Search results for: swarm intelligence

Authors: Libor Zachoval, Daire O Broin, Oisin Cawley

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E-learning platforms, such as Blackboard have two major shortcomings: limited data capture as a result of the limitations of SCORM (Shareable Content Object Reference Model), and lack of incorporation of Artificial Intelligence (AI) and machine learning algorithms which could lead to better course adaptations. With the recent development of Experience Application Programming Interface (xAPI), a large amount of additional types of data can be captured and that opens a window of possibilities from which online education can benefit. In a corporate setting, where companies invest billions on the learning and development of their employees, some learner behaviours can be troublesome for they can hinder the knowledge development of a learner. Behaviours that hinder the knowledge development also raise ambiguity about learner’s knowledge mastery, specifically those related to gaming the system. Furthermore, a company receives little benefit from their investment if employees are passing courses without possessing the required knowledge and potential compliance risks may arise. Using xAPI and rules derived from a state-of-the-art review, we identified three learner behaviours, primarily related to guessing, in a corporate compliance course. The identified behaviours are: trying each option for a question, specifically for multiple-choice questions; selecting a single option for all the questions on the test; and continuously repeating tests upon failing as opposed to going over the learning material. These behaviours were detected on learners who repeated the test at least 4 times before passing the course. These findings suggest that gauging the mastery of a learner from multiple-choice questions test scores alone is a naive approach. Thus, next steps will consider the incorporation of additional data points, knowledge estimation models to model knowledge mastery of a learner more accurately, and analysis of the data for correlations between knowledge development and identified learner behaviours. Additional work could explore how learner behaviours could be utilised to make changes to a course. For example, course content may require modifications (certain sections of learning material may be shown to not be helpful to many learners to master the learning outcomes aimed at) or course design (such as the type and duration of feedback).

Keywords: artificial intelligence, corporate e-learning environment, knowledge maintenance, xAPI

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Authors: John Hardy

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Over the last decade, the increasing utility of extremist narratives to the operational effectiveness of terrorist organizations has been evidenced by the proliferation of inspired or affiliated attacks across the world. Famous examples such as regional al-Qaeda affiliates and the self-styled “Islamic State” demonstrate the effectiveness of leveraging communication technologies to disseminate propaganda, recruit members, and orchestrate attacks. Terrorist organizations with the capacity to harness the communicative power offered by digital communication technologies and effective political narratives have held an advantage over their targets in recent years. Terrorists have leveraged the perceived legitimacy of grass-roots actors to appeal to a global audience of potential supporters and enemies alike, and have wielded a proficiency in profile-raising which remains unmatched by counter terrorism narratives around the world. In contrast, many attempts at propagating official counter-narratives have been received by target audiences as illegitimate, top-down and impersonally bureaucratic. However, the benefits provided by widespread communication and extremist narratives have come at an operational cost. Terrorist organizations now face a significant challenge in protecting their access to communications technologies and authority over the content they create and endorse. The dissemination of effective narratives has emerged as a core function of terrorist organizations with international reach via inspired or affiliated attacks. As such, it has become a critical function which can be targeted by intelligence and security forces. This study applies network targeting principles which have been used by coalition forces against a range of non-state actors in the Middle East and South Asia to the communicative function of terrorist organizations. This illustrates both a conceptual link between functional targeting and operational disruption in the abstract and a tangible impact on the operational effectiveness of terrorists by degrading communicative ability and legitimacy. Two case studies highlight the utility of applying functional targeting against terrorist organizations. The first case is the targeted killing of Anwar al-Awlaki, an al-Qaeda propagandist who crafted a permissive narrative and effective propaganda videos to attract recruits who committed inspired terrorist attacks in the US and overseas. The second is a series of operations against Islamic State propagandists in Syria, including the capture or deaths of a cadre of high profile Islamic State members, including Junaid Hussain, Abu Mohammad al-Adnani, Neil Prakash, and Rachid Kassim. The group of Islamic State propagandists were linked to a significant rise in affiliated and enabled terrorist attacks and were subsequently targeted by law enforcement and military agencies. In both cases, the disruption of communication between the terrorist organization and recruits degraded both communicative and operational functions. Effective functional targeting on member recruitment and operational tempo suggests that narratives are a critical function which can be leveraged against terrorist organizations. Further application of network targeting methods to terrorist narratives may enhance the efficacy of a range of counter terrorism techniques employed by security and intelligence agencies.

Keywords: countering violent extremism, counter terrorism, intelligence, terrorism, violent extremism

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Authors: Rojean Ghafariasar, Leili Nosrati

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The article introduces the categories and characteristics of cross-design, respectively, between industry and industry designers, artists, brands and brands, science, technology, and fashion. It focuses on the combination of technology and fashion cross-design methods, corresponding case studies on the combination of new technology fabrics, fashion design, smart devices, and also 3D printing technology, emphasizing the integration and application value of technology and fashion. The document also introduces design elements into fashion design through scientific and technological intelligence, promoting fashion innovation as well as research and development of new materials and functions, and incubates an ecosystem for the fashion industry through science and technology.

Keywords: fashion, design, industrial design, crossover design

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Authors: Shamie Kumar

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Background: Radiologists and radiographers (RR) need to educate themselves and their colleagues to ensure that AI is integrated safely, useful, and in a meaningful way with the direction it always benefits the patients. AI education and training are fundamental to the way RR work and interact with it, such that they feel confident using it as part of their clinical practice in a way they understand it. Methodology: This exploratory research will outline the current educational and training gaps for radiographers and radiologists in AI radiology diagnostics. It will review the status, skills, challenges of educating and teaching. Understanding the use of artificial intelligence within daily clinical practice, why it is fundamental, and justification on why learning about AI is essential for wider adoption. Results: The current knowledge among RR is very sparse, country dependent, and with radiologists being the majority of the end-users for AI, their targeted training and learning AI opportunities surpass the ones available to radiographers. There are many papers that suggest there is a lack of knowledge, understanding, and training of AI in radiology amongst RR, and because of this, they are unable to comprehend exactly how AI works, integrates, benefits of using it, and its limitations. There is an indication they wish to receive specific training; however, both professions need to actively engage in learning about it and develop the skills that enable them to effectively use it. There is expected variability amongst the profession on their degree of commitment to AI as most don’t understand its value; this only adds to the need to train and educate RR. Currently, there is little AI teaching in either undergraduate or postgraduate study programs, and it is not readily available. In addition to this, there are other training programs, courses, workshops, and seminars available; most of these are short and one session rather than a continuation of learning which cover a basic understanding of AI and peripheral topics such as ethics, legal, and potential of AI. There appears to be an obvious gap between the content of what the training program offers and what the RR needs and wants to learn. Due to this, there is a risk of ineffective learning outcomes and attendees feeling a lack of clarity and depth of understanding of the practicality of using AI in a clinical environment. Conclusion: Education, training, and courses need to have defined learning outcomes with relevant concepts, ensuring theory and practice are taught as a continuation of the learning process based on use cases specific to a clinical working environment. Undergraduate and postgraduate courses should be developed robustly, ensuring the delivery of it is with expertise within that field; in addition, training and other programs should be delivered as a way of continued professional development and aligned with accredited institutions for a degree of quality assurance.

Keywords: artificial intelligence, training, radiology, education, learning

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Authors: Ahmad Mohammad Sharqi, Mohamed Hechmi El Ouni, Saleh Alsulamy

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Due to the emerging technologies revolution worldwide, the need to exploit and employ innovative technologies for other functions and purposes in different aspects has become a remarkable matter. Saudi Arabia is considered one of the most powerful economic countries in the world, where the construction sector participates effectively in its economy. Thus, the construction sector in KSA should convoy the rapid digital revolution and transformation and implement smart devices on sites. A Smart Construction Site (SCS) includes smart devices, artificial intelligence, the internet of things, augmented reality, building information modeling, geographical information systems, and cloud information. This paper aims to study the level of implementation of SCS in KSA, analyze the obstacles and challenges of adopting SCS and find out critical success factors for its implementation. A survey of close-ended questions (scale and multi-choices) has been conducted on professionals in the construction sector of Saudi Arabia. A total number of twenty-nine questions has been prepared for respondents. Twenty-four scale questions were established, and those questions were categorized into several themes: quality, scheduling, cost, occupational safety and health, technologies and applications, and general perception. Consequently, the 5-point Likert scale tool (very low to very high) was adopted for this survey. In addition, five close-ended questions with multi-choice types have also been prepared; these questions have been derived from a previous study implemented in the United Kingdom (UK) and the Dominic Republic (DR), these questions have been rearranged and organized to fit the structured survey in order to place the Kingdom of Saudi Arabia in comparison with the United Kingdom (UK) as well as the Dominican Republic (DR). A total number of one hundred respondents have participated in this survey from all regions of the Kingdom of Saudi Arabia: southern, central, western, eastern, and northern regions. The drivers, obstacles, and success factors for implementing smart devices and technologies in KSA’s construction sector have been investigated and analyzed. Besides, it has been concluded that KSA is on the right path toward adopting smart construction sites with attractive results comparable to and even better than the UK in some factors.

Keywords: artificial intelligence, construction projects management, internet of things, smart construction sites, smart devices

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Authors: Chuping Lo

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This paper presents a simple model demonstrating that ceteris paribus countries with lower barriers to accessing global data tend to earn higher incomes than other countries. Therefore, large countries that inherently have greater data resources tend to have higher incomes than smaller countries, such that the former may be more hesitant than the latter to liberalize cross-border data flows to maintain this advantage. Furthermore, countries with higher artificial intelligence (AI) intensity in production technologies tend to benefit more from economies of scale in data aggregation, leading to higher income and more trade as they are better able to utilize global data.

Keywords: digital intensity, digital divide, international trade, scale of economics

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Authors: Sylvie Michel, Emmanuelle Gagnou, Joanne Hamet

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New major ethical dilemmas are posed by artificial intelligence. This article identifies an existing gap between the ethical questions that AI/robotics researchers grapple with in their research practice and those identified by literature review. The objective is to understand which ethical dilemmas are identified or concern AI researchers in order to compare them with the existing literature. This will enable to conduct training and awareness initiatives for AI researchers, encouraging them to consider these questions during the development of AI. Qualitative analyses were conducted based on direct observation of an AI/Robotics research team focused on collaborative robotics over several months. Subsequently, semi-structured interviews were conducted with 16 members of the team. The entire process took place during the first semester of 2023. The observations were analyzed using an analytical framework, and the interviews were thematically analyzed using Nvivo software. While the literature identifies three primary ethical concerns regarding AI—transparency, bias, and responsibility—the results firstly demonstrate that AI researchers are primarily concerned with the publication and valorization of their work, with the initial ethical concerns revolving around this matter. Questions arise regarding the extent to which to "market" publications and the usefulness of some publications. Research ethics are a central consideration for these teams. Secondly, another result shows that the researchers studied adopt a consequentialist ethics (though not explicitly formulated as such). They ponder the consequences of their development in terms of safety (for humans in relation to Robots/AI), worker autonomy in relation to the robot, and the role of work in society (can robots take over jobs?). Lastly, results indicate that the ethical dilemmas highlighted in the literature (responsibility, transparency, bias) do not explicitly appear in AI/Robotics research. AI/robotics researchers raise specific and pragmatic ethical questions, primarily concerning publications initially and consequentialist considerations afterward. Results demonstrate that these concerns are distant from the existing literature. However, the dilemmas highlighted in the literature also deserve to be explicitly contemplated by researchers. This article proposes that the journals these researchers target should mandate ethical reflection for all presented works. Furthermore, results suggest offering awareness programs in the form of short educational sessions for researchers.

Keywords: ethics, artificial intelligence, research, robotics

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Authors: Farzaneh Sasanpour, Saeed Amini Varaki

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Resilience is an important goal for cities as urban areas face an increasing range of challenges in the 21st century; therefore, according to the characteristics of risks, adopting an approach that responds to sensitive conditions in the risk management process is the resilience of cities. In the meantime, most of the resilience assessments have dealt with natural hazards and less attention has been paid to pandemics.In the covid-19 pandemic, the country of Iran and especially the metropolis of Tehran, was not immune from the crisis caused by its effects and consequences and faced many challenges. One of the methods that can increase the resilience of Tehran's metropolis against possible crises in the future is future studies. This research is practical in terms of type. The general pattern of the research will be descriptive-analytical and from the point of view that it is trying to communicate between the components and provide urban resilience indicators with pandemic crises and explain the scenarios, its future studies method is exploratory. In order to extract and determine the key factors and driving forces effective on the resilience of Tehran's urban areas against pandemic crises (Covid-19), the method of structural analysis of mutual effects and Micmac software was used. Therefore, the primary factors and variables affecting the resilience of Tehran's urban areas were set in 5 main factors, including physical-infrastructural (transportation, spatial and physical organization, streets and roads, multi-purpose development) with 39 variables based on mutual effects analysis. Finally, key factors and variables in five main areas, including managerial-institutional with five variables; Technology (intelligence) with 3 variables; economic with 2 variables; socio-cultural with 3 variables; and physical infrastructure, were categorized with 7 variables. These factors and variables have been used as key factors and effective driving forces on the resilience of Tehran's urban areas against pandemic crises (Covid-19), in explaining and developing scenarios. In order to develop the scenarios for the resilience of Tehran's urban areas against pandemic crises (Covid-19), intuitive logic, scenario planning as one of the future research methods and the Global Business Network (GBN) model were used. Finally, four scenarios have been drawn and selected with a creative method using the metaphor of weather conditions, which is indicative of the general outline of the conditions of the metropolis of Tehran in that situation. Therefore, the scenarios of Tehran metropolis were obtained in the form of four scenarios: 1- solar scenario (optimal governance and management leading in smart technology) 2- cloud scenario (optimal governance and management following in intelligent technology) 3- dark scenario (optimal governance and management Unfavorable leader in intelligence technology) 4- Storm scenario (unfavorable governance and management of follower in intelligence technology). The solar scenario shows the best situation and the stormy scenario shows the worst situation for the Tehran metropolis. According to the findings obtained in this research, city managers can, in order to achieve a better tomorrow for the metropolis of Tehran, in all the factors and components of urban resilience against pandemic crises by using future research methods, a coherent picture with the long-term horizon of 2050, from the path Provide urban resilience movement and platforms for upgrading and increasing the capacity to deal with the crisis. To create the necessary platforms for the realization, development and evolution of the urban areas of Tehran in a way that guarantees long-term balance and stability in all dimensions and levels.

Keywords: future research, resilience, crisis, pandemic, covid-19, Tehran

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Authors: Nashwah Alnajjar

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This research paper provides an overview of Replika, an AI chatbot application that uses natural language processing technology to engage in conversations with users. The app was developed to provide users with a virtual AI friend who can converse with them on various topics, including mental health. This study explores the experiences of Replika users using quantitative research methodology. A survey was conducted with 12 participants to collect data on their demographics, usage patterns, and experiences with the Replika app. The results showed that Replika has the potential to play a role in mental health support and well-being.

Keywords: Replika, chatbot, mental health, artificial intelligence, natural language processing

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Authors: Abbas Ahmadi, Bijan Raie, Mohammad Reza Neyshabouri, Mohammad Ali Ghorbani, Farrokh Asadzadeh

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In recent years, vast areas of Urmia Lake in Dasht-e-Tabriz has dried up leading to saline sediments exposure on the surface lake coastal areas being highly susceptible to wind erosion. This study was conducted to investigate wind erosion and its relevance to soil physicochemical properties and also modeling of wind erodibility (WE) using artificial intelligence techniques. For this purpose, 96 soil samples were collected from 0-5 cm depth in 414000 hectares using stratified random sampling method. To measure the WE, all samples (<8 mm) were exposed to 5 different wind velocities (9.5, 11, 12.5, 14.1 and 15 m s-1 at the height of 20 cm) in wind tunnel and its relationship with soil physicochemical properties was evaluated. According to the results, WE varied within the range of 76.69-9.98 (g m-2 min-1)/(m s-1) with a mean of 10.21 and coefficient of variation of 94.5% showing a relatively high variation in the studied area. WE was significantly (P<0.01) affected by soil physical properties, including mean weight diameter, erodible fraction (secondary particles smaller than 0.85 mm) and percentage of the secondary particle size classes 2-4.75, 1.7-2 and 0.1-0.25 mm. Results showed that the mean weight diameter, erodible fraction and percentage of size class 0.1-0.25 mm demonstrated stronger relationship with WE (coefficients of determination were 0.69, 0.67 and 0.68, respectively). This study also compared efficiency of multiple linear regression (MLR), gene expression programming (GEP), artificial neural network (MLP), artificial neural network based on genetic algorithm (MLP-GA) and artificial neural network based on whale optimization algorithm (MLP-WOA) in predicting of soil wind erodibility in Dasht-e-Tabriz. Among 32 measured soil variable, percentages of fine sand, size classes of 1.7-2.0 and 0.1-0.25 mm (secondary particles) and organic carbon were selected as the model inputs by step-wise regression. Findings showed MLP-WOA as the most powerful artificial intelligence techniques (R2=0.87, NSE=0.87, ME=0.11 and RMSE=2.9) to predict soil wind erodibility in the study area; followed by MLP-GA, MLP, GEP and MLR and the difference between these methods were significant according to the MGN test. Based on the above finding MLP-WOA may be used as a promising method to predict soil wind erodibility in the study area.

Keywords: wind erosion, erodible fraction, gene expression programming, artificial neural network

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Authors: George Kyriakopoulos, Photini Pazartzis, Anthi Koskina, Crystalie Bourcha

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The need for securing safe access to and return from outer space, as well as ensuring the viability of outer space operations, maintains vivid the debate over the promotion of organization of space traffic through a Space Traffic Management System (STM). The proliferation of outer space activities in recent years as well as the dynamic emergence of the private sector has gradually resulted in a diverse universe of actors operating in outer space. The said developments created an increased adverse impact on outer space sustainability as the case of the growing number of space debris clearly demonstrates. The above landscape sustains considerable threats to outer space environment and its operators that need to be addressed by a combination of scientific-technological measures and regulatory interventions. In this context, recourse to recent technological advancements and, in particular, to Artificial Intelligence (AI) and machine learning systems, could achieve exponential results in promoting space traffic management with respect to collision avoidance as well as launch and re-entry procedures/phases. New technologies can support the prospects of a successful space traffic management system at an international scale by enabling, inter alia, timely, accurate and analytical processing of large data sets and rapid decision-making, more precise space debris identification and tracking and overall minimization of collision risks and reduction of operational costs. What is more, a significant part of space activities (i.e. launch and/or re-entry phase) takes place in airspace rather than in outer space, hence the overall discussion also involves the highly developed, both technically and legally, international (and national) Air Traffic Management System (ATM). Nonetheless, from a regulatory perspective, the use of AI for the purposes of space traffic management puts forward implications that merit particular attention. Key issues in this regard include the delimitation of AI-based activities as space activities, the designation of the applicable legal regime (international space or air law, national law), the assessment of the nature and extent of international legal obligations regarding space traffic coordination, as well as the appropriate liability regime applicable to AI-based technologies when operating for space traffic coordination, taking into particular consideration the dense regulatory developments at EU level. In addition, the prospects of institutionalizing international cooperation and promoting an international governance system, together with the challenges of establishment of a comprehensive international STM regime are revisited in the light of intervention of AI technologies. This paper aims at examining regulatory implications advanced by the use of AI technology in the context of space traffic management operations and its key correlating concepts (SSA, space debris mitigation) drawing in particular on international and regional considerations in the field of STM (e.g. UNCOPUOS, International Academy of Astronautics, European Space Agency, among other actors), the promising advancements of the EU approach to AI regulation and, last but not least, national approaches regarding the use of AI in the context of space traffic management, in toto. Acknowledgment: The present work was co-funded by the European Union and Greek national funds through the Operational Program "Human Resources Development, Education and Lifelong Learning " (NSRF 2014-2020), under the call "Supporting Researchers with an Emphasis on Young Researchers – Cycle B" (MIS: 5048145).

Keywords: artificial intelligence, space traffic management, space situational awareness, space debris

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Authors: Aisha Adam

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This study critically examines the transformative impact of automated decision-making (ADM) and artificial intelligence (AI) systems on South African labour law. As AI technologies increasingly infiltrate workplaces, existing liability frameworks face challenges in addressing the unique complexities presented by these innovations. This article explores the necessity of redefining liability to accommodate the nuanced landscape of ADM and AI within South African labour law. It emphasises the importance of ensuring responsible deployment and safeguarding the rights of workers amid evolving technological dynamics. This research investigates the central concern of fairness, bias, and discrimination in ADM and AI decision-making. Focusing on algorithmic bias and discriminatory outcomes, the paper advocates for the integration of mechanisms within the South African legal framework, particularly under the Promotion of Equality and Prevention of Unfair Discrimination Act (PEPUDA) and the Employment Equity Act (EEA). The study scrutinises the shifting dynamics of the employment relationship, calling for clear guidelines on the responsibilities and liabilities of employers, employees, and technology providers. Furthermore, the article analyses legal and policy responses to ADM and AI within South African labour law, exploring potential amendments to legislation, guidelines, and codes of practice. It assesses the role of regulatory bodies, specifically the Commission for Conciliation, Mediation, and Arbitration (CCMA), in overseeing and enforcing responsible practices in the workplace. Lastly, the research evaluates the impact of ADM and AI on human and social rights in the South African context. Emphasising the protection of constitutional rights, including fair labour practices, privacy, and equality, the study proposes remedies and safeguards. It advocates for a multidisciplinary approach involving legal, technological, and ethical considerations to redefine liability in South African labour law effectively. The article contends that a shift from accountability to responsibility is crucial for promoting fairness, antidiscrimination, and the protection of human and social rights in the age of automated decision-making. It calls for collaborative efforts among stakeholders to shape responsible practices and redefine liability in this evolving technological landscape.

Keywords: automated decision-making, artificial intelligence, labour law, vicarious liability

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Authors: Taliah Foudah, Sana Masri, Jana Al Ghamdi, Rimaz Alzaaqi

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This research project explores the creative works of the app Metui, which allows consumers to edit their photos and use the new and popular AI feature, which turns any photo into a cartoon-like animated image with beautified enhancements. Studying this AI app demonstrates the significance of the ability in which AI can develop intricate designs which verily replicate the human mind. Our goal was to investigate the Metui app by asking our audience certain questions about its functionality and their personal feelings about its credibility as well as their beliefs as to how this app will add to the future of the AI generation, both positively and negatively. Their responses were further explored by analyzing the questions and responses thoroughly and calculating the results through pie charts. Overall, it was concluded that the Metui app is a powerful step forward for AI by replicating the intelligence of humans and its creativity to either benefit society or do the opposite.

Keywords: AI Art, Meitu, application, photo editing

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Authors: Fatima Faiza Ahmed, Syed Farrukh Hussain

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The last few decades have seen a large proportion of our population bending towards e-learning technologies, starting from learning tools used in primary and elementary schools to competency based e-learning systems specifically designed for applications like finance and marketing. The huge diversity in this crowd brings about a large number of challenges for the designers of these e-learning systems, one of which is the adaptability of such systems. This paper focuses on adaptability in the learning material in an e-learning course and how artificial intelligence and the semantic web can be used as an effective tool for this purpose. The study proved that the semantic web, still a hot topic in the area of computer science can prove to be a powerful tool in designing and implementing adaptable e-learning systems.

Keywords: adaptable e-learning, HTMLParser, information extraction, semantic web

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Authors: Javad Moghtader Kargaran

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Today, attention to knowledge and management Knowledge as a strategy is very important has taken with economy becoming knowledge-oriented, how and knowing the effective management and integration of different types Knowledge (obvious-implicit) to preserve and create advantage. Competition has become very important. Knowledge is a valuable resource for empowering organizations in the direction of innovation and competition. Due to the importance of human resources in the survival of organizations, extensive efforts are made to empower them. This knowledge can lead to awareness among employees. Employees and the knowledge that is in their minds are very valuable resources for the organization, which must be managed and developed. In fact, the ultimate goal of knowledge management is to increase the intelligence and productivity of employees and the organization.

Keywords: knowledge, management, productivity, human

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Authors: Raid R. O. Al-Nima, S. S. Dlay, W. L. Woo

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A relationship between face and signature biometrics is established in this paper. A new approach is developed to predict faces from signatures by using artificial intelligence. A multilayer perceptron (MLP) neural network is used to generate face details from features extracted from signatures, here face is the physical biometric and signatures is the behavioural biometric. The new method establishes a relationship between the two biometrics and regenerates a visible face image from the signature features. Furthermore, the performance efficiencies of our new technique are demonstrated in terms of minimum error rates compared to published work.

Keywords: behavioural biometric, face biometric, neural network, physical biometric, signature biometric

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Authors: Stamatis Zoras, Vasilis Evagelopoulos, Theodoros Staurakas

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The association between elevated air pollution levels and extreme climate conditions (temperature, particulate matter, ozone levels, etc.) and mental consequences has been, recently, the focus of significant number of studies. It varies depending on the time of the year it occurs either during the hot period or cold periods but, specifically, when extreme air pollution and weather events are observed, e.g. air pollution episodes and persistent heatwaves. It also varies spatially due to different effects of air quality and climate extremes to human health when considering metropolitan or rural areas. An air pollutant concentration and a climate extreme are taking a different form of impact if the focus area is countryside or in the urban environment. In the built environment the climate extreme effects are driven through the formed microclimate which must be studied more efficiently. Variables such as biological, age groups etc may be implicated by different environmental factors such as increased air pollution/noise levels and overheating of buildings in comparison to rural areas. Gridded air quality and climate variables derived from the land surface observations network of West Macedonia in Greece will be analysed against mortality data in a spatial format in the region of West Macedonia. Artificial intelligence (AI) tools will be used for data correction and prediction of health deterioration with climatic conditions and air pollution at local scale. This would reveal the built environment implications against the countryside. The air pollution and climatic data have been collected from meteorological stations and span the period from 2000 to 2009. These will be projected against the mortality rates data in daily, monthly, seasonal and annual grids. The grids will be operated as AI-based warning models for decision makers in order to map the health conditions in rural and urban areas to ensure improved awareness of the healthcare system by taken into account the predicted changing climate conditions. Gridded data of climate conditions, air quality levels against mortality rates will be presented by AI-analysed gridded indicators of the implicated variables. An Al-based gridded warning platform at local scales is then developed for future system awareness platform for regional level.

Keywords: air quality, artificial inteligence, climatic conditions, mortality

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Authors: Kerols Seif Said Botros

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The relationship between development and human rights has been debated for a long time. Various principles, from the right to development to development-based human rights, are applied to understand the dynamics between these two concepts. Despite the measures calculated, the connection between enhancement and human rights remains vague. Despite, the connection between these two opinions and the need to strengthen human rights have increased in recent years. It will then be examined whether the right to sustainable development is acceptable or not. In various human rights instruments and this is a good vibe to the request cited above. The book then cites domestic and international human rights treaties, as well as jurisprudence and regulations defining human rights institutions, to support this view.

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security.

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Authors: Lina Zhong, Sunny Sun, Rob Law

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Artificial intelligence has been developing rapidly. Previous studies have evaluated hotel technology either from an employee or consumer perspective. However, impacts, which mainly include the social and economic impacts of hotel robots, are unknown as they are newly introduced. To bridge the aforementioned research gap, this study evaluates hotel robots from contextual, diagnostic, evaluative, and strategic aspects using framework analysis as a basis to assist hotel managers in real-time hotel marketing strategy management, adjustment and revenue achievement. Findings show that, from a consumer perspective, the overall acceptance of hotel robots is low. The main implication is that the cost of hotel robots should be carefully estimated, and the investment should be made based on phases.

Keywords: application, evaluation, framework analysis, hotel robot

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Authors: Seyedabdolali Mortazavi Kamalabadi, Faegheh Taheran

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By giving an overview of previous arguments and findings concerned with the concept of competitive advantage, first, we define the overall concept of competitive advantage and discuss nuances of understanding such an important and strategic idea. Finally, by considering the major concerns of marketing academia, including globalization, AI-based technologies, consumer well-being, and internal coopetition between a firm’s units, fruitful avenues to be explored by future studies are presented in the form of research propositions. In the end, relevant gaps mentioned by numerous studies that are worth investigating are demonstrated.

Keywords: artificial intelligence, competitive advantage, consumer well-being, coopetition, globalization, literature review, temporary competitive advantage

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Authors: Mina Sadat Orooje, Mohammad Mehdi Latifi, Behnam Fereydooni Eftekhari

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The integration of cutting-edge Internet of Things (IoT) technologies with advanced Artificial Intelligence (AI) systems is revolutionizing healthcare facility management. However, the current landscape of hospital building maintenance suffers from slow, repetitive, and disjointed processes, leading to significant financial, resource, and time losses. Additionally, the potential of Building Information Modeling (BIM) in facility maintenance is hindered by a lack of data within digital models of built environments, necessitating a more streamlined data collection process. This paper presents a robust framework that harmonizes AI with BIM-IoT technology to elevate healthcare Facility Maintenance Management (FMM) and address these pressing challenges. The methodology begins with a thorough literature review and requirements analysis, providing insights into existing technological landscapes and associated obstacles. Extensive data collection and analysis efforts follow to deepen understanding of hospital infrastructure and maintenance records. Critical AI algorithms are identified to address predictive maintenance, anomaly detection, and optimization needs alongside integration strategies for BIM and IoT technologies, enabling real-time data collection and analysis. The framework outlines protocols for data processing, analysis, and decision-making. A prototype implementation is executed to showcase the framework's functionality, followed by a rigorous validation process to evaluate its efficacy and gather user feedback. Refinement and optimization steps are then undertaken based on evaluation outcomes. Emphasis is placed on the scalability of the framework in real-world scenarios and its potential applications across diverse healthcare facility contexts. Finally, the findings are meticulously documented and shared within the healthcare and facility management communities. This framework aims to significantly boost maintenance efficiency, cut costs, provide decision support, enable real-time monitoring, offer data-driven insights, and ultimately enhance patient safety and satisfaction. By tackling current challenges in healthcare facility maintenance management it paves the way for the adoption of smarter and more efficient maintenance practices in healthcare facilities.

Keywords: artificial intelligence, building information modeling, healthcare facility maintenance, internet of things integration, maintenance efficiency

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Authors: Amin Nazerzadeh, Afsaneh Nouri Houshyar , Azadeh Noori Hoshyar

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With the rapid growing of information technology, the industry and manufacturing systems are becoming more automated. Therefore, achieving the highly accurate automatic systems with reliable security is becoming more critical. Biometrics that refers to identifying individual based on physiological or behavioral traits are unique identifiers provide high reliability and security in different industrial systems. As biometric cannot easily be transferred between individuals or copied, it has been receiving extensive attention. Due to the importance of security applications, this paper provides an overview on biometrics and discuss about background, types and applications of biometric as an effective tool for the industrial applications.

Keywords: Industial and manufacturing applications, intelligence and security, information technology, recognition; security technology; biometrics

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Authors: Zheng Yuxun

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This review critically assesses the advancements and prospective developments in defect detection methodologies within the semiconductor industry, an essential domain that significantly affects the operational efficiency and reliability of electronic components. As semiconductor devices continue to decrease in size and increase in complexity, the precision and efficacy of defect detection strategies become increasingly critical. Tracing the evolution from traditional manual inspections to the adoption of advanced technologies employing automated vision systems, artificial intelligence (AI), and machine learning (ML), the paper highlights the significance of precise defect detection in semiconductor manufacturing by discussing various defect types, such as crystallographic errors, surface anomalies, and chemical impurities, which profoundly influence the functionality and durability of semiconductor devices, underscoring the necessity for their precise identification. The narrative transitions to the technological evolution in defect detection, depicting a shift from rudimentary methods like optical microscopy and basic electronic tests to more sophisticated techniques including electron microscopy, X-ray imaging, and infrared spectroscopy. The incorporation of AI and ML marks a pivotal advancement towards more adaptive, accurate, and expedited defect detection mechanisms. The paper addresses current challenges, particularly the constraints imposed by the diminutive scale of contemporary semiconductor devices, the elevated costs associated with advanced imaging technologies, and the demand for rapid processing that aligns with mass production standards. A critical gap is identified between the capabilities of existing technologies and the industry's requirements, especially concerning scalability and processing velocities. Future research directions are proposed to bridge these gaps, suggesting enhancements in the computational efficiency of AI algorithms, the development of novel materials to improve imaging contrast in defect detection, and the seamless integration of these systems into semiconductor production lines. By offering a synthesis of existing technologies and forecasting upcoming trends, this review aims to foster the dialogue and development of more effective defect detection methods, thereby facilitating the production of more dependable and robust semiconductor devices. This thorough analysis not only elucidates the current technological landscape but also paves the way for forthcoming innovations in semiconductor defect detection.

Keywords: semiconductor defect detection, artificial intelligence in semiconductor manufacturing, machine learning applications, technological evolution in defect analysis

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Authors: Mayar Almasri, Rosa Helmi, Rayana Enany

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This report describes the features of AutoCAD software released by Adobe. It explains how the program makes it easier for engineers and designers and reduces their time and effort spent using AutoCAD. Moreover, it highlights how AutoCAD works, how some of the commands used in it, such as Shortcut, make it easy to use, and features that make it accurate in measurements. The results of the report show that most users of this program are designers and engineers, but few people know about it and find it easy to use. They prefer to use it because it is easy to use, and the shortcut commands shorten a lot of time for them. The feature got a high rate and some suggestions for improving AutoCAD in Aperture, but it was a small percentage, and the highest percentage was that they didn't need to improve the program, and it was good.

Keywords: artificial intelligence, design, planning, commands, autodesk, dimensions

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Authors: Abdallah Al-ZoubI, Mamoun Dmour

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The spectrum of IoT devices is becoming widely diversified, entering almost all possible fields and finding applications in industry, health, finance, logistics, education, to name a few. The IoT active endpoint sensors and devices exceeded the 12 billion mark in 2021 and are expected to reach 27 billion in 2025, with over $34 billion in total market value. This sheer rise in numbers and use of IoT devices bring with it considerable concerns regarding data storage, analysis, manipulation and protection. IoT Blockchain-based systems have recently been proposed as a decentralized solution for large-scale data storage and protection. COVID-19 has actually accelerated the desire to utilize IoT devices as it impacted both demand and supply and significantly affected several regions due to logistic reasons such as supply chain interruptions, shortage of shipping containers and port congestion. An IoT-blockchain system is proposed to handle big data generated by a distributed network of sensors and controllers in an interactive manner. The system is designed using the Ethereum platform, which utilizes smart contracts, programmed in solidity to execute and manage data generated by IoT sensors and devices. such as Raspberry Pi 4, Rasbpian, and add-on hardware security modules. The proposed system will run a number of applications hosted by a local machine used to validate transactions. It then sends data to the rest of the network through InterPlanetary File System (IPFS) and Ethereum Swarm, forming a closed IoT ecosystem run by blockchain where a number of distributed IoT devices can communicate and interact, thus forming a closed, controlled environment. A prototype has been deployed with three IoT handling units distributed over a wide geographical space in order to examine its feasibility, performance and costs. Initial results indicated that big IoT data retrieval and storage is feasible and interactivity is possible, provided that certain conditions of cost, speed and thorough put are met.

Keywords: IoT devices, blockchain, Ethereum, big data

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Authors: Muhammad Bilal Shaikh

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Mineral processing plants play a pivotal role in extracting valuable minerals from raw ores, contributing significantly to various industries. However, the optimization of plant throughput remains a complex challenge, necessitating innovative approaches for increased efficiency and productivity. This research paper investigates the application of Multimodal Artificial Intelligence (MAI) techniques to address this challenge, aiming to improve overall plant throughput in mineral processing operations. The integration of multimodal AI leverages a combination of diverse data sources, including sensor data, images, and textual information, to provide a holistic understanding of the complex processes involved in mineral extraction. The paper explores the synergies between various AI modalities, such as machine learning, computer vision, and natural language processing, to create a comprehensive and adaptive system for optimizing mineral processing plants. The primary focus of the research is on developing advanced predictive models that can accurately forecast various parameters affecting plant throughput. Utilizing historical process data, machine learning algorithms are trained to identify patterns, correlations, and dependencies within the intricate network of mineral processing operations. This enables real-time decision-making and process optimization, ultimately leading to enhanced plant throughput. Incorporating computer vision into the multimodal AI framework allows for the analysis of visual data from sensors and cameras positioned throughout the plant. This visual input aids in monitoring equipment conditions, identifying anomalies, and optimizing the flow of raw materials. The combination of machine learning and computer vision enables the creation of predictive maintenance strategies, reducing downtime and improving the overall reliability of mineral processing plants. Furthermore, the integration of natural language processing facilitates the extraction of valuable insights from unstructured textual data, such as maintenance logs, research papers, and operator reports. By understanding and analyzing this textual information, the multimodal AI system can identify trends, potential bottlenecks, and areas for improvement in plant operations. This comprehensive approach enables a more nuanced understanding of the factors influencing throughput and allows for targeted interventions. The research also explores the challenges associated with implementing multimodal AI in mineral processing plants, including data integration, model interpretability, and scalability. Addressing these challenges is crucial for the successful deployment of AI solutions in real-world industrial settings. To validate the effectiveness of the proposed multimodal AI framework, the research conducts case studies in collaboration with mineral processing plants. The results demonstrate tangible improvements in plant throughput, efficiency, and cost-effectiveness. The paper concludes with insights into the broader implications of implementing multimodal AI in mineral processing and its potential to revolutionize the industry by providing a robust, adaptive, and data-driven approach to optimizing plant operations. In summary, this research contributes to the evolving field of mineral processing by showcasing the transformative potential of multimodal artificial intelligence in enhancing plant throughput. The proposed framework offers a holistic solution that integrates machine learning, computer vision, and natural language processing to address the intricacies of mineral extraction processes, paving the way for a more efficient and sustainable future in the mineral processing industry.

Keywords: multimodal AI, computer vision, NLP, mineral processing, mining

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Authors: Ashutosh Mishra, Nikhil Goyal

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A 3D facial emotion recognition model based on deep learning is proposed in this paper. Two convolution layers and a pooling layer are employed in the deep learning architecture. After the convolution process, the pooling is finished. The probabilities for various classes of human faces are calculated using the sigmoid activation function. To verify the efficiency of deep learning-based systems, a set of faces. The Kaggle dataset is used to verify the accuracy of a deep learning-based face recognition model. The model's accuracy is about 65 percent, which is lower than that of other facial expression recognition techniques. Despite significant gains in representation precision due to the nonlinearity of profound image representations.

Keywords: facial recognition, computational intelligence, convolutional neural network, depth map

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Authors: Alexander Heckman, Rohan Goswami, Zachi Attia, Paul Friedman, Peter Noseworthy, Demilade Adedinsewo, Pablo Moreno-Franco, Rickey Carter, Tathagat Narula

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Background: Postoperative atrial fibrillation (POAF) is associated with adverse health consequences, higher costs, and longer hospital stays. Utilizing existing predictive models that rely on clinical variables and circulating biomarkers, multiple societies have published recommendations on the treatment and prevention of POAF. Although reasonably practical, there is room for improvement and automation to help individualize treatment strategies and reduce associated complications. Methods and Results: In this retrospective cohort study of solid organ transplant recipients, we evaluated the diagnostic utility of a previously developed AI-based ECG prediction for silent AF on the development of POAF within 30 days of transplant. A total of 2261 non-cardiac transplant patients without a preexisting diagnosis of AF were found to have a 5.8% (133/2261) incidence of POAF. While there were no apparent sex differences in POAF incidence (5.8% males vs. 6.0% females, p=.80), there were differences by race and ethnicity (p<0.001 and 0.035, respectively). The incidence in white transplanted patients was 7.2% (117/1628), whereas the incidence in black patients was 1.4% (6/430). Lung transplant recipients had the highest incidence of postoperative AF (17.4%, 37/213), followed by liver (5.6%, 56/1002) and kidney (3.6%, 32/895) recipients. The AUROC in the sample was 0.62 (95% CI: 0.58-0.67). The relatively low discrimination may result from undiagnosed AF in the sample. In particular, 1,177 patients had at least 1 AI-ECG screen for AF pre-transplant above .10, a value slightly higher than the published threshold of 0.08. The incidence of POAF in the 1104 patients without an elevated prediction pre-transplant was lower (3.7% vs. 8.0%; p<0.001). While this supported the hypothesis that potentially undiagnosed AF may have contributed to the diagnosis of POAF, the utility of the existing AI-ECG screening algorithm remained modest. When the prediction for POAF was made using the first postoperative ECG in the sample without an elevated screen pre-transplant (n=1084 on account of n=20 missing postoperative ECG), the AUROC was 0.66 (95% CI: 0.57-0.75). While this discrimination is relatively low, at a threshold of 0.08, the AI-ECG algorithm had a 98% (95% CI: 97 – 99%) negative predictive value at a sensitivity of 66% (95% CI: 49-80%). Conclusions: This study's principal finding is that the incidence of POAF is rare, and a considerable fraction of the POAF cases may be latent and undiagnosed. The high negative predictive value of AI-ECG screening suggests utility for prioritizing monitoring and evaluation on transplant patients with a positive AI-ECG screening. Further development and refinement of a post-transplant-specific algorithm may be warranted further to enhance the diagnostic yield of the ECG-based screening.

Keywords: artificial intelligence, atrial fibrillation, cardiology, transplant, medicine, ECG, machine learning

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Authors: Bodhayan Nandi, Shubhajit Roy Chowdhury

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This paper presents the development of a portable blood glucose monitoring device based on Near-Infrared Spectroscopy. The system supports Internet connectivity through WiFi and uploads the time series data of glucose concentration of patients to a server. In addition, the server is given sufficient intelligence to predict the future pathophysiological state of a patient given the current and past pathophysiological data. This will enable to prognosticate the approaching critical condition of the patient much before the critical condition actually occurs.The server hosts web applications to allow authorized users to monitor the data remotely.

Keywords: non invasive, blood glucose concentration, microcontroller, IoT, application server, database server

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Authors: Julio Vega

Abstract:

Expression recognition is a field of Artificial Intelligence whose main objectives are to recognize basic forms of affective expression that appear on people’s faces and contributing to behavioral studies. In this work, a ROS node has been developed that, based on Deep Learning techniques, is capable of detecting the facial expressions of the people that appear in the image. These algorithms were optimized so that they can be executed in real time on an embedded platform. The experiments were carried out in a PC with a USB camera and in a Raspberry Pi 4 with a PiCamera. The final results shows a plausible system, which is capable to work in real time even in an embedded platform.

Keywords: python, low-cost, raspberry pi, emotion detection, human-robot interaction, ROS node

Procedia PDF Downloads 105