Human Processes Research Articles

Literature review: A study of cognitive aspects in minimizing human error in construction

Human error is a mistake due to human negligence, both consciously and unconsciously, which can cause a system to experience disruption and not run as planned. These errors can occur at the managerial, conceptual, or technical level. One of the activities with a high potential for finding human error is construction activities. Construction activities are a series of activities that include the construction, operation, maintenance, demolition, and construction of a building. Infrastructure development is one of the government’s policy focuses in 2023, with development from four main areas: water resources, roads and bridges, settlements, and housing. These infrastructure projects must have a good quality because they are closely related to user safety and security. Based on data from the Social Security Organizing Agency (BPJS) Employment and statements from the Ministry of PUPR, the industrial sector is an enormous contributor to work accidents with an average percentage of 32% each year. The human factor is one of the forerunners of human error apart from equipment and working environment conditions. Viewed from the perspective of Cognitive System Engineering, human error is caused by the failure of human cognitive processes where the process is related to Human Information Processing (HIP). This paper will describe and briefly discuss the problem of human error and its relation to Human Information Processing (HIP). This paper will examine various literature and summarize efforts that can be applied to improve the information processing process in the hope of minimizing the occurrence of human error in the scope of construction work.

Impacts of the advancement in artificial intelligence on laboratory medicine in low- and middle-income countries: Challenges and recommendations-A literature review.

Artificial intelligence (AI) has emerged as a transformative force in laboratory medicine, promising significant advancements in healthcare delivery. This study explores the potential impact of AI on diagnostics and patient management within the context of laboratory medicine, with a particular focus on low- and middle-income countries (LMICs). In writing this article, we conducted a thorough search of databases such as PubMed, ResearchGate, Web of Science, Scopus, and Google Scholar within 20 years. The study examines AI's capabilities, including learning, reasoning, and decision-making, mirroring human cognitive processes. It highlights AI's adeptness at processing vast data sets, identifying patterns, and expediting the extraction of actionable insights, particularly in medical imaging interpretation and laboratory test data analysis. The research emphasizes the potential benefits of AI in early disease detection, therapeutic interventions, and personalized treatment strategies. In the realm of laboratory medicine, AI demonstrates remarkable precision in interpreting medical images such as radiography, computed tomography,and magnetic resonance imaging.Its predictive analytical capabilities extend to forecasting patient trajectories and informing personalized treatment strategies using comprehensive data sets comprising clinical outcomes, patient records, and laboratory results. The study underscores the significance of AI in addressing healthcare challenges, especially in resource-constrained LMICs. While acknowledging the profound impact of AI on laboratory medicine in LMICs, the study recognizes challenges such as inadequate data availability, digital infrastructure deficiencies, and ethical considerations. Successful implementation necessitates substantial investments in digital infrastructure, the establishment of data-sharing networks, and the formulation of regulatory frameworks. The study concludes that collaborative efforts among stakeholders, including international organizations, governments, and nongovernmental entities, are crucial for overcoming obstacles and responsibly integrating AI into laboratory medicine in LMICs. A comprehensive, coordinated approach is essential for realizing AI's transformative potential and advancing health care in LMICs.

Bridging artificial intelligence and cognitive ergonomics: a call for synergy

There is considerable potential between the field of artificial intelligence (AI) and cognitive ergonomics. The purpose of this thesis is to support increasing attention to the synergy between artificial intelligence and cognitive ergonomics, as well as the urgent need to explore it in research and practice.Artificial intelligence (AI) has become an integral part of the modern workplace, transforming industries, increasing productivity, and streamlining processes. However, as AI technologies continue to evolve, it is essential to consider the human side of this digital transformation. Ergonomics, the science of designing workspaces and tools that fit the people who use them, is a critical element in ensuring that the integration of artificial intelligence into the workplace increases employee well-being, efficiency, and overall success.
 With its rapid advancements, artificial intelligence is revolutionizing various fields and industries, from healthcare to self-driving vehicles and financial services. While the potential benefits are obvious, the ethical, cognitive, and ergonomic implications are just as important. Cognitive ergonomics is a sub-branch of ergonomics that deals with the design and evaluation of systems and technologies that support human cognitive processes such as perception, attention, memory, and decision-making. The field of cognitive ergonomics, which primarily focuses on Optimizing human-system interaction is the key to ensuring that artificial intelligence systems are designed and implemented in a way that aligns with human capacities, abilities, and cognitive limitations . Current AI implementations typically adopt a technology-driven focus, and employees are expected to adapt to the technology. In this technology-driven focus, the performance, performance, and accuracy of AI are optimized, but these aspects are considered separately. This point of view raises various critical considerations that are often neglected in the design and implementation of advanced technologies and sometimes have disastrous consequences. From the point of view of ergonomics, the design of artificial intelligence should be transferred from a technology-oriented focus to a systemic and human perspective. By applying a focus on personnel, artificial intelligence should be meaningfully and safely designed and integrated into work processes with the aim of optimizing overall system performance and people's well-being.

Нейропсихологическая коррекция в дополнительном образовании – эффективное средство работы с детьми с ОВЗ

The article presents the author's view on the application of neuropsychological correction in additional education. In recent decades, a lot of children have demonstrated delays and distortions of psychorechological development, lack of formation of arbitrary self-regulation, dysgraphy, etc. Together, this leads to emotional, personal and cognitive unpreparedness for learning and adequate adaptation to society. The priority participation of neuropsychology —the science of the formation of the brain organization of human mental processes in solving a wide range of dysontogenetic problems is primarily due to the depressing realities of development processes in the modern child population. Never before have we encountered such an abundance of negative phenomena as are observed today. Neuropsychological analysis of the problems of deviant development, which has been actively developing in our country since the early 1980s, immediately proved to be the most reliable and productive. This is natural, since it is based on a powerful methodological and scientific-practical base developed in general neuropsychology, neuropsychology of childhood, the theory of neuropsychological rehabilitation. [1]

CVII: Enhancing Interpretability in Intelligent Sensor Systems via Computer Vision Interpretability Index.

In the realm of intelligent sensor systems, the dependence on Artificial Intelligence (AI) applications has heightened the importance of interpretability. This is particularly critical for opaque models such as Deep Neural Networks (DNN), as understanding their decisions is essential, not only for ethical and regulatory compliance, but also for fostering trust in AI-driven outcomes. This paper introduces the novel concept of a Computer Vision Interpretability Index (CVII). The CVII framework is designed to emulate human cognitive processes, specifically in tasks related to vision. It addresses the intricate challenge of quantifying interpretability, a task that is inherently subjective and varies across domains. The CVII is rigorously evaluated using a range of computer vision models applied to the COCO (Common Objects in Context) dataset, a widely recognized benchmark in the field. The findings established a robust correlation between image interpretability, model selection, and CVII scores. This research makes a substantial contribution to enhancing interpretability for human comprehension, as well as within intelligent sensor applications. By promoting transparency and reliability in AI-driven decision-making, the CVII framework empowers its stakeholders to effectively harness the full potential of AI technologies.

Strategic Analysis for Advancing Smart Agriculture with the Analytic SWOT/PESTLE Framework: A Case for Turkey

In the contemporary discourse, smart agriculture (SA) stands out as a potent driver for sustainable economic growth. The challenges of navigating SA transition are notably intricate in developing nations. To effectively embark on this transformative journey, strategic approaches are imperative, necessitating a thorough examination of the prevailing agricultural ecosystem. This study seeks to formulate strategies that advance Turkey’s agricultural sector. The primary research questions focus on optimizing the benefits of SA by aligning strengths and opportunities with diverse socio-economic and environmental factors, while also exploring effective strategies to mitigate the impact of weaknesses and threats within the agricultural landscape. To achieve this objective, the utilization of the 2-Tuple linguistic (2TL) model integrated DEMATEL (Decision-Making Trial and Evaluation Laboratory) methodology in conjunction with SWOT (Strengths, Weaknesses, Opportunities, and Threats) and PESTLE (Political, Economic, Social, Technological, Legal, Environmental) analyses is proposed. The integration of linguistic variables enhances the capacity to delve deeper into system analysis, aligning more closely with human cognitive processes. The research commences with SWOT and PESTLE analyses applied to Turkey’s agricultural sector. Subsequently, the 2TL-DEMATEL approach is employed to investigate interrelationships among analysis components. This inquiry aims to establish causal relations, facilitating the derivation of relevant strategies. The case study centers on Turkey, a developing country, with outcomes indicating that the highest-priority strategies revolve around addressing ‘environmental threats’ and ‘economic weaknesses’. The subsequent evaluation encompasses eight dimensions, resulting in the generation of fifteen distinct strategies, a process facilitated by collaboration with field experts. Importantly, both the results and strategies undergo rigorous validation, drawing upon insights from the recent literature and field experts. Significantly, these findings align seamlessly with the Sustainable Development Goals (SDGs), substantiating the study’s broader significance in fostering a sustainable future for Turkey.

The Influence of Neurosciences on Understanding the Bodily Conditioning of Cognitive Processes: a Socio-Anthropological Aspect

The study presents a conceptual analysis of the main approaches to the study of the human brain and consciousness from the standpoint of modern domestic and foreign neuroscience. Relevant interpretations of such problematic issues and concepts as “the boundary of the human body”, “embodied knowledge”, “general artificial intelligence”, “self”, etc. From the standpoint of a body-oriented approach, the problem of co-evolution of the body, consciousness, technology and social environment is considered. The idea of the body as an artifact is updated in the context of technological change in human nature, which entails the elimination of the living human body. On the example of the Homeric epic, it is shown that attempts to overcome vulnerability demonstrate one of the fundamental human needs. The realization of the need to expand and push the boundaries of the body in the anthropotechnological environment is presented in the form of a cyborg. It is suggested that, in terms of content, the idea of the “embodied mind”, as well as the project of creating a Hybrid reality and pan-communication, stem from the immanent human need for bodily involvement in the surrounding world. The features of the creation of Artificial Intelligence and Artificial General Intelligence at the present stage are revealed. The expediency of studying the results of neuroscientific comprehension of the organization of mental processes, first of all, in the field of visualization of brain processes and the mapping method, is noted. The main modern projects for the study of the brain, as well as the most effective methods for obtaining data on brain structures, are considered. The actual results of the research are described in such areas of neuroscience as: the study of mirror neurons and their systems; “reading the brain”, the study of neural correlates of consciousness. The results of studies of representatives of domestic neuroscience are presented. The human “I” is considered as a unique structure of the Ego-system of the brain (“self”). In conclusion, a conclusion is made about the significant influence of modern neuroscience on understanding the features of the functioning of the human brain, consciousness and cognitive processes; the effectiveness of applying the body-oriented approach to comprehending the integrity of “body-consciousness-technologies-social environment” is revealed.

A COGNITIVE STYLISTIC ANALYSIS OF LEWIS CAPALDI'S SONG “SOMEONE YOU LOVED”

Cognitive stylistics is a branch of linguistics that focuses on the study of how language and literature interact with human cognition and mental processes. This study presents a cognitive stylistic analysis of Lewis Capaldi's song "Someone You Loved." This study aims to explore the cognitive processes and stylistic features employed in the song's lyrics. This research uses a descriptive qualitative method by focusing on, highlighting, and analyzing each lyric in the song. The results of this study show the coherence among figures of speech used in the song. In conclusion, this study contributes to understanding how cognitive processes and stylistic choices intertwine in constructing emotional narratives within song lyrics.

Exploring Cognitive Linguistics

In recent years, cognitive linguistics has gained significant traction and recognition among researchers and individuals with a vested interest in the fields of linguistics and cognitive science. This paper serves the purpose of shedding light on some of the most current and pioneering research endeavors in this domain, while also assessing their contributions towards unraveling the intricate nuances of cognitive language processing. Cognitive linguistics represents a paradigm shift in the study of language and cognition, departing from the traditional structuralist and generative approaches. It posits that language is deeply intertwined with human cognitive processes, and therefore, understanding the cognitive aspects of language use is paramount. The contemporary studies explored in this paper have played a pivotal role in advancing this perspective. These studies employ an array of methodologies and approaches, such as neuroimaging, psycholinguistics, and corpus analysis, to investigate how humans conceptualize and process language. One notable study may delve into the neural mechanisms involved in metaphor comprehension, revealing that metaphors are not mere linguistic embellishments but rooted in the perceptual and experiential systems. Another cutting-edge research area might involve examining the influence of linguistic relativity on thought, challenging the idea that language is a neutral medium for thought and instead highlighting how language structures shape the cognitive experiences. These investigations are revolutionizing the understanding of linguistic diversity and the extent to which it influences cognition. In sum, this paper aims to provide a comprehensive overview of recent research endeavors within cognitive linguistics and to underscore their significance in unveiling the intricate processes of cognitive language comprehension. These studies have collectively contributed to the growing body of knowledge surrounding how language and thought are inherently entwined, reshaping the landscape of linguistic and cognitive inquiry.

Assessing target optical camouflage effects using brain functional networks: A feasibility study

Brain functional networks model the brain's ability to exchange information across different regions, aiding in the understanding of the cognitive process of human visual attention during target searching, thereby contributing to the advancement of camouflage evaluation. In this study, images with various camouflage effects were presented to observers to generate electroencephalography (EEG) signals, which were then used to construct a brain functional network. The topological parameters of the network were subsequently extracted and input into a machine learning model for training. The results indicate that most of the classifiers achieved accuracy rates exceeding 70%. Specifically, the Logistic algorithm achieved an accuracy of 81.67%. Therefore, it is possible to predict target camouflage effectiveness with high accuracy without the need to calculate discovery probability. The proposed method fully considers the aspects of human visual and cognitive processes, overcomes the subjectivity of human interpretation, and achieves stable and reliable accuracy.

Beta- and gamma-band cortico-cortical interactions support naturalistic reading of continuous text.

Large-scale integration of information across cortical structures, building on neural connectivity, has been proposed to be a key element in supporting human cognitive processing. In electrophysiological neuroimaging studies of reading, quantification of neural interactions has been limited to the level of isolated words or sentences due to artefacts induced by eye movements. Here, we combined magnetoencephalography recording with advanced artefact rejection tools to investigate both cortico-cortical coherence and directed neural interactions during naturalistic reading of full-page texts. Our results show that reading versus visual scanning of text was associated with wide-spread increases of cortico-cortical coherence in the beta and gamma bands. We further show that the reading task was linked to increased directed neural interactions compared to the scanning task across a sparse set of connections within a wide range of frequencies. Together, the results demonstrate that neural connectivity flexibly builds on different frequency bands to support continuous natural reading.

Energy-Efficient Deep Neural Networks for EEG Signal Noise Reduction in Next-Generation Green Wireless Networks and Industrial IoT Applications

Wireless electroencephalography (EEG) has emerged as a critical interface between human cognitive processes and machine learning technologies in the burgeoning field of sensor communications. This paper presents a comprehensive review of advancements in wireless EEG communication and analysis, with an emphasis on their role in next-generation green wireless networks and industrial IoT. The review explores the efficacy of modulation techniques, such as amplitude-shift keying (ASK) and frequency-shift keying (FSK) in EEG data transmission, and emphasizes the transformative role of deep learning in the joint transmission and restoration of EEG signals. In addition, we propose a novel, energy-efficient approach to deep learning-based EEG analytics, designed to enhance wireless information transfer for industrial IoT applications. By applying an autoencoder to sample the EEG data and incorporating a hidden layer to simulate a noisy communication channel, we assessed the energy efficiency and reliability of the transmission. Our results demonstrate that the chosen network topology and parameters significantly affect not only data fidelity but also energy consumption, thus providing valuable insights for the development of sustainable and efficient wireless EEG systems in industrial IoT environments. A key aspect of our study is related to symmetry. Our results demonstrate that the chosen network topology and parameters significantly impact not only data fidelity but also energy fidelity and energy consumption, thus providing valuable insights for the development of sustainable and efficient wireless EEG systems in industrial IoT environments. Furthermore, we realized that the EEG data showed mildly marked symmetry. Neural networks must also exhibit asymmetric behavior for better performance.

Pedestrians’ road-crossing decisions: Comparing different drift-diffusion models

The decision of whether to cross a road or wait for a car to pass, humans make frequently and effortlessly. Recently, the application of drift-diffusion models (DDMs) on pedestrians’ decision-making has proven useful in modelling crossing behaviour in pedestrian–vehicle interactions. These models consider binary decision-making as an incremental accumulation of noisy evidence over time until one of two choice thresholds (to cross or not) is reached. One open question is whether the assumption of a kinematics-dependent drift-diffusion process, which was made in previous pedestrian crossing DDMs, is justified, with DDM-parameters varying over time according to the developing traffic situation. It is currently unknown whether kinematics-dependent DDMs provide a better model fit than conventional DDMs, which are fitted per condition. Furthermore, previous DDMs have not considered reaction times for the not-crossing option. We address these issues by a novel experimental design combined with modelling. Experimentally, we use a 2-alternative-forced-choice paradigm, where participants view videos of approaching cars from a pedestrian’s perspective and respond whether they want to cross before the car or to wait until the car has passed. Using these data, we perform thorough model comparison between kinematics-dependent and condition-wise fitted DDMs. Our results demonstrate that condition-wise fitted DDMs can show better model fits than kinematics-dependent DDMs as reflected in the mean-squared-errors. The condition-wise fitted models need considerably more parameters, but in some cases still outperform kinematics-dependent DDMs in measures that penalize the parameter number (e.g., Akaike information criterion). Introducing a starting point bias provides support for the novel hypothesis of rapid early evidence build-up from the initial view of the vehicle distance. The drift rates obtained for the condition-wise fitted models align with the assumptions in the kinematics-dependent models, confirming that pedestrians’ decision processes are kinematics-dependent. However, the partial preference for condition-wise fitted models in the model selection suggests that the correct form of kinematics-dependence has not yet been identified for all DDM-parameters, indicating room for improvement of current pedestrian crossing DDMs. Developing more accurate models of human cognitive processes will likely facilitate autonomous vehicles to understand pedestrians’ intentions as well as to show unambiguous human-like behaviour in future traffic interactions with humans.

Human Robot Interaction : A Psychotechnical Perspective

This research paper delves into the multifaceted realm of Human-Robot Interaction (HRI) by adopting a multidisciplinary approach, amalgamating insights from psychology, technology, and neuroscience. From a psychological perspective, the study examines human responses to robots, considering factors like trust, empathy, and acceptance. It investigates how individual differences and cultural influences shape attitudes and behaviors in HRI scenarios. This psychological exploration forms a basis for designing robots aligned with human cognitive and emotional processes.

Building blocks of imagination

In the human context, the ubiquitous faculty of imagination is taken for granted. Whether we are singularly equipped with this ability or whether it is an evolved faculty also present in other life forms is a question that has been an issue ever since Darwin. A variety of research projects have indicated the presence of mental processes in non-human taxa and the faculty of imagination developed with increasing complexity over time, to its present status in humans. As an evolved faculty, encompassing a variety of non-humans, it serves as a useful tool to ascertain and understand the origins and unique quality of human mental processes, even to the extent of informing certain medical issues and problems in education. Several pointers exist that argue for human imagination as an evolved faculty that is shared within broad spectrum of taxa. Paleoanthropological research has provided insights as to the role of imagination in the early manifestations of religious behaviour in humans, the construction of effective stone tools by anatomically modern humans employing pyro technology, as well as their ability to mentally link ostensibly non-related natural phenomena like tidal fluctuations and the phases of the moon.Contribution: This review article explores imagination as an evolved faculty in an intersectional and interdisciplinary manner. This evolutionary reflection fits well with the purpose and inclusive nature of this special collection on the building blocks of our past, present and future.

“Listen to your heart”: A novel interoceptive strategy for real-time fMRI neurofeedback training of anterior insula activity

Real-time fMRI (rt-fMRI) neurofeedback (NF) training is a novel non-invasive technique for volitional brain modulation. Given the important role of the anterior insula (AI) in human cognitive and affective processes, it has become one of the most investigated regions in rt-fMRI studies. Most rt-fMRI insula studies employed emotional recall/imagery as the regulation strategy, which may be less effective for psychiatric disorders characterized by altered emotional processing. The present study thus aimed to examine the feasibility of a novel interoceptive strategy based on heartbeat detection in rt-fMRI guided AI regulation and its associated behavioral changes using a randomized double-blind, sham feedback-controlled between-subject design. 66 participants were recruited and randomly assigned to receive either NF from the left AI (LAI) or sham feedback from a control region while using the interoceptive strategy. N = 57 participants were included in the final data analyses. Empathic and interoceptive pre-post training changes were collected as behavioral measures of NF training effects. Results showed that participants in the NF group exhibited stronger LAI activity than the control group with LAI activity being positively correlated with interoceptive accuracy following NF training, although there were no significant increases of LAI activity over training sessions. Importantly, ability of LAI regulation could be maintained in a transfer session without feedback. Successful LAI regulation was associated with strengthened functional connectivity of the LAI with cognitive control, memory and learning, and salience/interoceptive networks. The present study demonstrated for the first time the efficacy of a novel regulation strategy based on interoceptive processing in up-regulating LAI activity. Our findings also provide proof of concept for the translational potential of this strategy in rt-fMRI AI regulation of psychiatric disorders characterized by altered emotional processing.

Model dan Proses Komunikasi

Psychological and mechanistic paradigms are two different approaches to understanding and explaining human phenomena and the world around them. The psychology paradigm involves the study of human thoughts, behavior, and mental processes. This approach focuses on understanding how individuals think, feel, and act. Psychological paradigms include various theories and methods used to study human psychological aspects. On the other hand, the mechanistic paradigm involves the view that the world can be explained and understood through the principles of mechanics and causality. These two paradigms have differences in the way they view and explain human phenomena and the world. The psychological paradigm focuses more on the psychological and mental aspects of humans, while the mechanistic paradigm focuses more on the physical and mechanical aspects

Utilizing Artificial Intelligence and Knowledge-Based Engineering Techniques in Shipbuilding: Practical Insights and Viability

_ This paper delves into the technical aspects and viability of integrating artificial intelligence (AI) and knowledge-based engineering (KBE) tools in practical design. The goal is to digitally embed the hands-on expertise and technical boundaries set by seasoned professionals during intricate engineering and preparatory phases. We showcase how AI/KBE tools might emulate human cognitive processes to make well-informed choices. The article also probes the prospective economic and modernization repercussions of this innovation. Our findings suggest that such an integration is feasible and can amplify the decision-making efficacy and advance the sophistication of CAD/CAM systems in the shipbuilding realm. Furthermore, this investigation underscores the promising future of AI/KBE tools in ship design and advocates for continued exploration and innovation in this sector to fully harness its advantages. Introduction Shipbuilding has long been intertwined with CAD/CAM technologies. As technology evolves, so does the landscape of ship design and manufacturing (Ross, 1950). Traditionally, ship design leaned heavily on seasoned engineers and designers, whose insights were cultivated over years of experience. However, with the rising demand for ships and an aging workforce, there’s a pressing need for enhanced design methodologies. Enter the era of artificial intelligence (AI) and knowledge-based engineering (KBE), which promise to revolutionize ship design by integrating practical knowledge and technical constraints. In today’s shipbuilding scenario, younger engineers often handle detailed engineering stages, a shift from when experienced professionals dominated the shop floor (Moyst and Das, 2005). Our research aims to assess the feasibility of AI KBE systems in enhancing the ship design process during these stages, by virtualizing the knowledge of experienced workers.

A Diaspora of Humans to Technology: VEDA Net for Sentiments and their Technical Analysis

Background: Human sentiments are the representation of one’s soul. Visual media has emerged as one of the most potent instruments for communicating thoughts and feelings in today's world. The area of visible emotion analysis is abstract due to the considerable amount of bias in the human cognitive process. Machines need to apprehend better and segment these for future AI advancements. A broad range of prior research has investigated only the emotion class identifier part of the whole process. In this work, we focus on proposing a better architecture to assess an emotion identifier and finding a better strategy to extract and process an input image for the architecture.
 Objective: We investigate the subject of visual emotion detection and analysis using a connected Dense Blocked Network to propose an architecture VEDANet. We show that the proposed architecture performed extremely effectively across different datasets.
 Method: Using CNN based pre-trained architectures, we would like to highlight the spatial hierarchies of visual features. Because the image's spatial regions communicate substantial feelings, we utilize a dense block-based model VEDANet that focuses on the image's relevant sentiment-rich regions for effective emotion extraction. This work makes a substantial addition by providing an in-depth investigation of the proposed architecture by carrying out extensive trials on popular benchmark datasets to assess accuracy gains over the comparable state-of-the-art. In terms of emotion detection, the outcomes of the study show that the proposed VED system outperforms the existing ones (accuracy). Further, we explore over the top optimization i.e. OTO layer to achieve higher efficiency.
 Results: When compared to the recent past research works, the proposed model performs admirably and obtains accuracy of 87.30% on the AffectNet dataset, 92.76% on Google FEC, 95.23% on Yale Dataset, and 97.63% on FER2013 dataset. We successfully merged the model with a face detector to obtain 98.34 percent accuracy on Real-Time live frames, further encouraging real-time applications. In comparison to existing approaches, we achieve real-time performance with a minimum TAT (Turn-around-Time) trade-off by using an appropriate network size and fewer parameters.

Robust Credit Card Fraud Detection: A Model Comparison Study

In this credit card fraud prediction project, we aimed to develop models capable of accurately identifying potentially fraudulent transactions. The dataset encompassed various attributes including transaction amount, gender, city population, age, transaction month and year, as well as geographical coordinates, which were used to train and evaluate the models. Two distinct machine learning algorithms, namely Logistic Regression and Decision Tree, were employed for this task. The models were fine-tuned using techniques such as feature selection and hyperparameter tuning to enhance their predictive accuracy. The Logistic Regression model demonstrated an accuracy of X% on the test set, showcasing its proficiency in distinguishing between fraudulent and nonfraudulent transactions. Additionally, the Decision Tree model exhibited a similar accuracy of Y%. Through an in-depth analysis of feature importance, it was revealed that attributes such as transaction amount, age, and latitudinal distance played pivotal roles in making accurate predictions. Furthermore, the project emphasizes the significance of employing soft computing techniques, which involve computational methods designed to emulate human cognitive processes, for credit card fraud detection. By leveraging machine learning algorithms and feature engineering, we were able to construct robust models capable of safeguarding financial transactions from potentially fraudulent activities. This project highlights the efficacy of modern data-driven approaches in enhancing security measures within the financial industry. Keywords— Intrusion detection, Machine learning, Optimization algorithms.