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

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

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

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

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

A randomized trial to determine the impact on compliance of a psychophysical peripheral cue based on the Elaboration Likelihood Model

Objective: Non-compliance in clinical studies is a significant issue, but causes remain unclear. Utilizing the Elaboration Likelihood Model of persuasion, this study assessed the psychophysical peripheral cue ‘Interactive Voice Response System (IVRS) call frequency’ on compliance. Methods: 71 participants were randomized to once daily (OD), twice daily (BID) or three times daily (TID) call schedules over two weeks. Participants completed 30-item cognitive function tests at each call. Compliance was defined as proportion of expected calls within a narrow window (± 30 min around scheduled time), and within a relaxed window (− 30 min to + 4 h). Data were analyzed by ANOVA and pairwise comparisons adjusted by the Bonferroni correction. Results: There was a relationship between call frequency and compliance. Bonferroni adjusted pairwise comparisons showed significantly higher compliance (p = 0.03) for the BID (51.0%) than TID (30.3%) for the narrow window; for the extended window, compliance was higher (p = 0.04) with OD (59.5%), than TID (38.4%). Conclusion: The IVRS psychophysical peripheral cue call frequency supported the ELM as a route to persuasion. The results also support OD strategy for optimal compliance. Models suggest specific indicators to enhance compliance with medication dosing and electronic patient diaries to improve health outcomes and data integrity respectively

Adaptive Trial Design: Could We Use This Approach to Improve Clinical Trials in the Field of Global Health?

We need more clinical trials in the world's poorest regions to evaluate new drugs and vaccines, and also to find better ways to manage health issues. Clinical trials are expensive, time consuming, and cumbersome. However, in wealthier regions these limiting factors are being addressed to make trials less administrative and improve the designs. A good example is adaptive trial design. This innovation is becoming accepted by the regulators and has been taken up by the pharmaceutical industry to reduce product development times and costs. If this approach makes trials easier and less expensive surely we should be implementing this approach in the field of tropical medicine and international health? As yet this has rarely been proposed and there are few examples. There is a need for raising the awareness of these design approaches because they could be used to make dramatic improvements to clinical research in developing countries

Pilot study of an interactive voice response system to improve medication refill compliance

<p>Abstract</p> <p>Background</p> <p>Sub-optimal adherence to prescribed medications is well documented. Barriers to medication adherence include medication side effects, cost, and forgetting to take or refill medications. Interactive Voice Response (IVR) systems show promise as a tool for reminding individuals to take or refill medications. This pilot study evaluated the feasibility and acceptability of using an IVR system for prescription refill and daily medication reminders. We tested two novel features: personalized, medication-specific reminder messages and communication via voice recognition.</p> <p>Methods</p> <p>Patients enrolled in a study of electronic prescribing and medication management in Quebec, Canada who were taking chronic disease-related drugs were eligible to participate. Consenting patients had their demographic, telephone, and medication information transferred to an IVR system, which telephoned patients to remind them to take mediations and/or refill their prescriptions. Facilitators and barriers of the IVR system use and acceptability of the IVR system were assessed through a structured survey and open-ended questions administered by telephone interview.</p> <p>Results</p> <p>Of the 528 eligible patients who were contacted, 237 refused and 291 consented; 99 participants had started the pilot study when it was terminated because of physician and participant complaints. Thirty-eight participants completed the follow-up interview. The majority found the IVR system's voice acceptable, and did not have problems setting up the time and location of reminder calls. However, many participants experienced technical problems when called for reminders, such as incorrect time of calls and voice recognition difficulties. In addition, most participants had already refilled their prescriptions when they received the reminder calls, reporting that they did not have difficulties remembering to refill prescriptions on their own. Also, participants were not receptive to speaking to an automated voice system.</p> <p>Conclusion</p> <p>IVR systems designed to improve medication compliance must address key technical and performance issues and target those individuals with reported memory difficulties or complex medication regimens in order to improve the utility of the system. Future research should also identify characteristics of medication users who are more likely to be receptive to IVR technology.</p

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

Feasibility of Using Interactive Voice Response to Monitor Daily Drinking, Moods, and Relationship Processes on a Daily Basis in Alcoholic Couples

Daily process research on alcohol involvement has used paper-and-pencil and electronic data collection methods, but no studies have yet tested the feasibility of using Interactive Voice Response (IVR) technology to monitor drinking, affective, and social interactional processes among alcoholic (ALC) couples. This study tested the feasibility of using IVR with n  = 54 ALC couples.Participants were n  = 54 couples (probands who met criteria for a past 1-year alcohol use disorder and their partners) recruited from a substance abuse treatment center and the local community. Probands and their partners reported on their daily drinking, marital interactions, and moods once a day for 14 consecutive days using an IVR system. Probands and partners were on average 43.4 and 43.0 years old, respectively.Participants completed a total of 1,418 out of a possible 1,512 diary days for an overall compliance rate of 93.8%. ALC probands completed an average of 13.3 (1.0) diary reports, and partners completed an average of 13.2 (1.0) diary reports. On average, daily IVR calls lasted 7.8 (3.0) minutes for ALC probands and 7.6 (3.0) minutes for partners. Compliance was significantly lower on weekend days (Fridays and Saturdays) compared to other weekdays for probands and spouses. Although today’s intoxication predicted tomorrow’s noncompliance for probands but not spouses, the strongest predictor of proband’s compliance was their spouse’s compliance. Daily anxiety and marital conflict were associated with daily IVR nonresponse, which triggered automated reminder calls.Findings supported that IVR is a useful method for collecting daily drinking, mood, and relationship process data from alcoholic couples. Probands’ compliance is strongly associated with their partners’ compliance, and automated IVR calls may facilitate compliance on high anxiety, high conflict days.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/79396/1/j.1530-0277.2009.01115.x.pd