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Dr. Dilbag Singh
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0 Image and Signal Processing
0 Meta Heuristic Algorithm
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Research article
Published: 31 July 2021 in Mobile Information Systems
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Power quality disturbance (PQD) is essential for devices consuming electricity and meeting today’s energy trends. This study contains an effective artificial intelligence (AI) framework for analyzing single or composite defects in power quality. A convolutional neural network (CNN) architecture, which has an output powered by a gated recurrent unit (GRU), is designed for this purpose. The proposed framework first obtains a matrix using a short-time Fourier transform (STFT) of PQD signals. This matrix contains the representation of the signal in the time and frequency domains, suitable for CNN input. Features are automatically extracted from these matrices using the proposed CNN architecture without preprocessing. These features are classified using the GRU. The performance of the proposed framework is tested using a dataset containing a total of seven single and composite defects. The amount of noise in these examples varies between 20 and 50 dB. The performance of the proposed method is higher than current state-of-the-art methods. The proposed method obtained 98.44% ACC, 98.45% SEN, 99.74% SPE, 98.45% PRE, 98.45% F1-score, 98.19% MCC, and 93.64% kappa metric. A novel power quality disturbance (PQD) system has been proposed, and its application has been represented in our study. The proposed system could be used in the industry and factory.

ACS Style

Enes Yiğit; Umut Özkaya; Şaban Öztürk; Dilbag Singh; Hassène Gritli. Automatic Detection of Power Quality Disturbance Using Convolutional Neural Network Structure with Gated Recurrent Unit. Mobile Information Systems 2021, 2021, 1 -11.

AMA Style

Enes Yiğit, Umut Özkaya, Şaban Öztürk, Dilbag Singh, Hassène Gritli. Automatic Detection of Power Quality Disturbance Using Convolutional Neural Network Structure with Gated Recurrent Unit. Mobile Information Systems. 2021; 2021 ():1-11.

Chicago/Turabian Style

Enes Yiğit; Umut Özkaya; Şaban Öztürk; Dilbag Singh; Hassène Gritli. 2021. "Automatic Detection of Power Quality Disturbance Using Convolutional Neural Network Structure with Gated Recurrent Unit." Mobile Information Systems 2021, no. : 1-11.

Journal article
Published: 20 July 2021 in IEEE Access
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Seagull Optimization Algorithm (SOA) is a metaheuristic algorithm that mimics the migrating and hunting behaviour of seagulls. SOA is able to solve continuous real-life problems, but not to discrete problems. The eight different binary versions of SOA are proposed in this paper. The proposed algorithm uses four transfer functions, S-shaped and V-shaped, which are used to map the continuous search space into discrete search space. Twenty-five benchmark functions are used to validate the performance of the proposed algorithm. The statistical significance of the proposed algorithm is also analysed. Experimental results divulge that the proposed algorithm outperforms the competitive algorithms. The proposed algorithm is also applied on data mining. The results demonstrate the superiority of binary seagull optimization algorithm in data mining application.

ACS Style

Vijay Kumar; Dinesh Kumar; Manjit Kaur; Dilbag Singh; Sahar Ahmed Idris; Hammam Alshazly. A Novel Binary Seagull Optimizer and its Application to Feature Selection Problem. IEEE Access 2021, 9, 103481 -103496.

AMA Style

Vijay Kumar, Dinesh Kumar, Manjit Kaur, Dilbag Singh, Sahar Ahmed Idris, Hammam Alshazly. A Novel Binary Seagull Optimizer and its Application to Feature Selection Problem. IEEE Access. 2021; 9 ():103481-103496.

Chicago/Turabian Style

Vijay Kumar; Dinesh Kumar; Manjit Kaur; Dilbag Singh; Sahar Ahmed Idris; Hammam Alshazly. 2021. "A Novel Binary Seagull Optimizer and its Application to Feature Selection Problem." IEEE Access 9, no. : 103481-103496.

Original research
Published: 14 July 2021 in Journal of Ambient Intelligence and Humanized Computing
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Asynchronous differential evolution (ADE) supports parallel optimization and effective exploration. The updation in population is done immediately when a vector with better fitness is found in ADE algorithm. The working of ADE and Differential Evolution (DE) is similar except the instant population updation feature and asynchronous nature. In this paper, we have integrated ADE with successful parent-selecting (SPS) framework and trigonometric mutation to enhance the performance. Additionally, the control parameters are updated in an adaptive manner to support better exploration as well as exploitation. The proposed algorithm is named as SPS embedded adaptive ADE with trigonometric mutation (SPS-AADE-TM). The modified mutation operation and adaptive parameters can increase the population diversity and the convergence speed. The parameter adaptation feature can automatically obtain the appropriate values of control parameters to enhance the robustness of SPS-AADE-TM. The proposed algorithm is tested over twenty-five widely used bench-mark functions and four engineering design problems. Two nonparametric statistical tests are also carried out to validate the performance of SPS-AADE-TM. The simulation results show that the proposed work provides promising results and outperforms the competitive algorithms.

ACS Style

Vaishali Yadav; Ashwani Kumar Yadav; Manjit Kaur; Dilbag Singh. Trigonometric mutation and successful-parent-selection based adaptive asynchronous differential evolution. Journal of Ambient Intelligence and Humanized Computing 2021, 1 -18.

AMA Style

Vaishali Yadav, Ashwani Kumar Yadav, Manjit Kaur, Dilbag Singh. Trigonometric mutation and successful-parent-selection based adaptive asynchronous differential evolution. Journal of Ambient Intelligence and Humanized Computing. 2021; ():1-18.

Chicago/Turabian Style

Vaishali Yadav; Ashwani Kumar Yadav; Manjit Kaur; Dilbag Singh. 2021. "Trigonometric mutation and successful-parent-selection based adaptive asynchronous differential evolution." Journal of Ambient Intelligence and Humanized Computing , no. : 1-18.

Journal article
Published: 14 June 2021 in Sensors
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A k-means algorithm is a method for clustering that has already gained a wide range of acceptability. However, its performance extremely depends on the opening cluster centers. Besides, due to weak exploration capability, it is easily stuck at local optima. Recently, a new metaheuristic called Moth Flame Optimizer (MFO) is proposed to handle complex problems. MFO simulates the moths intelligence, known as transverse orientation, used to navigate in nature. In various research work, the performance of MFO is found quite satisfactory. This paper suggests a novel heuristic approach based on the MFO to solve data clustering problems. To validate the competitiveness of the proposed approach, various experiments have been conducted using Shape and UCI benchmark datasets. The proposed approach is compared with five state-of-art algorithms over twelve datasets. The mean performance of the proposed algorithm is superior on 10 datasets and comparable in remaining two datasets. The analysis of experimental results confirms the efficacy of the suggested approach.

ACS Style

Tribhuvan Singh; Nitin Saxena; Manju Khurana; Dilbag Singh; Mohamed Abdalla; Hammam Alshazly. Data Clustering Using Moth-Flame Optimization Algorithm. Sensors 2021, 21, 4086 .

AMA Style

Tribhuvan Singh, Nitin Saxena, Manju Khurana, Dilbag Singh, Mohamed Abdalla, Hammam Alshazly. Data Clustering Using Moth-Flame Optimization Algorithm. Sensors. 2021; 21 (12):4086.

Chicago/Turabian Style

Tribhuvan Singh; Nitin Saxena; Manju Khurana; Dilbag Singh; Mohamed Abdalla; Hammam Alshazly. 2021. "Data Clustering Using Moth-Flame Optimization Algorithm." Sensors 21, no. 12: 4086.

Research article
Published: 09 June 2021 in Mathematical Problems in Engineering
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Optimization is a buzzword, whenever researchers think of engineering problems. This paper presents a new metaheuristic named dingo optimizer (DOX) which is motivated by the behavior of dingo (Canis familiaris dingo). The overall concept is to develop this method involving the collaborative and social behavior of dingoes. The developed algorithm is based on the hunting behavior of dingoes that includes exploration, encircling, and exploitation. All the above prey hunting steps are modeled mathematically and are implemented in the simulator to test the performance of the proposed algorithm. Comparative analyses are drawn among the proposed approach and grey wolf optimizer (GWO) and particle swarm optimizer (PSO). Some of the well-known test functions are used for the comparative study of this work. The results reveal that the dingo optimizer performed significantly better than other nature-inspired algorithms.

ACS Style

Amit Kumar Bairwa; Sandeep Joshi; Dilbag Singh. Dingo Optimizer: A Nature-Inspired Metaheuristic Approach for Engineering Problems. Mathematical Problems in Engineering 2021, 2021, 1 -12.

AMA Style

Amit Kumar Bairwa, Sandeep Joshi, Dilbag Singh. Dingo Optimizer: A Nature-Inspired Metaheuristic Approach for Engineering Problems. Mathematical Problems in Engineering. 2021; 2021 ():1-12.

Chicago/Turabian Style

Amit Kumar Bairwa; Sandeep Joshi; Dilbag Singh. 2021. "Dingo Optimizer: A Nature-Inspired Metaheuristic Approach for Engineering Problems." Mathematical Problems in Engineering 2021, no. : 1-12.

Original article
Published: 02 June 2021 in International Journal of System Assurance Engineering and Management
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Predicting the optimum availability of the physical processing unit of sewage treatment plant is defined as a Nondeterministic Polynomial time-hard problem. Recently many researchers have utilized soft computing techniques to handle this issue. However, the existing techniques are far from the optimal solutions as soft computing techniques suffer from various issues such as, poor computational speed, getting stuck in local optima, pre-mature convergence, etc. Therefore, in this work a novel mathematical model is designed and implemented using Markov process and Chapman-Kolmogorov equations derived by assuming arbitrary repair rates and exponentially distributed failure rates. Thereafter, Genetic Algorithm and Particle Swarm Optimization techniques are utilized to optimize the availability and performance of physical processing unit. The needed data has been collected with the help of plant personnel and results are also shared with them. Experimental results reveal that the Particle Swarm Optimization based proposed model outperforms the competitive techniques.

ACS Style

Deepak Sinwar; Monika Saini; Dilbag Singh; Drishty Goyal; Ashish Kumar. Availability and performance optimization of physical processing unit in sewage treatment plant using genetic algorithm and particle swarm optimization. International Journal of System Assurance Engineering and Management 2021, 1 -12.

AMA Style

Deepak Sinwar, Monika Saini, Dilbag Singh, Drishty Goyal, Ashish Kumar. Availability and performance optimization of physical processing unit in sewage treatment plant using genetic algorithm and particle swarm optimization. International Journal of System Assurance Engineering and Management. 2021; ():1-12.

Chicago/Turabian Style

Deepak Sinwar; Monika Saini; Dilbag Singh; Drishty Goyal; Ashish Kumar. 2021. "Availability and performance optimization of physical processing unit in sewage treatment plant using genetic algorithm and particle swarm optimization." International Journal of System Assurance Engineering and Management , no. : 1-12.

Journal article
Published: 26 May 2021 in PeerJ Computer Science
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Background Until now, there are still a limited number of resources available to predict and diagnose COVID-19 disease. The design of novel drug-drug interaction for COVID-19 patients is an open area of research. Also, the development of the COVID-19 rapid testing kits is still a challenging task. Methodology This review focuses on two prime challenges caused by urgent needs to effectively address the challenges of the COVID-19 pandemic, i.e., the development of COVID-19 classification tools and drug discovery models for COVID-19 infected patients with the help of artificial intelligence (AI) based techniques such as machine learning and deep learning models. Results In this paper, various AI-based techniques are studied and evaluated by the means of applying these techniques for the prediction and diagnosis of COVID-19 disease. This study provides recommendations for future research and facilitates knowledge collection and formation on the application of the AI techniques for dealing with the COVID-19 epidemic and its consequences. Conclusions The AI techniques can be an effective tool to tackle the epidemic caused by COVID-19. These may be utilized in four main fields such as prediction, diagnosis, drug design, and analyzing social implications for COVID-19 infected patients.

ACS Style

Vijay Kumar; Dilbag Singh; Manjit Kaur; Robertas Damaševičius. Overview of current state of research on the application of artificial intelligence techniques for COVID-19. PeerJ Computer Science 2021, 7, e564 .

AMA Style

Vijay Kumar, Dilbag Singh, Manjit Kaur, Robertas Damaševičius. Overview of current state of research on the application of artificial intelligence techniques for COVID-19. PeerJ Computer Science. 2021; 7 ():e564.

Chicago/Turabian Style

Vijay Kumar; Dilbag Singh; Manjit Kaur; Robertas Damaševičius. 2021. "Overview of current state of research on the application of artificial intelligence techniques for COVID-19." PeerJ Computer Science 7, no. : e564.

Journal article
Published: 08 April 2021 in Current Pharmaceutical Design
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Purpose: In cancer therapies, drug combinations have shown significant accuracy and minimal side effects than the single drug administration. Therefore, drug synergy has drawn great interest from pharmaceutical companies and researchers. Unfortunately, the prediction of drug synergy score was carried out based on the small group of drugs. Methods: Due to the advancement in high-throughput screening (HTS), the size of drug synergy datasets has grown enormously in recent years. Hence, machine learning models have been utilized to predict the drug synergy score. However, the majority of these machine learning models suffer from over-fitting and hyperparameters tuning issues. Results: A novel deep bidirectional mixture density network (BMDN) model is proposed. A dynamic mutationbased multi-objective differential evolution is used to optimize the hyper-parameters of BMDN. Extensive is conducted on the NCI-ALMANAC drug synergy dataset that consists of 2,90,000 synergy determinations. Conclusions: Experimental results reveal that BMDN outperforms the existing drug synergy models in terms of various performance metrics.

ACS Style

Dilbag Singh; Vijay Kumar. Drug Synergy Prediction Using Dynamic Mutation Based Differential Evolution. Current Pharmaceutical Design 2021, 27, 1103 -1111.

AMA Style

Dilbag Singh, Vijay Kumar. Drug Synergy Prediction Using Dynamic Mutation Based Differential Evolution. Current Pharmaceutical Design. 2021; 27 (8):1103-1111.

Chicago/Turabian Style

Dilbag Singh; Vijay Kumar. 2021. "Drug Synergy Prediction Using Dynamic Mutation Based Differential Evolution." Current Pharmaceutical Design 27, no. 8: 1103-1111.

Research article
Published: 01 March 2021 in Journal of Healthcare Engineering
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COVID-19 has affected the whole world drastically. A huge number of people have lost their lives due to this pandemic. Early detection of COVID-19 infection is helpful for treatment and quarantine. Therefore, many researchers have designed a deep learning model for the early diagnosis of COVID-19-infected patients. However, deep learning models suffer from overfitting and hyperparameter-tuning issues. To overcome these issues, in this paper, a metaheuristic-based deep COVID-19 screening model is proposed for X-ray images. The modified AlexNet architecture is used for feature extraction and classification of the input images. Strength Pareto evolutionary algorithm-II (SPEA-II) is used to tune the hyperparameters of modified AlexNet. The proposed model is tested on a four-class (i.e., COVID-19, tuberculosis, pneumonia, or healthy) dataset. Finally, the comparisons are drawn among the existing and the proposed models.

ACS Style

Manjit Kaur; Vijay Kumar; Vaishali Yadav; Dilbag Singh; Naresh Kumar; Nripendra Narayan Das. Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images. Journal of Healthcare Engineering 2021, 2021, 1 -9.

AMA Style

Manjit Kaur, Vijay Kumar, Vaishali Yadav, Dilbag Singh, Naresh Kumar, Nripendra Narayan Das. Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images. Journal of Healthcare Engineering. 2021; 2021 ():1-9.

Chicago/Turabian Style

Manjit Kaur; Vijay Kumar; Vaishali Yadav; Dilbag Singh; Naresh Kumar; Nripendra Narayan Das. 2021. "Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images." Journal of Healthcare Engineering 2021, no. : 1-9.

Original paper
Published: 20 February 2021 in Archives of Computational Methods in Engineering
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Real-time remote sensing imaging systems require high spatial resolution multispectral images. However, the remote sensing images obtained from a single satellite sensor do not provide a significant amount of information. Therefore, pansharpening techniques are desirable to provide high spatial resolution multispectral images. The hyperspectral pansharpening techniques are used to fuse the hyperspectral (HS) and the panchromatic (PAN) images to obtain an HS image with a significant amount of spatial and spectral information. The main objective of this paper is to provide a comprehensive review of the pansharpening techniques. Various categories of pansharpening techniques are also discussed. This paper provides three different summaries: initially, the conceptual aspects of pansharpening techniques are discussed. Thereafter, the comparative analyses are performed to evaluate the benefits and shortcomings of the existing pansharpening techniques. Finally, challenges and opportunities for future research in the field of pansharpening are discussed.

ACS Style

Gurpreet Kaur; Kamaljit Singh Saini; Dilbag Singh; Manjit Kaur. A Comprehensive Study on Computational Pansharpening Techniques for Remote Sensing Images. Archives of Computational Methods in Engineering 2021, 1 -18.

AMA Style

Gurpreet Kaur, Kamaljit Singh Saini, Dilbag Singh, Manjit Kaur. A Comprehensive Study on Computational Pansharpening Techniques for Remote Sensing Images. Archives of Computational Methods in Engineering. 2021; ():1-18.

Chicago/Turabian Style

Gurpreet Kaur; Kamaljit Singh Saini; Dilbag Singh; Manjit Kaur. 2021. "A Comprehensive Study on Computational Pansharpening Techniques for Remote Sensing Images." Archives of Computational Methods in Engineering , no. : 1-18.

Article
Published: 07 February 2021 in Applied Intelligence
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The extensively utilized tool to detect novel coronavirus (COVID-19) is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and consume critical time, around 6 to 9 hours to classify the subjects as COVID-19(+) or COVID-19(-). Due to the less sensitivity of RT-PCR, it suffers from high false-negative results. To overcome these issues, many deep learning models have been implemented in the literature for the early-stage classification of suspected subjects. To handle the sensitivity issue associated with RT-PCR, chest CT scans are utilized to classify the suspected subjects as COVID-19 (+), tuberculosis, pneumonia, or healthy subjects. The extensive study on chest CT scans of COVID-19 (+) subjects reveals that there are some bilateral changes and unique patterns. But the manual analysis from chest CT scans is a tedious task. Therefore, an automated COVID-19 screening model is implemented by ensembling the deep transfer learning models such as Densely connected convolutional networks (DCCNs), ResNet152V2, and VGG16. Experimental results reveal that the proposed ensemble model outperforms the competitive models in terms of accuracy, f-measure, area under curve, sensitivity, and specificity.

ACS Style

Dilbag Singh; Vijay Kumar; Manjit Kaur. Densely connected convolutional networks-based COVID-19 screening model. Applied Intelligence 2021, 51, 3044 -3051.

AMA Style

Dilbag Singh, Vijay Kumar, Manjit Kaur. Densely connected convolutional networks-based COVID-19 screening model. Applied Intelligence. 2021; 51 (5):3044-3051.

Chicago/Turabian Style

Dilbag Singh; Vijay Kumar; Manjit Kaur. 2021. "Densely connected convolutional networks-based COVID-19 screening model." Applied Intelligence 51, no. 5: 3044-3051.

Research article
Published: 06 February 2021 in Mathematical Problems in Engineering
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This study provides a comparative analysis of regression techniques to estimate the operating frequency of the C-like microstrip antenna. The performance of well-known regression techniques such as linear regression (LR), regression tree (RT), support vector regression (SVR), Gaussian regression (GR), and artificial neural network (ANN) is tested. For this purpose, 160 C-like microstrip antennas are simulated, of which 145 are used for training of regression techniques and 15 for testing. From the evaluated results, it is found that the pure quadratic Gaussian regression (PQGR) technique has the lowest error rates with 0.0109 mean absolute error (MAE), 0.0087 median error (ME), 0.0002 mean squared error (MSE), 0.0156 root mean squared error (RMSE), and 0.5981 average percentage error (APE). As can be seen in the comparative analysis, the PQGR method outperforms other regression methods on simulation and measurement data. Experimental analysis shows that the resonant frequency of the C-like patch antennas can be calculated very close to measurements.

ACS Style

Umut Özkaya; Enes Yiğit; Levent Seyfi; Şaban Öztürk; Dilbag Singh. Comparative Regression Analysis for Estimating Resonant Frequency of C-Like Patch Antennas. Mathematical Problems in Engineering 2021, 2021, 1 -8.

AMA Style

Umut Özkaya, Enes Yiğit, Levent Seyfi, Şaban Öztürk, Dilbag Singh. Comparative Regression Analysis for Estimating Resonant Frequency of C-Like Patch Antennas. Mathematical Problems in Engineering. 2021; 2021 ():1-8.

Chicago/Turabian Style

Umut Özkaya; Enes Yiğit; Levent Seyfi; Şaban Öztürk; Dilbag Singh. 2021. "Comparative Regression Analysis for Estimating Resonant Frequency of C-Like Patch Antennas." Mathematical Problems in Engineering 2021, no. : 1-8.

Original research
Published: 16 November 2020 in Journal of Ambient Intelligence and Humanized Computing
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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes novel coronavirus disease (COVID-19) outbreak in more than 200 countries around the world. The early diagnosis of infected patients is needed to discontinue this outbreak. The diagnosis of coronavirus infection from radiography images is the fastest method. In this paper, two different ensemble deep transfer learning models have been designed for COVID-19 diagnosis utilizing the chest X-rays. Both models have utilized pre-trained models for better performance. They are able to differentiate COVID-19, viral pneumonia, and bacterial pneumonia. Both models have been developed to improve the generalization capability of the classifier for binary and multi-class problems. The proposed models have been tested on two well-known datasets. Experimental results reveal that the proposed framework outperforms the existing techniques in terms of sensitivity, specificity, and accuracy.

ACS Style

Neha Gianchandani; Aayush Jaiswal; Dilbag Singh; Vijay Kumar; Manjit Kaur. Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images. Journal of Ambient Intelligence and Humanized Computing 2020, 1 -13.

AMA Style

Neha Gianchandani, Aayush Jaiswal, Dilbag Singh, Vijay Kumar, Manjit Kaur. Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images. Journal of Ambient Intelligence and Humanized Computing. 2020; ():1-13.

Chicago/Turabian Style

Neha Gianchandani; Aayush Jaiswal; Dilbag Singh; Vijay Kumar; Manjit Kaur. 2020. "Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images." Journal of Ambient Intelligence and Humanized Computing , no. : 1-13.

Article
Published: 10 October 2020 in Air Quality, Atmosphere & Health
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The WHO announced coronavirus disease a Public Health Emergency on 30 January 2020, and it spreads across the whole planet. Aftermath of outbreak of this disease at the global level is more frightening and panicking than anyone’s worst nightmare. With more than 23 mln positive coronavirus cases and more than 800,000 causalities all over the world, the potential of this virus cannot be undermined. This pandemic has victimized all human beings residing on 209 countries and territories of the world. It emerged as an unbeatable global challenge that the world has never witnessed before. Consequently, the affected countries have sealed their borders and made populations reside in their homes until the pandemic is over. Thus, the victims of coronavirus are not only the ones who are exposed to it but also the ones who are affected by the lockdown imposed by the governments. The paper aims to evaluate the effect of COVID-19 on air pollution of various countries. Papers indicating the relationship between air pollution levels and lockdown measures are analyzed. The dramatic U-turn from environmental degradation is definitely a silver lining in these black clouds. This paper reviews the repercussions of the pandemic in some nations, while war-like preparations continue to fight it. COVID-19 has dramatically improved the quality of air. Also, it has greatly affected the economy of various countries due to lockdown.

ACS Style

Ashish Girdhar; Himani Kapur; Vijay Kumar; Manjit Kaur; Dilbag Singh; Robertas Damasevicius. Effect of COVID-19 outbreak on urban health and environment. Air Quality, Atmosphere & Health 2020, 14, 389 -397.

AMA Style

Ashish Girdhar, Himani Kapur, Vijay Kumar, Manjit Kaur, Dilbag Singh, Robertas Damasevicius. Effect of COVID-19 outbreak on urban health and environment. Air Quality, Atmosphere & Health. 2020; 14 (3):389-397.

Chicago/Turabian Style

Ashish Girdhar; Himani Kapur; Vijay Kumar; Manjit Kaur; Dilbag Singh; Robertas Damasevicius. 2020. "Effect of COVID-19 outbreak on urban health and environment." Air Quality, Atmosphere & Health 14, no. 3: 389-397.

Journal article
Published: 10 October 2020 in International Journal of Pattern Recognition and Artificial Intelligence
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There are limited coronavirus disease 2019 (COVID-19) testing kits, therefore, development of other diagnosis approaches is desirable. The doctors generally utilize chest X-rays and Computed Tomography (CT) scans to diagnose pneumonia, lung inflammation, abscesses, and/or enlarged lymph nodes. Since COVID-19 attacks the epithelial cells that line our respiratory tract, therefore, X-ray images are utilized in this paper, to classify the patients with infected (COVID-19 [Formula: see text]ve) and uninfected (COVID-19 [Formula: see text]ve) lungs. Almost all hospitals have X-ray imaging machines, therefore, the chest X-ray images can be used to test for COVID-19 without utilizing any kind of dedicated test kits. However, the chest X-ray-based COVID-19 classification requires a radiology expert and significant time, which is precious when COVID-19 infection is increasing at a rapid rate. Therefore, the development of an automated analysis approach is desirable to save the medical professionals’ valuable time. In this paper, a deep convolutional neural network (CNN) approach is designed and implemented. Besides, the hyper-parameters of CNN are tuned using Multi-objective Adaptive Differential Evolution (MADE). Extensive experiments are performed by considering the benchmark COVID-19 dataset. Comparative analysis reveals that the proposed technique outperforms the competitive machine learning models in terms of various performance metrics.

ACS Style

Dilbag Singh; Vijay Kumar; Vaishali Yadav; Manjit Kaur. Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images. International Journal of Pattern Recognition and Artificial Intelligence 2020, 35, 1 .

AMA Style

Dilbag Singh, Vijay Kumar, Vaishali Yadav, Manjit Kaur. Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images. International Journal of Pattern Recognition and Artificial Intelligence. 2020; 35 (03):1.

Chicago/Turabian Style

Dilbag Singh; Vijay Kumar; Vaishali Yadav; Manjit Kaur. 2020. "Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images." International Journal of Pattern Recognition and Artificial Intelligence 35, no. 03: 1.

Journal article
Published: 15 August 2020 in Modern Physics Letters B
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To ensure the security of web applications and to reduce the constant risk of increasing cybercrime, basic security principles like integrity, confidentiality and availability should not be omitted. Even though Transport Layer Security/Secure Socket Layer (TLS/SSL) authentication protocols are developed to shield websites from intruders, these protocols also have their fair share of problems. Incorrect authentication process of websites can give birth to notorious attack like Man in The Middle attack, which is widespread in HTTPS websites. In MITM attack, the violator basically positions himself in a communication channel between user and website either to eavesdrop or impersonate the communicating party to achieve malicious goals. Initially, the MITM attack is defined as a binary machine learning problem. Deep Q learning is utilized to build the MITM attack classification model. Thereafter, training process is applied on 60% of the obtained dataset. Remaining 40% dataset is used for testing purpose. The experimental results indicate that the proposed technique performs significantly better than the existing machine learning technique-based MITM prediction techniques for SSL/TLS-based websites.

ACS Style

Saloni Manhas; Swapnesh Taterh; Dilbag Singh. Deep Q learning-based mitigation of man in the middle attack over secure sockets layer websites. Modern Physics Letters B 2020, 34, 1 .

AMA Style

Saloni Manhas, Swapnesh Taterh, Dilbag Singh. Deep Q learning-based mitigation of man in the middle attack over secure sockets layer websites. Modern Physics Letters B. 2020; 34 (32):1.

Chicago/Turabian Style

Saloni Manhas; Swapnesh Taterh; Dilbag Singh. 2020. "Deep Q learning-based mitigation of man in the middle attack over secure sockets layer websites." Modern Physics Letters B 34, no. 32: 1.

Article
Published: 12 August 2020 in Applied Physics A
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Hyperchaotic maps are generally used in the encryption to generate the secret keys. The number of hyperchaotic maps has been implemented so far. These maps involve a large number of state and control parameters. The major concern is the estimation of these parameters. Because the estimation requires extensive computational search. In this paper, a 7D hyperchaotic map is used to produce the secret keys for image encryption. As this hyperchaotic map require a large number of initial parameters, the manual estimation is computationally extensive. Therefore, minimax differential evolution is utilized to provide the optimal parameters to the hyperchaotic map. The fitness of the parameters is evaluated using correlation coefficient and entropy. The secrets keys are then produced by the proposed hyperchaotic map. These keys are further used to perform the diffusion operation on the input image to generate the encrypted images. Extensive experiments are conducted to investigate the performance of the proposed approach considering the well-known measures. The comparative results show that the proposed approach performs significantly better as compared to the competitive approaches.

ACS Style

Manjit Kaur; Dilbag Singh; Vijay Kumar. Color image encryption using minimax differential evolution-based 7D hyper-chaotic map. Applied Physics A 2020, 126, 1 -19.

AMA Style

Manjit Kaur, Dilbag Singh, Vijay Kumar. Color image encryption using minimax differential evolution-based 7D hyper-chaotic map. Applied Physics A. 2020; 126 (9):1-19.

Chicago/Turabian Style

Manjit Kaur; Dilbag Singh; Vijay Kumar. 2020. "Color image encryption using minimax differential evolution-based 7D hyper-chaotic map." Applied Physics A 126, no. 9: 1-19.

Article
Published: 09 August 2020 in Multidimensional Systems and Signal Processing
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Chaotic-based image encryption approaches have attracted great attention in the field of information security. The properties of chaotic maps such as randomness and sensitivity have given new ways to develop efficient encryption approaches. But chaotic maps require initial parameters to develop random sequences. The selection of these parameters is a tedious task. To obtain the optimal initial parameters, evolutionary optimization approaches have been utilized in image encryption. Therefore, in this paper, a hyper-chaotic map is optimized using a multiobjective evolutionary optimization approach. A dual local search based multiobjective optimization (DLS-MO) is used to obtain the optimal parameters of a hyper-chaotic map and encryption factors. Then, using optimal parameters, a hyper-chaotic map develops the secret keys. These secret keys are then used to perform permutation and diffusion on a plain image to develop the encrypted image. To perform encryption, permutation–permutation–diffusion–diffusion architecture is adopted for better confusion and diffusion. Experimental results show that the proposed approach provides better performance in comparison to existing competitive approaches.

ACS Style

Manjit Kaur; Dilbag Singh. Multiobjective evolutionary optimization techniques based hyperchaotic map and their applications in image encryption. Multidimensional Systems and Signal Processing 2020, 32, 281 -301.

AMA Style

Manjit Kaur, Dilbag Singh. Multiobjective evolutionary optimization techniques based hyperchaotic map and their applications in image encryption. Multidimensional Systems and Signal Processing. 2020; 32 (1):281-301.

Chicago/Turabian Style

Manjit Kaur; Dilbag Singh. 2020. "Multiobjective evolutionary optimization techniques based hyperchaotic map and their applications in image encryption." Multidimensional Systems and Signal Processing 32, no. 1: 281-301.

Original research
Published: 08 August 2020 in Journal of Ambient Intelligence and Humanized Computing
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The advancements in automated diagnostic tools allow researchers to obtain more and more information from medical images. Recently, to obtain more informative medical images, multi-modality images have been used. These images have significantly more information as compared to traditional medical images. However, the construction of multi-modality images is not an easy task. The proposed approach, initially, decomposes the image into sub-bands using a non-subsampled contourlet transform (NSCT) domain. Thereafter, an extreme version of the Inception (Xception) is used for feature extraction of the source images. The multi-objective differential evolution is used to select the optimal features. Thereafter, the coefficient of determination and the energy loss based fusion functions are used to obtain the fused coefficients. Finally, the fused image is computed by applying the inverse NSCT. Extensive experimental results show that the proposed approach outperforms the competitive multi-modality image fusion approaches.

ACS Style

Manjit Kaur; Dilbag Singh. Multi-modality medical image fusion technique using multi-objective differential evolution based deep neural networks. Journal of Ambient Intelligence and Humanized Computing 2020, 12, 2483 -2493.

AMA Style

Manjit Kaur, Dilbag Singh. Multi-modality medical image fusion technique using multi-objective differential evolution based deep neural networks. Journal of Ambient Intelligence and Humanized Computing. 2020; 12 (2):2483-2493.

Chicago/Turabian Style

Manjit Kaur; Dilbag Singh. 2020. "Multi-modality medical image fusion technique using multi-objective differential evolution based deep neural networks." Journal of Ambient Intelligence and Humanized Computing 12, no. 2: 2483-2493.

Research article
Published: 03 July 2020 in Journal of Biomolecular Structure and Dynamics
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Deep learning models are widely used in the automatic analysis of radiological images. These techniques can train the weights of networks on large datasets as well as fine tuning the weights of pre-trained networks on small datasets. Due to the small COVID-19 dataset available, the pre-trained neural networks can be used for diagnosis of coronavirus. However, these techniques applied on chest CT image is very limited till now. Hence, the main aim of this paper to use the pre-trained deep learning architectures as an automated tool to detection and diagnosis of COVID-19 in chest CT. A DenseNet201 based deep transfer learning (DTL) is proposed to classify the patients as COVID infected or not i.e. COVID-19 (+) or COVID (−). The proposed model is utilized to extract features by using its own learned weights on the ImageNet dataset along with a convolutional neural structure. Extensive experiments are performed to evaluate the performance of the propose DTL model on COVID-19 chest CT scan images. Comparative analyses reveal that the proposed DTL based COVID-19 classification model outperforms the competitive approaches. Communicated by Ramaswamy H. Sarma

ACS Style

Aayush Jaiswal; Neha Gianchandani; Dilbag Singh; Vijay Kumar; Manjit Kaur. Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning. Journal of Biomolecular Structure and Dynamics 2020, 1 -8.

AMA Style

Aayush Jaiswal, Neha Gianchandani, Dilbag Singh, Vijay Kumar, Manjit Kaur. Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning. Journal of Biomolecular Structure and Dynamics. 2020; ():1-8.

Chicago/Turabian Style

Aayush Jaiswal; Neha Gianchandani; Dilbag Singh; Vijay Kumar; Manjit Kaur. 2020. "Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning." Journal of Biomolecular Structure and Dynamics , no. : 1-8.