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Background: One of the latest issues in predicting cardiovascular disease is the limited performance of current risk prediction models. Although several models have been developed, they often fail to identify a significant proportion of individuals who go on to develop the disease. This highlights the need for more accurate and personalized prediction models. Objective: This study aims to investigate the effectiveness of the Grey Wolf Levenberg Model and Neural Networks in predicting cardiovascular diseases. The objective is to identify a synergistic approach that can improve the accuracy of predictions. Through this research, the authors seek to contribute to the development of better tools for early detection and prevention of cardiovascular diseases. Methods: The study used a quantitative approach to develop and validate the GWLM_NARX model for predicting cardiovascular disease risk. The approach involved collecting and analyzing a large dataset of clinical and demographic variables. The performance of the model was then evaluated using various metrics such as accuracy, sensitivity, and specificity. Results: the study found that the GWLM_NARX model has shown promising results in predicting cardiovascular disease. The model was found to outperform other conventional methods, with an accuracy of over 90%. The synergistic approach of Grey Wolf Levenberg Model and Neural Networks has proved to be effective in predicting cardiovascular disease with high accuracy. Conclusion: The use of the Grey Wolf Levenberg-Marquardt Neural Network Autoregressive model (GWLM-NARX) in conjunction with traditional learning algorithms, as well as advanced machine learning tools, resulted in a more accurate and effective prediction model for cardiovascular disease. The study demonstrates the potential of machine learning techniques to improve diagnosis and treatment of heart disorders. However, further research is needed to improve the scalability and accuracy of these prediction systems, given the complexity of the data associated with cardiac illness. Keywords: Cardiovascular data, Clinical data., Decision tree, GWLM-NARX, Linear model functions

Rainfall prediction is one of the most challenging task faced by researchers over the years. Many machine learning and AI based algorithms have been implemented on different datasets for better prediction purposes, but there is not a single solution which perfectly predicts the rainfall. Accurate prediction still remains a question to researchers. We offer a machine learning-based comparison evaluation of rainfall models for Kashmir province. Both local geographic features and the time horizon has influence on weather forecasting. Decision trees, Logistic Model Trees (LMT), and M5 model trees are examples of predictive models based on algorithms. GWLM-NARX, Gradient Boosting, and other techniques were investigated. Weather predictors measured from three major meteorological stations in the Kashmir area of the UT of J&K, India, were utilized in the models. We compared the proposed models based on their accuracy, kappa, interpretability, and other statistics, as well as the significance of the predictors utilized. On the original dataset, the DT model delivers an accuracy of 80.12 percent, followed by the LMT and Gradient boosting models, which produce accuracy of 87.23 percent and 87.51 percent, respectively. Furthermore, when continuous data was used in the M5-MT and GWLM-NARX models, the NARX model performed better, with mean squared error (MSE) and regression value (R) predictions of 3.12 percent and 0.9899 percent in training, 0.144 percent and 0.9936 percent in validation, and 0.311 percent and 0.9988 percent in testing.

The present research explores the process of diagnosing thyroid disorders using two different methods: traditional and ensemble. The traditional method implements the ID3 decision tree algorithm on 4000 records after several preprocessing stages. This method is evaluated using various statistical approaches, which defines the proportion of true agreement between observed and predicted events. The partition accuracy for the traditional method ranges from 66% to 70%. The ensemble method relies on the Random Forest algorithm, which creates multiple trees on random datasets and integrates predictions through voting. The partition accuracy for the ensemble method, assessed using k-fold cross-validation, ranges from 68% to 74%, with a mean accuracy of 72%. The ensemble method demonstrates higher partition accuracy in diagnosing thyroid disorders compared to the traditional method. These findings suggest that ensemble machine learning techniques, particularly Random Forest, can significantly enhance diagnostic accuracy for thyroid disorders. Future research should focus on integrating these advanced algorithms into clinical diagnostic processes to improve patient outcomes and further optimize diagnostic techniques. [ABSTRACT FROM AUTHOR]

Background: One of the latest issues in predicting cardiovascular disease is the limited performance of current risk prediction models. Although several models have been developed, they often fail to identify a significant proportion of individuals who go on to develop the disease. This highlights the need for more accurate and personalized prediction models. Objective: This study aims to investigate the effectiveness of the Grey Wolf Levenberg Model and Neural Networks in predicting cardiovascular diseases. The objective is to identify a synergistic approach that can improve the accuracy of predictions. Through this research, the authors seek to contribute to the development of better tools for early detection and prevention of cardiovascular diseases. Methods: The study used a quantitative approach to develop and validate the GWLM_NARX model for predicting cardiovascular disease risk. The approach involved collecting and analyzing a large dataset of clinical and demographic variables. The performance of the model was then evaluated using various metrics such as accuracy, sensitivity, and specificity. Results: the study found that the GWLM_NARX model has shown promising results in predicting cardiovascular disease. The model was found to outperform other conventional methods, with an accuracy of over 90%. The synergistic approach of Grey Wolf Levenberg Model and Neural Networks has proved to be effective in predicting cardiovascular disease with high accuracy. Conclusion: The use of the Grey Wolf Levenberg-Marquardt Neural Network Autoregressive model (GWLM-NARX) in conjunction with traditional learning algorithms, as well as advanced machine learning tools, resulted in a more accurate and effective prediction model for cardiovascular disease. The study demonstrates the potential of machine learning techniques to improve diagnosis and treatment of heart disorders. However, further research is needed to improve the scalability and accuracy of these prediction systems, given the complexity of the data associated with cardiac illness. [ABSTRACT FROM AUTHOR]

One of the earliest and most popular (and effective) machine learning methods is decision trees. Different decision tree changes have been suggested and put into practice over time. Selecting the model that best fits the situation is essential while examining the data. Numerous classification and regression specialists have suggested ensemble tactics for tabular data as well as diverse methods for solving classification and regression issues. In this study, the raw historical apple crop dataset is transformed into a discrete dataset using the Gini Index and information gain. On the resulting discrete dataset, the decision tree algorithm is used. Information Gain is determined for each attribute, and the attribute with the highest information gain is used as the splitting node, which is then applied recursively. With an accuracy of 84.54%, the decision tree algorithm used predicts the apple yield in Kashmir province. Later, a comparison between the accuracy of decision tree and other algorithms has also been made and it was observed that the decision tree performed better in accuracy and other statistics than all the other implemented algorithms. [ABSTRACT FROM AUTHOR]

Determining real-time machine simulation and functionalities of complex AI Engines is difficult to comprehend and is rarely discussed. We present a technique to analyze the workflow of one such engine, the NEAT engine, one of the fundamental and robust training engines in the current machine learning scenario. Computer Vision also presents a great approach towards working in real-time speedy functioning virtual simulators and visual platforms, whereas NEAT is not specialized in the same, but can perform to the best of the extent in the present day Machine Learning aspect. Technologies like Python, PyGame, and CsV were used to facilitate the research. So far, we have tested both the frameworks on real time cases, and it is safe to say that the NEAT module has presented an accurate trajectory, besides greater time complexity. Thus, we not only evaluate the accuracy but the other key factors as well. This study demonstrates that NEAT has the ability to address other difficult issues in the future and can produce excellent outcomes with a relatively small population. Robotics, artificial intelligence for video games, natural language processing, and healthcare are some of the potential future applications for NEAT. [ABSTRACT FROM AUTHOR]

It might be quite difficult to search for words or phrases in the many file formats that are based on various operating systems. To deal with this level of heterogeneity in different file systems, several academics have put up a number of alternative strategies. Given that data is stored in various formats and is controlled by several operating systems, such solutions, however, proved to be extremely time-consuming. The suggested techniques work best when the data is kept in a single source. The idea of bottlenecking and the resulting heterogeneity in file systems are two main issues with looking for a certain collection of data objects (folder, file, or directory) across many platforms (Windows, Linux, and so forth). We made an effort to suggest fundamental searching methods that may deal with the issue of heterogeneity while increasing efficiency and maintaining dependability. Our method makes use of a concept known as tree-based breadth first search (BFS) and depth first search techniques (DFS) to limit to an absolute minimum the amount of I/O operations that might be required in the heterogeneous environment. The experiment was run on Windows and Linux computers, and it was discovered that by using these strategies, the heterogeneity issues may be greatly decreased, leading to some encouraging outcomes. [ABSTRACT FROM AUTHOR]

Many recommendation systems make product or service suggestions based on existing knowledge of the user or the item. We must deal with two types of cold start problems: item-based and user-based cold start problems. In a user-based cold start dilemma, it is difficult for the system to suggest news to a new user whose information is not saved in the system. In this article, we attempt to address user based cold start problem by assuming that in the case of a user, we only know one type of information about the user, and that information is the user's location. Using BBC news data, an ID3 classification approach was developed, which incorporates eight explanatory factors such as News ID, News text, Keywords, date, Location, Shares count, followers, and so on. The classification accuracy of one of the best fit models constructed using (80-20)% training and test ratios is around 78%. Our technique is an effective tool for the cold-start problem because it outperforms the advice by a significant margin depending on the location. According to the results, our approach is competitive in terms of both accuracy and precision. [ABSTRACT FROM AUTHOR]

Chitosan is a biodegradable and biocompatible natural polymer that has been extensively explored in recent decades. The Food and Drug Administration has approved chitosan for wound treatment and nutritional use. Furthermore, chitosan has paved the way for advancements in different biomedical applications including as a nanocarrier and tissue-engineering scaffold. Its antibacterial, antioxidant, and haemostatic properties make it an excellent option for wound dressings. Because of its hydrophilic nature, chitosan is an ideal starting material for biocompatible and biodegradable hydrogels. To suit specific application demands, chitosan can be combined with fillers, such as hydroxyapatite, to modify the mechanical characteristics of pH-sensitive hydrogels. Furthermore, the cationic characteristics of chitosan have made it a popular choice for gene delivery and cancer therapy. Thus, the use of chitosan nanoparticles in developing novel drug delivery systems has received special attention. This review aims to provide an overview of chitosan-based nanoparticles, focusing on their versatile properties and different applications in biomedical sciences and engineering. [ABSTRACT FROM AUTHOR]

The way people learn has radically changed as a result of information technology. As an informal method of learning, fragmented learning has become a popular way to learn new technology and expertise. Academic organizations generate a large amount of heterogeneous data, and academic leaders want to make the most of it by analyzing the large amount of data in order to make better decisions. The volume isn't the only issue; the organization's data structure (structured, semi structured, and unstructured) adds to the complexity of academic work and decision-making on a daily basis. As big data has become more prevalent in educational settings, new data-driven techniques to enhance informed decision-making and efforts to improve educational efficacy have emerged. Traditional data sources and approaches were previously too expensive to obtain with digital traces of student behaviour, which offer more scalable and finer-grained comprehension and support of learning processes. This study provides a fragmented learning solution for students in a data environment that can suggest subjects to them based on their geographical location, gender, and district of residence, among other factors. This suggested framework is expected to play a key role in directing the development of a society that values lifelong learning. [ABSTRACT FROM AUTHOR]

When using machine learning to predict a class with a continuous numeric value, there are several issues. Only a few machine-learning approaches are capable of doing so, but it remains one of the most difficult jobs to do. In this paper, we show how to use the M5 Model Tree, an approach that can handle continuous numeric data. This method is a stepwise procedure that employs linear functions at the leaf nodes of any created decision tree inducer (such as CART). These M5 model trees provide basic practical formulas such as standard deviation (SD), standard deviation reduction (SDR), cost-complexity pruning (CCP), and so on, which may be simply applied to different benchmark data by another user. This study examines the M5 Model Tree algorithm's capabilities for analysing rainfall data in the Kashmir portion of India's Union Territory of Jammu & Kashmir. One of the best suited models was the M5 model tree, which was built using (70-30) percent training and test ratios, respectively, and predicted an RMSE of 2.593, an MAE of 1.68, and a correlation coefficient (R2) of 0.478. Furthermore, M5 model trees produce models with a minimal number of trails, requiring less computing effort and making them more practical to use. [ABSTRACT FROM AUTHOR]

While analyzing the data, it is crucial to choose the model that best matches the circumstance. Many experts in the field of classification and regression have proposed ensemble strategies for tabular data, as well as various approaches to classification and regression problems. In this paper, Gini Index is applied on raw geographical dataset to convert continuous data into discrete dataset. Decision tree algorithm is implemented on resultant discrete dataset, Information Gain is calculated for every attribute and the attribute with highest information gain is the splitting node, applied recursively. Decision tree algorithm implemented predicts the rainfall in Kashmir province with the accuracy of 81.5%. MDL pruning is applied on the resultant decision tree in order to reduce the size & complexity of the Decision tree. Pruning removes segments of the tree that contribute little towards classification; the accuracy is marginally reduced to 81.1%. Furthermore, after the implementation of Decision tree a boosting algorithm: gradient boosting has been implemented on the same set of data using decision tree as a base estimator. It was observed that the overall accuracy of the decision tree got increased to 87.5% after the implementation of gradient boosting model. Thus, the obtained results predict that gradient boosted-DT outperforms all other approaches with the highest accuracy measure and high susceptibility rate in rainfall prediction. [ABSTRACT FROM AUTHOR]

Decision trees are one of the oldest and most used (successful) machine learning algorithms. Over the years various modifications of decision tree have been proposed and implemented. In the recent past decision trees has many upgraded versions, including Random forests (RF), Random trees (RT), Distributed Decision trees (DDT) and Model Trees (MT) which are readily used nowadays. In this paper effort has been made to determine whether decision trees in its core form are still relevant in the era or obsolete. [ABSTRACT FROM AUTHOR]

Objective: The objective of study was to compare oral health knowledge, attitude and practice scores between medical and dental students of Karachi, Pakistan. Methods: It was cross-sectional study conducted at private medical and dental college of Karachi, Pakistan from Jul 2018-Jul 2019. Hundred medical and 100 dental students of age 22-26 years of either gender, enrolled in 2nd year of their medical and dental school were recruited for the study by using non-probability convenience sampling technique. The adapted questionnaire was used which consist of 16 items to assess knowledge, attitude and practice regarding oral health. SPSS version 23 was used to analyse data. Results: The mean age of participants was reported as 22.77 years. Among all medical students, the high mean knowledge score was observed for brushing timings (0.90), for attitude, the high mean score for use of floss/interdental brush was reported (4.41). Among all the dental students, the high mean score was observed for knowledge of brushing timings, frequency of replacing toothbrush and amount of toothpaste on your brush (0.93). According to attitude, high mean score for use of mouthwash was reported (4.45) followed by method of tooth brushing (4.35). According to practice the high mean score was reported for treatment of gum problems as 4.51.The knowledge, attitude and practice score of dental students were significantly high as compared to medical students (p<0.05). Among males, knowledge and attitude of dental students were significantly high as compared to medical students (p<0.05), whereas no significant difference was found between practice score. Among females, knowledge, attitude and practice score of dental students were significantly high as compared to medical students (p<0.05). Conclusion: The study ascertained that knowledge, attitude and practice regarding oral health was better in dental students as compared to medical students. [ABSTRACT FROM AUTHOR]

Synthesis of pharmaceutically active heterocycles is always appealing as the majority of the widely used drugs contain heterocyclic moieties as their core structure. So, the straightforward construction of heterocycles from readily available/accessible reagents is one of the prime targets of the synthetic chemists. In this context, C−H functionalization has emerged as an effective tool for the designing and synthesis of various heterocyclic moieties as it offers a straight‐forward and step‐economic pathway. On the other hand, the readily available/accessible conjugated carbonyls are well‐known reagents for the construction of carbocycles and heterocycles over the years. However, the employment of C−H functionalization of the two C−H bonds of the conjugated alkene in carbocycle/heterocycle synthesis was not so explored. In the last decade, much focus has been paid on the synthesis of various pharmaceutically active heterocycles through C−H bond functionalization of α,β‐unsaturated aldehydes/ketones. These protocols have been developed through either oxidative coupling of conjugated carbonyls with suitable coupling partners or intramolecular C−H bond functionalization of conjugated carbonyls. In this review, we will discuss all the methodologies developed for the synthesis of heterocycles employing intermolecular C−H bond functionalization of conjugated carbonyls. The mechanistic pathways and usefulness of the methodologies will be also highlighted. [ABSTRACT FROM AUTHOR]

The current evolving global pandemic caused by coronavirus disease-2019 (COVID-19) has dramatically impacted global health care systems, resulting in governments taking unprecedented measures to contain the spread of the infection, with adaptations by health care organizations. Research into understanding the pathophysiology behind this virus, to ascertain best medical management and treatment, has been accelerated to keep up with the rapidly evolving situation. There has been redeployment of medical and nursing staff to the frontlines and redistribution of health care resources. In addition, the cancellation of elective surgery and centralization of services to treat high-risk surgical cases will all, undeniably, have an impact on current surgical training with possible future implications. We aim to explore the impact COVID-19 is having on cardiac surgical training in the UK and what future implications this may have. [ABSTRACT FROM AUTHOR]

Objective: To assess the frequency and factors associated with dental caries in pregnant women attending antenatal clinic of a public sector hospital of Karachi, Pakistan. Methods: A descriptive cross-sectional study was conducted at the Sindh Institute of Oral Health Sciences, Jinnah Sindh Medical University, Karachi from July 2019-March 2020. Pregnant women of age 18-45 years visiting antenatal clinic were included. The dental examination was carried out to assess the dental caries in accordance with the WHO diagnosis criteria. Frequency of dental caries along with factors like age, socioeconomic status, educational status, occupation, BMI, residence, family structure, gestational hypertension, and gestational diabetes were noted. SPSS version 23 was used to analyze data. The univariate logistic regression was applied to identify the significant predictors of dental caries at p<0.10. The factors which were significant in univariate logistic regression model were added in final single multivariate logistic regression model. P<0.05 was taken as statistically significant Results: Of 375 pregnant women, mean age was 30.32 ±4.37 years. Most of the women were from low socio-economic class 237 (63.2%), uneducated 231 (61.6%) and belonged from rural areas 196 (52.3%). Frequency of dental caries was found to be 108 (28.8%). In multivariate logistic regression model, (aOR=9.53, 95% CI=3.93-23.10), uneducated females rural area residents (aOR=19.73, 95% CI= 7.50-51.88), presence of gestational diabetes (aOR=8.86, 95% CI=3.78-20.73) and presence of gestational hypertension (aOR=4.33, 95% CI=2.01-8.92) remained statistically significant (p<0.05). Conclusion: A higher frequency of dental caries was observed in pregnant women attending antenatal clinics. The significant factors that remained associated with dental caries in final multivariate model were education, residence, gestational hypertension and gestational diabetes mellitus. [ABSTRACT FROM AUTHOR]

Background Retrograde perfusion into coronary sinus during coronary artery bypass graft (CABG) surgery reduces the need for cardioplegic interruptions and ensures the distribution of cardioplegia to stenosed vessel territories, therefore enhancing the delivery of cardioplegia to the subendocardium. Peri-operative myocardial injury (PMI), as measured by the rise of serum level of cardiac biomarkers, has been associated with short and long-term clinical outcomes. We conducted a retrospective analysis to investigate whether the combination of antegrade and retrograde techniques of cardioplegia delivery is associated with a reduced PMI than that observed with the traditional methods of myocardial preservation. Methods Fifty-four consecutive patients underwent CABG surgery using either antegrade cold blood cardioplegia (group 1, n = 28) or cross-clamp fibrillation (group 2, n = 16) or antegrade retrograde warm blood cardioplegia (group 3, n = 10). The study primary end-point was PMI, evaluated with total area under the curve (AUC) of high-sensitivity Troponin-T (hsTnT), measured pre-operatively and at 6, 12, 24, 48 and 72 hours post-surgery. Secondary endpoints were acute kidney injury (AKI) and inotrope scores, length of intensive care unit (ICU) and hospital stay, new onset atrial fibrillation (AF) and clinical outcomes at 6 weeks (death, non-fatal myocardial infarction, coronary artery revascularization, stroke). Results There was evidence that mean total AUC of hsTnT was different among the three groups (P = 0.050). In particular mean total AUC of hsTnT was significantly lower in group 3 compared to both group 1 (-16.55; 95% CI: -30.08, -3.01; P = 0.018) with slightly weaker evidence of a lower mean hsTnT in group 3 when compared to group 2 (-15.13; 95% CI -29.87, -0.39; P = 0.044). There was no evidence of a difference when comparing group 2 to group 1 (-1.42,; 95% CI: -12.95, 10.12, P = 0.806). Conclusions Our retrospective analysis suggests that, compared to traditional methods of myocardial preservation, antegrade retrograde cardioplegia may reduce PMI in patients undergoing first time CABG surgery. [ABSTRACT FROM AUTHOR]

BACKGROUND: Dentistry may play its role in transmission of infection through dental impressions. Instructing dentists about infection control may decrease the odds of infection transmission. OBJECTIVE: This study investigated to determine the knowledge of dentists regarding the disinfection of impression materials. METHODOLOGY: A Cross sectional descriptive study was conducted on 51 dentists from 09 dental colleges of Karachi. A self-administered questionnaire having 30 open and closed ended questions on knowledge related to disinfection of impression materials was used to collect data by convenient sampling technique. Variables included type of disinfectant they used, its concentration, duration of disinfection for particular impression materials, importance of disinfecting impressions prior to handling and they were observed about their actual practices. Statistical analysis was done using SPSS 16 version. The collected data was analyzed by descriptive statistics and presented in frequency tables. RESULTS: : There were 17 (33.3%) male and 34 (66.7%) female dentists. Seniority wise, there were 27 (52.9%) house officers, 3 (5.9%) lecturers, 11 (21.6%) demonstrators, 6 (11.9%) senior registrars and 4 (7.8%) assistant professors. Almost 55% participants had an experience of less than a year, 20% of 1-3 years, 12% of more than 3 years and 14% of working 5 years in the prosthodontic department. Qualification wise, 88.2% were BDS. 5.9% MSC and only 2% were postgraduates. (MDS, MCPS or FCPS). Forty one (41%) of the participants washed impression trays before taking dental impressions. Moreover, only 1/3rd of the practitioners disinfected impression material and more than 1/3rd never practiced impression scrubbing after taking impression. It was also interesting to find out that majority of the study group stored dental impressions in tissue papers and only few in disinfectant soaked paper towels It was observed that almost half of the total participants did not know about the methods and type of disinfectants used to disinfect dental impressions. Out of 51 dentists, 19 (38 %) did not have knowledge on the method used for the disinfection of alginate impression material while 32 (62%) had knowledge. When inquired about the type of disinfectant used 22 (43.1%) did not know while 29 (56.9%) knew it. CONCLUSIONS: Dentists practicing in the Prosthodontic department had insufficient knowledge on disinfection of impression materials. [ABSTRACT FROM AUTHOR]