Publication - Samia Binta Hassan

Publications:

1. An analysis of supervised machine learning algorithms for spam email detection

Abstract:  Email users are increasing at a high rate and a huge number of people’s privacy is getting risked by spam email and it also kills valuable time of people most often. Spam email can be malicious as well as it can be of commercial use as in for marketing which are not desirable to us. Hence, detecting and filtering spam emails from several emails is a must work to do. There are enormous machine learning (ML) algorithms and some of them can be used to detect and analyze spam and unwanted emails. In this paper, we use the supervised ML technique on an existing email classification dataset where we explore Naïve Bayes, Support Vector Machine, Random Forest Classifier. Along with observing the accuracy from these algorithms, we showed other performance metrics like precision, recall and F1 score etc. We got a high rate of accuracy in each algorithm such as we got 98.8%, 97.6%, 91.5%, 97.8%, 98.5% accuracy in Multinomial Naïve Bayes, Bernoulli Naïve Bayes, Gaussian Naïve Bayes, Random forest classifier, Support vector machine (SVM) respectively.

Link: https://ieeexplore.ieee.org/abstract/document/9528108

2. Contactless Surveillance for Preventing Wind-Borne Disease using Deep Learning Approach.

Abstract: Covid-19 has been marked as a pandemic world-wide caused by the SARS-CoV-2 virus. Different studies are being conducted with a view to preventing and lessening the infections caused by covid-19. In future, many other wind-borne diseases may also appear and even emerge as “pandemic”. To prevent this, various measures should be an integral part of our daily life such as wearing face masks. It is tough to manually ensure individuals safety. The goal of this paper is to automate the process of contactless surveillance so that substantial prevention can be ensured against all kinds of wind-borne diseases. For automating the process, real time analysis and object detection is a must for which deep learning is the most efficient approach. In this paper, a deep learning model is used to check if a person takes any preventive measures. In our experimental analysis, we considered real time face mask detection as a preventive measure. We proposed a new face mask detection dataset. The accuracy of detecting a face mask along with the identity of a person achieved accuracy of 99.5%. The proposed model decreases time consumption as no human intervention is needed to check an individual person. This model helps to decrease infection risk by using a contactless automation system.

Link: https://search.proquest.com/openview/137fe1957af68a50aab1f586099df7f9/1?pq-origsite=gscholar&cbl=5444811

3. An Advanced Decision Tree-Based Deep Neural Network in Nonlinear Data Classification

Abstract: Deep neural networks (DNNs), the integration of neural networks (NNs) and deep learning (DL), have proven highly efficient in executing numerous complex tasks, such as data and image classification. Because the multilayer in a nonlinearly separable data structure is not transparent, it is critical to develop a specific data classification model from a new and unexpected dataset. In this paper, we propose a novel approach using the concepts of DNN and decision tree (DT) for classifying nonlinear data. We first developed a decision tree-based neural network (DTBNN) model. Next, we extend our model to a decision tree-based deep neural network (DTBDNN), in which the multiple hidden layers in DNN are utilized. Using DNN, the DTBDNN model achieved higher accuracy compared to the related and relevant approaches. Our proposal achieves the optimal trainable weights and bias to build an efficient model for nonlinear data classification by combining the benefits of DT and NN. By conducting in-depth performance evaluations, we demonstrate the effectiveness and feasibility of the proposal by achieving good accuracy over different datasets.

Link: https://www.mdpi.com/2227-7080/11/1/24

4. An Intelligent Road Accident Reduction System Using Device to Device Communication

     Abstract: Every year thousands of people are dying due to road accidents and most of the accidents are occurring in urban areas and highways. As the number of vehicles is increasing day by day, the probability of the occurrence of the accident is also increasing. A system has been introduced in this paper that can reduce road accidents by sending an alert to drivers via smartphones. For this purpose, an android-based application has been developed. To reduce accidents, every driver of the car must have an application. Firstly, the location of the vehicle will be collected from the driver’s smartphone, then the data will be sent to the server through the application. An algorithm has also been developed using Haversine Formula which calculates the distance among the vehicles. A total of five cases have been implemented according to the dataset and compared for the performance evaluation. An extensive experimental study and comparison have been performed with the other methods where a complete performance is focused that can be claimed to reduce road accidents via the device-to-device communication process.

Link: https://ijeecs.iaescore.com/index.php/IJEECS/article/view/30656