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The main aim of the research work is a day-to-day Novel energy consumption analysis system using virtual meters in proteus software compared with conventional energy utilization analysis methods. Using a virtual meter and 10 samples, an unique energy consumption analysis system for the house was designed, with a significance value of 0.02 when compared to the traditional energy utilization technique. The G power is 0.8, the alpha is 0.05, the beta is 0.2. This research discovered that a unique day-to-day energy consumption analysis method utilizing a virtual meter has a higher statistical significance value (p < 0.05). A unique remote application of real-time energy meter fault detection technology outperforms the traditional energy consumption analysis method by 30 percent. [ABSTRACT FROM AUTHOR]

For innovative categorization of water quality monitoring, supervised machine learning approaches were applied. The aim of this research work is to evaluate the performance of SVM in novel classification of normal water and polluted water by comparing with the k mean. A Dataset of 800 water samples were downloaded from Kaggle website. The water samples are divided into two folders, normal and polluted water each containing 300 samples and a variety of different resolutions. 300 water samples used for SVM (Group 1) and it is compared with k mean (Group 2). The dataset is split into two sections: training (70%) and testing (20%). Accuracy, precision, and sensitivity values are calculated using a confusion matrix to quantify the classification potential of the SVM algorithm. 0.8 is the G power. SVM algorithm has achieved accuracy, precision and sensitivity of 89.60%, 87.53% and 94.62% respectively in comparison to K Means algorithm of 84.26%, 81.38% and 90.17%. SVM has a significantly higher classification performance (p = 0.018) compared with k mean. The Support Vector Machine method outperforms the k mean approach in the categorization of water samples, according to this study. [ABSTRACT FROM AUTHOR]

An automated face detection and recognition system use the Eigenface principle component analysis (PSA) algorithm to compare the accuracy of pictures obtained from PGC and stereo vision using the new Violas-Jones technique. The Eigenface (PSA) Algorithm and the Novel Violas Jones Algorithm are two of the groups that this study uses, with a sample size of N = 40. The sample sizes are further divided using the G power (power = 0.85 and α = 0.05 as the setup parameters) to form two groups of size n = 20. In terms of effective face detection, the study's findings indicate that the violas jones approach has an accuracy of 95% and the eigenface detection system has an accuracy of 89%. P=0.012 is the significant value of the independent sample t-test. When compared to the Eigenface Principal Component Analysis technique, the Violas Jones algorithm outperformed it in terms of recognition and detection accuracy rate for images acquired by pixel grayscale and stereo vision cameras. [ABSTRACT FROM AUTHOR]

The major goal is to boost accuracy by comparing the Novel Gradient Boosting Regressor algorithm and the K Nearest Neighbours Classifier approach for analysis and prediction of solar power generation. Materials and Methods: To evaluate, compare, and understand the effectiveness of recommended algorithms, a total of sixty samples were gathered for the two study groups' SPSS analysis. Thirty samples from group 2's K Nearest Neighbours Classifier method and thirty samples from group 1's Novel Gradient Boosting Regressor (GBR) algorithm underwent an 80% power G-power analysis. The accuracy rates observed were 78.6% for the K Nearest Neighbours (KNN) Classifier and 93.3% for the Novel Gradient Boosting Regressor. Consequently, it can be concluded that the GBR method outperforms the KNN Classifier algorithm significantly. [ABSTRACT FROM AUTHOR]

The main objective of this work is to perform Cirrhosis Stage using Novel CatBoost Classifier Algorithm and Compare it with the Adaboost Classifier Algorithm to improve the accuracy. It went over the in-depth evaluations of recommended algorithms. Ten testing samples were conducted for each algorithm using the Cirrhosis Dataset, which was downloaded from the Kaggle.com website. For the SPSS study, close to ten samples were gathered in order to evaluate, compare, and determine the effectiveness of recommended algorithms. An alpha of 0.05 and a G power of 0.85 are used by the SPSS software. Ten samples each from the Novel CatBoost algorithm group 1 and the Adaboost algorithm group 2 are gathered in order to assess the predictive accuracy of Cirrhosis. We presented and spoke about the results of combining several feature extraction methods with classifiers. Novel CatBoost was shown to have the highest level of accuracy, scoring 92.1%. Based on the independent sample t-test, there is a statistically significant difference between the two groups (p=0.001; p<0.05). to evaluate the precision of the Cirrhosis stage prediction. To improve the accuracy of the Cirrhosis Stage prediction, increasing forecast precision is a very promising approach. The results of this study show that the Novel CatBoost algorithm performs significantly better than the Adaboost approach. [ABSTRACT FROM AUTHOR]

The main goal of this investigation is to enhance the precision of crop forecasting by transitioning from the Adaboost method to Naive Bayes analysis. The current inadequacy in food production and forecasting is attributed to anthropogenic climate change, resulting in low supply. This has significant adverse effects on farmers' economic conditions due to poor yield and the challenges associated with predicting future crops. This research initiative aims to assist novice farmers by leveraging machine learning to recommend suitable crops. The approach involves aggregating information on crop seeds along with specific requirements for optimal growth, encompassing nitrogen and phosphorus levels, temperature, humidity, pH, and rainfall. Statistical analysis, employing a t-test for independent samples with a significance level of 0.001 (p<0.05), G-power of 0.8, mean, and standard deviation, was conducted on data collected from diverse sources. In this experiment, a total of 20 samples were gathered and divided into two groups of 10 each. Group 1 utilized the Innovative Naive Bayes technique, while Group 2 employed the Adaboost methodology. The data analysis revealed that the Naïve Bayes Algorithm exhibited high accuracy (99.39%) compared to the Adaboost algorithm (79.45%) with a significance level of the independent sample t-test at 0.001 (p<0.05). This suggests that the Gaussian Naive Bayes algorithm is more accurate in predicting crop outcomes than the Adaboost algorithm. Key terms associated with this study include Crop Prediction, Crops, Adaboost Algorithm, Innovative Naive Bayes Algorithm, Machine Learning, Crop Yield Forecasting, and Maize. [ABSTRACT FROM AUTHOR]

Detection of malaria parasites using supervised machine learning algorithms. The aim of this study is to compare the performance of the Innovative Random forest algorithm with the K-Nearest Neighbors algorithm (KNN) in detecting malaria parasites from blood images. Materials and Methods: A Dataset of 600 images of parasite and uninfected cell images at different resolutions were collected from the Kaggle website. The entire dataset is split into two sets: one for training (70 percent) and one for testing (30 percent). In Random Forest (Group 1), 450 malaria cell images were used in the training phase and 150 malaria cell images were used in the testing phase, which was compared to KNN (Group 2). Accuracy, precision, and sensitivity values are calculated using a confusion matrix to quantify the performance of the Random forest algorithm. Results: Random forest algorithm has achieved accuracy, precision and sensitivity of 91.80%, 97.62% and 90.12% respectively compared to KNN algorithm of 86.60%, 96.20% and 83.50%. As compared to KNN, the Random forest algorithm has a significantly higher detection rate (p=0.02). Conclusion: In this study, it was found that the Random forest algorithm gives better performance than the KNN algorithm in detection and classification of malaria parasites. [ABSTRACT FROM AUTHOR]

The aim of this work is to design an Innovative sleeve dipole array antenna with high directivity and gain. Materials and Methods: For comparison the two antennas are separated into two groups. Group 1 is considered as a sleeve dipole array antenna and group 2 is considered as a microstrip patch antenna. Initially 10 samples are taken from each group but for accurate comparison sample size is taken as 6 for each group. The significance obtained was 0.01 (p<0.05). The alpha value is 0.05 and the beta value is 0.2 and the gpower value is 0.8. Results: The simulated results that are obtained in FEKO software are, directivity for the sleeve dipole array antenna at 2.4GHz is 10.57dBi and for microstrip patch antenna the directivity is 6.48dBi. The bandwidth for sleeve dipole array antenna is 0.61GHz and for microstrip patch it is 0.12GHz. Conclusion: By analyzing all the obtained results, the sleeve dipole array antenna will perform better than microstrip patch antenna for submarine communication. [ABSTRACT FROM AUTHOR]

The aim of this work is to design an Innovative sleeve dipole array antenna using an antenna array system and to obtain high gain for sleeve dipole array antenna and it is compared with ground plane monopole antenna. Materials and Methods: The sleeve dipole array antenna (group 1) which is designed by using an antenna array system and ground plane monopole antenna (group 2) are compared with gain as the parameter which is ranging from 1.9GHz to 2.4GHz. The gpower value is 0.8, the alpha and beta values are 0.05 and 0.2 respectively. For each group 10 samples of gain values were taken from the FEKO simulation tool but for accurate comparison the sample size is taken as six. Results: The simulated results show that the gain of the Sleeve dipole array antenna at 2.4GHz is 10.24dBi and for ground plane monopole antenna it is 3.67dBi at the same frequency. The significance value obtained in SPSS Statistical software is 0.02 which is p<0.05. Conclusion: From all the results and analysis it is clear that the sleeve dipole array antenna is better than the ground plane monopole antenna for submarine communication. [ABSTRACT FROM AUTHOR]