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The cardiac attack is considered to have high mortality due to lifestyle habits and dietary regimes. Prediction of heart disease based on medical history of patients is critical. As privacy of sensitive data is also of concern, digital fingerprint collected from vast amount of data generated by the healthcare sector is made easier with the help of Machine Learning (ML). With advances in technology application of internet of things (IoT) in healthcare is also gaining popularity. Application of algorithms to predict heart attack is also evident. In the current work, we propose to predict cardiac arrest using medical history for the patients data and select the features best suitable for based on a comparative analysis among the ML models. Different feature selection metrics and classification models are used for prediction. ML algorithms like Regression, Decision tree, Random Forest, Support vector machine, Naive Bayes, and K-nearest neighbor have been used. Our results show the importance of medical history in prediction of cardiac disease and diagnosis. [ABSTRACT FROM AUTHOR]

Cybercrime has been on a massive increase for several years. As more individuals conduct business and live their lives online, more criminals are turning to the internet to steal. With the developments in cybersecurity, adoption of best practices, cybersecurity awareness initiatives, and increased regulations and partnerships between businesses and governments to resolve the problems, cybercriminals may be feeling the pressure. In this paper, we are going to predict operating system vulnerabilities using machine learning algorithms. And evaluate their accuracies through training them. The aim of the paper is to use Machine Learning algorithms to predict with accuracy operating systems vulnerabilities. Different ML algorithms have been analyzed, such as Logistic Regression, Support Vector Machine and Random Forest. And, out of all of these algorithms, Random Forest had the highest accuracy of 99.33%. [ABSTRACT FROM AUTHOR]

The purpose of this study was to assess the accuracy of cybercrime predictions provided using Novel Random Forest and Support Vector Machine. Material and Procedure: Cybercrime predictions were made using the new random forest (N=10) and the support vector machine (N=10), which were then analysed. Random forest trumps SVM in terms of precision (by a margin of 84 percent to 85 percent). 81 percent is the percentage. The advanced random forest classifier performed better than the conventional SVM classifier. There is a significant difference in accuracy between the two approaches (p>0.005). The development of the new cybercrime prediction system used machine learning. This novel random forest technique outperformed SVM. [ABSTRACT FROM AUTHOR]

The purpose of this study is to apply machine learning methods to the problem of classifying musical genres. Here, signal processing is used to extract the rhythm pattern feature, which is then used in conjunction with machine learning algorithms to provide a multiclass categorization of musical genres. A preexisting internet music collection is used to generate two distinct datasets, one with undersampling and the other with oversampling to achieve class balance. Two types of models were compared for their efficacy in this research. The first model is an enhanced support vector machine that is fully trained to identify the musical genre of an input signal from its spectrogram. The Random Forest Classifier is the second model. G power for this research is 80%; there are two groups in total, and each has a sample size of 20. Experiments performed on the audio dataset show that an assembler classifier that takes use of both techniques achieves an AUC of 0.894. According to the findings, the support vector machine method provides a higher accuracy (69.24%) than the Random Forest Classifier (64.26%). Independent sample T-test significance level of P=0 (P 0.05, 2-tailed). The findings showed that the accuracy of the music genre classification using the enhanced support vector machine with an unique Rhythm pattern feature method was higher than using the Random Forest Classifier. [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]

Face recognition has increasingly favored over other biometric modalities such as fingerprint, iris, that have historically been seen to be more reliable. This is because face recognition has become more accurate over time. Face recognition has certain distinct advantages over other biometric modalities one of which is that it is less intrusive. Modern facial recognition systems just need users to be in the camera's field of view. It can be used in many environments, recognition is greater where the system does not expect people to cooperate, such as in security measures. Face recognition without occluded parts can no longer meet the needs of modern society. The face recognition in complex environment needs to be addressed w.r.t the real-world situation. Thus, we have proposed face recognition system for detecting the masked and unmasked images efficiently. The dataset used for the experiment consist of 50 individuals with 50 images with masked and 50 unmasked images. The collected images were in different orientation, illumination and poses. For the purpose of face recognition, only the area the face had to be cropped and saved. So, only the face from the image was cropped using Haar-Cascade model to identify the face from the image. For masked images, a dlib script MaskTheFace is used to mask the unmasked faces and prepare a synthetic dataset of masked faces. Further, feature extraction is done using different techniques such as Histograms of Oriented Gradients (HOG) and Local Binary Pattern (LBP). Finally, we have analyzed the performance of Machine Learning algorithms like Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) for face recognition using face as a trait for masked and unmasked persons. [ABSTRACT FROM AUTHOR]

The detection of chest diseases from X-ray images is a crucial aspect of medical diagnostics, playing a vital role in the early identification and treatment of respiratory conditions. This review paper gives a general overview of how machine learning algorithms are used to identify chest diseases from X-ray pictures. It emphasizes the importance of accurate and timely diagnosis of chest diseases, while acknowledging the challenges faced by conventional diagnostic methods. The review highlights the potential of machine learning as a promising approach to enhance diagnostic accuracy and efficiency in this field. It explores the utilization of both handcrafted features and deep learning-based approaches for extracting informative features from chest X-rays. Furthermore, it discusses the adoption of various machine learning algorithms, including support vector machines, random forests, and convolutional neural networks, for effective detection of chest diseases. The review paper also recognizes the significance of training and validation strategies in ensuring the robust development of models. Additionally, it addresses the potential impact of the proposed methodology on chest disease detection and patient outcomes. The review paper discusses challenges and limitations associated with this approach, such as data availability, interpretability, and ethical considerations. In summary, this extensive analysis strives to make a valuable contribution to the field by offering insights into cutting-edge machine learning techniques, recognizing existing challenges, and proposing potential avenues for future research. [ABSTRACT FROM AUTHOR]

Brain cancer is a highly lethal form of cancer with low survival rates and expensive treatment costs. Detecting tumors at an early stage is essential for improving patient outcomes, and the utilization of machine learning (ML) algorithms has demonstrated potential in augmenting the accuracy of brain tumor detection. This research aims to develop an ML model that predicts the likelihood of brain cancer by combining various factors. Four popular ML algorithms, namely Random Forest, Support Vector Machines (SVM), Gradient Boosting, and Decision Tree, are compared for their performance in this task. The dataset used in this study is obtained from Kaggle, a platform for data science competitions and projects. Among the four ML algorithms evaluated, Gradient Boosting achieved the highest accuracy of 83.9%, followed by Random Forest with 81%, SVM with 80.2%, and Decision Tree with 74.1%. To gain further insights, the research conducted feature importance analysis to identify the factors that contribute most significantly to the prediction of brain cancer likelihood. Additionally, addressing class imbalance using the Synthetic Minority Over-sampling Technique (SMOTE) was applied to improve the model's performance. [ABSTRACT FROM AUTHOR]

Malware is the term for harmful software that hackers use to infiltrate specific machines or an entire network of an organization. It takes advantage of flaws in legitimate software (such as a browser or plug in for an online application) that can be hijacked. According to a recent survey, 80% of smartphones were still vulnerable to malware attacks that might jeopardize personal information, identity, and business or financial information. Android mobile malware is the biggest threat to data security. To examine the efforts surrounding malware risks on Android mobile devices, a range of machine learning algorithms, including Naive Bayes, Support Vector Machine, K Nearest Neighbor, and Random Forest, are employed in this research. Each algorithm is evaluated according to various performance criteria to determine which algorithms are better suited to detecting malicious software. According to experimental findings, Support Vector Machine and Random Forest deliver the best results, making them the most efficient methods for malware detection. [ABSTRACT FROM AUTHOR]

Premature birth (PTB) is the most common public health issue in gynecology and obstetrics. It has a variety of consequences, ranging from increased infant mortality to chronic disease and disability throughout one's life. Many PTB prediction studies have been published in recent years to improve the accuracy of PTB prediction. However, no single study has addressed the issue of premature birth using a data set from the Iraqi community. There are many research from different countries that have been collected regarding premature birth, where the pregnant woman differs from one region to another in terms of lifestyle as well as the diagnostic methods used, where in Iraq only manual methods are adopted in the diagnosis. The aim of this paper is to provide and improve the prediction accuracy of premature births using real data from Iraqi hospitals. Additionally, provide a validation of the results in terms of reliability and accuracy. The novel approach is used to identify the best maternal characteristics (those associated with PTB) from an obstetric dataset. The following classifiers (Decision Tree, Random Forest, Logistic Regression, K-Neighbor Nearest, Naive Bayes Classifier, Neural Networks, Support Vector Machine) were used in this study to classify all birth cases into term normal birth and preterm birth. As a result, the Random Forest classifier outperformed all other classifiers with a 98% percent accuracy. Its worth noting that the classifiers' performance is quantified in terms of accuracy, sensitivity, Specificity, Precision, Recall and F1 score. [ABSTRACT FROM AUTHOR]

After a deep questioner among students, faculty members and higher education experts. The demand for qualified engineers in the specific field as understandably gone down. The situation is grimmer for tier-2 and tier-3 colleges in India. The purpose of opting engineering course is not to get expertise in the particular course, instead to get a job in government sector with valid degree. The problem identified in the field of engineering education towards improving the quality in education is addressed with the help of exploratory data analysis. The dataset used in our experiment is collect from UCI machine Learning Repository having 33 variables and 1044 observations. The contribution made in the paper is to identify the vital attributes using single and multi-variant regression techniques. To perform prediction using Decision Tree, Random Forest, Support vector Machine and compare their performance in terms of classification accuracy and F-Score. In addition to that a convolution Neural Network (CNN) model is established in which, the vital attributes identified using regression techniques are provided as inputs and weights at each stage is estimated using Gradient Decent algorithm with step size 0.5. The classification accuracy from 63% is improved to 97% with the help of CNN model in 26664 iterations. The finding of the present research states that student having backlogs are less frequently opting for higher studies. [ABSTRACT FROM AUTHOR]

Customer churn referred as customer who ceases his relationship with service provider. Customer churn analysis is performed to retain existing customers from churning. Churn prediction is gaining importance in telecom sector now a days because of its importance to analyze behaviors of different customers to predict who are in verge in leaving the subscription from service provider. Retaining existing clients is cheaper than gaining a newer one. This paper focuses on various machine learning and neural network methods to predict customer churn and identify the customers who are closer to leave their service from the respective service provider. Machine learning algorithms that are used to predict the customer churn such as Logistic Regression, Support Vector Machine, Random Forest, Adaboost and Artificial Neural Network. Data is collected from AT&T website and learning models are evaluated based on two criteria's i.e., Accuracy and Area under curve giving special weightage on Accuracy. Algorithm that proves to be the most accurate in all the test cases is compared and performance is measured. [ABSTRACT FROM AUTHOR]

Heart Disease, Diabetes and lung cancer are complex diseases and globally many people are suffering from these diseases. On time and efficient identification of these diseases, play a vital role in health care monitoring systems. In this article, we proposed an effective and precise framework to determine these and the framework depends on Machine Learning algorithms. The system is developed based on classification algorithms and includes Support vector machine, Logistic regression, K-nearest neighbor, Decision tree, Random Forest and XGBoost Classifiers. Firstly, Kaggle dataset is considered for testing the data. Later pre-processing of data is done. Then the greatest piece of any data analysis project is ensuring that the information is effectively organized and fixing it when it isn't. The initial segment of this interaction is managing Missing Data. After dealing with the missing data, we are ready to start formatting the data set for making classification tree. Formatting data is splitting data into dependent and independent variables. Finally, after formatting the data for classification tree, the data is split into training and testing sets. The experiments results show that the classifier Random Forest tree achieved high accuracy as compared to other classifiers. Additionally, the proposed system can be easily implemented in healthcare for identifying heart disease, diabetes and lung cancer. [ABSTRACT FROM AUTHOR]

Occupancy detection is critical during building operations, particularly in energy efficiency, space utility, heating, ventilation control, and air conditioning (HVAC) to optimize user comfort. However, applying occupancy sensors to detect occupants can violate a person's privacy, and the resulting data could be more precise. Therefore, this study aims to detect occupancy through environmental sensors by utilizing the accuracy of machine learning-based classification results. The ultimate goal of this study is to detect whether there are people in a room based on the benchmarks studied. Benchmarks for classifying whether people are in a room include CO2, humidity, lighting, temperature, and occupancy. This paper presents a method for comparing and evaluating a set of different machine learning techniques based on a given performance measure (for example, precision, accuracy, f1 score, and algorithm recall) and then using the algorithm with the best performance. This study uses five machine learning classification algorithms: K-Nearest Neighbors, Support Vector Machine, Decision Tree, Naive Bayes and Random Forest, and the method used to evaluate the results is cross-validation. Experiments were conducted using occupancy detection datasets from the UCI Machine Learning Repository. After performing the experiment, the predictive results of the five algorithms are high. That is, each has an accuracy value of more than 90%. Support Vector Machine has the highest accuracy value compared to other algorithms, namely 98.5%, and Decision Tree has the lowest accuracy value, namely 91.9%. The results show that selecting the right features and the suitable classification model can significantly impact prediction accuracy. [ABSTRACT FROM AUTHOR]

The IOT, in general, refers to devices being connected to a network. As a result, in order to protect network devices from security breaches, data must be secured. Because of the potential severity of the consequences of an IOT (IoT) failure, further investigation into IoT security vulnerabilities is warranted. The primary goal of IoT security is to protect user-privacy and confidentiality, as well as the architecture, communications, and technology that makes up an IoT ecosystem, as well as to ensure that the ecosphere services become inevitable. Since simulators, designers, and computation and analytic tools have become more widely available, research in IoT security has lately gained a lot of traction. An overview of the present level of IoT-security research, the essential tools, and IoT model and simulators are the core contribution of this study. Machine learning technologies will be used to identify attack threats in IoT security. Decision tree, Support Vector Machine (SVM), Bayes Algorithm, K-Nearest Neighbor (KNN), and Random Forest (RF) are the five most commonly used methods in IOT. [ABSTRACT FROM AUTHOR]

Eye recognition for person identification has been studied and implemented in many healthcare settings. This contactless person identification system is useful, especially in a hospital during the COVID-19 pandemic. It is also useful in critical treatment management, such as cardiac procedures. Unfortunately, low quality eye images may be obtained, which can result from, for example, a low-resolution camera on a mobile phone or moving eyes. In this study, a method to identify person in hospitals based on low-quality images is proposed. The eye images are captured using a low-resolution camera or moving camera. The proposed system employs an image segmentation algorithm and compares three different machine learning approaches to effectively classify each segmented region as the appropriate recognition type using Cascade Trainer: neural networks, support vector machines, and random forest decision trees. The use of a wrapper technique combined with recursive feature reduction has proven to be successful in maintaining the classifiers' performance while considerably lowering the number of required predictors. The results obtained with Jupyter demonstrate that classifiers based on fitted neural networks, random forest models, and support vector machines achieve high overall accuracy on testing with significant differences. The purpose of this project is to provide a consistent and robust methodological framework for the creation of trustworthy computational systems to assist in eye recognition for patient identification by using standard classification methods. The preliminary findings of this study suggest that, based on eye images of the classes collected in the dataset, they can be autonomously recognize. [ABSTRACT FROM AUTHOR]

Present investigation is targeted toward detection of credit card fake transactions. Random forest algorithm is used for innovative fraud detection in credit cards and its performance is tested by comparing with sup port vector machine algorithm. For present study two datasets were created one is training dataset [n=2,27,845 (80%)] and a test dataset [n=56,962 (20%)] (0.8g power). In the dataset 492 fraud transactions are present out of 284,807 transactions. Random forest classifier is compared with various machine learning algorithms using metrics like specificity, F-score, and accuracy. The detection accuracies of random forest algorithms and support vector machine are 97.2%, and 93.27% respectively. Findings of present study shows that the proposed random forest algorithm gives significantly enhanced performance as compared to the support vector machine algorithms in innovative fraud detection for fraudulent transactions in credit cards. [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]

The objective of this study is to implement machine learning (ML) to identify and classify the level of contamination in leftover cooked foods based on its aroma. An evaluation on the smell profiles using as a model local Malaysian lunch or evening foods that have always been stored as leftover cooked food is done in this study. To capture the data, a simple e-nose application is built and affixed to the food containers, which will accommodate four types of sensors sensitive to different gases and is programmed using the Arduino platform. To determine the aroma categorization of leftover Malaysian cuisine, samples are examined using RStudio. The results in this study demonstrated satisfactory performances by k-Nearest Neighbours (k-NN), Support Vector Machines (SVM), and Random Forest (RF) with accuracies ranging from 87.5% to 100% using the oversampling and undersampling techniques. Unfortunately, Linear Discriminant Analysis (LDA) gave poor performances (19.64% – 58.93%) in classifying the contamination level of the samples. Hence, the results obtained gave an indication that the electronic nose presented in this research was a promising for classification of contamination level for leftover cooked foods, allowing food to be better anticipated as to whether it is still edible or not. [ABSTRACT FROM AUTHOR]

Quick disease detection carries a great significance to the medical community. In this pandemic period, when people are falling prey to the deadly virus, it is clearly assume how much the frontline workers are risking their lives. In this period the technology driven laboratories plays an important role. These laboratories detect the dieses symptoms in a short span of time. However, affordability and reach off such medical facilities cannot reach to every citizen of a nation. Therefore, to make the things happening in a affordable way, we proposes an intelligent model for predicting human diseases such as covid19, diabetics, breast cancer, cardio diseases, kidney diseases, and liver diseases by using machine learning techniques. This model is an artificial intelligence based model, where various machine learning algorithms such as k-nearest neighbors, logistic regression, random forest, decision tree, support vector machine, Naive Bayes, and boosting techniques are used. [ABSTRACT FROM AUTHOR]

Though activity identification has already been studied for such a long period of time, the bulk of research and implementations have concerned with the physical activity recognition. Despite the fact that effective activity detection is necessary in a wide range of applications, research upon those activities has gotten less attention. One reason for this discrepancy is the scarcity of datasets to evaluate and compare different approaches. We developed the Open Laboratory Nursing Activity Identification Challenge, that focused on the detection of complex nursing duties, to stimulate study in such scenarios. The nursing field is one of the most likely to gain from activity recognition due to a scarcity of datasets. The competition was using the CARE COM Nurse Care Activity Dataset, which consisted of seven activities completed by eight persons in a controlled environment using accelerometer, motion tracking, and an indoor location sensor. In this article, the results of the competition are summarized. In this study, we employed widely used approaches such as Decision Based Tree Classification (DT), Random Forest Classification (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN). The accelerometer data was 71.2 percent accurate, the Meditag data was 81.1 percent accurate, and the Mobility Capture (Mocap) data was 99.99 percent accurate when we employed those approaches. [ABSTRACT FROM AUTHOR]

Over 17.3 million people are dying because of cardio-vascular disease. In past, predicting heart failure (HF) disease was a challenging task. In the modern era, we have relevant training data for HF prediction. Using state-of-the-art machine learning (ML) models, the HF can be predicted with high precision. In this paper, by employment of different ML algorithms, we predict whether a person has cardio-vascular disease (CVD) or not using relevant symptoms of the person. This research predicts the heart failure chances using discriminative attributes that are collected from the patients. A standard dataset from the university of California at Irvine (UCI) that contains 14 parameters related to heart disease has been examined in this study. Our machine learning models are trained using five different classification techniques. The algorithms are logistic regression, k-nearest neighbours (KNN), support vector machines (SVM), random forest, and gradient boosting. The SVM classifier has shown the highest accuracy of 86.84%. The accuracy of predictions has also been enhanced by suitable data pre-processing and cross validation techniques. [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]

Over few decades, there are quite a number of clinical guidelines are provided to healthcare workers regarding treatment suggested for homogenous patients, facing similar medical symptoms. Certainly, such recommendations may differ among guidelines, which results in tough scenario for clinicians to make a better decision. The scope of the work is to recognize homogeneous patients having similar medical signs and symptoms, demographics and health data. Distinguishing proof of comparative patient cases is helpful to work on clinical results. In view of the proposed model utilizing Machine Learning (ML) Algorithms like K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes, Random Forest, and expert opinion is utilized as a base for infection analysis and treatment idea. Better accuracy of 98% is achieved by SVM. It helps in the treatment or medication proposal of another patient, expectation of clinical outcomes, clinical help desk, research and in numerous other cases. [ABSTRACT FROM AUTHOR]

The rise in vehicle use as a means of transportation contributes to transportation problems occurring in big cities, including traffic congestion. This paper aims to provide insight into the prediction of traffic congestion by collecting the vehicle speed, the total number of vehicles, and time-based on two pre-recorded videos of congestion and non-congestion scenario for 30 seconds. The methodology used in this experiment uses OpenCV-Python to calculate and collect speed and the total number of vehicles in which these data will then be processed and analysed by using Orange with the help of machine learning (ML) techniques. ML techniques used are Decision Tree, Naive Bayes, Random Forest, and Support Vector Machine (SVM). This experiment's results illustrate that the ML technique that shows the best and most accurate for congestion prediction is Naive Bayes at 94.29%. Meanwhile, Decision Tree at 82.86%, Random Forest at 80%, and the lowest accuracy is SVM at only 20% for congestion prediction. All data processed are from a set of training data, where the correct classification has been determined beforehand. This study contributes to the effectiveness and accuracy of the method's congestion prediction, which can be used as a platform for future research to improve traffic congestion predictions. [ABSTRACT FROM AUTHOR]

Cricket is an outdoor game which is highly common and extremely followed in Asia. This game has a huge fan following across the globe. There are three formats of playing Cricket. The challenges posed by each format is different from other. When two equally skilled teams clash, the outcome of the cricket match becomes highly unpredictable. Each team comprises of eleven members and the collective effectiveness of a team determines its win.This work is focused towards performing a thorough review of the various approaches and techniques that are deployed across the game of cricket and how they are instrumental in predicting the outcome of the game. Also a basic prediction model that uses classifiers like Support Vector Machines (SVM), Decision Tree and Random Forest. [ABSTRACT FROM AUTHOR]

The implementation of high-speed broadband has resulted in a huge rise in internet use, which has skyrocketed as more people use remote access. Despite the fact that this rise in use allows for more online activities, the rapid growth of the Internet provides attackers with a wealth of network knowledge, and a large number of network threats has a negative effect on the network's development. Machine learning approaches are good at detecting unknown attacks. Random Forest, Decision Tree, Support Vector Machine, Logistic Regression, Gaussian Naive Bayes, and Gradient boosting Classifier are among the Supervised Machine Learning classification algorithms evaluated against the NSL-KDD dataset in this paper. Random Forest Classifier outperforms other methods in detecting attacks, according to the results of the experiments. [ABSTRACT FROM AUTHOR]

Currently, health management is a major concern all over the world. Particularly in India, due to population, the health issues are increasing rapidly. Diabetes has become a major issue that affects people all over India. With the advancement of Machine Learning approaches, many solutions have been developed to curb this serious issue. The goal of this paper is to design a model that can detect diabetes at preliminary stages with more accuracy. Therefore, various classification algorithms namely K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB) are used to solve this problem at earliest. Various performance metrics like accuracy, sensitivity, specificity, precision and recall are used for evaluation of the model. Evaluations are performed on PIMA Indian diabetes database, which is pulled from the Machine Learning cache. Results procured manifest that Random Forest outplay with the highest precision of 85.06%. The results are appropriately authenticated through a bar graph representation. [ABSTRACT FROM AUTHOR]

During the purchase of mobile phones, various features like memory, display, battery, camera, etc., are considered. People fail to make correct decisions, due to the non-availability of necessary resources to cross-validate the price. To address this issue, a machine learning model is developed using the data related to the key features of the mobile phone. The developed model is then used to predict the price range of the new mobile phone. Three machine learning algorithms namely Support Vector Machine (SVM), Random Forest Classifier (RFC), Logistic Regression are used to train the model and predict the output as low, medium, high or very high. The dataset used in this study is taken from Kaggle platform. In order to improve the classification accuracy, Chi-Squared based feature selection method is used. Among 21 features available in the dataset, only top 10 features namely RAM, pixel height, battery power, pixel width, mobile weight, internal memory, screen width, talk time, front camera and screen height are selected and used to train the model. Before applying feature selection, the accuracy obtained using SVM, RFC and Logistic Regression is 95%, 83% and 76% respectively. After feature selection, the accuracy of SVM, RFC and Logistic Regression improved to 97%, 87% and 81% respectively. From the experiments conducted, it is found that SVM gave superior performance when compared to other two classifiers. [ABSTRACT FROM AUTHOR]