In the telco industry, attracting new customers is no longer a good strategy since the cost of retaining existing customers is much lower. Churn management becomes instrumental in the telco industry. As there is limited study combining churn prediction and customer segmentation, this paper aims to propose an integrated customer analytics framework for churn management. There are six components in the framework, including data pre-processing, exploratory data analysis (EDA), churn prediction, factor analysis, customer segmentation, and customer behaviour analytics. This framework integrates churn prediction and customer segmentation process to provide telco operators with a complete churn analysis to better manage customer churn. Three datasets are used in the experiments with six machine learning classifiers. First, the churn status of the customers is predicted using multiple machine learning classifiers. Synthetic Minority Oversampling Technique (SMOTE) is applied to the training set to deal with the problems with imbalanced datasets. The 10-fold cross-validation is used to assess the models. Accuracy and F1-score are used for model evaluation. F1-score is considered to be an important metric to measure the models for imbalanced datasets since the premise of churn management is to be able to identify customers who will churn. Experimental analysis indicates that AdaBoost performed the best in Dataset 1, with accuracy of 77.19% and F1-score of 63.11%. Random Forest performed the best in Dataset 2, with accuracy of 93.6% and F1-score of 77.20%. Random Forest performed the best in Dataset 3 in terms of accuracy, at 63.09%, while Multi-layer Perceptron performed the best in terms of F1-score, at 42.84%. After implementing churn prediction, Bayesian Logistic Regression is used to conduct the factor analysis and to figure out some important features for churn customer segmentation. Churn customer segmentation is then carried out using K-means clustering. Customers are segmented into different groups, which allows marketers and decision makers to adopt retention strategies more precisely.

Phishing is an act of cybersecurity attack that tricks people into sharing sensitive data. Due to the inefficiency of the current security technologies, researchers have been paying much attention to employing machine learning methods for phishing detection lately. In our proposed solution, the effectiveness of machine learning techniques with feature selection techniques for phishing detection is investigated. To be specific, Random Forest (RF) and Artificial Neural Network (ANN) are integrated with feature selection techniques, Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE). The goal was to identify and classify the model with the highest accuracy. The experiments were evaluated using a dataset of 4,898 phishing sites and 6,157 legitimate sites, with the phishing data sourced from Kaggle.com. Our experiments demonstrate that the combination of RF model with PCA achieved 95.83% accuracy, while the ANN model with PCA reached 95.07% accuracy. The incorporation of PCA and RFE not only optimised the models' predictive performance but also improved computational efficiency. Overfitting can also be reduced. The experimental results also demonstrate that the proposed ANN with PCA method outperforms the state-of-the-art methods. Consequently, this research highlights the potential of combining advanced feature selection techniques with machine learning algorithms to develop robust solutions for phishing detection. Yet, this undoubtedly contributes to a safer internet environment.

Utilizing digital advancements, an integrated Flask-based platform has been engineered to centralize personal health records and facilitate informed healthcare decisions. The platform utilizes a Random Forest model-based symptom checker and an OpenAI API-powered chatbot for preliminary disease diagnosis and integrates Google Maps API to recommend proximal hospitals based on user location. Additionally, it contains a comprehensive user profile encompassing general information, medical history, and allergies. The system includes a medicine reminder feature for medication adherence. This innovative amalgamation of technology and healthcare fosters a user-centric approach to personal health management.

Masked face recognition (MFR) is an interesting topic in which researchers have tried to find a better solution to improve and enhance performance. Recently, COVID-19 caused most of the recognition system fails to recognize facial images since the current face recognition cannot accurately capture or detect masked face images. This paper introduces the proposed method known as histogram-based recurrent neural network (HRNN) MFR to solve the undetected masked face problem. The proposed method includes the feature descriptor of histograms of oriented gradients (HOG) as the feature extraction process and recurrent neural network (RNN) as the deep learning process. We have proven that the combination of both approaches works well and achieves a high true acceptance rate (TAR) of 99 percent. In addition, the proposed method is designed to overcome the underfitting problem and reduce computational burdens with large-scale dataset training. The experiments were conducted on two benchmark datasets which are RMFD (Real-World Masked Face Dataset) and Labeled Face in the Wild Simulated Masked Face Dataset (LFW-SMFD) to vindicate the viability of the proposed HRNN method.