In recent years, educational institutions have started using the tools of commercial data analytics in higher education. By gathering information about students as they navigate campus information systems, learning analytics “uses analytic techniques to help target instructional, curricular, and support resources” to examine student learning behaviors and change students’ learning environments. As a result, the information educators and educational institutions have at their disposal is no longer demarcated by course content and assessments, and old boundaries (...) between information used for assessment and information about how students live and work are blurring. Our goal in this paper is to provide a systematic discussion of the ways in which privacy and learning analytics conflict and to provide a framework for understanding those conflicts. -/- We argue that there are five crucial issues about student privacy that we must address in order to ensure that whatever the laudable goals and gains of learning analytics, they are commensurate with respecting students’ privacy and associated rights, including (but not limited to) autonomy interests. First, we argue that we must distinguish among different entities with respect to whom students have, or lack, privacy. Second, we argue that we need clear criteria for what information may justifiably be collected in the name of learning analytics. Third, we need to address whether purported consequences of learning analytics (e.g., better learning outcomes) are justified and what the distributions of those consequences are. Fourth, we argue that regardless of how robust the benefits of learning analytics turn out to be, students have important autonomy interests in how information about them is collected. Finally, we argue that it is an open question whether the goods that justify higher education are advanced by learning analytics, or whether collection of information actually runs counter to those goods. (shrink)

Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student’s demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however (...) defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution’s responsibility to its students. (shrink)

Research has shown that the construction of visual representations may have a positive effect on cognitive skills, including argumentation. In this paper we present a study on learning argumentation through computer-supported argument diagramming. We specifically focus on whether students, when provided with an argument-diagramming tool, create better diagrams, are more motivated, and learn more when working with other students or on their own. We use learning analytics to evaluate a variety of student activities: pre and post questionnaires (...) to explore motivational changes; the argument diagrams created by students to evaluate richness, complexity and completion; and pre and post knowledge tests to evaluate learning gains. (shrink)

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to (...) investigate the patient’s outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (shrink)

Heart disease is a major challenge for global health, along with high morbidity and mortality. The earlier it is diagnosed, the better the outcome of the patient given timely intervention. This project employs a form of machine learning to train and create a risk assessment model of heart disease from the user-submitted data. The model employs the Random Forest algorithm, one of the most accurate robust algorithms available. We will use a dataset having patient records, such as age, gender, (...) blood pressure, and others. Other medical indicators. Therefore, data preprocessing and feature selection will be applied to enhance the model performance. The main idea is to design an interactive web application which can provide easy access to predict heart disease risk assessment. Users input health parameters, and the application, developed with Streamlit, indicates an immediate prediction for heart disease risk. The interface can be accessed by anyone without technical backgrounds. After the submission, it will process the data through the machine learning model, and risk assessment will be shown. This immediately outcome gotten from this type of risk assessment may prove to be an early warning for patients as they are encouraged to seek medical evaluation in appropriate times. This project, therefore, aims to bridge the gap that exists between advanced machine learning and an actual setting of practical healthcare where users can be well cared for proactively with their hearts and in an effort to ease the burden of heart diseases through early detection and intervention. (shrink)

Elective mathematics has been an extra mathematics subject for pilot students of Eduardo Barretto Sr. National High School for quite some time now. Through this, many alumni testified how this helped them understand senior high school and college math. However, the teachers have also been struggling with the resources for specific areas of mathematics, such as Business Math, Statistics, Analytic Geometry, Trigonometry, and Calculus. When the pandemic hit the Philippines, contextualized learning material aligned with the Most Essential Learning (...) Competencies (MELC) helped elective mathematics teachers. The contextualized learning material was developed from April to August 2020 and was first validated in February 2021 by the students who participated in the pilot testing. However, following the due process, it was suggested that I revalidate the IDEA-formatted Analytic Geometry and Trigonometry instructional material. It examines Eduardo Barretto Sr. National High School mathematics teachers and SDO Calamba City experts' evaluation of the content's topics, objectives, concepts, directions, and activities and the format's prints, illustrations, design, and layout. Participants' perceptions of learning material were assessed using a 4-point Likert Scale survey. Hence, descriptive quantitative design. Participants were surveyed using a 4-point Likert Scale to assess their learning content impression. The findings reveal that the participants found the format and content of the developed learning material to be very satisfactory. The indicators "localized" and "can be done independently" were viewed as Fair and Satisfactory, respectively. The learning material was enhanced by localizing the activities and giving directions that could facilitate self-learning. (shrink)

Consumer behavior analytics has become a pivotal aspect for businesses to understand and predict customer preferences and actions. The advent of machine learning (ML) algorithms has revolutionized this field by providing sophisticated tools for data analysis, enabling businesses to make data-driven decisions. However, the effectiveness of these ML algorithms significantly hinges on the optimization techniques employed, which can enhance model accuracy and efficiency. This paper explores the application of various optimization techniques in consumer behaviour analytics using machine (...) learning algorithms. By focusing on the optimization of key parameters, the study aims to improve the predictive power of models and reduce computational costs. (shrink)

Consumer behavior analytics has become a pivotal aspect for businesses to understand and predict customer preferences and actions. The advent of machine learning (ML) algorithms has revolutionized this field by providing sophisticated tools for data analysis, enabling businesses to make data-driven decisions. However, the effectiveness of these ML algorithms significantly hinges on the optimization techniques employed, which can enhance model accuracy and efficiency. This paper explores the application of various optimization techniques in consumer behaviour analytics using machine (...) learning algorithms. By focusing on the optimization of key parameters, the study aims to improve the predictive power of models and reduce computational costs. (shrink)

“Big Data” and data analytics affect all of us. Data collection, analysis, and use on a large scale is an important and growing part of commerce, governance, communication, law enforcement, security, finance, medicine, and research. And the theme of this symposium, “Individual and Informational Privacy in the Age of Big Data,” is expansive; we could have long and fruitful discussions about practices, laws, and concerns in any of these domains. But a big part of the audience for this symposium (...) is students and faculty in higher education institutions (HEIs), and the subject of this paper is data analytics in our own backyards. Higher education learning analytics (LA) is something that most of us involved in this symposium are familiar with. Students have encountered LA in their courses, in their interactions with their law school or with their undergraduate institutions, instructors use systems that collect information about their students, and administrators use information to help understand and steer their institutions. More importantly, though, data analytics in higher education is something that those of us participating in the symposium can actually control. Students can put pressure on administrators, and faculty often participate in university governance. Moreover, the systems in place in HEIs are more easily comprehensible to many of us because we work with them on a day-to-day basis. Students use systems as part of their course work, in their residences, in their libraries, and elsewhere. Faculty deploy course management systems (CMS) such as Desire2Learn, Moodle, Blackboard, and Canvas to structure their courses, and administrators use information gleaned from analytics systems to make operational decisions. If we (the participants in the symposium) indeed care about Individual and Informational Privacy in the Age of Big Data, the topic of this paper is a pretty good place to hone our thinking and put into practice our ideas. (shrink)

Academia is a competitive environment. Early Career Researchers (ECRs) are limited in experience and resources and especially need achievements to secure and expand their careers. To help with these issues, this book offers a new approach for conducting research using the combination of mindsponge innovative thinking and Bayesian analytics. This is not just another analytics book. 1. A new perspective on psychological processes: Mindsponge is a novel approach for examining the human mind’s information processing mechanism. This conceptual framework (...) is used to construct models in studies. The framework is highly flexible and widely applicable for many different types of information processes. The mindsponge approach can help researchers discover interesting ideas or even formulate their very own theories when investigating psychosocial phenomena. This approach brings a fresh wind to the current landscape of social sciences and humanities (SSH). 2. Easy-to-follow analysis protocol: The Bayesian Mindsponge Framework (BMF analytics) is useful in terms of computing and visualizing power but also easy to learn and apply. Contrary to being intimidating, the Bayesian analytics section of this book is presented in a reader-friendly manner with a detailed yet clear step-by-step procedure. Examples are from published BMF articles, allowing readers to immediately practice the method and quickly create their own applications. With educational purposes in mind, the book is very suitable for ECRs who are looking to innovate their research methods. 3. Advocating for low-cost, high-quality research: Doing science can be very costly. Mindsponge innovative thinking and BMF analytics help produce impactful studies using openly available data on online repositories. This is based on the authors’ previous works and experiences. The book presents examples of employing the open R package bayesvl on secondary data from different sources. With less financial constraints, researchers can have more freedom of thought to pursue their curiosity and creativity. ECRs in low- and middle-income countries may find this aspect crucial in their careers. 4. Support and collaboration: The authors share their insights from experiences in the academic publishing system to help readers get through the processes of manuscript writing and peer-reviewing more easily. The authors are also ready to support other researchers with further inquiries and collaboration opportunities at the following website, mindsponge(dot)info. This book is for: a) ECRs whose only abundant resources are their innovation capacity and strength of will; b) Researchers in SSH who want to explore a novel approach to thinking and study conducting; c) Low- and middle-income countries’ researchers looking for a cost-effective research protocol; and, d) Innovative thinkers who want to turn their interesting thoughts into good publications. (shrink)

The research aims to identify the learning organizations and their role in achieving organizational excellence in the Palestinian universities in Gaza Strip. The researchers used descriptive analytical approach and used the questionnaire as a tool for information gathering. The questionnaires were distributed to senior management in the Palestinian universities. The study population reached (344) employees in senior management is dispersed over (3) Palestinian universities. A stratified random sample of (182) workers from the Palestinian universities was selected and the recovery (...) rate was (69.2%). Statistical analysis (SPSS) program was used for analysis and processing the data. The research found the following results: there is a fair degree of approval on "cognitive dimension", there is a high approval about the importance of "organizational dimension", there is moderately consent of the importance of "Community dimension", there is a large degree of consent about the importance of axis of the "leadership excellence", there is a large degree of consent about the importance of axis of the "service-excellence", there is a fair degree approval about the importance of the axis of "cognitive excellence", and there is a moderately consent of the importance of "Organizational Excellence". The research found a group of recommendations including: the need to develop appropriate strategies for the University, employ modern technology in information systems, in addition to provide appropriate environment that achieve learning organizations, develop technological infrastructure, adopt universities for knowledge management in the academic and administrative departments because knowledge is the core of the work of these departments, create technological incubators in universities to adopt excellence academic research projects, and protected them, support them, and market them. Establishing of centers of excellence for scientific research in the university.The need for cooperation and coordination between local, Arab and international universities for the exchange of knowledge, information, and participation programs and training courses, as it provides an opportunity to develop the capabilities and skills of personnel and expertise. (shrink)

Near the end of December 2019, the globe was hit with a major crisis, which is nothing but the coronavirusbased pandemic. The authorities at the train station should also keep in mind the need to limit the spread of the covid virus in the event of a global pandemic. When it comes to controlling the COVID-19 epidemic, public transportation facilities like train stations play a pivotal role because of the proximity of so many people who may be exposed to the (...) virus. Using common place CCTV cameras and deep learning with simple online and real-time (DeepSORT) methods, this study develops social distance monitoring using a YOLOv4 identification of a Surveillance Object Model. Based on experiments conducted with a minicomputer equipped with an Intel 11th Gen Intel(R) Core(TM) i3-1115G4 at 3.00GHz, 2995 Mhz, two Core(s), four Logical processor, four gigabytes of random-access memory (RAM), this paper makes use of CCTV surveillance, which was put into practice at the Guindy railway station, Chennai, Tamilnadu in India in order to detect the violation of social distancing. (shrink)

Predictive analytics, empowered by generative AI and cloud technologies, has the potential to revolutionize educational practices by facilitating early intervention and proactive support for students. This chapter explores the integration of predictive analytics in educational settings, focusing on how datadriven insights can identify at- risk students and tailor interventions to their specific needs. By leveraging generative AI algorithms, educators can analyze vast amounts of data, including academic performance, engagement levels, and socio- emotional factors, to predict potential challenges before (...) they escalate. The chapter highlights case studies that demonstrate successful implementations of predictive analytics in schools and universities, showcasing improved student outcomes, enhanced engagement, and more personalized learning experiences. Additionally, it discusses the ethical considerations and challenges associated with data privacy and bias in AI systems. (shrink)

This paper examines how predictive analytics enhances risk management in financial institutions. Advanced tools like machine learning and statistical modeling help predict risks, identify trends, and implement strategies to prevent losses by analyzing historical and real-time data. It covers the use of predictive analytics for credit risk, market risk, operational risk, and fraud detection, with practical case studies. Additionally, it discusses challenges, ethical issues, and prospects in this field.

ABSTRACT This is a review of David Bronstein's book "Aristotle on Knowledge and Learning: The Posterior Analytics" (Oxford: Oxford University Press, 2016).

I review David Bronstein's "Aristotle on Knowledge and Learning: The Posterior Analytics".

The study aimed to identify E- Learning strategies and their relation to the efficiency of research performance in foreign and Palestinian universities (University of Ottawa, Munster, Suez Canal, Al-Azhar, Islamic, Al-Aqsa). The analytical descriptive approach was used for this purpose, and relying on the questionnaire as a main tool for data collection. The study society is from the senior management, where the number of senior management in the universities in question is 206. The random stratified sample was selected and (...) (SPSS) was used for statistical data analysis. The study found a significant relationship between E- Learning strategies and the efficiency of research performance in universities. It also reached the participation of senior management in the research that develops the university performance in the Palestinian universities. The senior management indicated that they do not care to follow the policies of implementing the development of scientific research. While the senior management in foreign universities indicated that they are interested in following up the policies of implementing the development of scientific research. The study also showed that senior management in Palestinian universities does not care about providing the appropriate budget for E- Learning. The study recommended that the senior management of Palestinian universities should provide an E- Learning budget and encourage employees to continue using E- Learning strategies. The administration in the Palestinian universities should adopt and support outstanding research, and the need to encourage interest in the implementation of policies for the development of scientific research. (shrink)

This study aimed to identify the learning and business incubation processes and their impact on developing the performance of business incubators in Gaza Strip, and the study relied on the descriptive analytical approach, and the study population consisted of all employees working in business incubators in Gaza Strip in addition to experts and consultants in incubators where their total number reached (62) individuals, and the researchers used the questionnaire as a main tool to collect data through the comprehensive survey (...) method, where (55) questionnaires were retrieved with a recovery rate of (88.7%). The results of the study showed that there is a high approval of the variables of the study, where the dimension of incubation operations came with a relative weight (84.89%), followed by the learning dimension which got a relative weight (82.50%), while the performance level of the incubators got a relative weight (80.12%) and as the study results showed There is a positive relationship statistically between the two dimensions of learning and business incubation processes and performance development in business incubators, and the linear regression analysis model showed that the main effect in improving the performance of business incubators is "after business incubation operations" while it showed weak effect after learning. The study came out with several recommendations, the most important of which is that business incubators seek to reinforce the learning process and incubation processes for business, and the need to periodically evaluate their performance, and that a more effective mechanism be designed to follow up with companies after the end of the incubation period and monitor the progress of these companies. (shrink)

The research aims to identify the trends of Palestinian higher educational institutions in Gaza Strip as learning organizations from the perspective of senior management in the Palestinian universities in Gaza Strip. The researchers used descriptive analytical approach and used the questionnaire as a tool for information gathering. The questionnaires were distributed to senior management in the Palestinian universities. The study population reached (344) employees in senior management is dispersed over (3) Palestinian universities. A stratified random sample of (182) employees (...) from the Palestinian universities was selected and the recovery rate was (69.2%). Statistical analysis (SPSS) program was used for analysis and processing the data. The study found the following results: There is an agreement about: the importance of the focus of "organizational structure" with an average approval, the importance of "technological infrastructure" axis with high approval, and the importance of "strategic" with an average approval. The results concluded that the study sample agree on the importance of "organizational dimension" highly. The results showed that the sample believe that the "strategic leadership" level in the universities got medium-approval. There is a fair level agreement about the axis of interest "teams / committees". There is an agreement about the importance of the "human dimension" moderately. The results showed that the sample is highly agreed about the importance of focus of the "knowledge management", and the focus of the "continuing education". They agreed weakly to somewhat about the importance of the focus of "scientific research", moderately agree about the importance of the center of "institutional culture", agree moderately on the importance of "cognitive dimension". The results showed that the sample largely agree on the importance of the focus of "strategic partnerships and alliances". They agree highly on the axis "keep up with the labor market" and on the focus of the importance of "technology incubators". The results showed that the sample moderately agree about the importance of the theme on "consulting and training", the importance of the focus of "social responsibility", and the focus of the "Community dimension". The study found a group of recommendations including: there is a need to provide suitable environment that achieve learning organizations. There is a need to develop the technological infrastructure (hardware, software, networks, databases, and human skills) because of the great advantages that they offer. The universities need to adopt knowledge management in the academic and administrative departments because knowledge is the core of the work of these departments. The establishment of technology incubators in universities to adopt outstanding university research projects, to protect, to supports, and to market them; furthermore, to develop the capabilities and skills of employees in the field of information technology. (shrink)

Visual analytics (VA) combines the strengths of human and machine intelligence to enable the discovery of interesting patterns in challenging datasets. Historically, most attention has been given to developing the machine component—for example, machine learning or the human-computer interface. However, it is also essential to develop the abilities of the analysts themselves, especially at the beginning of their careers. -/- For the past several years, we at the University of British Columbia (UBC)—with the support of The Boeing Company—have (...) experimented with various ways of preparing undergraduate students for VA. Although inspired by the need to prepare students to become visual analysts, the result turned out to be fairly general in scope, applicable to other analytical approaches, as well as more general research. In hindsight, this makes considerable sense. Although the visual component of VA is necessary, it is insufficient; many analytical activities at the human end involve nonvisual skills, such as effective decision-making and the ability to quickly focus on the relevant parts of a problem. -/- The result of this experimentation is a third-year undergraduate course titled Cognitive Systems 303 (COGS 303) that focuses on “VA unplugged”—that is, on developing investigative abilities prior to training on the VA systems themselves. It was felt that if students focused on developing basic analytical habits of thought prior to learning VA systems, these habits would be reinforced by subsequent practice on “live” systems. (shrink)

The diversion of forest land for development requires accurate tree enumeration to assess environmental impact. Traditional methods, like manual counting and sampling, are labor-intensive, time-consuming, and prone to error. This project leverages high-resolution satellite and drone imagery, combined with advanced image processing and machine learning, to automate tree counting. Our system includes analytical tools, and provides authorities with historical environmental data (like rainfall, temperature, humidity) for informed decision-making. With a userfriendly interface and appealing data visualizations, it also integrates Google (...) Maps API for user-generated satellite imagery. This solution aims to enhance accuracy, efficiency, and scalability in tree enumeration, supporting sustainable forest management and regulatory compliance. (shrink)

Integrating medical imaging with computing technologies, such as Artificial Intelligence (AI) and its subsets: Machine learning (ML) and Deep Learning (DL) has advanced into an essential facet of present-day medicine, signaling a pivotal role in diagnostic decision-making and treatment plans (Huang et al., 2023). The significance of medical imaging is escalated by its sustained growth within the realm of modern healthcare (Varoquaux and Cheplygina, 2022). Nevertheless, the ever-increasing volume of medical images compared to the availability of imaging experts. (...) Biomedical experts and radiologists have resulted in a widening disparity, causing an excess and overwhelming workload on these healthcare professionals (Chen et al., 2021). Several studies indicate that the present-day biomedical radiologist is now saddled with the daunting task of interpreting an image almost every 10 seconds to keep pace with the burgeoning clinical demands (McDonald et al., 2015; Hosny et al., 2018; Lantsman et al., 2022). This cognitive drain has invariably led to inevitable consequences such as delays in diagnosis and an amplified risk of diagnostic errors – thus, the biomedical imaging aspect is in dire need of methods that would aid accurate diagnostics and analytics for improved decision making. In this review, the importance of AI-related technologies such as ML and/or DL methods are reviewed in relation to the processing of medical or biomedical images along with their potentials, challenges, and possible suggestions for future studies in the health landscape. The focus will be on machine learning methods associated with the medical field of image classification. (shrink)

The visual analytics platform provides comprehensive reports to healthcare providers, enabling them to monitor trends, identify risk factors, and make informed decisions. This approach significantly enhances patient care by minimizing delays in response and improving overall health outcomes. The system's architecture, based on big data frameworks, supports scalable and efficient data processing. The study demonstrates how the integration of predictive models and data visualization tools can revolutionize health alert systems, making them more responsive and adaptive to individual patient needs. (...) Future enhancements will focus on incorporating machine learning models for more personalized predictions and extending the system's capabilities to remote patient care. (shrink)

Customer churn prediction is a critical challenge for businesses in retaining their customer base and optimizing their marketing strategies. Machine learning (ML) techniques offer a powerful approach to predict customer churn by analyzing historical customer behavior, demographic information, and usage patterns. This paper provides an overview of machine learning-based models used for predicting customer churn, including classification algorithms such as logistic regression, decision trees, random forests, support vector machines (SVM), and neural networks. We explore how businesses can leverage (...) these models to identify customers who are likely to churn and take proactive measures to retain them. The paper also highlights the challenges in churn prediction, such as imbalanced data, overfitting, and the interpretability of models, and provides insights into the future of churn prediction models. Case studies in telecommunications, retail, and banking sectors are discussed to demonstrate practical applications. (shrink)

This is an instructor’s manual with student exercises for the Analytic Philosophy and Philosophy of Language course. It is intended to assist the instructor in teaching the subject to students for whom English is a second language. -/- This manual begins with a chapter that describes the types of learning activities during this course. Next are topic chapters, each of which has four sections: a synopsis of the lecture on the topic; a lecture lesson worksheet with tasks; a seminar (...) lesson worksheet with tasks; and assignments for essay writing. At the end of the manual is a list of key definitions for the course, a list of exam questions, and answer keys for seminar tasks and lecture worksheets. (shrink)

This research paper delves into the realm of Financial Network Analytics, unraveling the opportunities and challenges it presents in the domains of systemic risk management and investment analysis. The study navigates the intricate web of interconnected financial entities, employing advanced analytics techniques to uncover meaningful insights. Through a comprehensive exploration, the paper identifies opportunities for leveraging financial network data to enhance systemic risk detection mechanisms and refine investment strategies. Simultaneously, it addresses the inherent challenges, such as data complexity (...) and dynamic network structures, offering nuanced perspectives on their implications. The findings contribute to the evolving landscape of financial analytics, providing a foundation for the development of robust tools and methodologies that can aid financial institutions and investors in navigating the complexities of modern financial networks. (shrink)

Coronavirus disease has a crisis with high spread throughout the world during the COVID19 pandemic period. This disease can be easily spread to a group of people and increase the spread. Since it is a worldly disease and not plenty of vaccines available, social distancing is the only best approach to defend against the pandemic situation. All the affected countries' governments declared locked-down to implement social distancing. This social separation and persons not being in a mass group can slow down (...) the spread of COVID19. It reduces the physical contact between infected persons and normal healthy persons. Almost every health organization tells that to follow social distancing people should maintain at least 6 feet of distance from each other. This research proposes a deep learning approach for social distancing which is developed for tracking and detecting people who are in indoor as well as outdoor scenarios using YOLO V3 video analytic technique. This approach focuses to inspect whether the people are maintaining social distancing in many areas, using surveillance video with measuring the distance in real-time performance. Most of the early studies of detecting social distance monitoring were based on GPS for tracking the movements of people where the signals could be lost. On the other hand, some countries use drones to detect large gatherings of people who cannot have a clear view at night times [10]. In the future, the proposed system can be used fully for detecting threats in the public crowded or it can detect any person affected by critical situations (ie fainting, Cordia arrest) or planting the crops in the forms evenly with a uniform measurement. This proposal could be used in many fields like crowd analysis, autonomous vehicles, and human action recognition and could help the government authorities to redesign the public place layout and take precautionary action in the risk zones. This system analyses the social distancing of people by calculating the distance between people to slow downing the spread of the COVID 19 virus. (shrink)

P&C insurers have an important role in addressing financial risk management needs but now struggle to respond to the new forms of risk. Historical analysis and actuarial calculations, which form the backbone of classical approaches to risk measurement and management, are not well suited to such new kinds of risks as climate change, cyber risks, and business cycle risks. These conventional approaches are also a static method for selling, which has limited potential in changing quickly with new market and consumer (...) changes. However, the development of AI, big data, or advanced predictive analytics has provided a new paradigm to such challenges to cope with and analyze big data dynamically to derive Applying a combination of best Machine Learning (ML), natural language processing (NLP) and other AI techniques, it will become possible to predict risks exposures, as well as optimize underwriting and claims processing. In this paper, the author investigates the opportunities that AI carries within the sphere of reformation of risk evaluation approaches in P&C insurance. In particular, it discusses the ever-emerging field with AI models integrated into operations, underwriting, fraud detection, claims modelling, and loss computation. The applied research uses a comprehensive bibliographical review to present past and present practices and assess modern AI implementations. This paper uses research approaches and case studies to prove that AI can solve some problems and bring cost-saving solutions. Moreover, it emphasizes the ability of predictive analytics to enhance customer satisfaction at the policy level as well as optimize operations. However, the paper also explores the dirty side of implementation, including the stipulations surrounding compliance and data privacy and thus provides an objective analysis of the future of AI use in the P&C Insurance Industry. (shrink)

Sales forecasting plays a pivotal role in business planning, helping organizations predict future sales trends based on historical data. Traditional forecasting methods, such as moving averages and linear regression, often lack the flexibility and precision required to account for complex patterns in sales data. Predictive analytics, which leverages advanced machine learning techniques, offers a more robust and dynamic approach for forecasting sales. This paper explores the implementation of sales forecasting using predictive analytics, focusing on the application of (...) machine learning algorithms like decision trees, random forests, and time series models. By analyzing historical sales data and identifying key influencing factors such as seasonality, promotions, and economic indicators, predictive models can provide more accurate forecasts, enabling businesses to optimize inventory, staffing, and marketing strategies. This paper highlights key methodologies, benefits, and challenges associated with predictive sales forecasting and presents case studies demonstrating its impact in real-world applications. (shrink)

In today’s rapidly evolving business landscape, organizations must continuously adapt to stay competitive. AI-driven human resource (HR) analytics has emerged as a strategic tool to enhance workforce agility and inform decision-making processes. By leveraging advanced algorithms, machine learning models, and predictive analytics, HR departments can transform vast data sets into actionable insights, driving talent management, employee engagement, and overall organizational efficiency. AI’s ability to analyze patterns, forecast trends, and offer data-driven recommendations empowers HR professionals to make proactive (...) decisions in hiring, skill development, performance management, and retention. (shrink)

Microsoft Fabric represents a significant leap forward in analytics services, combining data management, analytics, and machine learning into a unified platform. This paper offers a detailed examination of Microsoft Fabric, covering its architecture, prominent features, advantages, and potential applications. Engineered to streamline data processes and foster collaboration among data experts, Microsoft Fabric supports real-time analytics, scalable data storage, advanced machine learning capabilities, and robust security protocols. Future developments for the platform include deeper AI integration, expanded (...) data connectivity, enhanced user experience, fortified security measures, sustainability initiatives, and collaborative tools. By harnessing Microsoft Fabric, organizations can gain comprehensive data insights and drive innovation effectively. (shrink)

The proposed system combines predictive analytics and historical data to flag potential emergencies before they occur. The visual analytics platform provides comprehensive reports to healthcare providers, enabling them to monitor trends, identify risk factors, and make informed decisions. This approach significantly enhances patient care by minimizing delays in response and improving overall health outcomes. The system's architecture, based on big data frameworks, supports scalable and efficient data processing. The study demonstrates how the integration of predictive models and data (...) visualization tools can revolutionize health alert systems, making them more responsive and adaptive to individual patient needs. Future enhancements will focus on incorporating machine learning models for more personalized predictions and extending the system's capabilities to remote patient care. (shrink)

The continuous growth of healthcare data has made it essential to develop efficient systems that not only alert healthcare providers but also visualize patient data in a comprehensible way. This study introduces a Health Alert System integrated with Report Visualization powered by Data Analytics to improve patient monitoring and alerting mechanisms. By leveraging real-time data from wearable sensors and hospital records, the system generates health alerts based on deviations from normal parameters. The proposed system combines predictive analytics and (...) historical data to flag potential emergencies before they occur. The visual analytics platform provides comprehensive reports to healthcare providers, enabling them to monitor trends, identify risk factors, and make informed decisions. This approach significantly enhances patient care by minimizing delays in response and improving overall health outcomes. The system's architecture, based on big data frameworks, supports scalable and efficient data processing. The study demonstrates how the integration of predictive models and data visualization tools can revolutionize health alert systems, making them more responsive and adaptive to individual patient needs. Future enhancements will focus on incorporating machine learning models for more personalized predictions and extending the system's capabilities to remote patient care. (shrink)

This dissertation consists of five chapters, each written as independent research papers that are unified by an overarching concern regarding information privacy and machine learning-based artificial intelligence (AI). This dissertation addresses the issues concerning privacy and AI by responding to the following three main research questions (RQs): RQ1. ‘How does an AI system affect privacy?’; RQ2. ‘How effectively does the General Data Protection Regulation (GDPR) assess and address privacy issues concerning both individuals and groups?’; and RQ3. ‘How can the (...) value of privacy be embedded into systems?’ -/- To respond to the RQs, this dissertation adopts the privacy impact assessment (PIA) as the overall methodology. A PIA encompasses three distinct stages; the first, the analytical stage, concerns the analysis of how AI (particularly focusing on inference as a process that includes inferred information, AI models’ performance, and accessing information uncovered by AI models) impacts privacy. Second, the legal assessment stage concerns whether AI that processes personal information and develops models complies with the GDPR. Finally, the design requirements stage features proposals for design requirements for systems aimed at protecting privacy. Accordingly, this dissertation is structured in three parts, each corresponding to a specific stage of a PIA and responding to one of the RQs. -/- Part I, which addresses the first stage of the PIA, comprises three chapters that altogether respond to RQ1. Chapter 2 analyses how AI impacts the descriptive aspect of privacy; this part argues that AI challenges the current definitions of privacy and that the ‘source control’ and ‘actual access’ definitions of privacy, once revised in the face of counter-examples involving inferred information, converge. Chapter 3, which considers how AI impacts the normative aspect of privacy, particularly the value of privacy, argues that AI affects the social value of privacy, which depends on trust, as this dimension of privacy is constituted when AI models perform accurately. Chapter 4 examines how AI impacts the normative aspect of privacy, particularly the right to privacy, and it argues that, although accessing information uncovered by AI models raises concerns about the privacy of algorithmically designed groups, the right to privacy cannot be recognised for such groups. This is the disruptive feature of AI that has led to consideration of new approaches other than the traditional one, which involves recognising the right to privacy, to protect the privacy of these groups. Instead of recognising the right to privacy for algorithmically designed groups, this chapter suggests taking a moral principle for the moral obligation of protecting vulnerable groups within an ethics of vulnerability. -/- Part II concerns the second stage of the PIA and consists of one chapter that responds to RQ2. Chapter 5, in addition to evaluating whether AI that processes personal information and develops models complies with the GDPR, also assesses whether the GDPR adequately addresses the privacy issues raised by AI. It specifically focuses on group privacy and argues that GDPR has limitations in protecting the privacy of algorithmically designed groups and that the privacy of such vulnerable entities must be considered in the context of privacy and data protection. -/- Part III, which is related to the third stage of the PIA, also consists of one chapter that responds to RQ3. Chapter 6 proposes design requirements to protect privacy by integrating privacy into systems and argues that privacy is instrumentally valuable for the sake of autonomy. Accordingly, to embed the value of privacy into systems, design requirements are articulated through the translation of norms that promote and protect autonomy. (shrink)

Urban resilience is critical for ensuring the sustainability and adaptability of cities in the face of growing challenges such as climate change, population growth, and infrastructure degradation. Predictive analytics, powered by Artificial Intelligence (AI) and the Internet of Things (IoT), offers a transformative approach to enhancing urban resilience. This paper explores how AI-driven predictive analytics can optimize disaster preparedness, infrastructure maintenance, and resource allocation in smart cities. By integrating real-time data from IoT sensors with advanced machine learning (...) models, cities can proactively address vulnerabilities and improve decision-making. This study highlights key applications, challenges, and future directions for leveraging predictive analytics to build resilient urban ecosystems. (shrink)

Matthias Mahlmann’s Mind and Rights (M&R) argues that the mentalist theory of moral cognition—premised on an approach to the mind most closely associated with generative linguistics—is the appropriate lens through which to understand moral judgment’s roots in the mind. Specifically, he argues that individuals possess an inborn moral faculty responsible for the principled generation of moral intuitions. These moral intuitions, once sufficiently abstracted, generalized, and universalized by individuals, gave rise to the idea of human rights embodied in such conventions as (...) the Universal Declaration of Human Rights. I am sympathetic to the fundamentals of this argument, particularly in the link between Universal Moral Grammar and the idea of human rights. I thus take my comments on M&R as an opportunity to preempt a topical challenge to the mentalist theory arising in statistical machine learning. Using M&R’s analytical theory of morality alongside the mentalist approach, I emphasize—through this preempted challenge—the significance of strong generative capacity in the moral domain, the distinction between actual acquisition of cognitive abilities and simulation of acquisition, and what Mahlmann calls the theory-dependence of data interpretation in machine learning modeling. (shrink)

The continuous growth of healthcare data has made it essential to develop efficient systems that not only alert healthcare providers but also visualize patient data in a comprehensible way. This study introduces a Health Alert System integrated with Report Visualization powered by Data Analytics to improve patient monitoring and alerting mechanisms. By leveraging real-time data from wearable sensors and hospital records, the system generates health alerts based on deviations from normal parameters. The proposed system combines predictive analytics and (...) historical data to flag potential emergencies before they occur. The visual analytics platform provides comprehensive reports to healthcare providers, enabling them to monitor trends, identify risk factors, and make informed decisions. This approach significantly enhances patient care by minimizing delays in response and improving overall health outcomes. The system's architecture, based on big data frameworks, supports scalable and efficient data processing. The study demonstrates how the integration of predictive models and data visualization tools can revolutionize health alert systems, making them more responsive and adaptive to individual patient needs. Future enhancements will focus on incorporating machine learning models for more personalized predictions and extending the system's capabilities to remote patient care. (shrink)

Acknowledging that many members of the SM3D Portal need reference documents related to Bayesian Mindsponge Framework (BMF) analytics to conduct research projects effectively, we present the essential materials and most up-to-date studies employing the method in this post. By summarizing all the publications and preprints associated with BMF analytics, we also aim to help researchers reduce the time and effort for information seeking, enhance proactive self-learning, and facilitate knowledge exchange and community dialogue through transparency.

Many spectacular claims about psychopaths are circulated. This contribution aims at providing the reader with the more complex reality of the phenomenon (or phenomena), and to point to issues of particular interest to philosophers working in moral psychology and moral theory. I first discuss the current evidence regarding psychopaths’ deficient empathy and decision-making skills. I then explore what difference it makes to our thinking whether we regard their deficit dimensionally (as involving abilities that are on or off) and whether we (...) focus on primary or secondary psychopathy. My conclusion is that most grand claims about psychopathy settling long-standing debates in moral philosophy and psychology are overblown, but there is much to be learnt from this disorder when it comes to formulating modern theories of moral psychology. (shrink)

The usage of scholarly journal articles in the academe is now gaining attention to cope with the ever dynamic and evolving teaching and learning processes. However, the use implies possible potential usage only because what is measured is the number of views and downloads of the articles. This paper explored how the teacher education faculty and students utilized scholarly journal articles in the teaching and learning of professional education courses. The study also determined the challenges in using these (...) primary sources and documented ways of overcoming them. Data were organized and analyzed using a thematic narrative technique of Riesman as the analytical framework. The inquiry was participated by six faculty and six students from the College of Education at a state university in Northern Mindanao in the first semester of the school year 2019-2020. The necessary protocol was followed, and ethical considerations were secured before the conduct of the study. An informed consent form was presented to each participant before they signed it, signifying that they are not forced to participate in the study. The faculty had a focus group discussion, while the students had a face-to-face interview with the researchers. Emerging themes revealed that scholarly journal articles were utilized by exposing learners to varied writings, enriching course content and learning, exposing them to process learning, preparing learners for classroom engagement, guiding learners to produce outputs, and allowing learners to acquire updates. The inquiry reported challenges in terms of the capability of the learners, interest and perception of learners, and the journal itself and its contents. Documented ways of overcoming these challenges include time management, frequency of use, selection criteria, and checking the author's background. (shrink)

I have been lecturing Analytic Philosophy course in English at Oles Honchar Dnipro National University, Ukraine, for three years. Teaching using CLIL (Content and Language Integrated Learning) techniques enables me to do it effectively. All three groups of my master’s-degree students who had learned analytic philosophy by CLIL techniques demonstrated good results of their learning. They not only achieved strong knowledge of analytic philosophy but also sufficiently improved their skills in English writing, speaking, reading, and listening.

Russell’s rejection in 1898 of the doctrine of internal relations — the view that all relations are grounded in the intrinsic properties of the terms related — was a decisive part of his break with Hegelianism and opened the way for his turn to analytic philosophy. Before rejecting it, Russell had given the doctrine little thought, though it played an essential role in the most intractable of the problems facing his attempt to construct a Hegelian dialectic of the sciences. I (...) argue that it was Russell’s early reading of Leibniz, in preparation for his lectures on Leibniz given at Cambridge in 1899, that most probably alerted him to the role the doctrine was playing in his own philosophy. Leibniz defended a similar doctrine and extricated it from difficulties like those faced by Russell by means of devices that were not open to Russell. Russell would have come across these views of Leibniz in writings by Leibniz that he read in the summer of 1898, just before he rejected the doctrine of internal relations. (shrink)

The research outlines a workflow that incorporates data collection, preprocessing, model training, and optimization. Real-world datasets from retail and e-commerce sectors are utilized to validate the proposed methodology, showcasing substantial improvements in model performance. The results indicate that optimized models not only provide better predictions of consumer behaviour but also enhance customer segmentation and targeting strategies. The study concludes with recommendations for future research, including the exploration of hybrid optimization techniques and the application of these methods in real-time analytics.

In the first book of the Prior Analytics, Aristotle sets out, for the first time in Greek philosophy, a logical system. It consists of a deductive system (I.4-22), meta-logical results (I.23-26), and a method for finding and giving deductions (I.27-29) that can apply in “any art or science whatsoever” (I.30). After this, Aristotle compares this method with Plato’s method of division, a procedure designed to find essences of natural kinds through systematic classification. This critical comparison in APr I.31 raises (...) an interpretive puzzle: how can Aristotle reasonably juxtapose two methods that differ so much in their aims and approach? What can be gained by doing so? Previous interpreters have failed to show how this comparison is legitimate or what important point Aristotle is making. The goal of this paper is to resolve the puzzle. In resolving this puzzle we not only learn more about the relationship be- tween division and the syllogistic in Aristotle. We will also learn something about the motivation for the syllogistic itself, by seeing the role that it plays in his philosophy of science. (shrink)

There is increasing concern about “surveillance capitalism,” whereby for-profit companies generate value from data, while individuals are unable to resist (Zuboff 2019). Non-profits using data-enabled surveillance receive less attention. Higher education institutions (HEIs) have embraced data analytics, but the wide latitude that private, profit-oriented enterprises have to collect data is inappropriate. HEIs have a fiduciary relationship to students, not a narrowly transactional one (see Jones et al, forthcoming). They are responsible for facets of student life beyond education. In addition (...) to classrooms, learning management systems, and libraries, HEIs manage dormitories, gyms, dining halls, health facilities, career advising, police departments, and student employment. HEIs collect and use student data in all of these domains, ostensibly to understand learner behaviors and contexts, improve learning outcomes, and increase institutional efficiency through “learning analytics” (LA). ID card swipes and Wi-Fi log-ins can track student location, class attendance, use of campus facilities, eating habits, and friend groups. Course management systems capture how students interact with readings, video lectures, and discussion boards. Application materials provide demographic information. These data are used to identify students needing support, predict enrollment demands, and target recruiting efforts. These are laudable aims. However, current LA practices may be inconsistent with HEIs’ fiduciary responsibilities. HEIs often justify LA as advancing student interests, but some projects advance primarily organizational welfare and institutional interests. Moreover, LA advances a narrow conception of student interests while discounting privacy and autonomy. Students are generally unaware of the information collected, do not provide meaningful consent, and express discomfort and resigned acceptance about HEI data practices, especially for non-academic data (see Jones et al. forthcoming). The breadth and depth of student information available, combined with their fiduciary responsibility, create a duty that HEIs exercise substantial restraint and rigorous evaluation in data collection and use. (shrink)

Abstract: This paper examined the influence of admission policy on the admission of students into Distance Learning Education Programme in University of Ibadan. Descriptive survey research method was adopted for the purpose of this study. The total number of staff who participated in this study was three hundred (300) which comprises of both non-academic and academic staff. But only two hundred and forty-five (245) staff returned questionnaire that were finally used for data collection. Chi-square test analytical method was used (...) for data analysis. This article revealed that quota system on admission policy is not applicable in distance learning education in University of Ibadan. That there is no Catchment area policy on admission in distance learning education university of Ibadan, and that there is no discriminatory school fee for students in distance learning education in university of Ibadan. This study recommended that there should be abolishment of the catchment area and quota policies of admission in Higher Education in Nigeria. Educationally backward states should develop remedial programmes for their indigenes. Policy on discriminatory fees should be moderated. (shrink)

This approach significantly enhances patient care by minimizing delays in response and improving overall health outcomes. The system's architecture, based on big data frameworks, supports scalable and efficient data processing. The study demonstrates how the integration of predictive models and data visualization tools can revolutionize health alert systems, making them more responsive and adaptive to individual patient needs. Future enhancements will focus on incorporating machine learning models for more personalized predictions and extending the system's capabilities to remote patient care.

This study was conducted by an academic research team at PRINCESS NOURAH BINT ABDULRAHMAN UNIVERSITY with the purpose of promoting the levels of healthy, value and ethical awareness among the students to limit the effects of covid-19. The study applied the descriptive, analytic survey approach to document the conceptions 0f the public education instructors throughout KSA concerning their role in raising the cognitive aspects and healthy and ethical skills for encountering coronavirus pandemic (COVID-19). The study population included all the instructors (...) of public education (male & female) in all the Kingdom governorates with its both public and private sectors amounting (525,610) instructors as per the MINISTRY OF EDUCAYION statistics for the school year (1441 H ) . The sample contained 357 instructors (male& female). For this purpose, the researchers designed a questionnaire. The study concluded that there is a high level of conceptions of the instructors about their cognitive skills, healthy and ethical responsibility for providing awareness about coronavirus pandemic (COVID-19) . The study proposes some supporting and effective strategies to achieve such online learning under this pandemic. (shrink)

In recent years, the significance of industrial heritage has seemed to become a growing trend in international heritage studies. Concerning their attributed values and the crucial needs for urban development, this branch of cultural heritage has been considered the important grid of cities. This has caused a great acceptance of adaptive reuse practices especially among developing countries which is a smart response to an ongoing debate to reach sustainable development. The flexibility of these buildings and sites seems an important criterion, (...) which can be improved through adaptive reuse practice. Therefore, this research aims to introduce the concept of flexibility in industrial heritage sites, evaluate its criteria among adaptive reuse practices, and make a comprehensive flexibility model for it. Indeed, the final goal is to determine the condition that based on the flexibility model, the adaptive reuse practice would be a proper way of encountering these sites. A historical-interpretation research method, analytical-description techniques, and questionnaire-based interviews are applied in this research. Results indicate that flexibility has genuinely been considered in this practice. Analysing flexibility techniques, this paper suggests a valuable framework to achieve the flexibility of industrial heritage as the presupposition of successful adaptive reuse in these sites. (shrink)