abnormal data detection and learning their behavior by abnormality and satisficing theory by masood abessi, Elahe Hajigol Yazdi

“…In this study, a new approach based on the abnormality theory and satisficing theory presented for confidence improvement of abnormal data detection and learning. First, the borders of abnormal and normal behavior clear using a combination approach based on abnormality theory then, satisfied solution extracted by means of satisficing theory. …”
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Asymptotic Behaviors of the Lorenz Curve for Left Truncated and Dependent Data by M. Bolbolian Ghalibaf

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Four‐year follow‐up of weight loss maintenance using electronic medical record data: The PROPEL trial by Peter T. Katzmarzyk, Emily F. Mire, Ronald Horswell, San T. Chu, Dachuan Zhang, Corby K. Martin, Robert L. Newton, John W. Apolzan, Eboni G. Price‐Haywood, Dan Fort, Thomas W. Carton, Kara D. Denstel, the PROPEL Research Group

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Transforming physical fitness and exercise behaviors in adolescent health using a life log sharing model by Shanshan Wang, Jingwu Liu

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Knowledge and behavior of dentistry patients about the use and misuse of antibiotics: A cross-sectional study by Nilay Er

“…Background: Antibiotics are being used frequently in dental infection and this study focused on the knowledge and behavior on antibiotic use of dentistry patients to reveal major mistakes leading to drug misuse. …”
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Cyberbullying victimization predicts substance use and mental health problems in adolescents: data from a large-scale epidemiological investigation by Cláudio Romualdo, Wanderlei Abadio de Oliveira, Luciana Bertoldi Nucci, José Eugenio Rodríguez Fernández, Laura Soares da Silva, Evelin Moreira Freires, Amanda Severo Lins Vitta, Fernando Ferreira Semolini, Lucas da Rosa Ferro, Denise de Micheli, André Luiz Monezi Andrade

“…PurposeThis study investigated the potential association of cyberbullying victimization (CyVic) on substance use and mental health-related behaviors among Brazilian adolescents, using data derived from the National Survey of School Health (PeNSE).MethodThe sample comprised 146,536 adolescents aged up to 17 years, who were selected through probabilistic and representative sampling. …”
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Survey of video behavior recognition by Huilan LUO, Chanjuan WANG, Fei LU

“…Behavior recognition is developing rapidly,and a number of behavior recognition algorithms based on deep network automatic learning features have been proposed.The deep learning method requires a large number of data to train,and requires higher computer storage and computing power.After a brief review of the current popular behavior recognition method based on deep network,it focused on the traditional behavior recognition methods.Traditional behavior recognition methods usually followed the processes of video feature extraction,modeling of features and classification.Following the basic process,the recognition process was overviewed according to the following steps,feature sampling,feature descriptors,feature processing,descriptor aggregation and vector coding.At the same time,the benchmark data set commonly used for evaluating the algorithm performance was also summarized.…”
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Analyzing the Strelitzia Juncea Cellulosic Fibers Mechanical Properties’ Experimental Data Using Various Statistical Methods by Soumia Teyar, Hassina Gheribi, Messaouda Boumaaza, Ahmed Belaadi, Boon Xian Chai, Mahmood M.S. Abdullah, Ibrahim M.H. Alshaikh, Amar Al-Khawlani, Djamel Ghernaout

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Identifying student help-seeking behavior patterns and help-seeking tendencies from student problem-solving and help-seeking behavior data: An educational data mining approach by Chih-Yueh Chou, Wei-Han Chen

“…This study applied an educational data mining approach, including clustering and classification, to analyze students’ problem-solving and help-seeking data in a computer assisted learning system to identify student help-seeking behavior patterns and tendencies. …”
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Urban Environmental Predictors of Group Size in Cliff Swallows (<i>Petrochelidon pyrrhonota</i>): A Test Using Community-Science Data by Cassie Rueda, Kevin J. McGraw

“…The urban heat island effect and shifts in resource availability (e.g., food, water) may also affect colonial birds. Here, we used five years of community-science data available in eBird to investigate urban impacts on group size in Cliff Swallows (<i>Petrochelidon pyrrhonota</i>), an abundant colonial bird species that now breeds readily under bridges and other built structures over or near water in Phoenix, Arizona, USA. …”
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Predicting errors in accident hotspots and investigating satiotemporal, weather, and behavioral factors using interpretable machine learning: An analysis of telematics big data. by Ali Golestani, Nazila Rezaei, Mohammad-Reza Malekpour, Naser Ahmadi, Seyed Mohammad-Navid Ataei, Sepehr Khosravi, Ayyoob Jafari, Saeid Shahraz, Farshad Farzadfar

“…This study aimed to utilize interpretable ML models to predict the occurrence of errors in road accident hotspots using telematics data in Iran and interpret the most influential predictors.…”
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Ensemble-based customer churn prediction in banking: a voting classifier approach for improved client retention using demographic and behavioral data by Ruchika Bhuria, Sheifali Gupta, Upinder Kaur, Salil Bharany, Ateeq Ur Rehman, Seada Hussen, Ghanshyam G. Tejani, Pradeep Jangir

“…Using a comprehensive dataset including demographic, financial, and behavioral data—such as credit score, account balance, tenure, and activity levels—the study employs the goal variable revealing if a customer has left the bank. …”
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Evaluating drivers’ braking behavior at mid-block pedestrian crosswalks using video data and a mixed logit model with heterogeneity in the means by Md Jamil Ahsan, Mohamed Abdel-Aty, Nafis Anwari, Natalia Barbour

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Predicting errors in accident hotspots and investigating satiotemporal, weather, and behavioral factors using interpretable machine learning: An analysis of telematics big data by Ali Golestani, Nazila Rezaei, Mohammad-Reza Malekpour, Naser Ahmadi, Seyed Mohammad-Navid Ataei, Sepehr Khosravi, Ayyoob Jafari, Saeid Shahraz, Farshad Farzadfar

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Quad-Tree-Based Driver Classification Using Deep Learning for Mild Cognitive Impairment Detection by Seyedeh Gol Ara Ghoreishi, Charles Boateng, Sonia Moshfeghi, Muhammad Tanveer Jan, Joshua Conniff, Kwangsoo Yang, Jinwoo Jang, Borko Furht, David Newman, Ruth Tappen, Monica Rosselli, Kelley L. Jackson

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Analyzing customer churn behavior using datamining approach: hybrid support vector machine and logistic regression in retail chain by Mohammad Barzegar, Aliakbar Hasani

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Behavior recognition technology based on deep learning used in pediatric behavioral audiometry by Wen Xie, Chunhua Li, Haisen Peng, Yuehui Liu, Zhilin Zhang, Xiaogang Cheng, Jiali Liu

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AI-Generated Questions in Context: A Contextualized Investigation Using Platform Data, Student Feedback, and Faculty Observations by Rachel Van Campenhout, Benny G. Johnson, Michelle Clark, Melissa Deininger, Shannon Harper, Kelly Odenweller, Erin Wilgenbusch

“…Previous research studies used aggregated data from all questions answered by all students to complete the largest evaluation of the performance metrics for automatically generated questions. …”
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A novel behavioral health care dataset creation from multiple drug review datasets and drugs prescription using EDA by Gantela Prabhakar, George Merlin Linda, Bandi Sudheer Reddy, Samineni Nagamani, Chary Sonkoju Nagarjuna

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ADOPTION OF IOT AND BIG DATA IN SHAPING AND ENHANCING ORGANIZATIONAL CITIZENSHIP BEHAVIOR by Pooja Kanojia, Rupa Khanna Malhotra, Amit Kumar Uniyal, Tannupriya Chaudhary

“…SPSS was used to analyze the data. The relationship between Big data and OCB also, demographic factors, and OCB is not significant. …”
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