Search results for: spiritual intelligence

Authors: Hina Iqbal, Mustansar Ali Ghazanfar, Sandor Szedmak

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Recommender systems uses artificial intelligence practices for filtering obscure information and can predict if a user likes a specified item. Kernel mapping Recommender systems have been proposed which are accurate and state-of-the-art algorithms and resolve recommender system’s design objectives such as; long tail, cold-start, and sparsity. The aim of research is to propose hybrid framework that can efficiently integrate different versions— namely item-based and user-based KMR— of KMR algorithm. We have proposed various heuristic algorithms that integrate different versions of KMR (into a unified framework) resulting in improved accuracy and elimination of problems associated with conventional recommender system. We have tested our system on publically available movies dataset and benchmark with KMR. The results (in terms of accuracy, precision, recall, F1 measure and ROC metrics) reveal that the proposed algorithm is quite accurate especially under cold-start and sparse scenarios.

Keywords: Kernel Mapping Recommender Systems, hybrid recommender systems, cold start, sparsity, long tail

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Authors: Pascal François Faye, Baye Mor Sall, Bineta Dembele, Jeanne Ana Awa Faye

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In agriculture, estimating crop yields is key to improving productivity and decision-making processes such as financial market forecasting and addressing food security issues. The main objective of this paper is to have tools to predict and improve the accuracy of crop yield forecasts using machine learning (ML) algorithms such as CART , KNN and SVM . We developed a mobile app and a web app that uses these algorithms for practical use by farmers. The tests show that our system (collection and deployment architecture, web application and mobile application) is operational and validates empirical knowledge on agro-climatic parameters in addition to proactive decision-making support. The experimental results obtained on the agricultural data, the performance of the ML algorithms are compared using cross-validation in order to identify the most effective ones following the agricultural data. The proposed applications demonstrate that the proposed approach is effective in predicting crop yields and provides timely and accurate responses to farmers for decision support.

Keywords: prediction, machine learning, artificial intelligence, digital agriculture

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Authors: Thavananthan Sivananthawerl

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Shifting cultivation (Chena, Slash & Burn) is a cultivation method of raising, primarily, food crops (mainly annual) where an area of land is cleared off for its vegetation and cultivated for a period, and the abandoned (fallow) for its fertility to be naturally restored. Although this is the oldest (more than 5000 years) farming system, it is still practiced by indigenous communities of several countries such as Sri Lanka, India, Indonesia, Malaysia, Myanmar, West & Central Africa, and Amazon rainforest area. In Sri Lanka, shifting cultivation is mainly practiced during the North-East monsoon (called as Maha season, from Sept. to Dec.) with no irrigation. The traditional system allows farmers to cultivate for a short period of cultivation and a long period fallow period. This was facilitated mainly by the availability of land with less population. In addition, in the old system, cultivation practices were mostly related to religious and spiritual practices (Astrology, dynamic farming, etc.). At present, the majority of the shifting cultivators (SC’s) are cultivating in government lands, and most of them are adopting new technology (seeds, agrochemicals, machineries). Due to the local demand, almost 70% of the SC’s growing maize is mono-crop, and the rest with mixed-crop, such as groundnut, cowpea, millet, and vegetables. To ensure continuous cultivation and reduce moisture stress, they established ‘dug wells’ and used pumps to lift water from nearby sources. Due to this, the fallow period has been reduced drastically to 1- 2 years. To have the future prosperous of system, farmers should be educated so that they can understand the harmful effects of shifting cultivation and require new policies and a framework for converting the land use pattern towards high economic returns (new crop varieties, maintaining soil fertility, reducing soil erosion) while protecting the natural forests. The practice of agroforestry should be encouraged in which both the crops and the tall trees are cared for by farmers simultaneously. To facilitate the continuous cultivation, the system needs to develop water harvesting, water-conserving technologies, and scientific water management for the limited rainy season. Even though several options are available, all the solutions vary from region to region. Therefore, it is only the government and cultivators together who can find solutions to the problems of the specific areas.

Keywords: shifting cultivation, agroforestry, fallow, economic returns, government, Sri Lanka

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Authors: Brindusa Grigoriu

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Inscribing itself in the field of the history of multilingual communities with a focus on the evolution of language didactics, our paper aims at providing a pragmatic-interactional approach on a corpus proposing to scholars of the international scientific community a relevant text of early modern European literature: the first romance in French, The Conte of Flore and Blanchefleur by Robert d’Orbigny (1150). The multicultural context described by the romance is one in which an Arab-speaking prince, Floire, and his Francophone protégée, Blanchefleur, learn Latin together at the court of Spain and become fluent enough to turn it into the language of their love. This learning process is made up of interactional patterns of affective relevance, in which the proficiency of the protagonists in the domain of emotive acts becomes a matter of linguistic and pragmatic emulation. From five to ten years old, the pupils are efficiently stimulated by their teacher of Latin, Gaidon – a Moorish scholar of the royal entourage – to cultivate their competencies of oral expression and reading comprehension (of Antiquity classics), while enjoying an ever greater freedom of written expression, including the composition of love poems in this second language of culture and emotional education. Another relevant parameter of the educational process at court is that Latin shares its prominent role as a language of culture with French, whose exemplary learner is the (Moorish) queen herself. Indeed, the adult 'First lady' strives to become a pupil benefitting from lifelong learning provided by a fortuitous slave-teacher with little training, her anonymous chambermaid and Blanchefleur’s mother, who, despite her status of a war trophy, enjoys her Majesty’s confidence as a cultural agent of change in linguistic and theological fields. Thus, the two foreign languages taught at Spains’s court, Latin and French – as opposed to Arabic -, suggest a spiritual authority allowing the mutual enrichment of intercultural pioneers of cross-linguistic communication, in the aftermath of religious wars. Durably, and significantly – if not everlastingly – the language of physical violence rooted in intra-cultural solipsism is replaced by two Romance languages which seem to embody, together and yet distinctly, the parlance of peace-making.

Keywords: multilingualism, history of European language learning, French and Latin learners, multicultural context of medieval romance

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Authors: Mubarak Saadu Nabunkari, Abdullahi Abdu Ibrahim, Muhammad Ilyas

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This study looks at how Internet of Things (IoT) devices are used in healthcare to monitor and treat patients better. However, using these devices in healthcare comes with security problems. The research explores using Security Information and Event Management (SIEM) systems with healthcare IoT devices to solve these security challenges. Reviewing existing literature shows the current state of IoT security and emphasizes the need for better protection. The main worry is that healthcare IoT devices can be easily hacked, putting patient data and device functionality at risk. To address this, the research suggests a detailed security framework designed for these devices. This framework, based on literature and best practices, includes important security measures like authentication, data encryption, access controls, and anomaly detection. Adding SIEM systems to this framework helps detect threats in real time and respond quickly to incidents, making healthcare IoT devices more secure. The study highlights the importance of this integration and offers guidance for implementing healthcare IoT securely, efficiently, and effectively.

Keywords: cyber security, threat intelligence, forensics, heath care

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Authors: Caroline Atef Shoukry Tadros

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Massive Urbanization growth threatens the sustainability of cities and the quality of city life. This raised the need for an alternate model of sustainability, so we need to plan the future cities in a smarter way with smarter mobility. Smart Mobility planning applications are solutions that use digital technologies and infrastructure advances to improve the efficiency, sustainability, and inclusiveness of urban transportation systems. They can contribute to meeting the needs of Urbanization growth by addressing the challenges of traffic congestion, pollution, accessibility, and safety in cities. Some example of a Smart Mobility planning application are Mobility-as-a-service: This is a service that integrates different transport modes, such as public transport, shared mobility, and active mobility, into a single platform that allows users to plan, book, and pay for their trips. This can reduce the reliance on private cars, optimize the use of existing infrastructure, and provide more choices and convenience for travelers. MaaS Global is a company that offers mobility-as-a-service solutions in several cities around the world. Traffic flow optimization: This is a solution that uses data analytics, artificial intelligence, and sensors to monitor and manage traffic conditions in real-time. This can reduce congestion, emissions, and travel time, as well as improve road safety and user satisfaction. Waycare is a platform that leverages data from various sources, such as connected vehicles, mobile applications, and road cameras, to provide traffic management agencies with insights and recommendations to optimize traffic flow. Logistics optimization: This is a solution that uses smart algorithms, blockchain, and IoT to improve the efficiency and transparency of the delivery of goods and services in urban areas. This can reduce the costs, emissions, and delays associated with logistics, as well as enhance the customer experience and trust. ShipChain is a blockchain-based platform that connects shippers, carriers, and customers and provides end-to-end visibility and traceability of the shipments. Autonomous vehicles: This is a solution that uses advanced sensors, software, and communication systems to enable vehicles to operate without human intervention. This can improve the safety, accessibility, and productivity of transportation, as well as reduce the need for parking space and infrastructure maintenance. Waymo is a company that develops and operates autonomous vehicles for various purposes, such as ride-hailing, delivery, and trucking. These are some of the ways that Smart Mobility planning applications can contribute to meeting the needs of the Urbanization growth. However, there are also various opportunities and challenges related to the implementation and adoption of these solutions, such as the regulatory, ethical, social, and technical aspects. Therefore, it is important to consider the specific context and needs of each city and its stakeholders when designing and deploying Smart Mobility planning applications.

Keywords: smart mobility planning, smart mobility applications, smart mobility techniques, smart mobility tools, smart transportation, smart cities, urbanization growth, future smart cities, intelligent cities, ICT information and communications technologies, IoT internet of things, sensors, lidar, digital twin, ai artificial intelligence, AR augmented reality, VR virtual reality, robotics, cps cyber physical systems, citizens design science

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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: Elizabeth Grier, Meg Gemmill, Mary Martin, Leora Reiter, Herman Tang, Alexandra Donaldson, Isis Lunsky, Mia Wu

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Background: Individuals with Cerebral Palsy (CP) and/or IDD face numerous physical and mental health challenges, including difficulty accessing effective palliative care. The aim of this study is to assess the knowledge and comfort of healthcare providers in providing community-based palliative care for patients with Cerebral Palsy (CP) and severe to profound Intellectual and Developmental Disabilities (IDD). Methods: This study includes a mixed methods approach obtaining both quantitative and qualitative data. Quantitative data from palliative care practitioners was obtained through an online survey assessing comfort in symptom management, grief assessment, and goals of care discussion. This survey was distributed to physicians and allied health practitioners across Canada through the College of Family Physicians of Canada Member Interest Groups for Palliative Care and for IDD. Survey results guided the development of a semi-structured interview template, which was used to conduct a focus group on the same topic. Participants were four palliative care providers (3 physicians and one spiritual care practitioner). The focus group transcript is currently undergoing thematic analysis using NVivo 12 software. Results: 57 palliative care practitioners completed the survey. 87% of participants indicated they have provided palliative care services for persons with CP and/or IDD. Findings suggest practitioners are somewhat confident in identifying specific physical symptoms (dyspnea, pressure ulcers) but less confident in identifying physical/emotional pain, addressing grief, and prognosticating life expectancy in this population. 54% of responses indicated they had little/no training on palliating those with CP or IDD, and 45% somewhat or strongly disagree members of their profession can manage symptoms for this population. Focus group analysis is underway, and results will be available at the time of the poster presentation. Conclusion: Persons with CP and IDD are more likely to experience severe health inequities when accessing palliative care. Results of this study suggest further education is needed for palliative care professionals to address the barriers and challenges in providing palliative care to this patient population.

Keywords: palliative care, symptom management, health equity, community healthcare, intellectual and developmental disabilities

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Authors: Mohamad Musa

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The history of mental health practices and services in the Middle East region has been deeply intertwined with its rich cultural, religious, and societal context. Tracing back to ancient times, mental health approaches were heavily influenced by the traditions of major monotheistic religions, with a strong emphasis on spiritual and traditional healing methods. As psychiatric institutions and Western medicine gradually gained a foothold in the region during the 20th century, a notable shift occurred. However, the integration of Western psychiatric practices faced significant challenges due to cultural barriers and deeply rooted beliefs. Families and communities often turned to traditional healers and religious practices as their initial recourse for mental health concerns, viewing Western interventions with skepticism and hesitation. Historically, mental health services in the Middle East have been overshadowed by a focus on physical health and the biomedical model. Mental illness carried substantial stigma, with individuals and families often reluctant to disclose mental health struggles due to fears of societal ostracization and discrimination. This stigma posed a significant barrier to accessing and accepting formal mental health support. Later in the 20th century, governments in the Middle East began recognizing the need for modernizing mental health services and integrating them into the broader healthcare system. However, this process was hindered by several factors, including limited resources, inadequate training for healthcare professionals, and ongoing conflicts and instability in certain regions, which disrupted the delivery of mental health services. As the 21st century progressed, several Middle Eastern nations, particularly those in the Arabian Gulf region, began implementing national mental health strategies and legislative reforms to address the growing need for comprehensive mental health care. These efforts aimed to destigmatize mental illness, protect the rights of individuals with mental health conditions, and promote public awareness and education. Despite these positive developments, the historical legacy of stigma, cultural barriers, and limited resources continues to pose challenges in the provision of accessible and culturally responsive mental health services across the diverse populations of the Middle East.

Keywords: mental health, history, middle east, literature review

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Authors: Hyunchul Ahn, William X. S. Wong

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Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM.

Keywords: corporate credit rating prediction, Feature selection, genetic algorithms, instance selection, multiclass support vector machines

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Authors: V. K. Banga

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Robots are now playing a very promising role in industries. Robots are commonly used in applications in repeated operations or where operation by human is either risky or not feasible. In most of the industrial applications, robotic arm manipulators are widely used. Robotic arm manipulator with two link or three link structures is commonly used due to their low degrees-of-freedom (DOF) movement. As the DOF of robotic arm increased, complexity increases. Instrumentation involved with robotics plays very important role in order to interact with outer environment. In this work, optimal control for movement of various DOFs of robotic arm using various soft computing techniques has been presented. We have discussed about different robotic structures having various DOF robotics arm movement. Further stress is on kinematics of the arm structures i.e. forward kinematics and inverse kinematics. Trajectory planning of robotic arms using soft computing techniques is demonstrating the flexibility of this technique. The performance is optimized for all possible input values and results in optimized movement as resultant output. In conclusion, soft computing has been playing very important role for achieving optimized movement of robotic arm. It also requires very limited knowledge of the system to implement soft computing techniques.

Keywords: artificial intelligence, kinematics, robotic arm, neural networks, fuzzy logic

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Authors: Nasim Nosoudi, Amir Zadeh, Hunter White, Joshua Conrad, Joon W. Shim

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De novo Malignancy has become one of the major causes of death after transplantation, so early cancer diagnosis and detection can drastically improve survival rates post-transplantation. Most previous work focuses on using artificial intelligence (AI) to predict transplant success or failure outcomes. In this work, we focused on predicting de novo malignancy after liver transplantation using AI. We chose the patients that had malignancy after liver transplantation with no history of malignancy pre-transplant. Their donors were cancer-free as well. We analyzed 254,200 patient profiles with post-transplant malignancy from the US Organ Procurement and Transplantation Network (OPTN). Several popular data mining methods were applied to the resultant dataset to build predictive models to characterize de novo malignancy after liver transplantation. Recipient's bilirubin, creatinine, weight, gender, number of days recipient was on the transplant waiting list, Epstein Barr Virus (EBV), International normalized ratio (INR), and ascites are among the most important factors affecting de novo malignancy after liver transplantation

Keywords: De novo malignancy, bilirubin, data mining, transplantation

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Authors: P.-W. Tsai, C.-Y. Chen, C.-W. Chen

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In this paper, the stability analysis of a GA-Based adaptive fuzzy sliding model controller for a nonlinear system is discussed. First, a nonlinear plant is well-approximated and described with a reference model and a fuzzy model, both involving FLC rules. Then, FLC rules and the consequent parameter are decided on via an Evolved Bat Algorithm (EBA). After this, we guarantee a new tracking performance inequality for the control system. The tracking problem is characterized to solve an eigenvalue problem (EVP). Next, an adaptive fuzzy sliding model controller (AFSMC) is proposed to stabilize the system so as to achieve good control performance. Lyapunov’s direct method can be used to ensure the stability of the nonlinear system. It is shown that the stability analysis can reduce nonlinear systems into a linear matrix inequality (LMI) problem. Finally, a numerical simulation is provided to demonstrate the control methodology.

Keywords: adaptive fuzzy sliding mode control, Lyapunov direct method, swarm intelligence, evolved bat algorithm

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Authors: Karen V. Lee

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The background and significance of this study involves an autoethnographic story that shares research results about how music and movies influence the suicide of a new music teacher working in a public school. The performative narrative duet demonstrates how music and movies highlight social issues when the new teacher cannot cope with allegations surrounding professional issues. Both university advisors are drawn into deep reflection about the wider political issues that arise around the transition from the student-teacher internship process to the teaching career with the stark reality of teaching profession in the 21st century. This performance of story and music creates a transformative composition of reading, hearing, feeling while provoking visceral and emotional responses. Sometimes, young teachers are forced to take a leave of absence to reflect upon their practice with adolescents. In this extreme circumstance, the outcome was suicide. The qualitative research method involves an autoethnographic story as the author is methodologist, theoretician, and participant. Sub-themes surround film, music education and how movie resources have influenced his tragic misguided decision regarding social, emotional, physical, spiritual, and practical strategies to cope with the allegations. Major findings from this story demonstrate how lived experiences can resonate the importance of providing more education and resources to new teachers. The research provides substantive contribution, aesthetic merit, as the impact of movies and music influences the suicide. The reflexive account of storied sensory experiences situated in culture settings becomes a way to describe and seek verisimilitude by evoking lifelike and believable feelings from others. Sadly, the circumstance surrounding the story involving the allegations of a teacher sexually harassing a student is not uncommon in society. However, the young teacher never received counseling to cope with the allegations but instead was influenced by music and movies and opted for suicide. In conclusion, stories share the implications for film and media studies as music and movies can encourage a moral mission to empower individuals with despair and emotional impairment to embrace professional support to assist with emotional and legal challenges encountered in the field of teaching. It is from media studies that education and awareness surrounding suicide can disseminate information about the tragic outcome.

Keywords: music, movies, suicide, narrative, autoethnography

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Authors: Waleed Al-Nuaimy, Luke Anastassiou, Manjinder Kainth

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As the process of teaching has evolved with the advent of new technologies over the ages, so has the process of learning. Educators have perpetually found themselves on the lookout for new technology-enhanced methods of teaching in order to increase learning efficiency and decrease ever expanding workloads. Shortly after the invention of the internet, web-based learning started to pick up in the late 1990s and educators quickly found that the process of providing learning material and marking assignments could change thanks to the connectivity offered by the internet. With the creation of early web-based virtual learning environments (VLEs) such as SPIDER and Blackboard, it soon became apparent that VLEs resulted in higher reported computer self-efficacy among students, but at the cost of students being less satisfied with the learning process . It may be argued that the impersonal nature of VLEs, and their limited functionality may have been the leading factors contributing to this reported dissatisfaction. To this day, often faced with the prospects of assigning colossal engineering cohorts their homework and assessments, educators may frequently choose optimally curated assessment formats, such as multiple-choice quizzes and numerical answer input boxes, so that automated grading software embedded in the VLEs can save time and mark student submissions instantaneously. A crucial skill that is meant to be learnt during most science and engineering undergraduate degrees is gaining the confidence in using, solving and deriving mathematical equations. Equations underpin a significant portion of the topics taught in many STEM subjects, and it is in homework assignments and assessments that this understanding is tested. It is not hard to see that this can become challenging if the majority of assignment formats students are engaging with are multiple-choice questions, and educators end up with a reduced perspective of their students’ ability to manipulate equations. Artificial intelligence (AI) has in recent times been shown to be an important consideration for many technologies. In our paper, we explore the use of new AI based software designed to work in conjunction with current VLEs. Using our experience with the software, we discuss its potential to solve a selection of problems ranging from impersonality to the reduction of educator workloads by speeding up the marking process. We examine the software’s potential to increase learning efficiency through its features which claim to allow more customized and higher-quality feedback. We investigate the usability of features allowing students to input equation derivations in a range of different forms, and discuss relevant observations associated with these input methods. Furthermore, we make ethical considerations and discuss potential drawbacks to the software, including the extent to which optical character recognition (OCR) could play a part in the perpetuation of errors and create disagreements between student intent and their submitted assignment answers. It is the intention of the authors that this study will be useful as an example of the implementation of AI in a practical assessment scenario insofar as serving as a springboard for further considerations and studies that utilise AI in the setting and marking of science and engineering assignments.

Keywords: engineering education, assessment, artificial intelligence, optical character recognition (OCR)

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Authors: Kaustubh Chakradeo, Michael Delves, Sofya Titarenko

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Malaria is a serious disease which affects hundreds of millions of people around the world, each year. If not treated in time, it can be fatal. Despite recent developments in malaria diagnostics, the microscopy method to detect malaria remains the most common. Unfortunately, the accuracy of microscopic diagnostics is dependent on the skill of the microscopist and limits the throughput of malaria diagnosis. With the development of Artificial Intelligence tools and Deep Learning techniques in particular, it is possible to lower the cost, while achieving an overall higher accuracy. In this paper, we present a VGG-based model and compare it with previously developed models for identifying infected cells. Our model surpasses most previously developed models in a range of the accuracy metrics. The model has an advantage of being constructed from a relatively small number of layers. This reduces the computer resources and computational time. Moreover, we test our model on two types of datasets and argue that the currently developed deep-learning-based methods cannot efficiently distinguish between infected and contaminated cells. A more precise study of suspicious regions is required.

Keywords: convolution neural network, deep learning, malaria, thin blood smears

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Authors: Sina Saadati

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Multiprocessors Systems-On-Chips (MPSOCs) are used widely on modern computers to execute sophisticated software and applications. These systems include different processors for distinct aims. Most of the proposed task schedulers attempt to improve energy consumption. In some schedulers, the processor's temperature is considered to increase the system's reliability and performance. In this research, we have proposed a new method for thermal-aware task scheduling which is based on an artificial neural network (ANN). This method enables us to consider a variety of factors in the scheduling process. Some factors like ambient temperature, season (which is important for some embedded systems), speed of the processor, computing type of tasks and have a complex relationship with the final temperature of the system. This Issue can be solved using a machine learning algorithm. Another point is that our solution makes the system intelligent So that It can be adaptive. We have also shown that the computational complexity of the proposed method is cheap. As a consequence, It is also suitable for battery-powered systems.

Keywords: task scheduling, MOSOC, artificial neural network, machine learning, architecture of computers, artificial intelligence

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Authors: Luis Miguel Méndez Díaz

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In this article, a mathematics teaching-learning strategy will be presented, specifically differential calculus in one variable, in a fun and competitive space in which the action on the part of the student is manifested and not only the repetition of information on the part of the teacher. Said action refers to motivating, problematizing, summarizing, and coordinating a game of dominoes whose thematic cards are designed around the basic and main contents of differential calculus. The strategies for teaching this area are diverse and precisely the game of dominoes is one of the most used strategies in the practice of mathematics because it stimulates logical reasoning and mental abilities. The objective on this investigation is to identify the way in which the game of dominoes affects the learning and understanding of fundamentals concepts of differential calculus in one variable through experimentation carried out on students of the first semester of the School of Engineering and Sciences of the Technological Institute of Monterrey Campus Querétaro. Finally, the results of this study will be presented and the use of this strategy in other topics around mathematics will be recommended to facilitate logical and meaningful learning in students.

Keywords: collaborative learning, logical-mathematical intelligence, mathematical games, multiple intelligences

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Authors: Yu Chen

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In line with existing philosophical approaches, this research proposes a conceptual model with an innovative spatial vision and aesthetic principles for Artificial Intelligence (AI) application in brand advertising. The model first identifies the major constituencies in contemporary advertising on three spatial levels—physical, psychological, and symbolic. The model further incorporates the relationships among AI, aesthetics, branding, and advertising and their interactions with the major actors in all spaces. It illustrates that AI may follow the aesthetic principles-- beauty, elegance, and simplicity-- to reinforce brand identity and consistency in advertising, to collaborate with stakeholders, and to satisfy different advertising objectives on each level. It proposes that, with aesthetic guidelines, AI may assist consumers to emerge into the physical, psychological, and symbolic advertising spaces and helps transcend the tangible advertising messages to meaningful brand symbols. Conceptually, the research illustrates that even though consumers’ engagement with brand mostly begins with physical advertising and later moves to psychological-symbolic, AI-assisted advertising should start with the understanding of brand symbolic-psychological and consumer aesthetic preferences before the physical design to better resonate. Limits of AI and future AI functions in advertising are discussed.

Keywords: AI, spatial, aesthetic, brand advertising

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Authors: Leah Ning

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This paper discusses the development of a machine learning algorithm that accurately detects metastatic breast cancer (cancer has spread elsewhere from its origin part) in selected images that come from pathology scans of lymph node sections. Being able to develop an accurate artificial intelligence (AI) algorithm would help significantly in breast cancer diagnosis since manual examination of lymph node scans is both tedious and oftentimes highly subjective. The usage of AI in the diagnosis process provides a much more straightforward, reliable, and efficient method for medical professionals and would enable faster diagnosis and, therefore, more immediate treatment. The overall approach used was to train a convolution neural network (CNN) based on a set of pathology scan data and use the trained model to binarily classify if a new scan were benign or malignant, outputting a 0 or a 1, respectively. The final model’s prediction accuracy is very high, with 100% for the train set and over 70% for the test set. Being able to have such high accuracy using an AI model is monumental in regard to medical pathology and cancer detection. Having AI as a new tool capable of quick detection will significantly help medical professionals and patients suffering from cancer.

Keywords: breast cancer detection, AI, machine learning, algorithm

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Authors: Malik Imtiaz Ahmad

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Human beings are the super organism granted the gift of consciousness of life by the Almighty God and endowed with an intrinsic legal value to their humanity that shall be guarded and protected respecting dignity regardless of your cultural, religious, race, or physical background; you want to be treated equally for a reason for being human. Islam graces the essential integrity of humanity and confirms the freedom and accountability impact on individuality and the open societal sphere, including the moral, economic, and political aspects. Human Rights allow people to live with dignity, equality, justice, freedom, and peace. The Kantian approach to morality expresses that ethical actions follow universal moral laws. Hence, human rights are based upon the normative approaches setting the international standards to promote, guard, and protect the fundamental rights of the people. Islam is a divine religion commanding human rights based upon the principles of social justice and regulates all facets of the moral and spiritual ethics of Muslims besides bringing balance abreast in the non-Muslims to respect their lives with safety and security and property. The Canon law manifests the faith and equality amongst Christianity, regulating the communal dignity to build and promote the sanctity of Holy life (can. 208 to 223). This concept of the community is developed after the insight of the Islamic 'canon law', which is the code of revelation itself and inseparable from the natural part of the salvation of mankind. The etymology and history of human rights is a polemical debate in a preview of Islamic and Western culture. On the other hand, international law is meticulous about the fundamental part of Conon law that focuses on the communal political, social and economic relationship. The evolving process of human rights is considered to be an exclusive universal thought regarding an open society that forms a legal base for the constituent of international instruments of the protection of Human Rights, viz. UDHR. On the other side, Muslim scholars emphasize that human rights are devolving around Islamic law. Both traditions need a dire explanation of contemporary openness for bringing the harmonious universal law acceptable and applicable to the international communities concerning the anthropology of political, economic, and social aspects of a human being.

Keywords: human rights-based approach (HRBA), human rights in Islam, evolution of universal human rights, conflict in western, Islamic human rights

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Authors: Michael Okeke, Andrew Blyth

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Supervisory Control And Data Acquisition (SCADA) as the state of the art Industrial Control Systems (ICS) are used in many different critical infrastructures, from smart home to energy systems and from locomotives train system to planes. Security of SCADA systems is vital since many lives depend on it for daily activities and deviation from normal operation could be disastrous to the environment as well as lives. This paper describes how No-Trust-Zone (NTZ) architecture could be incorporated into SCADA Systems in order to reduce the chances of malicious intent. The architecture is made up of two distinctive parts which are; the field devices such as; sensors, PLCs pumps, and actuators. The second part of the architecture is designed following lambda architecture, which is made up of a detection algorithm based on Particle Swarm Optimization (PSO) and Hadoop framework for data processing and storage. Apache Spark will be a part of the lambda architecture for real-time analysis of packets for anomalies detection.

Keywords: industrial control system (ics, no-trust-zone (ntz), particle swarm optimisation (pso), supervisory control and data acquisition (scada), swarm intelligence (SI)

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Authors: Scott N. Gerard, Aliza Heching, Susann M. Keohane, Samuel S. Adams

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The world-wide population of people over 60 years of age is growing rapidly. The explosion is placing increasingly onerous demands on individual families, multiple industries and entire countries. Current, human-intensive approaches to eldercare are not sustainable, but IoT and AI technologies can help. The Knowledge Reactor (KR) is a contextual, data fusion engine built to address this and other similar problems. It fuses and centralizes IoT and System of Record/Engagement data into a reactive knowledge graph. Cognitive applications and services are constructed with its multiagent architecture. The KR can scale-up and scaledown, because it exploits container-based, horizontally scalable services for graph store (JanusGraph) and pub-sub (Kafka) technologies. While the KR can be applied to many domains that require IoT and AI technologies, this paper describes how the KR specifically supports the challenging domain of cognitive eldercare. Rule- and machine learning-based analytics infer activities of daily living from IoT sensor readings. KR scalability, adaptability, flexibility and usability are demonstrated.

Keywords: ambient sensing, AI, artificial intelligence, eldercare, IoT, internet of things, knowledge graph

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Authors: Abe D. Desta

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This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking.

Keywords: artificial intelligence, computer vision, deep learning, fast-regional convolutional neural networks, feature extraction, vehicle tracking

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Authors: Venkataramana Sovenahalli Badigar, B. M. Suryakanth, Akshar Prasanna, Karthik Veeramalai, Vishwak Ram Vishwak Ram

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Technological advances have led to widespread autonomy in vehicles. Empowering these autonomous with the intelligence to cooperate amongst themselves leads to a more efficient use of the resources available to them. This paper proposes a demonstration of a swarm algorithm implemented on a group of autonomous vehicles. The demonstration involves two ground bots and an aerial drone which cooperate amongst them to locate an object of interest. The object of interest is modelled using a high-intensity light source which acts as a beacon. The ground bots are light sensitive and move towards the beacon. The ground bots and the drone traverse in random paths and jointly locate the beacon. This finds application in various scenarios in where human interference is difficult such as search and rescue during natural disasters, delivering crucial packages in perilous situations, etc. Experimental results show that the modified swarm algorithm implemented in this system has better performance compared to fully random based moving algorithm for object localization and tracking.

Keywords: swarm algorithm, object localization, ground bots, drone, beacon

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Authors: Mary M. Park

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Hollywood has had a long and enduring history of showcasing the United States military to civilian audiences, and the portrayals of soldiers in films have had a definite impact on the civilian perception of the US military. The growing gap between the civilian population and the military in the US has led to certain stereotypes of military personnel to proliferate, especially in the area of militarized masculinity, which has often been harmful to the psychological and spiritual wellbeing of military personnel. Examining Hollywood's portrayal of soldiers can serve to enhance our understanding of how civilians may be influenced in their perception of military personnel. Moreover, it can provide clues as to how male military personnel may also be influenced by Hollywood films as they form their own military identity. The post 9/11 era has seen numerous high budget films lionizing a particular type of soldier, the 'warrior-hero', who adheres to a traditional form of hegemonic masculinity and exhibits traits such as physical strength, bravery, stoicism, and an eagerness to fight. This paper examines how the portrayal of the 'warrior-hero' perpetuates a negative stereotype that soldiers are a blend of superheroes and emotionless robots and, therefore, inherently different from civilians. This paper examines the portrayal of militarized masculinity in three of the most successful war films of the post-9/11 era; Black Hawk Down (2001), The Hurt Locker (2008), and American Sniper (2014). The characters and experiences of the soldiers depicted in these films are contrasted with the lived experiences of soldiers during the Iraq and Afghanistan wars. Further, there is an analysis of popular films depicting ancient warriors, such as Troy (2004) and 300 (2007), which were released during the early years of the War on Terror. This paper draws on the concept of hegemonic militarised masculinity by leading scholars and feminist international relations theories on militarized masculinity. This paper uses veteran testimonies collected from a range of public sources, as well as previous studies on the link between traditional masculinity and war-related mental illness. This paper concludes that the seemingly exclusive portrayal of soldiers as 'warrior-heroes' in films in the post-9/11 era is misleading and damaging to civil-military relations and that the reality of the majority of soldiers' experiences is neglected in Hollywood films. As civilians often believe they are being shown true depictions of the US military in Hollywood films, especially in films that portray real events, it is important to find the differences between the idealized fictional 'warrior-heroes' and the reality of the soldiers on the ground in the War on Terror.

Keywords: civil-military relations, gender studies, militarized masculinity, social pyschology

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Authors: Andres Frederic

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We present the MindFlow system supporting the detection and the diagnosis of stresses. The heart of the system is a knowledge synthesis engine allowing occupational health stakeholders (psychologists, occupational therapists and human resource managers) to formulate queries related to stress and responding to users requests by recommending a pattern of stress if one exists. The stress pattern diagnosis is based on expert knowledge stored in the MindFlow stress ontology including stress feature vector. The query processing may involve direct access to the MindFlow system by occupational health stakeholders, online communication between the MindFlow system and the MindFlow domain experts, or direct dialog between a occupational health stakeholder and a MindFlow domain expert. The MindFlow knowledge model is generic in the sense that it supports the needs of psychologists, occupational therapists and human resource managers. The system presented in this paper is currently under development as part of a Dutch-Japanese project and aims to assist organisation in the quick diagnosis of stress patterns.

Keywords: occupational stress, stress management, physiological measurement, accident prevention

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Authors: Manar Amayri, Hussain Kazimi, Quoc-Dung Ngo, Stephane Ploix

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A general approach is proposed to determine occupant behavior (occupancy and activity) in residential buildings and to use these estimates for improved energy management. Occupant behaviour is modelled with a Bayesian Network in an unsupervised manner. This algorithm makes use of domain knowledge gathered via questionnaires and recorded sensor data for motion detection, power, and hot water consumption as well as indoor CO₂ concentration. Two case studies are presented which show the real world applicability of estimating occupant behaviour in this way. Furthermore, experiments integrating occupancy estimation and hot water production control show that energy efficiency can be increased by roughly 5% over known optimal control techniques and more than 25% over rule-based control while maintaining the same occupant comfort standards. The efficiency gains are strongly correlated with occupant behaviour and accuracy of the occupancy estimates.

Keywords: energy, management, control, optimization, Bayesian methods, learning theory, sensor networks, knowledge modelling and knowledge based systems, artificial intelligence, buildings

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Authors: Ian A. McCulloh, Madelaine Zinser, Jesse Patsolic, Michael Ramos

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Recommender systems are a common artificial intelligence (AI) application. For any given input, a search system will return a rank-ordered list of similar items. As users review returned items, they must decide when to halt the search and either revise search terms or conclude their requirement is novel with no similar items in the database. We present a statistically designed experiment that investigates the impact of similarity ratings on human judgement to conclude a search item is novel and halt the search. 450 participants were recruited from Amazon Mechanical Turk to render judgement across 12 decision tasks. We find the inclusion of ratings increases the human perception that items are novel. Percent similarity increases novelty discernment when compared with star-rated similarity or the absence of a rating. Ratings reduce the time to decide and improve decision confidence. This suggests the inclusion of similarity ratings can aid human decision-makers in knowledge search tasks.

Keywords: ratings, rankings, crowdsourcing, empirical studies, user studies, similarity measures, human-centered computing, novelty in information retrieval

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Authors: Muhamad Sahir Bin Ahmad Shatiry, Wan Normeza Wan Zakaria, Mohamad Zaki Hassan

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The pandemic Covid19 has significantly impacted the world; the spreading of the Covid19 virus initially from China has dramatically impacted the world's economy. Therefore, the world reacts with establishing the new way or norm in daily life. The rapid rise of the latest technology has been seen by introducing many technologies to ease human life to have a minor contract between humans and avoid spreading the virus Covid19. Digital twin technologies are one of the technologies created before the pandemic Covid19 but slow adoption in the industry. Throughout the Covid19, most of the companies in the world started to explore to use it. The digital twin technology provides the virtual platform to replicate the existing condition or setup for anything such as office, manufacturing line, factories' machine, building, and many more. This study investigates the effect on the economic perspective after the companies use the Digital Twin technology in the industry. To minimize the contact between humans and to have the ability to operate the system digitally remotely. In this study, the explanation of the digital twin technology impacts the world's microeconomic and macroeconomic.

Keywords: productivity, artificially intelligence, IoT, digital twin

Procedia PDF Downloads 193