Search results for: digital transformation artificial intelligence

Authors: Ilyes Athimni, Mouna Bouzazi, Mongi Boulehmi, Ahmed Ferchichi

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The principles of good governance, universal values, and digitization are among the tools to fight corruption and improve the quality of service delivery. In recent years, these tools have become one of the most controversial topics in the field of education and a concern of many international organizations and institutions against the problem of corruption. Corruption in the education sector, particularly in higher education, has negative impacts on the quality of education systems and on the quality of administrative or educational services. Currently, the health crisis due to the spread of the COVID-19 pandemic reveals the difficulties encountered by education systems in most countries of the world. Due to the poor governance of these systems, many educational institutions were unable to continue working remotely. To respond to these problems encountered by most education systems in many countries of the world, our initiative is to propose a methodology to reinvent education systems based on global values and digital technologies. This methodology includes a work strategy for educational institutions, whether in the provision of administrative services or in the teaching method, based on information and communication technologies (ICTs), intelligence artificial, and intelligent agents. In addition, we will propose a supervisory law that will be implemented and monitored by intelligent agents to improve accountability, transparency, and accountability in educational institutions. On the other hand, we will implement and evaluate a field experience by applying the proposed methodology in the operation of an educational institution and comparing it to the traditional methodology through the results of teaching an educational program. With these specifications, we can reinvent quality education systems. We also expect the results of our proposal to play an important role at local, regional, and international levels in motivating governments of countries around the world to change their university governance policies.

Keywords: artificial intelligence, corruption in education, distance learning, education systems, ICTs, intelligent agents, good governance

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Authors: Selcuk Aslan, Dervis Karaboga, Celal Ozturk

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Artificial Bee Colony (ABC) algorithm is one of the most successful swarm intelligence based metaheuristics. It has been applied to a number of constrained or unconstrained numerical and combinatorial optimization problems. In this paper, we presented a parallelized version of ABC algorithm by adapting employed and onlooker bee phases to the Compute Unified Device Architecture (CUDA) platform which is a graphical processing unit (GPU) programming environment by NVIDIA. The execution speed and obtained results of the proposed approach and sequential version of ABC algorithm are compared on functions that are typically used as benchmarks for optimization algorithms. Tests on standard benchmark functions with different colony size and number of parameters showed that proposed parallelization approach for ABC algorithm decreases the execution time consumed by the employed and onlooker bee phases in total and achieved similar or better quality of the results compared to the standard sequential implementation of the ABC algorithm.

Keywords: Artificial Bee Colony algorithm, GPU computing, swarm intelligence, parallelization

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Authors: Thomas Schalow

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This paper posits the need to take a cross-cultural approach to communication with non-human cultures and intelligences in order to meet the following three imminent contingencies: communicating with sentient biological intelligences, communicating with extraterrestrial intelligences, and communicating with artificial super-intelligences. The paper begins with a discussion of how intelligence emerges. It disputes some common assumptions we maintain about consciousness, intention, and language. The paper next explores cross-cultural communication among humans, including non-sapiens species. The next argument made is that we need to become much more serious about communicating with the non-human, intelligent life forms that already exist around us here on Earth. There is an urgent need to broaden our definition of communication and reach out to the other sentient life forms that inhabit our world. The paper next examines the science and philosophy behind CETI (communication with extraterrestrial intelligences) and how it has proven useful, even in the absence of contact with alien life. However, CETI’s assumptions and methodology need to be revised and based on the cross-cultural approach to communication proposed in this paper if we are truly serious about finding and communicating with life beyond Earth. The final theme explored in this paper is communication with non-biological super-intelligences using a cross-cultural communication approach. This will present a serious challenge for humanity, as we have never been truly compelled to converse with other species, and our failure to seriously consider such intercourse has left us largely unprepared to deal with communication in a future that will be mediated and controlled by computer algorithms. Fortunately, our experience dealing with other human cultures can provide us with a framework for this communication. The basic assumptions behind intercultural communication can be applied to the many types of communication envisioned in this paper if we are willing to recognize that we are in fact dealing with other cultures when we interact with other species, alien life, and artificial super-intelligence. The ideas considered in this paper will require a new mindset for humanity, but a new disposition will prepare us to face the challenges posed by a future dominated by artificial intelligence.

Keywords: artificial intelligence, CETI, communication, culture, language

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Authors: Yashar Ibragimov, Nino Berishvili

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Technological, economic and other innovation-related advances of the 21st century have influenced the old, traditional business models. Presently, organizational change has become an integral part of corporate strategy for the majority of businesses. Such shifts have resulted in both new challenges and opportunities. The expansion of the use of information and communication technologies has driven fundamental shifts towards digital change. Organizations are being forced to revise processes, goals and overall mission in order to stay competitive in the marketplace. However, the implementation of digital transformation brings uncertainty, causes stress and raises concerns about future jobs. The study employs systematic literature review to fill the gap in understanding relationship between employee motivation and commitment during the transformation. A conceptual model proposes the antecedents (OCB and Leader Member Exchange) of employee motivation and investigates its impact on employee commitment to change. The utilized model elucidates how to maintain employee motivation and commitment in the context of organizational transformation and sets the ground for future research.

Keywords: employee motivation, change commitment, change management, leader member exchange, organizational citizenship behavior

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Authors: Nabilah Filzah Mohd Radzuan, Andi Putra, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

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Precipitation forecast is important to avoid natural disaster incident which can cause losses in the involved area. This paper reviews three techniques logistic regression, decision tree, and random forest which are used in making precipitation forecast. These combination techniques through the vector auto-regression (VAR) model help in finding the advantages and strengths of each technique in the forecast process. The data-set contains variables of the rain’s domain. Adaptation of artificial intelligence techniques involved in rain domain enables the forecast process to be easier and systematic for precipitation forecast.

Keywords: logistic regression, decisions tree, random forest, VAR model

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Authors: Shalini Tripathi, Pardeep Kumar

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The method of translating observed symptoms into disease names is known as disease diagnosis. The ability to solve clinical problems in a complex manner is critical to a doctor's effectiveness in providing health care. The accuracy of his or her expertise is crucial to the survival and well-being of his or her patients. Artificial Intelligence (AI) has a huge economic influence depending on how well it is applied. In the medical sector, human brain-simulated intellect can help not only with classification accuracy, but also with reducing diagnostic time, cost and pain associated with pathologies tests. In light of AI's present and prospective applications in the biomedical, we will identify them in the paper based on potential benefits and risks, social and ethical consequences and issues that might be contentious but have not been thoroughly discussed in publications and literature. Current apps, personal tracking tools, genetic tests and editing programmes, customizable models, web environments, virtual reality (VR) technologies and surgical robotics will all be investigated in this study. While AI holds a lot of potential in medical diagnostics, it is still a very new method, and many clinicians are uncertain about its reliability, specificity and how it can be integrated into clinical practice without jeopardising clinical expertise. To validate their effectiveness, more systemic refinement of these implementations, as well as training of physicians and healthcare facilities on how to effectively incorporate these strategies into clinical practice, will be needed.

Keywords: Artificial Intelligence, medical diagnosis, virtual reality, healthcare ethical implications 

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Authors: Muneeb Ahmad, Ali Raza

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A computational method inspired by the immune system (IS) is presented, leveraging its shared characteristics of robustness, fault tolerance, scalability, and adaptability with swarm intelligence. This method aims to showcase flocking behaviors in a swarm of robots (SR). The innate part of the IS offers a variety of reactive and probabilistic cell functions alongside its self-regulation mechanism which have been translated to enable swarming behaviors. Although, the research is specially focused on flocking behaviors in a variety of simulated environments using e-puck robots in a physics-based simulator (CoppeliaSim); the artificial innate immune system (AIIS) can exhibit other swarm behaviors as well. The effectiveness of the immuno-inspired approach has been established with extensive experimentations, for scalability and adaptability, using standard swarm benchmarks as well as the immunological regulatory functions (i.e., Dendritic Cells’ Maturity and Inflammation). The AIIS-based approach has proved to be a scalable and adaptive solution for emulating the flocking behavior of SR.

Keywords: artificial innate immune system, flocking swarm, immune system, swarm intelligence

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Authors: Peter Burggräf, Matthias Dannapfel, Hanno Voet, Patrick-Benjamin Bök, Jérôme Uelpenich, Julian Hoppe

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The increasing digitalization of value chains can help companies to handle rising complexity in their processes and thereby reduce the steadily increasing planning and control effort in order to raise performance limits. Due to technological advances, companies face the challenge of smart value chains for the purpose of improvements in productivity, handling the increasing time and cost pressure and the need of individualized production. Therefore, companies need to ensure quick and flexible decisions to create self-optimizing processes and, consequently, to make their production more efficient. Lean production, as the most commonly used paradigm for complexity reduction, reaches its limits when it comes to variant flexible production and constantly changing market and environmental conditions. To lift performance limits, which are inbuilt in current value chains, new methods and tools must be applied. Digitalization provides the potential to derive these new methods and tools. However, companies lack the experience to harmonize different digital technologies. There is no practicable framework, which instructs the transformation of current value chains into digital pervasive value chains. Current research shows that a connection between lean production and digitalization exists. This link is based on factors such as people, technology and organization. In this paper, the introduced method for the determination of digitally pervasive value chains takes the factors people, technology and organization into account and extends existing approaches by a new dimension. It is the first systematic approach for the digital transformation of lean production and consists of four steps: The first step of ‘target definition’ describes the target situation and defines the depth of the analysis with regards to the inspection area and the level of detail. The second step of ‘analysis of the value chain’ verifies the lean-ability of processes and lies in a special focus on the integration capacity of digital technologies in order to raise the limits of lean production. Furthermore, the ‘digital evaluation process’ ensures the usefulness of digital adaptions regarding their practicability and their integrability into the existing production system. Finally, the method defines actions to be performed based on the evaluation process and in accordance with the target situation. As a result, the validation and optimization of the proposed method in a German company from the electronics industry shows that the digital transformation of current value chains based on lean production achieves a raise of their inbuilt performance limits.

Keywords: digitalization, digital transformation, Industrie 4.0, lean production, value chain

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Authors: Rattakorn Poonsuph

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Technological innovation plays a crucial role in virtual healthcare services. A growing number of telehealth platforms are concentrating on using digital tools to improve the quality and availability of care. As a result, telehealth represents an opportunity to redesign the way health services are delivered. The research objective is to discover a new business model for digital health services and related industries to participate with telehealth solutions. The business opportunity is valuable for healthcare investors as a startup company to further investigations or implement the telehealth platform. The paper presents a digital healthcare business model and business opportunities to related industries. These include digital healthcare services extending from a traditional business model and use cases of business opportunities to related industries. Although there are enormous business opportunities, telehealth is still challenging due to the patient adaption and digital transformation process within a healthcare organization.

Keywords: telehealth, Internet hospital, HealthTech, InsurTech

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Authors: Djeddi Khaled, Houssou Hind, Miloudi Abdellatif, Rabah Siham

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In the field of veterinary medicine, there is a growing application of artificial intelligence (AI) for diagnosing bovine mastitis, a prevalent inflammatory disease in dairy cattle. AI technologies, such as automated milking systems, have streamlined the assessment of key metrics crucial for managing cow health during milking and identifying prevalent diseases, including mastitis. These automated milking systems empower farmers to implement automatic mastitis detection by analyzing indicators like milk yield, electrical conductivity, fat, protein, lactose, blood content in the milk, and milk flow rate. Furthermore, reports highlight the integration of somatic cell count (SCC), thermal infrared thermography, and diverse systems utilizing statistical models and machine learning techniques, including artificial neural networks, to enhance the overall efficiency and accuracy of mastitis detection. According to a review of 15 publications, machine learning technology can predict the risk and detect mastitis in cattle with an accuracy ranging from 87.62% to 98.10% and sensitivity and specificity ranging from 84.62% to 99.4% and 81.25% to 98.8%, respectively. Additionally, machine learning algorithms and microarray meta-analysis are utilized to identify mastitis genes in dairy cattle, providing insights into the underlying functional modules of mastitis disease. Moreover, AI applications can assist in developing predictive models that anticipate the likelihood of mastitis outbreaks based on factors such as environmental conditions, herd management practices, and animal health history. This proactive approach supports farmers in implementing preventive measures and optimizing herd health. By harnessing the power of artificial intelligence, the diagnosis of bovine mastitis can be significantly improved, enabling more effective management strategies and ultimately enhancing the health and productivity of dairy cattle. The integration of artificial intelligence presents valuable opportunities for the precise and early detection of mastitis, providing substantial benefits to the dairy industry.

Keywords: artificial insemination, automatic milking system, cattle, machine learning, mastitis

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Authors: Sayor Ajfar Aaron, Mushfiqur Rahman, Sajjat Hossain Abir, Ashif Newaz

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This study delves into the development of an artificial intelligence application designed for real-time emotion recognition from human vocal patterns. Utilizing advanced machine learning algorithms, including deep learning and neural networks, the paper highlights both the technical challenges and potential opportunities in accurately interpreting emotional cues from speech. Key findings demonstrate the critical role of diverse training datasets and the impact of ambient noise on recognition accuracy, offering insights into future directions for improving robustness and applicability in real-world scenarios.

Keywords: artificial intelligence, convolutional neural network, emotion recognition, vocal patterns

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Authors: Sirine Sayed, Mostapha Tarfaoui, Abdelmalek Toumi, Youssef Qarssis, Mohamed Daly, Chokri Bouraoui

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This paper delves into the combination of additive manufacturing (AM) and artificial intelligence (AI) to solve challenges related to the mechanical behavior of AM-produced parts. The article highlights the fundamentals and benefits of additive manufacturing, including creating complex geometries, optimizing material use, and streamlining manufacturing processes. The paper also addresses the challenges associated with additive manufacturing, such as ensuring stable mechanical performance and material properties. The role of AI in improving the static behavior of AM-produced parts, including machine learning, especially the neural network, is to make regression models to analyze the large amounts of data generated during experimental tests. It investigates the potential synergies between AM and AI to achieve enhanced functions and personalized mechanical properties. The mechanical behavior of parts produced using additive manufacturing methods can be further improved using design optimization, structural analysis, and AI-based adaptive manufacturing. The article concludes by emphasizing the importance of integrating AM and AI to enhance mechanical operations, increase reliability, and perform advanced functions, paving the way for innovative applications in different fields.

Keywords: additive manufacturing, mechanical behavior, artificial intelligence, machine learning, neural networks, reliability, advanced functionalities

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Authors: Yuhanis Yusof, Zuriani Mustaffa, Siti Sakira Kamaruddin

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Data mining is a fundamental technique in identifying patterns from large data sets. The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical. Prior to that, data are consolidated so that the resulting mining process may be more efficient. This study investigates the effect of different data normalization techniques, which are Min-max, Z-score, and decimal scaling, on Swarm-based forecasting models. Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC). Forecasting models are later developed to predict the daily spot price of crude oil and gasoline. Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max. Nevertheless, the GWO is more superior that ABC as its model generates the highest accuracy for both crude oil and gasoline price. Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.

Keywords: artificial bee colony, data normalization, forecasting, Grey Wolf optimizer

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Authors: Jing-Ma

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Digital transformation (DT) is an important way for manufacturing enterprises to shape new competitive advantages, and how to choose an effective DT strategy is crucial for enterprise growth and sustainable development. Rooted in strategic change theory, this paper incorporates the dimensions of managers' digital cognition, organizational conditions, and external environment into the same strategic analysis framework and integrates the dynamic QCA method and PSM method to study the antecedent grouping of the DT strategy mode of manufacturing enterprises and its impact on corporate profitability based on the data of listed manufacturing companies in China from 2015 to 2019. We find that the synergistic linkage of different dimensional elements can form six equivalent paths of high-level DT, which can be summarized as the proactive change mode of resource-capability dominated as well as adaptive response mode such as industry-guided resource replenishment. Capacity building under complex environments, market-industry synergy-driven, forced adaptation under peer pressure, and the managers' digital cognition play a non-essential but crucial role in this process. Except for individual differences in the market industry collaborative driving mode, other modes are more stable in terms of individual and temporal changes. However, it is worth noting that not all paths that result in high levels of DT can contribute to enterprise profitability, but only high levels of DT that result from matching the optimization of internal conditions with the external environment, such as industry technology and macro policies, can have a significant positive impact on corporate profitability.

Keywords: digital transformation, strategy mode, enterprise profitability, dynamic QCA, PSM approach

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Authors: Xiaochen Mu

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Whether in common law countries that adhere to the "commercial copyright theory" or in civil law countries that center around "author's rights," the standards for judging originality have undergone continuous adjustments in response to the development of information technology. The adherence to originality standards does not arbitrarily dictate that all types of works be judged according to a single standard of originality, nor does it rigidly ignore the changes in creative methods and dissemination models brought about by technology. Adjustments and interpretations should be allowed based on the different forms of expression of works. Appropriate adjustments and interpretations are our response to technological advancements. However, what should be upheld are the principles and bottom lines of these adjustments and interpretations, namely the legislative intent and purpose of copyright law, which are to encourage the creation and dissemination of outstanding cultural works and to promote the flourishing of culture.

Keywords: generative artificial intelligence, originality, works, copyright

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Authors: Moshe Davidian, Shlomo Mark, Yotam Lurie

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With the rapid development of technology and the concomitant growth in the capability of Artificial Intelligence (AI) systems and their power, the ethical challenges involved in these systems are also evolving and increasing. In recent years, various organizations, including governments, international institutions, professional societies, civic organizations, and commercial companies, have been choosing to address these various challenges by publishing ethical codes for AI systems. However, despite the apparent agreement that AI should be “ethical,” there is debate about the definition of “ethical artificial intelligence.” This study investigates the various AI ethical codes and their key ethical values. From the vast collection of codes that exist, it analyzes and compares 25 ethical codes that were found to be representative of different types of organizations. In addition, as part of its literature review, the study overviews data collected in three recent reviews of AI codes. The results of the analyses demonstrate a convergence around seven key ethical values. However, the key finding is that the different AI ethical codes eventually reflect the type of organization that designed the code; i.e., the organizations’ role as regulator, user, or developer affects the view of what ethical AI is. The results show a relationship between the organization’s role and the dominant values in its code. The main contribution of this study is the development of a list of the key values for all AI systems and specific values that need to impact the development and design of AI systems, but also allowing for differences according to the organization for which the system is being developed. This will allow an analysis of AI values in relation to stakeholders.

Keywords: artificial intelligence, ethical codes, principles, values

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Authors: Samer Muthana Sarsam, Abdul Samad Shibghatullah, Chit Su Mon, Abd Aziz Alias, Hosam Al-Samarraie

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Generative Artificial Intelligence chatbots (GAI chatbots) have emerged as promising tools in various domains, including higher education. However, their specific role within the educational context and the level of legal support for their implementation remain unclear. Therefore, this study aims to investigate the role of Bard, a newly developed GAI chatbot, in higher education. To achieve this objective, English tweets were collected from Twitter's free streaming Application Programming Interface (API). The Latent Dirichlet Allocation (LDA) algorithm was applied to extract latent topics from the collected tweets. User sentiments, including disgust, surprise, sadness, anger, fear, joy, anticipation, and trust, as well as positive and negative sentiments, were extracted using the NRC Affect Intensity Lexicon and SentiStrength tools. This study explored the benefits, challenges, and future implications of integrating GAI chatbots in higher education. The findings shed light on the potential power of such tools, exemplified by Bard, in enhancing the learning process and providing support to students throughout their educational journey.

Keywords: generative artificial intelligence chatbots, bard, higher education, topic modelling, sentiment analysis

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Authors: Shahruhaida Adayu Mohd Paili, Abd Latif Abdul Rahman, Asmadi Mohammed Ghazali

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Digital library was developed by many organizations, especially universities. The digital library can be considered as a new information system. Digital library brings many benefits to the users. There are many researches that have investigated the importance of the digital library, the acceptance, and continuance use of digital library. The investigation towards the digital library is important and it is crucial to understand the reason why users accept and continued use of digital library. Users can search the information and available resources through the digital library website. It is important to know the user’s perception towards the aesthetics of the digital library. Besides that, because of digital library provided information to the users, the researcher also needed to investigate the quality of information in digital library. This study used Extending the Unified Theory of Acceptance and Use of Technology (UTAUT2) in order to know the user’s intention to continued use of digital library.

Keywords: digital library, aesthetics, information quality, intention to continued use of digital library, UTAUT2

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Authors: PR

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Renewable energy is a sustainable substitute to fossil fuels, which are depleting and attributing to global warming as well as greenhouse gas emissions. Renewable energy innovations including solar, wind, and geothermal have grown significantly and play a critical role in meeting energy demands recently. Consequently, Artificial Intelligence (AI) could further enhance the benefits of renewable energy systems. The combination of renewable technologies and AI could facilitate the development of smart grids that can better manage energy distribution and storage. AI thus has the potential to optimize the efficiency and reliability of renewable energy systems, reduce costs, and improve their overall performance. The conventional methods of using smart micro-grids are to connect these micro-grids in series or parallel or a combination of series and parallel. Each of these methods has its advantages and disadvantages. In this study, the proposal of using the method of connecting microgrids in a spiral manner is investigated. One of the important reasons for choosing this type of structure is the two-way reinforcement and exchange of each inner layer with the outer and upstream layer. With this model, we have the ability to increase energy from a small amount to a significant amount based on exponential functions. The geometry used to close the smart microgrids is based on nature.This study provides an overview of the applications of algorithms and models of AI as well as its advantages and challenges in renewable energy systems.

Keywords: artificial intelligence, renewable energy sources, spiral model, optimize

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Authors: Jinan Laurentia Woo

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This paper explores the intersection of technology, music education, and social-emotional learning (SEL) with a focus on Latin America. It delves into the impact of music education on social-emotional skills development, highlighting the universal significance of music across various life stages. The integration of artificial intelligence (AI) in music education is discussed, emphasizing its potential to enhance learning experiences. The paper also examines the implementation of SEL strategies in Latin American public schools, emphasizing the importance of fostering social-emotional well-being in educational settings. Challenges such as unequal access to technology and education in the region are addressed, calling for further research and investment in tech-assisted music education.

Keywords: music education, social emotional learning, educational technology, Latin America, artificial intelligence, music

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Authors: Shunzuo Wu, Xudong Luo, Yuanxiu Liao

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Recently, the rapid development of deep learning makes artificial intelligence (AI) penetrate into many fields, replacing manual work there. In particular, AI systems also become a research focus in the field of automatic office. To meet real needs in automatic officiating, in this paper we develop an automatic form filling system. Specifically, it uses two classical neural network models and several word embedding models to classify various relevant information elicited from the Internet. When training the neural network models, we use less noisy and balanced data for training. We conduct a series of experiments to test my systems and the results show that our system can achieve better classification results.

Keywords: artificial intelligence and office, NLP, deep learning, text classification

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Authors: Sandhya Rao Mehta

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As ChatGpt and other Artificial Intelligence (AI) forms are becoming part of our regular academic world, the consequences are being gradually discussed. The extent to which an essay written by a student is itself of any value if it has been downloaded by some form of AI is perhaps central to this discourse. A larger question is whether writing should be taught as an academic skill at all. In literature classrooms, this has major consequences as writing a traditional paper is still the single most preferred form of assessment. This study suggests that it is imperative to investigate alternative forms of assessment in literature, not only because the existing forms can be written by AI, but in a larger sense, students are increasingly skeptical of the purpose of such work. The extent to which an essay actually helps the students professionally is a question that academia has not yet answered. This paper suggests that using real-world tasks like creating podcasts, video tutorials, and websites is a far better way to evaluate students' critical thinking and application of ideas, as well as to develop digital skills which are important to their future careers. Using the example of a course in literature, this study will examine the possibilities and challenges of creating digital projects as a way of confronting the complexities of student evaluation in the future. The study is based on a specific university English as a Foreign Language (EFL) context.

Keywords: assessment, literature, digital humanities, chatgpt

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Authors: Antti Nurminen, Avleen Malhi

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Significant progress in intelligent systems in combination with exceedingly wide application domains having machine learning as the core technology are usually opaque, non-intuitive, and commonly complex for human users. We use innovative IoT technology which monitors and analyzes moisture, humidity, luminosity and temperature levels to assist end users for optimization of environmental conditions for their houseplants. For plant health monitoring, we construct a system yielding the Normalized Difference Vegetation Index (NDVI), supported by visual validation by users. We run the system for a selected plant, basil, in varying environmental conditions to cater for typical home conditions, and bootstrap our AI with the acquired data. For end users, we implement a web based user interface which provides both instructions and explanations.

Keywords: explainable artificial intelligence, intelligent agent, IoT, NDVI

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Authors: Xiaojiao Duan, Zhaoxia Yi, Maria Assumpta Komugabe, Munirpallam A. Venkataramanan

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This study explores stakeholders' perceptions and use of AI in university admissions using a conceptual model. The model suggests that AI expertise mediates the relationship between various factors (positions, experience, perceived benefits, concerns) and the desire to adopt AI. By reviewing existing research, the study identifies variables, correlations, and research gaps. The findings highlight the influence of institutional positions, AI expertise, knowledge, perceived advantages, and concerns on attitudes and intentions toward AI implementation. The review provides a framework for future research, emphasizes ethical AI use, and offers practical insights for admissions stakeholders.

Keywords: artificial intelligence, college admissions, ethical considerations, technology adoption, perceptions of AI

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Authors: Silvia Zanazzi

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Artificial intelligence (AI) has become an integral part of our world, transitioning from scientific exploration to practical applications that impact daily life. The emergence of generative AI is reshaping education, prompting new questions about the role of teachers, the nature of learning, and the overall purpose of schooling. While AI offers the potential for optimizing teaching and learning processes, concerns about discrimination and bias arising from training data and algorithmic decisions persist. There is a risk of a disconnect between the rapid development of AI and the goals of building inclusive educational environments. The prevailing discourse on AI in education often prioritizes efficiency and individual skill acquisition. This narrow focus can undermine the importance of collaborative learning and shared experiences. A growing body of research challenges this perspective, advocating for AI that enhances, rather than replaces, human interaction in education. This study aims to examine the relationship between AI and education critically. Reviewing existing research will identify both AI implementation’s potential benefits and risks. The goal is to develop a framework that supports the ethical and effective integration of AI into education, ensuring it serves the needs of all learners. The theoretical reflection will be developed based on a review of national and international scientific literature on artificial intelligence in education. The primary objective is to curate a selection of critical contributions from diverse disciplinary perspectives and/or an inter- and transdisciplinary viewpoint, providing a state-of-the-art overview and a critical analysis of potential future developments. Subsequently, the thematic analysis of these contributions will enable the creation of a framework for understanding and critically analyzing the role of artificial intelligence in schools and education, highlighting promising directions and potential pitfalls. The expected results are (1) a classification of the cognitive biases present in representations of AI in education and the associated risks and (2) a categorization of potentially beneficial interactions between AI applications and teaching and learning processes, including those already in use or under development. While not exhaustive, the proposed framework will serve as a guide for critically exploring the complexity of AI in education. It will help to reframe dystopian visions often associated with technology and facilitate discussions on fostering synergies that balance the ‘dream’ of quality education for all with the realities of AI implementation. The discourse on artificial intelligence in education, highlighting reductionist models rooted in fragmented and utilitarian views of knowledge, has the merit of stimulating the construction of alternative perspectives that can ‘return’ teaching and learning to education, human growth, and the well-being of individuals and communities.

Keywords: education, artificial intelligence, teaching, learning

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Authors: Bruno Vilić Belina, Ivan Župan

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Smart industry concepts and digital transformation are very popular in many industries. They develop their own digital platforms, which have an important role in innovations and transactions. The main idea of smart industry digital platforms is central data collection, industrial data integration, and data usage for smart applications and services. This paper presents the development of a digital platform for building critical infrastructure protection systems in smart industries. Different service contraction modalities in service level agreements (SLAs), customer relationship management (CRM) relations, trends, and changes in business architectures (especially process business architecture) for the purpose of developing infrastructural production and distribution networks, information infrastructure meta-models and generic processes by critical infrastructure owner demanded by critical infrastructure law, satisfying cybersecurity requirements and taking into account hybrid threats are researched.

Keywords: cybersecurity, critical infrastructure, smart industries, digital platform

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Authors: Parisa Mansour

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Introduction: Early diagnosis of coronavirus disease (COVID-19) is extremely important to isolate and treat patients in time, thus preventing the spread of the disease, improving prognosis and reducing mortality. High-resolution computed tomography (HRCT) chest imaging and artificial intelligence (AI)-based analysis of HRCT chest images can play a central role in the treatment of patients with COVID-19. Objective: To investigate different chest HRCT findings in different stages of COVID-19 pneumonia and to evaluate the potential role of artificial intelligence in the quantitative assessment of lung parenchymal involvement in COVID-19 pneumonia. Materials and Methods: This retrospective observational study was conducted between May 1, 2020 and August 13, 2020. The study included 2169 patients with COVID-19 who underwent chest HRCT. HRCT images showed the presence and distribution of lesions such as: ground glass opacity (GGO), compaction, and any special patterns such as septal thickening, inverted halo, mark, etc. HRCT findings of the breast at different stages of the disease (early: andlt) 5 days, intermediate: 6-10 days and late stage: >10 days). A CT severity score (CTSS) was calculated based on the extent of lung involvement on HRCT, which was then correlated with clinical disease severity. Use of artificial intelligence; Analysis of CT pneumonia and quot; An algorithm was used to quantify the extent of pulmonary involvement by calculating the percentage of pulmonary opacity (PO) and gross opacity (PHO). Depending on the type of variables, statistically significant tests such as chi-square, analysis of variance (ANOVA) and post hoc tests were applied when appropriate. Results: Radiological findings were observed in HRCT chest in 1438 patients. A typical pattern of COVID-19 pneumonia, i.e., bilateral peripheral GGO with or without consolidation, was observed in 846 patients. About 294 asymptomatic patients were radiologically positive. Chest HRCT in the early stages of the disease mostly showed GGO. The late stage was indicated by such features as retinal enlargement, thickening and the presence of fibrous bands. Approximately 91.3% of cases with a CTSS = 7 were asymptomatic or clinically mild, while 81.2% of cases with a score = 15 were clinically severe. Mean PO and PHO (30.1 ± 28.0 and 8.4 ± 10.4, respectively) were significantly higher in the clinically severe categories. Conclusion: Because COVID-19 pneumonia progresses rapidly, radiologists and physicians should become familiar with typical TC chest findings to treat patients early, ultimately improving prognosis and reducing mortality. Artificial intelligence can be a valuable tool in treating patients with COVID-19.

Keywords: chest, HRCT, covid-19, artificial intelligence, chest HRCT

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Authors: Chandan Hegde, K. Ashwini

Abstract:

Commonsense reasoning is the simulation of human ability to make decisions during the situations that we encounter every day. It has been several decades since the introduction of this subfield of artificial intelligence, but it has barely made some significant progress. The modern computing aids also have remained impotent in this regard due to the absence of a strong methodology towards commonsense reasoning development. Among several accountable reasons for the lack of progress, drawing inference out of commonsense knowledge-base stands out. This review paper emphasizes on a detailed analysis of representation of reasoning uncertainties and feasible prospects of programming aids for drawing inferences. Also, the difficulties in deducing and systematizing commonsense reasoning and the substantial progress made in reasoning that influences the study have been discussed. Additionally, the paper discusses the possible impacts of an effective inference technique in commonsense reasoning.

Keywords: artificial intelligence, commonsense reasoning, knowledge base, uncertainty in reasoning

Procedia PDF Downloads 187

Authors: M. Caramihai, Irina Severin

Abstract:

This paper deals with the design of an intelligent control structure for a bioprocess of Hansenula polymorpha yeast cultivation. The objective of the process control is to produce biomass in a desired physiological state. The work demonstrates that the designed Hybrid Control Techniques (HCT) are able to recognize specific evolution bioprocess trajectories using neural networks trained specifically for this purpose, in order to estimate the model parameters and to adjust the overall bioprocess evolution through an expert system and a fuzzy structure. The design of the control algorithm as well as its tuning through realistic simulations is presented. Taking into consideration the synergism of different paradigms like fuzzy logic, neural network, and symbolic artificial intelligence (AI), in this paper we present a real and fulfilled intelligent control architecture with application in bioprocess control.

Keywords: bioprocess, intelligent control, neural nets, fuzzy structure, hybrid techniques

Procedia PDF Downloads 424

Authors: Arwa Alnowaiser, Hala Shoukri

Abstract:

The ‘FreeAITherapist’ is an artificial intelligence application that uses the power of AI to offer advice and mental health counseling to its users through its chatbot services. The AI therapist is designed to understand users' issues, concerns, and problems and respond appropriately; it provides empathy and guidance and uses evidence-based therapeutic techniques. With its user-friendly platform, it ensures accessibility for individuals in need, regardless of their geographical location. This website was created in direct response to the growing demand for mental health support, aiming to provide a cost-effective and confidential solution. Through promising confidentiality, it considers user privacy and data security. The ‘FreeAITherapist’ strives to bridge the gap in mental health services, offering a reliable resource for individuals seeking guidance and counseling to improve their overall well-being.

Keywords: artificial intelligence, mental health, AI therapist, website, counseling

Procedia PDF Downloads 46