Analysis Of Behavior Patterns Research Articles

Comparative analysis of tracking and behavioral patterns between wild-type and genetically modified fruit flies using computer vision and statistical methods

Collective animal behavior occurs in groups and swarms at almost every biological scale, from single-celled organisms to the largest animals on Earth. The intriguing mysteries behind these group behaviors have attracted many scholars, and while it is known that models can reproduce qualitative features of such complex behaviors, this requires data from real animals to demonstrate, and obtaining data on the exact features of these groups is tricky. In this paper, we propose the Hidden Markov Unscented Tracker (HMUT), which combines the state prediction capability of HMM and the high-precision nonlinear processing capability of UKF. This prediction-driven tracking mechanism enables HMUT to quickly adjust tracking strategies when facing sudden changes in target motion direction or rapid changes in speed, reducing the risk of tracking loss. Videos of fruit fly swarm movement in an enclosed environment are captured using stereo cameras. For the captured fruit fly images, the thresholded AKAZE algorithm is first used to detect the positions of individual fruit flies in the images, and the motion of the fruit flies is modeled using a multidimensional hidden Markov model (HMM). Tracking is then performed using the Unscented Kalman Filter algorithm to obtain the flight trajectories of the fruit flies in two camera views. Finally, 3D reconstruction of the trajectories in both views is achieved through polar coordinate constraints, resulting in 3D motion data of the fruit flies. Additionally, the efficiency and accuracy of the proposed algorithm are evaluated by simulating fruit fly swarm movement using the Boids algorithm. Finally, based on the tracked fruit fly flight data, behavioral characteristics of the fruit flies are analyzed from two perspectives. The first is a statistical analysis of the differences between the two behaviors. The second dimension involves clustering trajectory similarity using the DTW method based on fruit fly flight trajectories, further analyzing the similarity within clusters and differences between clusters.

Monitoring virtual interactions of teachers and students in social networks

In the era of digitalization, the study of interaction between teachers and students in social networks gains particular relevance. The aim of this research is to monitor these virtual interactions for a deep analysis of opinions, beliefs, preferences, and behavioral patterns regarding the culture of online communication and digital etiquette in online communities. The participants included 227 students (M = 21.59; SD = 2.03) and 114 teachers (M = 40.17; SD = 8.83) from three higher education institutions in Kazakhstan. Specially developed scales of digital etiquette level and online communication quality were used for analysis, and a SWOT analysis (identifying strengths, weaknesses, opportunities, and threats) was conducted based on respondent interviews. The results indicated that there was no significant difference in the level of digital etiquette between students and teachers; however, a substantial difference was found in the quality of online communication, with students exhibiting better performance. The correlational analysis did not reveal a statistically significant relationship between the level of digital etiquette and the quality of online communication. The study identifies important aspects of the relationship between digital etiquette and the quality of online communication, providing valuable data for practitioners in the current field and emphasizing the need for further research.

A Logistic Regression Analysis of Behavioural Patterns in Threat Responses Using Online Experimental Data

A Logistic Regression Analysis of Behavioural Patterns in Threat Responses Using Online Experimental Data

Comparative characteristics of behavior models of nursing and midwifery specialists in a maternity hospital аnd an antenatal clinic

The article presents the results of a comparative analysis of behavioral patterns of nurses and midwives working in a maternit y hospital and antenatal clinic. Target. To analyze behavioral patterns of nursing and obstetric specialists depending on their place of work with the subsequent use of the results in the organizational and managerial process of a maternit y hospital and antenatal clinic. Results. The significant personal qualities for nursing and obstetrics specialists were determined, the leading psychological defenses for each group of specialists and the leading behavior models were identified. Conclusion. Nurses and midwives working in maternity hospitals and antenatal clinics play a special role in providing medical care to women and children, helping the former to experience the joy of motherhood, and the latter to be born. In both cases, it is necessary to have certain personal characteristics and the abilit y to interact with patients in any situation.

The Impact of a Digital Game-Based AI Chatbot on Students’ Academic Performance, Higher-Order Thinking, and Behavioral Patterns in an Information Technology Curriculum

In the age of intelligence, information technology (IT) education has become the focus of attention in the education sector. However, traditional teaching methods fall short in motivating students and fostering higher-order thinking and have difficulty providing a personalized learning experience. Although AI chatbots can provide instant feedback as an innovative teaching tool, it is still challenging to fully enhance learner engagement. Based on this, this study developed a digital game-based AI chatbot system to enhance students’ learning experience through digital game-based learning strategies. This study utilized a quasi-experimental design with the experimental group using a digital game-based AI chatbot and the control group using a traditional AI chatbot. A comparison was made between the two groups concerning student learning performance in IT courses, higher-order thinking (including problem-solving, computational thinking, and creativity), learning motivation, and flow experience. In addition, the behavioral patterns of high-achieving and low-achieving students in the experimental group were analyzed. The results showed that the experimental group was significantly better than the control group in academic performance, problem-solving, computational thinking, learning motivation, and flow experience, but there was no significant difference in creativity tendency. Behavioral pattern analysis showed that high-achieving students in the experimental group showed more systematic learning strategies, while low-achieving students relied more on immediate feedback and external help, but both high- and low-achieving groups were able to actively talk to the AI chatbot and actively explore problem-solving strategies in the digital game. Therefore, AI chatbots based on digital games can be effectively used in IT courses to help students construct knowledge and develop higher-order thinking.

Analysis of changing behavioral patterns in student parent domain using Reinforcement Learning and Machine Learning methods to predict Psychological Disorders

This study takes a critical look at the opportunities and difficulties faced by students in higher education who are moving from adolescence to young adulthood (18 to 25 years old). It integrates interdisciplinary viewpoints from psychology, education, and public health to comprehend and promote students' behavioral well-being. The identification of behavioral patterns, risk factors for scholastic challenges, mental health issues, and novel approaches such as machine learning for early intervention are among the main subjects. K-means, KNN, Clustering, Reinforcement learning, Actor-Critic Method, Multi Linear Regression, and Q-Learning are among the algorithms used. Keywords: Interdisciplinary perspectives, Psychology, Public health, K-Means, KNN, Clustering, Multi Linear Regression Reinforcement learning, Actor-Critic-Method, and Q-Learning.

Revolutionizing travel experiences: An in-depth analysis of intelligent booking systems and behavioral patterns

This study delves into the intricacies of booking behaviors, focusing on the preference for tropical coastal cities during colder seasons, given the burgeoning need for personalized and intelligent travel recommendations in the modern tourism landscape. Traditional booking systems, with their static algorithms, have often neglected the dynamic interplay of environmental factors and user preferences, necessitating an evolution towards more sophisticated, adaptive models. To address this, the research employed advanced machine learning and artificial intelligence techniques, amalgamating data from diverse sources like major tourism websites, hotel platforms, and social media. The methodology involved meticulous data preprocessing to ensure data quality and depth, followed by the implementation of deep learning models and time series analysis, which were instrumental in deciphering complex patterns and offering predictive insights. This comprehensive approach facilitated the integration of various environmental factors such as weather conditions and temporal events, enabling the system to craft hyper-personalized recommendations that resonate with user preferences and environmental nuances. The findings were illuminating, unveiling a significant inverse relationship between temperature and bookings and highlighting the influential role of temporal events in driving bookings. The advanced predictive models showcased commendable accuracy, underscoring the transformative potential of such intelligent systems in enhancing user experiences and engagement in the tourism sector. These insights pave the way for the development of innovative strategies that can significantly elevate user satisfaction and industry growth. The study concludes that the integration of machine learning and AI in booking systems can revolutionize the tourism industry by offering unprecedented levels of personalization and adaptability, catering to the diverse and evolving needs of modern travelers.

Analysis of activity rhythm and behavior pattern for plateau pika in degraded alpine meadow

This paper proposes a behavior observation method for plateau pika (Ochotona curzoniae) in degraded alpine meadow in the Source Zone of the Yellow River (SZYR) on the Qinghai–Tibet Plateau based on continuous monitoring videos to observe its activity rhythm. The video surveillance cameras are installed facing toward degraded alpine meadow in the SZYR and run from March 2021 to February 2022 to record time allocation of plateau pika behavior (i.e., digging, foraging, guarding, moving and other behaviors) and its daily, monthly, and seasonal activity rhythm. The results indicate that: (1) The most active ground activity time is between 10:00 and 16:00, meanwhile it is inactive at night, which proves it plateau pika is diurnal animals. (2) Digging and foraging are frequently occurring behaviors among its daily activities, which occurs from 9:00 to 12:00 and 16:00. (3) Compare to other season, winter sees relatively frequent occurrences of digging and foraging according to the seasonal analysis results. (4) From annual perspective, digging and foraging accounted for 66.13% of plateau pika activity, while guarding accounted for 13.35%, moving accounted for 11.64%, and other behaviors accounted for 8.88%. There is a significant difference in the frequency of each behavior between the two groups (P < 0.05). The behavior observation method using video surveillance camera contributes to behavioral ecology information of plateau pika.

Navigating New Markets: Uniqlo's Strategic Success in Canada

In a market characterized by fierce competition and diverse consumer preferences, the Japanese fast-fashion giant Uniqlo's strategic foray into the Canadian market is a significant move. Uniqlo's approach provides a compelling case study in international retail strategy. In this paper, the author scrutinizes various aspects of Uniqlos market entry from strategic marketing and consumer experience to business ethics. This study aims to uncover the intricacies of adapting a global brand to a local context, focusing on how Uniqlo navigated the Canadian retail environment's unique challenges. This involves analysis of consumer behavior patterns, market trends, and competitive dynamics to understand the factors behind Uniqlos performance in the Canadian market. Drawing from a range of sources, including industry reports, academic articles, and market analyses, the purpose of this research is to offer valuable insights into the complexities of global expansion and the nuanced strategies that drive success in new, diverse markets like Canada.

Utilizing enhanced deep learning methods for residential burglary detection via analysis of human behavior patterns

Utilizing enhanced deep learning methods for residential burglary detection via analysis of human behavior patterns

City Of Dreams: An Open-World Games

In recent years, the gaming industry has witnessed a remarkable evolution, with open-world games emerging as a dominant genre that captivates players with expansive virtual worlds, intricate narratives, and immersive gameplay experiences. Among these innovative titles, "City of Dreams" stands out as a pinnacle of open-world game design, offering players a richly detailed urban landscape to explore, inhabit, and shape according to their desires. This research paper provides a comprehensive analysis of "City of Dreams," examining its technical development, player engagement dynamics, societal impact, and broader implications within the gaming industry and digital culture. The technical development of "City of Dreams" is explored in detail, encompassing various aspects such as game engine selection, character animation, environment design, and physics implementation. By delving into the technical intricacies of game development, this research sheds light on the creative processes and technological innovations that underpin the creation of immersive gaming experiences. Furthermore, the paper investigates the player engagement dynamics of "City of Dreams," exploring how its diverse gameplay mechanics, immersive world design, and compelling narrative drive player engagement and immersion. Through an analysis of player behavior, feedback, and interaction patterns, insights are gained into the factors that contribute to the game's success and longevity. Keywords—open-world games, player experience, player behavior, game development, immersive environments, Non-Playable Characters (NPCs).

Passive Aggressive Vocabulary

This article provides a description, definition, and some research data on passive-aggressive personality structure, as well as practical recommendations and methods for working with such clients. The article outlines the main features of passive aggression and its implications for personality adaptation. It also offers practical recommendations for therapists, including techniques for working with clients, intervention strategies, and approaches to working with clients exhibiting a high degree of passive-aggressive defenses. The article introduces the “Dictionary of Passive Aggression”, formulated by practicing psychologists who participated in the practical course for psychologists “Therapeutic Skills” with Irina Pingareva (psychoanalyst, Certified Transactional Analyst of the European Registry CTA-P-EATA, body-oriented therapist, instructor at the International Institute of Developmental TA — MIR-TA, author of trainings and seminars). This dictionary was compiled through the analysis of typical expressions and behavioral patterns observed by participants in practice. The purpose of this dictionary is to help specialists identify and understand passive-aggressive expressions and their possible roots. This article may be beneficial for both psychotherapy specialists and a broader audience interested in understanding passive aggression in everyday life.

Quadrature Response Spectra Deep Neural Based Behavioral Pattern Analytics for Epileptic Seizure Identification

Abstract The brain’s Electroencephalogram (EEG) signals contain essential information about the brain and are widely used to support the analysis of epilepsy. By analyzing brain behavioral patterns, an accurate classification of different epileptic states can be made. The behavioral pattern analysis using EEG signals has become increasingly important in recent years. EEG signals are boisterous and non-linear, and it is a demanding mission to design accurate methods for classifying different epileptic states. In this work, a method called Quadrature Response Spectra-based Gaussian Kullback Deep Neural (QRS-GKDN) Behavioral Pattern Analytics for epileptic seizures is introduced. QRS-GKDN is divided into three processes. First, the EEG signals are preprocessed using the Quadrature Mirror Filter (QMF) and the Power Frequency Spectral (PFS) and Response Spectra (RS)-based Feature Extraction is applied for Behavioral Pattern Analytics. The QMF function is applied to the preprocessed EEG input signals. Then, relevant features for behavioral pattern analysis are extracted from the processed EEG signals using the PFS and RS function. Finally, Gaussian Kullback–Leibler Deep Neural Classification (GKDN) is implemented for epileptic seizure identification. Furthermore, the proposed method is analyzed and compared with dissimilar samples. The results of the Proposed method have superior prediction in a computationally efficient manner for identifying epileptic seizure based on the analyzed behavioral patterns with less error and validation time.

Ship Behavior Pattern Analysis Based on Multiship Encounter Detection

Ship Behavior Pattern Analysis Based on Multiship Encounter Detection

Piecewise linear convolutional deep belief classifier for consumer Behavior Analysis in Social Network in Gaussian-embedded clustering

ABSTRACT A novel technique Gaussian-Embedded Clustering-based Piecewise Linear Convolutional Deep Belief Classifier (GEC-PLCDBC) is introduced for enhancing the accuracy of consumer behaviors pattern prediction with minimum time consumption. The proposed GEC-PLCDBC technique performs three major processes, namely, preprocessing, clustering, and behavior pattern analysis. First, preprocessing is carried out using Gaussian distributive neighbor embedding technique to remove the repeated, retweets, and duplicate tweets from the dataset. After preprocessing, the clustering process is said to be performed using Tversky qualitative Hartigan–Wong clustering for grouping similar tweets to minimize the time complexity of the behavior pattern analysis. Finally, the Piecewise Linear Regressive Convolutional Deep Belief Neural Classifier (PLRCDBNC) is used for behavioral pattern prediction such as positive, negative, or neutral based on the extracted words from the tweets. In this way, the GEC-PLCDBC technique improves behavioral pattern prediction while maintaining a low error rate. GEC-PLCDBC technique’s experimental assessment is carried out with respect to clustering accuracy, behavior pattern prediction accuracy, behavior pattern prediction time, and error rate, with respect to different number of tweets. The quantitative results show that GEC-PLCDBC outperforms existing methods by 8% in clustering accuracy and 8% in prediction accuracy of behavior pattern prediction with 15% less time and 26% mean absolute error than the conventional methods.

A review of travel behavioural pattern using GPS dataset: A systematic literature review

In the last few years, the Global positioning system (GPS) has been used to study travel patterns with the advancement of data collection techniques. A GPS device precisely records the time and the location data of an individual travel. The accuracy of travel studies is usually improved by integrating travel surveys or travel activity diaries with GPS data. GPS surveys are implemented to replace paper-based techniques and reduce human error on trip-misreporting issues. Several reviews have been performed previously on GPS travel studies. Still, a detailed systematic review on the comparison of different modes and methods on the entire procedure of GPS data processing has not been thoroughly studied. This review identified 116 peer-reviewed studies and journal papers from 2013 to 2020, and 53 papers were studied. The review focused on travel behavior pattern (TBP) analysis using GPS studies only and a combination of travel surveys. Our review reveals that 53 only 40 have quality assessment scores of >50 %, meaning the remaining TBP studies are conventional methods. The study also benefits data analysis and researchers to select the most appropriate data collection techniques for studying TBP and improvise them further for meta-analysis travel behavior studies.

A comparative analysis of mechanical and optical behavioral patterns of translucent zirconia ceramics.

This research aimed to assess and compare the translucency and mechanical properties of partially stabilized zirconia in contrast to lithium disilicate, particularly within the context of translucent zirconia. The experimental design entails examining fifty samples, with ten drawn from each of the five distinct categories of ceramic materials, as part of this in vitro study. Translucency is measured using the Konica Minolta CM-3600D spectrophotometer, assessing Delta E through Lab values against white and black backgrounds. Flexural strength is analyzed via a 3-point bend test on a universal testing machine, with a controlled crosshead speed set at 1 mm/min. The study included the five categories of ceramic materials, each consisting of ten samples: High-strength zirconia (Katana HT), translucent zirconia (e.max Zircad MT and Cercon ht ML), and lithium disilicate (Press MT and LT). The Konica Minolta CM-3600D spectrophotometer is utilized to measure the translucency parameter. This involves determining the color difference (Delta E) by comparing the L*a*b values against both white and black backgrounds. The flexural strength (FS) of zirconia and lithium disilicate materials was analyzed through a 3-point bend test, aiming to compare their respective strengths. The testing procedure was carried out on a universal testing machine with a controlled crosshead speed set at 1 mm/min. The FS was calculated using the formula σ = FL/πR3 for circular disks, where σ represents the FS, F is the fracture load, L is the span length in millimeters, and R is the radius of the disk. The Student's t-test was employed for statistical analysis. The mean translucency parameter for e.max Press MT (6.33 ± 1.05) was significantly greater than all the specimens investigated. The Cercon ht ML exhibited a slightly higher translucency (2.18 ± 0.52) compared to e.max Zircad MT (1.49 ± 0.69), with a statistically significant difference (P = 0.022). Conversely, the FS of e.max Zircad MT (26.97 ± 2.06) was significantly greater (P < 0.001) than that of Cercon ht ML (23.25 ± 2.36). Notably, the Katana HT material demonstrated the highest load strength (32.92 ± 3.10), a statistically significant difference compared to its counterparts (P < 0.001). Among the materials tested, lithium disilicate ceramics exhibited the highest translucency, with its MT variant demonstrating the lowest strength. Katana HT displayed significantly greater biaxial FS compared to translucent zirconia, surpassing even lithium disilicate. Translucent zirconia proved to be notably more translucent than high-strength zirconia. Within the category of translucent zirconia, e.max Zircad MT exhibited substantially higher FS than Cercon.

가정간호 업무 관리를 돕는 캘린더 앱 디자인 연구

The purpose of this study is to propose a calendar app that can help manage schedules and work for Home Care Nurses who feel difficulty due to heavy workload. First, as a result of case analysis of related apps, it was found that various services other than calendar functions were provided, and that there was no app for nurses performing visiting schedules because all cases were produced for shift-working nurses. After conducting an in-depth interview, we were able to derive six key requirements by building and classifying them as an Affinity Diagram. Behavior pattern analysis was conducted using the data, and after grouping into two representative groups, a Persona and Circumstantial Scenario were produced to confirm the requirements of each aspect. Based on this, it was confirmed that effective services can be provided as a result of conducting usability evaluation after creating a prototype app. Finally, it concludes with conclusions and suggestions, and look forward to further research in the future.

Real-Time Malware Detection on IoT Devices using Behavior-Based Analysis and Neural Networks

IoT devices' constrained processing capabilities and malware's changing nature make traditional signature-based methods for malware detection ineffective. The focus of our suggested approach, in contrast, is on real-time analysis of IoT device behaviour patterns to find anomalies that might be signs of malicious activity. We can spot differences from typical behaviour on devices by continuously observing how they behave. These differences could indicate the existence of malware. We use deep neural networks to handle and analyse the enormous quantity of data produced by IoT devices in order to do this. Specifically, we use recurrent neural networks (RNNs) and convolutional neural networks (CNNs). These neural networks learn the anticipated behaviours of various IoT devices and their applications through training on historical data. They quickly detect unexpected behaviours that can be a sign of malware infestations or other harmful actions by comparing incoming data streams to these learned patterns in real-time. By reaching high detection rates while preserving low false-positive rates, our experimental results show the efficiency of the suggested approach. We can greatly improve the security posture of IoT devices or gateways by integrating this real-time malware detection technology into them, defending against new attacks in the ever-changing IoT landscape. By protecting the privacy and integrity of IoT-enabled environments, our research will help to mitigate the escalating cybersecurity challenges faced by IoT devices.

A STUDY OF THE RELATIONSHIP BETWEEN APARTMENT OCCUPANCY PATTERNS AND STUDENT BEHAVIOR PATTERNS Case Study: Gading Icon Apartment

Apartment is a room or several rooms (rooms) that are functioned or designated as a place to live, located in a building that usually has other rooms or rooms and the like. This study attempts to explore and analyze the relationship between residential space patterns and student behavior patterns in Jakarta. The behavioral patterns of students that affect the pattern of vertical residential spaces not only have an impact on the behavior and form of space but also see how student behavior shapes architecture in residential space patterns and architecture shapes student behavior. The purpose of this study is to understand the theory related to the relationship between vertical residential space patterns and student behavior patterns, to understand the characteristics of the relationship between residential space patterns and student behavior, to understand environmental factors for student behavior. The research method taken on the research approach uses a qualitative descriptive method. The data studied and obtained are physical and non-physical data obtained from interviews, observations, and mapping of student behavior patterns in residential spaces of Jakarta apartments. The approach that will be used is the Human Behavior approach to students in apartment housing. From the analysis of behavior patterns carried out, student activities are quite dense, causing the use of furniture according to needs and has ergonomic characteristics.