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Author

H. Parveen Sultana

Other affiliations: Universiti Teknologi Petronas
Bio: H. Parveen Sultana is an academic researcher from VIT University. The author has contributed to research in topics: Cloud computing & Wireless network. The author has an hindex of 7, co-authored 20 publications receiving 130 citations. Previous affiliations of H. Parveen Sultana include Universiti Teknologi Petronas.

Papers
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Journal ArticleDOI
TL;DR: A novel function for identifying optimal weights on the basis of population diversity function and tuning function and a novel fitness function for PSO with the help of Support Vector Machine (SVM) are presented.
Abstract: Machine learning is used as an effective support system in health diagnosis which contains large volume of data. More commonly, analyzing such a large volume of data consumes more resources and execution time. In addition, all the features present in the dataset do not support in achieving the solution of the given problem. Hence, there is a need to use an effective feature selection algorithm for finding the more important features that contribute more in diagnosing the diseases. The Particle Swarm Optimization (PSO) is one of the metaheuristic algorithms to find the best solution with less time. Nowadays, PSO algorithm is not only used to select the more significant features but also removes the irrelevant and redundant features present in the dataset. However, the traditional PSO algorithm has an issue in selecting the optimal weight to update the velocity and position of the particles. To overcome this issue, this paper presents a novel function for identifying optimal weights on the basis of population diversity function and tuning function. We have also proposed a novel fitness function for PSO with the help of Support Vector Machine (SVM). The objective of the fitness function is to minimize the number of attributes and increase the accuracy. The performance of the proposed PSO-SVM is compared with the various existing feature selection algorithms such as Info gain, Chi-squared, One attribute based, Consistency subset, Relief, CFS, Filtered subset, Filtered attribute, Gain ratio and PSO algorithm. The SVM classifier is also compared with several classifiers such as Naive Bayes, Random forest and MLP.

59 citations

Journal ArticleDOI
TL;DR: The approach of natural language processing and machine learning in order to solve the problem of fake news by using bag-of-words, n-grams, count vectorizer, and trained the data on five classifiers to investigate which of them works well for this specific dataset of labelled news statements.

55 citations

Journal ArticleDOI
TL;DR: Artificial gravitational cuckoo search algorithm along with particle bee optimized associative memory neural network is introduced to manage the features present in the earlier heart disease classification system.
Abstract: Now-a-days heart disease is one of the serious disease because most of the people affected by this disease that leads to create death. Due to the serious risk of this heart disease, it has been identified in the beginning stage for avoiding the risk factors. Then the earlier detection system has been created by utilizing optimized and hybridized techniques to recognize the heart disease in earlier stage. So, artificial gravitational cuckoo search algorithm along with particle bee optimized associative memory neural network is introduced to manage the features present in the earlier heart disease classification system. Initially, heart disease related information is collected from Heart Disease Data Set-UCI repository. The collected information is huge in dimension which is difficult to process, that reduces the efficiency of heart disease identification system. So, the dimensionality of the features are reduces according to the behavior of gravitational cuckoo search algorithm. The selected features are processed by above defined associative memory classifier. Then the efficiency of the system is evaluated with the help of MATLAB based experimental results.

51 citations

Journal ArticleDOI
TL;DR: Data analysis collected from the setup which is installed at various places across the VIT University, Vellore helps in deeper understanding of the air quality status such that people will be aware of what will happen if the same air quality continues for a longtime.

26 citations

Journal ArticleDOI
TL;DR: In this article, a hybridized Ruzzo-Tompa memetic based deep trained Neocognitron neural network is introduced to analyze the heart disease related features and predict the heart diseases in earlier stage.
Abstract: According to the survey 17.5 million deaths are happened due to the cardiovascular disease that leads to create heart attack, chest pain and stroke. Based on the survey it clearly concludes that most of the people affected by heart problem that need to be identified in the earlier stage for eliminating the future risk in patient health. The importance of the heart disease detection process helps to create the earlier detection system for identifying heart problem by using machine learning and optimized techniques but the developed forecasting systems are difficult to predict the heart problems in an accurate manner with minimum time. So, hybridized Ruzzo–Tompa memetic based deep trained Neocognitron neural network is introduced to analyze the heart disease related features and predict the heart disease in earlier stage. First, heart disease data has been collected from UCI repository, dimensionality of the data is minimized by hybridized Ruzzo–Tompa memetic approach. After reducing the number of features, that are trained by deep learning approach which analyze the features using maximum number of hidden layers that used to predict heart disease features successfully while making the Neocognitron neural network classification. Further efficiency of the system is evaluated using MATLAB based simulation results.

17 citations


Cited by
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Journal ArticleDOI
TL;DR: An improved version of Salp Swarm Algorithm (ISSA) is proposed in this study to solve feature selection problems and select the optimal subset of features in wrapper-mode and demonstrates that ISSA outperforms all baseline algorithms in terms of fitness values, accuracy, convergence curves, and feature reduction in most of the used datasets.
Abstract: Many fields such as data science, data mining suffered from the rapid growth of data volume and high data dimensionality. The main problems which are faced by these fields include the high computational cost, memory cost, and low accuracy performance. These problems will occur because these fields are mainly used machine learning classifiers. However, machine learning accuracy is affected by the noisy and irrelevant features. In addition, the computational and memory cost of the machine learning is mainly affected by the size of the used datasets. Thus, to solve these problems, feature selection can be used to select optimal subset of features and reduce the data dimensionality. Feature selection represents an important preprocessing step in many intelligent and expert systems such as intrusion detection, disease prediction, and sentiment analysis. An improved version of Salp Swarm Algorithm (ISSA) is proposed in this study to solve feature selection problems and select the optimal subset of features in wrapper-mode. Two main improvements were included into the original SSA algorithm to alleviate its drawbacks and adapt it for feature selection problems. The first improvement includes the use of Opposition Based Learning (OBL) at initialization phase of SSA to improve its population diversity in the search space. The second improvement includes the development and use of new Local Search Algorithm with SSA to improve its exploitation. To confirm and validate the performance of the proposed improved SSA (ISSA), ISSA was applied on 18 datasets from UCI repository. In addition, ISSA was compared with four well-known optimization algorithms such as Genetic Algorithm, Particle Swarm Optimization, Grasshopper Optimization Algorithm, and Ant Lion Optimizer. In these experiments four different assessment criteria were used. The rdemonstrate that ISSA outperforms all baseline algorithms in terms of fitness values, accuracy, convergence curves, and feature reduction in most of the used datasets. The wrapper feature selection mode can be used in different application areas of expert and intelligent systems and this is confirmed from the obtained results over different types of datasets.

224 citations

Journal ArticleDOI
TL;DR: A comparative analysis of different feature selection methods is presented, and a general categorization of these methods is performed, which shows the strengths and weaknesses of the different studied swarm intelligence-based feature selection Methods.

200 citations

Journal ArticleDOI
TL;DR: An IoT framework to evaluate heart disease more accurately using a Modified Deep Convolutional Neural Network (MDCNN) and demonstrates that the proposed MDCNN based heart disease prediction system performs better than other methods.
Abstract: Nowadays, heart disease is the leading cause of death worldwide. Predicting heart disease is a complex task since it requires experience along with advanced knowledge. Internet of Things (IoT) technology has lately been adopted in healthcare systems to collect sensor values for heart disease diagnosis and prediction. Many researchers have focused on the diagnosis of heart disease, yet the accuracy of the diagnosis results is low. To address this issue, an IoT framework is proposed to evaluate heart disease more accurately using a Modified Deep Convolutional Neural Network (MDCNN). The smartwatch and heart monitor device that is attached to the patient monitors the blood pressure and electrocardiogram (ECG). The MDCNN is utilized for classifying the received sensor data into normal and abnormal. The performance of the system is analyzed by comparing the proposed MDCNN with existing deep learning neural networks and logistic regression. The results demonstrate that the proposed MDCNN based heart disease prediction system performs better than other methods. The proposed method shows that for the maximum number of records, the MDCNN achieves an accuracy of 98.2 which is better than existing classifiers.

145 citations

Journal ArticleDOI
TL;DR: An IoMT framework for the diagnosis of heart disease using modified salp swarm optimization (MSSO) and an adaptive neuro-fuzzy inference system (ANFIS) is proposed, which improves the search capability using the Levy flight algorithm and achieves better accuracy than other approaches.
Abstract: The IoT has applications in many areas such as manufacturing, healthcare, and agriculture, to name a few Recently, wearable devices have become popular with wide applications in the health monitoring system which has stimulated the growth of the Internet of Medical Things (IoMT) The IoMT has an important role to play in reducing the mortality rate by the early detection of disease The prediction of heart disease is a key issue in the analysis of clinical dataset The aim of the proposed investigation is to identify the key characteristics of heart disease prediction using machine learning techniques Many studies have focused on heart disease diagnosis, but the accuracy of the findings is low Therefore, to improve prediction accuracy, an IoMT framework for the diagnosis of heart disease using modified salp swarm optimization (MSSO) and an adaptive neuro-fuzzy inference system (ANFIS) is proposed The proposed MSSO-ANFIS improves the search capability using the Levy flight algorithm The regular learning process in ANFIS is dependent on gradient-based learning and has a tendency to become trapped in local minima The learning parameters are optimized utilizing MSSO to provide better results for ANFIS The following information is taken from medical records to predict the risk of heart disease: blood pressure (BP), age, sex, chest pain, cholesterol, blood sugar, etc The heart condition is identified by classifying the received sensor data using MSSO-ANFIS A simulation and analysis is conducted to show that MSSA-ANFIS works well in relation to disease prediction The results of the simulation demonstrate that the MSSO-ANFIS prediction model achieves better accuracy than the other approaches The proposed MSSO-ANFIS prediction model obtains an accuracy of 9945 with a precision of 9654, which is higher than the other approaches

127 citations

Journal ArticleDOI
TL;DR: Algorithms on social media and financial news data are used to discover the impact of this data on stock market prediction accuracy for ten subsequent days and Random forest classifier is found to be consistent and highest accuracy is achieved by its ensemble.
Abstract: Accurate stock market prediction is of great interest to investors; however, stock markets are driven by volatile factors such as microblogs and news that make it hard to predict stock market index based on merely the historical data. The enormous stock market volatility emphasizes the need to effectively assess the role of external factors in stock prediction. Stock markets can be predicted using machine learning algorithms on information contained in social media and financial news, as this data can change investors’ behavior. In this paper, we use algorithms on social media and financial news data to discover the impact of this data on stock market prediction accuracy for ten subsequent days. For improving performance and quality of predictions, feature selection and spam tweets reduction are performed on the data sets. Moreover, we perform experiments to find such stock markets that are difficult to predict and those that are more influenced by social media and financial news. We compare results of different algorithms to find a consistent classifier. Finally, for achieving maximum prediction accuracy, deep learning is used and some classifiers are ensembled. Our experimental results show that highest prediction accuracies of 80.53% and 75.16% are achieved using social media and financial news, respectively. We also show that New York and Red Hat stock markets are hard to predict, New York and IBM stocks are more influenced by social media, while London and Microsoft stocks by financial news. Random forest classifier is found to be consistent and highest accuracy of 83.22% is achieved by its ensemble.

104 citations