Search results for: standardization artificial intelligence

Authors: Hiba El Assibi

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This study aims to weakly solve Kalah, a two-player board game, by developing a start-to-finish winning strategy using an optimized Minimax algorithm with Alpha-Beta Pruning. In weakly solving Kalah, our focus is on creating an optimal strategy from the game's beginning rather than analyzing every possible position. The project will explore additional enhancements like symmetry checking and code optimizations to speed up the decision-making process. This approach is expected to give insights into efficient strategy formulation in board games and potentially help create games with a fair distribution of outcomes. Furthermore, this research provides a unique perspective on human versus Artificial Intelligence decision-making in strategic games. By comparing the AI-generated optimal moves with human choices, we can explore how seemingly advantageous moves can, in the long run, be harmful, thereby offering a deeper understanding of strategic thinking and foresight in games. Moreover, this paper discusses the evaluation of our strategy against existing methods, providing insights on performance and computational efficiency. We also discuss the scalability of our approach to the game, considering different board sizes (number of pits and stones) and rules (different variations) and studying how that affects performance and complexity. The findings have potential implications for the development of AI applications in strategic game planning, enhancing our understanding of human cognitive processes in game settings, and offer insights into creating balanced and engaging game experiences.

Keywords: minimax, alpha beta pruning, transposition tables, weakly solving, game theory

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Authors: Pooria Norouzi, Nicholas Tsang, Adam van der Goes, Joseph Yu, Douglas Zheng, Sirine Maleej

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In this study, virtual meters will be designed and used for energy balance measurements of an air handling unit (AHU). The method aims to replace traditional physical sensors in heating, ventilation, and air conditioning (HVAC) systems with simulated virtual meters. Due to the inability to manage and monitor these systems, many HVAC systems have a high level of inefficiency and energy wastage. Virtual meters are implemented and applied in an actual HVAC system, and the result confirms the practicality of mathematical sensors for alternative energy measurement. While most residential buildings and offices are commonly not equipped with advanced sensors, adding, exploiting, and monitoring sensors and measurement devices in the existing systems can cost thousands of dollars. The first purpose of this study is to provide an energy consumption rate based on available sensors and without any physical energy meters. It proves the performance of virtual meters in HVAC systems as reliable measurement devices. To demonstrate this concept, mathematical models are created for AHU-07, located in building NE01 of the British Columbia Institute of Technology (BCIT) Burnaby campus. The models will be created and integrated with the system’s historical data and physical spot measurements. The actual measurements will be investigated to prove the models' accuracy. Based on preliminary analysis, the resulting mathematical models are successful in plotting energy consumption patterns, and it is concluded confidently that the results of the virtual meter will be close to the results that physical meters could achieve. In the second part of this study, the use of virtual meters is further assisted by artificial intelligence (AI) in the HVAC systems of building to improve energy management and efficiency. By the data mining approach, virtual meters’ data is recorded as historical data, and HVAC system energy consumption prediction is also implemented in order to harness great energy savings and manage the demand and supply chain effectively. Energy prediction can lead to energy-saving strategies and considerations that can open a window in predictive control in order to reach lower energy consumption. To solve these challenges, the energy prediction could optimize the HVAC system and automates energy consumption to capture savings. This study also investigates AI solutions possibility for autonomous HVAC efficiency that will allow quick and efficient response to energy consumption and cost spikes in the energy market.

Keywords: virtual meters, HVAC, artificial intelligence, energy consumption prediction

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Authors: Yasser F. Hassan

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Social networking involves use of an online platform or website that enables people to communicate, usually for a social purpose, through a variety of services, most of which are web-based and offer opportunities for people to interact over the internet, e.g. via e-mail and ‘instant messaging’, by analyzing the voting behavior and ratings of judges in a popular comments in social networks. While most of the party literature omits the electorate, this paper presents a model where elites and parties are emergent consequences of the behavior and preferences of voters. The research in artificial intelligence and psychology has provided powerful illustrations of the way in which the emergence of intelligent behavior depends on the development of representational structure. As opposed to the classical voting system (one person – one decision – one vote) a new voting system is designed where agents with opposed preferences are endowed with a given number of votes to freely distribute them among some issues. The paper uses ideas from machine learning, artificial intelligence and soft computing to provide a model of the development of voting system response in a simulated agent. The modeled development process involves (simulated) processes of evolution, learning and representation development. The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of structure. We employ agent-based computer simulation to demonstrate the formation and interaction of coalitions that arise from individual voter preferences. We are interested in coordinating the local behavior of individual agents to provide an appropriate system-level behavior.

Keywords: voting system, rough sets, multi-agent, social networks, emergence, power indices

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Authors: Pranav Ajmera, Amit Kharat, Tanveer Gupte, Richa Pant, Viraj Kulkarni, Vinay Duddalwar, Purnachandra Lamghare

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The cardiothoracic ratio (CTR) is the ratio of the diameter of the heart to the diameter of the thorax. An abnormal CTR, that is, a value greater than 0.55, is often an indicator of an underlying pathological condition. The accurate prediction of an abnormal CTR from chest X-rays (CXRs) aids in the early diagnosis of clinical conditions. We propose a deep learning-based model for automatic CTR calculation that can assist the radiologist with the diagnosis of cardiomegaly and optimize the radiology flow. The study population included 1012 posteroanterior (PA) CXRs from a single institution. The Attention U-Net deep learning (DL) architecture was used for the automatic calculation of CTR. A CTR of 0.55 was used as a cut-off to categorize the condition as cardiomegaly present or absent. An observer performance test was conducted to assess the radiologist's performance in diagnosing cardiomegaly with and without artificial intelligence (AI) assistance. The Attention U-Net model was highly specific in calculating the CTR. The model exhibited a sensitivity of 0.80 [95% CI: 0.75, 0.85], precision of 0.99 [95% CI: 0.98, 1], and a F1 score of 0.88 [95% CI: 0.85, 0.91]. During the analysis, we observed that 51 out of 1012 samples were misclassified by the model when compared to annotations made by the expert radiologist. We further observed that the sensitivity of the reviewing radiologist in identifying cardiomegaly increased from 40.50% to 88.4% when aided by the AI-generated CTR. Our segmentation-based AI model demonstrated high specificity and sensitivity for CTR calculation. The performance of the radiologist on the observer performance test improved significantly with AI assistance. A DL-based segmentation model for rapid quantification of CTR can therefore have significant potential to be used in clinical workflows.

Keywords: cardiomegaly, deep learning, chest radiograph, artificial intelligence, cardiothoracic ratio

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Authors: Hsiao-Chuan Huang, King-Lung Kuo, Mei-Hsin Lo, Hsiao-Yun Chou, Yusen Lin

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The most robust and economical method for laboratory diagnosis of TB is to identify mycobacterial bacilli (AFB) under acid-fast staining despite its disadvantages of low sensitivity and labor-intensive. Though digital pathology becomes popular in medicine, an automated microscopic system for microbiology is still not available. A new AI-assisted automated microscopic system, consisting of a microscopic scanner and recognition program powered by big data and deep learning, may significantly increase the sensitivity of TB smear microscopy. Thus, the objective is to evaluate such an automatic system for the identification of AFB. A total of 5,930 smears was enrolled for this study. An intelligent microscope system (TB-Scan, Wellgen Medical, Taiwan) was used for microscopic image scanning and AFB detection. 272 AFB smears were used for transfer learning to increase the accuracy. Referee medical technicians were used as Gold Standard for result discrepancy. Results showed that, under a total of 1726 AFB smears, the automated system's accuracy, sensitivity and specificity were 95.6% (1,650/1,726), 87.7% (57/65), and 95.9% (1,593/1,661), respectively. Compared to culture, the sensitivity for human technicians was only 33.8% (38/142); however, the automated system can achieve 74.6% (106/142), which is significantly higher than human technicians, and this is the first of such an automated microscope system for TB smear testing in a controlled trial. This automated system could achieve higher TB smear sensitivity and laboratory efficiency and may complement molecular methods (eg. GeneXpert) to reduce the total cost for TB control. Furthermore, such an automated system is capable of remote access by the internet and can be deployed in the area with limited medical resources.

Keywords: TB smears, automated microscope, artificial intelligence, medical imaging

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Authors: Anette Alén

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Intellectual property (IP) regimes have traditionally been designed to integrate various conflicting elements stemming from private entitlement and the public good. In IP laws and regulations, this design takes the form of specific uses of protected subject-matter without the right-holder’s consent, or exhaustion of exclusive rights upon market release, and the like. More recently, the pursuit of ‘balance’ has gained ground in the conceptualization of these conflicting elements both in terms of IP law and related policy. This can be seen, for example, in European Union (EU) copyright regime, where ‘balance’ has become a key element in argumentation, backed up by fundamental rights reasoning. This development also entails an ever-expanding dialogue between the IP regime and the constitutional safeguards for property, free speech, and privacy, among others. This study analyses the concept of ‘balance’ in EU copyright law: the research task is to examine the contents of the concept of ‘balance’ and the way it is operationalized and pursued, thereby producing new knowledge on the role and manifestations of ‘balance’ in recent copyright case law and regulatory instruments in the EU. The study discusses two particular pieces of legislation, the EU Digital Single Market (DSM) Copyright Directive (EU) 2019/790 and the finalized EU Artificial Intelligence (AI) Act, including some of the key preparatory materials, as well as EU Court of Justice (CJEU) case law pertaining to copyright in the digital era. The material is examined by means of document analysis, mapping the ways ‘balance’ is approached and conceptualized in the documents. Similarly, the interaction of fundamental rights as part of the balancing act is also analyzed. Doctrinal study of law is also employed in the analysis of legal sources. This study suggests that the pursuit of balance is, for its part, conducive to new battles, largely due to the advancement of digitalization and more recent developments in artificial intelligence. Indeed, the ‘balancing act’ rather presents itself as a way to bypass or even solidify some of the conflicting interests in a complex global digital economy. Indeed, such a conceptualization, especially when accompanied by non-critical or strategically driven fundamental rights argumentation, runs counter to the genuine acknowledgment of new types of conflicting interests in the copyright regime. Therefore, a more radical approach, including critical analysis of the normative basis and fundamental rights implications of the concept of ‘balance’, is required to readjust copyright law and regulations for the digital era. Notwithstanding the focus on executing the study in the context of the EU copyright regime, the results bear wider significance for the digital economy, especially due to the platform liability regime in the DSM Directive and with the AI Act including objectives of a ‘level playing field’ whereby compliance with EU copyright rules seems to be expected among system providers.

Keywords: balance, copyright, fundamental rights, platform liability, artificial intelligence

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Authors: Leonard Tiong, Sonit Singh, Kevin Ho Shon, Sarah Lewis

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Investigation into the use of artificial intelligence in radiology continues to develop at a rapid rate. During the coronavirus pandemic, the combination of an exponential increase in chest x-rays and unpredictable staff shortages resulted in a huge strain on the department's workload. There is a World Health Organisation estimate that two-thirds of the global population does not have access to diagnostic radiology. Therefore, there could be demand for a program that could detect acute changes in imaging compatible with infection to assist with screening. We generated a conventional neural network and tested its efficacy in recognizing changes compatible with coronavirus infection. Following ethics approval, a deidentified set of 77 normal and 77 abnormal chest x-rays in patients with confirmed coronavirus infection were used to generate an algorithm that could train, validate and then test itself. DICOM and PNG image formats were selected due to their lossless file format. The model was trained with 100 images (50 positive, 50 negative), validated against 28 samples (14 positive, 14 negative), and tested against 26 samples (13 positive, 13 negative). The initial training of the model involved training a conventional neural network in what constituted a normal study and changes on the x-rays compatible with coronavirus infection. The weightings were then modified, and the model was executed again. The training samples were in batch sizes of 8 and underwent 25 epochs of training. The results trended towards an 85.71% true positive/true negative detection rate and an area under the curve trending towards 0.95, indicating approximately 95% accuracy in detecting changes on chest X-rays compatible with coronavirus infection. Study limitations include access to only a small dataset and no specificity in the diagnosis. Following a discussion with our programmer, there are areas where modifications in the weighting of the algorithm can be made in order to improve the detection rates. Given the high detection rate of the program, and the potential ease of implementation, this would be effective in assisting staff that is not trained in radiology in detecting otherwise subtle changes that might not be appreciated on imaging. Limitations include the lack of a differential diagnosis and application of the appropriate clinical history, although this may be less of a problem in day-to-day clinical practice. It is nonetheless our belief that implementing this program and widening its scope to detecting multiple pathologies such as lung masses will greatly assist both the radiology department and our colleagues in increasing workflow and detection rate.

Keywords: artificial intelligence, COVID, neural network, machine learning

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Authors: Moyosola Jude Akomolafe

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Career commitment among employees is crucial to the success of any organization. However, career commitment has been reported to be very low among teachers in the public secondary schools in Nigeria. This study, therefore, examined the contributions of emotional intelligence and general self-efficacy to career commitment of among secondary school teachers in Nigeria. Descriptive research design of correlational type was adopted for the study. It made use of stratified random sampling technique was used in selecting two hundred and fifty (250) secondary schools teachers for the study. Three standardized instruments namely: The Big Five Inventory (BFI), Emotional Intelligence Scale (EIS), General Self-Efficacy Scale (GSES) and Career Commitment Scale (CCS) were adopted for the study. Three hypotheses were tested at 0.05 level of significance. Data collected were analyzed through Multiple Regression Analysis to investigate the predicting capacity of emotional intelligence and general self-efficacy on career commitment of secondary school teachers. The results showed that the variables when taken as a whole significantly predicted career commitment among secondary school teachers. The relative contribution of each variable revealed that emotional intelligence and general self-efficacy significantly predicted career commitment among secondary school teachers in Nigeria. The researcher recommended that secondary school teachers should be exposed to emotional intelligence and self-efficacy training to enhance their career commitment.

Keywords: career commitment, emotional intelligence, general self-efficacy, secondary school teachers

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Authors: Weeam Abdulrahman, Gawaher Al-Madwary, Fatima Al-Ammari, Razan Mohammad

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This research discusses the AI features in Replika which is a dialog with a customized characters application, interaction and communication with AI in different ways that is provided for the user. spreading a survey with questions on how the AI worked is one approach of exposing the app to others to utilize and also we made an analysis that provides us with the conclusion of our research as a result, individuals will be able to try out the app. In the methodology we explain each page that pops up in the screen while using replika and Specify each part and icon.

Keywords: Replika, AI, artificial intelligence, dialog app

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Authors: Nuno Biga

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Artificial Intelligence (AI) has the potential to transform management into several impactful ways. It allows machines to interpret information to find patterns in big data and learn from context analysis, optimize operations, make predictions sensitive to each specific situation and support data-driven decision making. The introduction of an 'artificial brain' in organization also enables learning through complex information and data provided by those who train it, namely its users. The "Assisted-BIGAMES" version of the Accident & Emergency (A&E) simulator introduces the concept of a "Virtual Assistant" (VA) sensitive to context, that provides users useful suggestions to pursue the following operations such as: a) to relocate workstations in order to shorten travelled distances and minimize the stress of those involved; b) to identify in real time existing bottleneck(s) in the operations system so that it is possible to quickly act upon them; c) to identify resources that should be polyvalent so that the system can be more efficient; d) to identify in which specific processes it may be advantageous to establish partnership with other teams; and e) to assess possible solutions based on the suggested KPIs allowing action monitoring to guide the (re)definition of future strategies. This paper is built on the BIGAMES© simulator and presents the conceptual AI model developed and demonstrated through a pilot project (BIG-AI). Each Virtual Assisted BIGAME is a management simulator developed by the author that guides operational and strategic decision making, providing users with useful information in the form of management recommendations that make it possible to predict the actual outcome of different alternative management strategic actions. The pilot project developed incorporates results from 12 editions of the BIGAME A&E that took place between 2017 and 2022 at AESE Business School, based on the compilation of data that allows establishing causal relationships between decisions taken and results obtained. The systemic analysis and interpretation of data is powered in the Assisted-BIGAMES through a computer application called "BIGAMES Virtual Assistant" (VA) that players can use during the Game. Each participant in the VA permanently asks himself about the decisions he should make during the game to win the competition. To this end, the role of the VA of each team consists in guiding the players to be more effective in their decision making, through presenting recommendations based on AI methods. It is important to note that the VA's suggestions for action can be accepted or rejected by the managers of each team, as they gain a better understanding of the issues along time, reflect on good practice and rely on their own experience, capability and knowledge to support their own decisions. Preliminary results show that the introduction of the VA provides a faster learning of the decision-making process. The facilitator designated as “Serious Game Controller” (SGC) is responsible for supporting the players with further analysis. The recommended actions by the SGC may differ or be similar to the ones previously provided by the VA, ensuring a higher degree of robustness in decision-making. Additionally, all the information should be jointly analyzed and assessed by each player, who are expected to add “Emotional Intelligence”, an essential component absent from the machine learning process.

Keywords: artificial intelligence, gamification, key performance indicators, machine learning, management simulators, serious games, virtual assistant

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Authors: Barna Arnold Keserű

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In the last few years (what is called by many scholars the big data era) artificial intelligence (hereinafter AI) get more and more attention from the public and from the different branches of sciences as well. What previously was a mere science-fiction, now starts to become reality. AI and robotics often walk hand in hand, what changes not only the business and industrial life, but also has a serious impact on the legal system. The main research of the author focuses on these impacts in the field of private law, with special regards to liability and intellectual property issues. Many questions arise in these areas connecting to AI and robotics, where the boundaries are not sufficiently clear, and different needs are articulated by the different stakeholders. Recognizing the urgent need of thinking the Committee on Legal Affairs of the European Parliament adopted a Motion for a European Parliament Resolution A8-0005/2017 (of January 27th, 2017) in order to take some recommendations to the Commission on civil law rules on robotics and AI. This document defines some crucial usage of AI and/or robotics, e.g. the field of autonomous vehicles, the human job replacement in the industry or smart applications and machines. It aims to give recommendations to the safe and beneficial use of AI and robotics. However – as the document says – there are no legal provisions that specifically apply to robotics or AI in IP law, but that existing legal regimes and doctrines can be readily applied to robotics, although some aspects appear to call for specific consideration, calls on the Commission to support a horizontal and technologically neutral approach to intellectual property applicable to the various sectors in which robotics could be employed. AI can generate some content what worth copyright protection, but the question came up: who is the author, and the owner of copyright? The AI itself can’t be deemed author because it would mean that it is legally equal with the human persons. But there is the programmer who created the basic code of the AI, or the undertaking who sells the AI as a product, or the user who gives the inputs to the AI in order to create something new. Or AI generated contents are so far from humans, that there isn’t any human author, so these contents belong to public domain. The same questions could be asked connecting to patents. The research aims to answer these questions within the current legal framework and tries to enlighten future possibilities to adapt these frames to the socio-economical needs. In this part, the proper license agreements in the multilevel-chain from the programmer to the end-user become very important, because AI is an intellectual property in itself what creates further intellectual property. This could collide with data-protection and property rules as well. The problems are similar in the field of liability. We can use different existing forms of liability in the case when AI or AI led robotics cause damages, but it is unsure that the result complies with economical and developmental interests.

Keywords: artificial intelligence, intellectual property, liability, robotics

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Authors: Adithya Nadig

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In the field of production, characterization of surface roughness plays a vital role in assessing the quality of a manufactured product. The defined parameters for this assessment, each, have their own drawbacks in describing a profile surface. From the purview of small-scale and medium-scale industries, an increase in time spent for manual inspection of a product for various parameters adds to the cost of the product. In order to reduce this, a uniform and established standard is necessary for quantifying a profile of a manufactured product. The inspection procedure in the small and medium-scale manufacturing units at Jigani Industrial area, Bangalore, was observed. The parameters currently in use in those industries are described in the paper and a change in the inspection method is proposed.

Keywords: efficiency of quality assessment, manual areal profiling technique, manufacturing in small and medium-scale industries product-oriented inspection, standardization of manual inspection, surface roughness characterization

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Authors: Leila Najeh Bel'Kiry

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Artificial intelligence (AI) brought about many language robots, with ChatGPT being the most sophisticated thanks to its human-like linguistic capabilities. This aspect raises the idea of using ChatGPT in learning foreign languages. Starting from the premise that positions ChatGPT as a mediator between the language and the leaner, functioning as a “ghost teacher" offering a peaceful and secure learning space, this study aims to explore the attitudes of Tunisian students of English towards ChatGPT as a “Foreign Language Teacher” . Forty-five students, in their third year of fundamental English at Tunisian universities and high institutes, completed a Likert scale questionnaire consisting of thirty-two items and covering various aspects of language (phonology, morphology, syntax, semantics, and pragmatics). A scale ranging from 'Strongly Disagree,' 'Disagree,' 'Undecided,' 'Agree,' to 'Strongly Agree.' is used to assess the attitudes of the participants towards the integration of ChaGPTin learning a foreign language. Results indicate generally positive attitudes towards the reliance on ChatGPT in learning foreign languages, particularly some compounds of language like syntax, phonology, and morphology. However, learners show insecurity towards ChatGPT when it comes to pragmatics and semantics, where the artificial model may fail when dealing with deeper contextual and nuanced language levels.

Keywords: artificial language model, attitudes, foreign language learning, ChatGPT, linguistic capabilities, Tunisian English language learners

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Authors: Loris Franchi, Daniele Calvi, Sabrina Corpino

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This paper presents a design methodology in which stakeholders are assisted with the exploration of a so-called negotiation space, aiming to the maximization of both group social welfare and single stakeholder’s perceived utility. The outcome results in less design iterations needed for design convergence while obtaining a higher solution effectiveness. During the early stage of a space project, not only the knowledge about the system but also the decision outcomes often are unknown. The scenario is exacerbated by the fact that decisions taken in this stage imply delayed costs associated with them. Hence, it is necessary to have a clear definition of the problem under analysis, especially in the initial definition. This can be obtained thanks to a robust generation and exploration of design alternatives. This process must consider that design usually involves various individuals, who take decisions affecting one another. An effective coordination among these decision-makers is critical. Finding mutual agreement solution will reduce the iterations involved in the design process. To handle this scenario, the paper proposes a design methodology which, aims to speed-up the process of pushing the mission’s concept maturity level. This push up is obtained thanks to a guided negotiation space exploration, which involves autonomously exploration and optimization of trade opportunities among stakeholders via Artificial Intelligence algorithms. The negotiation space is generated via a multidisciplinary collaborative optimization method, infused by game theory and multi-attribute utility theory. In particular, game theory is able to model the negotiation process to reach the equilibria among stakeholder needs. Because of the huge dimension of the negotiation space, a collaborative optimization framework with evolutionary algorithm has been integrated in order to guide the game process to efficiently and rapidly searching for the Pareto equilibria among stakeholders. At last, the concept of utility constituted the mechanism to bridge the language barrier between experts of different backgrounds and differing needs, using the elicited and modeled needs to evaluate a multitude of alternatives. To highlight the benefits of the proposed methodology, the paper presents the design of a CubeSat mission for the observation of lunar radiation environment. The derived solution results able to balance all stakeholders needs and guaranteeing the effectiveness of the selection mission concept thanks to its robustness in valuable changeability. The benefits provided by the proposed design methodology are highlighted, and further development proposed.

Keywords: concurrent engineering, artificial intelligence, negotiation in engineering design, multidisciplinary optimization

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Authors: Ahmad Haidar

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The rapid adoption of Artificial Intelligence (AI) technology holds unforeseen risks like privacy violation, unemployment, and algorithmic bias, triggering research institutions, governments, and companies to develop principles of AI ethics. The extensive and diverse literature on AI lacks an analysis of the evolution of principles developed in recent years. There are two fundamental purposes of this paper. The first is to provide insights into how the principles of AI ethics have been changed recently, including concepts like risk management and public participation. In doing so, a NOISE (Needs, Opportunities, Improvements, Strengths, & Exceptions) analysis will be presented. Second, offering a framework for building Ethical AI linked to sustainability. This research adopts an explorative approach, more specifically, an inductive approach to address the theoretical gap. Consequently, this paper tracks the different efforts to have “trustworthy AI” and “ethical AI,” concluding a list of 12 documents released from 2017 to 2022. The analysis of this list unifies the different approaches toward trustworthy AI in two steps. First, splitting the principles into two categories, technical and net benefit, and second, testing the frequency of each principle, providing the different technical principles that may be useful for stakeholders considering the lifecycle of AI, or what is known as sustainable AI. Sustainable AI is the third wave of AI ethics and a movement to drive change throughout the entire lifecycle of AI products (i.e., idea generation, training, re-tuning, implementation, and governance) in the direction of greater ecological integrity and social fairness. In this vein, results suggest transparency, privacy, fairness, safety, autonomy, and accountability as recommended technical principles to include in the lifecycle of AI. Another contribution is to capture the different basis that aid the process of AI for sustainability (e.g., towards sustainable development goals). The results indicate data governance, do no harm, human well-being, and risk management as crucial AI for sustainability principles. This study’s last contribution clarifies how the principles evolved. To illustrate, in 2018, the Montreal declaration mentioned eight principles well-being, autonomy, privacy, solidarity, democratic participation, equity, and diversity. In 2021, notions emerged from the European Commission proposal, including public trust, public participation, scientific integrity, risk assessment, flexibility, benefit and cost, and interagency coordination. The study design will strengthen the validity of previous studies. Yet, we advance knowledge in trustworthy AI by considering recent documents, linking principles with sustainable AI and AI for sustainability, and shedding light on the evolution of guidelines over time.

Keywords: artificial intelligence, AI for sustainability, declarations, framework, regulations, risks, sustainable AI

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Authors: Siddhartha Khare, Nitish Kr Boro, Omm Animesh Mishra

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Recent food price hikes may signal the end of an era of predictable global grain crop plenty due to climate change, population expansion, and dietary changes. Food consumption will treble in 20 years, requiring enormous production expenditures. Climate and the atmosphere changed owing to rainfall and seasonal cycles in the past decade. India's tropical agricultural relies on evapotranspiration and monsoons. In places with limited resources, the global environmental change affects agricultural productivity and farmers' capacity to adjust to changing moisture patterns. Motivated by these difficulties, satellite remote sensing might be combined with near-surface imaging data (smartphones, UAVs, and PhenoCams) to enable phenological monitoring and fast evaluations of field-level consequences of extreme weather events on smallholder agriculture output. To accomplish this technique, we must digitally map all communities agricultural boundaries and crop kinds. With the improvement of satellite remote sensing technologies, a geo-referenced database may be created for rural Indian agriculture fields. Using AI, we can design digital agricultural solutions for individual farms. Main objective is to Geo-enable each farm along with their seasonal crop information by combining Artificial Intelligence (AI) with satellite and near-surface data and then prepare long term crop monitoring through in-depth field analysis and scanning of fields with satellite derived vegetation indices. We developed an AI based algorithm to understand the timelapse based growth of vegetation using PhenoCam or Smartphone based images. We developed an android platform where user can collect images of their fields based on the android application. These images will be sent to our local server, and then further AI based processing will be done at our server. We are creating digital boundaries of individual farms and connecting these farms with our smart phone application to collect information about farmers and their crops in each season. We are extracting satellite-based information for each farm from Google earth engine APIs and merging this data with our data of tested crops from our app according to their farm’s locations and create a database which will provide the data of quality of crops from their location.

Keywords: artificial intelligence, satellite remote sensing, crop monitoring, android and web application

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Authors: Priyanka Bhatnagar, Malkeshkumar Patel, Joondong Kim, Joonpyo Hong

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Artificial skin and sensory memory platforms are produced using a flexible, transparent photovoltaic (TPV) device. The TPV device is composed of a metal oxide heterojunction (nZnO/p-NiO) and transmits visible light (> 50%) while producing substantial electric power (0.5 V and 200 μA cm-2 ). This TPV device is a transparent energy interface that can be used to detect signals and propagate information without an external energy supply. The TPV artificial skin offers a temperature detection range (0 C75 C) that is wider than that of natural skin (5 C48 °C) due to the temperature-sensitive pyrocurrent from the ZnO layer. Moreover, the TPV thermoreceptor offers sensory memory of extreme thermal stimuli. Much like natural skin, artificial skin uses the nociceptor mechanism to protect tissue from harmful damage via signal amplification (hyperalgesia) and early adaption (allodynia). This demonstrates the many features of TPV artificial skin, which can sense and transmit signals and memorize information under self-operation mode. This transparent photovoltaic skin can provide sustainable energy for use in human electronics.

Keywords: transparent, photovoltaics, thermal memory, artificial skin, thermoreceptor

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Authors: Stephens Oluyemi Adetunji, Nel Norma Margaret, Krogs Sozein

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There has been a shift in the role of sports from being a form of entertainment and relaxation to becoming a huge business concern and high money spinning venture. This shift has placed a greater demand on sport coaches as regards expectations for high performance from investors as well as other stake holders. The responsibility of sports coaches in ensuring high performance of sports men and women has become increasingly more demanding from both spectators and sports organisers. Coaches are leaders who should possess soft skills such as emotional intelligence aside from employing skills and drills to ensure high performance of athletes. This study is, therefore, designed to determine the emotional intelligence of sports coaches in South Africa. An assessment of the emotional intelligence of sports coaches would enable the researchers to identify those who have low emotional intelligence and to design an intervention program that could improve their emotional intelligence. This study will adopt the pragmatic world view of research using the mixed methods research design of the quantitative and qualitative approach. The non-probability sampling technique will be used to select fifty sports coaches for the quantitative study while fifteen sports coaches will be purposively selected for the qualitative study. One research question which seeks to ascertain the level of emotional intelligence of sports coaches will be raised to guide this study. In addition, two research hypotheses stating that there will be no significant difference in the level of emotional intelligence of sports coaches on the basis of gender and type of sports will be formulated and statistically analysed at 0.05 level of significance. For the quantitative study, an emotional intelligence test will be used to measure the emotional intelligence of sport coaches. Focus group interviews and open ended questions will be used to obtain the qualitative data. Quantitative data obtained will be statistically analysed using the SPSS version 22.0 while the qualitative data will be analysed using atlas ti. Based on the findings of this study, recommendations will be made.

Keywords: emotional intelligence, high performance, sports coaches, South Africa

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Authors: Soumya Dutta

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This research paper aims at investigating the impact of emotional intelligence and cognitive intelligence on radio presenter’s performance in the All India Radio, Kolkata (India’s public service broadcaster). The ancient concept of productivity is the ratio of what is produced to what is required to produce it. But, father of modern management Peter F. Drucker (1909-2005) defined productivity of knowledge work and knowledge workers in a new form. In the other hand, the concept of Emotional Intelligence (EI) originated back in 1920’s when Thorndike (1920) for the first time proposed the emotional intelligence into three dimensions, i.e., abstract intelligence, mechanical intelligence, and social intelligence. The contribution of Salovey and Mayer (1990) is substantive, as they proposed a model for emotional intelligence by defining EI as part of the social intelligence, which takes measures the ability of an individual to regulate his/her personal and other’s emotions and feeling. Cognitive intelligence illustrates the specialization of general intelligence in the domain of cognition in ways that possess experience and learning about cognitive processes such as memory. The outcomes of past research on emotional intelligence show that emotional intelligence has a positive effect on social- mental factors of human resource; positive effects of emotional intelligence on leaders and followers in terms of performance, results, work, satisfaction; emotional intelligence has a positive and significant relationship with the teachers' job performance. In this paper, we made a conceptual framework based on theories of emotional intelligence proposed by Salovey and Mayer (1989-1990) and a compensatory model of emotional intelligence, cognitive intelligence, and job performance proposed by Stephen Cote and Christopher T. H. Miners (2006). For investigating the impact of emotional intelligence and cognitive intelligence on radio presenter’s performance, sample size consists 59 radio presenters (considering gender, academic qualification, instructional mood, age group, etc.) from All India Radio, Kolkata station. Questionnaires prepared based on cognitive (henceforth called C based and represented by C1, C2,.., C5) as well as emotional intelligence (henceforth called E based and represented by E1, E2,., E20). These were sent to around 59 respondents (Presenters) for getting their responses. Performance score was collected from the report of program executive of All India Radio, Kolkata. The linear regression has been carried out using all the E-based and C-based variables as the predictor variables. The possible problem of autocorrelation has been tested by having the Durbinson-Watson (DW) Statistic. Values of this statistic, almost within the range of 1.80-2.20, indicate the absence of any significant problem of autocorrelation. The possible problem of multicollinearity has been tested by having the Variable Inflation Factor (VIF) value. Values of this statistic, around within 2, indicates the absence of any significant problem of multicollinearity. It is inferred that the performance scores can be statistically regressed linearly on the E-based and C-based scores, which can explain 74.50% of the variations in the performance.

Keywords: cognitive intelligence, emotional intelligence, performance, productivity

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Authors: Uranus Saadat

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ChatGPT, as one of the well-known artificial intelligence (AI) tools, has recently been integrated into English language education and has had several impacts on learners. Accordingly, concerns regarding the overuse of this tool among EFL/ESL learners are rising, which could lead to several disadvantages in their writing skills development. The use of ChatGPT in facilitating writing skills is a novel concept that demands further studies in different contexts and learners. In this study, a qualitative case study is applied to investigate the impact of ChatGPT on the writing skills of a group of EFL bachelor’s students from Uzbekistan studying Teaching English as the Second Language (TESL) at a private university in Malaysia. The data was collected through the triangulation of document analysis, semi-structured interviews, classroom observations, and focus group discussions. Subsequently, the data was analyzed by using thematic analysis. Some of the emerging themes indicated that ChatGPT is helpful in engaging students by reducing their anxiety in class and providing them with constructive feedback and support. Conversely, certain emerging themes revealed excessive reliance on ChatGPT, resulting in a decrease in students’ creativity and critical thinking skills, memory span, and tolerance for ambiguity. The study suggests a number of strategies to alleviate its negative impacts, such as peer review activities, workshops for familiarizing students with AI, and gradual withdrawal of AI support activities. This study emphasizes the need for cautious AI integration into English language education to cultivate independent learners with higher-order thinking skills.

Keywords: ChatGPT, EFL/ESL learners, English writing skills, artificial intelligence tools, critical thinking skills

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Authors: Ivan Stanev, Maria Koleva

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Described are challenges to the Bulgarian e-Customs (BeC) related to low level of interoperability and standardization, inefficient use of available infrastructure, lack of centralized identification and authorization, extremely low level of software process automation, and insufficient quality of data stored in official registers. The technical requirements for BeC are prepared with a focus on domain independent common platform, specialized customs and excise components, high scalability, flexibility, and reusability. The Knowledge Based Automated Software Engineering (KBASE) Common Platform for Automated Programming (CPAP) is selected as an instrument covering BeC requirements for standardization, programming automation, knowledge interpretation and cloud computing. BeC stage 3 results are presented and analyzed. BeC.S3 development trends are identified.

Keywords: service oriented architecture, cloud computing, knowledge based automated software engineering, common platform for automated programming, e-customs

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Authors: Farnoosh Attar

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Nowadays, buffalo’s milk due to has highly nutritional properties, has a special place among consumers and its application for the production of dairy products due to the high technological properties is increasing day by day. In the present study, the physicochemical characteristics of Iranian buffalo’s milk were compared with cow's milk. According to chemical analysis, the amount of fat, protein, and total solid was higher in buffalo milk than cow's milk (respectively, 8.2%, 4.73%, and 15.92% compared with 3.5%, 3.25%, and 12.5%). Also, the percentage of cholesterol buffalo’s milk was less than in cow's milk. In contrast, no significant difference between the pH, acidity, and specific gravity was observed. The size of buffalo milk fat globules was larger than cow's milk. In addition, the profile of buffalo free fatty acids milk showed the relatively high distribution of long chain saturated fatty acids. The presence of four major bands related to αs casein, β casein, β-lactoglobulin, and α-lactalbumin with quite higher intensity than cow’s milk was also observed. The results obtained will provide a reference investigation to improve the developing of buffalo milk standard.

Keywords: buffalo milk, physicochemical characterization, standardization, dairy products

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Authors: Artur Matuck, Guilherme F. Nobre

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Machines can be either tool, media, or social agents. Advances in technology have been delivering machines capable of autonomous expression, both through communication and art. This paper deals with models (theoretical approach) and experiments (applied approach) related to artificial agents. On one hand it traces how social sciences' scholars have worked with topics such as text automatization, man-machine writing cooperation, and communication. On the other hand it covers how computer sciences' scholars have built communicative and artistic machines, including the programming of creativity. The aim is to present a brief survey on artificially intelligent communicators and artificially creative writers, and provide the basis to understand the meta-authorship and also to new and further man-machine co-authorship.

Keywords: artificial communication, artificial creativity, artificial writers, meta-authorship, robotic art

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Authors: Geetha Baskaran, Andrzej Barjiela, Rong Qu

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Motivation: Nurse scheduling is a complex combinatorial optimization problem. It is also known as NP-hard. It needs an efficient re-scheduling to minimize some trade-off of the measures of violation by reducing selected constraints to soft constraints with measurements of their violations. Problem Statement: In this paper, we extend our novel approach to solve the nurse scheduling problem by transforming it through Information Granulation. Approach: This approach satisfies the rules of a typical hospital environment based on a standard benchmark problem. Generating good work schedules has a great influence on nurses' working conditions which are strongly related to the level of a quality health care. Domain transformation that combines the strengths of operation research and artificial intelligence was proposed for the solution of the problem. Compared to conventional methods, our approach involves judicious grouping (information granulation) of shifts types’ that transforms the original problem into a smaller solution domain. Later these schedules from the smaller problem domain are converted back into the original problem domain by taking into account the constraints that could not be represented in the smaller domain. An Integer Programming (IP) package is used to solve the transformed scheduling problem by expending the branch and bound algorithm. We have used the GNU Octave for Windows to solve this problem. Results: The scheduling problem has been solved in the proposed formalism resulting in a high quality schedule. Conclusion: Domain transformation represents departure from a conventional one-shift-at-a-time scheduling approach. It offers an advantage of efficient and easily understandable solutions as well as offering deterministic reproducibility of the results. We note, however, that it does not guarantee the global optimum.

Keywords: domain transformation, nurse scheduling, information granulation, artificial intelligence, simulation

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Authors: Adane Kassa Shikur

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Today’s supply chains (SC) have become vulnerable to unexpected and ever-intensifying disruptions from myriad sources. Consequently, the concept of supply chain resilience (SCRes) has become crucial to complement the conventional risk management paradigm, which has failed to cope with unexpected SC disruptions, resulting in severe consequences affecting SC performances and making business continuity questionable. Advancements in cutting-edge technologies like artificial intelligence (AI) and their potential to enhance SCRes by improving critical antecedents in the different phases have attracted the attention of scholars and practitioners. The research from academia and the practical interest of the industry have yielded significant publications at the nexus of AI and SCRes during the last two decades. However, the applications and examinations have been primarily conducted independently, and the extant literature is dispersed into research streams despite the complex nature of SCRes. To close this research gap, this study conducts a systematic literature review of 106 peer-reviewed articles by curating, synthesizing, and consolidating up-to-date literature and presents the state-of-the-art development from 2010 to 2022. Bayesian networks are the most topical ones among the 13 AI techniques evaluated. Concerning the critical antecedents, visibility is the first ranking to be realized by the techniques. The study revealed that AI techniques support only the first 3 phases of SCRes (readiness, response, and recovery), and readiness is the most popular one, while no evidence has been found for the growth phase. The study proposed an AI-SCRes framework to inform research and practice to approach SCRes holistically. It also provided implications for practice, policy, and theory as well as gaps for impactful future research.

Keywords: ANNs, risk, Bauesian networks, vulnerability, resilience

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Authors: Temitope Francis Abiodun

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The practice of intelligence is not limited to the machinery of a nation state alone, yet not much research or analysis has been directed towards the spy-crafts and tradecrafts engaged in by violent non-state actors (VNSAs) in the international community. The rise of 'private sector intelligence' in more recent years has only just begun to be interrogated by practitioners and academics. However, the use of intelligence by insurgents and other groups assembled to achieve varied forms of politico-military outcomes has often been overlooked. This paper examined the factors and conditions that gave rise to an increase in violent non-state actors (VNSAs), strategies aiding their deployment of insurgent intelligence, and as well the implications of their activities on global security. The failed state theory was adopted, while a descriptive research design served as the framework for the study. Data were collected from primary and secondary sources. The paper, however, revealed there were massive deployments of insurgent intelligence by violent non-state actors in contrast to a faulty pre-conception that insurgents were not as highly trained in deployment of intelligence as state actors, having assumed that the VNSAs lacked the sophistication to produce intelligence. However, the strategic objectives of insurgents (VNSAs) were revealed to depend on well-organized information gathering operations that feed into the tactical executions of their insurgency. The paper recommends, therefore, there is a need for adequate training on the part of security personnel in the states to be alive to their responsibilities; and there is also a need to ensure adequate border control and management to checkmate the influx of the various violent or deadly movements across global frontiers.

Keywords: terrorism, non-violent state actors, private sector intelligence, security

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Authors: Syed Qasim Gilani, Muhammad Umair, Muhammad Noman, Syed Bilawal Shah, Aqib Abbasi, Muhammad Waheed

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Cardiovascular Diseases(CVD's) are the major cause of death in the world. The main reason for these deaths is the unavailability of first aid for heart failure. In many cases, patients die before reaching the hospital. We in this paper are presenting innovative online health service for Cardiac Patients. The proposed online health system has two ends. Users through device developed by us can communicate with their doctor through a mobile application. This interface provides them with first aid.Also by using this service, they have an easy interface with their doctors for attaining medical advice. According to the proposed system, we developed a device called Cardiac Care. Cardiac Care is a portable device which a patient can use at their home for monitoring heart condition. When a patient checks his/her heart condition, Electrocardiogram (ECG), Blood Pressure(BP), Temperature are sent to the central database. The severity of patients condition is checked using Artificial Intelligence Algorithm at the database. If the patient is suffering from the minor problem, our algorithm will suggest a prescription for patients. But if patient's condition is severe, patients record is sent to doctor through the mobile Android application. Doctor after reviewing patients condition suggests next step. If a doctor identifies the patient condition as critical, then the message is sent to the central database for sending an ambulance for the patient. Ambulance starts moving towards patient for bringing him/her to hospital. We have implemented this model at prototype level. This model will be life-saving for millions of people around the globe. According to this proposed model patients will be in contact with their doctors all the time.

Keywords: cardiovascular disease, classification, electrocardiogram, blood pressure

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Authors: Ahmed Hossam ElMolla, Mohamed Hatem Saleh, Hamza Mostafa, Lara Mamdouh, Yassin Wael

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Artificial intelligence (AI) is reshaping industries, and its potential to revolutionize renewable energy and data center operations is immense. By harnessing AI's capabilities, we can optimize energy consumption, predict fluctuations in renewable energy generation, and improve the efficiency of data center infrastructure. This convergence of technologies promises a future where energy is managed more intelligently, sustainably, and cost-effectively. The integration of AI into renewable energy systems unlocks a wealth of opportunities. Machine learning algorithms can analyze vast amounts of data to forecast weather patterns, solar irradiance, and wind speeds, enabling more accurate energy production planning. AI-powered systems can optimize energy storage and grid management, ensuring a stable power supply even during intermittent renewable generation. Moreover, AI can identify maintenance needs for renewable energy infrastructure, preventing costly breakdowns and maximizing system lifespan. Data centers, which consume substantial amounts of energy, are prime candidates for AI-driven optimization. AI can analyze energy consumption patterns, identify inefficiencies, and recommend adjustments to cooling systems, server utilization, and power distribution. Predictive maintenance using AI can prevent equipment failures, reducing energy waste and downtime. Additionally, AI can optimize data placement and retrieval, minimizing energy consumption associated with data transfer. As AI transforms renewable energy and data center operations, modified Key Performance Indicators (KPIs) will emerge. Traditional metrics like energy efficiency and cost-per-megawatt-hour will continue to be relevant, but additional KPIs focused on AI's impact will be essential. These might include AI-driven cost savings, predictive accuracy of energy generation and consumption, and the reduction of carbon emissions attributed to AI-optimized operations. By tracking these KPIs, organizations can measure the success of their AI initiatives and identify areas for improvement. Ultimately, the synergy between AI, renewable energy, and data centers holds the potential to create a more sustainable and resilient future. By embracing these technologies, we can build smarter, greener, and more efficient systems that benefit both the environment and the economy.

Keywords: data center, artificial intelligence, renewable energy, energy efficiency, sustainability, optimization, predictive analytics, energy consumption, energy storage, grid management, data center optimization, key performance indicators, carbon emissions, resiliency

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Authors: Laiba Amin, Rashna H. Sukhia, Mubassar Fida

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Introduction: This study evaluates the accuracy of responses from ChatGPT, Google Bard, and Microsoft Copilot regarding dentofacial orthopedic appliances. As artificial intelligence (AI) increasingly enhances various fields, including healthcare, understanding its reliability in specialized domains like orthodontics becomes crucial. By comparing the accuracy of different AI models, this study aims to shed light on their effectiveness and potential limitations in providing technical insights. Materials and Methods: A total of 110 questions focused on dentofacial orthopedic appliances were posed to each AI model. The responses were then evaluated by five experienced orthodontists using a modified 5-point Likert scale to ensure a thorough assessment of accuracy. This structured approach allowed for consistent and objective rating, facilitating a meaningful comparison between the AI systems. Results: The results revealed that Google Bard demonstrated the highest accuracy at 74%, followed by Microsoft Copilot, with an accuracy of 72.2%. In contrast, ChatGPT was found to be the least accurate, achieving only 52.2%. These results highlight significant differences in the performance of the AI models when addressing orthodontic queries. Conclusions: Our study highlights the need for caution in relying on AI for orthodontic insights. The overall accuracy of the three chatbots was 66%, with Google Bard performing best for removable Class II appliances. Microsoft Copilot was more accurate than ChatGPT, which, despite its popularity, was the least accurate. This variability emphasizes the importance of human expertise in interpreting AI-generated information. Further research is necessary to improve the reliability of AI models in specialized healthcare settings.

Keywords: artificial intelligence, large language models, orthodontics, dentofacial orthopaedic appliances, accuracy assessment.

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Authors: Olumuyiwa Ojo, Masengo Ilunga

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Artificial neural network (ANN) has broken the bounds of the convention programming, which is actually a function of garbage in garbage out by its ability to mimic the human brain. Its ability to adopt, adapt, adjust, evaluate, learn and recognize the relationship, behavior, and pattern of a series of data set administered to it, is tailored after the human reasoning and learning mechanism. Thus, the study aimed at modeling wastewater treatment process in order to accurately diagnose water control problems for effective treatment. For this study, a stage ANN model development and evaluation methodology were employed. The source data analysis stage involved a statistical analysis of the data used in modeling in the model development stage, candidate ANN architecture development and then evaluated using a historical data set. The model was developed using historical data obtained from Daspoort Wastewater Treatment plant South Africa. The resultant designed dimensions and model for wastewater treatment plant provided good results. Parameters considered were temperature, pH value, colour, turbidity, amount of solids and acidity. Others are total hardness, Ca hardness, Mg hardness, and chloride. This enables the ANN to handle and represent more complex problems that conventional programming is incapable of performing.

Keywords: ANN, artificial neural network, wastewater treatment, model, development

Procedia PDF Downloads 149