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Disease diagnosis is a challenging task due to a large number of associated factors. Uncertainty in the diagnosis process arises from inaccuracy in patient attributes, missing data, and limitation in the medical expert’s ability to define cause and effect relationships when there are multiple interrelated variables. This paper aims to demonstrate an integrated view of deploying smart disease diagnosis using the Internet of Things (IoT) empowered by the fuzzy inference system (FIS) to diagnose various diseases. The Fuzzy System is one of the best systems to diagnose medical conditions because every disease diagnosis involves many uncertainties, and fuzzy logic is the best way to handle uncertainties. Our proposed system differentiates new cases provided symptoms of the disease. Generally, it becomes a time-sensitive task to discriminate symptomatic diseases. The proposed system can track symptoms firmly to diagnose diseases through IoT and FIS smartly and efficiently. Different coefficients have been employed to predict and compute the identified disease’s severity for each sign of disease. This study aims to differentiate and diagnose COVID-19, Typhoid, Malaria, and Pneumonia. This study used the FIS method to figure out the disease over the use of given data related to correlating with input symptoms. MATLAB tool is utilised for the implementation of FIS. Fuzzy procedure on the aforementioned given data presents that affectionate disease can derive from the symptoms. The results of our proposed method proved that FIS could be utilised for the diagnosis of other diseases. This study may assist doctors, patients, medical practitioners, and other healthcare professionals in early diagnosis and better treat diseases. © 2022 Tech Science Press. All rights reserved.

COVID-19 is a pandemic that has affected nearly every country in the world At present, sustainable development in the area of public health is considered vital to securing a promising and prosperous future for humans However, widespread diseases, such as COVID-19, create numerous challenges to this goal, and some of those challenges are not yet defined In this study, a Shallow Single-Layer Perceptron Neural Network (SSLPNN) and Gaussian Process Regression (GPR) model were used for the classification and prediction of confirmed COVID-19 cases in five geographically distributed regions of Asia with diverse settings and environmental conditions: namely, China, South Korea, Japan, Saudi Arabia, and Pakistan Significant environmental and non-environmental features were taken as the input dataset, and confirmed COVID-19 cases were taken as the output dataset A correlation analysis was done to identify patterns in the cases related to fluctuations in the associated variables The results of this study established that the population and air quality index of a region had a statistically significant influence on the cases However, age and the human development index had a negative influence on the cases The proposed SSLPNN-based classification model performed well when predicting the classes of confirmed cases During training, the binary classification model was highly accurate, with a Root Mean Square Error (RMSE) of 0 91 Likewise, the results of the regression analysis using the GPR technique with Matern 5/2 were highly accurate (RMSE = 0 95239) when predicting the number of confirmed COVID-19 cases in an area However, dynamic management has occupied a core place in studies on the sustainable development of public health but dynamic management depends on proactive strategies based on statistically verified approaches, like Artificial Intelligence (AI) In this study, an SSLPNN model has been trained to fit public health associated data into an appropriate class, allowing GPR to predict the number of confirmed COVID-19 cases in an area based on the given values of selected parameters Therefore, this tool can help authorities in different ecological settings effectively manage COVID-19

Cloud robotics is a field of robotics that endeavors to summon cloud advances, for instance, cloud computing, and other internet advances focused on the advantages of merged framework and shared administrations for robotics. In this research, an intelligent task planning mechanism is proposed to attain a certain level of attention for knowledge sharing among cloud-based robots using mamdani fuzzy inference system (MFIS). The proposed task planning mechanism using mamdani fuzzy inference system (TPM-MFIS), can classify the different stages of attention such as very high, high, medium, low, very low and none. In this recommended intelligent system, cloud robotics services are used to maintain the workload of each robot. Whereas, all fuzzification and decision-making policies handled on cloud to avoid the redundancy task planning consumption on individual level. This system has two input variables that are facial expression and motion rate. Input variables detect the output level of attention to be very high, high, medium, low, very low, and none. This paper presents an analysis of the result’s accuracy using proposed TPM-MFIS to model the task planning process.

Purpose:It is generally believed that sports can play an important role in developing life skills and positive youth development. The purpose of this study was to determine the views of Gomal University students’ athletes on the role that sports played in developing the important life skill of decision making. The time management, planning, dealing with adversity and adapting new situation were taken as decision-making skills. Materials:A sample of n=375 (male=334, female=41 completed questionnaires. The researchers collected the required information with the help of self-administered structured questionnaire encompassing the selected variables of the study. The responses were tabulated and analyzed with the help of computer software. Results:The results showed participants attributed a significant role for sports in developing decision-making skills such as time management, planning, dealing with adversity and adapting new situation (603** at 0.01) and significant correlation between sports participation and the development of various decision-making skills among its participants (603** at 0.01). The results indicated that demographic characteristic such as gender, age, and level of sports participation of the athletes brings variations in the mean score of research variables (0.003, 0.004 & 0.001 < 0.05). conclusions:Results of the study indicated that sports provide an excellent opportunity for students to learn skills that can help them in taking positive as well as effective decision. The present exploration affirmed that students can acquire a) time management skill, b) proper planning, c) dealing with adversity, and d) the ability to adjust in a new situation through active participation in sports activities. However, some demographic characteristics of the sports participants such as gender, sports experience, and format of sports have an influence upon the development of these skills through sports.

The research is to pronounce the socialization with humans after identification of relationships between cognitive agents recognized with the perspective of focus, selective attention, intention and decision making. Machine learning is used to understand environment complexity, dynamic collaboration, noise, features, domain and range on different parameters. Range of view is an interesting approach for relationship identification with respect to time, distance and face direction in a settled boundary that are trying to answer socialized behavior between those multiple agents. In resultant, the system agent finds friend, best friend and stranger relationships between other agents by using top down approach. The application can play a wonderful role for security purposes, gaming, labs, and intelligence agencies etc.

Malignant mesothelioma (MM) is a rare cancer type arising from mesothelial cells. The current clinical diagnosis is based on contrast-enhanced computed tomography, magnetic resonance imaging, and positron emission tomography that are either invasive or costly. The failure to diagnose malignantly can lead to an increased risk of multiple medical conditions, including cardiovascular diseases, emotional distress, anemia, and diabetes. To date, there is a limited number of prognostic factors that can be used for diagnosis. Most existing work has considered the MM disease as a classification task. In contrast, our study has initiated a knowledge extraction problem and proposed a machine learning-based framework. The performance status, age, and sex of patients are currently the most substantial clinical prognostic factors, but other histopathological and clinical prognostic factors are still unclear. This study aims to search for clinical prognostic, radiological, and histopathological factors in MM. In this study, the latest dataset from a public repository (UCI) has been utilised, including patients' medical, socio-economic, histopathological, and clinical factors. Association rule mining-based algorithms (Apriori and frequent pattern (FP) growth method) and feature selection techniques have been employed to extract significant features. The performance of the proposed framework has been evaluated based on support, confidence, and lift. We set the support, confidence, and lift between 0.5–1.0, 0.5–1.0, and 1.0–1.6 respectively. Our results showed five significant prognosis factors with the values for the identification of MM: Pleural lactate dehydrogenase >500 IU/L, C-reactive protein >10/μL, pleural albumin

In this paper, we present and analyze the performance of a parallel interference cancellation (PIC) scheme for multicarrier (MC) direct-sequence code-division multiple-access (DS-CDMA) systems. In order to mitigate the multi-path interference (MPI) in the DS CDMA system. At each cancellation stage in the proposed PIC scheme, on each subcarrier, a weighted sum of the soft outputs of the other users in the current stage is cancelled from the soft output of the desired user to form the input to the next stage. At the last stage, the interference cancelled outputs from all the subcarriers are maximal ratio combined (MRC) to form the decision statistic. Parallel interference elimination is first proposed in this paper the multi-path interference are evaluated by tentative decision and known user information. Then the performance over Rayleigh fading channel are analyzed and compared to Matched filter, Decorelator, successive interference cancellation (SIC) and conventional parallel interference cancellation (PIC). It is shown that PIC performance can be improved greatly by using this method with simple structure and easy implementation.

kids with Autism Spectrum problems (ASD) have conversation deficits and problems with social interaction. A lack of social conduct can bog down healing interventions and may lessen the potential to examine social talents. Robots had been shown to initiate proactive social conduct in youngsters with ASD. improvement of robot systems able to appearing as catalysts for social conduct in the context of ASD remedy is the need of contemporary era. A methodology and theoretical framework is being supplied here in this paper for a robotic system that can not only compare the effects and stage of ASD, however, can assist the victim consequently.