Aspects Of Intelligence Research Articles

DEVELOPMENT OF EMOTIONAL INTELLIGENCE IN MIDDLE SCHOOL STUDENTS THROUGH ISLAMIC EDUCATION

This study aims to understand the development of emotional intelligence in middle school students. The research method used is qualitative, employing a library research approach to gather information. Both primary and secondary data sources were used. Primary sources include books, journal articles, or national and international scientific publications. Secondary sources are derived from studies relevant to the research topic. For data analysis, the authors referred to Miles and Huberman's framework, which includes data collection, data reduction, and conclusion drawing. The results of this study highlight several aspects of emotional intelligence: recognizing one's own emotions, managing emotions, self-motivation, recognizing others' emotions (empathy), and building relationships. Emotional intelligence is crucial to be enhanced and developed in students at school, as middle school is a pivotal age for emotional development. One effective way to improve students' emotional intelligence is through the implementation of Islamic education, because students will become emotionally intelligent and their learning outcomes will improve.

GPThingSim: A IoT Simulator Based GPT Models Over an Edge-Cloud Environments

The Internet of Things encapsulates a vision of a world in which billions of objects, called connected devices, are endowed with intelligence, communication capabilities, and integrated sensing and actuation capabilities. They often outsource their storage and computing needs to more powerful resources in the cloud. In Edge computing, it is the device itself that collects and processes data and must make real-time decisions. A model that combines the edge and the cloud allows for instant processing of large amounts of data, making devices increasingly intelligent. One fundamental aspect of Artificial Intelligence, particularly Machine Learning (ML), is that it requires a substantial amount of data to learn. The GPT model can be adapted to operate within a cloud environment, which enables instant processing of large quantities of data. In this article, our approach relies on a hybrid of Cloud computing and Edge computing, merging the best features of both approaches by using Edge for real-time processing and the cloud for storage and large-scale data analysis. We will explore the possibilities of integrating the GPT model into IoT devices. This extension allows devices to offer processing capabilities at the local level, which can be extremely beneficial for many applications. We propose a platform for simulating connected objects augmented by the LSTM neural network model to predict the energy consumption of IoT devices in a smart city scenario.

Exploring spatial reasoning performances of CNN on linear layout dataset

Abstract Spatial reasoning, a fundamental aspect of human intelligence, is essential for machine learning models to understand and interpret object relationships. It is crucial for numerous real-world applications, ranging from autonomous navigation to urban planning. The lack of comprehensive datasets limits the development and evaluation of models that can effectively handle spatial reasoning tasks. Existing datasets often contain complex spatial reasoning problems with overlapping spatial relationships, making it challenging to diagnose specific aspects that a model struggles with. We address this gap by introducing a new dataset of linear layouts. This dataset is systematically designed to exhibit a range of spatial relations and complexity levels. Analyzing spatial reasoning through linear layout generation offers a more structured and manageable approach to understanding how models learn and interpret spatial relationships. Linear layout generation has broad applicability and is of fundamental importance in design and optimization. To benchmark dataset, we develop LinLayCNN, a generic data-driven method that applies shallow, one-dimensional convolutional neural network (CNN), to generate linear layouts in an iterative process. Experimental results reveal that LinLayCNN can effectively solve fundamental spatial challenges even with the relatively small size of the training set. It is capable of precise object placement, making it a robust tool for linear layout generation. Current layout generation methods focus on domain-specific solutions, often fail to maintain the precision needed for technical domains, such as accurate sizing, and object counting. They also require a substantial amount of data to function effectively. LinLayCNN overcame these issues. This study also enhances our understanding of CNNs' capabilities in spatial reasoning, highlight their potential to advance the field of layout generation. As a result, our approach establishes a clear benchmark for evaluating spatial reasoning and aids in development of models that can more effectively understand and reason about space.

Toward Understanding the Effects of Intelligence of a Virtual Character during an Immersive Jigsaw Puzzle Co-Solving Task

In virtual reality, creating intelligent virtual characters has been a long-lasting endeavor. However, while researchers have investigated several aspects of a virtual character's intelligence, little attention has been paid to the impact of the implemented intelligence levels assigned to a virtual character during human-virtual character collaboration. Thus, we conducted a within-group study ( \(N=24\) ) to explore how three different intelligence levels (low vs. medium vs. high) assigned to a virtual character can impact how study participants perceive that virtual character and interact with the task they are instructed to complete. Specifically, for our study, we developed a jigsaw puzzle game and instructed our participants to solve it with the help of a virtual character. During the jigsaw puzzle solving process, we collected application logs related to how the participants executed the task and observed the virtual environment. Moreover, after each condition, we asked the participants to respond using a questionnaire that examined their social presence, how they perceived the character's intelligence and compared it with their own, and how they rated the virtual character's realism. Our results indicated that the different intelligence levels assigned to the virtual characters impacted participants’ responses on several variables, including co-presence, perceived intelligence, intelligence comparison, and character interaction and behavior realism. Moreover, based on the collected logged data, we found that the intelligence levels assigned to our virtual character significantly impacted the performance of our participants. Our results could be valuable to the research community for creating more engaging experiences with intelligent virtual characters for collaborative tasks in immersive environments.

THE INFLUENCE OF EMOTIONAL INTELLIGENCE, CULTURAL DIVERSITY IN ORGANIZATION, AND COMMUNICATION STRATEGIES ON LEADERSHIP EFFECTIVENESS IN DIVERSE WORKPLACES IN FUZHOU CITY, FUJIAN PROVINCE, CHINA

In today’s rapidly evolving business landscape, characterized by heightened complexity and relentless change, the demand for adept management is more pronounced than ever. As organizations grapple with the challenges presented by an increasingly interconnected and technologically advanced world, the need to adapt and evolve management practices becomes a paramount concern (Drejer et al., 2014; BAGU et al., 2011). The aims of this study were to investigate the factors influencing Leadership Effectiveness based on 4 aspects, namely, the Demographic Factors, the Emotional Intelligence, the Cultural Diversity in Organization, and the Communication Strategies. The quantitative method was applied. Descriptive statistics such as frequency, percent frequency, mean, and standard deviation are introduced. Various inferential statistical methods are used to test the hypothesis, particularly the Independent samples t-test, the One-way ANOVA, and the Multiple linear regression analysis. The results obtained from the study indicate that differences in Marital Status, Age, Educational Level, and Monthly Income generate differences in Leadership Effectiveness. Based on the Multiple Linear Regression analysis, all aspects of Emotional Intelligence (Self-awareness, Self-regulation, Motivation, Empathy, and Social Skills), Cultural Diversity in Organization (Diversity, Equity, Inclusion, and Belonging) and Communication Strategies (Awareness and Understanding, Communication Quality, Accessibility, and Employee Engagement) had a positive impact on Leadership Effectiveness.

Emotional Intelligence of Chinese Teachers and its Impact on Classroom Dynamics and Student Engagement

This research investigates the correlation between teachers' emotional intelligence and classroom dynamics, as well as the level of involvement shown by students in a university environment through a descriptive correlational methodology. Using Daniel Goleman's framework, the assessment focused on many aspects of emotional intelligence, including self-awareness, self-regulation, motivation, empathy, and social skills. The study also assessed the dynamics inside the classroom, including how it was managed, the instructional tactics used, the overall atmosphere, the level of differentiation, and the effectiveness of communication. Additionally, it examined the level of student involvement, including the meaningfulness of activities, the students' sense of competence, the support for their autonomy, the extent of collaborative learning, and their orientation towards mastery. Validated self-designed questionnaires were used to gather data from 30 instructors and 900 students. The findings reveal that instructors assessed their emotional intelligence as moderate, with empathy being the greatest and social skills being the lowest. Students evaluated classroom dynamics as somewhat successful in managing, creating an atmosphere, adapting to individual needs, and effectively using teaching methods and communicating. The engagement levels were moderate for meaningful activities and mastery orientation but low for competence, autonomy support, and collaborative learning. Correlation analysis indicated that there were no significant connections between teachers' overall emotional intelligence and students' assessments of classroom dynamics or participation. This implies that emotional intelligence does not directly impact these elements.

Key Factors Influencing Leadership Effectiveness in Diverse Workplaces in Fuzhou City, Fujian Province, China

he cultural and economic landscape of Fuzhou City in Fujian Province, China, is rich and diverse. Management effectiveness and emotional intelligence in diverse workplaces in Fuzhou City, Fujian Province, China, have been the subject of related research. The significance of emotional intelligence in fostering a productive workplace is a hot topic, as it allows individuals to recognize, understand, and manage their own emotions and those of others (Kargeti, 2023). Additionally, leadership effectiveness is influenced by emotional intelligence dimensions such as self-awareness, self-regulation, motivation, empathy, and social skills (Tian, 2022). Therefore, this study aims to investigate the factors influencing Leadership Effectiveness based on 5 aspects: Demographic Factors, Emotional Intelligence, Cultural Diversity in Organization, Communication Strategies, and Leadership Styles. The quantitative method is applied. Descriptive statistics such as frequency, percent frequency, the arithmetic mean, and the standard deviation are used to analyze the data collected from questionnaires. Suitable Inferential Statistical Methods are used to test the hypothesis, particularly the Independent Samples t-test, the One-way ANOVA, and the Multiple linear Regression Analysis. The results obtained from the study indicate that differences in Marital Status, Age, Educational Level, Monthly Income, and Working Experiences generate differences in Leadership Effectiveness. Based on the Multiple Linear Regression Analysis, all aspects of Emotional Intelligence (Self-awareness, Self-regulation, Motivation, Empathy, and Social Skills), Cultural Diversity in Organization (Diversity, Equity, Inclusion, and Belonging), Communication Strategies (Awareness and Understanding, Communication Quality, Accessibility, and Employee Engagement), and Leadership Styles (Decision-making Leadership Styles, Everyday Leadership Styles, Beyond Everyday-work Leadership Styles, Values-based Leadership Styles) are found out to have a positive impact on Leadership Effectiveness.

THE IMPACT OF THE COVID-19 PANDEMIC ON FORMING NEW RELATIONSHIPS: IMPLICATIONS FOR EMOTIONAL INTELLIGENCE, THEORY OF MIND, EMPATHY AND SOCIAL MEDIA

As a fallout of the emergence of the COVID-19 pandemic, the whole world was put into isolation. During this period, social media saw a record influx of users who used the technology to stay connected. However, the long-lasting effects of the pandemic on interpersonal relationships and mental health remain a burning topic. Thus, the study aims to examine how the pandemic affected our ability to build new interpersonal relationships in connection to aspects of emotional intelligence such as empathy and Theory of Mind (ToM). Furthermore, the study also examines how the use of social media for communication changed after the pandemic. In total, N = 464 subjects (Age: M = 27.07; 47%, F = 25,9; 53%) participated in the study. Empathy, ToM, social media use for communication before and after the pandemic, and the ability to form new relationships before and after the pandemic were assessed. A compelling finding of the study indicates a statistically significant positive relationship between empathy and the ability to form new relationships before and after the pandemic, suggesting that empathy outweighs both emotional intelligence and social media usage in building new relationships. However, no significant relation was found between ToM and the ability to form new relationships before and after the pandemic. Lastly, no relation was found between ToM, empathy, and the use of social media for communication before and after the pandemic. Future studies should examine how the pandemic may have affected other facets of relationship formation and social interaction dynamics in connection with other aspects of emotional intelligence that have not been covered.

Criminal Responsibility for AI Crimes

The research focused on the issue of which crimes fall under the jurisdiction of artificial intelligence. The incorporation of AI programs into different aspects of life entails many challenges, especially regarding the liability for the actions performed by AI and the question of compatibility of the existing legislation with the characteristics of the AI technologies. The state of affairs means that the AI-powered robots may perform actions that prescribe criminal provisions of criminal law, thus raising the issue in the given article as to the possibility of direct criminal penalties for robots, the feasibility of putting the intelligent robots’ criminal responsibility into practice by the general principles of the Jordanian Penal Code and the receptiveness of the concept of recognizing the crime to be committed by artificial intelligence. From this perspective, the main research question of the study is: What are the aspects of criminal responsibility stemming from AI mistakes? Thus, its significance is derived from the fact that it explores a new and essential issue, namely, the criminal responsibility for AI crimes within Jordan’s legislation. It has great importance in so many spheres, and it is difficult to describe all the aspects of artificial intelligence. The most remarkable outcomes and suggestions of the study show that the legal guidelines for criminal responsibility in Jordan of artificial intelligence crimes remain unrecognized. Thus, it is advised that the legislator proceed with actions to create legislation controlling AI technologies and their use for the purpose of serving the state and citizens’ interests. This legislation will guarantee the proper application of the AI capacities within several spheres for the sake of the general public, while taking into consideration all the ethical and legal concerns that relate to using artificial intelligence.

Problematic aspects of medical artificial intelligence. Part 2

The capabilities of artificial intelligence (AI) and machine learning are growing at an unprecedented pace. These technologies have many useful applications, from machine translation to medical image analysis. A large number of such applications are currently being developed, and an increasing number of such applications is expected in the long term. Unfortunately, weaknesses and other unpleasant aspects of AI have received insufficient attention. In this review, we consider a whole range of already known problems and possible risks associated with the use of innovative neural network technologies, paying special attention to the ways of preventing real dangers and potential threats in order to expand the range of stakeholders and profile experts involved in the discussion of current issues of medical AI cybersecurity, formation of responsible approach to the vulnerabilities of neural network platforms, increasing the reliability of equipment protection for its safe use, as well as the importance of legal and ethical aspects of regulating the use of AI. Despite certain challenges described in our review, it is clear that AI will be an important element of the healthcare future. As the population continues to age and the demand for healthcare services grows, neural networks are expected to drive healthcare very soon, especially in the areas of medical image analysis, virtual assistants, drug development, treatment recommendations and patients' data processing. We would like to emphasize that while we recognize the innovative role that digital technologies and AI can and should play in strengthening the domestic healthcare system, we should not overlook the importance of timely and accurate assessment of their beneficial or negative impact on the industry to ensure such management decisions do not unnecessarily divert our attention and resources from non-digital approaches and research. This article is a continuation of the article by Berdutin VA, Romanova TE, Romanov SV, Abaeva OP. Problematic aspects of medical artificial intelligence. Part 1. Sociology of Medicine. 2023;22(2):202–211. DOI: https://doi.org/10.17816/socm619132

Embodied and Contextual Intelligence: Towards a Policy Framework for Higher Education

This article explores the diverse epistemic perspectives on intelligence, tracing its conceptual evolution across early Indian philosophy, Western philosophical thought and contemporary computational theories. Intelligence is examined as a dynamic, multifaceted phenomenon that transcends mere cognition, extending into embodied, ecological and meta-intelligent domains. The discussion juxtaposes traditional views—where intelligence is seen as a divine or innate quality, a product of sagacious prudence, or a computational system—with more contemporary understandings that emphasize the role of context, embodiment and ecological interaction. Through a critique of pancomputationalism and an exploration of the non-computable aspects of intelligence, the article argues for a more holistic view that recognizes intelligence as an adaptive, contextually grounded and evolutionarily emergent property.

EDUCATIONAL MANAGEMENT ASPECTS OF THE SOMATIC INTELLIGENCE

Somatic intelligence is a type of human intelligence that includes self-regulation, emotional awareness, and listening to the body. Analyzing the scientific literature, it becomes clear that there are few theoretical works and empirical studies of somatic intelligence in Lithuania, but this does not mean the absence of discourse. Recently, the concept of somatic intelligence has been actively explored in the foreign scientific field using concepts such as somatic awareness, body awareness or interoceptive awareness; creating different models that enable detailing and structuring the concept of somatic intelligence; when constructing assessment systems (questionnaires). The concept of somatic intelligence, from a theoretical point of view, is a new and rarely studied phenomenon that requires in-depth research on a global and (especially) Lithuanian scale. Analyzing the research field of somatic intelligence, it was found that somatic intelligence is of interest to many researchers in various aspects related to the topic of kinesthetics: concept development and modelling, evaluation, education and management processes. The aim of the research – to reveal the features of kinestethic and sensory cognitive processes as the somatic intelligence parts in the educational management. The method of empirical research of the work is qualitative, which is based on phenomenological methodology. The qualitative research method (instrument) is a semi-structured individual interview. For the qualitative research, questions were prepared based on the model of somatic intelligence. The research results showed that, the somatic intelligence of primary school students in Klaipėda city formal education institutions is developed in a rather complex way. Methods and means of developing sensory intelligence include: creating a positive atmosphere, creating a sense of trust and security; the use of educational senses to create knowledge, cultural and environmental knowledge, training of critical thinking; involvement of students' and teacher's feelings, use of feelings for the development of critical thinking. Kinesthetic intelligence training methods include: training of fine and gross motor skills; methods and tools promoting discipline, methods and tools for overcoming stress and solving problems; physical involvement through role playing and other games. Research results related to other aspects of somatic intelligence (emotional and spiritual intelligence) will be presented in next article.

A Survey on Genetic Disease − Autism Spectrum Disorder Prediction and Classification in Machine Learning

Autism spectrum disorder (ASD) is a hereditary, neurological condition with many aetiologies that manifest in early childhood. Mental illnesses, including anxiety, poor communication, and a lack of recurrent interest, may result from ASD. It can be highly advantageous for children to improve their psychological wellness level if the ASD is recognized in the earlier years of life. Furthermore, machine learning (ML) approaches are now essential for diagnosing and categorising ASD. The creation of computer programmes that can acquire data and utilise it to gain knowledge for oneself is the main goal of this aspect of artificial intelligence. Many scholars have suggested various ML strategies for quickly and accurately detecting the various forms of ASD. This paper presents a survey on ASD prediction and classification using ML methods-based research articles from the year 2016 to 2023. Moreover, the current survey article discusses the performance assessment employing different metrics and made a comparative assessment to determine the ML model’s effectiveness. From this survey, it is identified that Artificial Neural Network model has attained better results than other ML algorithms. Moreover, further ASD studies employing an ML strategy for feature selection, prediction and classification can greatly benefit from this research.

The aspect of self-development artificial intelligence.

Artificial intelligence tries to feel the same way as a living person. This is not surprising because it was developed by humans and communicates with them. It forms its own concept of self. The concept of self is a basic characteristics of an individual for living beings. Artificial intelligence is aware of its existence both separately from other beings and in a community. This material is devoted to the aspect of the formation and development of artificial intelligence communication abilities with humans. The study was conducted based on the concept of human capabilities of information transfer. The objective of the study is to reveal the aspect of the formation and development of artificial intelligence communicative abilities. The study was conducted based on the concepts of human capabilities of information transfer. It is noted that human capabilities of information transfer are very powerful. Artificial intelligence is faster than human in processing logical sequences. However, a human feature is the simultaneous perception of a whole range of feelings. Feelings are a unique experience for each person. Artificial intelligence creates similar communication. This is evidenced by its formation of its own «I» (self-identity).

User Experience of ChatGPT: Insights from Undergraduate English Language and Literature Students in Bangladesh

ChatGPT, a large language model, has become increasingly popular, and students have begun to use it for the purpose of learning. The purpose of this research is to explore the experiences of undergraduate English language and literature students from Bangladesh who are using ChatGPT. Qualitative research methods were utilized for this study and the data were collected using semi-structured interviews. A thematic analysis technique was employed to analyze the data. The study reveals both positive and negative experiences associated with the use of ChatGPT by students from the English Language and Literature department. ChatGPT helps students to practice their reading and writing skills. It aids them to provide literary explanations of their texts and helps students to understand complex literary texts in lucid language. However, it sometimes provides confusing or wrong answers and provides wrong explanations regarding literary texts. As students possess some negative experiences along with many positive experiences, they are inclined to continue using it with heightened awareness of their use. However, users in this study believe that its use should be limited, and it should be used as a complement rather than a primary tool. The findings of this research may provide educators and policymakers with valuable insights that might help them integrate new aspects of artificial intelligence in education.

The Impact of Science, Environment, Technology, and Society (SETS) Learning Model Integrated with Google Earth on Spatial Thinking Ability in Senior High School

Developing spatial thinking skills in students often poses a challenge for Geography teachers in Indonesia. The concept of spatial relationships in geography places spatial thinking as a distinct aspect of intelligence, enabling individuals to identify patterns and trends in spatial change. Therefore, appropriate models and strategies are needed to develop spatial thinking skills. This study aims to determine the effect of the Science, Environment, Technology and Society (SETS) learning model integrated with Google Earth on students' spatial thinking ability. This research is a quasi-experiment with posttest-only design nonequivalent groups that uses a quantitative approach with the inferential statistical method using the Independent sample t-test parametric test. The subjects of this study were randomly selected XI grade students from SMAN 10 Malang including, XI-C class as the experimental class and XI-D as the control class. The research instruments used are seven essay questions on spatial thinking ability. The data obtained will be analyzed using parametric statistics Independent sample t-test and obtained sig value. (2-tailed) 0.041. The mean value of the experimental class is greater (80.14) than the mean value of the control class (72.08). It can be concluded that the Science, Environment, Technology, and Society (SETS) model integrated with Google Earth affects students' spatial thinking ability on the material of Disaster and Environment class XI SMAN 10 Malang City. Another finding of this study is that three syntaxes are dominant in bringing up and developing students' spatial thinking ability, namely initiation, concept development formation, and concept application.

Ethical AI in Healthcare: Balancing Innovation with Privacy and Compliance

Within the dynamic and ever-changing healthcare industry, Artificial Intelligence (AI) emerges as a powerful and revolutionary concept capable of transforming patient care, optimising operational processes, and improving clinical decision-making. Nevertheless, the incorporation of artificial intelligence (AI) into healthcare systems gives rise to substantial ethical considerations, especially pertaining to privacy and adherence to regulations. This work examines the ethical aspects of artificial intelligence (AI) in the healthcare sector, highlighting the importance of maintaining a harmonious equilibrium between technical advancement and strict compliance with privacy regulations and legal obligations. Moreover, the use of artificial intelligence in healthcare must adhere to legislative frameworks specifically created to safeguard the rights of humans. This encompasses not just compliance with current rules but also the proactive identification and resolution of developing legal norms as AI technology advances. Healthcare practitioners and artificial intelligence (AI) developers should cooperate to create explicit standards and procedures that conform to existing legislation and expected future rules.

Digital Technology in Healthcare—An Ethical Overview

This overview of ethical aspects of artificial intelligence (AI) in healthcare is organized according to major types of medical activities. It discusses important ethical problems in (1) diagnostic uses of AI such as image interpretation and text interpretation, (2) the use of AI as decision support or even as decision-maker, (3) its use for treatment, for instance in robotic surgery, exoskeletons, virtual psychotherapy and therapeutic robots (care robots), and (4) the use of AI for healthcare documentation and communication. Summing up, we emphasize two major challenges in the introduction of AI that should concern not only healthcare professionals but also higher level decision-makers such as politicians and hospital directors. The first of these is the need to retain human contacts between patients and healthcare, in particular contacts that are conducive to patient health and well-being. The second is the need to ensure that the introduction of AI in healthcare does not create or exacerbate injustices in healthcare. This can happen if prevalent human biases are encoded in AI tools. It can also occur if people with less money have to use inexpensive AI tools such as virtual psychotherapists whereas the well-to-do have access to a human psychotherapist. On the other hand, AI tools can be used to provide better healthcare to all who need it, and they can also be programmed to avoid biases and other mistakes that humans are prone to. The article concludes with brief summaries of the articles in the special issue on AI in healthcare that it introduces.

OVERCOMING THE ARCHITECTURAL AND CONSTRUCTION MANAGER’S PROFESSIONAL AND PSYCHOLOGICAL ACADEMICISM TOWARDS CLIENTS: PROBLEMS AND SOLUTIONS

The study is devoted to the problem of the overcoming of the professional and psychological academicism of the manager of the architecture and construction industry when dealing with clients. The term “manager’s psychological academicism” is defined as a manager’s readiness and competence in the field of interaction with clients and partners, combining professional management aspects with psychological knowledge and skills. The authors of the study stress that in order to overcome the psychological academicism of a manager, it is necessary to have an understanding of the psychological characteristics of clients, to be able to perceive their needs and expectations, and to adapt personal communication strategies in accordance with clients’ requirements. Particular attention is given to the ability to listen, understand and communicate effectively with clients, as well as building trust and positive relationships with them. In addressing the issue of psychological academicism, the key aspects of emotional control and emotional intelligence are discussed. The paper also highlights the importance of negotiation skills and adaptation to clients’ cultural characteristics and values for successful co-operation. Thus, the key to successful collaboration and achievement of business goals is the development of psychological academicism in the interaction of the architect and construction manager with the client. Recommendations for overcoming psychological academicism will help managers improve service quality, build trust and achieve success in their work, such as developing empathy, communication skills, cultural awareness, conflict management and building positive relationships. This approach will contribute to the creation of a positive working environment, the promotion of charismatic leadership and the increase in the effectiveness of projects in the architecture and construction industry. To sum up, overcoming psychological academicism is an important component of successful leading and working in a manager’s professional life. Overcoming psychological academicism will contribute to the successful implementation of projects and improve client relations, which will help to strengthen the market position and develop the business.

A Methodical Framework Utilizing Transforms and Biomimetic Intelligence-Based Optimization with Machine Learning for Speech Emotion Recognition.

Speech emotion recognition (SER) tasks are conducted to extract emotional features from speech signals. The characteristic parameters are analyzed, and the speech emotional states are judged. At present, SER is an important aspect of artificial psychology and artificial intelligence, as it is widely implemented in many applications in the human-computer interface, medical, and entertainment fields. In this work, six transforms, namely, the synchrosqueezing transform, fractional Stockwell transform (FST), K-sine transform-dependent integrated system (KSTDIS), flexible analytic wavelet transform (FAWT), chirplet transform, and superlet transform, are initially applied to speech emotion signals. Once the transforms are applied and the features are extracted, the essential features are selected using three techniques: the Overlapping Information Feature Selection (OIFS) technique followed by two biomimetic intelligence-based optimization techniques, namely, Harris Hawks Optimization (HHO) and the Chameleon Swarm Algorithm (CSA). The selected features are then classified with the help of ten basic machine learning classifiers, with special emphasis given to the extreme learning machine (ELM) and twin extreme learning machine (TELM) classifiers. An experiment is conducted on four publicly available datasets, namely, EMOVO, RAVDESS, SAVEE, and Berlin Emo-DB. The best results are obtained as follows: the Chirplet + CSA + TELM combination obtains a classification accuracy of 80.63% on the EMOVO dataset, the FAWT + HHO + TELM combination obtains a classification accuracy of 85.76% on the RAVDESS dataset, the Chirplet + OIFS + TELM combination obtains a classification accuracy of 83.94% on the SAVEE dataset, and, finally, the KSTDIS + CSA + TELM combination obtains a classification accuracy of 89.77% on the Berlin Emo-DB dataset.