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The proposed research work anticipates the regular workout patterns of users by utilising the data collected from their smartphones. The study indicates that the Random Forest algorithm is more effective in predicting human exercise routines with higher precision than the Linear regression algorithm. A dataset of 3000 entry with the headings 'user','activity','Timestamp', and so on are used to compare, with two groups of 10 sample size for each being studied. Linear Regression recorded an accuracy of 73.67%, while the innovative Random Forest Algorithm gives an precision of 76.51. The difference between these groups is statistically significant at 0.033, which is smaller than 0.05 (p<0.05). It is obvious that these groups differ statistically significantly from one another. Compared to the results of the linear regression, the predictions made by the Random Forest algorithm model are more accurate. [ABSTRACT FROM AUTHOR]

The aim of this study is an analysis on obesity levels prediction based on smoking habits using stepwise linear regression algorithm in comparison with random forest classifier for improved accuracy. Estimating the incidence of obesity in people using information acquired from the open-access website Kaggle Insufficient weight, normal weight, overweight levels I and II, overweight levels I and II, and obesity types I, II, and III are the categories that can be used to group the 2111 records of the data that have 17 qualities. A comparison between the random forest algorithm (Group 2) and the stepwise linear regression algorithm (Group 1) using 20 records each. The Gpower is 80 % (The values for g power are alpha(α)=0.05 and power=0.85). The research uses the stepwise linear regression algorithm and obtained accuracy of 84.8% while random forest classifier got 81.9 %. The significance value is found to be p=0.001 (p<0.05) after analyzing the results from Independent. This perspective compares the random forest classifier and stepwise linear regression algorithm for predicting obesity levels based on smoking habits. In comparison the stepwise linear regression method performs more accurately than a random forest classifier. [ABSTRACT FROM AUTHOR]

Across the globe, diabetes is recognized as one of the many causes of deaths, especially in Third World countries as there is a lack of treatment for diabetes, especially in the early stages. In study, the presence of diabetes will be classified within the community, thus contributing to the existing technology within the healthcare system. Our discovery can help doctors to predict the existence of diabetes accurately and alert patients to seek early treatments. Four data mining algorithms were used within this study which consists of both single and ensemble classifiers. The two single classifiers are decision tree, and logistic regression classifier while the ensemble classifiers are random forest, and stacking. These classifiers are chosen as they are efficient and high in performance. This research uses the PIMA diabetes dataset as it can be obtained by the general public. The stratify cross-validation is used to ensure the efficiency of the models. Ensemble classifiers show better or similar testing results compared to single classifiers. From data visualisation, two important features are discovered. [ABSTRACT FROM AUTHOR]

The aim of the study is to diminish the power loss with reference to sensitivity and precision in power transmissions and consumptions. As power theft is considered as non-technical loss, it is hard to track the total transmission. So the Random forest algorithm is proposed over the K-NN Algorithm to compare accuracy in power theft detection. Materials and Methods: There are two groups in this study each with a sample size of 21400 per group. Analysis is done with the pretest power 0.8. Results: The mean value for accuracy in the Random Forest algorithm is 90.6215 which is high when compared to K-NN algorithm whose accuracy is 82.2200. It has an insignificant value of 0.07 (p>0.05). These values are evaluated using SPSS for statistical analysis. Conclusion: This analysis shows that the novel Random Forest algorithm has more reliable accuracy and sensitivity of power theft detection compared to the K-NN algorithm. [ABSTRACT FROM AUTHOR]

The fundamental objective of this research is to investigate several approaches that may be used to enhance the precision of recent machine learning algorithms that are used to anticipate road accidents, in particular the Random Forest Algorithm and the Naive Bayes Algorithm. Both the Materials and the Procedures are: Both the Random Forest and the Naive Bayes algorithms were run through 20 iterations in order to see how well they could predict future traffic incidents. The results show that in terms of accuracy, the Random Forest approach performs better than the Naive Bayes algorithm (89.95 percent). The significant independent samples T-test for the Random Forest approach is 0.001, as stated in the previous sentence (p0.05). When the data were compared, it was evident that the Random Forest approach surpassed the Naive Bayes algorithm in terms of the prediction of traffic accidents. This was the conclusion reached after analysing the data. [ABSTRACT FROM AUTHOR]

An IDS built using the Information Gain Algorithm and an unique One Class Support Vector Machine with Random Forest Feature Selection (Group 1) for detecting denial-of-service attacks in wireless ad hoc networks are compared for accuracy and detection rate (Group 2). Substances and Methods: An IDS model was created using CIC-IDS 2017. The IDSs were modelled using the above methods and analysed using SPSS with 19 samples per group. The innovative One Class SVM with Random Forest Feature Selection IDS had a detection rate of 90% and an accuracy of 99.0%, whereas the Information Gain Algorithm IDS had 82.0 percent and 80.0 percent. The significance threshold was 0.05. (Accuracy - 0.00; Detection rate - 0.00). Finally, this data shows that the innovative One Class SVM with Random Forest Feature Selection IDS beats the Information Gain Algorithm IDS by a large margin. [ABSTRACT FROM AUTHOR]

The goal of this research is to catalogue all of the undesirable materials available on rival and popular social media platforms. Tools and methods: The dataset for training and testing the proposed prediction models was constructed using the messages from a number of popular social media messaging platforms, with a minimum of 5 attributes and 150 messages. Random Forest, a machine learning algorithm, was used to design the framework, and it was compared favourably to the Support Vector Machine algorithm. Discussion and Remarks: Accuracy in retrieval is 90.30 percent for the Random forest algorithm and 94.36 percent for the Support vector machine learning algorithm (countvectorizer). The significance level between the two algorithms is high at p=0.02 (p0.05). This study confirms that the SVM (countvectorizer) Machine Learning algorithm has a higher accuracy rate than the Random Forest algorithm and creates a novel framework to identify spam words. [ABSTRACT FROM AUTHOR]

This paper focuses on finding the best classification algorithm model in the case study of student performance prediction and comparing the algorithm performance before and after using the normalization method. To achieve this goal, this study uses data mining classification techniques to analyse student performance at Vocational High School in 2020-2021. The steps of the research carried out include: data collection, data pre-processing, build algorithm models without using normalization and with using normalization, and final step are comparing algorithm performance before and after using normalization. The algorithms that will be used include: Random Forest, Decision Tree, Logistic Regression, SVM, Naive Bayes, and KNN. While the normalization methods used are Standard Scaler, Min-Max Scaler, and Robust Scaler. The result of this research is that the normalization method is able to significantly increase the accuracy of the model. Based on the tests and evaluations carried out, the normalization method using the Min-Max Scaler has the biggest impact in improving the overall model performance and the algorithm with the best performance is Random Forest. This paper reviews the effect of the normalization method to improve algorithm performance in predicting student performance, where based on previous research no one has used the normalization method to gain accuracy of the model which actually has a considerable impact on gaining accuracy of the model. [ABSTRACT FROM AUTHOR]

The target of this work is to assess the presentation involving arbitrary woodland and contrast and KNN for the recognition of mind growth with Novel division. In this work, an AI approach is acquain ted with identifying growths in an MRI picture of the cerebrum. A sum of 31619 examples was gathered from three liver illness datasets accessible in Kaggle. These examples were partitioned into preparing dataset bunch 1 (n = 22133 [70%]) and test dataset b unch 2 (n = 9486 [30%]). The necessary examples for this examination are finished by G power computation is 80%. Irregular backwoods accomplished exactness, accuracy, and review upsides of 91.66 %, 92.43 % and 92.53 % are huge qualities and better contrasted with KNN calculation which showed exactness, accuracy, and review upsides of 81.48 %, 95.65 % and 95.65 % with P < 0.02. In this study, it is seen that the KNN calculation displayed essentially preferable execution over Random Forest calculation for mind growth recognition of the datasets. [ABSTRACT FROM AUTHOR]

To predict the accuracy percentage of Short Message Services (SMS) spam detection using machine learning classifiers. Two ensemble learning algorithms named random forest algorithm and naive bayes are applied to data. The algorithms have been implemented and tested over a dataset which consists of 5574 records. Ensemble learning methods combined several models trained with a given learning algorithm to improve accuracy. After performing the experiment as result shows mean accuracy of 89.75 % by using random forest and compared naive bayes algorithm mean accuracy is 88.05% for SMS spam detection. There is a statistically significant difference in accuracy for two algorithms is p<0.05 by performing independent samples t-tests. This paper is intended to implement Innovative Classification for prediction of SMS spam detection. The comparison results shows that the naive bayes algorithm has appeared to be better performance than Random Forest Algorithm. [ABSTRACT FROM AUTHOR]

In order to improve false review detection and determine the accuracy of real-time fake review detection, this work compares the semi-supervised algorithm and the random forest approach. By adjusting the SSA parameter and random forest parameter to maximise the pH, novel semi-supervised algorithm (N = 32) and random forest algorithm (N = 32) techniques are simulated. For two groups, the sample size is estimated using Gpower 80%, and 64 samples were employed in this study. According to the results, SSA's accuracy (82.60%) is much higher than Random Forest's accuracy (80.60%). The difference in statistical significance between the random forest algorithm and the semi-supervised approach was found to be 0.006 (p < 0.05). When detecting bogus reviews, semi-supervised algorithms perform better than random forest method. [ABSTRACT FROM AUTHOR]

The study's main objective is to increase the accuracy percentage for identifying slow learners using Novel Random Forest algorithm in comparison to K-Mode algorithm on online social media. Using the Random Forest algorithm (N=10) and the K-Mode algorithm (N=10) for classification, the algorithms' accuracy was compared. Accuracy achieved forNovel Random Forest algorithm is (94.20%) and K-Mode algorithm is (90.15%). Random Forest algorithm performs significantly better than K-Mode algorithm (p=0.0267) (p<0.05). Random Forest algorithm analyzing the slow learners with more accuracy. These algorithms have been tested to perform better accuracy on finding the slow learners over online social media, and here Random Forest show more accuracy than K-Mode algorithm. [ABSTRACT FROM AUTHOR]

To implement a matrix analysis of personal loan fraud detection using linear regression algorithm and random forest algorithm. The sample size for the algorithm for both groups (n=10) for finding accuracy in personal loan fraud detection. Based on the obtained results, the accuracy rate of the random forest algorithm is 96.88% which is higher than the accuracy rate of linear regression algorithm 87.76%. Statistical significance difference observed between two groups is p=0.00 (p<0.05) 2-tailed based on an independent sample T test. In this proposed paper, the novel random forest algorithm has more accuracy than linear regression algorithm. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to improve the accuracy for automatic classification of meal waste from innovative trash data with the help of image processing. There are 2572 images for the classification of meal waste were used for this paper. The images are labeled as "Cardboard", "Plastic", "Paper", "Metal", "Glass", "Trash" and there are 20 number of images have been used for RF classifier taken as first set of machine learning algorithm and is compared with SVM algorithm taken as second set of machine learning algorithm With a g-power value of 80%, the revolutionary garbage data images, a threshold of 0.05%, a confidence interval of 95%, and a standard deviation, these photographs were gathered from various web sources. When compared to the SVM method, which had an accuracy of 61.45%, the proposed system's accuracy was enhanced to 84.81%, with a significant value of 0.001 (p 0.05) with a 95% confidence interval. This study found the meal waste from trash using the RF is significantly better than SVM algorithm. [ABSTRACT FROM AUTHOR]

In recent years, the internet of things is prevalent and widely used. The new problem with IoT is security, which needs to be considered carefully because of the technology heterogeneity. These threats can affect IoT performance; therefore, it is necessary for effective monitoring. This paper examines several machine learning methods in intrusion detection systems that possibly run on IoT. Random Forests and Decision Tree are employed in this study for performance comparison. The experimental results show that the Random Forest and Decision tree algorithms application produces good performance with a faster response time and possible running on IoT. [ABSTRACT FROM AUTHOR]

Lung cancer is the most common cancer that kills many people across the world. Many computer-assisted algorithms are used for the analysis of this cancer. In this research, Random Forest and Support Vector Machine (SVM) algorithms are used to predict Lung Cancer. K-NN Algorithm is used for pre-processing the dataset and then, the existing algorithms such as Random Forest and SVM are applied in the dataset for diagnosing the lung cancer in the patients and a newly proposed algorithm, Optimized RF Algorithm combines the characteristics of Random Forest and SVM is used in this research. When compared with the Random Forest algorithm and Support Vector Machine, the results show that the Optimized RF Algorithm (Optimized-RF) gives better performance than the existing algorithms. [ABSTRACT FROM AUTHOR]

Sentiment analysis of text data has become very much popular in past few years, because of the interesting challenges it can offer. Among these challenges, sarcasm is unique. We can address sarcasm as the 'Achilles heel ' of sentiment analysis. Detection of sarcasm from the given data is same time complicated and interesting. It is interesting because, if researchers can find the optimized solutions for finding sarcastic words in the data, it will enhance the sentiment analysis of that data. Even humans also have difficulties in understanding the actual interpretation of a sentence, if it is presented indirectly. This means, a person stated something, but he meant contradictory to the word meaning of the statement. This makes sarcasm detection an interesting topic for researchers. In this paper, we evaluated the performance of several machine learning models like Support vector machine, Naïve Bayes, Decision tree etc., and different ensemble models like Random Forest, XGBoost and AdaBoost etc., with the collaboration of various feature extraction methods such as Term Frequency-Inverse Document Frequency etc. The main evaluation metrics we used to evaluate the performance are accuracy, precision, f-score and recall. Based on the results, we concluded that the models such as XGBoost, LightGBM and Bagging classifiers provide better results in detecting sarcasm with respect to other machine learning models. [ABSTRACT FROM AUTHOR]

With their digital marketing efforts, banks are now attempting to address the needs of their existing clients. By storing large-scale data gathered from marketing studies, it is known to provide statistical results in order to forecast client behaviour in artificial intelligence applications. Using real banking marketing data, including customer profiles, this study compared the performance of random forest and logistic regression methods. These algorithms were also tested on the WEKA, Google Colab, and MATLAB platforms to compare performance. At the conclusion of the trial, the random forest algorithm on the WEKA platform produced the best results, with 94.8 percent accuracy, 93.9 percent sensitivity, 94.8 percent recall, 94.4 percent f1-score, and 98.7 percent AUC value. Furthermore, when compared to previous investigations, the obtained performance values generate superior outcomes. [ABSTRACT FROM AUTHOR]

To Improve performance analysis in classification with accuracy of innovative phishing websites using the innovative Random Forest algorithm by comparing with the Support Vector Machine algorithm. Random Forest algorithm (N=20) is compared with the Support Vector Machine algorithm (N=20) in order to get high accuracy. The framework depends on Machine Learning. Random forest has the highest accuracy (92.11%) in comparison to the Support Vector Machine algorithm (90.26%) and the independent T-test was carried out and shows that it is statistically insignificant (α=0.354) with a confidence value of 95%. Random Forest algorithm obtained seems to be better accuracy than the Support Vector Machine algorithm in the detection of phishing Websites. [ABSTRACT FROM AUTHOR]

The study's goal is to create a wireless ad hoc network intrusion detection system that can identify Denial of Service attacks using the random forest approach and compare its performance to that of an intrusion detection system built using the K-NN algorithm. Here, CIC-IDS 2017 dataset was taken to design the IDS model. To classify the normal and abnormal nodes, a random forest classifier technique (Group1) is used. And this IDS performance is compared with an IDS which is designed using the K-NN algorithm (Group2). For each group 19 samples were taken into consideration for the analysis with the SPSS tool. The performance of the IDS is measured by using accuracy, detection rate and false positive rate. Using a random forest method to construct the IDS, the researchers found that it has a detection rate of 78%, an accuracy of 100%, and a false positive rate of just 1.4 percent. The IDS which is designed using the K-NN algorithm has accuracy 99%, detection rate 72% and false positive rate 0.03%. It concludes that the IDS which uses random forest method appears to perform significantly better than the IDS which is designed using the K-NN algorithm, since its accuracy and detection rate is more. But the false positive rate is little bit more than the IDS which uses K-NN. [ABSTRACT FROM AUTHOR]

Lungs are a pair of spongy, air-filled organs that are a part of the Human Respiratory System. Cancer is a disease where the cell growth in a body goes out of control. Such uncontrollable abnormal cell growth in the lungs leads to lung cancer. Lung tumors can be categorized as small cell lung cancer and non – small cell lung cancer depending on the size of the cell as perceived under a microscope. More people are affected by non-small lung cancer than small-lung cancer. Tumors, which are detected at a prior stage have more likelihood of getting treated at a faster period, however, if left undetected or undiagnosed for a long period, it could lead to various obstructions. As a fact, the traditional methods used for diagnosing are time-consuming and have more probability of chances of errors. Thus a non – invasive method of diagnosing has been studied and discussed. In the proposed study, PET-CT images have been collected from various online databases and processed using Gradient Vector Flow (GVF) Algorithm. As a result of the study, the proposed gradient vector flow algorithm provides a precise segmentation in the lung images. Statistical features like mean, kurtosis, skewness, standard deviation, have been studied and compared between the normal and abnormal images. Thus a most precise and fast programmed method has been implemented to segment the lung tumor images using a gradient vector flow algorithm. Machine learning classifiers like KNN, Naïve Bayes, multilayer perceptron and random forest were analyzed. It was observed that the accuracy value of KNN classifier is 91.8%, Naive Bayes Classifier is 91.9%, Multilayer Perceptron is 89.1% and Random Forest Classifier is 87.9%. [ABSTRACT FROM AUTHOR]

The paper is present a comparison of modern approaches for predicting the spatial distribution in the upper soil layer of a chemical element chromium (Cr), which had spots of anomalously high concentration in the investigated region. The distribution of a normally distributed element copper (Cu) was also predicted. The data were obtained as a result of soil screening in the city of Tarko-Sale, Russia. Models based on artificial neural networks (multilayer perceptron MLP), random forests (RF), and also a model based on a random forest in which MLP used as a tree - a random perceptron forest (RMLPF) - were considered. The models were implemented in MATLAB. Approaches using artificial neural networks (MLP and RMLPF) were significantly more accurate for anomalously distributed Cr. Models based on RF algorithms proved to be more accurate for normally distributed copper. In general, the proposed model RMLPF was the most universal and accurate. [ABSTRACT FROM AUTHOR]