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The major objective of the researchers doing this study was to determine the effects of the Novel Coronavirus pandemic on the sleeping patterns of persons residing in both rural and urban areas. It was found that around 454 answers were genuine. The dietary influence on urban and rural people of Kanchipuram and Chennai was examined in this research using SPSS software and the chi-square test. To determine the sample size, an 80% G-power, a 0.05 α-value, and a 95% confidence interval were used. A significant portion of the participants (78.36% of the total, p<0.001) reported that stress and their past sleeping habits either disrupted or worsened their sleep patterns. A decrease in nighttime sleep duration and an increase in daytime naps were noticeable changes between the pre- and post-lockdown periods. Health care practitioners were untouched by these impacts, even though they were obvious across all sectors. Even so, they affected the majority of persons in the labour force. The night's sleep was dull for everyone. There was a correlation between the duration of sleep and symptoms of depression. There is a new COVID-19 lockdown that is associated with people's sleeping patterns, including their quantity and time of sleep every night. Despite these changes being associated with higher incidence of emotional symptoms, innovative cross-sectional data analysis makes it difficult to determine whether sleep deprivation causes psychological discomfort or if the pain causes sleep deprivation. [ABSTRACT FROM AUTHOR]

The study aims to evaluate the performance of the Music Genre Prediction System using a Novel Support Vector Machine and Decision Tree. The MARSYAS website provided the GTZAN dataset utilised in this investigation, which is comprised of one thousand.au-formatted music files and is intended for use in Music Information Retrieval. It is currently considered the gold standard dataset. Mel-frequency cepstralcoefficients (MFCC) are retrieved from music files and used to make predictions about the genre. All steps, from analysing data to training a model to testing it, take place within Jupyter. SPSS is used to analyse the mean accuracies of two algorithms side-by-side. Accuracy, precision, etc., numbers came out differently depending on the size of the input data set. SVM and Decision Tree are used for classification, with N equal to 20 for each of the two groups (proposed and comparative). The value of the pre-test achieved is 0.08. This research shows that compared to the Decision Tree method, Novel SVM is more effective. In the Research study, SVM with 81.10% accuracy is found more effective, efficient, and faster when compared to the Decision Tree with 59.88% accuracy. [ABSTRACT FROM AUTHOR]

The primary goal of this study is to compare the performance of the Novel Random Forest (RF) algorithm, Decision Tree (DT), and Logistic Regression in forecasting loan default (LR). The 346-record loan dataset that Novel Random Forest is associated with. It has been suggested and assessed how well the revolutionary methods of Random Forest, Decision Tree, and Logistic Regression can forecast loan defaults in the banking and finance industry. There were a total of 17 participants in each study group. The classifier's efficacy in terms of accuracy and precision is measured and documented. On this dataset, the Logistic Regression model predicts loan default with an accuracy of 81%, while the Decision Tree model achieves 93% and the Random Forest model achieves 95%. (p 0.031) is statistically significant. That's why it's clear that Novel Random Forest outperforms both Decision Tree and Logistic Regression. When compared to Decision Tree and Logistic Regression, Novel Random forest has superior accuracy and precision. [ABSTRACT FROM AUTHOR]

This research employs a novel approach to headcounting by comparing the performance of the ANN algorithm with that of the SVM algorithm. To determine how many individuals are moving under an overhead monitoring system, researchers have developed a revolutionary paper counting approach based on head recognition and tracking. Substances and Techniques: Twenty samples in all were taken from an electronic resource. We maintained a significance level of 0.05, a confidence interval of 95% (95% CI), an enrollment ratio of 1, and a significance level of 80% (G power). The precision value is calculated using the SPSS software. Result: The results of two separate t-tests show that the ANN algorithm outperformed the SVM method, although the difference is not statistically significant (p=0.721; p>0.05). In summary, it seems that the ANN algorithm outperforms the SVM method on the analysed datasets. [ABSTRACT FROM AUTHOR]

The main aim is to assist the visually impaired to dole out money from the ATM in autonomous space using innovative ratified biometry. Materials and methods: The Raspberry Pi is a microcontroller that acts to convert analog input signals to digital output signals. A Biometric system with IVRS at an ATM promotes the transaction to succeed.SPSS was used for determining the significance between ratified and smart ATMs. The parameters were maintained as threshold 0.05, enrollment ratio 1, confidence level 95%, and G-power 80%. Results: There exists a statistically insignificant difference (p=0.935, p>0.05) between ratified and smart ATMs. Ratified ATMs were found to perform better than smart ATMs and helped the visually impaired to withdraw money safely. Conclusion: This method not only aids people with visual impairment but also illiterates, dyslexia, and other disabilities. [ABSTRACT FROM AUTHOR]

The primary objective of this research is to categorise photos using the INCEPTION algorithm and compare its performance to that of the VGG19 method. G power is calculated by setting the power and error rate to 0.80 and 0.05, respectively, yielding a sample size of 60000. CIFAR-10 dataset samples to the tune of 60k. For efficient picture classification, the VGG19 and INCEPTION algorithms are used to both the training data (n=45,000; 80% of the total) and the test samples (n=15,000; 20% of the total). When compared to the 88.32% accuracy produced by the VGG19 algorithm, which was developed before the INCEPTION method, the INCEPTION algorithm performed much better. When compared to the VGG19 algorithm, the INCEPTION achieved a higher level of accuracy at a significance level of p=0.004 (p0.05). The INCEPTION classifier has been shown to outperform the VGG19 classifier by a wide margin. [ABSTRACT FROM AUTHOR]

The purpose of this research is to compare the results of utilising the manual approach vs STAAD Pro to assess and design a roof for a circular water tank that is lying on the ground. Two groups were compared using a t-test based on an independent sample. Both groups, 1 (the control) and 2 (the experimental) each have 9 samples total (STAAD Pro). Using G power of 50 and alpha of 0.05, we were able to acquire an accurate sample size of 18. Manual and STAAD Pro were used for the analysis and design. Shear stress, bending moment (BM), and deflection were all tested using an independent t-test, and the results indicated no statistically significant differences between the groups (p-values of 0.25, 0.88, and 0.20, respectively; p>0.05). Since p>0.05, it may be inferred that there are no statistically significant differences between the manual and STAAD pro approaches to design. With less work, STAAD Pro produces outcomes that are on par with those of a solution developed in accordance with the requirements of the IS code. Thus, STAAD Pro is an effective tool for modelling, analysing, and designing lightweight buildings. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop a novel method for controlling the heart rates of people with atrial fibrillation who are being treated with pacemaker devices. To achieve the system's goals, the P and PID controller methods are used. In all, there are forty personnel spread over two sets of controllers: proportional (P) and proportional (PID) controllers. The current parameters for the sample size are as follows: 80% significance, 80% Gpower, 80% confidence, 95% confidence, and a 1% enrollment ratio. Overshoot, rise/fall/settling times, and peak/settling times are some of the metrics used to assess the performance of P and PID controllers. Rise Time, Peak Time, Settling Time, and Overshoot averaged 0.0633, 0.0633, 6.1700, and 48.2290 for the proportional controller (P). Rise Time (0.0586), Peak Time (0.0586), Settling Time (7.1667), and Overshoot (51.0441) were often seen in PID controllers. Independent sample T-test results suggested that the Proportional Integral Derivative (PID) Controller was slightly more effective than the Proportional (P) Controller, but after examining all of the relevant factors, we were unable to identify a statistically significant difference between the two groups. [ABSTRACT FROM AUTHOR]

The main objective of this research is to compare the sodium (Na) concentration present in human seminal plasma of normospermia and asthenospermia (sperms with motility issues) semen samples for the clinical diagnosis. Semen samples were collected from Normospermia (N=22) and Asthenospermia (N=22) from the Andrology lab, Milan fertility centre, Banglore. Sample size for this research has been calculated by using previous literature in clinicalc.com by keeping G power at 80%, threshold as 0.05. By using Atomic Absorption Spectroscopy, sodium concentration has been evaluated. Semen analysis was done according to the world health organisation protocol. Results were evaluated by using SPSS software. Independent sample T-test is done for sodium concentration of Normospermia and Asthenospermia which were obtained as (P=0.389) insignificant. It is found that sodium concentration is higher in Normospermia (5.4 mg/ml) than in Asthenospermia (2.3 mg/ml) samples.sodium is important for the reabsorption of luminal fluid and increases the concentration of sperm, and its absence or less concentration may lead to infertility issues. In Normospermia sodium concentration is high and in Asthenospermia sodium concentration is less which leads to motility issues. [ABSTRACT FROM AUTHOR]

The core of this work presents an assembly method of EEG signals for the early detection of brain tumors using a novel support vector machine (SVM) algorithm compared with a decision tree algorithm Materials and methods: Collected from UCI repository 1472 samples contain 19 attributes. Early detection of brain tumors is performed using two groups, where group 1 is a SVM and group 2 is a decision tree. For the analysis of groups 1 and 2, 40 samples were used. Results: The accuracy of the SVM algorithm was 93%, while the accuracy of the decision tree was 82%. A significant value of 0.037 (P<0.05) was obtained and determined by independent samples test. Conclusion: The support vector machine algorithm has significantly higher accuracy than the decision tree algorithm. [ABSTRACT FROM AUTHOR]

The primary goal of this research is to improve upon the standard practise of fabricating E-shaped microstrip patch antennas by stacking them. A ten-person sample uses an E-shaped microstrip patch antenna constructed using the stacking approach and compares it to a ten-person sample using an E-shaped microstrip patch antenna designed with the traditional method. The suggested antenna's properties, such Bandwidth and Gain, are derived using Altair Feko modelling programme. The results for the E-shaped microstrip patch antenna constructed using the stacking approach are Gain=9 dB and Bandwidth=284.2 MHz, whereas the results for the E-shaped microstrip patch antenna designed using the traditional method are Bandwidth=250.4 MHz. According to the data, the level of significance for directivity is 0.029. By adopting a stacking approach rather than a traditional one, the bandwidth seems to be increased by 13 percent, from 250.4MHz to 284.2 MHz. [ABSTRACT FROM AUTHOR]

When the Viola-Jones machine learning algorithm is used to forecast a driver's sleepiness behaviour, it may help save people from nodding off behind the wheel and causing accidents that can leave them seriously injured or dead. To determine the predictive accuracy of the Viola-Jones machine learning system for Blink frequency, the sample size was varied from 100 times. The sensitivity of Viola-Jones is much higher. Viols Jones has a higher blink frequency percentage than HOG (86.14 percent) (75.84 percent). The correlation between Voila-Jones and HOGt was significant (p=0.000). The Viola-Jones model improves the sensitivity % for forecasting driver fatigue. If the algorithm determines that the driver is, in fact, sleepy, an alert will sound. [ABSTRACT FROM AUTHOR]

The main objective of this research is to compare the sodium (Na) concentration present in human seminal plasma of various infertile conditions and normospermia semen samples for the clinical diagnosis and prognosis of human male infertility. Materials and Methods: Semen samples were collected normospermia (N=22) and asthenospermia (N=22), oligospermia (N=22), azoospermia (N=22) from the andrology lab, Milan fertility centre, Banglore. Sample size was calculated based on clinicalc.com by keeping G power 80% and threshold at 0.05. By using novel atomic absorption spectroscopy, sodium concentration has been evaluated in this research. Semen analysis was done according to the world health organisation protocol. Results: Results were evaluated by using SPSS software. Independent sample T-test is done for sodium concentration of normospermia and infertile groups like asthenospermia, azoospermia and oligospermia which were obtained as (P=0.411) statistically insignificant. It was found that sodium concentration was higher in normospermia (5.4 mg/ml) than in infertile groups like asthenospermia (2.3 mg/ml), azoospermia and oligospermia samples. Conclusion: Sodium is important for the reabsorption of luminal fluid and increases the concentration of sperm, and its absence or less concentration may lead to infertility issues. In normospermia sodium concentration was high and in infertile groups sodium concentration was less which leads to motility issues. [ABSTRACT FROM AUTHOR]

The study's primary objective is to compare the efficacy of the state-of-the-art SVM method for image prediction with that of KNN, Logistic Regression, Random Forest, and Decision Tree. The UCI Machine Learning Laboratory provides a total of 569 samples. Groups like SVM, KNN, Decision Tree, Random Forest, and Logistic Regression are used to the samples after they have been separated into benign and malignant cells so that their respective performances may be compared. G power calculation is used to determine how many samples are needed for this analysis. The maximum acceptable error is set at 0.5, and the minimum power of analysis is set at 0.8. Predictions made using Logistic Regression appear to have a higher accuracy(95%) than those made using SVM, KNN, Decision Tree, or Random Forest(92%,90%,85%, and 91%). This proposed system has a probability importance of 0.55. The Wisconsin dataset was used to compare Logistic Regression against SVM, KNN, Decision Tree, and Random Forest for the detection of breast cancer. [ABSTRACT FROM AUTHOR]

The aim of the study was to distinguish fingerprints using state-of-the-art artificial neural networks and support vector machines. Materials and methods: This study compared support vector machines and innovative artificial neural network techniques (N=10). The total sample size was calculated using the g-Power software with an alpha of 0.05, an enrollment rate of 0.1, a confidence interval of 95%, and a pretest power of 80%. Results: The ANN achieves 93% accuracy while the SVM classifier achieves 83% accuracy. In SPSS statistical analysis, the accuracy was found to be significant (p<0.05). Conclusion: The novel ANN classifier provides significantly better fingerprinting performance than the SVM classifier. [ABSTRACT FROM AUTHOR]

The goal of this study is to determine how much of an effect glass fibre has on the compressive strength of M20 concrete. There are two groups in this investigation, and 18 samples have been prepared for each. Group 1 consists of your standard M20 concrete, whereas Group 2 features M20 concrete that has been strengthened with glass fibres. CLINICAL software was used to determine the sample size based on values taken from previously published studies. Several tools and resources were used to get the samples ready for analysis. The independent sample T-test, performed in SPSS version 21, shows that the addition of polymer coating to cured fiber-reinforced M20 grade concrete with GGBS slag (20 percent) significantly raises the concrete's compressive strength. According to the results of the compressive testing machine, the increase in strength is 17.435 percent. Table 6 shows that the significance of these occurrences was 0.034. (p0.05). The M20 grade glass-reinforced concrete had a standard deviation of 1.42683, as shown in table 5. Based on the results of this experiment, it can be said that M20 grade concrete that has been reinforced with glass fibres has higher compressive strength than regular M20 grade concrete. [ABSTRACT FROM AUTHOR]

This research uses MATLAB theoretical assumptions to compare retrograde ejaculation to normospermia in sperm flow pattern, maximum, and mean velocity. Retrograde ejaculation was done on 28 normospermic men, and the gold standard World Health Organization technique was used to report sperm quality (WHO). Based on past research, Clincialc.com calculated sample sizes with a threshold of 0.05, G power of 80%, and enrollment ratio of 1. We analysed the two groups' flow rates and velocities in Matlab using assumed and standard values from the literature. The independent samples t-test showed no significant differences in flow rate, maximum velocity, or mean velocity between retrograde ejaculation and non-reprograde groups (P= 0.193, 0.374, 0.067). The mathematical models indicated that normospermia had a larger flow rate (0.09817m3/sec), mean velocity (0.03125m2/sec), and maximum velocity (0.0625m3/sec) than retrograde ejaculation. These data highlight the role of blood flow rate and velocity in retrograde diseases like erectile dysfunction. [ABSTRACT FROM AUTHOR]

The purpose of this research is to compare and contrast how well the random forest algorithm and the decision tree method perform in classifying fire and smoke photographs. With a g power of 0.8 and an alpha of 0.05, we find that out of a total of 4000 photos, 2000 depict fire and the remaining 2000 depict smoke. In this case, we split the dataset into a training set (n=3200, or 80%) and a validation set (n=800, or 20%). Classification tasks are executed with the help of the Sklearn machine learning package in Python. Precision, recall, f score, and accuracy numbers are some of the metrics used to measure an algorithm's effectiveness. When compared to the Decision Tree algorithm's 90.54, 90.00, 90.9, and 89.70 percent accuracy, F-score, recall, and precision, Random Forest achieved 97.42, 97.28, 97.43, and 97.15 percent values, respectively (p0.001(2-tailed)). The results of this research show that the random forest method outperforms the decision tree algorithm by a wide margin. [ABSTRACT FROM AUTHOR]

Aim: The main aim of this study is to assist the visually impaired to dole out money from the innovative ratified ATM compared with smart ATM. Materials and methods: The Raspberry Pi which is a microcontroller acts to convert analog input signals to the digital output signal. A Biometric system with IVRS at an ATM promotes the transaction to succeed. The significance between the two groups was calculated using SPSS. The parameters were maintained as threshold 0.05, enrollment ratio as 1, confidence level 95%, and G-power 80%. Results: Ratified ATM was found to perform better than smart ATM in aiding the visually impaired. There exists a statistically significant difference (p=0.878, p>0.05) between ratified and smart ATM groups. The execution of this method has been found promising on the account of its sensitivity to the recognition of the customer's fingerprint as contained in the database. Conclusion: This method not only helps the visually impaired but also illiterates, dyslexia, and people with visual disabilities to withdraw money safely. [ABSTRACT FROM AUTHOR]

This study intends to create a novel heart rate regulation technique for pacemaker-using atrial fibrillation patients. This study will focus on people in this situation. System uses Proportional controller (P) and Proportional Integral Derivative (PID) controllers. The PID controller controls the control group, whereas the P controller manages research. The following categories and subcategories describe sample size features: The enrollment ratio and cutoff must be one and 0.05. Gpower is required for 80% of the population. Rise Time, Peak Time, Settling Time, and Overshoot assess proportional (P) and PID control efficacy. Overshoot is the sixth time to evaluate proportional (P) and PID controllers. The sixth choice is overshoot. The proportional controller's Rise and Peak averaged 0.0633, Settling 6.1700, and Overshoot 48.2290. (P). In a proportional integral derivative (PID) controller, rising, peak, settling, and overshoot times were 0.0586, 0.0586, 7.1667, and 51.0441 milliseconds, respectively. An independent sample T-test suggested that the Proportional Integral Derivative (PID) Controller was somewhat better than the Proportional (P) Controller, although other parameters were similar between the two groups (p>0.05). This showed that the PID Controller is not much superior than the P Controller. [ABSTRACT FROM AUTHOR]

The goal of this study is to determine how much of an effect glass fibre has on the compressive strength of M20 concrete. There are two groups in this investigation, and 18 samples have been prepared for each. Group 1 consists of your standard M20 concrete, whereas Group 2 features M20 concrete that has been strengthened with glass fibres. CLINICAL software was used to determine the sample size based on values taken from previously published studies. Several tools and resources were used to get the samples ready for analysis. The independent sample T-test, performed in SPSS version 21, shows that the addition of polymer coating to cured fiber-reinforced M20 grade concrete with GGBS slag (20 percent) significantly raises the concrete's compressive strength. According to the results of the compressive testing machine, the increase in strength is 17.435 percent. Table 6 shows that the significance of these occurrences was 0.034. (p0.05). The M20 grade glass-reinforced concrete had a standard deviation of 1.42683, as shown in table 5. Based on the results of this experiment, it can be said that M20 grade concrete that has been reinforced with glass fibres has higher compressive strength than regular M20 grade concrete. [ABSTRACT FROM AUTHOR]

This study examines and discusses how to properly monitor water stagnation in agricultural areas in order to boost crop production. This research work is to analyse and discuss the proper monitoring of water stagnation in cultivation land to increase crop production. Materials and methods: Fuzzy Logic Controller (FLC) algorithm and Proportional and Integral (PI) algorithms are implemented to analyse the accuracy of monitoring the water stagnation in the field. For two groups, the sample size is estimated using Gpower, and 14 samples were employed in this study. Gpower is assumed to be 0.8. There is a 0.003 (p <0.05) significant difference between the two groups. Result: In dry condition, the FLC provides 29.34 % and PI provides 24.3 % accuracy in water stagnation monitoring. In wet conditions the FLC and PI provide 17.5 % and 20.34% of accuracy respectively. In both scenarios, FLC outperforms PI controllers in terms of performance. Conclusion: In light of the findings, the FLC approach performs noticeably better than the PI method. [ABSTRACT FROM AUTHOR]

The major aim of the study is to compare BPNN (Back propagation neural network) and SRNN (Simple recurrent neural network) algorithms for automatic weed detection with improved accuracy in wheat field, chickpea fields. We collected different types of wheat, chickpea field images (weed) based on the sample size BPNN (N=10) and SRNN (N=10) calculated by using previous literature through clinicalc.com by keeping threshold at 0.05, G power at 80% and enrollment ratio as 1, and we have done with image segmentation and textural features were extracted. Weed detection was evaluated by using artificial intelligence (AI) for both the groups by considering the parameters like accuracy and sensitivity. Independent sample T-test was done which reveals that p<0.01 found to be statistically significant in BPNN when compared with SRNN for effective detection of weed in wheat, chickpea fields. In case of accuracy and sensitivity values BPNN was better when compared with SRNN. Accuracy value in BPNN was 91.95% and the accuracy value in SRNN was 88.95%.BPNN was yielded with better results when compared to the SRNN in case of accuracy and sensitivity for detecting weeds in wheat, chickpea fields. [ABSTRACT FROM AUTHOR]

This study's goal is to maximise the output voltage of a Brushless Direct Current Motor (BLDC) drive by implementing an unique Fuzzy Logic Controller (FLC) and comparing it to a more traditional Proportional and Integral (PI) controller. The Components and Procedures: DC output voltage of BLDC drive can be increased by modifying the voltage gain with the help of two controllers, FLC and PI. In total, 14 samples are used to conduct the Gpower calculations for two groups. The obtained value of 0.031 is statistically significant (p 0.05). The DC output voltage of a BLDC motor drive fed by an FLC is measured and compared to that of a BLDC motor drive fed by a PI controller. It can be seen from the results that the BLDC drive with the FLC controller produces a higher DC voltage (220V) than the BLDC drive with the PI controller (125V). Results indicate that the FLC controller outperforms the PI controller. [ABSTRACT FROM AUTHOR]

The study's goal was to evaluate the efficacy and reach of internet education during the CoV-19 epidemic by surveying people in both urban and rural settings. Screen time, device type, and weekly itinerary were only some of the metrics used to analyse the 454 respondents who filled out the study. The effectiveness of online learning was studied using a chi-square analysis in SPSS software, and the stress brought on by excessive mobile device usage was evaluated in both the urban and rural populations of Chennai and Kanchipuram. The sample size was determined using a confidence interval of 95%, a significance level of =0.05, and a G-power of 80%. Online courses caused higher physical discomfort and mental strain for respondents living in urban areas (43.61%) than those living in rural areas (31.58 percent). Chi-square values of 23.173 and 20.993 for urban and rural areas, respectively, using three variables as predictors, are statistically significant at the P0.001 level. The results showed that urbanites adapted to online learning faster than those in rural areas, but they also reported higher levels of stress due to their increased reliance on electronic devices and the pressures of implementing educational changes grounded on cutting-edge research. [ABSTRACT FROM AUTHOR]

The main objective of this work is to create an innovative heart rate control technique for pacemaker devices used by patients with Bradycardia. Materials and Methods: This method employs the proportional control (P) and integral derivative (ID) controller methods, with the Proportional Controller (P) acting as the control group and the Integral Derivative(ID) Controller serving as the study group, each with a sample size of 40 calculated by keeping G power 80% and threshold as 0.05. Results: The efficiency of both Proportional (P) and Integral Derivative (ID) controllers was measured using Rise Time, Peak Time, Settling Time, and Overshoot. In Proportional Controller (P), the mean Rise Time is 0.0633, the mean Peak Time is 0.0633, the mean Settling Time was 6.1700, and the mean Overshoot was 48.2288. In an Integral Derivative (ID) Controller, the mean Rise Time was 0.0530, the mean Peak Time was 0.0530, the mean Settling Time was 6.4123, and the mean Overshoot was 52.9560. Conclusion: A statistical analysis result confirms that Integral Derivative (ID) Controller was significantly better than Proportional (P) Controller but there was no statistical significance for all the parameters when compared between groups. [ABSTRACT FROM AUTHOR]