AI is used in behavior analysis to analyze patterns and behaviors from data sources such as video surveillance or user interactions. It employs machine learning and computer vision techniques to detect anomalies, predict actions, and provide insights for applications like security, retail analytics, and customer behavior understanding.
Researchers present the MPDB dataset, capturing physiological responses of 35 participants during a driving simulator experiment. Combining EEG, ECG, EMG, GSR, and eye-tracking data with driving behaviors, the dataset offers insights into human cognitive functions while driving. Detailed collection methods, storage structures, and validation procedures ensure the dataset's reliability and effectiveness in studying driver behavior, paving the way for advancements in traffic psychology and behavior modeling.
Researchers introduce FulMAI, a cutting-edge system utilizing LiDAR, video tracking, and deep learning for accurate, markerless tracking and analysis of marmoset behavior. Achieving high accuracy and long-term monitoring capabilities, FulMAI offers valuable insights into marmoset behavior and facilitates research in brain function, development, and disease without causing stress to the animals.
Researchers unveil MouseVUER, an open-source deep learning-based system, facilitating three-dimensional video monitoring of laboratory mice in their home cages. With high image quality, low data volume, and compatibility with various software, this innovative tool promises transformative insights into natural mouse behavior while overcoming limitations of existing monitoring systems.
Researchers introduce the Social Behavior Atlas (SBeA), a pioneering computational framework for studying animal social behavior. Leveraging few-shot learning, 3D pose estimation, and identity recognition, SBeA overcomes data limitations, addresses occlusion challenges, and unveils previously unnoticed social behavior phenotypes across various species, showcasing its potential as a transformative tool in the field.
Researchers focus on improving pedestrian safety within intelligent cities using AI, specifically support vector machine (SVM). Leveraging machine learning and authentic pedestrian behavior data, the SVM model outperforms others in predicting crossing probabilities and speeds, demonstrating its potential for enhancing road traffic safety and integrating with intelligent traffic simulations. The study emphasizes the significance of SVM in accurately predicting real-time pedestrian behaviors, contributing to refined decision models for safer road designs.
This article critically reviews the challenges and advancements in intelligent vehicle safety within complex multi-vehicle interactions. Addressing data collection methods, vehicle interaction dynamics, and risk evaluation techniques, the study categorizes risk assessment into state inference-based and trajectory prediction-based methods. It underscores the need for deeper analysis of multi-vehicle behaviors and emphasizes the advantages and limitations of existing risk assessment approaches.
Researchers proposed an IoT and ML-based approach to analyze ornamental goldfish behavior in response to environmental changes, particularly real-time water temperature and dissolved oxygen concentration. Utilizing IoT sensors and machine learning classifiers like Decision Tree, Naïve Bayes, Linear Discriminant Analysis, and K-Nearest Neighbor, the study demonstrated the effectiveness of the Decision Tree classifier in accurately classifying behavioral changes.
This study introduces a sophisticated pedestrian detection algorithm enhancing the lightweight YOLOV5 model for autonomous vehicles. Integrating extensive kernel attention mechanisms, lightweight coordinate attention, and adaptive loss tuning, the algorithm tackles challenges like occlusion and positioning inaccuracies. Experimental results show a noticeable accuracy boost, especially for partially obstructed pedestrians, offering promising advancements for safer interactions between vehicles and pedestrians in complex urban environments.
Researchers have introduced a cutting-edge Driver Monitoring System (DMS) that employs facial landmark estimation to monitor and recognize driver behavior in real-time. The system, using an infrared (IR) camera, efficiently detects inattention through head pose analysis and identifies drowsiness through eye-closure recognition, contributing to improved driver safety and accident prevention.
This research combines Radio-Frequency Identification (RFID) technology and machine learning to analyze customer browsing behaviors in physical retail stores. By using methods like Isolation Forest (iForest) and Adaptive Synthetic Sampling (ADASYN), the model achieved remarkable accuracy and can be integrated into a web-based application, providing valuable insights for store optimization and customer experience enhancement.
Researchers present a distributed, scalable machine learning-based threat-hunting system tailored to the unique demands of critical infrastructure. By harnessing artificial intelligence and machine learning techniques, this system empowers cyber-security experts to analyze vast amounts of data in real-time, distinguishing between benign and malicious activities, and paving the way for enhanced threat detection and protection.
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