To date there are no studies examining the ability to make a moral/conventional transgression distinction in adolescent offenders with psychopathic traits. Based on the Psychopathy Checklist: Youth Version, we compared males with high (HP, n = 45), medium (MP, n = 31) and low psychopathy scores (LP, n = 39) on the moral convention distinction task. Under normal rule conditions the psychopathy groups did not differ in their ability to make a moral/conventional distinction. The HP group tended to view both (...) transgression types as more permissible and conventional transgressions as less serious, than the LP group. Under modified rule conditions, the HP group exhibited reduced moral/conventional distinction scores com- pared to the MP group. The findings only partially replicate findings from previous M/C studies in children and adults with psychopathic traits. The work fits with more recent reports suggesting that psychopathy is not strongly associated with marked difficulties in cognitive theory of mind, perspective taking and moral judgements. Future studies should focus on the affective aspects of moral reasoning in offender samples. (shrink)

Growing-Block theorists hold that past and present things are real, while future things do not yet exist. This generates a puzzle: how can Growing-Block theorists explain the fact that some sentences about the future appear to be true? Briggs and Forbes develop a modal ersatzist framework, on which the concrete actual world is associated with a branching-time structure of ersatz possible worlds. They then show how this branching structure might be used to determine the truth values of future contingents. They (...) point out three different ways of interpreting the logical connectives, which give rise to three different logics of the open future: one supervaluationist, one corresponding to Lukasiewicz's strong Kleene logic, and one intuitionist. (shrink)

We reply to recent papers by John Turri and Ben Bronner, who criticise the dispositionalised Nozickian tracking account we discuss in “Mad, Bad and Dangerous to Know.” We argue that the account we suggested can handle the problems raised by Turri and Bronner. In the course of responding to Turri and Bronner’s objections, we draw three general lessons for theories of epistemic dispositions: that epistemic dispositions are to some extent extrinsic, that epistemic dispositions can have manifestation conditions concerning circumstances where (...) their bearers fail to exist, and that contrast is relevant to disposition attributions. (shrink)

Anscombe’s published writings, lectures and notes on sensation point toward a sophisticated critique of sense-data, representationalist and direct realist theories of perception (in both their historical and contemporary forms), and a novel analysis of the concept of sensation. Her philosophy of perception begins with the traditional question, ‘What are the objects of sensation?’, but the response is a grammatical rather than ontological enquiry. What, she asks, are the characteristics of the grammatical object of sensation verbs? Anscombe’s answer is: sensation verbs (...) take ‘intentional objects’, where an ‘intentional object’ is a description which has the characteristics of the concept of intention—characteristics elucidated in great detail in her Intention. This simple manoeuvre allows Anscombe to reject two opposing positions in the philosophy of perception - that the objects of sensation are sense-data and that the objects of sensation are tables and chairs (etc). Both views, she argues, fail to recognise the grammatical fact that the verbs of sensation take intentional objects. This chapter outlines the case for a grammatical methodology in philosophy of perception, by framing an analysis of the ways in which the sense-data and direct-realist theories of perception arise out of the theoretical demands that are generated when the grammar of the concept of sensation is left unexplored. Along the way, it will be shown that Anscombe’s philosophy of perception should not be read as fore-running contemporary representationalism, and that reading it as such in fact runs counter not only to the letter but the spirit of Ancombe’s proposal. (shrink)

Rachael Briggs and Daniel Nolan have recently proposed an improved version of Nozick’s tracking account of knowledge. I show that, despite its virtues, the new proposal suffers from three serious problems.

CATEGORY: Philosophy play; historical fiction; comedy; social criticism. -/- STORYLINE: Tim, a physics professor with a certain taste for young female university students, recently got a new appointment at a London university. But, as it turns out, he is still unsatisfied. Why? Is it because Rachael unexpectedly left him under strange circumstances? Or does it have to do with his sudden departure from another university? Or is it his research? When Tim meets Christianus for a brown-bag discussion on philosophy (...) and science, new facts and perspectives are revealed. -/- TOPICS: In the course of the play, Tim and Christianus discuss different metaphysical, epistemological, and ontological ideas. Many of these are either related to isssues in the philosophy of science (explanation, methodology, scientific investigation, etc.), or to issues in human psychology and the philosophy of mind (the self vs. the mind, dualism, ‘Who am I?’, etc.). In one scene, for example, Tim tries to invoke ‘Ockham’s Razor’ (or ‘Occam’s Razor’) to quickly dismiss some of Christianus’s metaphysical ideas (see Scene VII: Ockham’s Raisin; Scene XIII: The Raisin Tale Revisited); in another scene, Christianus introduces his ‘Postman’ scenario, and the idea that successful (‘scientific’) prediction or forecasting is not necessarily a sure sign of true understanding (see Scene XV: The Postman Always Turns Twice). -/- NOTES: This work features elaborate footnotes and comments (including full bibliographical references) by the author, to enhance the reader's experience of the play and its philosophizing characters. (shrink)

Rachael Briggs and Daniel Nolan attempt to improve on Nozick’s tracking theory of knowledge by providing a modified, dispositional tracking theory. The dispositional theory, however, faces more problems than those previously noted by John Turri. First, it is not simply that satisfaction of the theory’s conditions is unnecessary for knowledge – it is insufficient as well. Second, in one important respect, the dispositional theory is a step backwards relative to the original tracking theory: the original but not the dispositional (...) theory can avoid Gettier-style counterexamples. Future attempts to improve the tracking theory would be wise to bear these problems in mind. (shrink)

Memory is everywhere in Blade Runner 2049. From the dead tree that serves as a memorial and a site of remembrance (“Who keeps a dead tree?”), to the ‘flashbulb’ memories individuals hold about the moment of the ‘blackout’, when all the electronic stores of data were irretrievably erased (“everyone remembers where they were at the blackout”). Indeed, the data wiped out in the blackout itself involves a loss of memory (“all our memory bearings from the time, they were all damaged (...) in the blackout”). Memory, and lack of it, permeates place, where from the post-blackout Las Vegas Deckard remembers it as somewhere you could “forget your troubles.” Memory is a commodity, called upon and consumed by the Wallace Corporation, purchased from the memory-maker, Dr Ana Stelline, who constructs and implants “the best memories” in replicants so as to instil in them real human responses. Memory is ubiquitous in Blade Runner 2049, involving humans, replicants, objects, and machines. Even “God,” we are told, “remembered Rachael.” Nowhere, though, is the depiction of memory more important than in the attempt to solve a question of identity. Officer K has a memory of his past. Even though he knows it is an implant, it is a memory he is emotionally attached to, frequently narrating it to Joi, his digital girlfriend. But it is a memory that starts to puzzle and trouble him. When K discovers the remains of a dead replicant, a female NEXUS-7, he uncovers a secret—this replicant was pregnant and died during childbirth, a discovery that could “break the world.” K is charged with hunting down the child and making the problem disappear. Yet as K starts seeking answers to the question of the child’s identity he gets inextricably caught up in the mystery. Is he merely Officer K, or is he Joe, the miracle child of Rachael and Deckard? The answer to this question hinges on K’s memory. But is the memory genuine? Is the memory his? Blade Runner 2049 encourages us to think deeply about the nature of memory, identity, and the relation between them. Indeed, the film does not just serve as a starting point for thinking about philosophical issues related to memory and identity. Rather, as we show in this chapter, the film seems to offer a view on these philosophical issues. Blade Runner 2049 offers us a view of memory as spread out over people, objects, and the environment, and it shows us that memory’s role in questions of identity goes beyond merely accurately recalling one’s past. Identity depends not on memory per se, but partly on what we use memory for. (shrink)

The Reflection Principle can be defended with a Diachronic Dutch Book Argument (DBA), but it is also defeated by numerous compelling counter-examples. It seems then that Diachronic DBAs can lead us astray. Should we reject them en masse—including Lewis’s Diachronic DBA for Conditionalization? Rachael Briggs’s “suppositional test” is supposed to differentiate between Diachronic DBAs that we can safely ignore (including the DBA for Reflection) and Diachronic DBAs that we should find compelling (including the DBA for Conditionalization). I argue that (...) Brigg’s suppositional test is wrong: it sets the bar for coherence too high and places certain cases of self-doubt on the wrong side of the divide. Given that the suppositional test is unsatisfactory, we are left without any justification for discriminating between Diachronic DBAs and ought to reject them all—including the DBA for Conditionalization. (shrink)

In recent years, the concept of evolvability has been gaining in prominence both within evolutionary developmental biology (evo-devo) and the broader field of evolutionary biology. Despite this, there remains considerable disagreement about what evolvability is. This article offers a solution to this problem. I argue that, in focusing too closely on the role played by evolvability as an explanandum in evo-devo, existing philosophical attempts to clarify the evolvability concept have been overly narrow. Within evolutionary biology more broadly, evolvability offers a (...) robust explanation for the evolutionary trajectories of populations. Evolvability is an abstract, robust, dispositional property of populations, which captures the joint causal influence of their internal features on the outcomes of evolution (as opposed to the causal influence of selection, which is often characterized as external). When considering the nature of the physical basis of this disposition, it becomes clear that the many existing definitions of evolvability at play within evo-devo should be understood as capturing only aspects of a much broader phenomenon. 1 Introduction2 The Problem of Evolvability3 The Theoretical Role of Evolvability in Evolutionary Biology3.1 The explanatory targets of evolutionary biology3.2 Selection-based explanations3.3 Lineage explanations3.4 Evolvability-based explanations3.5 What properties must evolvability have?4 What Evolvability Really Is4.1 Making sense of ft4.2 Making sense of x and b5 What of the Limbs? The Power of E6 Conclusion. (shrink)

Philosophers often talk about the things we say, or believe, or think, or mean. The things are often called ‘propositions’. A proposition is what one believes, or thinks, or means when one believes, thinks, or means something. Talk about propositions is ubiquitous when philosophers turn their gaze to language, meaning and thought. But what are propositions? Is there a single class of things that serve as the objects of belief, the bearers of truth, and the meanings of utterances? How do (...) our utterances express propositions? Under what conditions do two speakers say the same thing, and what (if anything) does this tell us about the nature of propositions? There is no consensus on these questions—or even on whether propositions should be treated as things at all. During the second Propositions and Same-Saying workshop, which took place on July 19–21 2010 at the University of Sydney, philosophers debated these (and related) questions. The workshop covered topics in the philosophy of language, perception, and metaphysics. The present volume contains revised and expanded versions of the papers presented at the workshop. (shrink)

Traditional accounts of the role of learning in evolution have concentrated upon its capacity as a source of fitness to individuals. In this paper I use a case study from invasive species biology—the role of conditioned taste aversion in mitigating the impact of cane toads on the native species of Northern Australia—to highlight a role for learning beyond this—as a source of evolvability to populations. This has two benefits. First, it highlights an otherwise under-appreciated role for learning in evolution that (...) does not rely on social learning as an inheritance channel nor “special” evolutionary processes such as genetic accommodation (both of which many are skeptical about). Second, and more significantly, it makes clear important and interesting parallels between learning and exploratory behaviour in development. These parallels motivate the applicability of results from existing research into learning and learning evolution to our understanding of the evolution of evolvability more generally. (shrink)

Background. The literature on Theory of Mind (ToM) in antisocial samples is limited despite evidence that the neural substrates of theory of mind task involve the same circuits implicated in the pathogenesis of antisocial behaviour. Method. Eighty-nine male DSM-IV Antisocial Personality Disordered subjects (ASPDs) and 20 controls (matched for age and IQ) completed a battery of ToM tasks. The ASPD group was categorized into psychopathic and non-psychopathic groups based on a cut-off score of 18 on the Psychopathy Checklist: Screening Version. (...) Results. There were no significant group (control v. psychopath v. non-psychopathic ASPD) dif- ferences on basic tests of ToM but both psychopathic and non-psychopathic ASPDs performed worse on subtle tests of mentalizing ability (faux pas tasks). ASPDs can detect and understand faux pas, but show an indifference to the impact of faux pas. On the face/eye task non-psychopathic ASPDs showed impairments in the recognition of basic emotions compared with controls and psychopathic ASPDs. For complex emotions, no significant group differences were detected largely due to task difficulty. Conclusions. The deficits in mentalizing ability in ASPD are subtle. For the majority of criminals with ASPD and psychopathy ToM abilities are relatively intact and may have an adaptive function in maintaining a criminal lifestyle. Our findings suggest the key deficits appear to relate more to their lack of concern about the impact on potential victims than the inability to take a victim perspective. The findings tentatively also suggest that ASPDs with neurotic features may be more impaired in mentalizing ability than their low anxious psychopathic counterparts. (shrink)

The aim of this paper is to evaluate the critical success factors and investigate the benefits that might be gained once implementing Electronic Customer Relationship Management at HEI from employee perspective. The study conducted at Al Quds Open University in Palestine and data collected from (300) employee through a questionnaire which consist of four variables. A number of statistical tools were intended for hypotheses testing and data analysis, including Spearman correlation coefficient for Validity, reliability correlation using Cronbach’s alpha, and Frequency (...) and Descriptive analysis. The overall findings of the current study show that all the features were important for staff and it was critical success factors, at the same time, websites were providing all the features discussed by the theory whereas staff showed their willingness to use those features if provided. It is also discovered that implementing Electronic Customer Relationship Management can cause staff retention, were provided efficiently and needed to be improved. Research limitations: The survey findings were based on QOU employee in Palestine, UAE and KSA branches not included in the study. (shrink)

This paper presents the design and development of intelligent tutoring system for teaching Oracle. The Oracle Intelligent Tutoring System (OITS) examined the power of a new methodology to supporting students in Oracle programming. The system presents the topic of Introduction to Oracle with automatically generated problems for the students to solve. The system is dynamically adapted at run time to the student’s individual progress. An initial evaluation study was done to investigate the effect of using the intelligent tutoring system on (...) the performance of students. (shrink)

Learning programming is thought to be troublesome. One doable reason why students don’t do well in programming is expounded to the very fact that traditional way of learning within the lecture hall adds more stress on students in understanding the Material rather than applying the Material to a true application. For a few students, this teaching model might not catch their interest. As a result, they'll not offer their best effort to grasp the Material given. Seeing however the information is (...) applied to real issues will increase student interest in learning. As a consequence, this may increase their effort to be taught. In the current paper, we try to help students learn C# programming language using Intelligent Tutoring System. This ITS was developed using ITSB authoring tool to be able to help the student learn programming efficiently and make the learning procedure very pleasing. A knowledge base using ITSB authoring tool style was used to represent the student's work and to give customized feedback and support to students. (shrink)

In these days, there is an increasing technological development in intelligent tutoring systems. This field has become interesting to many researchers. In this paper, we present an intelligent tutoring system for teaching mathematics that help students understand the basics of math and that helps a lot of students of all ages to understand the topic because it's important for students of adding and subtracting. Through which the student will be able to study the course and solve related problems. An evaluation (...) of the intelligent tutoring systems was carried out and the results were encouraging. (shrink)

Abstract: Artificial Intelligence (AI) is rapidly becoming a cornerstone of modern healthcare, offering unprecedented capabilities in diagnostics, treatment planning, patient care, and healthcare management. This paper explores the transformative impact of AI on the healthcare sector, examining how it enhances patient outcomes, improves the efficiency of medical practices, and introduces new ethical and operational challenges. By analyzing current applications such as AI-driven diagnostic tools, personalized medicine, and hospital management systems, this paper highlights the significant advancements AI has brought to the (...) field. However, the integration of AI also raises concerns related to data privacy, algorithmic bias, and the displacement of healthcare professionals. This study provides a comprehensive overview of these developments, offering insights into the future potential of AI in revolutionizing healthcare while addressing the ethical considerations that must accompany its adoption. The findings suggest that while AI holds the promise of transforming healthcare delivery, careful consideration of its implications is essential to ensure equitable and effective implementation. (shrink)

The evolution of Intelligent Tutoring System (ITS) is the result of the amount of research in the field of education and artificial intelligence in recent years. English is the third most common languages in the world and also is the internationally dominant in the telecommunications, science and trade, aviation, entertainment, radio and diplomatic language as most of the areas of work now taught in English. Therefore, the demand for learning English has increased. In this paper, we describe the design of (...) an Intelligent Tutoring System for teaching English language grammar to help students learn English grammar easily and smoothly. The system provides all topics of English grammar and generates a series of questions automatically for each topic for the students to solve. The system adapts with all the individual differences of students and begins gradually with students from easier to harder level. The intelligent tutoring system was given to a group of students of all age groups to try it and to see the impact of the system on students. The results showed a good satisfaction of the students toward the system. (shrink)

Abstract: Artificial Intelligence (AI) is transforming the agricultural sector, enhancing both productivity and sustainability. This paper delves into the impact of AI technologies on agriculture, emphasizing their application in precision farming, predictive analytics, and automation. AI-driven tools facilitate more efficient crop and resource management, leading to higher yields and a reduced environmental footprint. The paper explores key AI technologies, such as machine learning algorithms for crop monitoring, robotics for automated planting and harvesting, and data analytics for optimizing resource use. Additionally, (...) it discusses challenges like data privacy, barriers to technology adoption, and the ethical implications of AI in farming. Integrating AI into agricultural practices holds the promise of greater efficiency and sustainability, paving the way for future innovations. (shrink)

Abstract: Smoke detectors are critical devices for early fire detection and life-saving interventions. This research paper explores the application of Artificial Neural Networks (ANNs) in smoke detection systems. The study aims to develop a robust and accurate smoke detection model using ANNs. Surprisingly, the results indicate a 100% accuracy rate, suggesting promising potential for ANNs in enhancing smoke detection technology. However, this paper acknowledges the need for a comprehensive evaluation beyond accuracy. It discusses potential challenges, such as overfitting, dataset size, (...) and class imbalance, and presents strategies employed to address these issues. Additionally, the paper emphasizes the importance of alternative evaluation metrics, including precision, recall, and F1-score, to assess the model's performance comprehensively. Detailed information regarding the model architecture, data preprocessing, and hyperparameter tuning is provided to enhance the reproducibility of the research. The study also discusses ethical considerations and the implications of false positives and false negatives in smoke detection applications. While achieving 100% accuracy is a notable accomplishment, this paper calls for cautious interpretation and further validation in real-world settings. It concludes by highlighting areas for future research and improvement in smoke detection technology, emphasizing the need for practical deployment and the reduction of false alarms. (shrink)

Abstract: Artificial Intelligence (AI) has rapidly advanced, offering significant potential to transform business decision-making. This paper delves into how AI can be harnessed to enhance strategic decision-making within business contexts. It investigates the integration of AI-driven analytics, predictive modeling, and automation, emphasizing their role in improving decision accuracy and operational efficiency. By examining current applications and case studies, the paper underscores the opportunities AI offers, including improved data insights, risk management, and personalized customer experiences. It also addresses the challenges businesses (...) face when adopting AI, such as data privacy concerns, integration difficulties, and the need for skilled professionals. The paper concludes by offering recommendations for successfully implementing AI solutions to maximize benefits while mitigating potential challenges. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into medical imaging represents a transformative shift in healthcare, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. This paper explores the application of AI technologies in the analysis of medical images, focusing on techniques such as convolutional neural networks (CNNs) and deep learning models. We discuss how these technologies are applied to various imaging modalities, including X-rays, MRIs, and CT scans, to enhance disease detection, image segmentation, and diagnostic support. Additionally, the (...) paper addresses the challenges faced in AI-driven medical imaging, including data quality, model interpretability, and ethical considerations. By examining recent advancements and real-world case studies, this paper provides insights into the current state of AI in medical imaging and its potential future directions. The findings highlight the ongoing evolution of AI technologies and their crucial role in advancing medical diagnostics and treatment strategies. (shrink)

Cardiovascular diseases, including heart disease, pose a significant global health challenge, contributing to a substantial burden on healthcare systems and individuals. Early detection and accurate prediction of heart disease are crucial for timely intervention and improved patient outcomes. This research explores the potential of neural networks in predicting heart disease using a dataset collected from Kaggle, consisting of 1025 samples with 14 distinct features. The study's primary objective is to develop an effective neural network model for binary classification, identifying the (...) presence or absence of heart disease. The neural network architecture includes an input layer, a hidden layer, and an output layer, designed to capture intricate relationships within the dataset. Rigorous training and validation processes, accompanied by data preprocessing steps, ensure the model's robustness and generalization capabilities. The results demonstrate promising performance, with an accuracy of 92% and an average error of 0.062. Moreover, an analysis of feature importance highlights key predictors, including "oldpeak," "thalach," "trestbps," "ca," "thal," "cp," "chol," "sex," "restecg," "age," "slope," "fbs," and "exang." This research contributes to the field of predictive healthcare by leveraging neural networks to enhance heart disease prediction. The developed model offers the potential for early identification of individuals at risk, facilitating timely medical interventions and ultimately improving public health. Further exploration of machine learning techniques in healthcare promises to reshape disease prediction and prevention strategies. (shrink)

Intelligent tutoring system (ITS) is a computer system which aims to provide immediate and customized or reactions to learners, usually without the intervention of human teacher's instructions. Secretariats professional to have the common goal of learning a meaningful and effective manner through the use of a variety of computing technologies enabled. There are many examples of professional Secretariats used in both formal education and in professional settings that have proven their capabilities. There is a close relationship between private lessons intelligent, (...) cognitive learning and design theories; and there are ongoing to improve the effectiveness of ITS research. And it aims to find a solution to the problem of over-reliance on students' teachers for quality education. The program aims to provide access to high-quality education to every student, and therefore the reform of the education system as a whole. In this paper, we will use Intelligent Tutoring System Builder (ITSB) to build an education system on cloud computing in terms of the concept of cloud computing and components and how to take advantage of cloud computing in the field. (shrink)

Abstract: The integration of artificial intelligence (AI) into drug discovery is revolutionizing the pharmaceutical industry by accelerating the development of novel therapeutics. AI-powered tools enable researchers to process vast datasets, identify drug candidates, and predict their efficacy and safety with unprecedented speed and accuracy. This paper explores the transformative impact of AI on drug discovery, highlighting key advancements in machine learning algorithms, deep learning, and predictive modeling. Additionally, it addresses the challenges associated with AI implementation, including data quality, regulatory hurdles, (...) and ethical considerations. By analyzing case studies of AI-driven pharmaceutical breakthroughs, this paper underscores AI's potential to streamline drug development, reduce costs, and address unmet medical needs. The future of drug discovery is poised to shift dramatically as AI continues to advance, offering promising solutions for improving human health. (shrink)

In this paper, we present an intelligent tutoring system developed to help students in learning Computer Theory. The Intelligent tutoring system was built using ITSB authoring tool. The system helps students to learn finite automata, pushdown automata, Turing machines and examines the relationship between these automata and formal languages, deterministic and nondeterministic machines, regular expressions, context free grammars, undecidability, and complexity. During the process the intelligent tutoring system gives assistance and feedback of many types in an intelligent manner according to (...) the behavior of the student. An evaluation of the intelligent tutoring system has revealed reasonably acceptable results in terms of its usability and learning abilities are concerned. (shrink)

In this paper, we designed and developed an intelligent tutoring system for teaching Photoshop. We designed the lessons, examples, and questions in a way to teach and evaluate student understanding of the material. Through the feedback provided by this tool, you can assess the student's understanding of the material, where there is a minimum overshoot questions stages, and if the student does not pass the level of questions he is asked to return the lesson and read it again. Eventually this (...) administration is a special teacher for the students and can continue with him until he fully understands the lesson without weariness or boredom, regardless of the level of student. (shrink)

An Intelligent Tutoring System (ITS) is a computer system that offers an instant, adapted instruction and customized feedback to students without human teacher interference. Reciting "Tajweed" the Holy Quran in the appropriate way is very important for all Muslims and is obligatory in Islamic devotions such as prayers. In this paper, the researchers introduce an intelligent tutoring system for teaching Reciting "Tajweed". Our "Tajweed" tutoring system is limited to "Tafkhim and Tarqiq in TAJWEED" the Holy Quran, Rewaya: Hafs from ‘Aasem. (...) The system was evaluated by reciting teachers and students, and the results were auspicious. (shrink)

Abstract: The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, necessitating accurate and timely forecasting of cases for effective mitigation strategies. In this research paper, we present a novel approach to predict COVID-19 cases using Artificial Neural Networks (ANNs), harnessing the power of machine learning for epidemiological forecasting. Our ANNs-based forecasting model has demonstrated remarkable efficacy, achieving an impressive accuracy rate of 97.87%. This achievement underscores the potential of ANNs in providing precise and data-driven insights into the dynamics (...) of the pandemic. However, this paper underscores the critical importance of a comprehensive evaluation beyond accuracy, including metrics such as sensitivity, specificity, and the area under the ROC curve (AUC-ROC), to assess the model's performance robustness. The research paper offers detailed insights into the architecture of the ANN model, encompassing critical hyperparameters, data preprocessing techniques, and regularization strategies employed to optimize model accuracy. Ethical considerations surrounding data privacy and potential biases within the COVID-19 dataset are also addressed. While the achieved accuracy is a significant milestone, this study underscores the dynamic and evolving nature of the pandemic, necessitating continuous model refinement and validation. Furthermore, it emphasizes the importance of considering false positives and false negatives in the context of public health decision-making. In conclusion, this research contributes to the arsenal of tools available for pandemic management by showcasing the potential of ANNs in COVID-19 case forecasting. It encourages ongoing exploration and adaptation of predictive models to enhance their applicability in real-world public health scenarios, ultimately contributing to more effective pandemic control and response efforts. (shrink)

This study explores experiences of ex-incels—men who have withdrawn from incel communities—through eleven qualitative interviews analysed using R.W. Connell’s hegemonic masculinity (HM) framework. Findings reveal some ex-incels adopt flexible masculinities, while others struggle with prescriptive norms perpetuated by the anti-feminist ‘manosphere’. Findings spotlight identity reconstructions, where men both reject and remain influenced by rigid archetypes, performing hybrid masculinities. This study deepens understanding of incel ideology, its impact on identity, and interplay between inceldom and masculinities via contributing to hybrid masculinities theorising. (...) Insights presents applications for gender theory and inform further research on HM’s influence within unique cultural contexts. (shrink)

Abstract: Colon cancer is a prevalent and life-threatening disease, necessitating accurate and timely diagnosis for effective treatment and improved patient outcomes. This research paper presents the development of a knowledge-based system for diagnosing colon cancer using the CLIPS language. Knowledge-based systems offer the potential to assist healthcare professionals in making informed diagnoses by leveraging expert knowledge and reasoning mechanisms. The methodology involves acquiring and structuring medical knowledge specific to colon cancer, followed by the implementation of a knowledge- based system using (...) CLIPS. The system's design integrates relevant medical databases and employs advanced reasoning mechanisms to facilitate accurate diagnosis. Evaluation results demonstrate the system's effectiveness, with high accuracy and sensitivity rates compared to existing methods. The findings highlight the potential of knowledge-based systems in improving clinical practice and patient care. The research contributes to the growing field of medical informatics by demonstrating the feasibility and effectiveness of CLIPS-based knowledge systems for colon cancer diagnosis. Future research can focus on enhancing system performance and expanding its scope to encompass other types of cancer diagnoses. (shrink)

Abstract: The Knowledge-Based System for Diagnosing Breast Cancer aims to support medical students in enhancing their education regarding diagnosis and counseling. The system facilitates the analysis of biopsy images under a microscope, determination of tumor type, selection of appropriate treatment methods, and identification of disease-related questions. According to the Ministry of Health's annual report in Gaza, there were 7,069 cases of breast cancer between 2009 and 2014, with 1,502 cases reported in 2014. In an era dominated by visual information, where (...) 65% of the population are visual learners, the Knowledge-Based System serves as an effective tool to aid long-term information retention by utilizing visual and textual resources. The system features a user-friendly interface, enabling students to diagnose the disease and expand their understanding. Furthermore, it can be accessed via smartphones. The Knowledge-Based System comprises three primary components: Firstly, it provides specific patient-oriented questions to familiarize students with the questioning process. Secondly, it facilitates the analysis of biopsy sample images to determine their integrity and identify potential infections. Lastly, the system incorporates animated videos illustrating various approaches for treating breast cancer. (shrink)

In this paper we show how a student can learn the basics of the system databases using (W3school CSS) which was built as intelligent tutoring educational system by using the authoring tool called (ITSB). The learning material contains CSS and HTML. We divided the material in a group of lessons for novice learner which combines relational system and lessons in the process of learning. The student can learn using example of CSS, and types of CSS color. Furthermore, the intelligent tutoring (...) system supports not only lessons; but exercises of different difficult levels for each lesson. When a student finish successfully the first difficulty level in a lesson, the student is allowed to move to the next difficulty level of the exercises of the lesson. (shrink)

Abstract: Early detection of breast cancer plays a pivotal role in improving patient prognosis and reducing mortality rates. Recent technological advancements have significantly enhanced the accuracy and effectiveness of breast cancer screening methods. This paper explores the latest innovations in early detection, including the evolution of digital mammography, the impact of 3D mammography (tomosynthesis), and the use of advanced imaging techniques such as molecular imaging and MRI. Furthermore, the integration of artificial intelligence (AI) in diagnostic tools is discussed, highlighting how (...) machine learning algorithms are refining imaging analyses and reducing diagnostic errors. Advances in genetic screening, liquid biopsies, and biomarkers are also examined, showcasing their potential in identifying high-risk individuals and enabling personalized treatment plans. This paper provides insights into the future of breast cancer detection, outlining both the opportunities and challenges that lie ahead in adopting these innovative technologies to improve patient outcomes. (shrink)

Abstract: The incorporation of Artificial Intelligence (AI) into educational technology has dramatically transformed learning through Intelligent Tutoring Systems (ITS). These systems utilize AI to offer personalized, adaptive instruction tailored to each student's needs, thereby improving learning outcomes and engagement. This paper examines the development and impact of ITS, focusing on AI technologies such as machine learning, natural language processing, and adaptive algorithms that drive their functionality. Through various case studies and applications, it illustrates how ITS have revolutionized traditional educational methods (...) and addresses the challenges and limitations, including data privacy and system biases. The discussion also covers future directions in ITS development, highlighting emerging trends and potential advancements. The goal is to provide a thorough understanding of how ITS use AI to enhance educational experiences and suggest avenues for future research in this evolving field. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into leadership practices is rapidly transforming organizational dynamics and decision-making processes. This paper explores the ways in which AI enhances leadership effectiveness by providing data- driven insights, optimizing decision-making, and automating routine tasks. Additionally, it examines the challenges leaders face when adopting AI, including ethical considerations, potential biases in AI systems, and the need for upskilling. By analyzing current applications of AI in leadership and discussing future trends, this study aims to offer a (...) comprehensive overview of the opportunities AI presents for effective leadership, as well as the strategies required to address its associated challenges. (shrink)

Brain tumors pose significant challenges in modern healthcare, with accurate and timely diagnosis crucial for determining appropriate treatment strategies. Artificial intelligence has made significant advancements in recent years. Rule-based expert systems (if-then rule-based systems) have emerged as a promising approach for clinical decision-making in brain tumor diagnosis. In this paper, we present "A CLIPS-Based Expert System for Brain Tumor Diagnosis," which leverages a set of 14 if-then rules to diagnose brain tumors with three possible outcomes: 1) Confirm the diagnosis of (...) a brain tumor, 2) Consider the possibility of a brain tumor that has metastasized, and 3) Consider the possibility of a brain tumor. Our expert system offers a user-friendly interface, enabling users to select symptoms and receive a diagnosis based on the provided information. This paper discusses the expert system's development, implementation, and evaluation, highlighting its potential to facilitate brain tumor diagnosis and decision-making in clinical settings. Additionally, we provide a literature review that contextualizes our expert system within the broader landscape of rule-based expert systems for brain tumor diagnosis, examining their effectiveness, limitations, and challenges. (shrink)

Abstract: In recent years, the outbreak of various poultry diseases has posed a significant threat to the global poultry industry. Therefore, the accurate and timely detection of chicken diseases is critical to reduce economic losses and prevent the spread of diseases. In this study, we propose a method for classifying chicken diseases using a convolutional neural network (CNN). The proposed method involves preprocessing the chicken images, building and training a CNN model, and evaluating the performance of the model. The dataset (...) used in this study comprises chicken images of three different diseases: Newcastle disease, avian influenza, and infectious bursal disease. The proposed method achieved an overall F1-score of 99.04% in classifying the chicken diseases. The results demonstrate the effectiveness of using CNN in classifying chicken diseases and provide a promising approach for early detection and diagnosis of poultry diseases. (shrink)

Lately in the past couple of years, there are an increasing in the normal rate of playing computer games or video games compared to the E-learning content that are introduced for the safety of our children, and the impact of the video game addictiveness that ranges from (Musculoskeletal issues, Vision problems and Obesity). Furthermore, this paper introduce an intelligent tutoring system for both parent and their children for enhancement the experience of gaming and tell us about the health problems and (...) how we can solve them, with an easy user interface that way can our children be happy and excited about the information and their health. (shrink)

Abstract: Lotteries have long been a source of fascination and intrigue, offering the tantalizing prospect of unexpected fortunes. In this research paper, we delve into the world of lottery predictions, employing cutting-edge AI techniques to unlock the secrets of lottery outcomes. Our dataset, obtained from Kaggle, comprises historical lottery draws, and our goal is to develop predictive models that can anticipate future winning numbers. This study explores the use of deep learning and time series analysis to achieve this elusive feat. (...) Through rigorous experimentation and data-driven approaches, we seek to determine the viability of AI in the realm of lottery predictions. Our findings reveal both the promise and limitations of AI in this context, shedding light on the complexities of lottery data and the potential need for quantum computing as a last resort. (shrink)

Abstract: The sinking of the RMS Titanic in 1912 remains a poignant historical event that continues to captivate our collective imagination. In this research paper, we delve into the realm of data-driven analysis by applying Artificial Neural Networks (ANNs) to predict the chances of survival for passengers aboard the Titanic. Our study leverages a comprehensive dataset encompassing passenger information, demographics, and cabin class, providing a unique opportunity to explore the complex interplay of factors influencing survival outcomes. Our ANN-based predictive model (...) achieved an accuracy rate of 78%, shedding light on the underlying patterns within the data. However, this paper underscores the multifaceted nature of survival prediction and acknowledges several challenges. These include missing data imputation, feature engineering, and the inherent noise within historical datasets. We further discuss the importance of model evaluation metrics, showcasing not only accuracy but also precision, recall, and F1-score as essential indicators of predictive performance. The research paper meticulously outlines the architecture of the ANN model, emphasizing the key hyperparameters, activation functions, and regularization techniques employed in model development. Additionally, we address ethical considerations related to data handling and potential biases within the dataset. While our achieved accuracy is a notable achievement, this study emphasizes the importance of interpreting the results with due caution. It underscores the need for a holistic perspective that considers the practical implications of false positives and false negatives, especially in the context of a disaster scenario. In conclusion, this research contributes to our understanding of survival prediction in historical events like the Titanic disaster using modern machine learning techniques. It calls for further exploration and refinement of predictive models, with a focus on enhancing the interpretability and generalizability of such models to real-world applications. (shrink)

This research paper presents the development and evaluation of an expert system for diagnosing kidney diseases. The expert system utilizes a decision-making tree approach and is implemented using the CLIPS and Delphi frameworks. The system's accuracy in diagnosing kidney diseases and user satisfaction were evaluated. The results demonstrate the effectiveness of the expert system in providing accurate diagnoses and high user satisfaction.

Abstract: This research focuses on utilizing Artificial Neural Networks (ANNs) to predict Audit Risk accurately, a critical aspect of ensuring financial system integrity and preventing fraud. Our dataset, gathered from Kaggle, comprises 18 diverse features, including financial and historical parameters, offering a comprehensive view of audit-related factors. These features encompass 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' 'History_score,' 'score,' and 'Risk,' with a total of 774 samples. Our proposed neural network architecture, consisting of three (...) layers (1 input, 1 hidden, and 1 output), forms the core of this study. While it may seem simple, the power of ANNs lies not in complexity but in their ability to uncover intricate data patterns. The model underwent rigorous training and validation, resulting in remarkable outcomes—an accuracy of 100% and an average error rate of 0.000015. In addition to performance metrics, our research investigates feature importance, revealing the key contributors to Audit Risk prediction. Notably, 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' and 'History_score' emerged as the most influential factors, shedding light on the elements crucial for precise risk assessment. This study advances Audit Risk prediction models and highlights the potential of ANNs in strengthening financial system integrity and fraud prevention. The insights gained from our findings offer practical guidance for stakeholders in the audit and financial sectors. (shrink)

Abstract: Context: grapevine leaves are an important agricultural product that is used in many Middle Eastern dishes. The species from which the grapevine leaf originates can differ in terms of both taste and price. Method: In this study, we build a deep learning model to tackle the problem of grape leaf classification. 500 images were used (100 for each species) that were then increased to 10,000 using data augmentation methods. Convolutional Neural Network (CNN) algorithms were applied to build this model (...) specifically using the pre-trained model on top of the VGG16 architecture. Then, dense layers were added to classify the output of the Convolutional layers and classify outputs to the five classes (species) the leaf belonged to. Results: It was found that feature extraction without fine-tuning the convolutional layers yielded poor results, about 86% accuracy, while training the whole network along with some data preprocessing gave the best results, about 99.45% accuracy on the testing dataset. Conclusions: The proposed CNN model is an effective one for the problem of classification of grape leaf species. (shrink)

The research aims to study the role of strategic and operational planning as approach for crises management in UNRWA - Gaza Strip field- Palestine. Several descriptive analytical methods were used for this purpose and a survey as a tool for data collection. Community size was (881), and the study sample was stratified random (268). The overall findings of the current study show that strategic and operational planning is performed in UNRWA. The results of static analysis show that there are a (...) relation between strategic and operational planning and crises management. In spite this relation existence, it need more improvement and expanding. Also there are shortcomings in the way that organization manages the crises before and after they occur. A crisis management is only practicing during the crisis. The study suggests that UNRWA must invest in more resources to enhance strategic and operational palming and improve other methods to face potential crises in the future. (shrink)

Vertigo is a common symptom that can result from various underlying diseases and conditions, ranging from benign to severe. Accurate and timely diagnosis of the cause of vertigo is crucial for appropriate management and treatment. In this research, we propose the development of an expert system for vertigo diseases diagnosis, utilizing artificial intelligence (AI) and the proposed Expert System which was produced to help assist healthcare professionals in diagnosing the cause of vertigo based on a patient's symptoms, medical history, and (...) other relevant clinical information. The proposed expert system presents an overview about vertigo diseases are given, the cause of disease is outlined and the treatment or recommendation of disease whenever possible is given out. CLIPS language was used for designing and implementing the proposed expert system. The potential of the proposed expert system lies in its ability to enhance the accuracy and efficiency of vertigo diagnosis, as well as assist in the proper referral and management of patients. (shrink)

Water quality assessment is critical for environmental sustainability and public health. This research employs neural networks to predict water quality, utilizing a dataset of 21 diverse features, including metals, chemicals, and biological indicators. With 8000 samples, our neural network model, consisting of four layers, achieved an impressive 94.22% accuracy with an average error of 0.031. Feature importance analysis revealed arsenic, perchlorate, cadmium, and others as pivotal factors in water quality prediction. This study offers a valuable contribution to enhancing water quality (...) monitoring and decision-making for stakeholders and policymakers. (shrink)

Abstract: This research paper presents a novel approach to predict audit risks using a neural network model. The dataset used for this study was obtained from Kaggle and comprises 774 samples with 18 features, including Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, History_score, score, and Risk. The proposed neural network architecture consists of three layers, including one input layer, one hidden layer, and one output layer. The neural network model was trained and validated, achieving (...) an impressive accuracy of 100% and an average error of 0.000015, indicating its robust predictive capability. Moreover, we conducted feature importance analysis to identify the most influential features for predicting audit risk. The key features found to be critical for classifying fraudulent activities in audit risk prediction are Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, and History_score. This research contributes to the field of audit risk prediction by demonstrating the effectiveness of a neural network-based approach and highlighting the importance of specific features in detecting fraudulent activities. The findings have significant implications for auditors and organizations seeking to enhance their audit risk assessment processes, ultimately leading to improved financial transparency and fraud detection. (shrink)