Artificial Neural Network for Predicting COVID 19 Using JNN

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

Streamlined Book Rating Prediction with Neural Networks

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

Prediction Heart Attack using Artificial Neural Networks (ANN)

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

Predicting Audit Risk Using Neural Networks: An In-depth Analysis.

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

Classification of plant Species Using Neural Network

Abstract: In this study, we explore the possibility of classifying the plant species. We collected the plant species from Kaggle website. This dataset encompasses 544 samples, encompassing 136 distinct plant species. Recent advancements in machine learning, particularly Artificial Neural Networks (ANNs), offer promise in enhancing plant Species classification accuracy and efficiency. This research explores plant Species classification, harnessing neural networks' power. Utilizing a rich dataset from Kaggle, containing 544 entries, 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 0.002. Beyond performance metrics, we delve into the intricacies of plant Species classification through feature importance analysis. The most influential features— Vegsout, durflow, semiros, pdias, begflow, wind, leafy, autopoll and insects— 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 plant safety, and bolstering global trade

The Palestinian primary ciliary dyskinesia population: first results of the diagnostic, and genetic spectrum

BACKGROUND: Diagnostic testing for primary ciliary dyskinesia (PCD) started in 2013 in Palestine. We aimed to describe the diagnostic, genetic and clinical spectrum of the Palestinian PCD population. METHODS: Individuals with symptoms suggestive of PCD were opportunistically considered for diagnostic testing: nasal nitric oxide (nNO) measurement, transmission electron microscopy (TEM) and/or PCD genetic panel or whole-exome testing. Clinical characteristics of those with a positive diagnosis were collected close to testing including forced expiratory volume in 1 s (FEV1) Global Lung Index z-scores and body mass index z-scores. RESULTS: 68 individuals had a definite positive PCD diagnosis, 31 confirmed by genetic and TEM results, 23 by TEM results alone, and 14 by genetic variants alone. 45 individuals from 40 families had 17 clinically actionable variants and four had variants of unknown significance in 14 PCD genes. CCDC39, DNAH11 and DNAAF11 were the most commonly mutated genes. 100% of variants were homozygous. Patients had a median age of 10.0 years at diagnosis, were highly consanguineous (93%) and 100% were of Arabic descent. Clinical features included persistent wet cough (99%), neonatal respiratory distress (84%) and situs inversus (43%). Lung function at diagnosis was already impaired (FEV1 z-score median −1.90 (−5.0–1.32)) and growth was mostly within the normal range (z-score mean −0.36 (−3.03–2.57). 19% individuals had finger clubbing. CONCLUSIONS: Despite limited local resources in Palestine, detailed geno- and phenotyping forms the basis of one of the largest national PCD populations globally. There was notable familial homozygosity within the context of significant population heterogeneity

Interactive voice response technology for symptom monitoring and as an adjunct to the treatment of chronic pain

Chronic pain is a medical condition that severely decreases the quality of life for those who struggle to cope with it. Interactive voice response (IVR) technology has the ability to track symptoms and disease progression, to investigate the relationships between symptom patterns and clinical outcomes, to assess the efficacy of ongoing treatments, and to directly serve as an adjunct to therapeutic treatment for chronic pain. While many approaches exist toward the management of chronic pain, all have their pitfalls and none work universally. Cognitive behavioral therapy (CBT) is one approach that has been shown to be fairly effective, and therapeutic interactive voice response technology provides a convenient and easy-to-use means of extending the therapeutic gains of CBT long after patients have discontinued clinical visitations. This review summarizes the advantages and disadvantages of IVR technology, provides evidence for the efficacy of the method in monitoring and managing chronic pain, and addresses potential future directions that the technology may take as a therapeutic intervention in its own right

Global burden of peripheral artery disease and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: Peripheral artery disease is a growing public health problem. We aimed to estimate the global disease burden of peripheral artery disease, its risk factors, and temporospatial trends to inform policy and public measures. Methods: Data on peripheral artery disease were modelled using the Global Burden of Disease, Injuries, and Risk Factors Study (GBD) 2019 database. Prevalence, disability-adjusted life years (DALYs), and mortality estimates of peripheral artery disease were extracted from GBD 2019. Total DALYs and age-standardised DALY rate of peripheral artery disease attributed to modifiable risk factors were also assessed. Findings: In 2019, the number of people aged 40 years and older with peripheral artery disease was 113 million (95% uncertainty interval [UI] 99·2–128·4), with a global prevalence of 1·52% (95% UI 1·33–1·72), of which 42·6% was in countries with low to middle Socio-demographic Index (SDI). The global prevalence of peripheral artery disease was higher in older people, (14·91% [12·41–17·87] in those aged 80–84 years), and was generally higher in females than in males. Globally, the total number of DALYs attributable to modifiable risk factors in 2019 accounted for 69·4% (64·2–74·3) of total peripheral artery disease DALYs. The prevalence of peripheral artery disease was highest in countries with high SDI and lowest in countries with low SDI, whereas DALY and mortality rates showed U-shaped curves, with the highest burden in the high and low SDI quintiles. Interpretation: The total number of people with peripheral artery disease has increased globally from 1990 to 2019. Despite the lower prevalence of peripheral artery disease in males and low-income countries, these groups showed similar DALY rates to females and higher-income countries, highlighting disproportionate burden in these groups. Modifiable risk factors were responsible for around 70% of the global peripheral artery disease burden. Public measures could mitigate the burden of peripheral artery disease by modifying risk factors. Funding: Bill & Melinda Gates Foundation