Search results for: artificial intelligence in education

Authors: Amarendar Reddy Addula

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Definition of Artificial Intelligence: Artificial intelligence is the simulation of mortal intelligence processes by machines, especially computer systems. Explicit operations of AI comprise expert systems, natural language processing, and speech recognition, and machine vision. Artificial Intelligence (AI) is an original medium for digital business, according to a new report by Gartner. The last 10 times represent an advance period in AI’s development, prodded by the confluence of factors, including the rise of big data, advancements in cipher structure, new machine literacy ways, the materialization of pall computing, and the vibrant open- source ecosystem. Influence of AI to a broader set of use cases and druggies and its gaining fashionability because it improves AI’s versatility, effectiveness, and rigidity. Edge AI will enable digital moments by employing AI for real- time analytics closer to data sources. Gartner predicts that by 2025, further than 50 of all data analysis by deep neural networks will do at the edge, over from lower than 10 in 2021. Responsible AI is a marquee term for making suitable business and ethical choices when espousing AI. It requires considering business and societal value, threat, trust, translucency, fairness, bias mitigation, explainability, responsibility, safety, sequestration, and nonsupervisory compliance. Responsible AI is ever more significant amidst growing nonsupervisory oversight, consumer prospects, and rising sustainability pretensions. Generative AI is the use of AI to induce new vestiges and produce innovative products. To date, generative AI sweats have concentrated on creating media content similar as photorealistic images of people and effects, but it can also be used for law generation, creating synthetic irregular data, and designing medicinals and accoutrements with specific parcels. AI is the subject of a wide- ranging debate in which there's a growing concern about its ethical and legal aspects. Constantly, the two are varied and nonplussed despite being different issues and areas of knowledge. The ethical debate raises two main problems the first, abstract, relates to the idea and content of ethics; the alternate, functional, and concerns its relationship with the law. Both set up models of social geste, but they're different in compass and nature. The juridical analysis is grounded on anon-formalistic scientific methodology. This means that it's essential to consider the nature and characteristics of the AI as a primary step to the description of its legal paradigm. In this regard, there are two main issues the relationship between artificial and mortal intelligence and the question of the unitary or different nature of the AI. From that theoretical and practical base, the study of the legal system is carried out by examining its foundations, the governance model, and the nonsupervisory bases. According to this analysis, throughout the work and in the conclusions, International Law is linked as the top legal frame for the regulation of AI.

Keywords: artificial intelligence, ethics & human rights issues, laws, international laws

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Authors: Samuel Ntsanwisi

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This study investigates the transformative role of Artificial Intelligence (AI) and Virtual Reality (VR) in the preservation of endangered African languages. The study is contextualized within the film and television industry, highlighting disparities in screen representation for certain languages in South Africa, underscoring the need for increased visibility and preservation efforts; with globalization and cultural shifts posing significant threats to linguistic diversity, this research explores approaches to language preservation. By leveraging AI technologies, such as speech recognition, translation, and adaptive learning applications, and integrating VR for immersive and interactive experiences, the study aims to create a framework for teaching and passing on endangered African languages. Through digital documentation, interactive language learning applications, storytelling, and community engagement, the research demonstrates how these technologies can empower communities to revitalize their linguistic heritage. This study employs a dual-method approach, combining a rigorous literature review to analyse existing research on the convergence of AI, VR, and language preservation with primary data collection through interviews and surveys with ten filmmakers. The literature review establishes a solid foundation for understanding the current landscape, while interviews with filmmakers provide crucial real-world insights, enriching the study's depth. This balanced methodology ensures a comprehensive exploration of the intersection between AI, VR, and language preservation, offering both theoretical insights and practical perspectives from industry professionals.

Keywords: language preservation, endangered languages, artificial intelligence, virtual reality, interactive learning

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Authors: Angelis P. Barlampas

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The purpose of this study is to inform the reader, about the various applications of artificial intelligence (AI), in cardiac imaging. AI grows fast and its role is crucial in medical specialties, which use large amounts of digital data, that are very difficult or even impossible to be managed by human beings and especially doctors.Artificial intelligence (AI) refers to the ability of computers to mimic human cognitive function, performing tasks such as learning, problem-solving, and autonomous decision making based on digital data. Whereas AI describes the concept of using computers to mimic human cognitive tasks, machine learning (ML) describes the category of algorithms that enable most current applications described as AI. Some of the current applications of AI in cardiac imaging are the follows: Ultrasound: Automated segmentation of cardiac chambers across five common views and consequently quantify chamber volumes/mass, ascertain ejection fraction and determine longitudinal strain through speckle tracking. Determine the severity of mitral regurgitation (accuracy > 99% for every degree of severity). Identify myocardial infarction. Distinguish between Athlete’s heart and hypertrophic cardiomyopathy, as well as restrictive cardiomyopathy and constrictive pericarditis. Predict all-cause mortality. CT Reduce radiation doses. Calculate the calcium score. Diagnose coronary artery disease (CAD). Predict all-cause 5-year mortality. Predict major cardiovascular events in patients with suspected CAD. MRI Segment of cardiac structures and infarct tissue. Calculate cardiac mass and function parameters. Distinguish between patients with myocardial infarction and control subjects. It could potentially reduce costs since it would preclude the need for gadolinium-enhanced CMR. Predict 4-year survival in patients with pulmonary hypertension. Nuclear Imaging Classify normal and abnormal myocardium in CAD. Detect locations with abnormal myocardium. Predict cardiac death. ML was comparable to or better than two experienced readers in predicting the need for revascularization. AI emerge as a helpful tool in cardiac imaging and for the doctors who can not manage the overall increasing demand, in examinations such as ultrasound, computed tomography, MRI, or nuclear imaging studies.

Keywords: artificial intelligence, cardiac imaging, ultrasound, MRI, CT, nuclear medicine

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Authors: Li Yong

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Using the data of 135 financial institutions in China from 2018 to 2022, this paper deeply analyzes the underlying theoretical mechanism of artificial intelligence (AI) technology promoting financial development and examines the impact of AI technology on the digital transformation performance of financial enterprises. It is found that the level of AI technology has a significant positive impact on the development of finance. Compared with the impact on the expansion of financial scale, AI technology plays a greater role in improving the performance of financial institutions, reflecting the trend characteristics of the current AI technology to promote the evolution of financial structure. By investigating the intermediary transmission effects, we found that AI technology plays a positive role in promoting the performance of financial institutions by reducing operating costs and improving customer satisfaction, but its function in innovating financial products and mitigating financial risks is relatively limited. In addition, the promotion of AI technology in financial development has significant heterogeneity in terms of the type, scale, and attributes of financial institutions.

Keywords: artificial intelligence technology, financial development, China, heterogeneity

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Authors: Han-Yu Cheng

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This study aims to explore the impact of AI-generated illustration works on the aesthetic symbol consumption of consumers in Taiwan. The advancement of artificial intelligence drawing has lowered the barriers to entry, enabling more individuals to easily enter the field of illustration. Using Japanese anime style as an example, with the development of Generative Artificial Intelligence (Generative AI), an increasing number of illustration works are being generated by machines, sparking discussions about aesthetics and art consumption. Through surveys and the analysis of consumer perspectives, this research investigates how this influences consumers' aesthetic experiences and the resulting changes in the traditional art market and among creators. The study reveals that among consumers in Taiwan, particularly those interested in Japanese anime style, there is a pronounced interest and curiosity surrounding the emergence of Generative AI. This curiosity is particularly notable among individuals interested in this style but lacking the technical skills required for creating such artworks. These works, rooted in elements of Japanese anime style, find ready acceptance among enthusiasts of this style due to their stylistic alignment. Consequently, they have garnered a substantial following. Furthermore, with the reduction in entry barriers, more individuals interested in this style but lacking traditional drawing skills have been able to participate in producing such works. Against the backdrop of ongoing debates about artistic value since the advent of artificial intelligence (AI), Generative AI-generated illustration works, while not entirely displacing traditional art, to a certain extent, fulfill the aesthetic demands of this consumer group, providing a similar or analogous aesthetic consumption experience. Additionally, this research underscores the advantages and limitations of Generative AI-generated illustration works within this consumption environment.

Keywords: generative AI, anime aesthetics, Japanese anime illustration, art consumption

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Authors: Ying Zhu

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Recent years have witnessed a boom of foreign investments in the field of artificial intelligence (AI). Foreign investments provide critical capital for AI development but also trigger national security concerns of host states. A notable example is an increasing number of cases in which the Committee on Foreign Investment in the United States (CFIUS) has denied Chinese acquisitions of US technology companies on national security grounds. On July 19, 2018, the Congress has reached a deal on the final draft of a new provision to strengthen CFIUS’s authority to review overseas transactions involving sensitive US technology. The question is: how to reconcile the emerging tension between, on the one hand, foreign AI investors’ expectations of a predictable investment environment, and on the other hand, host states’ regulatory power on national security? This paper provides a methodology to reconcile this tension under international investment law. Based on an examination, the national security exception clauses in international investment treaties and the application of national security justification in investor-state arbitration jurisprudence, the paper argues that a traditional interpretation of the national security exception, based on the necessity concept in customary international law, fails to take into account new risks faced by countries, including security concerns over strategic industries such as AI. To overcome this shortage, the paper proposes to incorporate an integrated national security clause in international investment treaties, which includes a two-tier test: a ‘self-judging’ test in the pre-establishment period and a ‘proportionality’ test in the post-establishment period. At the end, the paper drafts a model national security clause for future treaty-drafting practice.

Keywords: foreign investment, artificial intelligence, international investment law, national security exception

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Authors: Ethan James

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Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

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Authors: Mahasa Zahirnia

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The purpose of this paper is to explore new and effective ways of optimizing satellite images using artificial intelligence, and the process of implementing reinforcement learning to enhance the quality of data captured within the image. In our implementation of Bellman's Reinforcement Learning equations, associated state diagrams, and multi-stage image processing, we were able to enhance image quality, detect and define objects. Reinforcement learning is the differentiator in the area of artificial intelligence, and Q-Learning relies on trial and error to achieve its goals. The reward system that is embedded in Q-Learning allows the agent to self-evaluate its performance and decide on the best possible course of action based on the current and future environment. Results show that within a simulated environment, built on the images that are commercially available, the rate of detection was 40-90%. Reinforcement learning through Q-Learning algorithm is not just desired but required design criteria for image optimization and enhancements. The proposed methods presented are a cost effective method of resolving uncertainty of the data because reinforcement learning finds ideal policies to manage the process using a smaller sample of images.

Keywords: Q-learning, image optimization, reinforcement learning, Markov decision process

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Authors: Warnakulasuriya Arachichige Malisha Ann Rosary Fernando, Udalamatta Gamage Omila Chalanka Jinadasa, Bihini Pabasara Amandi Amarasinghe, Manul Thisuraka Mandalawatta, Uthpala Samarakoon, Manori Gamage

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The surge in sexually transmitted diseases (STDs) has become a critical public health crisis demanding urgent attention and action. Like many other nations, Sri Lanka is grappling with a significant increase in STDs due to a lack of education and awareness regarding their dangers. Presently, the available applications for tracking and managing STDs cover only a limited number of easily detectable infections, resulting in a significant gap in effectively controlling their spread. To address this gap and combat the rising STD rates, it is essential to leverage technology and data. Employing technology to enhance the tracking and management of STDs is vital to prevent their further propagation and to enable early intervention and treatment. This requires adopting a comprehensive approach that involves raising public awareness about the perils of STDs, improving access to affordable healthcare services for early detection and treatment, and utilizing advanced technology and data analysis. The proposed mobile application aims to cater to a broad range of users, including STD patients, recovered individuals, and those unaware of their STD status. By harnessing cutting-edge technologies like image detection, symptom-based identification, prevention methods, doctor and clinic recommendations, and virtual counselor chat, the application offers a holistic approach to STD management. In conclusion, the escalating STD rates in Sri Lanka and across the globe require immediate action. The integration of technology-driven solutions, along with comprehensive education and healthcare accessibility, is the key to curbing the spread of STDs and promoting better overall public health.

Keywords: STD, machine learning, NLP, artificial intelligence

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Authors: F. Allag, S. Bouharati, M. Belmahdi, R. Zegadi

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The rapid expansion of deserts in recent decades as a result of human actions combined with climatic changes has highlighted the necessity to understand biological processes in arid environments. Whereas physical processes and the biology of flora and fauna have been relatively well studied in marginally used arid areas, knowledge of desert soil micro-organisms remains fragmentary. The objective of this study is to conduct a diversity analysis of bacterial communities in unvegetated arid soils. Several biological phenomena in hot deserts related to microbial populations and the potential use of micro-organisms for restoring hot desert environments. Dry land ecosystems have a highly heterogeneous distribution of resources, with greater nutrient concentrations and microbial densities occurring in vegetated than in bare soils. In this work, we found it useful to use techniques of artificial intelligence in their treatment especially artificial neural networks (ANN). The use of the ANN model, demonstrate his capability for addressing the complex problems of uncertainty data.

Keywords: desert soil, climatic changes, bacteria, vegetation, artificial neural networks

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Authors: Meruyert Serik, Nassipzhan Duisegaliyeva, Danara Tleumagambetova, Madina Ermaganbetova

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This research paper explores the process of creating a proctoring system by evaluating the implementation of practical face recognition algorithms. Students of educational programs reviewed the research work "6B01511-Computer Science", "7M01511-Computer Science", "7M01525- STEM Education," and "8D01511-Computer Science" of Eurasian National University named after L.N. Gumilyov. As an outcome, a proctoring system will be created, enabling the conduction of tests and ensuring academic integrity checks within the system. Due to the correct operation of the system, test works are carried out. The result of the creation of the proctoring system will be the basis for the automation of the informational, educational portal developed by machine learning.

Keywords: artificial intelligence, education portal, face recognition, machine learning, proctoring

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Authors: Thomas Schalow

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It is commonly recognized that our present curriculum is not preparing students to function in the 21st century. This is particularly true in regard to communication needs across cultures - both human and non-human. In this paper, a comprehensive theory of communication-based on communication with non-human cultures and intelligences is presented 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 the argument that we need to become much more serious about communicating with the non-human, intelligent life forms that already exists around us here on Earth. We need to broaden our definition of communication and reach out to other sentient life forms in order to provide humanity with a better perspective of its place within our ecosystem. The paper next examines the science and philosophy behind CETI (communication with extraterrestrial intelligences) and how it could prove useful even in the absence of contact with alien life. However, CETI’s assumptions and methodology need to be revised in accordance with the communication theory being 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. Humanity has 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 cultures can provide us with a framework for this communication. The basic concepts behind intercultural communication can be applied to the three 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 yield substantial gains. A curriculum that is truly ready for the 21st century needs to be aligned with this new theory of communication.

Keywords: artificial intelligence, CETI, communication, language

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Authors: Edward W. Staley, Corban G. Rivera, Ashley J. Llorens

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Advances in reinforcement learning (RL) have resulted in recent breakthroughs in the application of artificial intelligence (AI) across many different domains. An emerging landscape of development environments is making powerful RL techniques more accessible for a growing community of researchers. However, most existing frameworks do not directly address the problem of learning in complex operating environments, such as dense urban settings or defense-related scenarios, that incorporate distributed, heterogeneous teams of agents. To help enable AI research for this important class of applications, we introduce the AI Arena: a scalable framework with flexible abstractions for distributed multi-agent reinforcement learning. The AI Arena extends the OpenAI Gym interface to allow greater flexibility in learning control policies across multiple agents with heterogeneous learning strategies and localized views of the environment. To illustrate the utility of our framework, we present experimental results that demonstrate performance gains due to a distributed multi-agent learning approach over commonly-used RL techniques in several different learning environments.

Keywords: reinforcement learning, multi-agent, deep learning, artificial intelligence

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Authors: Kshama Sharma

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Education is one of the most powerful tools to transform the world into a just, sustainable, and more peaceful place for existing lives across the globe. However, its recent objective approach focused on materialistic, factual, and existing knowledge, has a constraint of human experiences that is limited to certain dimensions only. And leads to a materialistic world which is deprived of spiritual approaches and makes it less compassionate, and more grades oriented. To make it more comprehensive, education should explore the subjective approaches towards spiritualism to connect lives with the greater self and consciousness of cosmic intelligence. This approach will bring a major shift in the orientation of pedagogical processes, assessment strategies, and administrative management of the present education system. Spirituality often related to the religious aspect of human civilization and development, however, when universal consciousness /cosmic intelligence (which is often claimed as dark energy) and the human mind competencies works in coherence and coordination then the efficiency of human mind reaches to a different dimension and achieve extraordinary level of human understanding. Quantitative analysis of the existing secondary data from the different agencies working in the field of meditation had been analyzed to conclude its implications on human mind and further how it can effectively use in education to bring the desired and expected results. Any kind of meditation practice affects the cognitive, mental, physical, emotional, and conscious state of mind. If aligned with the teaching and learning methodology will lead to conscious learner and peaceful world.

Keywords: spirituality, cosmic intelligence, consciousness, mind competencies

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Authors: B. S. Akhmetov, S. T. Akhmetova, A. I. Ivanov, T. S. Kartbayev, A. Y. Malygin

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The problem of training of a network of artificial neurons in biometric appendices is that this process has to be completely automatic, i.e. the person operator should not participate in it. Therefore, this article discusses the issues of training the network of artificial neurons and the description of the non-iterative learning algorithm of artificial neuron.

Keywords: artificial neuron, biometrics, biometrical applications, learning of neuron, non-iterative algorithm

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Authors: S. Sudhakar, Geetha Manjunath, Siva Teja Kakileti, Himanshu Madhu

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Diagnosis of breast cancer at early stages has seen better clinical and survival outcomes. Survival rates in developing countries like India are very low due to accessibility and affordability issues of screening tests such as Mammography. In addition, Mammography is not much effective in younger women with dense breasts. This leaves a gap in current screening methods. Thermalytix is a new technique for detecting breast abnormality in a non-contact, non-invasive way. It is an AI-enabled computer-aided diagnosis solution that automates interpretation of high resolution thermal images and identifies potential malignant lesions. The solution is low cost, easy to use, portable and is effective in all age groups. This paper presents the results of a retrospective comparative analysis of Thermalytix over Mammography and Clinical Breast Examination for breast cancer screening. Thermalytix was found to have better sensitivity than both the tests, with good specificity as well. In addition, Thermalytix identified all malignant patients without palpable lumps.

Keywords: breast cancer screening, radiology, thermalytix, artificial intelligence, thermography

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Authors: Martin Popescu D. Dana Maria

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The aims of this research are to broaden the study on the relationship between emotional intelligence and counterproductive work behavior (CWB). The study sample consisted in 441 Romanian employees from companies all over the country. Data has been collected through web surveys and processed with SPSS. The results indicated an average correlation between the two constructs and their sub variables, employees with a high level of emotional intelligence tend to be less aggressive. In addition, labeling was considered an individual difference which has the power to influence the level of employee aggression. A regression model was used to underline the importance of emotional intelligence together with labeling as predictors of CWB. Results have shown that this regression model enforces the assumption that labeling and emotional intelligence, taken together, predict CWB. Employees, who label themselves as victims and have a low degree of emotional intelligence, have a higher level of CWB.

Keywords: aggression, CWB, emotional intelligence, labeling

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Authors: Sepinoud Hamedi

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Over the past few decades, companies have appeared developing concerns in connection to the natural affect of their fabricating exercises. Green supply chain administration has been considered by the producers as a attainable choice to decrease the natural affect of operations whereas at the same time moving forward their operational execution. Contemporaneously the coming of digitalization and globalization within the supply chain space has driven to a developing acknowledgment of the importance of data preparing methodologies, such as enormous information analytics and fake insights innovations, in improving and optimizing supply chain execution. Also, supply chain collaboration in part intervenes the relationship between manufactured innovation and supply chain execution Ponders appear that the use of BDA-AI advances includes a significant impact on natural handle integration and green supply chain collaboration conjointly underlines that both natural handle integration and green supply chain collaboration have a critical affect on natural execution. Correspondingly savvy supply chain contributes to green execution through overseeing green connections and setting up green operations.

Keywords: green supply chain, artificial intelligence, manufacturers, technology, environmental

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Authors: Shatha Ghareeb, Rawaa Al-Jumeily, Thar Baker

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In Abu Dhabi, there are many different education curriculums where sector of private schools and quality assurance is supervising many private schools in Abu Dhabi for many nationalities. As there are many different education curriculums in Abu Dhabi to meet expats’ needs, there are different requirements for registration and success. In addition, there are different age groups for starting education in each curriculum. In fact, each curriculum has a different number of years, assessment techniques, reassessment rules, and exam boards. Currently, students that transfer curriculums are not being placed in the right year group due to different start and end dates of each academic year and their date of birth for each year group is different for each curriculum and as a result, we find students that are either younger or older for that year group which therefore creates gaps in their learning and performance. In addition, there is not a way of storing student data throughout their academic journey so that schools can track the student learning process. In this paper, we propose to develop a computational framework applicable in multicultural countries such as UAE in which multi-education systems are implemented. The ultimate goal is to use cloud and fog computing technology integrated with Artificial Intelligence techniques of Machine Learning to aid in a smooth transition when assigning students to their year groups, and provide leveling and differentiation information of students who relocate from a particular education curriculum to another, whilst also having the ability to store and access student data from anywhere throughout their academic journey.

Keywords: admissions, algorithms, cloud computing, differentiation, fog computing, levelling, machine learning

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Authors: Umamaheswari Shanmugam, Silvia Ronchi, Radu Vornicu

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Software, artificial intelligence, and machine learning can improve healthcare through innovative and advanced technologies that are able to use the large amount and variety of data generated during healthcare services every day. As we read the news, over 500 machine learning or other artificial intelligence medical devices have now received FDA clearance or approval, the first ones even preceding the year 2000. One of the big advantages of these new technologies is the ability to get experience and knowledge from real-world use and to continuously improve their performance. Healthcare systems and institutions can have a great benefit because the use of advanced technologies improves the same time efficiency and efficacy of healthcare. Software-defined as a medical device, is stand-alone software that is intended to be used for patients for one or more of these specific medical intended uses: - diagnosis, prevention, monitoring, prediction, prognosis, treatment or alleviation of a disease, any other health conditions, replacing or modifying any part of a physiological or pathological process–manage the received information from in vitro specimens derived from the human samples (body) and without principal main action of its principal intended use by pharmacological, immunological or metabolic definition. Software qualified as medical devices must comply with the general safety and performance requirements applicable to medical devices. These requirements are necessary to ensure high performance and quality and also to protect patients’ safety. The evolution and the continuous improvement of software used in healthcare must take into consideration the increase in regulatory requirements, which are becoming more complex in each market. The gap between these advanced technologies and the new regulations is the biggest challenge for medical device manufacturers. Regulatory requirements can be considered a market barrier, as they can delay or obstacle the device approval, but they are necessary to ensure performance, quality, and safety, and at the same time, they can be a business opportunity if the manufacturer is able to define in advance the appropriate regulatory strategy. The abstract will provide an overview of the current regulatory framework, the evolution of the international requirements, and the standards applicable to medical device software in the potential market all over the world.

Keywords: artificial intelligence, machine learning, SaMD, regulatory, clinical evaluation, classification, international requirements, MDR, 510k, PMA, IMDRF, cyber security, health care systems.

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Authors: M. E. Joret, F. Germeys, P. Van de Craen

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Background: Previous studies have shown that ‘full’ bilingualism seems to enhance the executive functions in children, young adults and elderly people. Executive functions refer to a complex cognitive system responsible for self-controlled and planned behavior and seem to predict academic achievement. The present study aimed at investigating whether similar effects could be found in children learning their second language at school in immersion education programs. Methods: In this study, 44 children involved in immersion education for 4 to 5 years were compared to 48 children in traditional schools. All children were between 9 and 11 years old. To assess executive functions, the Simon task was used, a neuropsychological measure assessing executive functions with reaction times and accuracy on congruent and incongruent trials. To control for background measures, all children underwent the Raven’s coloured progressive matrices, to measure non-verbal intelligence and the Echelle de Vocabulaire en Images Peabody (EVIP), assessing verbal intelligence. In addition, a questionnaire was given to the parents to control for other confounding variables, such as socio-economic status (SES), home language, developmental disorders, etc. Results: There were no differences between groups concerning non-verbal intelligence and verbal intelligence. Furthermore, the immersion learners showed overall faster reaction times on both congruent and incongruent trials compared to the traditional learners, but only after 5 years of training, not before. Conclusion: These results show that the cognitive benefits found in ‘full’ bilinguals also appear in children involved in immersion education, but only after a sufficient exposure to the second language. Our results suggest that the amount of second language training needs to be sufficient before these cognitive effects may emerge.

Keywords: bilingualism, executive functions, immersion education, Simon task

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Authors: Azucena Yearby

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This study examined if cultural intelligence can be developed through the study of a foreign language. Specifically, the study sought to determine if strategies such as the Arts/History, Vocabulary and Real or Simulated Experiences have an effect on the development of cultural intelligence in the foreign language classroom. Students enrolled in Spanish 1114 or level 1 Spanish courses at the University of Central Oklahoma (UCO) completed Linn Van Dyne’s 20-item questionnaire that measures Cultural Intelligence (CQ). Results from the study indicated a slight cultural intelligence increase in those students who received an intervention. Therefore, the study recommended that foreign language educators implement the considered strategies in the classroom in order to increase their students’ cultural intelligence.

Keywords: cultural competency, cultural intelligence, foreign language, language

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Authors: Begum Koru, Isik Aybay, Demet Celik Ulusoy

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The discipline of law is quite complex and has its own terminology. Apart from written legal rules, there is also living law, which refers to legal practice. Basic legal rules aim at the happiness of individuals in social life and have different characteristics in different branches such as public or private law. On the other hand, law is a national phenomenon. The law of one nation and the legal system applied on the territory of another nation may be completely different. People who are experts in a particular field of law in one country may have insufficient expertise in the law of another country. Today, in addition to the local nature of law, international and even supranational law rules are applied in order to protect basic human values and ensure the protection of human rights around the world. Systems that offer algorithmic solutions to legal problems using artificial intelligence (AI) tools will perhaps serve to produce very meaningful results in terms of human rights. However, algorithms to be used should not be developed by only computer experts, but also need the contribution of people who are familiar with law, values, judicial decisions, and even the social and political culture of the society to which it will provide solutions. Otherwise, even if the algorithm works perfectly, it may not be compatible with the values of the society in which it is applied. The latest developments involving the use of AI techniques in legal systems indicate that artificial law will emerge as a new field in the discipline of law. More AI systems are already being applied in the field of law, with examples such as predicting judicial decisions, text summarization, decision support systems, and classification of documents. Algorithms for legal systems employing AI tools, especially in the field of prediction of judicial decisions and decision support systems, have the capacity to create automatic decisions instead of judges. When the judge is removed from this equation, artificial intelligence-made law created by an intelligent algorithm on its own emerges, whether the domain is national or international law. In this work, the aim is to make a general analysis of this new topic. Such an analysis needs both a literature survey and a perspective from computer experts' and lawyers' point of view. In some societies, the use of prediction or decision support systems may be useful to integrate international human rights safeguards. In this case, artificial law can serve to produce more comprehensive and human rights-protective results than written or living law. In non-democratic countries, it may even be thought that direct decisions and artificial intelligence-made law would be more protective instead of a decision "support" system. Since the values of law are directed towards "human happiness or well-being", it requires that the AI algorithms should always be capable of serving this purpose and based on the rule of law, the principle of justice and equality, and the protection of human rights.

Keywords: AI and law, artificial law, protection of human rights, AI tools for legal systems

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Authors: Go Woon Kwak, Siyoung Jun, Soyun Maeng, Haeyoung Lee

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Entry is an education platform used in South Korea, created to help students learn to program, in which they can learn to code while playing. Using the online version of the entry, teachers can easily assign programming homework to the student and the students can make programs simply by linking programming blocks. However, the programs may be made by others, so that the authors of the programs should be identified. In this paper, as the first step toward author identification of entry programs, we present an artificial neural network based classification approach to identify genders of authors of a program written in an entry. A neural network has been trained from labeled training data that we have collected. Our result in progress, although preliminary, shows that the proposed approach could be feasible to be applied to the online version of entry for gender identification of authors. As future work, we will first use a machine learning technique for age identification of entry programs, which would be the second step toward the author identification.

Keywords: artificial intelligence, author identification, deep neural network, gender identification, machine learning

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Authors: Mahda Foroughi, Bruno de Anderade, Ana Pereira Roders

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Social media platforms have been increasingly used by locals and tourists to express their opinions about buildings, cities, and built heritage in particular. Most recently, scholars have been using social media to conduct innovative research on built heritage and heritage management. Still, the application of artificial intelligence (AI) methods to analyze social media data for heritage management is seldom explored. This paper investigates the potential of short texts (sentences and hashtags) shared through social media as a data source and artificial intelligence methods for data analysis for revealing the cultural significance (values and attributes) of built heritage. The city of Yazd, Iran, was taken as a case study, with a particular focus on windcatchers, key attributes conveying outstanding universal values, as inscribed on the UNESCO World Heritage List. This paper has three subsequent phases: 1) state of the art on the intersection of public participation in heritage management and social media research; 2) methodology of data collection and data analysis related to coding people's voices from Instagram and Twitter into values of windcatchers over the last ten-years; 3) preliminary findings on the comparison between opinions of locals and tourists, sentiment analysis, and its association with the values and attributes of windcatchers. Results indicate that the age value is recognized as the most important value by all interest groups, while the political value is the least acknowledged. Besides, the negative sentiments are scarcely reflected (e.g., critiques) in social media. Results confirm the potential of social media for heritage management in terms of (de)coding and measuring the cultural significance of built heritage for windcatchers in Yazd. The methodology developed in this paper can be applied to other attributes in Yazd and also to other case studies.

Keywords: social media, artificial intelligence, public participation, cultural significance, heritage, sentiment analysis

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Authors: Shryas Bhurat, Aryan Vashistha, Sampreet Dinakar Nayak, Ayush Gupta

Abstract:

As the technology is advancing more towards high computational resources, there is a paradigm shift in the usage of these resources to optimize the design process. This paper discusses the usage of ‘Generative Design using Artificial Intelligence’ to build better models that adapt the operations like selection, mutation, and crossover to generate results. The human mind thinks of the simplest approach while designing an object, but the intelligence learns from the past & designs the complex optimized CAD Models. Generative Design takes the boundary conditions and comes up with multiple solutions with iterations to come up with a sturdy design with the most optimal parameter that is given, saving huge amounts of time & resources. The new production techniques that are at our disposal allow us to use additive manufacturing, 3D printing, and other innovative manufacturing techniques to save resources and design artistically engineered CAD Models. Also, this paper discusses the Genetic Algorithm, the Non-Domination technique to choose the right results using biomimicry that has evolved for current habitation for millions of years. The computer uses parametric models to generate newer models using an iterative approach & uses cloud computing to store these iterative designs. The later part of the paper compares the topology optimization technology with Generative Design that is previously being used to generate CAD Models. Finally, this paper shows the performance of algorithms and how these algorithms help in designing resource-efficient models.

Keywords: genetic algorithm, bio mimicry, generative modeling, non-dominant techniques

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Authors: Asifa Amara, Prachi Singh, Kanishka, Debargho Pathak, Akshat Kumar, Jayakumar Eravelly

Abstract:

Today's generation is totally dependent on technology that uses data as its fuel. The present study is all about innovations and developments in data science and gives an idea about how efficiently to use the data provided. This study will help to understand the core concepts of data science. The concept of artificial intelligence was introduced by Alan Turing in which the main principle was to create an artificial system that can run independently of human-given programs and can function with the help of analyzing data to understand the requirements of the users. Data science comprises business understanding, analyzing data, ethical concerns, understanding programming languages, various fields and sources of data, skills, etc. The usage of data science has evolved over the years. In this review article, we have covered a part of data science, i.e., machine learning. Machine learning uses data science for its work. Machines learn through their experience, which helps them to do any work more efficiently. This article includes a comparative study image between human understanding and machine understanding, advantages, applications, and real-time examples of machine learning. Data science is an important game changer in the life of human beings. Since the advent of data science, we have found its benefits and how it leads to a better understanding of people, and how it cherishes individual needs. It has improved business strategies, services provided by them, forecasting, the ability to attend sustainable developments, etc. This study also focuses on a better understanding of data science which will help us to create a better world.

Keywords: data science, machine learning, data analytics, artificial intelligence

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Authors: Xiao Yihong, Yu Kexin, Burra Venkata Durga Kumar

Abstract:

With the accelerating process of urbanization, the problem of urban road congestion is becoming more and more serious. Intelligent transportation system combining distributed and artificial intelligence has become a research hotspot. As the core development direction of the intelligent transportation system, Cooperative Intelligent Transportation System (C-ITS) integrates advanced information technology and communication methods and realizes the integration of humans, vehicle, roadside infrastructure, and other elements through the multi-agent distributed system. By analyzing the system architecture and technical characteristics of C-ITS, the report proposes a distributed multi-agent C-ITS. The system consists of Roadside Sub-system, Vehicle Sub-system, and Personal Sub-system. At the same time, we explore the scalability of the C-ITS and put forward incorporating local rewards in the centralized training decentralized execution paradigm, hoping to add a scalable value decomposition method. In addition, we also suggest introducing blockchain to improve the safety of the traffic information transmission process. The system is expected to improve vehicle capacity and traffic safety.

Keywords: distributed system, artificial intelligence, multi-agent, cooperative intelligent transportation system

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Authors: Saradha Priya

Abstract:

Developing virtual human is very important to meet the challenges occurred in many applications where human find difficult or risky to perform the task. A robot is a machine that can perform a task automatically or with guidance. Robotics is generally a combination of artificial intelligence and physical machines (motors). Computational intelligence involves the programmed instructions. This project proposes a robotic vehicle that has a camera, PIR sensor and text command based movement. It is specially designed to perform surveillance and other few tasks in the most efficient way. Serial communication has been occurred between a remote Base Station, GUI Application, and PC.

Keywords: Zigbee, camera, pirsensor, wireless transmission, DC motor

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Authors: Animish Jain

Abstract:

This study deals with using Multiplicative Weight Update within artificial intelligence and machine learning to create models that can diagnose skin cancer using microscopic images of cancer samples. In this study, the multiplicative weight update method is used to take the predictions of multiple models to try and acquire more accurate results. Logistic Regression, Convolutional Neural Network (CNN), and Support Vector Machine Classifier (SVMC) models are employed within the Multiplicative Weight Update system. These models are trained on pictures of skin cancer from the ISIC-Archive, to look for patterns to label unseen scans as either benign or malignant. These models are utilized in a multiplicative weight update algorithm which takes into account the precision and accuracy of each model through each successive guess to apply weights to their guess. These guesses and weights are then analyzed together to try and obtain the correct predictions. The research hypothesis for this study stated that there would be a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The SVMC model had an accuracy of 77.88%. The CNN model had an accuracy of 85.30%. The Logistic Regression model had an accuracy of 79.09%. Using Multiplicative Weight Update, the algorithm received an accuracy of 72.27%. The final conclusion that was drawn was that there was a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The conclusion was made that using a CNN model would be the best option for this problem rather than a Multiplicative Weight Update system. This is due to the possibility that Multiplicative Weight Update is not effective in a binary setting where there are only two possible classifications. In a categorical setting with multiple classes and groupings, a Multiplicative Weight Update system might become more proficient as it takes into account the strengths of multiple different models to classify images into multiple categories rather than only two categories, as shown in this study. This experimentation and computer science project can help to create better algorithms and models for the future of artificial intelligence in the medical imaging field.

Keywords: artificial intelligence, machine learning, multiplicative weight update, skin cancer

Procedia PDF Downloads 49