In this paper, we analyze empirically the robustness and performances of six machine learning algorithms against two types of adversarial attacks by using four different datasets and three metrics. Therefore, it is significant to determine the performance and the robustness of a machine learning algorithm against attacks. This makes machine learning algorithms a target of adversaries, which is an important problem for systems that use such algorithms. Moreover, machine learning algorithms are used to solve some hard problems of systems that may contain very critical information.

Generally, machine learning is used by various industries to enhance their performances. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.įor the past decade, machine learning technology has increasingly become popular and it has been contributing to many areas that have the potential to influence the society considerably. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight.

In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. The performance metrics used were accuracy, precision, recall, and f1-score, along with the ROC curve. The results showed that SVM performed better than all the other algorithms when trained with TF-IDF feature vectors. For feature extraction, both Bag of Words & TF-IDF models were used separately and performance with both of these approaches was also compared. This paper showcases the performance evaluation of some selected supervised Machine Learning algorithms namely Naive Bayes Classifier, Support Vector Machine, Random Forest, & XG-Boost for spam email classification on a combination of three different datasets. Machine Learning algorithms are being extensively used in the classification of spam emails. Filtering these spam emails is very essential in order to keep the email space fluent in its functioning. These spam emails are sent with many different targets that range from advertisement to data theft. The irrelevant and unwanted emails are termed email spam. Billions of emails are sent every day in our world but not every one of them is relevant or of importance. We recommended deep leaning and deep adversarial learning as the future techniques that can effectively handle the menace of spam emails.Įmail is one of the most popular modes of communication we have today. Our review compares the strengths and drawbacks of existing machine learning approaches and the open research problems in spam filtering. Discussion on general email spam filtering process, and the various efforts by different researchers in combating spam through the use machine learning techniques was done. The preliminary discussion in the study background examines the applications of machine learning techniques to the email spam filtering process of the leading internet service providers (ISPs) like Gmail, Yahoo and Outlook emails spam filters.

Our review covers survey of the important concepts, attempts, efficiency, and the research trend in spam filtering. We present a systematic review of some of the popular machine learning based email spam filtering approaches. Machine learning methods of recent are being used to successfully detect and filter spam emails.

The upsurge in the volume of unwanted emails called spam has created an intense need for the development of more dependable and robust antispam filters.

2019 spamsieve review