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Aims: The objective of this research is to develop an effective cardiovascular disease prediction framework using machine learning techniques and to achieve high accuracy for the prediction of cardiovascular disease. Methods: In this paper, we have utilized machine learning algorithms to predict cardiovascular disease on the basis of symptoms such as chest pain, age and blood pressure. This study incorporated five distinct datasets: Heart UCI, Stroke, Heart Statlog, Framingham and Coronary Heart dataset obtained from online sources. For the implementation of the framework, RapidMiner tool was used. The three‐step approach includes pre‐processing of the dataset, applying feature selection method on pre‐processed dataset and then applying classification methods for prediction of results. We addressed missing values by replacing them with mean, and class imbalance was handled using sample bootstrapping. Various machine learning classifiers were applied out of which random forest with AdaBoost dataset using 10‐fold cross‐validation provided the high accuracy. Results: The proposed model provides the highest accuracy of 99.48% on Heart Statlog, 93.90% on Heart UCI, 96.25% on Stroke dataset, 86% on Framingham dataset and 78.36% on Coronary heart disease dataset, respectively. Conclusions: In conclusion, the results of the study have shown remarkable potential of the proposed framework. By handling imbalance and missing values, a significantly accurate framework has been established that could effectively contribute to the prediction of cardiovascular disease at early stages. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of Things (IoT) has become one of the most familiar names creating a benchmark and scaling new heights. IoT an indeed future of the communication that has transformed the objects (things) of the real world into smarter devices. With the advent of IoT technology, this decade is witnessing a transformation from traditional agriculture approaches to the most advanced ones. Limited research has been carried out in this direction. Thus, herein we present various technological aspects involved in IoT-based cultivation. The role and the key components of smart farming using IoT were examined, with a focus on network technologies, including layers, protocols, topologies, network architecture, etc. We also delve into the integration of relevant technologies such as cloud computing, big data analytics, and the integration of IoT-based cultivation. We explored various security issues in modern IoT cultivation and also emphasized the importance of safeguarding sensitive agricultural data. Additionally, a comprehensive list of applications based on sensors and mobile devices is provided, offering refined solutions for greenhouse management. The principles and regulations established by different countries for IoT-based cultivation systems are presented, demonstrating the global recognition of these technologies. Furthermore, a selection of successful use cases and real-world scenarios and applications were presented. Finally, the open research challenges and solutions in modern IoT-based cultivation were discussed. [ABSTRACT FROM AUTHOR]

The primary objective of the study was to explore the leadership style that best enhances workplace performance among employees, and concurrently, assess the influence of leadership styles such as Transformational and Transactional leadership on employee performance. Effectively managing an organization necessitates adept leadership, with various leadership styles associated with distinct outcomes. This investigation delved into the presence of leadership styles, their correlation with employee performance, and the effects of transformational and transactional leadership on concepts like organizational commitment, organizational behavior, and job satisfaction. The City Schools of Peshawar served as the case study. Initially, a questionnaire was distributed to teaching staff to gauge the impact of independent variables on the dependent variable. In theoretical framework, we placed transformational leadership and transactional leadership as repressors and a employee performance as regressends. The sample size comprised 140 respondents, with 104 questionnaires selected using the "Yamani" formula. Primary data was collected through a structured 5-point Likert scale questionnaire. Analysis, conducted using SPSS, involved correlation matrix, multicollinearity (VIF) analysis, heteroscedasticity analysis, and regression. The findings affirmed a positive impact of the tested leadership styles on employee performance. The research concluded by recommending that managers emphasize the use of transformational leadership to cultivate higher levels of organizational commitment, organizational behavior, and job satisfaction. [ABSTRACT FROM AUTHOR]

The global population is increasing rapidly as compared to food production; approximately three times more food would be required in 2050. Climate change affects crop production by causing sudden changes in weather conditions, including rain, storms, heat waves, doughiness, and water shortages. Farming with smart technology provides a productive solution. Smart farming is a productive solution that provides a great resource of income and improves the countries' economy by exporting consumable goods and preventing food security problems. Smart agriculture provides a combination of flexibility, remote access, and automation through the use of intelligent control technologies. Many countries are working towards smart and intelligent agriculture farming that analyzes crop, soil fertility, pests and weeds, and other problems caused by mismanagement and incompetence. However, smart agricultural farming is less widely adopted in agriculture as a result of high costs and little understanding of technology. In this study, An artificial climate control chamber (ACCC) was designed for cultivating plants by controlling the optimal parameters, especially the light spectrum. In ACCC, influential plant factors such as light, moisture, humidity, and fertilizer concentration have been controlled intelligently. Light spectrum was controlled by time periods in the previous system, while in the system proposed in this study, the light was controlled by image processing. In an artificial control chamber, the plant growth stages have been determined through image processing techniques. Datasets of image images have been used to organize specific intensities of the light spectrum. This intelligent system provides aid in the speed breeding procedure through variant spectrums of light and fertilizers combinations. In the research study, the yield and quality of intelligent farming are enhanced. [ABSTRACT FROM AUTHOR]

OBJECTIVE: The purpose of this study was to investigate the prevalence and configuration of Middle Mesial Canals (MMC) and isthmi in mandibular molars in Pakistani population using Cone Beam Computed Tomography (CBCT). METHODOLOGY: Sixty CBCTs of patients were selected randomly from database of University College of Dentistry, Lahore. First and second permanent mandibular molars without evidence of previous endodontic treatment, full coverage restoration or root resorption were evaluated for the number of roots, canals and the presence and configuration of MMC and isthmi. The age and gender of patients were also recorded. SPSS was used for statistical analysis using chi-square test. Significance level was set at 5% (P < 0.05). RESULTS: Out of 189 mandibular molars, MMCs were found in 9 and isthmi in 62 teeth. No statistical association was found between the presence of MMC and age, gender, side or tooth type. Significantly more isthmi were found in second molars, females and younger patients, P < 0.05. CONCLUSION: MMC and isthmi in mesial roots of mandibular molars are not unusual findings in our population and CBCT can be a valuable tool for their detection. [ABSTRACT FROM AUTHOR]

Gauss-type nested implicit Runge--Kutta methods exhibit many vital properties of implicit Runge--Kutta methods, such as stability, highorder accuracy, and symmetry. Moreover, nested implicit Runge--Kutta methods have a cheap practical implementation in comparison of implicit Runge--Kutta methods because of their explicit nature of internal stages. In this paper, we provide a complete mechanism for the construction, formulation, and implementation of 4th- and 6th-order nested implicit Runge--Kutta methods. The numerical testing is performed using 6th-order nested implicit Runge--Kutta method that uses an embedded 4th-order nested implicit Runge--Kutta method for the local error estimation on a collection of test problems from stiff detest. We also compare the nested implicit Runge--Kutta method with the MATLAB integrator ode 15s and assess the effectiveness of four variations on the local error estimation. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the accuracy and efficiency of Gauss-type nested implicit Runge-Kutta (NIRK) methods. These methods possess many important practical properties of implicit Runge-Kutta (IRK) methods, such as, good stability, high order, and symmetry. Moreover, as with IRK methods, NIRK methods can also be presented in embedded form that helps local error estimation. However, the implementation of NIRK methods is practically cheaper than other classes of IRK methods as the NIRK formulas have explicit internal stages. We test an order six NIRK method that uses an embedded order four method for estimating the local error. Our test problems are those from stiff DETEST. As part of the testing, we compare the method with the Matlab integrator ODE15s and assess the effectiveness of four variations on the local error estimation. [ABSTRACT FROM AUTHOR]