of high-dimensional data,and then the outlier detection was achieved based on a one-class SVM with the linear kernel function.High-dimensional data sets in UCI machine learning repository were selected to experiment,result shows that the algorithm has obvious advantages in detection accuracy and computational complexity.Compared with the PCA-SVDD algorithm,the detection accuracy is improved by 4.65%.Compared with the automatic encoder algorithm,its training time and testing time decrease significantly.…”
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Artificial intelligence in data storage systems by V. V. Zhilin, O. A. Safar'yan

“…The use of AI in organizing a data warehouse will speed up the system. Artificial intelligence with built-in machine-learning algorithms will provide responding to a situation that affects the state of the sys-tem. …”
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ARC-LIGHT: Algorithm for Robust Characterization of Lunar Surface Imaging for Ground Hazards and Trajectory by Alexander Cushen, Ariana Bueno, Samuel Carrico, Corrydon Wettstein, Jaykumar Ishvarbhai Adalja, Mengxiang Shi, Naila Garcia, Yuliana Garcia, Mirko Gamba, Christopher Ruf

“…The feasibility of this technology was validated through development of a prototype algorithm, which was trained on data from a purpose-built testbed that simulates imaging through a dusty environment. …”
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Detecting Personally Identifiable Information Through Natural Language Processing: A Step Forward by Luca Mainetti, Andrea Elia

“…This information is then processed by a pre-trained Bidirectional Encoder Representations from Transformers (BERT) algorithm. We achieved high performance in detecting PII, reaching an accuracy of 99.558%. …”
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K-Nearest Neighbors for Anomaly Detection and Predictive Maintenance in Water Pumping Systems by João Pablo Santos da Silva, André Laurindo Maitelli

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Drunk Driver Detection Using Multiple Non-Invasive Biosignals by Sang Hyuk Kim, Hyo Won Son, Tae Mu Lee, Hyun Jae Baek

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Enhancing placental pathology detection with GAMatrix-YOLOv8 model by Weirui Wu, Zhifa Jiang, Jingwen Liu, Jiahui Ji, Xiaoyan Wei, Xiangyun Ye, Zhen Zhang

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Marine soundscape forecasting: A deep learning-based approach by Shashidhar Siddagangaiah

“…Despite the rapid development of anomaly detection algorithms and deep-learning models for forecasting, their application to marine soundscapes remains unexplored. …”
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Review of applications of deep learning in veterinary diagnostics and animal health by Sam Xiao, Navneet K. Dhand, Zhiyong Wang, Kun Hu, Kun Hu, Peter C. Thomson, John K. House, Mehar S. Khatkar, Mehar S. Khatkar

“…Deep learning (DL), a subfield of artificial intelligence (AI), involves the development of algorithms and models that simulate the problem-solving capabilities of the human mind. …”
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Comparison of Deep Learning and Clinician Performance for Detecting Referable Glaucoma from Fundus Photographs in a Safety Net Population by Van Nguyen, MD, Sreenidhi Iyengar, Haroon Rasheed, MD, Galo Apolo, Zhiwei Li, Aniket Kumar, Hong Nguyen, Austin Bohner, MD, Kyle Bolo, MD, Rahul Dhodapkar, MD, Jiun Do, MD, PhD, Andrew T. Duong, MD, Jeffrey Gluckstein, MD, Kendra Hong, MD, Lucas L. Humayun, Alanna James, MD, Junhui Lee, MD, Kent Nguyen, OD, Brandon J. Wong, MD, Jose-Luis Ambite, PhD, Carl Kesselman, PhD, Lauren P. Daskivich, MD, Michael Pazzani, PhD, Benjamin Y. Xu, MD, PhD

“…Purpose: Develop and test a deep learning (DL) algorithm for detecting referable glaucoma. …”
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An Algorithm for Mining the Living Habits of Elderly People Living Alone Based on AIoT by Jiaxuan Wu, Yuxin Lu, Yueqiu Jiang

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Vehicle maneuver recognition and correction algorithm for road quality measurement system optimization by Roland Nagy, István Szalai

“…We introduce a novel, computationally efficient soft-sensor algorithm that detects and isolates aggressive maneuvers using sensor data from existing road quality measurement systems, classifying them into four categories. …”
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