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Amy Neustein; Parikshit N. Mahalle; Mohd. Shafi Pathan; Nilanjan Dey. Preface: Special Section: Special Issue on Next Generation Acoustic Signal Processing (Articles 1–9). International Journal of Speech Technology 2021, 1 -2.
AMA StyleAmy Neustein, Parikshit N. Mahalle, Mohd. Shafi Pathan, Nilanjan Dey. Preface: Special Section: Special Issue on Next Generation Acoustic Signal Processing (Articles 1–9). International Journal of Speech Technology. 2021; ():1-2.
Chicago/Turabian StyleAmy Neustein; Parikshit N. Mahalle; Mohd. Shafi Pathan; Nilanjan Dey. 2021. "Preface: Special Section: Special Issue on Next Generation Acoustic Signal Processing (Articles 1–9)." International Journal of Speech Technology , no. : 1-2.
The Internet of Things (IoT) paradigm is a predominant research domain for smart cities, smart villages, society, and industry 4.0. The introduction of Unmanned Aircraft Systems (UAS) in an ultra-low latency network with fog, dews, and edge computing gives the researcher ample scope to establish a decentralized architecture for ultra-high-speed message exchange between IoT devices. This work mainly focused on Social Internet of Things ecosystem and its design to efficiently handle large group social gatherings, events, and emergency service management. We propose a layered message transfer framework for the social IoT scenario. We also establish network connection through flying ad hoc network architecture. The standard IoT message transfer protocol is redesigned by amalgamating with an opportunistic routing mechanism and deployed within 6G software-defined network (SDN) slice. We use seven distinguished network slices for different services and corresponding access. The study reveals nearly 99% of message delivery rate with a latency upper bound of 2300 ms by opportunistic message transfer scheme in a dense network scenario for QoS 2. It also shows 95% of the bandwidth utilization per slice and 97% of network coverage under SDN in quality of service level 2.
Amartya Mukherjee; Nilanjan Dey; Atreyee Mondal; Debashis De; Rubén González Crespo. iSocialDrone: QoS aware MQTT middleware for social internet of drone things in 6G-SDN slice. Soft Computing 2021, 1 -17.
AMA StyleAmartya Mukherjee, Nilanjan Dey, Atreyee Mondal, Debashis De, Rubén González Crespo. iSocialDrone: QoS aware MQTT middleware for social internet of drone things in 6G-SDN slice. Soft Computing. 2021; ():1-17.
Chicago/Turabian StyleAmartya Mukherjee; Nilanjan Dey; Atreyee Mondal; Debashis De; Rubén González Crespo. 2021. "iSocialDrone: QoS aware MQTT middleware for social internet of drone things in 6G-SDN slice." Soft Computing , no. : 1-17.
This paper presents a deep learning-based machine translation (MT) system that translates a sentence of subject-object-verb (SOV) structured language into subject-verb-object (SVO) structured language. This system uses recurrent neural networks (RNNs) and Encodings. Encode embedded RNNs generate a set of numbers from the input sentence, where the second RNNs generate the output from these sets of numbers. Three popular datasets of SOV structured language i.e., EMILLE corpus, Prothom-Alo corpus and Punjabi Monolingual Text Corpus ILCI-II are used as two different case-study to validate. In our experimental case-study 1, for the EMILLE corpus and Prothom-Alo corpus dataset, we have achieved 0.742, 4.11 and 0.18, respectively as Bilingual Evaluation Understudy (BLEU), NIST (metric) and tertiary entrance rank scores. Another case-study for Punjabi Monolingual Text Corpus ILCI-II dataset achieved a BLEU score of 0.75. Our results can be compared with the state-of-the-art results.
Nawab Yousuf Ali; Lizur Rahman; Jyotismita Chaki; Nilanjan Dey; K. C. Santosh. Machine translation using deep learning for universal networking language based on their structure. International Journal of Machine Learning and Cybernetics 2021, 1 -12.
AMA StyleNawab Yousuf Ali, Lizur Rahman, Jyotismita Chaki, Nilanjan Dey, K. C. Santosh. Machine translation using deep learning for universal networking language based on their structure. International Journal of Machine Learning and Cybernetics. 2021; ():1-12.
Chicago/Turabian StyleNawab Yousuf Ali; Lizur Rahman; Jyotismita Chaki; Nilanjan Dey; K. C. Santosh. 2021. "Machine translation using deep learning for universal networking language based on their structure." International Journal of Machine Learning and Cybernetics , no. : 1-12.
Investment in the share market helps generate more profit than the other financial instruments but has the threat of market risk that might lead to a high loss. This risk factor refrains many potential investors from investing in the share market directly. Instead, they invest in different mutual funds that are being managed by experienced portfolio managers. To avoid the risk factors and increase the gain, they put the accumulated capital in multiple stocks. They need to perform many calculations and predictions to overcome the uncertainties and unpredictability and need to ensure higher gains to the investors of that mutual fund. In this research work initially, a data mining based approach employs a curve fitting/regression technique to forecast the individual stock price. Based on the above analysis, we propose a framework to diversify the investment of the capital fund. This method employs buy and hold strategy using both statistical features and basic domain knowledge of the share market. The proposed framework distributes the capital first, by distributing sector-wise, and then for each sector, investing company-wise, as a diversified approach among different stocks for higher return but maintaining lower risks. Experimental results show that the proposed framework performs well and generates a good yield compared to some benchmark and ranked mutual funds in the Indian stock market.
Giridhar Maji; Debomita Mondal; Nilanjan Dey; Narayan C. Debnath; Soumya Sen. Stock prediction and mutual fund portfolio management using curve fitting techniques. Journal of Ambient Intelligence and Humanized Computing 2021, 1 -14.
AMA StyleGiridhar Maji, Debomita Mondal, Nilanjan Dey, Narayan C. Debnath, Soumya Sen. Stock prediction and mutual fund portfolio management using curve fitting techniques. Journal of Ambient Intelligence and Humanized Computing. 2021; ():1-14.
Chicago/Turabian StyleGiridhar Maji; Debomita Mondal; Nilanjan Dey; Narayan C. Debnath; Soumya Sen. 2021. "Stock prediction and mutual fund portfolio management using curve fitting techniques." Journal of Ambient Intelligence and Humanized Computing , no. : 1-14.
Biomedical imaging is considered main procedure to acquire valuable physical information about the human body and some other biological species. It produces specialized images of different parts of the biological species for clinical analysis. It assimilates various specialized domains including nuclear medicine, radiological imaging, Positron emission tomography (PET), and microscopy. From the early discovery of X-rays, progress in biomedical imaging continued resulting in highly sophisticated medical imaging modalities, such as magnetic resonance imaging (MRI), ultrasound, Computed Tomography (CT), and lungs monitoring. These biomedical imaging techniques assist physicians for faster and accurate analysis and treatment. The present chapter discussed the impact of intelligent computing methods for biomedical image analysis and healthcare. Different Artificial Intelligence (AI) based automated biomedical image analysis are considered. Different approaches are discussed including the AI ability to resolve various medical imaging problems. It also introduced the popular AI procedures that employed to solve some special problems in medicine. Artificial Neural Network (ANN) and support vector machine (SVM) are active to classify different types of images from various imaging modalities. Different diagnostic analysis, such as mammogram analysis, MRI brain image analysis, CT images, PET images, and bone/retinal analysis using ANN, feed-forward back propagation ANN, probabilistic ANN, and extreme learning machine continuously. Various optimization techniques of ant colony optimization (ACO), genetic algorithm (GA), particle swarm optimization (PSO) and other bio-inspired procedures are also frequently conducted for feature extraction/selection and classification. The advantages and disadvantages of some AI approaches are discussed in the present chapter along with some suggested future research perspectives.
Shouvik Chakraborty; Sankhadeep Chatterjee; Amira S. Ashour; Kalyani Mali; Nilanjan Dey. Intelligent Computing in Medical Imaging. Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms 2021, 592 -608.
AMA StyleShouvik Chakraborty, Sankhadeep Chatterjee, Amira S. Ashour, Kalyani Mali, Nilanjan Dey. Intelligent Computing in Medical Imaging. Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms. 2021; ():592-608.
Chicago/Turabian StyleShouvik Chakraborty; Sankhadeep Chatterjee; Amira S. Ashour; Kalyani Mali; Nilanjan Dey. 2021. "Intelligent Computing in Medical Imaging." Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms , no. : 592-608.
This paper demonstrates an efficient programmable Cellular Automata (CA) based hybrid encryption technique (PCHET) for chatting applications involving multiple clients who can chat simultaneously with each other. The proposed scheme is a symmetric key encryption technique, still very lightweight and it is easy to implement. The base of the work lies in the attributes of various CA rules and their cryptographic properties. These principles rely upon the state data of its neighbors and that of its own. Further, it creates chaotic and random next state data which is extremely difficult to anticipate and its inversion is practically exorbitant in all aspects. This work is implemented in java. The evaluation outcomes of different randomness tests prescribed by National Institute of Standards and Technology (NIST) and DIEHARD test suits show that the scheme is capable of generating high degree of randomness in the ciphertext. In addition to these, a thorough comparative investigation is done with existing similar kind of ciphers such as Data Encryption Standard (DES), 3DES and Advanced Encryption Standard (AES). The execution time of the proposed work is much better than the existing scheme as it is practically required for end-to-end encryption in case of chatting applications involving multiple clients. PCHET has achieved an improvement of 1.65% in terms of execution time.
Satyabrata Roy; Rohit Kumar Gupta; Umashankar Rawat; Nilanjan Dey; Ruben Gonzalez Crespo. PCHET: An efficient programmable cellular automata based hybrid encryption technique for multi-chat client-server applications. Journal of Information Security and Applications 2020, 55, 102624 .
AMA StyleSatyabrata Roy, Rohit Kumar Gupta, Umashankar Rawat, Nilanjan Dey, Ruben Gonzalez Crespo. PCHET: An efficient programmable cellular automata based hybrid encryption technique for multi-chat client-server applications. Journal of Information Security and Applications. 2020; 55 ():102624.
Chicago/Turabian StyleSatyabrata Roy; Rohit Kumar Gupta; Umashankar Rawat; Nilanjan Dey; Ruben Gonzalez Crespo. 2020. "PCHET: An efficient programmable cellular automata based hybrid encryption technique for multi-chat client-server applications." Journal of Information Security and Applications 55, no. : 102624.
H. R. Bhapkar; Parikshit N. Mahalle; Nilanjan Dey; K. C. Santosh. Revisited COVID-19 Mortality and Recovery Rates: Are we Missing Recovery Time Period? Journal of Medical Systems 2020, 44, 202 .
AMA StyleH. R. Bhapkar, Parikshit N. Mahalle, Nilanjan Dey, K. C. Santosh. Revisited COVID-19 Mortality and Recovery Rates: Are we Missing Recovery Time Period? Journal of Medical Systems. 2020; 44 (12):202.
Chicago/Turabian StyleH. R. Bhapkar; Parikshit N. Mahalle; Nilanjan Dey; K. C. Santosh. 2020. "Revisited COVID-19 Mortality and Recovery Rates: Are we Missing Recovery Time Period?" Journal of Medical Systems 44, no. 12: 202.
In this article, a new algorithm, namely the multi-objective competitive swarm optimizer (MOCSO), is introduced to handle multi-objective problems. The algorithm has been principally motivated from the competitive swarm optimizer (CSO) and the NSGA-II algorithm. In MOCSO, a pair wise competitive scenario is presented to achieve the dominance relationship between two particles in the population. In each pair wise competition, the particle that dominates the other particle is considered the winner and the other is consigned as the loser. The loser particles learn from the respective winner particles in each individual competition. The inspired CSO algorithm does not use any memory to remember the global best or personal best particles, hence, MOCSO does not need any external archive to store elite particles. The experimental results and statistical tests confirm the superiority of MOCSO over several state-of-the-art multi-objective algorithms in solving benchmark problems.
Prabhujit Mohapatra; Kedar Nath Das; Santanu Roy; Ram Kumar; Nilanjan Dey. A Novel Multi-Objective Competitive Swarm Optimization Algorithm. International Journal of Applied Metaheuristic Computing 2020, 11, 114 -129.
AMA StylePrabhujit Mohapatra, Kedar Nath Das, Santanu Roy, Ram Kumar, Nilanjan Dey. A Novel Multi-Objective Competitive Swarm Optimization Algorithm. International Journal of Applied Metaheuristic Computing. 2020; 11 (4):114-129.
Chicago/Turabian StylePrabhujit Mohapatra; Kedar Nath Das; Santanu Roy; Ram Kumar; Nilanjan Dey. 2020. "A Novel Multi-Objective Competitive Swarm Optimization Algorithm." International Journal of Applied Metaheuristic Computing 11, no. 4: 114-129.
With the growing prevalence of Internet connectivity in the civilized world, smart grid technology has become more practically relevant to implement. The smart electric grid is more than just a generation and transmission infrastructure. Modernizing such electric grids to automate the process of tracking the electricity consumption at multiple locations, while intelligently managing the supply is an exciting transformation, which offers both challenges as well as opportunities. Further, it must consider the change in prices with demand throughout the day. Advanced communication policy and intelligent sensing mechanism and decision making must be adapted to collect, monitor, and analyze real-time information, performing automatic metering, home automation, and inter-grid communication within vast geographical distance. In this paper, two crucial issues in the domain of smart grid management and communication have been addressed and the potential solution is provided. Firstly Delay Tolerant Network assisted the Internet of Drone Things based communication paradigm has been modeled for smart-grid communication in intermittent connective smart grid networks. The hybrid cluster-based 3D mobility has been engineered to that pursue the information sharing and offloading within IoT based cloud infrastructure. The routing mechanism results in 97% message delivery in 5 MB buffer size and about 8.5 × 106 J data transmission energy dissipation. In the latter half of this work, a load forecasting strategy is proposed, combining the mathematically robust gradient boosting strategies and a popular Deep Learning methodology, termed the Long Short-Term Memory approach. A hybrid architecture is developed for enhanced prediction in the presence of noise and faulty transmission of data from the physical layer. Further, the proposed model is also capable of generalization to a variegated set of data and produces forecast results with 0.167 MSE score, 7.231 MAE score, and 4.9% resource utilization which are better than the conventional frameworks on resource-constrained edge computing platforms.
Amartya Mukherjee; Prateeti Mukherjee; Debashis De; Nilanjan Dey. iGridEdgeDrone: Hybrid Mobility Aware Intelligent Load Forecasting by Edge Enabled Internet of Drone Things for Smart Grid Networks. International Journal of Parallel Programming 2020, 49, 285 -325.
AMA StyleAmartya Mukherjee, Prateeti Mukherjee, Debashis De, Nilanjan Dey. iGridEdgeDrone: Hybrid Mobility Aware Intelligent Load Forecasting by Edge Enabled Internet of Drone Things for Smart Grid Networks. International Journal of Parallel Programming. 2020; 49 (3):285-325.
Chicago/Turabian StyleAmartya Mukherjee; Prateeti Mukherjee; Debashis De; Nilanjan Dey. 2020. "iGridEdgeDrone: Hybrid Mobility Aware Intelligent Load Forecasting by Edge Enabled Internet of Drone Things for Smart Grid Networks." International Journal of Parallel Programming 49, no. 3: 285-325.
Sakshi Ahuja; Bijaya Ketan Panigrahi; Nilanjan Dey; Venkatesan Rajinikanth; Tapan Kumar Gandhi. Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. 2020, 1 -15.
AMA StyleSakshi Ahuja, Bijaya Ketan Panigrahi, Nilanjan Dey, Venkatesan Rajinikanth, Tapan Kumar Gandhi. Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. . 2020; ():1-15.
Chicago/Turabian StyleSakshi Ahuja; Bijaya Ketan Panigrahi; Nilanjan Dey; Venkatesan Rajinikanth; Tapan Kumar Gandhi. 2020. "Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices." , no. : 1-15.
Lung abnormality is one of the common diseases in humans of all age group and this disease may arise due to various reasons. Recently, the lung infection due to SARS-CoV-2 has affected a larger human community globally, and due to its rapidity, the World-Health-Organisation (WHO) declared it as pandemic disease. The COVID-19 disease has adverse effects on the respiratory system, and the infection severity can be detected using a chosen imaging modality. In the proposed research work; the COVID-19 is detected using transfer learning from CT scan images decomposed to three-level using stationary wavelet. A three-phase detection model is proposed to improve the detection accuracy and the procedures are as follows; Phase1- data augmentation using stationary wavelets, Phase2- COVID-19 detection using pre-trained CNN model and Phase3- abnormality localization in CT scan images. This work has considered the well known pre-trained architectures, such as ResNet18, ResNet50, ResNet101, and SqueezeNet for the experimental evaluation. In this work, 70% of images are considered to train the network and 30% images are considered to validate the network. The performance of the considered architectures is evaluated by computing the common performance measures. The result of the experimental evaluation confirms that the ResNet18 pre-trained transfer learning-based model offered better classification accuracy (training = 99.82%, validation = 97.32%, and testing = 99.4%) on the considered image dataset compared with the alternatives.
Sakshi Ahuja; Bijaya Ketan Panigrahi; Nilanjan Dey; Venkatesan Rajinikanth; Tapan Kumar Gandhi. Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. Applied Intelligence 2020, 51, 571 -585.
AMA StyleSakshi Ahuja, Bijaya Ketan Panigrahi, Nilanjan Dey, Venkatesan Rajinikanth, Tapan Kumar Gandhi. Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. Applied Intelligence. 2020; 51 (1):571-585.
Chicago/Turabian StyleSakshi Ahuja; Bijaya Ketan Panigrahi; Nilanjan Dey; Venkatesan Rajinikanth; Tapan Kumar Gandhi. 2020. "Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices." Applied Intelligence 51, no. 1: 571-585.
The coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a machine learning–based pipeline to detect COVID-19 infection using lung computed tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19–affected CTI using social group optimization–based Kapur’s entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection, and fusion to classify the infection. Principle component analysis–based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test, and validate four different classifiers namely Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree. Experimental results using benchmark datasets show a high accuracy (> 91%) for the morphology-based segmentation task; for the classification task, the KNN offers the highest accuracy among the compared classifiers (> 87%). However, this should be noted that this method still awaits clinical validation, and therefore should not be used to clinically diagnose ongoing COVID-19 infection.
Nilanjan Dey; V. Rajinikanth; Simon James Fong; M. Shamim Kaiser; Mufti Mahmud. Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images. Cognitive Computation 2020, 12, 1011 -1023.
AMA StyleNilanjan Dey, V. Rajinikanth, Simon James Fong, M. Shamim Kaiser, Mufti Mahmud. Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images. Cognitive Computation. 2020; 12 (5):1011-1023.
Chicago/Turabian StyleNilanjan Dey; V. Rajinikanth; Simon James Fong; M. Shamim Kaiser; Mufti Mahmud. 2020. "Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images." Cognitive Computation 12, no. 5: 1011-1023.
The handwritten digit recognition issue turns into one of the well-known issues in machine learning and computer vision applications. Numerous machine learning methods have been utilized to resolve the handwritten digit recognition problem. However, sometimes the digit is not completely present in the image due to issues related to scanning or environmental conditions (light, illumination, dirt, etc.). Although different efficient methodologies of handwritten digit recognition are proposed, there is not much work done on fragmented handwritten digit recognition. The objective of the proposed research work is to handle this circumstance to assemble a consistent digit recognition system that can precisely handle three types (English, Bangla, and Devanagari) of fragmented handwritten digit images. To solve the confusion, a technique is created to classify handwritten digits based on geometrical functions that are utilized to calculate handwritten digit features to assess if a digit belongs to a specific class. A grading scheme and a set of specified fuzzy rules determine the performance of classification. Experiments have been directed on the three familiar datasets, i.e., MNIST database (English), NumtaDB (Bangla) and Deva numeral database (Devanagari). Since fragmented digit delivers a lesser amount of information, the work also attempts to create a tentative size threshold above which outcomes become erratic and whether such thresholds are standardized or vary depending on other factors. Since the fragmented handwritten digital image does not have a public database, a method is formed to produce repeatable fragmented handwritten digital images from the entire image. Experimental outcomes validate that the proposed approach is effective in recognizing fragmented handwritten digits to an acceptable degree of fragmentation.
Jyotismita Chaki; Nilanjan Dey. Fragmented handwritten digit recognition using grading scheme and fuzzy rules. Sādhanā 2020, 45, 1 -23.
AMA StyleJyotismita Chaki, Nilanjan Dey. Fragmented handwritten digit recognition using grading scheme and fuzzy rules. Sādhanā. 2020; 45 (1):1-23.
Chicago/Turabian StyleJyotismita Chaki; Nilanjan Dey. 2020. "Fragmented handwritten digit recognition using grading scheme and fuzzy rules." Sādhanā 45, no. 1: 1-23.
Image registration has an imperative role in medical imaging. In this work, a grey-wolf optimizer (GWO)-based non-rigid demons registration is proposed to support the retinal image registration process. A comparative study of the proposed GWO-based demons registration framework with cuckoo search, firefly algorithm, and particle swarm optimization-based demons registration is conducted. In addition, a comparative analysis of different demons registration methods, such as Wang’s demons, Tang’s demons, and Thirion’s demons which are optimized using the proposed GWO is carried out. The results established the superiority of the GWO-based framework which achieved 0.9977 correlation, and fast processing compared to the use of the other optimization algorithms. Moreover, GWO-based Wang’s demons performed better accuracy compared to the Tang’s demons and Thirion’s demons framework. It also achieved the best less registration error of 8.36 × 10−5.
Sayan Chakraborty; Ratika Pradhan; Amira S. Ashour; Luminita Moraru; Nilanjan Dey. Grey-Wolf-Based Wang’s Demons for Retinal Image Registration. Entropy 2020, 22, 659 .
AMA StyleSayan Chakraborty, Ratika Pradhan, Amira S. Ashour, Luminita Moraru, Nilanjan Dey. Grey-Wolf-Based Wang’s Demons for Retinal Image Registration. Entropy. 2020; 22 (6):659.
Chicago/Turabian StyleSayan Chakraborty; Ratika Pradhan; Amira S. Ashour; Luminita Moraru; Nilanjan Dey. 2020. "Grey-Wolf-Based Wang’s Demons for Retinal Image Registration." Entropy 22, no. 6: 659.
COVID-19 is a pandemic that has affected over 170 countries around the world. The number of infected and deceased patients has been increasing at an alarming rate in almost all the affected nations. Forecasting techniques can be inculcated thereby assisting in designing better strategies and in taking productive decisions. These techniques assess the situations of the past thereby enabling better predictions about the situation to occur in the future. These predictions might help to prepare against possible threats and consequences. Forecasting techniques play a very important role in yielding accurate predictions. This study categorizes forecasting techniques into two types, namely, stochastic theory mathematical models and data science/machine learning techniques. Data collected from various platforms also play a vital role in forecasting. In this study, two categories of datasets have been discussed, i.e., big data accessed from World Health Organization/National databases and data from a social media communication. Forecasting of a pandemic can be done based on various parameters such as the impact of environmental factors, incubation period, the impact of quarantine, age, gender and many more. These techniques and parameters used for forecasting are extensively studied in this work. However, forecasting techniques come with their own set of challenges (technical and generic). This study discusses these challenges and also provides a set of recommendations for the people who are currently fighting the global COVID-19 pandemic.
Gitanjali R. Shinde; Asmita B. Kalamkar; Parikshit N. Mahalle; Nilanjan Dey; Jyotismita Chaki; Aboul Ella Hassanien. Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art. SN Computer Science 2020, 1, 1 -15.
AMA StyleGitanjali R. Shinde, Asmita B. Kalamkar, Parikshit N. Mahalle, Nilanjan Dey, Jyotismita Chaki, Aboul Ella Hassanien. Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art. SN Computer Science. 2020; 1 (4):1-15.
Chicago/Turabian StyleGitanjali R. Shinde; Asmita B. Kalamkar; Parikshit N. Mahalle; Nilanjan Dey; Jyotismita Chaki; Aboul Ella Hassanien. 2020. "Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art." SN Computer Science 1, no. 4: 1-15.
The novel discovered disease coronavirus popularly known as COVID19 is a lung infection disease that causes adverse effects on the human respiratory system. It is caused due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and declared a pandemic by the World Health Organization (WHO). For COVID-19 detection, chest radiography, i.e., computerized tomography(CT) scan, X-rays, etc. are widely investigated. In the proposed work, a deep learning model, i.e., truncated VGG16(Visual Geometry Group from Oxford) is implemented to screen COVID-19 CT scans. The VGG16 architecture is fine-tuned and used to extract features from CT Scan images. Further Principal Component Analysis (PCA) is used for feature selection. The final classification is performed using four different classifiers, namely deep convolutional neural network(CNN) , Extreme Learning Machine (ELM), Online sequential ELM, and Bagging Ensemble with support vector machine (SVM) . The best performing classifier Bagging Ensemble with SVM within 385 ms achieved an accuracy of 95.7%, precision of 95.8%, Area Under Curve (AUC) of 0.958, and an F1 score of 95.3% on 208 test images. The results obtained on diverse datasets prove the superiority and robustness of the proposed work in comparison to the techniques available in the literature.
Mukul Singh; Shrey Bansal; Sakshi Ahuja; Rahul Kumar Dubey; Bijaya Ketan Panigrahi; Nilanjan Dey. Transfer learning based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data. 2020, 1 .
AMA StyleMukul Singh, Shrey Bansal, Sakshi Ahuja, Rahul Kumar Dubey, Bijaya Ketan Panigrahi, Nilanjan Dey. Transfer learning based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data. . 2020; ():1.
Chicago/Turabian StyleMukul Singh; Shrey Bansal; Sakshi Ahuja; Rahul Kumar Dubey; Bijaya Ketan Panigrahi; Nilanjan Dey. 2020. "Transfer learning based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data." , no. : 1.
Medical science has taken great steps to enable us to live longer and healthier lives
Robert Simon Sherratt; Nilanjan Dey. Low-Power Wearable Healthcare Sensors. Electronics 2020, 9, 892 .
AMA StyleRobert Simon Sherratt, Nilanjan Dey. Low-Power Wearable Healthcare Sensors. Electronics. 2020; 9 (6):892.
Chicago/Turabian StyleRobert Simon Sherratt; Nilanjan Dey. 2020. "Low-Power Wearable Healthcare Sensors." Electronics 9, no. 6: 892.
Image registration has an imperative role in medical imaging. In this work, a grey-wolf optimizer (GWO) based non-rigid demons registration is proposed to support the retinal image registration process. A comparative study of the proposed GWO-based demons registration framework with cuckoo search, firefly algorithm, and particle swarm optimization- based demons registration is conducted. In addition, a comparative analysis of different demons registration methods, such as Wang’s demons, Tang’s demons, and Thirion’s demons which are optimized using the proposed GWO is carried out. The results established the superiority of the GWO-based framework which achieved 0.9977 correlation, and fast processing compared to the use of the other optimization algorithms. Moreover, GWO-based Wang’s demons performed better accuracy compared to the Tang’s demons and Thirion’s demons framework. It also achieved the best less registration error of 8.36×10-5.
Sayan Chakraborty; Ratika Pradhan; Amira S. Ashour; Luminita Moraru; Nilanjan Dey. Grey Wolf Based Wang’s Demons for Retinal Image Registration. 2020, 1 .
AMA StyleSayan Chakraborty, Ratika Pradhan, Amira S. Ashour, Luminita Moraru, Nilanjan Dey. Grey Wolf Based Wang’s Demons for Retinal Image Registration. . 2020; ():1.
Chicago/Turabian StyleSayan Chakraborty; Ratika Pradhan; Amira S. Ashour; Luminita Moraru; Nilanjan Dey. 2020. "Grey Wolf Based Wang’s Demons for Retinal Image Registration." , no. : 1.
The Coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared as a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a Machine Learning based pipeline to detect the COVID-19 infection using the lung Computed Tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19 affected CTI using Social-Group-Optimization and Kapur’s Entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection and fusion to classify the infection. PCA based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test and validate four different classifiers namely Random Forest, k-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree. Experimental results using benchmark datasets show a high accuracy (> 91%) for the morphology-based segmentation task and for the classification task the KNN offers the highest accuracy among the compared classifiers (> 87%). However, this should be noted that this method still awaits clinical validation, and therefore should not be used to clinically diagnose the ongoing COVID-19 infection.
Nilanjan Dey; V. Rajinikant; Simon James Fong; M. Shamim Kaiser; Mufti Mahmud. Social-Group-Optimization Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images. 2020, 1 .
AMA StyleNilanjan Dey, V. Rajinikant, Simon James Fong, M. Shamim Kaiser, Mufti Mahmud. Social-Group-Optimization Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images. . 2020; ():1.
Chicago/Turabian StyleNilanjan Dey; V. Rajinikant; Simon James Fong; M. Shamim Kaiser; Mufti Mahmud. 2020. "Social-Group-Optimization Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images." , no. : 1.
(1) Background: In this research, we aimed to identify and validate a set of relevant features to distinguish between benign nevi and melanoma lesions. (2) Methods: Two datasets with 70 melanomas and 100 nevi were investigated. The first one contained raw images. The second dataset contained images preprocessed for noise removal and uneven illumination reduction. Further, the images belonging to both datasets were segmented, followed by extracting features considered in terms of form/shape and color such as asymmetry, eccentricity, circularity, asymmetry of color distribution, quadrant asymmetry, fast Fourier transform (FFT) normalization amplitude, and 6th and 7th Hu’s moments. The FFT normalization amplitude is an atypical feature that is computed as a Fourier transform descriptor and focuses on geometric signatures of skin lesions using the frequency domain information. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were employed to ascertain the relevance of the selected features and their capability to differentiate between nevi and melanoma. (3) Results: The ROC curves and AUC were employed for all experiments and selected features. A comparison in terms of the accuracy and AUC was performed, and an evaluation of the performance of the analyzed features was carried out. (4) Conclusions: The asymmetry index and eccentricity, together with F6 Hu’s invariant moment, were fairly competent in providing a good separation between malignant melanoma and benign lesions. Also, the FFT normalization amplitude feature should be exploited due to showing potential in classification.
Felicia Anisoara Damian; Simona Moldovanu; Nilanjan Dey; Amira S. Ashour; Luminita Moraru. Feature Selection of Non-Dermoscopic Skin Lesion Images for Nevus and Melanoma Classification. Computation 2020, 8, 41 .
AMA StyleFelicia Anisoara Damian, Simona Moldovanu, Nilanjan Dey, Amira S. Ashour, Luminita Moraru. Feature Selection of Non-Dermoscopic Skin Lesion Images for Nevus and Melanoma Classification. Computation. 2020; 8 (2):41.
Chicago/Turabian StyleFelicia Anisoara Damian; Simona Moldovanu; Nilanjan Dey; Amira S. Ashour; Luminita Moraru. 2020. "Feature Selection of Non-Dermoscopic Skin Lesion Images for Nevus and Melanoma Classification." Computation 8, no. 2: 41.