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Balakrishna Gokaraju
Feed . Journal article Thermal Imagery Feature Extraction Techniques and the Effects on Machine Learning Models for Smart HVAC Efficiency in Building Energy Yaa Takyiwaa Acquaah Balakrishna Gokaraju Raymond C. Tesiero Gregory H. Monty https://doi.org/10.3390/rs13193847 Published: 26 September 2021 in Remote Sensing . Reads? 0 Downloads? 0 Abstract Cite All recommendations The control of thermostats of a heating, ventilation, and air-conditioning (HVAC) system installed in commercial and residential buildings remains a pertinent problem in building energy efficiency and thermal comfort research. The ability to determine the number of people at a particular time in an area is imperative for energy efficiency in order to condition only occupied regions and thermally deficient regions. In this study of the best features comparison for detecting the number of people in an area, feature extraction techniques including wavelet scattering, wavelet decomposition, grey-level co-occurrence matrix (GLCM) and feature maps convolution neural network (CNN) layers were explored using thermal camera imagery. Specifically, the pretrained CNN networks explored are the deep residual (Resnet-50) and visual geometry group (VGG-16) networks. The discriminating potential of Haar, Daubechies and Symlets wavelet statistics on different distributions of data were investigated. The performance of VGG-16 and ResNet-50 in an end-to-end manner utilizing transfer learning approach was investigated. Experimental results showed the classification and regression trees (CART) model trained on only GLCM and Haar wavelet statistic features, individually achieved accuracies of approximately 80% and 84%, respectively, in the detection problem. Moreover, k-nearest neighbors (KNN) trained on the combined features of GLCM and Haar wavelet statistics achieved an accuracy of approximately 86%. In addition, the performance accuracy of the multi classification support vector machine (SVM) trained on deep features obtained from layers of pretrained ResNet-50 and VGG-16 was between 96% and 97%. Furthermore, ResNet-50 transfer learning outperformed the VGG-16 transfer learning model for occupancy detection using thermal imagery. Overall, the SVM model trained on features extracted from wavelet scattering emerged as the best performing classifier with an accuracy of 100%. A principal component analysis (PCA) on the wavelet scattering features proved that the first twenty (20) principal components achieved a similar accuracy level instead of training on the whole feature set to reduce the execution time. The occupancy detection models can be integrated into HVAC control systems for energy efficiency and security systems, and aid in the distribution of resources to people in an area. ACS Style Yaa Takyiwaa Acquaah; Balakrishna Gokaraju; Raymond C. Tesiero; Gregory H. Monty. Thermal Imagery Feature Extraction Techniques and the Effects on Machine Learning Models for Smart HVAC Efficiency in Building Energy. Remote Sensing 2021 , 13 , 3847 . AMA Style Yaa Takyiwaa Acquaah, Balakrishna Gokaraju, Raymond C. Tesiero, Gregory H. Monty. Thermal Imagery Feature Extraction Techniques and the Effects on Machine Learning Models for Smart HVAC Efficiency in Building Energy. Remote Sensing. 2021; 13 (19):3847. Chicago/Turabian Style Yaa Takyiwaa Acquaah; Balakrishna Gokaraju; Raymond C. Tesiero; Gregory H. Monty. 2021. "Thermal Imagery Feature Extraction Techniques and the Effects on Machine Learning Models for Smart HVAC Efficiency in Building Energy." Remote Sensing 13, no. 19: 3847. Journal article Big Data Mining and Classification of Intelligent Material Science Data Using Machine Learning Swetha Chittam Balakrishna Gokaraju Zhigang Xu Jagannathan Sankar Kaushik Roy https://doi.org/10.3390/app11188596 Published: 16 September 2021 in Applied Sciences . Reads? 0 Downloads? 0 Abstract Cite All recommendations There is a high need for a big data repository for material compositions and their derived analytics of metal strength, in the material science community. Currently, many researchers maintain their own excel sheets, prepared manually by their team by tabulating the experimental data collected from scientific journals, and analyzing the data by performing manual calculations using formulas to determine the strength of the material. In this study, we propose a big data storage for material science data and its processing parameters information to address the laborious process of data tabulation from scientific articles, data mining techniques to retrieve the information from databases to perform big data analytics, and a machine learning prediction model to determine material strength insights. Three models are proposed based on Logistic regression, Support vector Machine SVM and Random Forest Algorithms. These models are trained and tested using a 10-fold cross validation approach. The Random Forest classification model performed better on the independent dataset, with 87% accuracy in comparison to Logistic regression and SVM with 72% and 78%, respectively. ACS Style Swetha Chittam; Balakrishna Gokaraju; Zhigang Xu; Jagannathan Sankar; Kaushik Roy. Big Data Mining and Classification of Intelligent Material Science Data Using Machine Learning. Applied Sciences 2021 , 11 , 8596 . AMA Style Swetha Chittam, Balakrishna Gokaraju, Zhigang Xu, Jagannathan Sankar, Kaushik Roy. Big Data Mining and Classification of Intelligent Material Science Data Using Machine Learning. Applied Sciences. 2021; 11 (18):8596. Chicago/Turabian Style Swetha Chittam; Balakrishna Gokaraju; Zhigang Xu; Jagannathan Sankar; Kaushik Roy. 2021. "Big Data Mining and Classification of Intelligent Material Science Data Using Machine Learning." Applied Sciences 11, no. 18: 8596. Journal article Secure Cyber Defense: An Analysis of Network Intrusion-Based Dataset CCD-IDSv1 with Machine Learning and Deep Learning Models Niraj Thapa Zhipeng Liu Addison Shaver Albert Esterline Balakrishna Gokaraju Kaushik Roy https://doi.org/10.3390/electronics10151747 Published: 21 July 2021 in Electronics . Reads? 0 Downloads? 0 Abstract Cite All recommendations Anomaly detection and multi-attack classification are major concerns for cyber defense. Several publicly available datasets have been used extensively for the evaluation of Intrusion Detection Systems (IDSs). However, most of the publicly available datasets may not contain attack scenarios based on evolving threats. The development of a robust network intrusion dataset is vital for network threat analysis and mitigation. Proactive IDSs are required to tackle ever-growing threats in cyberspace. Machine learning (ML) and deep learning (DL) models have been deployed recently to detect the various types of cyber-attacks. However, current IDSs struggle to attain both a high detection rate and a low false alarm rate. To address these issues, we first develop a Center for Cyber Defense (CCD)-IDSv1 labeled flow-based dataset in an OpenStack environment. Five different attacks with normal usage imitating real-life usage are implemented. The number of network features is increased to overcome the shortcomings of the previous network flow-based datasets such as CIDDS and CIC-IDS2017. Secondly, this paper presents a comparative analysis on the effectiveness of different ML and DL models on our CCD-IDSv1 dataset. In this study, we consider both cyber anomaly detection and multi-attack classification. To improve the performance, we developed two DL-based ensemble models: Ensemble-CNN-10 and Ensemble-CNN-LSTM. Ensemble-CNN-10 combines 10 CNN models developed from 10-fold cross-validation, whereas Ensemble-CNN-LSTM combines base CNN and LSTM models. This paper also presents feature importance for both anomaly detection and multi-attack classification. Overall, the proposed ensemble models performed well in both the 10-fold cross-validation and independent testing on our dataset. Together, these results suggest the robustness and effectiveness of the proposed IDSs based on ML and DL models on the CCD-IDSv1 intrusion detection dataset. ACS Style Niraj Thapa; Zhipeng Liu; Addison Shaver; Albert Esterline; Balakrishna Gokaraju; Kaushik Roy. Secure Cyber Defense: An Analysis of Network Intrusion-Based Dataset CCD-IDSv1 with Machine Learning and Deep Learning Models. Electronics 2021 , 10 , 1747 . AMA Style Niraj Thapa, Zhipeng Liu, Addison Shaver, Albert Esterline, Balakrishna Gokaraju, Kaushik Roy. Secure Cyber Defense: An Analysis of Network Intrusion-Based Dataset CCD-IDSv1 with Machine Learning and Deep Learning Models. Electronics. 2021; 10 (15):1747. Chicago/Turabian Style Niraj Thapa; Zhipeng Liu; Addison Shaver; Albert Esterline; Balakrishna Gokaraju; Kaushik Roy. 2021. "Secure Cyber Defense: An Analysis of Network Intrusion-Based Dataset CCD-IDSv1 with Machine Learning and Deep Learning Models." Electronics 10, no. 15: 1747. Journal article Comparison of Machine Learning and Deep Learning Models for Network Intrusion Detection Systems Niraj Thapa Zhipeng Liu Dukka B. Kc Balakrishna Gokaraju Kaushik Roy https://doi.org/10.3390/fi12100167 Published: 30 September 2020 in Future Internet . Reads? 0 Downloads? 0 Abstract Cite All recommendations The development of robust anomaly-based network detection systems, which are preferred over static signal-based network intrusion, is vital for cybersecurity. The development of a flexible and dynamic security system is required to tackle the new attacks. Current intrusion detection systems (IDSs) suffer to attain both the high detection rate and low false alarm rate. To address this issue, in this paper, we propose an IDS using different machine learning (ML) and deep learning (DL) models. This paper presents a comparative analysis of different ML models and DL models on Coburg intrusion detection datasets (CIDDSs). First, we compare different ML- and DL-based models on the CIDDS dataset. Second, we propose an ensemble model that combines the best ML and DL models to achieve high-performance metrics. Finally, we benchmarked our best models with the CIC-IDS2017 dataset and compared them with state-of-the-art models. While the popular IDS datasets like KDD99 and NSL-KDD fail to represent the recent attacks and suffer from network biases, CIDDS, used in this research, encompasses labeled flow-based data in a simulated office environment with both updated attacks and normal usage. Furthermore, both accuracy and interpretability must be considered while implementing AI models. Both ML and DL models achieved an accuracy of 99% on the CIDDS dataset with a high detection rate, low false alarm rate, and relatively low training costs. Feature importance was also studied using the Classification and regression tree (CART) model. Our models performed well in 10-fold cross-validation and independent testing. CART and convolutional neural network (CNN) with embedding achieved slightly better performance on the CIC-IDS2017 dataset compared to previous models. Together, these results suggest that both ML and DL methods are robust and complementary techniques as an effective network intrusion detection system. ACS Style Niraj Thapa; Zhipeng Liu; Dukka B. Kc; Balakrishna Gokaraju; Kaushik Roy. Comparison of Machine Learning and Deep Learning Models for Network Intrusion Detection Systems. Future Internet 2020 , 12 , 167 . AMA Style Niraj Thapa, Zhipeng Liu, Dukka B. Kc, Balakrishna Gokaraju, Kaushik Roy. Comparison of Machine Learning and Deep Learning Models for Network Intrusion Detection Systems. Future Internet. 2020; 12 (10):167. Chicago/Turabian Style Niraj Thapa; Zhipeng Liu; Dukka B. Kc; Balakrishna Gokaraju; Kaushik Roy. 2020. "Comparison of Machine Learning and Deep Learning Models for Network Intrusion Detection Systems." Future Internet 12, no. 10: 167. .
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