Aspects Of Intelligence Research Articles

Pengaruh Model Pembelajaran Problem Based Learning Terhadap Kecerdasan Emosional dan Hasil Belajar Siswa Pada Mata Pelajaran Fiqh di Madrasah Tsanawiyah Kecamatan Sakra Barat Tahun Pelajaran 2023/2024

This study aims to evaluate the influence of Problem Based Learning (PBL) model on students' emotional intelligence and learning outcomes in the subject of Fiqh at Madrasah Tsanawiyah in the Sakra Barat Subdistrict for the academic year 2023/2024. A quantitative research method was employed with a pretest-posttest control group design experiment. Data collection was conducted through emotional intelligence tests and Fiqh learning outcome tests. Data analysis involved comparing the mean pretest-posttest differences between the control and experimental groups using independent t-tests. The results indicate that the implementation of PBL significantly enhances students' emotional intelligence and learning outcomes compared to conventional methods. The implication of this study underscores the importance of considering appropriate teaching models to improve not only cognitive but also emotional intelligence aspects in Fiqh education. PBL can serve as an effective alternative in achieving these goals at Madrasah Tsanawiyah, providing a deeper understanding of the subject matter and fostering social and emotional skills necessary for daily life and religious practice.

Real-time Speech Emotion Recognition using Machine Learning

This project is centred around examining spoken language emotion, a critical aspect of human-computer interaction and artificial intelligence. Leveraging advanced techniques such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models, we aim to decipher and understand the nuanced expressions of emotions conveyed through speech. The investigation draws from diverse datasets, including RAVDESS, SAVEE, TESS, and CREAM-D, each carefully chosen to encapsulate a broad spectrum of emotions and real-world speech scenarios. The project unfolds by elucidating the methodologies employed in dataset selection, the prepossessing of raw audio data, and the intricacies of utilizing CNN and LSTM techniques for speech emotion analysis. The main objective is to create a robust and reliable outcome model capable of accurately classifying and interpreting emotions in speech across various contexts. Practical applications of this Research extend to fields such as sentiment analysis and potential contributions to mental health monitoring. Through this project, we contribute valuable insights to the evolving landscape of speech emotion analysis, addressing the inherent challenges and exploring opportunities for enhancing human-computer interaction and emotional intelligence in artificial systems.

Pengembangan Manajemen Pengetahuan Madrasah & Pesantren

The rapid advancement of science and technology in the global era is currently felt in various fields of community life, especially in the fields of education, social and culture, including in boarding school education. In order to face the demands of society, community educational institutions including Islamic boarding schools must be functional, because educational institutions as a forum in society can be used as a gateway in facing the demands of society, science and technology that continue to change. Therefore, in order to become an ideal boarding school, it is necessary to organize the management and development of the boarding school. Islamic boarding school as one of the educational institutions that combines these three aspects, not only emphasizes the cognitive intelligence aspect, but also emphasizes the affective and psychomotor aspects, namely by teaching values and norms in accordance with Islamic law and equipping students with various skills (life skills). The development of management and management of education at PPMI Assalam consists of management elements including man, money, material, method, macine, and market. While the realization steps in order to implement the program are planning, organizing, implementing, and monitoring.

BIOLOGY OF HUMAN BEHAVIOR: ITS ESSENTIAL ASPECTS

In terms of Biology humans are just another species on this planet, but when it comes to explaining the biology of the context of Human behavior, it really becomes very tough. Humans have the physiology and anatomy similar to that of a cave man, yet the level of our intelligence has far evolved in comparison to the extent that the cave man anatomy and physiology becomes inadequate to serve to the needs of certain aspects of human intelligence of modern times which essentially functions beyond the level of just survival (for example in animals), and is capable of performing such advanced functions as retaining a huge past memory and can anticipate a future situation. This in many ways has made us far superior to the animal world, yet many of us get messed up in handling our intelligence properly and either develop chronic stress, indulge in violence or mishandle the adolescence related issues of our teenagers. Biologically speaking human behavior is determined largely by how our brains are shaped by developmental and evolutionary processes, at the heart of which lies the genetic and epigenetic changes and environmental impact. Because at any critical moment brains are to decide about any bit of a behavior, therefore the knowledge of underlying brain anatomy and physiology is important. This article is about explaining how biology makes a sense to human behavior and is essentially focused on its three important aspects, i.e. adolescence, chronic stress and violence, which are described in detail in the following paragraphs. KEYWORDS: Amygdala, Brain Reward System, Dopamine, Endocrinology, Homeostasis, Neuropsychology, Prefrontal Cortex. Striatum.

Prediction of Engineering Students Learning Outcomes using Machine Learning

Data science and machine learning, over the years have proven very well-organized and significant in many sectors including education. Machine learning is an aspect of artificial intelligence in which a computing system can able to learn from data and make conclusions. The recent development in education sector provides assessment tools to predict the student performance by exploring education data using machine learning and data mining techniques. Student performance assessment is an important measurement metrics in education which affects the university accreditation. Student performance improvement plan must be implemented in those universities, by counselling the low performer students. It helps both students and teachers to overcome the problems experienced by the student during studies and teaching techniques of teachers. In this review paper, different student performance prediction literature related to find out low performer student. The survey results indicated that different machine learning techniques are used to overcome the problems related to predicting student at risk and assessment of student performance. Machine learning techniques plays an important role in progress and prediction of student performance, thus improving student performance prediction system

A Systematic Review of the Application of Computational Technology in Microtia.

Microtia is a congenital and morphological anomaly of one or both ears, which results from a confluence of genetic and external environmental factors. Up to now, extensive research has explored the potential utilization of computational methodologies in microtia and has obtained promising results. Thus, the authors reviewed the achievements and shortcomings of the research mentioned previously, from the aspects of artificial intelligence, computer-aided design and surgery, computed tomography, medical and biological data mining, and reality-related technology, including virtual reality and augmented reality. Hoping to offer novel concepts and inspire further studies within this field.

Legal framework for AI applications

Artificial intelligence (AI), the most widespread technological term, has become an integral part of human daily life. People are becoming increasingly dependent on AI-powered devices; thus, it is now essential to have a legal framework to oversee artificial intelligence's various uses and legal peculiarities. As artificial intelligence has entered all areas of life and into various sectors, which requires examining in detail the legal nature of artificial intelligence, determining the laws that must be applied to it, and identifying the people or entities responsible for it to determine the scope of their responsibilities for the resulting damages that may be caused to others as a result of the use of intelligence. Artificial, this research aimed to examine the adequacy of the legal rules in Jordanian legislation regulating the provisions of artificial intelligence in light of the diversity of its applications and its different legal nature. The research is divided into two chapters. The first chapter presents the concept of artificial intelligence, and the second chapter discusses the legal liability of artificial intelligence. The results of the research revealed that the Jordanian legislator did not specify the legal nature of the artificial intelligence and contented himself with stipulating it in separate texts, Legal liability resulting from utilizing artificial intelligence systems can be challenged under regulations of the defect in manufacturing, responsibility for guarding things, and the distinction between responsibilities due to the degree of independence and intelligence of artificial intelligence, In determining the liability of artificial intelligence, the legislator should consider the types of artificial intelligence systems, their different capabilities, and their independence from humans, The process of enacting special laws regulating all aspects of artificial intelligence must be expedited. This law should be characterized by flexibility that enables it to keep pace with and rapid development witnessed in this field.

Development of Voice-Instructed Smart Fire Alarm Using Arduino and IoT Functionality

Fires can destroy and cause significant damage to property and human life. Fires break out unexpectedly, anywhere, and at any time, and it is essential to get accurate information on fire causes to minimize casualties. Therefore, it is imperative to set an alarm appropriately. Traditional fire alarms sound simply noisy without much guiding detail. Sometimes, building occupants even escape toward the flames. In this study, an intelligent Arduino system was created for a verbal guide with more precise information. It informed the location, sources of fire, and seriousness while alarming the fire department through the email alert system. The self-surveyed scoring tests demonstrated that our voice-instructed smart fire system was significantly informative and superior in the aspects of audibility, intelligibility, reliability, and acceptability, compared to the traditional fire alarm systems. At the same time, further study might be necessary to develop more effective fire alarms.

Exploring the Impact of CHATBOT AI on Empathic Responses and Emotional Intelligence: A Mixed-Methods Study

Abstract: This study investigates the impact of Artificial Intelligence (AI) CHATBOTS on users' empathic abilities and emotional intelligence (EI). The study includes a comparative analysis of people who use and don't use AI CHATBOTS for emotional support. The objectives included assessing how the effect of AI CHATBOTS on empathy affects users' empathic responses, investigating changes in emotional intelligence associated with the use of AI CHATBOTS, and identifying factors contributing to the effectiveness of AI CHATBOTS in improving empathy ability. The method involved a comparative study of 140 participants between the ages of 18 and 25, divided into users and non-users of an AI CHATBOT aimed at providing emotional support. Data collection included self-report surveys measuring CHATBOT usage, Interpersonal Reactivity index (IRI), and emotional intelligence (EI). Statistical analysis reveals significant differences in scores for perspective taking, fantasy, and personal distress between users and non-users of AI CHATBOTS, suggesting that technology-mediated empathy may be associated with specific aspects of empathic responses. Qualitative analysis of participants' responses reveals different experiences and perceptions related to the use of AI CHATBOTS, such as emotional and academic support and platform choice (e.g., CHATGPT, SNAPCHAT). The findings provide valuable insight into the complex relationship between technology-mediated empathy and human emotional responses. In conclusion, this study highlights the importance of considering the role of technology in shaping human interactions and emotional experiences. This result suggests that exposure to AI CHATBOTS may influence empathic responses and aspects of emotional intelligence. However, further research is recommended to explore the underlying mechanisms and ethical considerations related to AI-mediated empathy.

Metode Profile Matching Dalam Sistem Pendukung Keputusan Pemilihan Pegawai Terbaik

Tri Jaya Mart has implemented a best employee selection system to motivate its employees to have a better career path. However, the selection of employees currently only looks at one employee with the highest attendance rate every month and if there is the same then it is chosen subjectively. Selection in this way, researchers feel less effective because it only uses one criterion by not paying attention to other supporting criteria and often more than one best employee candidate is obtained. As well as conflicts between employees due to injustice during promotion. The purpose of this research is to find out the best employee selection process that is currently running where currently out of five employees who work at Tri Jaya Mart, in the process of selecting the best employee, implementing the profile matching method in making a decision so as to provide alternative problem solving in choosing the best employee to be promoted to store manager. In this study using three criteria, namely aspects of work attitude, personality aspects and intelligence aspects. From several steps that have been applied using the profile matching method, the results obtained are the right decision and as expected for stakeholders in choosing the best employee in the order of employee two, employee four, employee one, employee three and employee five.

Voice-based personal assistant (VPA) trust: Investigating competence and integrity

While intelligence of voice-based personal assistants (VPAs) is evolving, limited research has investigated how various intelligence attributes impact consumer trust and adoption of VPAs. Drawn from social agency theory and the technology acceptance model, the current study developed and empirically tested a holistic research framework of the effects of intelligence attributes on consumers’ behavioral responses to VPAs through trust. We identified three aspects of intelligence attributes as functional (ease of use, usefulness), hedonic (enjoyment, innovativeness), and anthropomorphic attributes (humanness, autonomy) and two types of trust in VPAs as competence and integrity. Results showed dynamic relationships among VPAs’ intelligence attributes, consumer trust, and behavioral intentions. Specifically, both functional and hedonic attributes promote competence, whereas anthropomorphic attributes increase integrity. Further, both competence and integrity lead to positive word-of-mouth (WOM), while only competence positively influences consumers’ usage intention. In addition, a user's level of technology anxiety (an individual difference variable) was found to serve as a moderator in the trust model. This study offers both theoretical contributions and practical implications regarding consumer trust development in the context of VPAs.

Beyond Gates and Metal Detectors

Innovation is of vital importance in professional sport organizations, with various segments and departments catering to diverse managerial functions. While previous research has focused on ticketing, marketing, and event management, the critical aspect of security and intelligence in sports requires further exploration. With the safety of fans, players, coaches, staff, and the local community at stake, security and intelligence play a paramount role. The current research addresses the literature gap pertaining to cybersecurity through in-depth interviews involving 12 participants from major U.S. professional sports leagues and the cybersecurity industry, delving into physical security, personnel security, and cybersecurity domains.The results of the study highlighted an overall awareness and success in physical and personnel security, but also revealed a concerning deficiency in the understanding of cybersecurity threats. This shortfall emphasizes the pressing need for innovative cybersecurity solutions in the sport industry to ensure the integrity and safety of professional sport organizations in a complex environment. Through such innovative approaches, professional sport organizations can enhance their ability to effectively combat cybersecurity challenges, safeguarding their operations and protecting all stakeholders involved.

The relationship between emotional intelligence and academic performance among medical students in Mysuru

Introduction: In medical education, it was known that only abstract intelligence was enough for patient treatment, but frequent failures and inabilities to handle social issues by students have led researchers to come out to explore other aspects of intelligence. Materials and Methods: It is a cross-sectional study for 3 months. The sample size was 110, and the data were collected using a Schutte self-assessment questionnaire from medical students in a medical college. The data were analyzed using mean, Pearson’s correlation, Student’s t-test, and ANOVA test using SPSS version 22. Results: The mean emotional intelligence (EI) of the participants was 121.11 ± 12.778. Most of the participants belonged to the age group of 19–23 years. Most belonged to the nuclear family (80%) and most fathers were professionals (81.8%). Sixty-five percentage of the students scored between 65% and 75% in their examinations. There was a negative correlation between EI and academic scores. High EI can mean that they can handle crises and emotional turmoil well but may not necessarily mean that it would translate to a good academic performance according to the current assessment techniques in medical universities. Conclusion: A negative correlation between EI and academic performance can be implied that either academic performance is independent of EI or the current education system is considering the cognitive domain in academic performance assessment which is not influenced by EI. The following core competencies are addressed in this article: Practice-based learning and improvement, Professionalism.

Methodological aspects of diagnosing the linguistic intelligence in early school-age students

This article highlights the essence and importance of the possession of linguistic intelligence by students of young school age as a valuable acquisition in learning English language. In this sense, a tool is proposed to diagnose the level of linguistic intelligence purchased by students of young school age, namely a test that contains items related to the four aspects of linguistic intelligence: phonology, semantics, syntax and praxis. The results of the application of the respective test are provided by statistical processing methods such as: the Statistical Package for the Social Sciences (SPSS) program, Cronbach's Alpha Coefficient, frequency analysis, cross-frequency analysis, T-test for paired samples, Pearson Correlation. The article also includes estimates of the value of the findings, including the areas of their exploitation.

Towards explainable artificial intelligence through expert-augmented supervised feature selection

This paper presents a comprehensive framework for expert-augmented supervised feature selection, addressing pre-processing, in-processing, and post-processing aspects of Explainable Artificial Intelligence (XAI). As part of pre-processing XAI, we introduce the Probabilistic Solution Generator through the Information Fusion (PSGIF) algorithm, leveraging ensemble techniques to enhance the exploration and exploitation capabilities of a Genetic Algorithm (GA). Balancing explainability and prediction accuracy, we formulate two multi-objective optimization models that empower expert(s) to specify a maximum acceptable sacrifice percentage. This approach enhances explainability by reducing the number of selected features and prioritizing those considered more relevant from the domain expert's perspective. This contribution aligns with in-processing XAI, incorporating expert opinions into the feature selection process as a multi-objective problem. Traditional feature selection techniques lack the capability to efficiently search the solution space considering our explainability-focused objective function. To overcome this, we leverage the Genetic Algorithm (GA), a powerful metaheuristic algorithm, optimizing its parameters through Bayesian optimization. For post-processing XAI, we present the Posterior Ensemble Algorithm (PEA), estimating the predictive power of features. PEA enables a nuanced comparison between objective and subjective importance, identifying features as underrated, overrated, or appropriately rated. We evaluate the performance of our proposed GAs on 16 publicly available datasets, focusing on prediction accuracy in a single objective setting. Moreover, we test our multi-objective model on a classification dataset to show the applicability and effectiveness of our framework. Overall, this paper provides a holistic and nuanced approach to explainable feature selection, offering decision-makers a comprehensive understanding of feature importance.

THE CORRELATIONAL STUDY OF EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE OF HOME-ECONOMICS STUDENTS

For effective engagement of students in their academics and for enhancing their academic performance it is very necessary to develop the emotional intelligence capabilities of students. The purpose of study was to determine whether there is relationship between emotional intelligence and students' college-level academic performance by assessing the influence of both the individual elements of emotional intelligence and emotional intelligence as a whole. The total of 252 students from three different colleges of Khyber Pakhtunkhwa participated in study. The predictors have shown significant association with criterion variable. The students' academic performance and emotional intelligence were both positively and strongly correlated with relationship management. In predicting students' academic performance, a significant positive relationship amid emotional intelligence and academic performance was found. Emotional intelligence was a significant factor for students’ academic performance that shows that achievement in academia depends not only on cognitive dimensions of intelligence but on emotional skills. Consequently, the institutions should focus upon aspect of emotional intelligence of the students which will result in good academic performance of the students.

Electroencephalography (EEG) Evidence for the Psychological Processes of Humor Generation: A Comparison Perspective on Humor and Creativity.

(1) Background: Humor stands out as the most dynamic and innovative aspect of human intelligence. Drawing on the cognitive parallels between humor and creativity, this study explored the EEG alpha frequency band activity patterns during humor generation by comparing the process of generating humorous and creative ideas. (2) Methods: Thirty-six participants were randomly assigned to either the humor generation group or the creative generation group, and the dependent variable was the neural oscillation in both low-frequency and high-frequency alpha during the early, middle, and late stages of both humor and creative generation. (3) Results: In the early stages, both humor and creative generation exhibited significantly higher power in low-frequency alpha and high-frequency alpha in the temporal region compared to the middle and late stages. In the middle and late stages, the low-frequency alpha oscillation in the frontal region for humor generation was significantly higher than that for creative generation. (4) Conclusions: Humor and creative generation share similar neural activation patterns in the early stages, involving the activation and retrieval of long-term memory information based on contextual cues. The differences between the two primarily manifest in the middle and late stages, where the selection of humorous ideas requires inhibiting not only irrelevant or ordinary ideas, akin to creative generation but also novel yet non-humorous ideas. This study sheds light on the neurocognitive mechanisms of humor generation and provides insights into the cognitive parallels and distinctions between humor generation and creative generation.

Analysis the improvements of YOLOv5 algorithms: NRT-YOLO, MR-YOLO and YPH-YOLOv5

Computer Vision (CV) is a fundamental aspect of artificial intelligence, with applications spanning multiple domains. The YOLO (You Only Look Once) algorithm has significantly contributed to real-time object recognition in CV. This paper explores the evolution of the YOLO algorithm, focusing on the improvements brought by three specialized variants: NRT-YOLO, MR-YOLO, and TPH-YOLOv5. NRT-YOLO addresses the challenge by introducing the C3NRT module, enhancing precision while maintaining low complexity. MR-YOLO optimizes YOLOv5 for industrial quality control, improving speed and accuracy. TPH-YOLOv5 enhances object detection in drone-captured images by introducing additional prediction heads and transformer modules. Despite these advancements, YOLO algorithms have limitations, including difficulty in detecting small objects and issues in complex scenes. Nevertheless, the research sheds light on the continuous evolution of YOLO algorithms, offering insights into real-time object detection, with applications in manufacturing, healthcare, transportation, and more. The significance of this research lies in showcasing the adaptability and potential of the YOLO framework. As YOLO continues to evolve, it promises to revolutionize various industries and applications, providing robust, real-time object detection solutions.

Working Memory Capacity of ChatGPT: An Empirical Study

Working memory is a critical aspect of both human intelligence and artificial intelligence, serving as a workspace for the temporary storage and manipulation of information. In this paper, we systematically assess the working memory capacity of ChatGPT, a large language model developed by OpenAI, by examining its performance in verbal and spatial n-back tasks under various conditions. Our experiments reveal that ChatGPT has a working memory capacity limit strikingly similar to that of humans. Furthermore, we investigate the impact of different instruction strategies on ChatGPT's performance and observe that the fundamental patterns of a capacity limit persist. From our empirical findings, we propose that n-back tasks may serve as tools for benchmarking the working memory capacity of large language models and hold potential for informing future efforts aimed at enhancing AI working memory.

Algorithms, Sociology of Law and Justice

Objective: to determine the possibility of professional legal activity automation and to identify the limitations that in a democratic society are associated with the use of software capable of generating effective legal solutions in conflict situations.Methods: the conducted empirical research is based on the methodology of social sciences, sociology of law, principles of communicative and communicational theories of law, and formal-legal analysis of legal documents.Results: the paper presents examples of functioning of computer systems that imitate some specific aspects of human intelligence in decision-making. The concept of algorithm and the main characteristics of tasks performed by artificial intelligence systems are defined. The relevance, methods and achievements of sociology of law are outlined, which underlie the systems or computer programs helping to resolve legal conflicts. It is found that the research tools developed as a methodology within the sociology of law since its emergence will be more widely used in the future, due to the growing use of information and communication technologies in legal activities. It is shown that in the administration of justice it is impossible to generate solutions only on the basis of artificial intelligence, since law and the process of making legal decisions on its basis has more complex characteristics in a democratic society. It is emphasized that obtaining the desired legal result is not limited to the processes of algorithmization, categorization or formal exegetics of legal texts. It rather consists in understanding and thinking in accordance with the accepted values, meanings, evaluative criteria, strategies, perspectives, etc.Scientific novelty: the article reveals the significance of the principles of communicative and communicational theories of law under the development of information and communication technologies in legal activity. The main limitations of the use of artificial intelligence in legal activity and, in particular, in justice are identified.Practical significance: the formulated provisions help, while improving legal regulation, to avoid insufficiently justified decisions on the automation of law enforcement, as well as to take into account the increasing importance of the principles of communicative and communicational theories of law in such types of legal activities as creation, interpretation and application of laws.