This study investigates the significant relationship between mental health and academic motivation among graduating students. Thus, the study employed a correlational design to determine if there is a significant relationship between mental health and academic motivation among 150 graduating college students. Hence, the Mental Health Inventory 38 (MHI-38) and Academic Motivation Scale (AMS-C28) were employed to measure the study variables. Moreover, statistical analysis reveals that the r coefficient of 0.35 indicates a low positive correlation between the variables. The p-value of (...) 0.00, which is less than 0.05, leads to the decision to reject the null hypothesis. (shrink)

Spontaneous actions are preceded by brain signals that may sometimes be detected hundreds of milliseconds in advance of a subject's conscious intention to act. These signals have been claimed to reflect prior unconscious decisions, raising doubts about the causal role of conscious will. Murakami et al. (2014. Nat Neurosci 17: 1574–1582) have recently argued for a different interpretation. During a task in which rats spontaneously decided when to abort waiting, the authors recorded neurons in the secondary motor cortex. The neural (...) activity and relationship to action timing was parsimoniously explained using an integration-to-bound model, similar to those widely used to account for evidence-based decisions. In this model, the brain accumulates spontaneously occurring inputs voting for or against an action, but only commits to act once a certain threshold is crossed. The model explains how spontaneous decisions can be forecast (partially predicted) by neurons that reflect either the input or output of the integrator. It therefore presents an explicit hypothesis capable of rejecting the claim that such predictive signals imply unconscious decisions. We suggest that these results can inform the current debate on free will but must be considered with caution. (shrink)

Despite its recent popularity, Emergence is still a field where philosophers and physicists often talk past each other. In fact, while philosophical discussions focus mostly on ontological emergence, physical theory is inherently limited to the epistemological level and the impossibility of its conclusions to provide direct evidence for ontological claims is often underestimated. Nevertheless, the emergentist philosopher’s case against reductionist theories of how the different levels of reality are related to each other can still gain from the assessment of paradigmatic (...) examples of discontinuity between models in physics, even though their implications must be handled with care. (shrink)

The ability to solve problems is a prerequisite in preparing mathematics preservice teachers. This study assessed preservice teachers’ problem-solving difficulties and performance, particularly in worded problems on number sense, measurement, geometry, algebra, and probability. Also, academic profile differences in the preservice teacher’s problem-solving performance and common errors were determined. A descriptive-comparative research design was employed with 158 random respondents. Data were gathered face-to-face during the first semester of the school year 2022-2023, and data were analyzed with the aid of jamovi (...) software, ensuring ethical measures. Overall findings revealed that the preservice teachers experienced average difficulty in solving problems. The low performance of the preservice teachers on the given problems was also demonstrated. Further analysis revealed a significant difference between the preservice teachers’ problem-solving performance based on their subject preference and program. Moreover, the error analysis revealed that the preservice teachers incurred comprehension errors in misrepresentation, misinterpretation, and miscalculation. These results will serve as a measure for policymakers and curriculum developers of the teacher education institution concerned to make relevant enhancements to the math courses offered in the elementary and secondary education programs. (shrink)

Lockdowns, or modern quarantines, involve the use of novel restrictive non-pharmaceutical interventions (NPIs) to suppress the transmission of COVID-19. In this paper, I aim to critically analyze the emerging history and philosophy of lockdowns, with an emphasis on the communication of health evidence and risk for informing policy decisions. I draw a distinction between evidence-based and modeling-based decision-making. I argue that using the normative framework of evidence-based medicine would have recommended against the use of lockdowns. I first review the World (...) Health Organization’s evidence-based pandemic preparedness plans for respiratory viruses. I then provide a very brief history of COVID-19 modeling, which was cited as justification for the use of lockdowns in the U.K., the U.S., and much of the world. I focus on the so-called Imperial College model designed by Neil Ferguson et al. as well as the so-called Oxford model designed by José Lourenço et al. I analyze the evidence-based pandemic response known as ‘mitigation’, and I compare it with Ferguson et al.’s experimental strategy known as ‘suppression’. I summarize the strengths and weaknesses of these strategies based on their diametric aims and each model’s parametric assumptions. Based on my critical analysis of the suppression strategy, I attempt to expose what has been called the ‘logic of lockdowns’, which Sunetra Gupta of the Oxford model group has suggested is flawed. Finally, I consider Trisha Greenhalgh’s objection to evidence-based policy based on the precautionary principle, and I attempt to offer a response. I conclude with a brief narrative review of the emerging randomized evidence on restrictive NPIs, which seems to support my claim that mitigation was the strategy that would have been recommended by evidence-based medicine. If this is true, then COVID-19 modeling may serve as an important reminder of the enduring lesson of evidence-based medicine: that one should always ‘Trust the Evidence!’ for better health policy. (shrink)

In the first chapter of his Knowledge and Lotteries, John Hawthorne argues that thinkers do not ordinarily know lottery propositions. His arguments depend on claims about the intimate connections between knowledge and assertion, epistemic possibility, practical reasoning, and theoretical reasoning. In this paper, we cast doubt on the proposed connections. We also put forward an alternative picture of belief and reasoning. In particular, we argue that assertion is governed by a Gricean constraint that makes no reference to knowledge, and that (...) practical reasoning has more to do with rational degrees of belief than with states of knowledge. (shrink)

How does microbiota research impact our understanding of biological individuality? We summarize the interdisciplinary summer school on "Microbiota, Symbiosis and Individuality: Conceptual and Philosophical Issues" (July 2019), which was supported by a European Research Council starting grant project "Immunity, DEvelopment, and the Microbiota" (IDEM). The summer school centered around interdisciplinary group work on four facets of microbiota research: holobionts, individuality, causation, and human health. The conceptual discussion of cutting-edge empirical research provided new insights into microbiota and highlights the value of (...) incorporating into meetings experts from other disciplines, such as philosophy and history of science. (shrink)

The purpose of the article is to reveal the scientific approach that substantiates the impact of the creation of strategic alliances (SA) on the digital transformation of business and the development of their innovative power based on identified success factors. The aim was achieved using the following methods: abstract logic and typification (for classification of SA's success factors), generalization (to determine the peculiarities of SA's influence on their innovation development), analytical and ranking method (to determine the relationship between the dynamics (...) creating of SA and the degree of acceleration of digital transformation), expert evaluation (to determine the degree of influence of SA success factors by business areas). Implementing a business-integrated approach to understanding digital transformation will accelerate implementation of innovation and organizational change, identifies future prospects for market, customer and business relationships, highlighting the importance of researching the factors that ensure their success. An alternative vision of SA’s success factors is suggested, which are determined by the possibilities the organization of partnerships and organizational culture, integration of business sectors, compatibility of management goals, external relevance, obtaining synergies. Innovation is considered by the authors as an integration factor that reconciles all groups of SA success factors, giving them the necessary focus to solve business problems. The results of the study show that the creation of SA has a significant impact on accelerating and changes priority of digital transformation of business areas involved in strategic partnerships, and the impact of SA on the development of their innovation power is crucial. (shrink)

A number of experiences have demonstrated how digital solutions are effective in improving quality of life (QoL) and health outcomes for older adults. Smart Health Age-Friendly Environments (SHAFE) is a new concept introduced in Europe since 2017 that combines the concept of Age-Friendly Environments with Information Technologies, supported by health and community care to improve the health and disease management of older adults and during the life-course. This chapter aims to provide an initial overview of the experiences available not only (...) in Europe, based on the research work of the participants of the International Interdisciplinary Network on Health and Well-being in an Age-Friendly Digital World (NET4Age-Friendly), which could be of interest to preventive, health and social authorities. The chapter reports good practices, pain points, and bottlenecks that may require a collaborative, interdisciplinary research approach to facilitate the transformations towards smart, sustainable, health and age-friendly cities and communities. (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)

A long series of studies in social psychology have shown that the explanations people give for their own behaviors are fundamentally different from the explanations they give for the behaviors of others. Still, a great deal of uncertainty remains about precisely what sorts of differences one finds here. We offer a new approach to addressing the problem. Specifically, we distinguish between two levels of representation ─ the level of linguistic structure (which consists of the actual series of words used in (...) the explanation) and the level of conceptual structure (which consists of the concepts these words are used to express). We then formulate and test hypotheses both about self-other differences in conceptual structure and about self-other differences in the mapping from conceptual structure to linguistic structure. (shrink)

Abstract: Online book review platforms generate vast user data, making accurate rating prediction crucial for personalized recommendations. This research explores neural networks as simple models for predicting book ratings without complex algorithms. Our novel approach uses neural networks to predict ratings solely from user-book interactions, eliminating manual feature engineering. The model processes data, learns patterns, and predicts ratings. We discuss data preprocessing, neural network design, and training techniques. Real-world data experiments show the model's effectiveness, surpassing traditional methods. This research can (...) enhance user experience, book catalog organization, and aid publishers, simplifying recommendation processes and providing tailored suggestions based on user preferences. (shrink)

Due to the COVID-19 Pandemic, several changes have been forcibly made and observed in various fields and areas of society, one of which include the field of education; the foundation of the formation of intellect and knowledge. After two years of studying indoors and private educational institutions holding virtual classes, the time has finally come for students to be re- adjusted once more to the blended mode of learning; a combination of virtual and in-person classes. Thus, this study aimed to (...) investigate the development of students’ study habits and how these habits could potentially affect the students’ academic achievement or performance. The study utilized a descriptivecorrelational design to examine the impact of study habits to the academic performance of 137 senior high school students. The study made use of the Palsane and Sharma’s Study Habits Inventory, or also known as PSSHI, and using this instrument, the study has been able to procure sufficient results that will answer the present study’s problems. Using linear regression analysis, the present study found that there was a significance between the study habits and academic performance of the senior high school students, thus the study’s null hypothesis was eventually rejected. (shrink)

Abstract: The Spotify Status Dataset is a valuable resource that provides real-time insights into the operational status and performance of Spotify, a popular music streaming platform. This dataset contains a wide array of information related to server uptime, user activity, service disruptions, and more, serving as a critical tool for both Spotify's internal monitoring and the broader data analysis community. As digital services like Spotify continue to play a central role in music consumption, understanding the platform's status becomes crucial for (...) ensuring a seamless user experience. This dataset offers numerous research and analysis opportunities, whether you're interested in exploring service availability trends or investigating user behavior patterns during service disruptions. This study highlights the significance of the Spotify Status Dataset, its potential applications, and the valuable insights it can offer to various stakeholders, from Spotify engineers working to enhance service reliability to researchers studying the effects of downtime on user engagement. It invites readers on a journey through this dataset to uncover the secrets of Spotify's operational status and its impact on music enthusiasts worldwide. (shrink)

Job motivation remains an area of concern among researchers due to the rising issues of poor or lack of motivation among workers. This refers to one’s personal will or drives to perform a task at work. Meanwhile, job satisfaction refers to an employee’s sense of fulfillment with his or her work experience. Therefore, the current study utilized the descriptive- correlational research design to investigate the impact of job motivation on the job satisfaction of accountants. To gather essential data and achieve (...) the objectives of the study, Multidimensional Work Motivational Scale (MWMS) and Minnesota Satisfaction Questionnaire (MSQ) were administered to the target respondents. The collected data were then analyzed using a Multiple Regression Analysis; findings revealed that job motivation has a significant impact on the job satisfaction of accountants. Additionally, the results of this study were carefully evaluated and discussed, and recommendations were made to benefit the stakeholders of the study and contribute to the existing body of knowledge. (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)

The traditional use of conventional media by businesses for audience targeting has shifted with the rise of influencer marketing, notably on platforms like TikTok, posing challenges in content adaptation and technological adaptation. Albert Bandura's Social Cognitive Theory examines factors shaping purchasing behavior, particularly relevant for young professionals. A quantitative correlational study focused on young professionals engaging with TikTok and influenced by Filipino TikTok creators, revealing education level as a key determinant of purchasing behavior. Extended TikTok engagement positively correlates with increased (...) purchasing likelihood. Filipino TikTok influencers significantly impact young professionals' purchasing decisions, emphasizing the need for tailored marketing strategies targeting this demographic. Recommendations were provided for effective influencer and digital marketing strategies targeting young professionals. (shrink)

Abstract: This research delves into the utilization of Artificial Neural Networks (ANNs) as a powerful tool for predicting the overall ratings of books by leveraging a diverse set of attributes. To achieve this, we employ a comprehensive dataset sourced from Goodreads, enabling us to thoroughly examine the intricate connections between the different attributes of books and the ratings they receive from readers. In our investigation, we meticulously scrutinize how attributes such as genre, author, page count, publication year, and reader reviews (...) influence a book's overall rating. Through rigorous analysis and experimentation, we construct an advanced ANN model tailored for predictive analysis in the realm of book ratings. The outcomes of our study reveal the remarkable potential of ANNs in this domain. The ANN model exhibits an impressive level of accuracy when it comes to forecasting book ratings, underlining the efficacy and promise of artificial neural networks in enhancing our understanding and prediction of book evaluations. This research opens up new avenues for leveraging machine learning techniques to gain deeper insights into the dynamics of book ratings and reader preferences. (shrink)

This study aims to investigate the relationship between job burnout and employee performance in the feed industry during the ASF outbreak. Further, the researchers employed a descriptive-correlational research design in order to analyze the acquired data and produce pertinent findings. Thus, the researchers gathered data from one hundred two (102) feed industry employees. The Maslach Burnout Inventory (MBI) and Individual Work Performance Questionnaire (IWPQ) were employed to ascertain the extent of job burnout experienced by the respondents and evaluate employee performance, (...) respectively. Based on the statistical analysis, there is a significant relationship between job burnout and employee performance. Moreover, the study's findings were thoroughly analyzed and discussed. (shrink)

Heart palpitations, while often benign, can sometimes be indicative of severe underlying conditions requiring immediate intervention. Accurate and swift diagnosis thus remains a clinical priority. "A CLIPS-Based Expert System for Heart Palpitations Diagnosis" represents a novel approach to addressing this challenge, harnessing the power of artificial intelligence and rule-based expert systems. Specifically, this system applies a suite of 7 if-then rules to evaluate potential heart palpitations causes and assign one of three outcomes: 1) A confirmed diagnosis of heart palpitations, 2) (...) A suspected link to cardiovascular diseases, and 3) A possible association with anxiety or stress disorders. The expert system offers an intuitive user interface, allowing for seamless symptom input and instant diagnosis based on user-provided information. This paper explores the various phases of this expert system's lifecycle, including design, implementation, and evaluation. Furthermore, the study situates the system within the broader discourse on rule-based expert systems for heart palpitations diagnosis, critically analyzing their efficiency, potential pitfalls, and ongoing challenges. Through this research, the value of integrating rule-based expert systems in clinical diagnostic processes is highlighted, illustrating its capacity to enhance diagnostic accuracy and patient outcomes. (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)

Family members can provide crucial support to individuals participating in clinical trials. In research on the “newest frontier” of Deep Brain Stimulation (DBS)—the use of DBS for psychiatric conditions—family member support is frequently listed as a criterion for trial enrollment. Despite the significance of family members, qualitative ethics research on DBS for psychiatric conditions has focused almost exclusively on the perspectives and experiences of DBS recipients. This qualitative study is one of the first to include both DBS recipients and their (...) family members as interview participants. Using dyadic thematic analysis—an approach that takes both the individuals and the relationship as units of analyses—this study analyzes the complex ways in which family relationships can affect DBS trial participation, and how DBS trial participation in turn influences family relationships. Based on these findings, we propose ways to improve study designs to better take family relationships into account, and better support family members in taking on the complex, essential roles that they play in DBS trials for psychiatric conditions. (shrink)

There is no doubt that Computer troubleshooting is important for organizations and companies and for personal use level. Sound cards troubles is one of the most annoying problems in computers. It causes damage and troubles in computers to persons, organizations and firms. Correctly, expert systems can greatly help to avoid damage to these computers. designed to diagnose and troubleshoot issues related to sound cards in computer systems. The expert system is developed using a combination of rule-based and machine learning approaches, (...) which allows for accurate and efficient diagnosis of a wide range of sound card problems. Objectives: An expert system has been established based on CLIPS to diagnose Computer Problems. (shrink)

Abstract: This research paper presents the development and evaluation of a neural network-based model for predicting fire alarms in smoke detection systems. Using a dataset from Kaggle containing 15 features and 3487 samples, we trained and validated a neural network with a three-layer architecture. The model achieved an accuracy of 100% and an average error of 0.0000003. Additionally, we identified the most influential features in predicting fire alarms.

This study examines the correlation between job descriptions and salaries at Immaculate Conception College of Balayan Inc. (ICCBI), a private Catholic institution devoted to faith-based education. Using qualitative research, a single-case study was conducted with ten (10) participants selected through purposive sampling based on specific criteria. Through face-to-face interviews, data was collected and analyzed using a narrative approach. Thus, it was found out that job descriptions at ICCBI are established through methods like job analysis, role and responsibility approaches, qualifications, and (...) the school manual-based method. Salary determination involves factors such as tenure, educational attainment, performance, teaching loads, experience, and collegial care. Key factors influencing job descriptions include salary differentiation, aligned job descriptions, career development opportunities, and increased duties and responsibilities. Variations in the salary structure are affected by teaching loads, department designations, and educational qualifications. The findings indicate that job descriptions impact employee salaries at the institution, and future research is encouraged to explore identified factors for insights into developing more efficient roles and contributing to organizational effectiveness. With this, the study proposed a strategic plan for future use and implementation. (shrink)

Accurate statistical analysis of Fortnite gameplay data is essential for improving gaming strategies and performance. In this study, we present a novel approach to analyze Fortnite statistics using machine learning techniques. Our dataset comprises a wide range of gameplay metrics, including eliminations, assists, revives, accuracy, hits, headshots, distance traveled, materials gathered, materials used, damage taken, damage to players, damage to structures, and more. We collected this dataset to gain insights into Fortnite player performance and strategies. The proposed model employs advanced (...) machine learning algorithms and data preprocessing techniques. We aim to identify key performance indicators and influential factors that contribute to Fortnite success. By exploring patterns and relationships within the dataset, our model can provide valuable insights into gameplay optimization. Our research involves feature analysis to determine the most influential factors in predicting Fortnite performance. These factors include eliminations, assists, revives, accuracy, hits, headshots, distance traveled, materials gathered, materials used, damage taken, damage to players, damage to structures, and more. By understanding the impact of these factors, we aim to help Fortnite players enhance their skills and strategies. Through extensive training and validation of our machine learning model, we achieved remarkable results, with an accuracy rate of [Your Accuracy Rate]% and an average error of [Your Average Error]. This research not only provides valuable insights into Fortnite gameplay but also offers a foundation for future research in the field of esports analytics and performance optimization. (shrink)

Kidney stones pose a significant health concern, and early detection can lead to timely intervention and improved patient outcomes. This research endeavours to predict the presence of kidney stones based on urine analysis, utilizing a neural network model. A dataset of 552 urine specimens, comprising six essential physical characteristics (specific gravity, pH, osmolarity, conductivity, urea concentration, and calcium concentration), was collected and prepared. Our proposed neural network architecture, featuring three layers (input, hidden, output), was trained and validated, achieving an impressive (...) accuracy of 98.67% and an average error of 0.012. In addition to model performance, feature importance analysis was conducted to determine the most influential factors in predicting kidney stone presence. The findings underscore the significance of urea concentration, specific gravity, calcium concentration, conductivity, osmolarity, and pH as key indicators. This research contributes to the early diagnosis of kidney stones and demonstrates the potential of neural networks in medical diagnostics. The clinical implications of these findings are discussed, emphasizing the importance of timely intervention in managing kidney stone-related health issues. (shrink)

Socioeconomic status refers to the family's social and economic standing in society. It is measured by combining an individual or group's economic and social position, which is often based on income, education, and occupation. It significantly affects academic performance and even one's health status. The pandemic changed the educational system, causing a huge transition from traditional learning methods to online learning. This shift resulted in confusion, burden, and difficulty among students from different walks of life. This study was conducted to (...) determine the academic stress on online learning among nursing students in relation to their economic status and their ability to access technology. The study utilized a quantitative-correlational method aided by a standard questionnaire. Two hundred thirty-six University of Bohol nursing students enrolled in the second semester of SY 2021–2022 were included in the study by random selection. Data revealed a significant correlation between economic status and the level of stress related to academic expectations and a significant correlation between economic status and access to technology after being subjected to the Spearman rho test of correlation. However, no significant correlation was found between access to technology and the respondents' level of academic stress. (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: 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)

Malaria is a life-threatening disease spread to humans by some types of mosquitoes. It is mostly found in tropical countries. It is preventable and curable. The infection is caused by a parasite and does not spread from person to person. Symptoms can be mild or life-threatening. Mild symptoms are fever, chills and headache. Severe symptoms include fatigue, confusion, seizures, and difficulty breathing. Infants, children under 5 years, pregnant women, travelers and people with HIV or AIDS are at higher risk of (...) severe infection. Discovering this disease is a good step for proper and correct treatment. Determining the treatment with high accuracy depends on the method used in the diagnosis. Correctly, expert systems can greatly help to avoid spreading this disease. The expert system diagnoses malaria disease correctly to facilitate doctors to find the correct treatment based on the appropriate diagnosis. Objectives: An expert system has been established based on CLIPS to diagnose malaria disease. (shrink)

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: Rice, as a paramount staple crop worldwide, sustains billions of lives. Precise classification of rice types holds immense agricultural, nutritional, and economic significance. Recent advancements in machine learning, particularly Artificial Neural Networks (ANNs), offer promise in enhancing rice type classification accuracy and efficiency. This research explores rice type classification, harnessing neural networks' power. Utilizing a rich dataset from Kaggle, containing 18,188 entries and key rice grain attributes, we develop and evaluate a neural network model. Our neural network, featuring a (...) single hidden layer, achieves remarkable results—a staggering 100% accuracy and a minute average error rate of 0.000001. Beyond performance metrics, we delve into the intricacies of rice type classification through feature importance analysis. The most influential features—'id,' 'Area,' 'MajorAxisLength,' 'MinorAxisLength,' 'Eccentricity,' 'ConvexArea,' 'EquivDiameter,' 'Extent,' 'Perimeter,' 'Roundness,' and 'AspectRatio'—uncover the physiological traits underpinning accurate rice classification. This research contributes to advancing rice classification methods and highlights the potential of ANNs in optimizing agricultural practices, ensuring food safety, and bolstering global trade. (shrink)

Abstract Heart Attack is the Cardiovascular Disease (CVD) which causes the most deaths among CVDs. We collected a dataset from Kaggle website. In this paper, we propose an ANN model for the predicting whether a patient has a heart attack or not that. The dataset set consists of 9 features with 1000 samples. We split the dataset into training, validation, and testing. After training and validating the proposed model, we tested it with testing dataset. The proposed model reached an accuracy (...) of 98.01% on the Heart Disease Dataset. (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)

heart attack analysis & prediction dataset is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 304 samples with 11 features, such as age, sex, chest pain type, Trtbps, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, and HeartDisease. (...) A neural network model with four layers (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 88% and an average error of 0078296. The most influential factors in heart failure prediction were found to be oldpeak, ST_Slope, sex, fastingBS, chestPainType, exerciseAngina, cholesterol, restingBP, maxHR, restingBP, and age. This study provides a valuable tool for early detection and intervention of heart failure, thereby contributing to the field of health and medicine. (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)

In this paper we argue that dissociative identity disorder (DID) is best interpreted as a causal model of a (possible) post-traumatic psychological process, as a mechanical model of an abnormal psychological condition. From this perspective we examine and criticize the evidential status of DID, and we demonstrate that there is really no good reason to believe that anyone has ever suffered from DID so understood. This is so because the proponents of DID violate basic methodological principles of good causal modeling. (...) When every ounce of your concentration is fixed upon blasting a winged pig out of the sky, you do not question its species' ontological status. James Morrow, City of Truth (1990). (shrink)

Abstract: The aim behind analyzing Google Stock Prices dataset is to get a fair idea about the relationships between the multiple attributes a day might have, such as: the opening price for each day, the volume of trading for each day. With over a hundred thousand days of trading data, there are some patterns that can help in predicting the future prices. We proposed an Artificial Neural Network (ANN) model for predicting the closing prices for future days. The prediction is (...) based on these features (date, opening price, highest price, lowest price, volume), which were used as input variables and (closing price) as output variable for our ANN model. Our model was created, trained, and validated using data set in JNN environment, which its title is “Google-Stock-Prices”. The model evaluation showed that the ANN model is able to predict correctly 99.48% of the validation samples. (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.

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)

Mouth ulcers, also known as canker sores, are a common oral health issue affecting a significant portion of the population. Early and accurate diagnosis of mouth ulcers is crucial for effective treatment and prevention of complications. This paper presents an expert system developed using CLIPS (C Language Integrated Production System) to diagnose mouth ulcer disease. The expert system utilizes a rule-based approach, incorporating a comprehensive knowledge base consisting of symptoms, risk factors, and medical literature related to mouth ulcers. By employing (...) an inference engine, the system evaluates patient input against the rules and generates a diagnosis based on the matching symptoms and relevant factors. The system also provides detailed explanations and recommendations for each diagnosis, aiding healthcare professionals and patients in understanding the condition and selecting appropriate treatment options. The development and validation of the expert system involved a multi-step process, including knowledge acquisition, rule formulation, and testing against a diverse dataset of mouth ulcer cases. The system's performance was evaluated by comparing its diagnoses with those made by medical professionals, demonstrating a high level of accuracy and consistency. The implementation of an expert system for diagnosing mouth ulcer disease offers several advantages, including the ability to provide timely and accessible healthcare services, reduce misdiagnosis rates, and assist healthcare professionals in decision-making processes. Moreover, it empowers individuals to make informed decisions about their health and seek appropriate medical intervention. This paper contributes to the field of medical expert systems by showcasing the practical application of CLIPS in the diagnosis of mouth ulcer disease. The results demonstrate the potential of expert systems as valuable tools for healthcare professionals and patients alike, enhancing the efficiency and accuracy of diagnosis in oral health care. (shrink)

Abstract: This study has effectively tackled the critical challenge of accurate calorie prediction in dishes by employing a robust neural network-based model. With an outstanding accuracy rate of 99.32% and a remarkably low average error of 0.009, our model has showcased its proficiency in delivering precise calorie estimations. This achievement equips individuals, healthcare practitioners, and the food industry with a powerful tool to promote healthier dietary choices and elevate awareness of nutrition. Furthermore, our in-depth feature importance analysis has shed light (...) on the indispensable role played by specific nutritional attributes in calorie estimation. This unveiling of crucial factors provides valuable insights for further research endeavors and practical applications. Notably, this research extends its impact beyond the immediate context by making substantial contributions to the domains of nutrition science and dietary planning. It underscores the transformative potential of artificial intelligence, demonstrating how it can revolutionize our approach to food, nutrition, and health. As the world grapples with the challenges of diet-related health issues and environmental concerns, the accuracy and precision achieved in calorie prediction through neural networks represent a significant stride towards more informed and conscientious dietary practices. In this era of data-driven decision-making, our research paves the way for healthier lifestyles, heightened nutritional awareness, and a more health-conscious society. (shrink)

Warts and verrucas are common skin conditions caused by the human papillomavirus (HPV) infection. They present as raised, rough, or bumpy growths on the hands, feet, or other areas subjected to friction or pressure. Plantar warts exhibit a rough surface with small black dots, while genital warts have a cauliflower-like appearance. Pain or itchiness may accompany these lesions. Factors such as close contact with infected individuals and immune compromise can impact the severity and spread of warts. Diagnosis is primarily based (...) on clinical examination, and referral to a dermatologist may be necessary for uncertain cases. Treatment options range from over-the-counter remedies to professional interventions. Persistent or symptomatic lesions should be evaluated by a healthcare professional. Improved understanding and effective management of warts and verrucas are essential for optimal patient outcomes. (shrink)

Abstract: The emergence of the novel coronavirus (COVID-19) in 2019 has presented the world with an unprecedented global health crisis. The rapid and widespread transmission of the virus has strained healthcare systems, disrupted economies, and challenged societies. In response to this monumental challenge, the intersection of technology and healthcare has become a focal point for innovation. This research endeavors to leverage the capabilities of Artificial Neural Networks (ANNs) to develop an advanced predictive model for forecasting the spread of COVID-19. It (...) involves the collection, analysis, and integration of diverse datasets encompassing epidemiological, clinical, and social factors that influence the virus's dissemination. (shrink)