1
|
Saravagi D, Agrawal S, Saravagi M, Chatterjee JM, Agarwal M, Hošovský A. Diagnosis of Lumbar Spondylolisthesis Using Optimized Pretrained CNN Models. Computational Intelligence and Neuroscience 2022; 2022:1-12. [PMID: 35432510 PMCID: PMC9007141 DOI: 10.1155/2022/7459260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 02/12/2022] [Accepted: 02/18/2022] [Indexed: 02/05/2023]
Abstract
Spondylolisthesis refers to the slippage of one vertebral body over the adjacent one. It is a chronic condition that requires early detection to prevent unpleasant surgery. The paper presents an optimized deep learning model for detecting spondylolisthesis in X-ray radiographs. The dataset contains a total of 299 X-ray radiographs from which 156 images are showing the spine with spondylolisthesis and 143 images are of the normal spine. Image augmentation technique is used to increase the data samples. In this study, VGG16 and InceptionV3 models were used for the image classification task. The developed model is optimized by utilizing the TFLite model optimization technique. The experimental result shows that the VGG16 model has achieved a 98% accuracy rate, which is higher than InceptionV3's 96% accuracy rate. The size of the implemented model is reduced up to four times so it can be used on small devices. The compressed VGG16 and InceptionV3 models have achieved 100% and 96% accuracy rate, respectively. Our finding shows that the implemented models were outperformed in the diagnosis of lumbar spondylolisthesis as compared to the model suggested by Varcin et al. (which had a maximum of 93% accuracy rate). Also, the developed quantized model has achieved higher accuracy rate than Zebin and Rezvy's (VGG16 + TFLite) model with 90% accuracy. Furthermore, by evaluating the model's performance on other publicly available datasets, we have generalised our approach on the public platform.
Collapse
|
2
|
Yadav S, Gulia P, Gill NS, Chatterjee JM. A Real-Time Crowd Monitoring and Management System for Social Distance Classification and Healthcare Using Deep Learning. J Healthc Eng 2022; 2022:2130172. [PMID: 35422976 PMCID: PMC9005306 DOI: 10.1155/2022/2130172] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/11/2022] [Accepted: 03/18/2022] [Indexed: 02/05/2023]
Abstract
Coronavirus born COVID-19 disease has spread its roots in the whole world. It is primarily spread by physical contact. As a preventive measure, proper crowd monitoring and management systems are required to be installed in public places to limit sudden outbreaks and impart improved healthcare. The number of new infections can be significantly reduced by adopting social distancing measures earlier. Motivated by this notion, a real-time crowd monitoring and management system for social distance classification is proposed in this research paper. In the proposed system, people are segregated from the background using the YOLO v4 object detection technique, and then the detected people are tracked by bounding boxes using the Deepsort technique. This system significantly helps in COVID-19 prevention by social distance detection and classification in public places using surveillance images and videos captured by the cameras installed in these places. The performance of this system has been assessed using mean average precision (mAP) and frames per second (FPS) metrics. It has also been evaluated by deploying it on Jetson Nano, a low-cost embedded system. The observed results show its suitability for real-time deployment in public places for COVID-19 prevention by social distance monitoring and classification.
Collapse
Affiliation(s)
- Sangeeta Yadav
- Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, India
| | - Preeti Gulia
- Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, India
| | - Nasib Singh Gill
- Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, India
| | - Jyotir Moy Chatterjee
- Department of Information Technology, Lord Buddha Education Foundation, Kathmandu, Nepal
| |
Collapse
|
3
|
Khatri A, Agrawal S, Chatterjee JM, Gupta P. Wheat Seed Classification: Utilizing Ensemble Machine Learning Approach. Scientific Programming 2022; 2022:1-9. [DOI: 10.1155/2022/2626868] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Recognizing and authenticating wheat varieties is critical for quality evaluation in the grain supply chain, particularly for methods for seed inspection. Recognition and verification of grains are carried out manually through direct visual examination. Automatic categorization techniques based on machine learning and computer vision offered fast and high-throughput solutions. Even yet, categorization remains a complicated process at the varietal level. The paper utilized machine learning approaches for classifying wheat seeds. The seed classification is performed based on 7 physical features: area of wheat, perimeter of wheat, compactness, length of the kernel, width of the kernel, asymmetry coefficient, and kernel groove length. The dataset is collected from the UCI library and has 210 occurrences of wheat kernels. The dataset contains kernels from three wheat varieties Kama, Rosa, and Canadian, with 70 components chosen at random for the experiment. In the first phase, K-nearest neighbor, classification and regression tree, and Gaussian Naïve Bayes algorithms are implemented for classification. The results of these algorithms are compared with the ensemble approach of machine learning. The results reveal that accuracies calculated for KNN, decision, and Naïve Bayes classifiers are 92%, 94%, and 92%, respectively. The highest accuracy of 95% is achieved through the ensemble classifier in which decision is made based on hard voting.
Collapse
|
4
|
Sujatha R, Chatterjee JM, Priyadarshini I, Hassanien AE, Mousa AAA, Alghamdi SM. Self-organizing Maps and Bayesian Regularized Neural Network for Analyzing Gasoline and Diesel Price Drifts. INT J COMPUT INT SYS 2022; 15. [DOI: 10.1007/s44196-021-00060-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Any nation’s growth depends on the trend of the price of fuel. The fuel price drifts have both direct and indirect impacts on a nation’s economy. Nation’s growth will be hampered due to the higher level of inflation prevailing in the oil industry. This paper proposed a method of analyzing Gasoline and Diesel Price Drifts based on Self-organizing Maps and Bayesian regularized neural networks. The US gasoline and diesel price timeline dataset is used to validate the proposed approach. In the dataset, all grades, regular, medium, and premium with conventional, reformulated, all formulation of gasoline combinations, and diesel pricing per gallon weekly from 1995 to January 2021, are considered. For the data visualization purpose, we have used self-organizing maps and analyzed them with a neural network algorithm. The nonlinear autoregressive neural network is adopted because of the time series dataset. Three training algorithms are adopted to train the neural networks: Levenberg-Marquard, scaled conjugate gradient, and Bayesian regularization. The results are hopeful and reveal the robustness of the proposed model. In the proposed approach, we have found Levenberg-Marquard error falls from − 0.1074 to 0.1424, scaled conjugate gradient error falls from − 0.1476 to 0.1618, and similarly, Bayesian regularization error falls in − 0.09854 to 0.09871, which showed that out of the three approaches considered, the Bayesian regularization gives better results.
Collapse
|
5
|
Sujatha R, Ephzibah E, Chatterjee JM. An adaptive neuro-fuzzy inference for blockchain-based smart job recommendation system. IJIDS 2022. [DOI: 10.1504/ijids.2022.10047264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
6
|
Sujatha R, Venkata Siva Krishna B, Moy Chatterjee J, Rahul Naidu P, Jhanjhi NZ, Charita C, Nerin Mariya E, Baz M. Prediction of Suitable Candidates for COVID-19 Vaccination. Intelligent Automation & Soft Computing 2022. [DOI: 10.32604/iasc.2022.021216] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
7
|
Sujatha R, Moy Chatterjee J, Jhanjhi NZ, A. Tabbakh T, A. Almusaylim Z. Heart Failure Patient Survival Analysis with Multi Kernel Support Vector Machine. Intelligent Automation & Soft Computing 2022. [DOI: 10.32604/iasc.2022.019133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
8
|
Priyadarshini I, Chatterjee JM, Sujatha R, Jhanjhi N, Karime A, Masud M. Exploring Internet Meme Activity during COVID-19 Lockdown Using Artificial Intelligence Techniques. Applied Artificial Intelligence 2021. [DOI: 10.1080/08839514.2021.2014218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Ishaani Priyadarshini
- Department of Electrical and Computer Engineering, University of Delaware, Newark, Delaware, USA
| | | | - R. Sujatha
- School of Information Technology Engineering, Site, Vellore Institute of Technology, Vellore, India
| | - Nz Jhanjhi
- School of Computer Science and Engineering, Sce, Taylor’s University, Malaysia
| | - Ali Karime
- Department of Electrical & Computer Engineering, Royal Military College of Canada, Kingston, ON, Canada
| | - Mehedi Masud
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
| |
Collapse
|
9
|
AlQAHERI H, SUJATHA R, CHATTERJEE JM, SHOORIYA S, KUMAR J. SA, SATISH N. Toward an Autonomous Incubation System for Monitoring Premature Infants. STUD INFORM CONTROL 2021. [DOI: 10.24846/v30i4y202111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
10
|
Khairandish M, Sharma M, Jain V, Chatterjee J, Jhanjhi N. A Hybrid CNN-SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2021.06.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
|
11
|
Pattnaik CR, Mohanty SN, Mohanty S, Chatterjee JM, Jana B, Diaz VG. A fuzzy multi-criteria decision-making method for purchasing life insurance in India. Bulletin EEI 2021; 10:344-56. [DOI: 10.11591/eei.v10i1.2275] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Life insurance is an agreement between an insured and an insurer, where the insurer pays out a sum of money either on a specific period or the death of the insured. Now a day, People can buy a policy through an online platform. There are a lot of insurance companies available in the market, and each company has various policies. Selecting the best insurance company for purchasing an online term plan is a very complex problem. People may confuse to choose the best insurance company for buying an online term. It is a multi-criteria decision making (MCDM) problem, and the problem consists of different criteria and various alternatives. Here in this paper, a model has been proposed to solve this decision-making problem. In this model, a fuzzy multi-criteria decision-making approach combined with technique for order preference by similarity to ideal solution (TOPSIS) and it has been applied to rank the different insurance companies based on online term plans. The experimental results show that the life insurance corporation of India (LIC) gets the top rank out of 12 companies for purchasing an online term plan. A sensitivity analysis has been performed to validate the proposed model.
Collapse
|
12
|
Sujatha R, Chatterjee JM, Jhanjhi N, Brohi SN. Performance of deep learning vs machine learning in plant leaf disease detection. Microprocessors and Microsystems 2021; 80:103615. [DOI: 10.1016/j.micpro.2020.103615] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
13
|
Rathore PS, Chatterjee JM, Kumar A, Sujatha R. Energy-efficient cluster head selection through relay approach for WSN. J Supercomput 2021. [DOI: 10.1007/s11227-020-03593-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
|
14
|
Sujatha R, Mareeswari V, Chatterjee JM, Mousa AAA, Hassanien AE. A Bayesian Regularized Neural Network for Analyzing Bitcoin Trends. IEEE Access 2021. [DOI: 10.1109/access.2021.3063243] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
15
|
Dhamodharavadhani S, Rathipriya R, Chatterjee JM. COVID-19 Mortality Rate Prediction for India Using Statistical Neural Network Models. Front Public Health 2020; 8:441. [PMID: 32984242 PMCID: PMC7485390 DOI: 10.3389/fpubh.2020.00441] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 07/17/2020] [Indexed: 02/05/2023] Open
Abstract
The primary aim of this study is to investigate suitable Statistical Neural Network (SNN) models and their hybrid version for COVID-19 mortality prediction in Indian populations and is to estimate the future COVID-19 death cases for India. SNN models such as Probabilistic Neural Network (PNN), Radial Basis Function Neural Network (RBFNN), and Generalized Regression Neural Network (GRNN) are applied to develop the COVID-19 Mortality Rate Prediction (MRP) model for India. For this purpose, we have used two datasets as D1 and D2. The performances of these models are evaluated using Root Mean Square Error (RMSE) and "R," a correlation value between actual and predicted value. To improve prediction accuracy, the new hybrid models have been constructed by combining SNN models and the Non-linear Autoregressive Neural Network (NAR-NN). This is to predict the future error of the SNN models, which adds to the predicted value of these models for getting better MRP value. The results showed that the PNN and RBFNN-based MRP model performed better than the other models for COVID-19 datasets D2 and D1, respectively.
Collapse
Affiliation(s)
| | - R Rathipriya
- Department of Computer Science, Periyar University, Salem, India
| | | |
Collapse
|
16
|
Radhakrishnan S, Lakshminarayanan AS, Chatterjee JM, Hemanth DJ. Forest data visualization and land mapping using support vector machines and decision trees. Earth Sci Inform 2020. [DOI: 10.1007/s12145-020-00492-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
17
|
Rath M, Chatterjee JM. Exploration of Information Retrieval Approaches With Focus on Medical Information Retrieval. ONTOLOGY‐BASED INFORMATION RETRIEVAL FOR HEALTHCARE SYSTEMS 2020. [DOI: 10.1002/9781119641391.ch13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
18
|
Sujatha R, Chatterjee JM, Hassanien AE. Correction to: A machine learning forecasting model for COVID-19 pandemic in India. Stoch Environ Res Risk Assess 2020; 34:1681. [PMID: 32840243 PMCID: PMC7383123 DOI: 10.1007/s00477-020-01843-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- R. Sujatha
- School of Information Technology & Engineering, Vellore Institute of Technology, Vellore, India
| | - Jyotir Moy Chatterjee
- Department of IT, Lord Buddha Education Foundation (Asia Pacific University), Kathmandu, Nepal
| | - Aboul Ella Hassanien
- Faculty of Computers and Artificial Intelligence, Cairo University and Scientific Research Group in Egypt (SRGE), Giza, Egypt
| |
Collapse
|
19
|
Iwendi C, Bashir AK, Peshkar A, Sujatha R, Chatterjee JM, Pasupuleti S, Mishra R, Pillai S, Jo O. COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm. Front Public Health 2020; 8:357. [PMID: 32719767 PMCID: PMC7350612 DOI: 10.3389/fpubh.2020.00357] [Citation(s) in RCA: 173] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 06/23/2020] [Indexed: 02/05/2023] Open
Abstract
Integration of artificial intelligence (AI) techniques in wireless infrastructure, real-time collection, and processing of end-user devices is now in high demand. It is now superlative to use AI to detect and predict pandemics of a colossal nature. The Coronavirus disease 2019 (COVID-19) pandemic, which originated in Wuhan China, has had disastrous effects on the global community and has overburdened advanced healthcare systems throughout the world. Globally; over 4,063,525 confirmed cases and 282,244 deaths have been recorded as of 11th May 2020, according to the European Centre for Disease Prevention and Control agency. However, the current rapid and exponential rise in the number of patients has necessitated efficient and quick prediction of the possible outcome of an infected patient for appropriate treatment using AI techniques. This paper proposes a fine-tuned Random Forest model boosted by the AdaBoost algorithm. The model uses the COVID-19 patient's geographical, travel, health, and demographic data to predict the severity of the case and the possible outcome, recovery, or death. The model has an accuracy of 94% and a F1 Score of 0.86 on the dataset used. The data analysis reveals a positive correlation between patients' gender and deaths, and also indicates that the majority of patients are aged between 20 and 70 years.
Collapse
Affiliation(s)
- Celestine Iwendi
- BCC of Central South University of Forestry and Technology, Changsha, China
| | - Ali Kashif Bashir
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom
| | - Atharva Peshkar
- Department of Information Technology, G H Raisoni College of Engineering, Nagpur, India
| | - R. Sujatha
- School of Information Technology and Engineering, VIT University, Vellore, India
| | - Jyotir Moy Chatterjee
- Department of Information Technology, Lord Buddha Education Foundation, Kathmandu, Nepal
| | - Swetha Pasupuleti
- School of Civil Engineering, Galgotias University, Greater Noida, India
| | - Rishita Mishra
- Department of Electronics and Telecommunications Engineering, G H Raisoni College of Engineering, Nagpur, India
| | - Sofia Pillai
- School of Civil Engineering, Galgotias University, Greater Noida, India
| | - Ohyun Jo
- Department of Computer Science, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si, South Korea
| |
Collapse
|
20
|
Abstract
Coronavirus disease (COVID-19) is an inflammation disease from a new virus. The disease causes respiratory ailment (like influenza) with manifestations, for example, cold, cough and fever, and in progressively serious cases, the problem in breathing. COVID-2019 has been perceived as a worldwide pandemic and a few examinations are being led utilizing different numerical models to anticipate the likely advancement of this pestilence. These numerical models dependent on different factors and investigations are dependent upon potential inclination. Here, we presented a model that could be useful to predict the spread of COVID-2019. We have performed linear regression, Multilayer perceptron and Vector autoregression method for desire on the COVID-19 Kaggle data to anticipate the epidemiological example of the ailment and pace of COVID-2019 cases in India. Anticipated the potential patterns of COVID-19 effects in India dependent on data gathered from Kaggle. With the common data about confirmed, death and recovered cases across India for over the time length helps in anticipating and estimating the not so distant future. For extra assessment or future perspective, case definition and data combination must be kept up persistently.
Collapse
Affiliation(s)
- R Sujath
- Vellore Institute of Technology, Vellore, India
| | | | - Aboul Ella Hassanien
- Faculty of Computers and Artificial Intelligence, Cairo University and Scientific Research Group in Egypt (SRGE), Giza, Egypt
| |
Collapse
|
21
|
Kumar A, Chatterjee JM, Rathore PS. Smartphone Confrontational Applications and Security Issues. International Journal of Risk and Contingency Management 2020. [DOI: 10.4018/ijrcm.2020040101] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
In today's society, there is a high volume of smartphones, with Android being the most popular and most commonly used smartphones. In the last few years, the Android market has been booming, making lots of developers join the industry so as to create various mobile applications that are a benefit to people's lives. However, its over-popularity has brought many crime issues, including security. One of the major common incidents to mobile users is having their mobile phones lost or stolen. Since most mobile users want to find their lost phones, they are looking for the most reliable features that can help them locate their smartphones. Luckily, there are some developed applications and services that have been designed to track down and locate lost or stolen smartphones. In this work, the authors tried to identify a collection of these applications and the information they send to the user in aiding them to find their phone. Since some applications are able to send location information or a photo, this work will look at what metadata is usually sent with the message.
Collapse
Affiliation(s)
- Abhishek Kumar
- Chitkara University Research and Innovation Network (CURIN), Chitkara University, Punjab, India
| | - Jyotir Moy Chatterjee
- Lord Buddha Education Foundation (Asia Pacific University of Technology & Innovation), Kathmandu, Nepal
| | | |
Collapse
|
22
|
Kumar A, Chatterjee JM, Díaz VG. A novel hybrid approach of SVM combined with NLP and probabilistic neural network for email phishing. IJECE 2020; 10:486. [DOI: 10.11591/ijece.v10i1.pp486-493] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Phishing attacks are one of the slanting cyber-attacks that apply socially engineered messages that are imparted to individuals from expert hackers going for tricking clients to uncover their delicate data, the most mainstream correspondence channel to those messages is through clients' emails. Phishing has turned into a generous danger for web clients and a noteworthy reason for money related misfortunes. Therefore, different arrangements have been created to handle this issue. Deceitful emails, also called phishing emails, utilize a scope of impact strategies to convince people to react, for example, promising a fiscal reward or summoning a feeling of criticalness. Regardless of far reaching alerts and intends to instruct clients to distinguish phishing sends, these are as yet a pervasive practice and a worthwhile business. The creators accept that influence, as a style of human correspondence intended to impact others, has a focal job in fruitful advanced tricks. Cyber criminals have ceaselessly propelling their techniques for assault. The current strategies to recognize the presence of such malevolent projects and to keep them from executing are static, dynamic and hybrid analysis. In this work we are proposing a hybrid methodology for phishing detection incorporating feature extraction and classification of the mails using SVM. At last, alongside the chose features, the PNN characterizes the spam mails from the genuine mails with more exactness and accuracy.
Collapse
|
23
|
Swain M, Kisan S, Moy J, Supramaniam M, Nandan S, Jhanjhi NZ, Abdullah A. Hybridized Machine Learning based Fractal Analysis Techniques for Breast Cancer Classification. IJACSA 2020. [DOI: 10.14569/ijacsa.2020.0111024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
24
|
Jain A, Kumar A, Chatterjee JM, Rathore PS. An assessment of classification with hybrid methodology for neural network classifier against different classifier. IJCI 2020. [DOI: 10.1504/ijci.2020.111659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
25
|
Chatterjee JM, Priyadarshini I, Shankeys, Le D. Fog Computing and Its security issues. SECURITY DESIGNS FOR THE CLOUD, IOT, AND SOCIAL NETWORKING 2019. [DOI: 10.1002/9781119593171.ch4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
26
|
Garg S, Chatterjee JM, Le D. Implementation of Rest Architecure‐Based Energy‐Efficient Home Automation System. SECURITY DESIGNS FOR THE CLOUD, IOT, AND SOCIAL NETWORKING 2019. [DOI: 10.1002/9781119593171.ch9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
27
|
Nguyen V, Pham B, Vu B, Prakash I, Jha S, Shahabi H, Shirzadi A, Ba D, Kumar R, Chatterjee J, Tien Bui D. Hybrid Machine Learning Approaches for Landslide Susceptibility Modeling. Forests 2019; 10:157. [DOI: 10.3390/f10020157] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
This paper presents novel hybrid machine learning models, namely Adaptive Neuro Fuzzy Inference System optimized by Particle Swarm Optimization (PSOANFIS), Artificial Neural Networks optimized by Particle Swarm Optimization (PSOANN), and Best First Decision Trees based Rotation Forest (RFBFDT), for landslide spatial prediction. Landslide modeling of the study area of Van Chan district, Yen Bai province (Vietnam) was carried out with the help of a spatial database of the area, considering past landslides and 12 landslide conditioning factors. The proposed models were validated using different methods such as Area under the Receiver Operating Characteristics (ROC) curve (AUC), Mean Square Error (MSE), Root Mean Square Error (RMSE). Results indicate that the RFBFDT (AUC = 0.826, MSE = 0.189, and RMSE = 0.434) is the best method in comparison to other hybrid models, namely PSOANFIS (AUC = 0.76, MSE = 0.225, and RMSE = 0.474) and PSOANN (AUC = 0.72, MSE = 0.312, and RMSE = 0.558). Thus, it is reasonably concluded that the RFBFDT is a promising hybrid machine learning approach for landslide susceptibility modeling.
Collapse
|
28
|
Son PH, Son LH, Jha S, Kumar R, Chatterjee JM. Governing mobile Virtual Network Operators in developing countries. Utilities Policy 2019. [DOI: 10.1016/j.jup.2019.01.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
|
29
|
Abstract
The rapid growth in the digital world in form of exponentiation to accommodate huge amount of structured, semi-structured, unstructured and hybrid data received from different sources. By using the conventional data management tools, it is quite impossible to manage this semi-structured and unstructured data for which a non-relational database management system such as NoSQL and NewSQL are used to handle such types of data. These types of semi-structured and structured data are generally considered ‘Big Data.' This article describes the basic characteristics, background and the models of NoSQL used for big data applications. In this work, the authors surveyed different NoSQL characteristics used by the researchers and try to compare the strength and weakness of different NoSQL databases.
Collapse
Affiliation(s)
| | - Ajaya Kumar Jena
- School of Computer Engineering, KIIT University, Bhubaneswar, India
| | | | - Raghvendra Kumar
- Department of Computer Science and Engineering, LNCT College, Bhopal, India
| | - Dac-Nhuong Le
- Faculty of Information Technology, Haiphong University, Haiphong, Vietnam
| |
Collapse
|
30
|
Son LH, Chiclana F, Kumar R, Mittal M, Khari M, Chatterjee JM, Baik SW. ARM–AMO: An efficient association rule mining algorithm based on animal migration optimization. Knowl Based Syst 2018; 154:68-80. [DOI: 10.1016/j.knosys.2018.04.038] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
31
|
Jha S, Kumar R, Son LH, Chatterjee JM, Khari M, Yadav N, Smarandache F. Neutrosophic soft set decision making for stock trending analysis. Evolving Systems 2019; 10:621-7. [DOI: 10.1007/s12530-018-9247-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
|
32
|
|
33
|
|
34
|
Narayan G. A Review on Intrusion Detection System and Various Attacks on Network. IJRASET 2018. [DOI: 10.22214/ijraset.2018.6010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
|
35
|
|
36
|
Son LH, Jha S, Kumar R, Chatterjee JM, Khari M. Collaborative handshaking approaches between internet of computing and internet of things towards a smart world: a review from 2009–2017. Telecommun Syst 2019; 70:617-34. [DOI: 10.1007/s11235-018-0481-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
|
37
|
|
38
|
Kumar R, Le D, Moy Chatterjee J; Department of Computer Science and Engineering, LNCT College, Jabalpur, MP, India. Validation Lamina for Maintaining Confidentiality within the Hadoop. IJIEEB 2018; 10:42-50. [DOI: 10.5815/ijieeb.2018.02.06] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
39
|
Khari M, Kumar R, Le DN, Chatterjee JM. Interconnect Network on Chip Topology in Multi-core Processors: A Comparative Study. IJCNIS 2017. [DOI: 10.5815/ijcnis.2017.11.06] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
40
|
Chatterjee JM, Son LH, Ghatak S, Kumar R, Khari M. BitCoin exclusively informational money: a valuable review from 2010 to 2017. Qual Quant 2017. [DOI: 10.1007/s11135-017-0605-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
41
|
Jyotir Moy Chatterjee. Privacy Preservation in Data Centric Environment: Analysis and Segregation. IJERT 2017. [DOI: 10.17577/ijertv6is050071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
|