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  • Brain stroke detection from computed tomography images using deep learning algorithms

    Aykut Diker; Abdullah Elen; Subasi, Abdulhamit; External Collaboration; NA; NA; NA; Computer Science; NA; Aykut Diker (Academic Press, 2023-01-01)
    Stroke is one of the common causes of death worldwide. Stroke is the inability of a focus to be fed in the brain due to clogged or bleeding of the vessels feeding the brain. Because early stroke treatment and diagnosis are related to a favorable patient outcome, time is a critical aspect of successful stroke treatment. In this chapter, we examine the stroke classification from Brain Stroke CT Dataset, with deep learning architectures. In the experimental study, a total of 2501 brain stroke computed tomography (CT) images were used for testing and training. For this purpose, numerus widely known pretrained convolutional neural networks (CNNs) such as GoogleNet, AlexNet, VGG-16, VGG-19, and Residual CNN were used to classify brain stroke CT images as normal and as stroke. Several performance metrics such as accuracy (ACC), specificity (SPE), sensitivity (SEN), and F-score are used to evaluate the performances of the classifier. The best classification results are achieved by VGG-19 with ACC 97.06%, SEN 97.41%, SPE 96.49%, and F-score 96.95%.
  • Breast tumor detection in ultrasound images using artificial intelligence

    Omkar Modi; Subasi, Abdulhamit; External Collaboration; NA; NA; NA; Computer Science; NA; Omkar Modi (Academic Press, 2023-01-01)
    Leveraging artificial intelligence (AI) for categorizing breast tumors as malignant or benign from breast ultrasound images can provide an effective and relatively low-cost method for the diagnosis of breast cancer. Presently, many machine learning (ML) and deep learning (DL) algorithms have been used for early-stage breast cancer detection. AI algorithms have shown promising results in breast cancer detection tasks. The use of deep convolutional neural network approaches has provided solutions for the efficient analysis of breast ultrasound images. Convolutional neural network (CNN) models analyze the image data in multiple layers and extract features which helps in better feature extractions and better performance in comparison to the conventional ML algorithms. Apart from conventional learning algorithms, we use the transfer learning technique which uses knowledge from its previous training in another related problem set. In this chapter, we have demonstrated the use of DL models through transfer learning, deep feature extraction, machine learning models, and comparison of their performances.
  • Breast cancer detection from mammograms using artificial intelligence

    Subasi, Abdulhamit; Aayush Dinesh Kandpal; Kolla Anant Raj; Ulas Bagci; External Collaboration; NA; NA; NA; Computer Science; NA; et al. (Academic Press, 2023-01-01)
    Breast cancer is one of the fastest-growing forms of cancer in the world today. Breast cancer is primarily found in women, and its frequency has been gaining significantly in the last few years. The key to tackle the rising cases of breast cancer is early detection. Many studies have shown that early detection significantly reduces the mortality rate of those affected. Machine learning and deep learning techniques have been adopted in the present scenario to help detect breast cancer in an early stage. Deep learning models such as the convolutional neural networks (CNNs) are suited explicitly to image data and overcome the drawbacks of machine learning models. To improve upon conventional approaches, we apply deep CNNs for automatic feature extraction and classifier building. In this chapter, we have demonstrated thoroughly the use of deep learning models through transfer learning, deep feature extraction, and machine learning models. Computer-aided detection or diagnosis systems have recently been developed to help health-care professionals increase diagnosis accuracy. This chapter presents early breast cancer detection from mammograms using artificial intelligence (AI). Various models have been presented along with an in-depth comparative analysis of the different state-of-the-art architectures, custom CNN networks, and classifiers trained on features extracted from pretrained networks. Our findings have indicated that deep learning models can achieve training accuracies of up to 99%, while both validation and test accuracies up to 96%. We conclude by suggesting various improvements that could be made to existing architectures and how AI techniques could help further improve and help in the early detection of breast cancer.
  • Artificial intelligence-based retinal disease classification using optical coherence tomography images

    Sohan Patnaik; Subasi, Abdulhamit; External Collaboration; NA; NA; NA; Computer Science; NA; Sohan Patnaik (Academic Press, 2023-01-01)
    Optical coherence tomography (OCT) is a noninvasive imaging technology used to obtain high-resolution cross-sectional images of the retina. The layers within the retina can be differentiated and retinal thickness can be measured to aid in the early detection and diagnosis of retinal diseases and conditions. Notwithstanding the proven utility of OCT images, diagnosing large datasets of OCT images using the manual method still remains a challenge. In this chapter, we propose a deep learning-based approach, namely, the use of convolutional neural networks (CNN) and some pretrained image classification models on top of CNNs to get a proper and faster diagnosis of the OCT images. We also experiment with the features extracted using pretrained image classification models. Mainly three diseases—drusen, diabetic macular edema, choroidal neovascularization are addressed in this study. Our technique achieves an accuracy score of 0.9948 and an F1 score of 0.9948 on the test set. The outcomes suggest that the proposed model could serve as an analytic module to alert users and/or medical experts when retinal diseases are suspected.
  • Artificial intelligence-based skin cancer diagnosis

    Subasi, Abdulhamit; Saqib Ahmed Qureshi; External Collaboration; NA; NA; NA; Computer Science; NA; Saqib Ahmed Qureshi (Academic Press, 2023-01-01)
    The first melanoma tumor has affected millions of people across the globe and taken many human lives. It can be diagnosed in its early stage, therefore it becomes very important to detect it before it becomes lethal. The melanoma skin cancer can be detected from the images of tumor by applying various techniques of deep learning. Medical science has progressed to a large extent in recent times. Its progress can be catalyzed further with the help of technology such as artificial intelligence or deep learning. In the first stage of our study, we used CNN (convolutional neural network) and transfer learning for differentiating between normal and melanoma tumors. In the next stage, features of the image were extracted from different pretrained models and then these features were passed through global average pooling layer and a classifier was put on top of it. In the first stage, that is, end-to-end learning, MobileNet architecture achieved the highest F1 score of 0.8014. In feature extraction technique, the model in which features were extracted from MobileNet architecture and XGBoost was used as a classifier achieved the highest F1 score of 0.818.
  • Detection and classification of Diabetic Retinopathy Lesions using deep learning

    Siddhesh Shelke; Subasi, Abdulhamit; External Collaboration; NA; NA; NA; Computer Science; NA; Siddhesh Shelke (Academic Press, 2023-01-01)
    Diabetic retinopathy (DR) is a frequent consequence of diabetes mellitus that induces retinal lesions, which affect vision. DR can lead to poor vision and blindness if not treated quickly. Unfortunately, DR is not reversible, and therapy just prolongs vision. As a result, tools are needed that initially identify and prevent poor vision in diabetics at an early stage. Early identification and treatment of DR can decrease the risk of vision loss considerably. Unlike computer-aided diagnosis systems, the manual diagnosis of DR retina fundus images by ophthalmologists is time-consuming and is prone to misdiagnosis. Recent technological advances have brought optical imaging systems to the market in relation to smartphones, which allows for low power, DR viewing in a variety of settings. On the other hand, deep learning (DL) has recently emerged as one of the most widely used approaches for improving performance in a variety of fields, including medical image analysis and classification. The purpose of this chapter is to use DL models to create an automated DR detection for the modern eye model. Moreover, DL models are implemented with the color fundus retina images. Transfer learning models such as InceptionResNet, VGG, and DenseNet architectures are also utilized for the color fundus retina image analysis. F1 scores, accuracy, area under the receiver operating characteristic curve (AUC - Area under the ROC Curve), and Kappa score are utilized to measure the performance of DL models for DR detection. It contributes significantly to improve DR identification by using different artificial intelligence (AI) methods with a variety of the color fundus retina public datasets.
  • Diagnosis of breast cancer from histopathological images with deep learning architectures

    Emrah Hancer; Subasi, Abdulhamit; External Collaboration; NA; NA; NA; Computer Science; NA; Emrah Hancer (Academic Press, 2023-01-01)
    Breast cancer is one of the most common cancer types among women worldwide. If not treated in earlier stages, it may be fatal. Therefore early diagnosis of breast cancer can minimize the human life risk. Mammograms and ultrasound imaging technologies play a crucial role to detect intraductal papillomas. However, the determination process of intraductal papillomas requires histopathological image analysis which may be mostly time-consuming, subjective, and tedious if carried out manually by the experts. To cover issue, computer-aided diagnosis (CAD) systems came into consideration. However, earlier CAD systems could not achieve significant improvement in the diagnosis process and their usage of them did not become widespread for more than a decade. Since deep learning has made so many significant advances in a wide variety of image applications, CAD systems that use its principles perform as well as the experts in stand-alone mode, and even perform better when used in support mode. In this chapter, we utilized various deep learning architectures for the detection process of breast cancer on the invasive ductal carcinoma (IDC) dataset which is one of the most popular and remarkable datasets in this field. According to the results, the pretrained VGG16 and MobileNet architectures obtain the best detection performance, reaching nearly 92% classification accuracy.
  • Magnetic resonance imagining-based automated brain tumor detection using deep learning techniques

    Abhranta Panigrahi; Subasi, Abdulhamit; External Collaboration; NA; NA; NA; Computer Science; NA; Abhranta Panigrahi (Academic Press, 2023-01-01)
    A brain tumor refers to an accumulation or growth of anomalous cells in the brain. The mass can either be benign (noncancerous) or malignant (cancerous). Cancerous brain tumors are the source of morbidity for which diagnosis and treatment require extensive resource allocation, experienced and skilled radiologists and doctors, and sophisticated diagnostic and therapeutic technology. Early detection of brain tumors is the basic requirement for the treatment of the patient. Manual detection of the brain tumor is an invasive process and hence is extremely risky. So, advancements in medical imaging techniques, such as magnetic resonance imagining (MRI), have proved to be an important tool in the early detection of brain tumors. Even with the advancements in medical imaging, it remains a very exigent task for radiologists. In many scenarios, the unavailability of a skilled radiologist or doctor can lead to improper diagnosis of the patient. Artificial intelligence and computer vision have successfully been used to achieve human-like accuracy in various image classification tasks. In this chapter, we propose various algorithms to detect the presence of brain tumors in MRI scans of the brain. We use various state-of-the-art convolutional neural networks and apply transfer learning to achieve this goal. We have also used machine learning algorithms that were trained on the embedding of the MRI scans, which were acquired through deep feature extraction, to detect the presence of abnormalities. Moreover, this chapter compares the performance of various models and techniques for automatic brain tumor detection using deep learning.
  • Artificial intelligence based Alzheimer’s disease detection using deep feature extraction

    Manav Nitin Kapadnis; Abhijit Bhattacharyya; Subasi, Abdulhamit; External Collaboration; NA; NA; NA; Computer Science; NA; Manav Nitin Kapadnis (Academic Press, 2023-01-01)
    Alzheimer’s disease (AD) is an acute brain disease that affects neural functions and destroys the memories and abilities of human beings. AD causes severe chronic, progressive, and irreversible cognitive declination and brain damage. It is one of the most common forms of dementia that affects the elderly. Early identification of AD is critical for developing new treatment options. Artificial intelligence (AI) is an excellent tool for detecting AD since these methods are used in clinical settings as a computer-aided diagnosis (CAD) system and play an important role in detecting alterations in brain images for AD detection. This chapter discusses the recent methods and developments in medical image analysis and image processing for AD detection using AI. The primary objective of this chapter is the development of easy-to-implement methods that promote early AD detection based on deep feature extraction methods. We developed a deep feature extraction methodology with machine learning approaches to achieve a good performance in AD detection. Furthermore, some of the techniques that were used by previous researchers are reviewed. A discussion on the existing state-of-the-art methods, a review of emerging trends, and future research problems will round up the chapter.
  • Digital twins in healthcare and biomedicine

    Subasi, Abdulhamit; External Collaboration; NA; NA; NA; Computer Science; NA; Abdulhamit Subasi (Academic Press, 2024-01-01)
    A digital twin (DT) is a three-part idea, which includes a virtual counterpart, a physical model, and the interaction between the two. This intersection of medicine and computer science represents a new area with numerous possible applications. DT technology can evaluate the correlations between a physical cancer patient and a comparable digital counterpart to isolate predictors of disease. DT can be created in healthcare for both patients and the disease risk assessment and therapy process, and they can be used to inform quantitatively adaptive risk assessment, diagnosis, and therapy decision-making, as well as personalization and optimization of health outcomes, prediction and prevention of adverse events, and intervention planning. In an ideal world, the DT concept may be used to patients to enhance diagnoses and therapy. The goal is to (1) create an unlimited number of replicas of network models of all phenotypic, molecular, and environmental factors related to disease mechanisms in individual patients; (2) computationally treat those DTs with thousands of drugs to find the best-performing drug; and (3) treat the patient with this drug and observe the side effects. To address multistage risk assessment and therapy selection models, which include both related disease and side-effect considerations in which a digital replica or DT of a physical process or entity is virtually recreated, with similar elements and dynamics, to achieve real-time optimization and testing, is used. This chapter presents the notion that data science may supplement clinical expertise to scientifically guide disease diagnosis, treatment planning, and prognosis. In particular, digital twins could forecast disease obstacles by using them in precision medicine, disease care and treatment modeling, machine learning, and predictive analytics and combining distinct scales of clinician viewpoints.
  • Lung cancer detection from histopathological lung tissue images using deep learning

    Aayush Rajput; Subasi, Abdulhamit; External Collaboration; NA; NA; NA; Computer Science; NA; Aayush Rajput (Academic Press, 2023-01-01)
    Lung cancer is a disease in which the growth of cells in the lung goes out of control. This disease can be lethal if the treatment to stop the growth of cells is not given to the patient in its early stages. Hence, it is very crucial to correctly recognize lung cancer in less time. Using the traditional method where each tissue is observed by a medical practitioner is time-consuming as well as error-prone; moreover, the practitioner should be very skilled. All these problems can be solved by using automated methods to detect lung cancer. In this chapter different deep learning models and techniques are used to detect lung cancer using histopathological images. The accuracy achieved by these models is very high and takes negligible time to give the results. Using a pretrained ResNet model combined with a support vector machine accuracy of 98.57% is achieved on the test data.
  • Introduction to artificial intelligence techniques for medical image analysis

    Subasi, Abdulhamit; No Collaboration; NA; NA; NA; Computer Science; NA; Abdulhamit Subasi (Academic Press, 2023-01-01)
    As the main goal of artificial intelligence (AI) is to provide inference from a sample, it employs statistics theory to develop mathematical models. When a model is constructed, its description and algorithmic solution for understanding must be competent. In some cases, the AI algorithm’s competency may be just as crucial as its classification accuracy. AI is applied in a variety of domains, such as anomaly detection, forecasting, medical signal/image analysis as a decision support component, and so on. The goal of this chapter is to assist scientists in selecting an acceptable AI approach and then guiding them in determining the best strategy by utilizing medical imaging. Furthermore, to introduce readers with the fundamentals of AI before digging into tackling real-world issues with AI methodologies. Machine learning, deep learning, and transfer learning are examples of basic ideas discussed. Topics relating to the various AI methodologies, such as supervised and unsupervised learning, will be covered. As a result, the key AI algorithms are discussed briefly in this chapter. Relevant PYTHON programming codes and routines are provided in each section.
  • Automated detection of colon cancer using deep learning

    Aayush Rajput; Subasi, Abdulhamit; External Collaboration; NA; NA; NA; Computer Science; NA; Aayush Rajput (Academic Press, 2023-01-01)
    Colon cancer is a type of cancer that affects the large population. In the beginning, small ploys are formed in the large intestine, which if left untreated become cancer. Detection of colon cancer in its early stages reduces the risk of life to large extent and makes treatment easier and reduces the cost of treatment. The traditional way of detection of colon cancer is very time-consuming and often can be wrong if not done by a skilled person. The development of deep learning has enabled us to detect colon cancer accurately using histopathological images. In this chapter different pretrained models and techniques are used. The accuracy of results is very good; using ResNet50 99.8% accuracy is achieved on the test data which is very good. Using these techniques, the time for detection of colon cancer can be reduced significantly.
  • Clustering potential metaverse users with the use of a value-based framework: Exploiting perceptions and attitudes on the use and adoption of metaverse for bold propositions

    Rsha Mirza; Lytras, Miltiadis; Ohoud Alzamzami; Lama Al Khuzayem; Hajar Alharbi; Sultanah Alshammari; Alaa Bafail; Arwa Basbrain; Eaman Alharbi; Nada Bajnaid; et al. (Pergamon, 2024-03-01)
    Metaverse is a new emerging platform that enables users to interact with each other and engage in many activities inside a collective virtual shared space. The metaverse is rapidly evolving, and it is essential to understand the perception and attitude of its potential users. Therefore, this research aims to study the users of the metaverse and obtain their opinions on some of the key variables of the metaverse. These variables include the core metaverse concept, readiness, ease of use, intention to belong to metaverse, intention to adopt, and value realization. We designed an online questionnaire that aims to measure these variables. Then, a deep analysis of the collected data was conducted using various exploratory descriptive statistics on all participants. Subsequently, we performed further descriptive and correlation analysis on the participants from Saudi Arabia, which resulted in clustering participants into three groups. The result of this work can help in understanding the current and potential users of the metaverse, especially the users from Saudi Arabia. This study will consequently help in developing the metaverse space, enhancing its features, and providing users with the best experiences. Our research contributes to the theory of the metaverse by justifying a bold value-based framework for metaverse adoption. This study also introduces three clusters, Skeptical, Unaware, and Optimists, of potential users of metaverse platforms, providing a clear description of each group.
  • An improved clustering method using particle swarm optimization algorithm and mitochondrial fusion model (PSO-MFM)

    Elkafrawy, Passent; Nasef, Mohamed; Hashim, Amal; External Collaboration; Virtual Reality Lab; 0; 0; Computer Science; 1; Nasef, Mohamed (IOS Press BV, 2024-02-16)
    Computational models are foundational concepts in computer science; many of these models such as P systems are based on natural biological processes. P systems represent a wide framework for a variety of concepts of data mining, as models of data clustering approaches. Data clustering is a technique for analyzing data based on its structure that is widely utilized for many applications. In this paper, the proposed model (PSO-MFM) has combined the Particle Swarm Optimization algorithm (PSO) with Mitochondrial Fusion Model to overcome some constraints of clustering techniques. The solving of clustering problem based on particle swarm is investigated in the proposed model when mutual dynamic rules are used. It can find the best cluster centers for a data set and improve clustering performance by utilizing the distributed parallel computing concept of mutual dynamic rules of mitochondrial fusion model. The comparative results demonstrate that the proposed strategy outperforms competition models when it comes to clustering accuracy, stability and the most efficient in time complexity.
  • Advancing precision medicine in medical education: Integrated, precise and data-driven smart solutions

    Paraskevi Papadopoulou; Lytras, Miltiadis; External Collaboration; NA; NA; NA; Computer Science; NA; Paraskevi Papadopoulou (2023-12-02)
    Advances in “Precision Medicine” initiative, also known as “Personalized Medicine” is an emerging approach for disease treatment and prevention that has already led to innovative discoveries and has created “smart” applications and solutions tailored to a person's or a group of individuals' genetic profile, lifestyle, and environment interaction. Already many physicians as part of patient care routinely prescribe various molecular/genetic and other tests enabling them to select personalized treatments that improve the chances of survival and reduce exposure to adverse effects. This initiative should provide to medical and healthcare professionals with adequate resources and readily available solutions so that the target to specific treatments and care of the illnesses is achieved while at the same time protecting the privacy and safety of the individual is secured as well as the Electronic Health Records (EHRs) and whatever additional data is necessary within the context of Precision Medicine. This study, through a literature review mostly, and some case study analysis, examine whether personalized medicine is delivered to the patient in an “accurate” and “precise” way, as expected. This requires that Health and Human Services and other stakeholders and agencies collaborate to solicit the right input from patients while at the same time can identify and address any educational, practical, legal, and technical issues and providing smart solutions. The lack of proper medical education and advanced infrastructure are still major barriers to the adoption of Precision Medicine, therefore, the role of medical training in Precision Medicine is also examined and analyzed. Specific examples are discussed in an integrated, precise, and data-driven manner to provide “smart” solutions.
  • Translating a value-based framework for resilient e-learning impact in post COVID-19 times: Research-based Evidence from Higher Education in Kuwait

    Afnan Alkhaldi; Sawsan Malik; Rashed Alhaimer; Abdullah Alshaheen; Lytras, Miltiadis; External Collaboration; NA; NA; NA; Computer Science; et al. (Elsevier, 2024-01-30)
    The covid-19 pandemic has changed people’s daily lives and ehaviors all across the world and has impacted practically every element of human existence. The introduction of remote education systems and the move toward online learning have had some of the most ignificant effects. The on-site operations of educational institutions, such as schools, colleges, and universities, have had to be suspended in order to stop the virus’ spread. In order to effectively disseminate instructional material and guarantee the unbroken progression of students’ academic endeavors, educators have been forced to look for novel approaches. The study used the Value-Based Adoption Model (VAM) as a conceptual framework to look into the factors that affected Kuwait’s e-learning outcomes in the midst of the covid-19 pandemic. 382 students at Kuwaiti universities and colleges were the source of quantitative data collection. The findings revealed that peer interaction emerged as the most influential factor in shaping utcomes within the educational context of Kuwait, while instructors and course design factors were not significant. Using the VAM, this study investigated the impact of several factors on students’ e-learning results during times of crisis. The research expands the existing knowledge base in the field on this subject and suggests developing a well-organized online learning crisis approach. The main contribution of this work is summarized on (i) An integrated framework for the quality of the e-learning experience in universities in post-covid-19 times and (ii) A resilient higher education institutional learning strategy model in post-covid-19 times. The findings of this paper can be generalizable to other Gulf Corporation Council (GCC) countries such as Kingdom of Saudi Arabia, Qatar, United Arab Emirates (UAE), Bahrain and Oman. This is due to the shared cultural traditions and values, along with similar educational systems among these nations.
  • Advancing Smart Cities: Sustainable Practices, Digital Transformation

    Bibri, Elias S.; Visvizi, Anna; Troisi, Orlando; External Collaboration; Artificial Intelligence & Cyber Security Lab; none; none; Entrepreneurship; none; Bibri, Elias S. (Springer, 2024-04-27)
    This book presents a comprehensive exploration of the transformative journey toward smart cities and the implementation of cutting-edge technologies in urban development. Divided into four distinct parts, it covers a broad range of topics that contribute to sustainable, efficient, and innovative urban living. Encompassing diverse research from IEREK's Future Smart Cities (FSC) conference, it focuses on smart city advancement through sustainable practices, digital transformation, and IoT integration. Covering topics such as smart buildings, urban planning during pandemics, and IoT applications in health care and agriculture, this book shapes the future of urban living. It delves further into opportunities in city regeneration, human-centric smart design, IoT data effectiveness, and more. A valuable resource for academics, researchers, and policymakers, it offers insights into telecommunications, AI, smart manufacturing, and methodologies for urban ecosystem improvement.
  • Higher Education Institutions and Covid-19 Toward Resilience and Sustainability Through Emergencies

    Visvizi, Anna; Kozlowski, Krzysztof; Nawaz, Raheel; External Collaboration; Artificial Intelligence & Cyber Security Lab; none; none; Entrepreneurship; none; Visvizi, Anna (Routledge, 2023-09-23)
    Offering insights into the adaptational strategies that were employed by higher education institutions worldwide during the Covid-19 pandemic, this volume considers the lasting effects of adaptation and change, as well as the perception of universities’ role in society and desired ways of operating. Nearly overnight, the pandemic forced university leaders and faculty across the world to switch to remote models, not only of teaching and learning but also of managing an entire institution. This book recognizes how the scale of challenges as well as the range of measures specific universities had to undertake was uneven, with some being better equipped than the others. Using a selection of international case studies, it offers an insight into strategies employed by institutions worldwide to navigate the crisis, and highlights the targets and objectives addressed by them during these processes. In so doing, it offers invaluable lessons for the years to come. An indispensable study into strategies that result in resilience and sustainability for universities, this book is essential reading for scholars of education, pedagogy, and organizational change in the higher education sector, as well as educational leaders around the world.
  • Research and Innovation Forum 2023. Navigating Shocks and Crises in Uncertain Times—Technology, Business, Society

    Visvizi, Anna; Troisi, Orlando; Corvello, Vincenzo; External Collaboration; Artificial Intelligence & Cyber Security Lab; none; none; Entrepreneurship; none; Visvizi, Anna (Springer, 2024-01-01)
    This book features research presented and discussed during the Research & Innovation Forum (Rii Forum) 2023. As such, this book offers a unique insight into emerging topics, issues and developments pertinent to the fields of technology, innovation and education and their social impact. Papers included in this book apply inter- and multi-disciplinary approaches to query such issues as technology-enhanced teaching and learning, smart cities, information systems, cognitive computing and social networking. What brings these threads of the discussion together is the question of how advances in computer science—which are otherwise largely incomprehensible to researchers from other fields—can be effectively translated and capitalized on so as to make them beneficial for society as a whole. In this context, Rii Forum and Rii Forum proceedings offer an essential venue where diverse stakeholders, including academics, the think tank sector and decision-makers, can engage in a meaningful dialogue with a view to improving the applicability of advances in computer science.

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