Detection of child depression using machine learning methods. by Umme Marzia Haque, Enamul Kabir, Rasheda Khanam

“…The variables of yes/no value of low correlation with the target variable (depression status) have been eliminated. The Boruta algorithm has been utilized in association with a Random Forest (RF) classifier to extract the most important features for depression detection among the high correlated variables with target variable. …”
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ANALYTICS AND DATA SCIENCE APPLIED TO THE TRAJECTORY OUTLIER DETECTION by Alexis J. LOPEZ, Perfecto M. QUINTERO, Ana K. HERNANDEZ

“…The experimental results show that the algorithm detects optimally the abnormal routes using historical data as a base. …”
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Deep Learning Algorithm Analysis of Potato Disease Classification for System on Chip Implementation by John Adebisi, Sesham Srinu, Varqa Mitonga

“…The broad range of crops has witnessed setbacks in different capacities due to climate change among other factors, hence leading to diseases and infections; thereby leading to negatively impacted nutrition. This work uses a deep learning algorithm to investigate the classification of potato diseases as a case study which can be leveraged on by other agricultural products for reference.  …”
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Enhanced object detection in low-visibility haze conditions with YOLOv9s. by Yang Zhang, Bin Zhou, Xue Zhao, Xiaomeng Song

“…Low-visibility haze environments, marked by their inherent low contrast and high brightness, present a formidable challenge to the precision and robustness of conventional object detection algorithms. This paper introduces an enhanced object detection framework for YOLOv9s tailored for low-visibility haze conditions, capitalizing on the merits of contrastive learning for optimizing local feature details, as well as the benefits of multiscale attention mechanisms and dynamic focusing mechanisms for achieving real-time global quality optimization. …”
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Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease by Ivar R. deVries, Judith O. E. H. vanLaar, Marieke B. van der Hout‐van der Jagt, Sally‐Ann B. Clur, Rik Vullings

“…Furthermore, a positive effect of measurement length on the detection performance was observed, reaching optimal performance when using 14 electrocardiography segments (37.5 min) or more. …”
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Text Detection Method With Emphasis on Text Component Importance and Lightweight Design by Lanlan Yin, Qiming Wu

“…The proposed method exhibited a peak memory occupation rate of only 24.16%, with an average parameter volume of 13.57 MB over 100 tests, a value that is considerably lower than that observed in comparative algorithms. In practical applications, the proposed method consistently demonstrates optimal performance, with minimal instances of false positives or false negatives. …”
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Integrating Information Gain and Chi-Square for Enhanced Malware Detection Performance by Fauzi Adi Rafrastara, Wildanil Ghozi, Ramadhan Rakhmat Sani, Lekso Budi Handoko, Abdussalam Abdussalam, Elkaf Rahmawan Pramudya, Faizal M. Abdollah

“…Recent studies have shown that this challenge can be addressed by employing machine learning algorithms for detection. Some studies have also implemented various feature selection methods to optimize detection efficiency. …”
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Anomaly Detection in Wireless Sensor Networks Using Immune-Based Bioinspired Mechanism by Ramsha Rizwan, Farrukh Aslam Khan, Haider Abbas, Sajjad Hussain Chauhdary

“…In this paper, we propose a bioinspired solution using Negative Selection Algorithm (NSA) of the AIS for anomalies detection in WSNs. …”
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Preliminary analysis of acoustic detection of the Red-throated Caracara in northern Costa Rica by Roberto Vargas-Masís, Diego Quesada

“…Advances in automatic acoustic detection have transformed bird ecology, allowing researchers to analyze bird populations using pattern matching algorithms, machine learning, and random forest models. …”
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Development of machine learning algorithms to predict viral load suppression among HIV patients in Conakry (Guinea) by Degninou Yehadji, Degninou Yehadji, Geraldine Gray, Carlos Arias Vicente, Petros Isaakidis, Petros Isaakidis, Abdourahimi Diallo, Saa Andre Kamano, Thierno Saidou Diallo

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Classification Model for Bot-IoT Attack Detection Using Correlation and Analysis of Variance by Firgiawan Faira, Dandy Pramana Hostiadi

“…The novelty of this research lies in the application of a data aggregation technique to address class imbalance, significantly improving machine learning model performance and optimizing training time. These findings contribute to the development of robust cybersecurity systems to effectively detect IoT-related threats.…”
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SARS-CoV-2 Prediction Strategy Based on Classification Algorithms from a Full Blood Examination by C. F. Choukhan, I. Lasri, R. El Hatimi, M. R. Lemnaouar, M. Esghir

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Comparison of K-Nearest Neighbors and Naive Bayes Classifier Algorithms in Sentiment Analysis of 2024 Election in Twitter (X) by Lola Enjelia, Yana Cahyana, Rahmat, Deden Wahiddin

“…These findings offer practical implications for election authorities, policymakers, and digital campaign strategists, particularly in optimizing public communication strategies, early detection of potential conflicts, and designing public opinion monitoring systems based on real-time sentiment analysis. …”
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Integrating Cognitive Intelligence and VANET for Effective Traffic Congestion Detection in Smart Urban Mobility by Anita Mohanty, Ambarish G. Mohapatra, Subrat Kumar Mohanty, Tiansheng Yang, Rajkumar Singh Rathore, Ahmed Alkhayyat, Deepak Gupta

“…This approach promises a more effective congestion detection methodology with minimal installation costs. …”
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An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification by Khaoula Taji, Ali Sohail, Tariq Shahzad, Bilal Shoaib Khan, Muhammad Adnan Khan, Khmaies Ouahada

“…The ensemble feature vector is optimized using three different meta-heuristic algorithms that are Binary Dragonfly algorithm (BDA), Ant Colony Optimization algorithm and Moth Flame Optimization algorithm (MFO). …”
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Real-time Detection of Imperfect Wheat Grains on Wheat Pile Surface Based on IDS-YOLO by FAN Jiawei, WU Lan, YAN Jingjing

“…To address the high missed detection rate of imperfect grains in target detection algorithms and to enhance the model detection speed, this study optimized the lightweight network model YOLOV4-Tiny. …”
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Detection Method for Safety Helmet Wearing on Construction Sites Based on UAV Images and YOLOv8 by Xin Jiao, Cheng Li, Xin Zhang, Jian Fan, Zhenwei Cai, Zhenglong Zhou, Ying Wang

“…To address these issues, this study proposes a helmet detection method based on unmanned aerial vehicles (UAVs) and the YOLOv8 object detection algorithm. …”
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Research on load frequency control system attack detection method based on multi-model fusion by Feng Zheng, Weixun Li, Huifeng Li, Libo Yang, Zengjie Sun

“…A multi-model fusion attack detection framework is proposed, integrating (Long Short-Term Memory) LSTM supervised learning and autoencoder unsupervised learning algorithms, with an adaptive weight adjustment mechanism that dynamically optimizes detection strategies. …”
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Validation study of health administrative data algorithms to identify individuals experiencing homelessness and estimate population prevalence of homelessness in Ontario, Canada by Lucie Richard, Stephen W Hwang, Cheryl Forchuk, Rosane Nisenbaum, Kristin Clemens, Kathryn Wiens, Richard Booth, Mahmoud Azimaee, Salimah Z Shariff

“…Two reference standard definitions of homelessness were adopted: the housing episode and the annual housing experience (any homelessness within a calendar year).Main outcome measures Sensitivity, specificity, positive and negative predictive values and positive likelihood ratios of 30 case ascertainment algorithms for detecting homelessness using up to eight health service databases.Results Sensitivity estimates ranged from 10.8% to 28.9% (housing episode definition) and 18.5% to 35.6% (annual housing experience definition). …”
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A deep contrastive learning-based image retrieval system for automatic detection of infectious cattle diseases by Veerayuth Kittichai, Morakot Kaewthamasorn, Apinya Arnuphaprasert, Rangsan Jomtarak, Kaung Myat Naing, Teerawat Tongloy, Santhad Chuwongin, Siridech Boonsang

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