Cognitive Computing Research Articles

Research on the optimization of emotion expression movement based on cognitive computing in dance creation

Abstract Cognitive computing technology realizes the processing of complex data and problems by simulating the human cognitive way, which is inspiring for the optimization of emotional expression in dance creation. In this paper, we propose a cognitive computing-based model for optimizing dance action emotions, using the improved 3DCNN model for dance action recognition and then CNN-BLSTM based on deep and shallow feature fusion for dance emotion recognition. The experimental results show that the students in the experimental group improved their movement emotion performance by 117% compared with the control group. Their professional level score was 8.42 points higher than that of the control group, the expert assessment score reached 43.98 points, and 82.5% of the students showed interest in dance learning. It shows that the optimization method for expressing dance emotions in this paper has good effects.

Cognitive Computing and Machine Intelligence in Fog-Cloud Infrastructure for Industry 5.0

Cognitive Computing and Machine Intelligence in Fog-Cloud Infrastructure for Industry 5.0

Cognitive Systems for Education: Architectures, Innovations, and Comparative Analyses

In recent years, education has been closely linked to the continued development of technology, especially smart systems based on the use of artificial intelligence with cognitive capabilities. The emphasis here is on the significant potential of cognitive computing in the domain of education and learning. This association implies a transformative impact on how education is delivered, accessed, and personalized through the integration of advanced cognitive systems in the learning and teaching process. This article conducts an overview of the several cognitive computing technologies in the context of education used to enhance learning and teaching activities. This study identifies three conceptual architectures of these systems, Layered Architecture, Agent-Based Architecture, and Hybrid Architecture; then describes their components. Finally, it explores well-known platforms that are used in the education field namely IBM Watson, Kenwton, Carnegie Learning, and DreamBox Learning.

Personalized Learning Path Design for Civic Education Content in Colleges and Universities Based on Cognitive Computing

Personalized Learning Path Design for Civic Education Content in Colleges and Universities Based on Cognitive Computing

Trends in the knowledge area of organizations in Industry 5.0: perspectives and theoretical references

The field of organizational studies has undergone significant transformations in the 21st century, initially focused on business structure and management to maximize effectiveness and performance. Currently there is talk of Industry 5.0, which seeks to integrate technologies such as the collective network of connected devices, cognitive computing, nanotechnology, robotization, artificial intelligence and digitization of human processes. This research seeks to analyze the trends in knowledge of organizations, through a post-positivist approach based on empirical observation and experimentation to assess the study variable, select contemporary scientific literature, and review legal foundations, using databases of high impact and scientific rigor. The results are oriented by the strategic management of knowledge and intellectual assets, innovation and creativity, technology, and information. It is concluded that these are influenced by the requirements of the environment with a sustainable and sustainable approach, it is imperative to adapt to these trends to maintain a holistic perspective to ensure the permanence in time of organizations in the face of dynamic market changes.

Research on cognitive computing model based on machine learning algorithm in artificial intelligence environment

Abstract In the artificial intelligence environment, constructing cognitive computational models using machine learning algorithms is the main direction of computer development. By outlining the three cognitive levels of machine learning, the feature space composition of the cognitive computational model is exposed based on the data acquired by the human brain monitoring equipment. The Gaussian decision tree algorithm is used to construct the cognitive computation model, and the anthropomorphic effects of machine cognitive computation are explored in two directions: auditory features and visual features. In terms of auditory features, the model in this paper maintains 95.03% ± 2.49% feature recognition rate. In contrast, in terms of visual features, the algorithm proposed in this paper maintains a high tracking success rate of 88.83%. Based on the auditory and visual feature analysis results, the cognitive computing model based on the Gaussian decision tree algorithm has been confirmed to perform excellently.

Generative AI for Consumer Electronics: Enhancing User Experience with Cognitive and Semantic Computing

Generative AI for Consumer Electronics: Enhancing User Experience with Cognitive and Semantic Computing

계산적 인지과학의 관점에서 바라본 과학적 설명

This paper introduces the process of explanation in computational cognitive science, which involves elucidating why phenomena or events occur within the framework of causal relationships. It highlights the similarity between everyday explanations and scientific explanations when examined through the lens of cognitive processes, noting that scientific explanations are more systematic, rigorous, and precise.
 The three crucial stages of explanation discussed are firstly gathering information to provision the system's structure, secondly generating hypotheses to advance the explanation, and thirdly evaluating competitive hypotheses or explanations. The paper goes on to introduce the computational implementation of cognitive science frameworks for scientific explanation through deductive, probabilistic, and neural network approaches.
 In the deductive approach, the discussion revolves around Hempel's (1965, 1966) explanatory model, illustrating its simulation as a rule-based system when given laws and conditions. The probabilistic approach, primarily utilizing Bayesian networks, is shown to involve tasks such as generating hypotheses and assessing suitable hypotheses through conditional probabilities. Similarly, the neural network theoretical approach demonstrates that hypothesis generation or explanation through identifying causes can occur through the interaction of neural cells.
 Notably, one significant advantage of neural network theory is its ability to perform abductive inference or hypothesis generation not only based on linguistic expressions but also by accepting visual, emotional, and non-linguistic information as inputs and producing non-linguistic outputs. This advantage is ultimately seen as the ability to accommodate a variety of expression forms, termed as a "multi-modal mode."

A Study to Know AI in Tracking Consumer Buying Impulses and Stimulus

The development of intelligent machines that can do activities that are typically performed by people, such as learning, reasoning, problem-solving, vision, and natural language processing, is known as artificial intelligence (AI), often referred to as machine intelligence. Numerous industries, including healthcare, banking, transportation, education, and entertainment, can benefit from the application of AI technologies. They are designed to operate independently and adjust to shifting conditions and surroundings. AI technology includes, among other things, machine learning, natural language processing, computer vision, robotics, and cognitive computing. While AI has the potential to revolutionise many aspects of daily life, it also brings up ethical, social, and legal issues that need to be thoroughly considered and addressed. In general, artificial intelligence (AI) is revolutionising the marketing sector by empowering advertisers to develop more customised, successful, and efficient marketing programmes that foster client loyalty and company expansion. Marketers may obtain a competitive edge and remain ahead of the curve in a field that is changing quickly by utilising AI-powered tools. International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 07 Issue: 12 | December - 2023 SJIF Rating: 8.176 ISSN: 2582-3930 © 2023, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM27813 | Page 2 This essay seeks to comprehend how contemporary technology, such artificial intelligence, affect Indian consumers' impulsive purchasing habits, particularly in the retail fashion sector. AI was used to investigate the effects of factors including the length of the transaction, suggested items, product information, and human interaction on impulsive purchases. Using artificial intelligence and how it might increase sales by drawing customers to their stores or online. Key word: Artificial intelligence, Marketing

Integration of Industry 4.0 with reliability centered maintenance to enhance sustainable manufacturing

AbstractAchieving sustainability in manufacturing requires optimizing reliability and productivity while reducing environmental impact. This article introduces a novel approach by integrating Industry 4.0 technologies with reliability centered maintenance (RCM) to create a “smart RCM” model. In an era where Industry 4.0 leverages the Internet of Things (IoT), cyber physical systems (CPSs), cloud computing, and cognitive computing, this integration transforms conventional RCM into a data‐driven and efficient system. A case study conducted in a cement plant illustrates the benefits of smart RCM implementation. Key performance indicators (KPI), including increased production volume, reduced downtime, enhanced mean time between failures (MTBF), and improved overall equipment effectiveness (OEE), showcase the positive impact of smart RCM. Furthermore, specific environmental performance indicators, such as energy consumption, carbon footprint, and water consumption, exhibit favorable changes, contributing to sustainability. The implementation of smart RCM also presents an attractive payback period, making it an appealing approach for industries looking to optimize maintenance practices and environmental performance. Despite potential challenges in workforce adaptation and resistance, the integration of Industry 4.0 with RCM emerges as a transformative strategy.

Next-generation predictive maintenance: leveraging blockchain and dynamic deep learning in a domain-independent system.

The fourth industrial revolution, often referred to as Industry 4.0, has revolutionized the manufacturing sector by integrating emerging technologies such as artificial intelligence (AI), machine and deep learning, Industrial Internet of Things (IIoT), cloud computing, cyber physical systems (CPSs) and cognitive computing, throughout the production life cycle. Predictive maintenance (PdM) emerges as a critical component, utilizing data analytic to track machine health and proactively detect machinery failures. Deep learning (DL), is pivotal in this context, offering superior accuracy in prediction through neural networks' data processing capabilities. However, DL adoption in PdM faces challenges, including continuous model updates and domain dependence. Meanwhile, centralized DL models, prevalent in PdM, pose security risks such as central points of failure and unauthorized access. To address these issues, this study presents an innovative decentralized PdM system integrating DL, blockchain, and decentralized storage based on the InterPlanetary File System (IPFS) for accurately predicting Remaining Useful Lifetime (RUL). DL handles predictive tasks, while blockchain secures data orchestration. Decentralized storage safeguards model metadata and training data for dynamic models. The system features synchronized two DL pipelines for time series data, encompassing prediction and training mechanisms. The detailed material and methods of this research shed light on the system's development and validation processes. Rigorous validation confirms the system's accuracy, performance, and security through an experimental testbed. The results demonstrate the system's dynamic updating and domain independence. Prediction model surpass state-of-the-art models in terms of the root mean squared error (RMSE) score. Blockchain-based scalability performance was tested based on smart contract gas usage, and the analysis shows efficient performance across varying input and output data scales. A comprehensive CIA analysis highlights the system's robust security features, addressing confidentiality, integrity, and availability aspects. The proposed decentralized predictive maintenance (PdM) system, which incorporates deep learning (DL), blockchain technology, and decentralized storage, has the potential to improve predictive accuracy and overcome significant security and scalability obstacles. Consequently, this system holds promising implications for the advancement of predictive maintenance in the context of Industry 4.0.

Q-rung orthopair fuzzy decision-making method of multi-source information based on the compression mapping and inverse score function

Intelligent computing is an independent algorithm system that imitates the laws and principles of nature's biological chain to design and solve practical problems in modern society, it can help people better handle some big data problems through artificial intelligence technology and different cognitive characteristics. Especially, when dealing with group decision problems, it is often considered as human behavior based on cognitive computing. The q-rung orthopair fuzzy set (q-ROFS) is not only a generalization of Pythagorean fuzzy set (PFS), but also can deal with the decision problem of multi-source information in a wider area. In this paper, we first give the detailed explanations of the relationship between the q-ROFS and q-rung orthopair fuzzy number (q-ROFN)by an example, and itis pointed out that there are some defects in the existing two ranking methods for the Pythagorean fuzzy numbers (PFNs) and intuitionistic fuzzy numbers (IFNs) through counterexamples, and then all q-ROFNs are mapped into the unit triangle of the first quadrant through q-compression mapping, and the concept of distance factor for q-ROFNs is proposed by Euclidean distance between each q-ROFN and the maximum element (1,0).Secondly, a novel inverse score function for q-ROFNs are put forward by the distance factor and hesitancy degree, and the monotonicity of the inverse score function are discussed. Finally, a new ranking criterion of q-ROFNs is given by the new score formula, and then the rationality of the ranking criterion is proved, and through the method of solving the conditional extreme value of multivariate function (Lagrange multiplier method), there is indeed an extreme value point (corresponding to the maximum q-ROFN) on υ=μ. Besides, a q-rung orthopair fuzzy decision-making method for multi-source information is given by the new ranking criterion and aggregation operations, and the comparison of examples shows that the inverse score function and decision-making method do have many advantages in the q-ROFN environment.

Digital Transformation in Business Operations Management

The development of technology and digital transformation are important factors in improving business performance in Indonesia, with the government and companies needing to face challenges and implement efficient operational management to stay competitive in the digital era. The purpose of this study is to determine the application of digital transformation in business operational management. The method used in this research is a literature study method with a qualitative approach. The data analysis uses Miles and Huberman analysis, namely Data Reduction, Data Display, Conclusion Drawing / Verification. Research results in the advancement of information and communication technology have triggered digital transformation in business operational management, changing the way companies plan, organize, manage, and supervise the production of goods and services. This transformation enables companies to adopt digital technologies such as the Internet of Things, artificial intelligence, cognitive computing, advanced robotics, cloud technologies, blockchain, and big data to improve their efficiency, productivity, and competitiveness. Successful operations managers are those who understand and utilize these technologies, so as to better achieve company goals, respond quickly to market changes, and avoid gaps between customer expectations and traditional business models. Digital transformation has become key in managing efficient and sustainable business operations in an ever-changing era

СТРАТЕГІЯ РОЗВИТКУ ДИЗАЙН-ОСВІТИ В ПОВОЄННІЙ УКРАЇНІ: ТРАНСДИСЦИПЛІНАРНИЙ ПІДХІД

The activities of designers in the new conditions require a qualitatively new way of thinking. The features of training designers with a new level of professional thinking are considered. A set of problems related to the specifics of the process of design thinking formation has been studied as a means of adapting to social transformations in post-war Ukraine. And also as a means of creating and transmitting new ideas in a single cognitive space of dynamic events. Design, forming material and visual culture, becomes a tool for understanding the properties of the surrounding world and human capabilities. By creating a material, spatial, subject environment, design is a key tool for innovation and competition, growth and development. This is important for the effective revival of post-war Ukraine and its flourishing. Therefore, a triad of actions is strategically important for design education - the formation of hybrid design thinking in the learning process, the expansion of the worldview and mastering the principles of risk prediction. New tools and technologies are valuable for design education, which contribute to creative and innovative activity through the triadic technology of forming natural design thinking, forming balanced creative-critical thinking, as well as 3D visualization of information flows of various nature in a single cognitive space. The growth of the influence of algorithms and artificial intelligence in modern society has created a new reality that negatively affects mental health and thinking, human cognition and interpersonal communication. Therefore, the transformation of design education on a transdisciplinary basis will allow solving urgent problems in post-war Ukraine. In our opinion, the key to the formation of design thinking is the triadic technology for solving systemic problems, the use of which contributes to the formation of balanced creative-critical thinking. It is important to systematically analyze the viability of biological objects and objects in extreme conditions using a code-symbol and a code-algorithm. Note that the heuristic metamodels of complex dynamic systems (cyber-physical systems, fractal systems), as a rule, have the form of the Star of David. Harmonization of interrelations and inversion of harmoniously connected opposites in information flows of various nature makes it possible to form hybrid complex-systemic thinking in the learning process, using metaheuristic models of cyber-physical systems, cognitive computing and artificial intelligence look like the Star of David. Its new interpretation as a code-algorithm for finding a balance between creative and critical thinking contributes to the formation of innovative thinking in the process of design activity.

Cognitive computing and 3D facial tracking method to explore the ethical implication associated with the detection of fraudulent system in online examination

Following the Covid-19 pandemic, the rapid spread of online education and tests demanded the implementation of cheating detection tools to ensure academic integrity. While advances in technology such as face recognition, face expression recognition, head posture analysis, eye gaze tracking, network data traffic analysis, and IP spoofing detection have shown promising results in detecting fraudulent behavior, their integration raises ethical concerns that must be carefully considered. This work presents a cognitive computing strategy for investigating the ethical implications of using cheating detection systems in online tests. This study attempts to examine the potential impact on students’ privacy, fairness, and trust in the examination process by employing cognitive computing, which models human cognitive capacities. A thorough literature review is used in the process to uncover existing ethical norms and regulatory frameworks linked to online assessments and cheating detection. Soft computing approaches are also used to evaluate the effectiveness and dependability of the aforementioned cheating detection strategies. The study looks into how far facial recognition and expression analysis can go in terms of privacy, as well as the possibility of bias in head posture analysis and eye gaze tracking algorithms. Furthermore, it investigates the ethical implications of monitoring network data traffic and detecting IP spoofing, with a focus on data security and user permission. The cognitive computing model, based on the analysis, presents a comprehensive framework for ethical decision-making when installing cheating detection technologies. The findings of this study contribute to the continuing discussion about the ethical concerns of using modern technologies to identify cheating in online exams. It provides educational institutions and policymakers with practical ideas for striking a balance between academic integrity and protecting students’ rights and dignity. By emphasizing ethical issues, this study aims to ensure that the implementation of cheating detection systems adheres to values of fairness, transparency, and privacy protection, promoting a trusting and supportive online learning environment for all parties involved.

NAVIGATING THE AI LANDSCAPE: A SYSTEMATIC GUIDE TO SOLVING COMPLEX CHALLENGES

Artificial Intelligence (AI) has become an integral part of modern technological advancements, offering solutions to complex problems across various domains. However, the development of effective AI systems often encounters challenges that require specialized problem-solving approaches. This article presents a comprehensive analysis of common AI problems and proposes a systematic problem-solving framework to address them. We examine typical AI challenges such as optimization, pattern recognition, natural language processing, and decision-making. Drawing on established methodologies and cutting- edge research, we outline a step-by-step problem-solving approach that integrates algorithm design, data preprocessing, model selection, and performance evaluation. Furthermore, we discuss the significance of leveraging machine learning, deep learning, and other AI techniques in creating robust and adaptable solutions. By emphasizing the importance of a structured problem-solving methodology, this article aims to provide researchers and practitioners with a valuable guide to effectively tackle complex AI problems and foster advancements in the field. Keywords: AI Problem Solving, Algorithmic Solutions, Cognitive Computing, Machine Learning Techniques, Computational Intelligence.

Thought Processes as Data in Eye Care Education and Research.

Working memory is a component of the human memory system in which all thought processes take place during an activity. Asking participants to verbalize their thoughts (think-aloud method) is a well-established technique in cognitive psychology and computer science for obtaining access to mental processes in order to investigate in-performance cognitions. Few attempts have been made to use such data in eye care research. This article provides a concise overview of the think-aloud technique and discusses potential research areas where this method can produce useful data.

83 Computational Modeling of Planning and Inhibition in the Tower of London

Objective:The Tower of London is commonly used to assess planning ability. Deficient outcomes may however have different causes: A participant may not have the ability to think a sufficient number of steps into the future, or may become, for example, impatient to evaluate different possible paths. Outcomes are thus not pure measures of the "planning" construct of primary interest, which may have contributed to findings of low reliability and low validity of these outcomes in the literature. The advent of computerized testing combined with computational modeling potentially allows to go beyond traditional outcomes such as "total number of moves" and "total time taken" and disentangle different processes that are of primary interest. The goal of the current study is to establish whether a model that consists of "planning ability" and "response inhibition" parameters can be used to describe Tower of London data.Participants and Methods:We constructed an algorithm that produces Tower of London data, and a computational model that uses every single decision of a participant as input (e.g., whether a participant moves the red or the blue ball to the right peg in setting 15 when trying to get to setting 28). There are 210 unique decision situations that participants can encounter. Our algorithm and Bayesian hierarchical model uses two parameters for each participant as well as a guessing rule, that together determine the participant's decision at every step. The appropriateness of the model was evaluated in a simulation study, where the simulated distribution of data implied by this model is compared to the empirical distribution of total number of moves observed in real datasets. Data were simulated for 10 items with a sample size of 200 participants.Results:Our simulation study shows that with our model the empirical distribution of total number of moves is successfully replicated in the distribution of the simulated data.Conclusions:Computational modeling provides a new window into Tower of London performance by identifying different processes. Modeling thus allows us to go beyond aspecific descriptions of planning ability. Furthermore, using the high-resolution data of computerized testing allows us to estimate these parameters reliably without requiring "big data", keeping participant burden low. This study will be followed up in three ways. First, predictions will be preregistered and tested for these new cognitive outcomes in several large oncological patient samples. Second, the model will be extended to include reaction times, to include an additional metric of cognitive computation. Third, the new cognitive process outcomes will be analyzed in conjunction with cognitive process outcomes on other tests to establish process communalities.

Building Human-Like Artificial Agents: A General Cognitive Algorithm for Emulating Human Decision-Making in Dynamic Environments

One of the early goals of artificial intelligence (AI) was to create algorithms that exhibited behavior indistinguishable from human behavior (i.e., human-like behavior). Today, AI has diverged, often aiming to excel in tasks inspired by human capabilities and outperform humans, rather than replicating human cogntion and action. In this paper, I explore the overarching question of whether computational algorithms have achieved this initial goal of AI. I focus on dynamic decision-making, approaching the question from the perspective of computational cognitive science. I present a general cognitive algorithm that intends to emulate human decision-making in dynamic environments, as defined in instance-based learning theory (IBLT). I use the cognitive steps proposed in IBLT to organize and discuss current evidence that supports some of the human-likeness of the decision-making mechanisms. I also highlight the significant gaps in research that are required to improve current models and to create higher fidelity in computational algorithms to represent human decision processes. I conclude with concrete steps toward advancing the construction of algorithms that exhibit human-like behavior with the ultimate goal of supporting human dynamic decision-making.

Facial Emotion Recognition and Classification Using the Convolutional Neural Network-10 (CNN-10)

The importance of facial expressions in nonverbal communication is significant because they help better represent the inner emotions of individuals. Emotions can depict the state of health and internal wellbeing of individuals. Facial expression detection has been a hot research topic in the last couple of years. The motivation for applying the convolutional neural network-10 (CNN-10) model for facial expression recognition stems from its ability to detect spatial features, manage translation invariance, understand expressive feature representations, gather global context, and achieve scalability, adaptability, and interoperability with transfer learning methods. This model offers a powerful instrument for reliably detecting and comprehending facial expressions, supporting usage in recognition of emotions, interaction between humans and computers, cognitive computing, and other areas. Earlier studies have developed different deep learning architectures to offer solutions to the challenge of facial expression recognition. Many of these studies have good performance on datasets of images taken under controlled conditions, but they fall short on more difficult datasets with more image diversity and incomplete faces. This paper applied CNN-10 and ViT models for facial emotion classification. The performance of the proposed models was compared with that of VGG19 and INCEPTIONV3. The CNN-10 outperformed the other models on the CK + dataset with a 99.9% accuracy score, FER-2013 with an accuracy of 84.3%, and JAFFE with an accuracy of 95.4%.