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In the current information and technology era, business enterprises are focusing in performing the process effectively by reducing the waiting time in completing the work, reduce latency and deploy the resources effectively so as to service the patient, medical practitioners, societies, and other stakeholders in an efficient manner. Hence, several organisations are using the emerging technologies so as to obtain high performance and enhance competitive edge. The advancement in machine learning, deep learning, business analytics, etc. enables the health care industry to identify the patterns based on the data collected and create a pivotal position and enhance revenues and profits in a sustainable manner. Machine learning models are considered as computational algorithms which will enable in collected the data, analyze them, and provide the necessary reports to the experts and management in order to make informed decision making. The application of advanced machine learning enables the organisation to process the image effectively, recognize the voice and enable in servicing the customers, process the available data, and identify the patterns so as to make informed decision making. The basic purpose of the study is to analyze the overall implementation of advanced machine learning approaches towards health care services for providing enhanced services, better patient engagement, and support in creating better life for them, the researchers intend to collect the closed-ended questionnaire from employees in different medical centers covering: apprehend the nature of designing and implementation of machine learning approaches in the organisation and understand the effectiveness of these tools in enhancing the sustainable growth and development of the organisation., Competing Interests: The authors declare that they have no conflict of interest., (Copyright © 2022 M. Kiruthiga Devi et al.)

The paper “Design and Implementation of a Smart Home Energy Management System Using IoT and Machine Learning” proposes a system that aims to optimize energy consumption in a smart home environment. The system uses Internet of Things (IoT) devices to collect real-time data on energy usage and machine learning algorithms to predict future consumption patterns. This paper proposes the use of deep neural networks (DNNs) for the design and implementation of a smart home energy management system using IoT and machine learning techniques. The authors demonstrate the effectiveness of the system through experimental results, showing significant energy savings compared to traditional methods. The DNN is built using Keras or Tensor Flow and is trained on extracted features from energy consumption data collected using IoT sensors. The system is implemented with a real-time monitoring system and a user interface for remote access. The proposed system has the potential to save energy and reduce energy costs for households while providing real-time feedback to the user.

Data transmission through a wireless network has faced various signal problems in the past decades. The orthogonal frequency division multiplexing (OFDM) technique is widely accepted in multiple data transfer patterns at various frequency bands. A recent wireless communication network uses OFDM in longterm evolution (LTE) and 5G, among others. The main problem faced by 5G wireless OFDM is distortion of transmission signals in the network. This transmission loss is called peak-to-average power ratio (PAPR). This wireless signal distortion can be reduced using various techniques. This study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless communication. Partial transmit sequence (PTS) helps in the fast transfer of data in wireless LTE. PTS is merged with deep belief neural network (DBNet) for the efficient processing of signals in wireless 5G networks. Result indicates that the proposed system outperforms other existing techniques. Therefore, PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm optimization. Hence, the specified design supports in improving the proposed PAPR reduction architecture. [ABSTRACT FROM AUTHOR]

Food is required for human survival. Paddy is a vital food crop serving 60% of the Indian population. Food quality is determined by the plant yield. Unfavorable environmental circumstances, soil fertility, bacteria, viruses, nematodes, fertilizer use, and the absence of nutritional shortages substantially influence plant yield. As a result, it is critical to protect the plants from illness. Crop yield must be improved to meet food scarcity of growing population. Although disease symptoms are apparent in various parts of plant like leaves, stem, fruits and stem, the infections are commonly observed in the leaves. Understanding plant pathology plays a vital role in disease detection. Early detection of diseases is a prompt intervention that aids the farmers in controlling disease spread, resulting in increased agricultural quantity and quality. Image processing techniques with advanced computer vision technologies like machine learning and deep learning have proven the automation of plant disease diagnosis precisely. The main objectives of this research are to investigate computer vision technologies for the early identification of plant diseases and help novice researchers in the same domain learn about plant diseases and the methodologies for disease detection in paddy plant leaves. Consequently this manuscript reviews significant paddy plant infections, highlights related study of tools and techniques, current research, limitations and conclusions for future research in this field. [ABSTRACT FROM AUTHOR]

Compression strength refers to a structure ability to endure loads that cause it to shrink in size. The ultimate load in Newtones scaled by the gross cross section area of the block in square millimeters yields the bearing capacity of a concrete masonry unit. The machine learning technology could extend the interdisciplinary field for the estimation of concrete block compressive strength. With this review, this project aims to estimate the concrete block compressive strength retrieved from the KAGGLE machine-learning repository. The compressive concrete blocks dataset contains 9 features and 1050 concrete block and it is processed with encoding and missing values. The exploratory data analysis is done to analyze the correlation of all the features. The raw dataset is applied to linear regression, Linear Kernel SVR, Polynomial Kernel SVR, Radial Basis Function SVR, Sigmoid Kernel SVR, Decision Tree to analyze the performance of compressive strength estimation of concrete blocks. The proposed model uses Fully Connected Deep Neural Network (FCDNN) where the compressive concrete blocks dataset is applied to estimate the compressive strength estimation of concrete blocks. The performance metrics utilized for analysis are Mean Squared Error, R-Squared error. Experimental results shows that the Fully Connected Deep Neural Network shows Mean Squared Error of 29.5196 and R-Squared error of 0.885 when analyzed with the all regression models. [ABSTRACT FROM AUTHOR]

It is established that exercise will increase immunity against covid-19. This data can be statistically verified by employing a Support Vector Machine model and a K-Means method. The number of push-ups taken per day/week and the occurrence covid-19 infection can be modeled into a Support Vector Machine algorithm. Similarly the minutes of jogging per day and breathing difficulty occurrences can be modeled into a K-Means algorithm. The training data set will give a predictive model to assess the importance of exercise against covid-19. [ABSTRACT FROM AUTHOR]

With the advent of computing and communication technologies, it has become possible for a learner to expand his or her knowledge irrespective of the place and time. Web-based learning promotes active and independent learning. Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process. This digital learning improves the quality of teaching and also promotes educational equity. However, the challenges in e-learning platforms include dissimilarities in learner's ability and needs, lack of student motivation towards learning activities and provision for adaptive learning environment. The quality of learning can be enhanced by analyzing the online learner's behavioral characteristics and their application of intelligent instructional strategy. It is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning course. It is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user's interest level. In this research work, a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner's objective attainment. Catering to the demands of e-learner, an intelligent model is presented in this study for e-learning system that apply supervised machine learning algorithm. An adaptive e-learning system suits every category of learner, improves the learner's performance and paves way for offering personalized learning experiences. [ABSTRACT FROM AUTHOR]

With the current improvement of development towards pharmaceutical, distinctive ultrasound methodologies are open to find the fetal prosperity. It is analyzed with diverse clinical parameters with 2-D imaging and other test. In any case, prosperity desire of fetal heart still remains an open issue due to unconstrained works out of the hatchling, the minor heart appraise and inadequate of data in fetal echocardiography. The machine learning strategies can find out the classes of fetal heart rate which can be utilized for earlier evaluating. With this background, we have utilized Cardiotocographic Fetal heart rate dataset removed from UCI Machine Learning Store for predicting the fetal heart rate health classes. The Prediction of fetal health rate are achieved in six ways. Firstly, the data set is preprocessed with Feature Scaling and missing values. Secondly, exploratory data investigation is done and the dispersion of target feature is visualized. Thirdly, the raw data set is fitted to all the classifiers and the performance is analysed before and after feature scaling. Fourth, the raw data set is subjected to undersampling methods like ClusterCentroids, RepeatedENN, AllKNN, CondensedNearestNeighbour, EditedNearestNeighbours, InstanceHardnessThreshold and NearMiss. Fifth, the undersampled dataset by above mentioned methods are fitted to all the classifiers and the performance is analyzed before and after feature scaling. Sixth, performance analysis is done using metrics like Precision, Recall, F-score, Accuracy and running time. The execution is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that the Decision Tree classifier tends to retain 98% before and after feature scaling for the undersampling with EditedNearestNeighbours, RepeatedENN and AllKNN methods. [ABSTRACT FROM AUTHOR]

Concurring to information from the Centres for Illness Control and Anticipation, instruction and religion are both capable indicators of lasting or dissolving unions. The chance of a marriage finishing in separate was lower for individuals with more knowledge, with over half of relational unions of those who did not complete high school having finished in separate compared with roughly 30 percent of relational unions of college graduates. With this overview, the divorce rate dataset from UCI dataset repository is used for predicting the divorce class target with the following contributions. Firstly, the Divorce rate dataset is subjected with the data cleaning and exploratory data analysis. Secondly, the data set is settled with different classifiers to look at the classification before and after feature scaling. Thirdly, the dataset is processed with various cross validation of training and testing dataset i.e 80:20, 30:70, 40:60, 50:50 to improve the accuracy of all the classifiers. Fourth, the dataset is processed with 15, 20 and 30 components of principal component analysis and then applied with all classifier algorithm to analyze the accuracy of divorce rate prediction. Fifth, the performance analysis is done with precision, recall, accuracy, fscore and running time to infer the classification before and after feature scaling. Experimental results show that the Random Forest classifier is found to have the accuracy of 98% for all PCA reduced dataset with 15, 20 and 30 components. The result shows that Random Forest classifier is found to have the accuracy of 98% for 40:60, 50:50 of training and testing dataset. [ABSTRACT FROM AUTHOR]

Most forms of human papillomavirus can create alterations on a woman's cervix that can lead to cervical cancer in the long run, while others can produce genital or epidermal tumors. Cervical cancer is a leading cause of morbidity and mortality among women in low- and middle-income countries. The prediction of cervical cancer still remains an open challenge as there are several risk factors affecting the cervix of the women. By considering the above, the cervical cancer risk factor dataset from KAGGLE data warehouse is executed for predicting the cervical cancer risk classes. The cervical cancer data set is normalised with incomplete data and Pattern Calibration. Secondly, the interpretive data analysis is carried out, and the target feature's dispersion of the cervical cancer risk is visualised. Thirdly, several classifiers are fitted to the unprocessed data set, and the performance is measured with pre and post feature scaling. Fourth, oversampling methodologies are applied to the pre - processed data set. Fifth, the oversampled dataset by different methods are applied to all the classifiers and the performance is compared with pre and post feature scaling. Sixth, Precision, recall, F-score, accuracy, and running time are some of the metrics used in performance analysis. The code is written in Python and executed with Anaconda Navigator on the Spyder framework. The findings of the experiments reveal that the Random forest classifier tends to sustain 96% accuracy pre and post scaling for unprocessed dataset. Similarly the same classifier tends to sustain 98% accuracy for all the oversampling techniques. [ABSTRACT FROM AUTHOR]

A lot of nursing home residents suffer health issues such as pressure ulcers, night walking and incontinence. Previous research shows that these issues have a clear link with motion patterns of patients during bedtime. This paper examines if unobtrusive activity tracking can be done in the bed via accelerometers on the mattress. We show that, with enough training on accelerometer data from variable conditions, a high accuracy can be achieved. [ABSTRACT FROM AUTHOR]

Agriculture sector is recognized as the backbone of the Indian economy that plays a crucial role in the growth of the nation's economy. It imparts on weather and other environmental aspects. Some of the factors on which agriculture is reliant are Soil, climate, flooding, fertilizers, temperature, precipitation, crops, insecticides, and herb. The soil fertility is dependent on these factors and hence difficult to predict. However, the Agriculture sector in India is facing the severe problem of increasing crop productivity. Farmers lack the essential knowledge of nutrient content of the soil, selection of crop best suited for the soil and they also lack efficient methods for predicting crop well in advance so that appropriate methods have been used to improve crop productivity. This paper presents different Supervised Machine Learning Algorithms such as Decision tree, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) to predict the fertility of soil based on macro-nutrients and micro-nutrients status found in the dataset. Supervised Machine Learning algorithms are applied on the training dataset and are tested with the test dataset, and the implementation of these algorithms is done using R Tool. The performance analysis of these algorithms is done using different evaluation metrics like mean absolute error, cross-validation, and accuracy. Result analysis shows that the Decision tree is produced the best accuracy of 99% with a very less mean square error (MSE) rate. [ABSTRACT FROM AUTHOR]

With the current growth of technology towards medicine, various ultrasound methods are available to find the fetal health. It is accessed with various clinical parameters with 2-D imaging and other test. However, health prediction of fetal heart still remains an open issue due to spontaneous activities of the fetus, the minor heart size and deficiency of knowledge in fetal echocardiography. The machine learning techniques can find out the classes of fetal heart rate which can be used for earlier forecasting. With this overview, we have used Cardiotocographic Fetal heart rate dataset extracted from UCI Machine Learning Repository for predicting the fetal heart rate health classes. The classification of fetal heart rate are achieved in four ways. Firstly, the data set is preprocessed with Feature Scaling and Missing Values. Secondly, raw data set is fitted to all the classifiers and the performance is analyzed. Thirdly, the raw data set is subjected to dimensionality reduction using PCA with 10 components and then fitted to all the classifiers and the performance is analyzed. Fourth, data set is subjected to variants of PCA like Kernel PCA, Incremental PCA, Sparse PCA and Minibatch Sparse PCA with 10 components and then fitted to all the classifiers and the performance is analyzed. Fifth, performance analysis is done using metrics like Precision, Recall, Accuracy and F-score before and after feature scaling. The implementation is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that Kernel SVM classifier is found to retain the accuracy of 98.5% in PCA, Kernel PCA, Incremental PCA, Sparse PCA and Minibatch Sparse PCA after feature scaling. [ABSTRACT FROM AUTHOR]

Web page classification plays a major role in information management and retrieval task. Feature selection is an important process for accurate classification of Web pages. Web pages contain several features, and more number of features reduce the classification accuracy. We propose a hybrid feature selection approach which is both efficient and effective for automatic Web page classification problem and also helps the Web search tool to get relevant results in the relevant category. Experiments were conducted by us with various feature selection methods for Web page classification and keyword search problem. From these experiments, it was found that some features present in the initial feature set (IFS) are irrelevant, redundant, and noisy, and they consume more memory space, increase computational time, and give a poor predictive performance. These features can be eliminated using evaluator methods such as principal component analysis, consistency subset evaluator, and search methods such as genetic search and rank search, resulting in minimal and more relevant features. We call these features as intermediate feature set (IMFS), and further optimization in this feature set gives more accurate results. Finally, attribute-selected classifier which is a part of machine learning meta-classifier was applied to the IMFS to get final feature set (FFS), and it was found that accuracy has increased up to 97% and computational time for all classifiers is minimized compared to IFS using WebKb (Faculty and Course) and ODP (Sports) benchmarking datasets. The proposed method yields better classification performance and reduces space requirements and search time in the Web documents compared with the existing methods. [ABSTRACT FROM AUTHOR]