Anurag Rana
Verified @gmail.com
Associate Professor Yogananda School of AI Computers and Data Sciences
EDUCATION
Post Doctoral (AI & AGI)
Doctor of Philosophy Computer Science and Engineering ( Artificial Intelligence / Machine Learning &Data Science)
Master of Technology Computer Science and Engineering ( Neural Networks)
RESEARCH, TEACHING, or OTHER INTERESTS
Artificial Intelligence, Computer Engineering, Information Systems, Computer Science
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Ashish, Sonia, Monika Arora, Hemraj, Anurag Rana, and Gaurav Gupta
IEEE
Fake news is one of the major issues in today’s world because a piece of false information can ruin someone’s life easily. So, to identify these types of crimes, researchers introduced a fake news detection system through machine learning. Fake news identification is becoming more and more popular and widely used. Many businesses are investing in the sector, either for their needs or to offer it as a service to others. Machine learning (ML) and deep learning (DL) are two methods used for determining whether the news results to be authentic or not. Numerous methodologies exist for discerning false news through the utilization of both Machine Learning and Deep Learning methodologies. Assessing the need of the time, through this paper, an identification of fake news and analysis has been done using machine learning techniques. After a detailed review, it has been discovered that numerous Machine Learning and Deep Learning algorithms are applied. The most often used Machine Learning approach is SVM (Support Vector Machine), and the most widely used Deep Learning technique is LSTM (Long Short-Term Memory).
Hemraj, Sonia, Ashish, Gaurav Gupta, Anurag Rana and Anitya Gupta
IEEE
Machine learning methodologies have become indispensable in augmenting the efficacy of intrusion detection systems (IDS). This paper furnishes a comprehensive survey of machine learning-driven IDS strategies, encompassing diverse classification algorithms, feature selection methodologies, and anomaly detection techniques. The study scrutinizes the utilization of singular, amalgamated, and ensemble classifiers within the machine learning paradigm for intrusion detection purposes. Moreover, it investigates innovative amalgamated methodologies integrating Genetic Algorithm (GA), Artificial Neural Network (ANN), Artificial Bee Colony (ABC), Discrete Wavelet Transform (DWT), and Support Vector Machine (SVM) to enhance intrusion detection accuracy. Additionally, the paper investigates the importance of kernel methods in intrusion identification, introducing a novel array of kernels tailored for anomaly detection. Lastly, it addresses the challenges associated with the extensive deployment of anomaly-based intrusion detectors. This research offers valuable insights into contemporary machine learning techniques for intrusion detection, providing a roadmap for researchers and practitioners in developing and deploying effective IDS solutions.
Anurag Rana, Pankaj Vaidya, and Yu-Chen Hu
Springer Science and Business Media LLC
Anurag Rana, Pankaj Vaidya, and Rohit Kumar
Springer Nature Singapore
Sunil Kumar, Iqra Ali, Faheem Abbas, Anurag Rana, Sadanand Pandey, Manoj Garg, and Deepak Kumar
Informa UK Limited
Lung cancer is a complex and heterogeneous disease, which has been associated with various molecular alterations, including the overexpression and mutations of the epidermal growth factor receptor (EGFR). In this study, designed a library of 1843 benzimidazole-1,2,3-triazole hybrids and carried out pharmacophore-based screening to identify potential EGFR inhibitors. The 164 compounds were further evaluated using molecular docking and molecular dynamics simulations to understand the binding interactions between the compounds and the receptor. In-si-lico ADME and toxicity studies were also conducted to assess the drug-likeness and safety of the identified compounds. The results of this study indicate that benzimidazole-1,2,3-triazole hybrids BENZI-0660, BENZI-0125, BENZI-0279, BENZI-0415, BENZI-0437, and BENZI-1110 exhibit dock scores of -9.7, -9.6, -9.6, -9.6, -9.6, -9.6 while referencing molecule -7.9 kcal/mol for EGFR (PDB ID: 4HJO), respectively. The molecular docking and molecular dynamics simulations revealed that the identified compounds formed stable interactions with the active site of EGFR, indicating their potential as inhibitors. The in-silico ADME and toxicity studies showed that the compounds had favorable drug-likeness properties and low toxicity, further supporting their potential as therapeutic agents. Finally, performed DFT studies on the best-selected ligands to gain further insights into their electronic properties. The findings of this study provide important insights into the potential of benzimidazole-1,2,3-triazole hybrids as promising EGFR inhibitors for the treatment of lung cancer. This research opens up a new avenue for the discovery and development of potent and selective EGFR inhibitors for the treatment of lung cancer.Communicated by Ramaswamy H. Sarma.
Anurag Rana, Pankaj Vaidya, Sandeep Kautish, Manoj Kumar, and Supriya Khaitan
IOS Press
Parameters related to earthquake origins can be broken down into two broad classes: source location and source dimension. Scientists use distance curves versus average slowness to approximate the epicentre of an earthquake. The shape of curves is the complex function to the epicentral distance, the geological structures of Earth, and the path taken by seismic waves. Brune’s model for source is fitted to the measured seismic wave’s displacement spectrum in order to estimate the source’s size by optimising spectral parameters. The use of ANFIS to determine earthquake magnitude has the potential to significantly alter the playing field. ANFIS can learn like a person using only the data that has already been collected, which improves predictions without requiring elaborate infrastructure. For this investigation’s FIS development, we used a machine with Python 3x running on a core i5 from the 11th generation and an NVIDIA GEFORCE RTX 3050ti GPU processor. Moreover, the research demonstrates that presuming a large number of inputs to the membership function is not necessarily the best option. The quality of inferences generated from data might vary greatly depending on how that data is organised. Subtractive clustering, which does not necessitate any type of normalisation, can be used for prediction of earthquakes magnitude with a high degree of accuracy. This study has the potential to improve our ability to foresee quakes larger than magnitude 5. A solution is not promised to the practitioner, but the research is expected to lead in the right direction. Using Brune’s source model and high cut-off frequency factor, this article suggests using machine learning techniques and a Brune Based Application (BBA) in Python. Application accept input in the Sesame American Standard Code for Information Interchange Format (SAF). An application calculates the spectral level of low frequency displacement (Ω0), the corner frequency at which spectrum decays with a rate of 2(fc), the cut-off frequency at which spectrum again decays (fmax), and the rate of decay above fmax on its own (N). Seismic moment, stress drop, source dimension, etc. have all been estimated using spectral characteristics, and scaling laws. As with the maximum frequency, fmax, its origin can be determined through careful experimentation and study. At some sites, the moment magnitude was 4.7 0.09, and the seismic moment was in the order of (107 0.19) 1023. (dyne.cm). The stress reduction is 76.3 11.5 (bars) and the source-radius is (850.0 38.0) (m). The ANFIS method predicted pretty accurately as the residuals were distributed uniformly near to the centrelines. The ANFIS approach made fairly accurate predictions, as evidenced by the fact that the residuals were distributed consistently close to the centerlines. The R2, RMSE, and MAE indices demonstrate that the ANFIS accuracy level is superior to that of the ANN.
Anurag Rana, Gaurav Gupta, Pankaj Vaidya, Waleed Salehi, Shakila Basheer, and Madhulika Bhatia
Hindawi Limited
This research looked into the viability of using metaheuristic algorithms in conjunction with an adaptive neurofuzzy system to predict seismicity and earthquakes. Different metaheuristic algorithms have been combined with an artificial intelligence (AI) algorithm. Subjected to seismicity is a promising factor. The new sensors have many advantages over the older, more impressive-looking ones, including (a) a generally linear relationship between the measured values and real ground motion (described above), (b) the ability to measure three orthogonal components of ground movement in a single unit, (c) sensitivity to a very broad range of frequencies, and (d) high dynamic range, which allows for the detection of both very small and fairly large tremors. To accept the acquired results as a hybrid model of an adaptive neurofuzzy inference system with particle swarm optimization (PSO), genetic algorithm (GA), and extreme machine learning (ELM) (ANFIS-PSO-GA-ELM) implemented. According to the dataset, all approaches produce excellent and realistic predictions of seismic loads; however, the method ANFIS-PSO produces better results. All the strategies demonstrated a high level of predictability. Finally, this research urges researchers to investigate the performance of triple hybrid MT algorithms using a variety of hybrid metaheuristic methodologies, rather than the existing double hybrid MT algorithms.
Anurag Rana, Pankaj Vaidya, and Gaurav Gupta
Elsevier BV
Anurag Rana, Pankaj Vaidya, and Yu-Chen Hu
IEEE
The ANN (Artificial Neural Networks) & ANFIS (Adaptive Neuro-Fuzzy Inference System) approaches were used to identify between discrete and continuous readings in seismic recorded data and precursor radon all over the world. The significance of “precursor radon” in earthquake forecasting has been investigated. For time series modelling, the intelligence system ANN and ANFIS approaches were applied. The knowledge contained in the trained networks can be described as a fuzzy rule base using ANFIS techniques. The results of the research on the use of ANN and ANFIS approaches to forecast earthquakes are presented in this paper. The ANFIS algorithms outperform ANN modelling approaches in terms of accuracy. The varied techniques of ANN and ANFIS in various viewpoints of the seismic domain are also investigated in this research.
Anurag Rana, Pankaj Vaidya, Puneet Kapoor, and Gaurav Gupta
IEEE
The employment of the included offered algorithm, the most recent strategy successfully boosts the unique contrast inside digital photos. Gradient-based smoothing will also be employed to retain the edges and borders. Furthermore, the proposed technique has produced some really valuable outcomes, making it even more useful. Python programming is used to build and implement the proposed approach. The recommended solution is created and implemented with the Scikit image processing library. Many different sorts of images have also been studied for purpose of testing. Different quality criteria for evaluating the efficiency of the suggested technique are also given. The suggested technique outperforms the alternatives, as evidenced by a comparison of existing and new methodologies. Some picture performance measures such as root mean square error and edge preservation index will be used to compare new and current median filter algorithms.