Search results for: artificial intelligence in medicine

Authors: Boumaraf Malak

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The development of intelligent transportation systems (ITS) and artificial intelligence (AI) are spurring us to pave the way for the widespread adoption of autonomous vehicles (AVs). This is open again opportunities for smart roads, smart traffic safety, and mobility comfort. A highly intelligent decision-making system is essential for autonomous driving around dense, dynamic objects. It must be able to handle complex road geometry and topology, as well as complex multiagent interactions, and closely follow higher-level commands such as routing information. Autonomous vehicles have become a very hot research topic in recent years due to their significant ability to reduce traffic accidents and personal injuries. Using new artificial intelligence-based technologies handles important functions in scene understanding, motion planning, decision making, vehicle control, social behavior, and communication for AV. This paper focuses only on deep reinforcement learning-based methods; it does not include traditional (flat) planar techniques, which have been the subject of extensive research in the past because reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. The DRL algorithm used so far found solutions to the four main problems of autonomous driving; in our paper, we highlight the challenges and point to possible future research directions.

Keywords: deep reinforcement learning, autonomous driving, deep deterministic policy gradient, deep Q-learning

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Authors: Shih-Fang Lo, Tzu-Wei Guo, Chih-Hsuan Lee

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Tropical fisheries are vulnerable to the physical and biogeochemical oceanic changes associated with climate change. Warmer temperatures and extreme weather have beendamaging the abundance and growth patterns of aquatic organisms. In recent year, the shrinking of fish stock and labor shortage have increased the threat to global aquacultural production. Thus, building a climate-resilient and sustainable mechanism becomes an urgent, important task for global citizens. To tackle the problem, Taiwanese fishermen applies the artificial intelligence (AI) technology. In brief, the AI system (1) measures real-time water quality and chemical parameters infish ponds; (2) monitors fish stock through segmentation, detection, and classification; and (3) implements fishermen’sprevious experiences, perceptions, and real-life practices. Applying this system can stabilize the aquacultural production and potentially increase the labor force. Furthermore, this AI technology can build up a more resilient and sustainable system for the fishermen so that they can mitigate the influence of extreme weather while maintaining or even increasing their aquacultural production. In the future, when the AI system collected and analyzed more and more data, it can be applied to different regions of the world or even adapt to the future technological or societal changes, continuously providing the most relevant and useful information for fishermen in the world.

Keywords: aquaculture, artificial intelligence (AI), real-time system, sustainable fishery

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Authors: Arpita Sabath, Jytoika Samuel

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Emotional Intelligence is conceptualized as a confluence of learned abilities resulting in wise behavior, high achievement and mental health. This case study is done on IT Sector employees of CAREERNET consultancy at Bangalore. Thus the present study intends to find out the difference in different dimensions of El and Js Scales among male and female employees and the existing relationship between emotional intelligence and job satisfaction for the beginner age group of employees (25 yrs - 40 yrs) in order to enhance the employees productivity level in the present scenario of recession in employment. It is observed that all promotions and increment are achieved at these 25 yrs - 40 yrs age group employees. Therefore, the sample is selected randomly and grouped. Survey method with the administration of Emotional Intelligence Scale and opinionScedule is used. The findings of the study has revealed that there is a positive relationship between emotional intelligence and performance excellence. The study is concluded with a remark that the relevance of this study should be followed by the administrative body of IT sectors to motivate them and to get more productive work from their employees

Keywords: emotional intelligence, job satisfaction, organisational behavior, IT sector

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Authors: Sylvester Akpah, Selasi Vondee

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Unmanned Aerial Vehicles (UAV) as they are also known, drones have attracted increasing attention in recent years due to their ubiquitous nature and boundless applications in the areas of communication, surveying, aerial photography, weather forecasting, medical delivery, surveillance amongst others. Operated remotely in real-time or pre-programmed, drones can fly autonomously or on pre-defined routes. The application of these aerial vehicles has successfully penetrated the world due to technological evolution, thus a lot more businesses are utilizing their capabilities. Unfortunately, while drones are replete with the benefits stated supra, they are riddled with some problems, mainly attributed to the complexities in learning how to master drone flights, collision avoidance and enterprise security. Additional challenges, such as the analysis of flight data recorded by sensors attached to the drone may take time and require expert help to analyse and understand. This paper presents an autonomous drone control system using a chatbot. The system allows for easy control of drones using conversations with the aid of Natural Language Processing, thus to reduce the workload needed to set up, deploy, control, and monitor drone flight missions. The results obtained at the end of the study revealed that the drone connected to the chatbot was able to initiate flight missions with just text and voice commands, enable conversation and give real-time feedback from data and requests made to the chatbot. The results further revealed that the system was able to process natural language and produced human-like conversational abilities using Artificial Intelligence (Natural Language Understanding). It is recommended that radio signal adapters be used instead of wireless connections thus to increase the range of communication with the aerial vehicle.

Keywords: artificial ntelligence, chatbot, natural language processing, unmanned aerial vehicle

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Authors: Sameh Ibrahim Ghali Hanna

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Information system (IS) are fairly crucial in the operation of private and public establishments in growing and developed international locations. Growing countries are saddled with many project failures throughout the implementation of records systems. However, successful information systems are greatly wished for in developing nations in an effort to decorate their economies. This paper is extraordinarily critical in view of the high failure fee of data structures in growing nations, which desire to be decreased to minimal proper levels by means of advocated interventions. This paper centers on a review of IS development in developing international locations. The paper gives evidence of the IS successes and screw-ups in developing nations and posits a version to deal with the IS failures. The proposed model can then be utilized by means of growing nations to lessen their IS mission implementation failure fee. A contrast is drawn between IS improvement in growing international locations and evolved international locations. The paper affords valuable records to assist in decreasing IS failure, and growing IS models and theories on IS development for developing countries.

Keywords: research information systems (RIS), research information, heterogeneous sources, data quality, data cleansing, science system, standardization artificial intelligence, AI, enterprise information system, EIS, integration developing countries, information systems, IS development, information systems failure, information systems success, information systems success model

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Authors: Akriti Srivastava, Soumi Awasthy

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One of the disciplines that provoked increased interest in the importance of intelligence is the management and organization development literature. Organization intelligence is a key enabling force underlying many vital activities and processes dominating organizational life. Hence, the factors which lead to organizational intelligence and the result which comes out of the whole procedure is important to be understood with the understanding of OI. The focus of this research was to uncover potential antecedents and consequences of organizational intelligence, thus a non-experimental explanatory survey research design was used. A non-experimental research design is in which the manipulation of variables and randomization of samples are not present. The data was collected with the help of the questionnaire from 321 scientists from different laboratories of an R & D organization. Out of which 304 data were found suitable for the analysis. There were 194 males (age, M= 35.03, SD=7.63) and 110 females (age, M= 34.34, SD=8.44). This study tested a conceptual model linking antecedent variables (leadership and organizational culture) to organizational intelligence, followed by organizational innovational capability and organizational performance. Structural equation modeling techniques were used to analyze the hypothesized model. But, before that, confirmatory factor analysis of organizational intelligence scale was done which resulted in an insignificant model. Then, exploratory factor analysis was done which gave six factors for organizational intelligence scale. This structure was used throughout the study. Following this, the final analysis revealed relatively good fit of data to the hypothesized model with certain modifications. Leadership and organizational culture emerged out as the significant antecedents of organizational intelligence. Organizational innovational capability and organizational performance came out to be the consequent factors of organizational intelligence. But organizational intelligence did not predict organizational performance via organizational innovational capability. With this, additional significant pathway emerged out between leadership and organizational performance. The model offers a fresh and comprehensive view of the organizational intelligence. In this study, prior studies in related literature were reviewed to offer a basic framework of organizational intelligence. The study proved to be beneficial for organizational intelligence scholarship, seeing its importance in the competitive environment.

Keywords: leadership, organizational culture, organizational intelligence, organizational innovational capability

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Authors: Abdul Basit Kiani, Maryam Kiani

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Online developers now have the tools necessary to create online apps that are not only reliable but also highly interactive, thanks to the introduction of JavaScript frameworks and APIs. The objective is to give a broad overview of the recent advances in the area. The fusion of machine learning (ML) and artificial intelligence (AI) has expanded the possibilities for web development. Modern websites now include chatbots, clever recommendation systems, and customization algorithms built in. In the rapidly evolving landscape of modern websites, it has become increasingly apparent that user engagement and personalization are key factors for success. To meet these demands, websites now incorporate a range of innovative technologies. One such technology is chatbots, which provide users with instant assistance and support, enhancing their overall browsing experience. These intelligent bots are capable of understanding natural language and can answer frequently asked questions, offer product recommendations, and even help with troubleshooting. Moreover, clever recommendation systems have emerged as a powerful tool on modern websites. By analyzing user behavior, preferences, and historical data, these systems can intelligently suggest relevant products, articles, or services tailored to each user's unique interests. This not only saves users valuable time but also increases the chances of conversions and customer satisfaction. Additionally, customization algorithms have revolutionized the way websites interact with users. By leveraging user preferences, browsing history, and demographic information, these algorithms can dynamically adjust the website's layout, content, and functionalities to suit individual user needs. This level of personalization enhances user engagement, boosts conversion rates, and ultimately leads to a more satisfying online experience. In summary, the integration of chatbots, clever recommendation systems, and customization algorithms into modern websites is transforming the way users interact with online platforms. These advanced technologies not only streamline user experiences but also contribute to increased customer satisfaction, improved conversions, and overall website success.

Keywords: Javascript, machine learning, artificial intelligence, web development

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Authors: Michael S. Goodman

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The Joint Intelligence Committee (JIC) has been the senior most analytical body in the UK since its creation in 1936. At various points in its history, most notably and recently in 2004, in the wake of the Iraq war, questions have been asked about its analytical process. In 1968 the British intelligence community saw one of its biggest transformations: the creation of an independent, central cadre of analysts. The ‘Assessments Staff’ was a novel attempt to improve the quality of analysis by fostering independence from departmental biases that had long plagued British intelligence. Seconded into the Cabinet Office, staff were allocated a ‘desk,’ and their role was to produce high level assessments for the most senior readers in the land. At the same time, efforts were made to ‘professionalise’ the analysts. This paper is based on a detailed archival examination of the JIC’s documentary files. It will recount the reasons behind this organisational reform, what the changes entailed, and whether they were a success. The changes were immediately brought to bear with the intelligence assessments prior to the Soviet invasion of Czechoslovakia, something that the JIC failed to appreciate.

Keywords: intelligence, cold war history, analysis, united kingdom

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Authors: Abdal-Hafeez Alhussein

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Artificial Intelligence (AI) has become a transformative force in the field of information technologies, reshaping how data is processed, analyzed, and utilized across various domains. This paper explores the multifaceted applications of AI within information technology, focusing on three key areas: automation, scalability, and data-driven decision-making. We delve into how AI-powered automation is optimizing operational efficiency in IT infrastructures, from automated network management to self-healing systems that reduce downtime and enhance performance. Scalability, another critical aspect, is addressed through AI’s role in cloud computing and distributed systems, enabling the seamless handling of increasing data loads and user demands. Additionally, the paper highlights the use of AI in cybersecurity, where real-time threat detection and adaptive response mechanisms significantly improve resilience against sophisticated cyberattacks. In the realm of data analytics, AI models—especially machine learning and natural language processing—are driving innovation by enabling more precise predictions, automated insights extraction, and enhanced user experiences. The paper concludes with a discussion on the ethical implications of AI in information technologies, underscoring the importance of transparency, fairness, and responsible AI use. It also offers insights into future trends, emphasizing the potential of AI to further revolutionize the IT landscape by integrating with emerging technologies like quantum computing and IoT.

Keywords: artificial intelligence, information technology, automation, scalability

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Authors: Amarachukwu B. Isiaka, Vivian N. Anakwenze, Chinyere C. Ezemba, Chiamaka R. Ilodinso, Chikodili G. Anaukwu, Chukwuebuka M. Ezeokoli, Ugonna H. Uzoka

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Infectious diseases continue to pose significant threats to global public health, necessitating advanced and timely detection methods for effective outbreak management. This study explores the integration of artificial intelligence (AI) in the early detection and management of infectious disease outbreaks. Leveraging vast datasets from diverse sources, including electronic health records, social media, and environmental monitoring, AI-driven algorithms are employed to analyze patterns and anomalies indicative of potential outbreaks. Machine learning models, trained on historical data and continuously updated with real-time information, contribute to the identification of emerging threats. The implementation of AI extends beyond detection, encompassing predictive analytics for disease spread and severity assessment. Furthermore, the paper discusses the role of AI in predictive modeling, enabling public health officials to anticipate the spread of infectious diseases and allocate resources proactively. Machine learning algorithms can analyze historical data, climatic conditions, and human mobility patterns to predict potential hotspots and optimize intervention strategies. The study evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. The implementation of an AI-driven early detection system requires collaboration between public health agencies, healthcare providers, and technology experts. Ethical considerations, privacy protection, and data security are paramount in developing a framework that balances the benefits of AI with the protection of individual rights. The synergistic collaboration between AI technologies and traditional epidemiological methods is emphasized, highlighting the potential to enhance a nation's ability to detect, respond to, and manage infectious disease outbreaks in a proactive and data-driven manner. The findings of this research underscore the transformative impact of harnessing AI for early detection and management, offering a promising avenue for strengthening the resilience of public health systems in the face of evolving infectious disease challenges. This paper advocates for the integration of artificial intelligence into the existing public health infrastructure for early detection and management of infectious disease outbreaks. The proposed AI-driven system has the potential to revolutionize the way we approach infectious disease surveillance, providing a more proactive and effective response to safeguard public health.

Keywords: artificial intelligence, early detection, disease surveillance, infectious diseases, outbreak management

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Authors: Juan Sebastian Huertas Piedrahita, Jaime Arturo Lopez Duque, Eduardo Luis Perez Londoño, Julián S. Rodríguez

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The process of generating computer vision is called artificial vision. The artificial vision is a branch of artificial intelligence that allows the obtaining, processing, and analysis of any type of information especially the ones obtained through digital images. Actually the artificial vision is used in manufacturing areas for quality control and production, as these processes can be realized through counting algorithms, positioning, and recognition of objects that can be measured by a single camera (or more). On the other hand, the companies use assembly lines formed by conveyor systems with actuators on them for moving pieces from one location to another in their production. These devices must be previously programmed for their good performance and must have a programmed logic routine. Nowadays the production is the main target of every industry, quality, and the fast elaboration of the different stages and processes in the chain of production of any product or service being offered. The principal base of this project is to program a computer that recognizes geometric figures (circle, square, and triangle) through a camera, each one with a different color and link it with a group of conveyor systems to organize the mentioned figures in cubicles, which differ from one another also by having different colors. This project bases on artificial vision, therefore the methodology needed to develop this project must be strict, this one is detailed below: 1. Methodology: 1.1 The software used in this project is QT Creator which is linked with Open CV libraries. Together, these tools perform to realize the respective program to identify colors and forms directly from the camera to the computer. 1.2 Imagery acquisition: To start using the libraries of Open CV is necessary to acquire images, which can be captured by a computer’s web camera or a different specialized camera. 1.3 The recognition of RGB colors is realized by code, crossing the matrices of the captured images and comparing pixels, identifying the primary colors which are red, green, and blue. 1.4 To detect forms it is necessary to realize the segmentation of the images, so the first step is converting the image from RGB to grayscale, to work with the dark tones of the image, then the image is binarized which means having the figure of the image in a white tone with a black background. Finally, we find the contours of the figure in the image to detect the quantity of edges to identify which figure it is. 1.5 After the color and figure have been identified, the program links with the conveyor systems, which through the actuators will classify the figures in their respective cubicles. Conclusions: The Open CV library is a useful tool for projects in which an interface between a computer and the environment is required since the camera obtains external characteristics and realizes any process. With the program for this project any type of assembly line can be optimized because images from the environment can be obtained and the process would be more accurate.

Keywords: artificial intelligence, artificial vision, binarized, grayscale, images, RGB

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Authors: Zheni S. Stefanova, Kandethody M. Ramachandran

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With the increasing dependency on our computer devices, we face the necessity of adequate, efficient and effective mechanisms, for protecting our network. There are two main problems that Intrusion Detection Systems (IDS) attempt to solve. 1) To detect the attack, by analyzing the incoming traffic and inspect the network (intrusion detection). 2) To produce a prompt response when the attack occurs (intrusion prevention). It is critical creating an Intrusion detection model that will detect a breach in the system on time and also challenging making it provide an automatic and with an acceptable delay response at every single stage of the monitoring process. We cannot afford to adopt security measures with a high exploiting computational power, and we are not able to accept a mechanism that will react with a delay. In this paper, we will propose an intrusion response mechanism that is based on artificial intelligence, and more precisely, reinforcement learning techniques (RLT). The RLT will help us to create a decision agent, who will control the process of interacting with the undetermined environment. The goal is to find an optimal policy, which will represent the intrusion response, therefore, to solve the Reinforcement learning problem, using a Q-learning approach. Our agent will produce an optimal immediate response, in the process of evaluating the network traffic.This Q-learning approach will establish the balance between exploration and exploitation and provide a unique, self-learning and strategic artificial intelligence response mechanism for IDS.

Keywords: cyber security, intrusion prevention, optimal policy, Q-learning

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Authors: Abigail Gragg

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The goal of this research study was to adjudicate if implementing Howard Gardner’s multiple levels of intelligence would enhance student achievement levels in an Algebra 1 College Preparatory class. This was conducted within every class by incorporating one level of the eight levels of intelligence into small group work in stations. Every class was conducted utilizing small-group instruction. Achievement levels were measured through various forms of collected data that expressed student understandings in class through formative assessments versus student understandings on summative assessments. The data samples included: assessments (i.e. summative and formative assessments), observable data, video recordings, a daily log book, student surveys, and checklists kept during the observation periods. Formative assessments were analyzed during each class period to measure in-class understanding. Summative assessments were dissected per question per accuracy to review the effects of each intelligence implemented. The data was collated into a coding workbook for further analysis to conclude the resulting themes of the research. These themes include 1) there was no correlation to multiple levels of intelligence enhancing student achievement, 2) bodily-kinesthetic intelligence showed to be the intelligence that had the most improvement on test questions and 3) out of all of the bits of intelligence, interpersonal intelligence enhanced student understanding in class.

Keywords: stations, small group instruction, multiple levels of intelligence, Mathematics, Algebra 1, student achievement, secondary school, instructional Pedagogies

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Authors: Ananda Perera

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Computer-Assisted Medical Evaluation of Symptoms (CAMEOS) is a medical expert system designed to help General Practices (GPs) make an accurate diagnosis. CAMEOS comprises a knowledge base, user input, inference engine, reasoning module, and output statement. The knowledge base was developed by the author. User input is an Html file. The physician user collects data in the consultation. Data is sent to the inference engine at servers. CAMEOS uses set theory to simulate diagnostic reasoning. The program output is a list of differential diagnoses, the most probable diagnosis, and the diagnostic reasoning.

Keywords: CDSS, computerized decision support systems, expert systems, general practice, diagnosis, diagnostic systems, primary care diagnostic system, artificial intelligence in medicine

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Authors: Ali Heyrani, Maryam Saidyousefi

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The aim of the present study was to investigate the relationship between emotional intelligence and Self-Efficacy of physical education teachers in Ilam province. The research method is descriptive correlational. The study participants were of 170 physical education teachers (90 males, 80 females) with an age range of 20 to 50 years, who were selected randomly. The instruments for data collection were Emotional Intelligence Questionnaire Bar-on (1997) to assess the Emotional Intelligence teachers and Self-Efficacy Questionnaire to measure their Self-Efficacy. The questionnaires used in the interior are reliable and valid. To analyze the data, descriptive statistics and inferential tests (Kolmogorov-Smirnov test, Pearson correlation and multiple regression) at a significance level of P <0/ 05 were used. The Results showed that there is a significant positive relationship between totall emotional intelligence and Self-Efficacy of teachers, so the more emotional intelligence of physical education teachers the better the extent of Self-Efficacy. Also, the results arising from regression analysis gradually showed that among components of emotional intelligence, three components, the General Mood, Adaptability, and Interpersonal Communication to Self-Efficacy are of a significant positive relationship and are able to predict the Self-Efficacy of physical education teachers. It seems the application of this study ҆s results can help to education authorities to promote the level of teachers’ emotional intelligence and therefore the improvement of their Self-Efficacy and success in learners’ teaching and training.

Keywords: emotional intelligence, self-efficacy, physical education teachers, Ilam province

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Authors: Mariangela Viviani, Daniele Germano, Simone Colace, Agostino Forestiero, Giuseppe Papuzzo, Sara Laurita

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Nowadays, people spend most of their time in closed environments, in offices, or at home. Therefore, secure and highly livable environmental conditions are needed to reduce the probability of aerial viruses spreading. Also, to lower the human impact on the planet, it is important to reduce energy consumption. Heating, Ventilation, and Air Conditioning (HVAC) systems account for the major part of energy consumption in buildings [1]. Devising systems to control and regulate the airflow is, therefore, essential for energy efficiency. Moreover, an optimal setting for thermal comfort and air quality is essential for people’s well-being, at home or in offices, and increases productivity. Thanks to the features of Artificial Intelligence (AI) tools and techniques, it is possible to design innovative systems with: (i) Improved monitoring and prediction accuracy; (ii) Enhanced decision-making and mitigation strategies; (iii) Real-time air quality information; (iv) Increased efficiency in data analysis and processing; (v) Advanced early warning systems for air pollution events; (vi) Automated and cost-effective m onitoring network; and (vii) A better understanding of air quality patterns and trends. We propose AIR SAFE, an IoT-based infrastructure designed to optimize air quality and thermal comfort in indoor environments leveraging AI tools. AIR SAFE employs a network of smart sensors collecting indoor and outdoor data to be analyzed in order to take any corrective measures to ensure the occupants’ wellness. The data are analyzed through AI algorithms able to predict the future levels of temperature, relative humidity, and CO₂ concentration [2]. Based on these predictions, AIR SAFE takes actions, such as opening/closing the window or the air conditioner, to guarantee a high level of thermal comfort and air quality in the environment. In this contribution, we present the results from the AI algorithm we have implemented on the first s et o f d ata c ollected i n a real environment. The results were compared with other models from the literature to validate our approach.

Keywords: air quality, internet of things, artificial intelligence, smart home

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Authors: Hemad Zareiforoush, Saeed Minaei, Ahmad Banakar, Mohammad Reza Alizadeh

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The quality inspection of rice (Oryza sativa L.) during its various processing stages is very important. In this research, an artificial intelligence-based model coupled with computer vision techniques was developed as a decision support system for qualitative grading of rice grains. For conducting the experiments, first, 25 samples of rice grains with different levels of percentage of broken kernels (PBK) and degree of milling (DOM) were prepared and their qualitative grade was assessed by experienced experts. Then, the quality parameters of the same samples examined by experts were determined using a machine vision system. A grading model was developed based on fuzzy logic theory in MATLAB software for making a relationship between the qualitative characteristics of the product and its quality. Totally, 25 rules were used for qualitative grading based on AND operator and Mamdani inference system. The fuzzy inference system was consisted of two input linguistic variables namely, DOM and PBK, which were obtained by the machine vision system, and one output variable (quality of the product). The model output was finally defuzzified using Center of Maximum (COM) method. In order to evaluate the developed model, the output of the fuzzy system was compared with experts’ assessments. It was revealed that the developed model can estimate the qualitative grade of the product with an accuracy of 95.74%.

Keywords: machine vision, fuzzy logic, rice, quality

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Authors: Joshna Rani S., Ahmadi Banu

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Artificial Intelligence (AI) has a significant task to carry out in the medical care contributions of things to come. As AI, it is the essential capacity behind the advancement of accuracy medication, generally consented to be a painfully required development in care. Albeit early endeavors at giving analysis and treatment proposals have demonstrated testing, we anticipate that AI will at last dominate that area too. Given the quick propels in AI for imaging examination, it appears to be likely that most radiology, what's more, pathology pictures will be inspected eventually by a machine. Discourse and text acknowledgment are now utilized for assignments like patient correspondence and catch of clinical notes, and their utilization will increment. The best test to AI in these medical services areas isn't regardless of whether the innovations will be sufficiently skilled to be valuable, but instead guaranteeing their appropriation in day by day clinical practice. For far reaching selection to happen, AI frameworks should be affirmed by controllers, coordinated with EHR frameworks, normalized to an adequate degree that comparative items work likewise, instructed to clinicians, paid for by open or private payer associations, and refreshed over the long haul in the field. These difficulties will, at last, be survived, yet they will take any longer to do as such than it will take for the actual innovations to develop. Therefore, we hope to see restricted utilization of AI in clinical practice inside 5 years and more broad use inside 10 years. It likewise appears to be progressively evident that AI frameworks won't supplant human clinicians for a huge scope, yet rather will increase their endeavors to really focus on patients. Over the long haul, human clinicians may advance toward errands and work plans that draw on remarkably human abilities like sympathy, influence, and higher perspective mix. Maybe the lone medical services suppliers who will chance their professions over the long run might be the individuals who will not work close by AI

Keywords: artificial intellogence, health care, breast cancer, AI applications

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Authors: Mohita Singh, Sunil Sachdev, Amrita Singh

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Background and Aim: Physical aerobic exercises promote positive changes in one’s mental health, intelligence, and ability to cope with stressful encounters. The present study was designed to explore the correlation between intelligence and stress parameters and to assess the correlation between the same parameters after the practice of one month of moderate-intensity physical exercise. Method: The study was conducted on thirty-five healthy male volunteer students to assess the correlation between stress parameters in subjects with varying level of general intelligence (GI) and emotional intelligence (EI). Correlation studies were again conducted after one month between the same parameters to evaluate the effect of moderate-intensity physical exercise (MIPE). Baseline values were recorded using standard scales. Result: IQ and EQ correlated negatively with both acute and chronic stress parameters and positively with each other. A positive correlation was found between acute and chronic stress. With the practice of one month of moderate-intensity physical exercise, there was significant increment between the parameters under study and hence improved results. Conclusion: MIPE improved correlation between GI, EI, stress parameters, and thus reduced stress and improved intelligence.

Keywords: emotional intelligence, general intelligence, moderate intensity physical exercise, stress response

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Authors: Gurmail Singh

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Effective supply chain is the main priority of every organization which is the outcome of strategic corporate investments with deliberate management action. Value-driven supply chain is defined through development, procurement and by configuring the appropriate resources, metrics and processes. However, responsiveness of the supply chain can be improved by proper coordination. So the Bullwhip effect (BWE) and Net stock amplification (NSAmp) values were anticipated and used for the control of inventory in organizations by both discrete wavelet transform-Artificial neural network (DWT-ANN) and Adaptive Network-based fuzzy inference system (ANFIS). This work presents a comparative methodology of forecasting for the customers demand which is non linear in nature for a multilevel supply chain structure using hybrid techniques such as Artificial intelligence techniques including Artificial neural networks (ANN) and Adaptive Network-based fuzzy inference system (ANFIS) and Discrete wavelet theory (DWT). The productiveness of these forecasting models are shown by computing the data from real world problems for Bullwhip effect and Net stock amplification. The results showed that these parameters were comparatively less in case of discrete wavelet transform-Artificial neural network (DWT-ANN) model and using Adaptive network-based fuzzy inference system (ANFIS).

Keywords: bullwhip effect, hybrid techniques, net stock amplification, supply chain flexibility

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Authors: Tran Duong Nguyen, Marwan Shagar, Qinghao Zeng, Aras Maqsoodi, Pardis Pishdad, Eunhwa Yang

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Construction supply chain management (CSCM) involves the collaboration of many disciplines and actors, which generates vast amounts of data. However, inefficient, fragmented, and non-standardized data storage often hinders this data exchange. The industry has adopted building information modeling (BIM) -a digital representation of a facility's physical and functional characteristics to improve collaboration, enhance transmission security, and provide a common data exchange platform. Still, the volume and complexity of data require tailored information categorization, aligning with stakeholders' preferences and demands. To address this, artificial intelligence (AI) can be integrated to handle this data’s magnitude and complexities. This research aims to develop an integrated and efficient approach for data exchange in CSCM by utilizing AI. The paper covers five main objectives: (1) Investigate existing framework and BIM adoption; (2) Identify challenges in data exchange; (3) Propose an integrated framework; (4) Enhance data transmission security; and (5) Develop data exchange in CSCM. The proposed framework demonstrates how integrating BIM and other technologies, such as cloud computing, blockchain, and AI applications, can significantly improve the efficiency and accuracy of data exchange in CSCM.

Keywords: construction supply chain management, BIM, data exchange, artificial intelligence

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Authors: Sheelagh Heugh, Michael Upton, Kriya Kalidas, Stephen Breen

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This article aims to create awareness of the opportunities and issues the artificial intelligence (AI) tool GPT-3 (Generative Pre-trained Transformer-3) brings to higher education. Technological disruptors have featured in higher education (HE) since Konrad Klaus developed the first functional programmable automatic digital computer. The flurry of technological advances, such as personal computers, smartphones, the world wide web, search engines, and artificial intelligence (AI), have regularly caused disruption and discourse across the educational landscape around harnessing the change for the good. Accepting AI influences are inevitable; we took mixed methods through participatory action research and evaluation approach. Joining HE communities, reviewing the literature, and conducting our own research around Chat GPT-3, we reviewed our institutional approach to changing our current practices and developing policy linked to assessments and the use of Chat GPT-3. We review the impact of GPT-3, a high-powered natural language processing (NLP) system first seen in 2020 on HE. Historically HE has flexed and adapted with each technological advancement, and the latest debates for educationalists are focusing on the issues around this version of AI which creates natural human language text from prompts and other forms that can generate code and images. This paper explores how Chat GPT-3 affects the current educational landscape: we debate current views around plagiarism, research misconduct, and the credibility of assessment and determine the tool's value in developing skills for the workplace and enhancing critical analysis skills. These questions led us to review our institutional policy and explore the effects on our current assessments and the development of new assessments. Conclusions: After exploring the pros and cons of Chat GTP-3, it is evident that this form of AI cannot be un-invented. Technology needs to be harnessed for positive outcomes in higher education. We have observed that materials developed through AI and potential effects on our development of future assessments and teaching methods. Materials developed through Chat GPT-3 can still aid student learning but lead to redeveloping our institutional policy around plagiarism and academic integrity.

Keywords: artificial intelligence, Chat GPT-3, intellectual property, plagiarism, research misconduct

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Authors: Mahnaz Khalafehnilsaz, Rozina Rahnama

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Introduction: The elderly population is growing rapidly, and with this growth comes an increased demand for healthcare services. Artificial intelligence (AI) has the potential to revolutionize the delivery of healthcare services to the elderly population. In this study, the various ways in which AI is used to create health content for elderly people and its transformative impact on the healthcare industry will be explored. Method: A systematic review of the literature was conducted to identify studies that have investigated the role of AI in creating health content specifically for elderly people. Several databases, including PubMed, Scopus, and Web of Science, were searched for relevant articles published between 2000 and 2022. The search strategy employed a combination of keywords related to AI, personalized health content, and the elderly. Studies that utilized AI to create health content for elderly individuals were included, while those that did not meet the inclusion criteria were excluded. A total of 20 articles that met the inclusion criteria were identified. Finding: The findings of this review highlight the diverse applications of AI in creating health content for elderly people. One significant application is the use of natural language processing (NLP), which involves the creation of chatbots and virtual assistants capable of providing personalized health information and advice to elderly patients. AI is also utilized in the field of medical imaging, where algorithms analyze medical images such as X-rays, CT scans, and MRIs to detect diseases and abnormalities. Additionally, AI enables the development of personalized health content for elderly patients by analyzing large amounts of patient data to identify patterns and trends that can inform healthcare providers in developing tailored treatment plans. Conclusion: AI is transforming the healthcare industry by providing a wide range of applications that can improve patient outcomes and reduce healthcare costs. From creating chatbots and virtual assistants to analyzing medical images and developing personalized treatment plans, AI is revolutionizing the way healthcare is delivered to elderly patients. Continued investment in this field is essential to ensure that elderly patients receive the best possible care.

Keywords: artificial intelligence, health content, older adult, healthcare

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Authors: Sylvie Michel, Sylvie Gerbaix, Marc Bidan

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Artificial intelligence (AI) can be held accountable for its detrimental impacts. This question gains heightened relevance given AI's pervasive reach across various domains, magnifying its power and potential. The expanding influence of AI raises fundamental ethical inquiries, primarily centering on biases, responsibility, and transparency. This encompasses discriminatory biases arising from algorithmic criteria or data, accidents attributed to autonomous vehicles or other systems, and the imperative of transparent decision-making. This article aims to stimulate reflection on AI accountability, denoting the necessity to elucidate the effects it generates. Accountability comprises two integral aspects: adherence to legal and ethical standards and the imperative to elucidate the underlying operational rationale. The objective is to initiate a reflection on the obstacles to this "accountability," facing the challenges of the complexity of artificial intelligence's system and its effects. Then, this article proposes to mobilize Edgar Morin's complex thought to encompass and face the challenges of this complexity. The first contribution is to point out the challenges posed by the complexity of A.I., with fractional accountability between a myriad of human and non-human actors, such as software and equipment, which ultimately contribute to the decisions taken and are multiplied in the case of AI. Accountability faces three challenges resulting from the complexity of the ethical issues combined with the complexity of AI. The challenge of the non-neutrality of algorithmic systems as fully ethically non-neutral actors is put forward by a revealing ethics approach that calls for assigning responsibilities to these systems. The challenge of the dilution of responsibility is induced by the multiplicity and distancing between the actors. Thus, a dilution of responsibility is induced by a split in decision-making between developers, who feel they fulfill their duty by strictly respecting the requests they receive, and management, which does not consider itself responsible for technology-related flaws. Accountability is confronted with the challenge of transparency of complex and scalable algorithmic systems, non-human actors self-learning via big data. A second contribution involves leveraging E. Morin's principles, providing a framework to grasp the multifaceted ethical dilemmas and subsequently paving the way for establishing accountability in AI. When addressing the ethical challenge of biases, the "hologrammatic" principle underscores the imperative of acknowledging the non-ethical neutrality of algorithmic systems inherently imbued with the values and biases of their creators and society. The "dialogic" principle advocates for the responsible consideration of ethical dilemmas, encouraging the integration of complementary and contradictory elements in solutions from the very inception of the design phase. Aligning with the principle of organizing recursiveness, akin to the "transparency" of the system, it promotes a systemic analysis to account for the induced effects and guides the incorporation of modifications into the system to rectify deviations and reintroduce modifications into the system to rectify its drifts. In conclusion, this contribution serves as an inception for contemplating the accountability of "artificial intelligence" systems despite the evident ethical implications and potential deviations. Edgar Morin's principles, providing a lens to contemplate this complexity, offer valuable perspectives to address these challenges concerning accountability.

Keywords: accountability, artificial intelligence, complexity, ethics, explainability, transparency, Edgar Morin

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Authors: Hamid Rostami Jaz, Kamran Ameri Siahooei

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Up to now different methods have been used to forecast the index returns and the index rate. Artificial intelligence and artificial neural networks have been one of the methods of index returns forecasting. This study attempts to carry out a comparative study on the performance of different Radial Base Neural Network and Feed-Forward Perceptron Neural Network to forecast investment returns on the index. To achieve this goal, the return on investment in Tehran Stock Exchange index is evaluated and the performance of Radial Base Neural Network and Feed-Forward Perceptron Neural Network are compared. Neural networks performance test is applied based on the least square error in two approaches of in-sample and out-of-sample. The research results show the superiority of the radial base neural network in the in-sample approach and the superiority of perceptron neural network in the out-of-sample approach.

Keywords: exchange index, forecasting, perceptron neural network, Tehran stock exchange

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Authors: Gina L. Solano

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This project endeavors to enhance learning experiences for undergraduate pre-service teachers and graduate K-12 educators by leveraging artificial intelligence (AI). Firstly, the initiative delves into integrating AI within undergraduate education courses, fostering traditional literacy skills essential for academic success and extending their applicability beyond the classroom. Education students will explore AI tools to design literacy-focused activities aligned with their curriculum. Secondly, the project investigates the utilization of AI to craft instructional materials employing gamification strategies (e.g., digital and classic games, badges, quests) to amplify student engagement and motivation in mastering course content. Lastly, it aims to create a professional repertoire that can be applied by pre-service and current teachers in P-12 classrooms, promoting seamless integration for those already in teaching positions. The project's impact extends to benefiting college students, including pre-service and graduate teachers, as they enhance literacy and digital skills through AI. It also benefits current P-12 educators who can integrate AI into their classrooms, fostering innovative teaching practices. Moreover, the project contributes to faculty development, allowing them to cultivate low-risk and engaging classroom environments, ultimately enriching the learning journey. The insights gained from this project can be shared within and beyond the discipline to advance the broader field of study.

Keywords: artificial intelligence, gamification, learning experiences, literacy skills, engagement

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Authors: Said S. Aldhafri, Marwa N. Alrajhi

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This study examines gender differences in emotional intelligence levels in the Sultanate of Oman, an Arabic country in the Middle East. As a collective culture, the Omani culture rears children differently based on the Islamic beliefs and the Arabic culture. Gender differences across the different dimensions of emotional intelligence are possible within this collective culture. Emotional intelligences reflect the ability to understand and act upon one’s and others’ emotions. The sample of the study consisted of 338 (50.6% were females) adults from different regions in Oman. The participants completed a 25-item emotional intelligence scale, using 5-point Likert type responses. The results showed that the total scores of the scale as well as the scores from the five dimensions (self management, self motivation, social skills, empathy, and self-awareness) all have good reliability coefficients. Using independent sample t-tests, the findings show that female adults scored higher than male adults. The differences were all statistically significant across the five dimensions of emotional intelligence. The findings are discussed from a cultural perspective and applications for the development of emotional intelligence skills are outlined.

Keywords: emotional intelligence, gender, Arab, Oman

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Authors: Yogesh C. Sharma, A. Seetharaman

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The decision-making process in humans is a complicated system influenced by a variety of intrinsic and extrinsic factors. Human decisions have a ripple effect on subsequent decisions. In this study, the scope of human decision making is limited to employees. In an organisation, a person makes a variety of decisions from the time they are hired to the time they retire. The goal of this research is to identify various elements that influence decision-making. In addition, the environment in which a decision is made is a significant aspect of the decision-making process. Employees in today's workplace use artificial intelligence (AI) systems for automation and decision augmentation. The impact of AI systems on the decision-making process is examined in this study. This research is designed based on a systematic literature review. Based on gaps in the literature, limitations and the scope of future research have been identified. Based on these findings, a research framework has been designed to identify various factors affecting employee decision making. Employee decision making is influenced by technological advancement, data-driven culture, human trust, decision automation-augmentation, and workplace motivation. Hybrid human-AI systems require the development of new skill sets and organisational design. Employee psychological safety and supportive leadership influences overall job satisfaction.

Keywords: employee decision making, artificial intelligence (AI) environment, human trust, technology innovation, psychological safety

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Authors: Alexander Scott English, Yilin Ma, Xiaoying Liu

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The widespread use of artificial intelligence in everyday life has triggered a fervent discussion covering a wide range of areas. However, to date, research on the influence of gender in various segments and factors from a social science perspective is still limited. This study aims to explore whether there are gender differences in human trust in AI for its application in basic everyday life and correlates with human perceived similarity, perceived emotions (including competence and warmth), and attractiveness. We conducted a study involving 321 participants using a two-subject experimental design with a two-factor (masculinized vs. feminized voice of the AI) multiplied by a two-factor (pitch level of the AI's voice) between-subject experimental design. Four contexts were created for the study and randomly assigned. The results of the study showed significant gender differences in perceived similarity, trust, and perceived emotion of the AIs, with females rating them significantly higher than males. Trust was higher in relation to AIs presenting the same gender (e.g., human female to female AI, human male to male AI). Mediation modeling tests indicated that emotion perception and similarity played a sufficiently mediating role in trust. Notably, although trust in AIs was strongly correlated with human gender, there was no significant effect on the gender of the AI. In addition, the study discusses the effects of subjects' age, job search experience, and job type on the findings.

Keywords: artificial intelligence, gender differences, human-robot trust, mediation modeling

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Authors: Vasileios Anastopoulos

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Cyber threat intelligence assists organizations in understanding the threats they face and helps them make educated decisions on preparing their defenses. Sharing of threat intelligence and threat information is increasingly leveraged by organizations and enterprises, and various software solutions are already available, with the open-source malware information sharing platform (MISP) being a popular one. In this work, a methodology for the production of cyber threat intelligence using the threat information stored in MISP is proposed. The methodology leverages the discipline of social network analysis and the diamond model, a model used for intrusion analysis, to produce cyber threat intelligence. The workings are demonstrated with a case study on a production MISP instance of a real organization. The paper concluded with a discussion on the proposed methodology and possible directions for further research.

Keywords: cyber threat intelligence, diamond model, malware information sharing platform, social network analysis

Procedia PDF Downloads 178