Aditya Chellam

Bio: Aditya Chellam is an academic researcher from VIT University. The author has contributed to research in topics: Brand awareness & Market analysis. The author has an hindex of 1, co-authored 2 publications receiving 12 citations.

Data Visualization: Visualization of Social Media Marketing Analysis Data to Generate Effective Business Revenue Model

Abstract: Right from its inception, social media has played a pivotal role in shaping the marketing strategies of today’s business. Businesses use marketing to successfully grow their market presence and improve brand awareness. The most effective marketing approach is one where social media and traditional marketing mixes are used in tandem. Social media marketing is a lucrative option for business owners as the cost of marketing is low and user feedback on social media Web sites and forums can be utilized effectively to constantly update the marketing strategy for maximizing gains. This chapter focuses on analyzing the Facebook marketing strategy of a certain company and providing a comparative study of visualization methodologies that present the client sentiment in the most lucid manner, thereby allowing the business owner to devise an effective business model with maximum returns and minimum expenditure.

Machine learning approaches to predict adsorption capacity of Azolla pinnata in the removal of methylene blue

Abstract: Background In this study, the adsorption of methylene blue (MB) dye using an aquatic plant, Azolla pinnata (AP) was modelled using several various supervised machine learning (ML) algorithms, aiming to accurately predict the adsorption capacity under various experimental conditions. Methods The ML algorithms used in this study are the artificial neural network (ANN), random forests (RF), support vector regression (SVR), and instance-based learner (IbK). The SVR algorithm was trained using three kernels: radial basis function (RBF), Pearson VII universal kernel (PUK), and polynomial kernel (PolyK). The experimental data (adsorbent dosage, pH, ionic strength, initial dye concentration, and contact time) served as input for training the algorithms and with the adsorption capacity as the output. The performance of the algorithms was optimised based on the values of correlation coefficient (R) and fine-tuned using several error functions (e.g. mean absolute error, root mean square error, and non-linear chi-squared). Findings The best performing ML algorithm in this study is SVR-RBF which achieves the highest value in R (0.994) and has the lowest error.

Machine learning approaches to predict adsorption capacity of Azolla pinnata in the removal of methylene blue

Abstract: In this study, the adsorption of methylene blue (MB) dye using an aquatic plant, Azolla pinnata (AP) was modelled using several various supervised machine learning (ML) algorithms, aiming to accurately predict the adsorption capacity under various experimental conditions. The ML algorithms used in this study are the artificial neural network (ANN), random forests (RF), support vector regression (SVR), and instance-based learner (IbK). The SVR algorithm was trained using three kernels: radial basis function (RBF), Pearson VII universal kernel (PUK), and polynomial kernel (PolyK). The experimental data (adsorbent dosage, pH, ionic strength, initial dye concentration, and contact time) served as input for training the algorithms and with the adsorption capacity as the output. The performance of the algorithms was optimised based on the values of correlation coefficient (R) and fine-tuned using several error functions (e.g. mean absolute error, root mean square error, and non-linear chi-squared). The best performing ML algorithm in this study is SVR-RBF which achieves the highest value in R (0.994) and has the lowest error.

A novel method for feature learning and network intrusion classification

Abstract: With the rapid advancement in technology, network systems are becoming prone to more sophisticated types of intrusions. However, machine learning (ML) based strategies are among the most efficient and popular methods to identify the network intrusions or attacks. In this study, we examined the important and discriminative features, in order to recognize the various attacks by applying the Structural Sparse Logistic Regression (SSPLR) and Support Vector Machine (SVMs) methods. The SVMs are standard ML-based techniques, which provide the reasonable performance, however, they have few shortcomings, such as, interpretability and huge computational cost. On the other hand, the sparse modeling (SSPLR) is considered as the advanced method for the data examination and processing through regularization. The structural sparse modeling can be used to simultaneously select the distinct features or the group of discriminative features from the repository of the data set to determine the coefficient of the linear classifier, where, prior information of the feature’s structure can be mapped on various sparsity-inducing regularizations. In this way, the particular group of features yielded by the most significant network attacks are selected and potentially identified. The experiments and discussion, show that the proposed techniques have improved performance compared to the most state-of-the-art techniques, used for the Intrusion Detection System (IDS).

Network Attack Prediction By Random Forest : Classification Method

Abstract: Proposed system comprised of 32 honeypots with reports from 17M login attempts provided by many countries from 6000 different source IP addresses. Increasing number of attacks for blocking network connections in switch level was handled by Software Defined Network because of decoupled data in control plane. The Motive of software defined networks was defining rules based on the SDN controller for blocking unauthorized network connections. An historical network attack detects a data, blocks the untrusted connections. Though, each attacker provides solutions cannot effectively against chain attacks that contain many IP address utilized. In this paper, a machine learning algorithm on historical network attack data is trained for detecting potential unauthorized connections and potential attack destinations. Decision Tree (DT) and Naive-Bayes is used predicted host attacked depending on the historical data. Software attack pattern was predicted to identify unauthorized user that is same as Identifying intrusion in system. The Proposed system shows that average accuracy prediction using Bayesian networks are 91.68%.

Network intrusion detection system by using genetic algorithm

Abstract: Developing a better intrusion detection systems (IDS) has attracted many researchers in the area of computer network for the past decades. In this paper, Genetic Algorithm (GA) is proposed as a tool that capable to identify harmful type of connections in a computer network. Different features of connection data such as duration and types of connection in network were analyzed to generate a set of classification rule. For this project, standard benchmark dataset known as KDD Cup 99 was investigated and utilized to study the effectiveness of the proposed method on this problem domain. The rules comprise of eight variables that were simulated during the training process to detect any malicious connection that can lead to a network intrusion. With good performance in detecting bad connections, this method can be applied in intrusion detection system to identify attack thus improving the security features of a computer network.