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Coronavirus (COVID-19) is an epidemic that is rapidly spreading and causing a severe healthcare crisis resulting in more than 40 million confirmed cases across the globe. There are many intensive studies on AI-based technique, which is time consuming and troublesome by considering heavyweight models in terms of more training parameters and memory cost, which leads to higher time complexity. To improve diagnosis, this paper is aimed to design and establish a unique lightweight deep learning-based approach to perform multi-class classification (normal, COVID-19, and pneumonia) and binary class classification (normal and COVID-19) on X-ray radiographs of chest. This proposed CNN scheme includes the combination of three CBR blocks (convolutional batch normalization ReLu) with learnable parameters and one global average pooling (GP) layer and fully connected layer. The overall accuracy of the proposed model achieved 98.33% and finally compared with the pre-trained transfer learning model (DenseNet-121, ResNet-101, VGG-19, and XceptionNet) and recent state-of-the-art model. For validation of the proposed model, several parameters are considered such as learning rate, batch size, number of epochs, and different optimizers. Apart from this, several other performance measures like tenfold cross-validation, confusion matrix, evaluation metrics, sarea under the receiver operating characteristics, kappa score and Mathew’s correlation, and Grad-CAM heat map have been used to assess the efficacy of the proposed model. The outcome of this proposed model is more robust, and it may be useful for radiologists for faster diagnostics of COVID-19.
Soumya Ranjan Nayak; Janmenjoy Nayak; Utkarsh Sinha; Vaibhav Arora; Uttam Ghosh; Suresh Chandra Satapathy. An Automated Lightweight Deep Neural Network for Diagnosis of COVID-19 from Chest X-ray Images. Arabian Journal for Science and Engineering 2021, 1 -18.
AMA StyleSoumya Ranjan Nayak, Janmenjoy Nayak, Utkarsh Sinha, Vaibhav Arora, Uttam Ghosh, Suresh Chandra Satapathy. An Automated Lightweight Deep Neural Network for Diagnosis of COVID-19 from Chest X-ray Images. Arabian Journal for Science and Engineering. 2021; ():1-18.
Chicago/Turabian StyleSoumya Ranjan Nayak; Janmenjoy Nayak; Utkarsh Sinha; Vaibhav Arora; Uttam Ghosh; Suresh Chandra Satapathy. 2021. "An Automated Lightweight Deep Neural Network for Diagnosis of COVID-19 from Chest X-ray Images." Arabian Journal for Science and Engineering , no. : 1-18.
In the contemporary world, with ever-evolving internet models in the process of automating and digitalizing various industrial and domestic implementations, the Internet of Things (IoT) has made remarkable advancements in sharing the healthcare data and triggering the associated necessary actions. Healthcare-related data sharing among the intermediate nodes, privacy, and data integrity are the two critical challenges in the present-day scenario. Data needs to be encrypted to ensure the confidentiality of the sensitive information shared among the nodes, especially in the case of healthcare-related data records. Implementing the conventional encryption algorithms over the intermediate node may not be technically feasible, and too much burden on the intermediate nodes is not advisable. This article has focused on various security challenges in the existing mechanism, existing strategies in security solutions for IoT driven healthcare monitoring frameworks and proposes a context-aware state of art model based on Blockchain technology that has been deployed for encrypting the data among the nodes in the architecture of a 5G network. The proposed strategy was examined through various performance evaluation metrics, and the proposed approach had outperformed compared to its counterparts.
Parvathaneni Srinivasu; Akash Bhoi; Soumya Nayak; Muhammad Bhutta; Marcin Woźniak. Blockchain Technology for Secured Healthcare Data Communication among the Non-Terminal Nodes in IoT Architecture in 5G Network. Electronics 2021, 10, 1437 .
AMA StyleParvathaneni Srinivasu, Akash Bhoi, Soumya Nayak, Muhammad Bhutta, Marcin Woźniak. Blockchain Technology for Secured Healthcare Data Communication among the Non-Terminal Nodes in IoT Architecture in 5G Network. Electronics. 2021; 10 (12):1437.
Chicago/Turabian StyleParvathaneni Srinivasu; Akash Bhoi; Soumya Nayak; Muhammad Bhutta; Marcin Woźniak. 2021. "Blockchain Technology for Secured Healthcare Data Communication among the Non-Terminal Nodes in IoT Architecture in 5G Network." Electronics 10, no. 12: 1437.
The mutually beneficial blend of artificial intelligence with internet of things has been enabling many industries to develop smart information processing solutions. The implementation of technology enhanced industrial intelligence systems is challenging with the environmental conditions, resource constraints and safety concerns. With the era of smart homes and cities, domains like automated license plate recognition (ALPR) are exploring automate tasks such as traffic management and fraud detection. This paper proposes an optimized decision support solution for ALPR that works purely on edge devices at night-time. Although ALPR is a frequently addressed research problem in the domain of intelligent systems, still they are generally computationally intensive and unable to run on edge devices with limited resources. Therefore, as a novel approach, we consider the complex aspects related to deploying lightweight yet efficient and fast ALPR models on embedded devices. The usability of the proposed models is assessed in real-world with a proof-of-concept hardware design and achieved competitive results to the state-of-the-art ALPR solutions that run on server-grade hardware with intensive resources.
Jithmi Shashirangana; Heshan Padmasiri; Dulani Meedeniya; Charith Perera; Soumya R. Nayak; Janmenjoy Nayak; Shanmuganthan Vimal; Seifidine Kadry. License plate recognition using neural architecture search for edge devices. International Journal of Intelligent Systems 2021, 1 .
AMA StyleJithmi Shashirangana, Heshan Padmasiri, Dulani Meedeniya, Charith Perera, Soumya R. Nayak, Janmenjoy Nayak, Shanmuganthan Vimal, Seifidine Kadry. License plate recognition using neural architecture search for edge devices. International Journal of Intelligent Systems. 2021; ():1.
Chicago/Turabian StyleJithmi Shashirangana; Heshan Padmasiri; Dulani Meedeniya; Charith Perera; Soumya R. Nayak; Janmenjoy Nayak; Shanmuganthan Vimal; Seifidine Kadry. 2021. "License plate recognition using neural architecture search for edge devices." International Journal of Intelligent Systems , no. : 1.
Customization of products or services is a strategy that the business sector has embraced to build a better relationship with the customers to cater to their individual needs and thus providing them a fulfilling experience. This whole process is known as customer relationship management (CRM). In this context, we extensively surveyed 138 papers published between 1996 and 2021 in the area of analytical CRM. Although this study consisted of papers from different business sectors, a fair share of focus was directed to the telecommunication industry and generalized CRM techniques usages. Different science and engineering-based data repositories were studied to ascertain significant studies published in scientific journals, conferences, and articles. The research works on CRM were considered and separated into IT and non-IT-based techniques to study the methods used in different business sectors. The main target behind implementing CRM is for the better revenue growth of the company. Different IT and non-IT-based techniques are used in the analytical CRM area to achieve this target, and researchers have been actively involved in this domain. The purpose of the research was to show the impact of IT-based techniques in the business world. A detailed future course of research in this area was discussed.
Lewlisa Saha; Hrudaya Tripathy; Soumya Nayak; Akash Bhoi; Paolo Barsocchi. Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review. Sustainability 2021, 13, 5279 .
AMA StyleLewlisa Saha, Hrudaya Tripathy, Soumya Nayak, Akash Bhoi, Paolo Barsocchi. Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review. Sustainability. 2021; 13 (9):5279.
Chicago/Turabian StyleLewlisa Saha; Hrudaya Tripathy; Soumya Nayak; Akash Bhoi; Paolo Barsocchi. 2021. "Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review." Sustainability 13, no. 9: 5279.
Fractal Dimension (FD) estimation in digital image analysis has received much attention due to its dimensional significance and therefore has become an active area of research over the year. The earlier FD-based techniques often followed traditional box-counting and its different variation of differential box-counting (DBC) paradigms, in which the proper choice of box count has remained a major concern. However, most of the state-of-the-art DBC variants suffer from considerable limitations like over-counting (OC), under-counting (UC), and limited their application only to square-shaped images, and it is still a major research problem! In this backdrop, the current investigation proposes a generalized box-counting (graylevel invariant DBC); and compares it with other state-of-the-art techniques. The proposed model is evaluated on five benchmark texture datasets (which include real and generated synthetic images) and obtained better results than the existing methods and achieved all desired outcomes by eliminating both OC and UC problems. This algorithm works for any arbitrarily sized (both squared and rectangular) images. It gives a higher rate of accuracy in terms of less fitting error in detecting exact surface roughness from given datasets.
Soumya Ranjan Nayak; Jibitesh Mishra. FRACTAL DIMENSION-BASED GENERALIZED BOX-COUNTING TECHNIQUE WITH APPLICATION TO GRAYSCALE IMAGES. Fractals 2021, 29, 1 .
AMA StyleSoumya Ranjan Nayak, Jibitesh Mishra. FRACTAL DIMENSION-BASED GENERALIZED BOX-COUNTING TECHNIQUE WITH APPLICATION TO GRAYSCALE IMAGES. Fractals. 2021; 29 (03):1.
Chicago/Turabian StyleSoumya Ranjan Nayak; Jibitesh Mishra. 2021. "FRACTAL DIMENSION-BASED GENERALIZED BOX-COUNTING TECHNIQUE WITH APPLICATION TO GRAYSCALE IMAGES." Fractals 29, no. 03: 1.
Verifying the genuineness of official documents, such as bank checks, certificates, contract forms, bonds, etc., remains a challenging task when it comes to accuracy and robustness. Here, the genuineness is related to the degree of match of the signature contained in the documents relating to the original signatures of the authorized person. Signatures of authorized persons are considered known in advance. In this paper, a novel feature set is introduced based on quasi-straightness of boundary pixel runs for signature verification. We extract the quasi-straight line segments using elementary combinations of the directional codes from the signature boundary pixels and subsequently we obtain the feature set from various quasi-straight line classes. The quasi-straight line segments provide a blending of straightness and small curvatures resulting in a robust feature set for the verification of signatures. We have used Support Vector Machine (SVM) for classification and have shown results on standard signature datasets like CEDAR (Center of Excellence for Document Analysis and Recognition) and GPDS-100 (Grupo de Procesado Digital de la Senal). The results establish how the proposed method outperforms the existing state of the art.
Ajij; Sanjoy Pratihar; Soumya Ranjan Nayak; Thomas Hanne; Diptendu Sinha Roy. Off-line signature verification using elementary combinations of directional codes from boundary pixels. Neural Computing and Applications 2021, 1 -18.
AMA StyleAjij, Sanjoy Pratihar, Soumya Ranjan Nayak, Thomas Hanne, Diptendu Sinha Roy. Off-line signature verification using elementary combinations of directional codes from boundary pixels. Neural Computing and Applications. 2021; ():1-18.
Chicago/Turabian StyleAjij; Sanjoy Pratihar; Soumya Ranjan Nayak; Thomas Hanne; Diptendu Sinha Roy. 2021. "Off-line signature verification using elementary combinations of directional codes from boundary pixels." Neural Computing and Applications , no. : 1-18.
Graphical processing unit (GPU) has gained more popularity among researchers in the field of decision making and knowledge discovery systems. However, most of the earlier studies have GPU memory utilization, computational time, and accuracy limitations. The main contribution of this paper is to present a novel algorithm called the Mixed Mode Database Miner (MMDBM) classifier by implementing multithreading concepts on a large number of attributes. The proposed method use the quick sort algorithm in GPU parallel computing to overcome the state of the art limitations. This method applies the dynamic rule generation approach for constructing the decision tree based on the predicted rules. Moreover, the implementation results are compared with both SLIQ and MMDBM using Java and GPU with the computed acceleration ratio time using the BP dataset. The primary objective of this work is to improve the performance with less processing time. The results are also analyzed using various threads in GPU mining using eight different datasets of UCI Machine learning repository. The proposed MMDBM algorithm have been validated on these chosen eight different dataset with accuracy of 91.3% in diabetes, 89.1% in breast cancer, 96.6% in iris, 89.9% in labor, 95.4% in vote, 89.5% in credit card, 78.7% in supermarket and 78.7% in BP, and simultaneously, it also takes less computational time for given datasets. The outcome of this work will be beneficial for the research community to develop more effective multi thread based GPU solution in GPU mining to handle large set of data in minimal processing time. Therefore, this can be considered a more reliable and precise method for GPU computing.
Soumya Ranjan Nayak; S Sivakumar; Akash Kumar Bhoi; Gyoo-Soo Chae; Pradeep Kumar Mallick. Mixed-mode database miner classifier: Parallel computation of graphical processing unit mining. The International Journal of Electrical Engineering & Education 2021, 1 .
AMA StyleSoumya Ranjan Nayak, S Sivakumar, Akash Kumar Bhoi, Gyoo-Soo Chae, Pradeep Kumar Mallick. Mixed-mode database miner classifier: Parallel computation of graphical processing unit mining. The International Journal of Electrical Engineering & Education. 2021; ():1.
Chicago/Turabian StyleSoumya Ranjan Nayak; S Sivakumar; Akash Kumar Bhoi; Gyoo-Soo Chae; Pradeep Kumar Mallick. 2021. "Mixed-mode database miner classifier: Parallel computation of graphical processing unit mining." The International Journal of Electrical Engineering & Education , no. : 1.
SARS Cov-2, COVID-19 (Coronavirus) emerged in Wuhan in early December 2019 and then spread exponentially across the globe. Although, a series of prevention strategies (lockdown, social-distancing) have been enforced to control this pandemic. In this study, we have made statistical analysis in terms of Gaussian modeling, ANOVA test and probabilistic model. After applying ANOVA we can conclude that the recovery rate for all the countries are significantly higher than the mortality rate except for the UK where the mortality rate is significantly higher than the recovery rate. Gaussian modeling applied here was able to predict the original peak values of confirmed cases of countries. Using the probabilistic model we were able to predict that there is around 5% probability that a person in India will be tested positive for COVID-19 on 100 tests.
Soumya Ranjan Nayak; Vaibhav Arora; Utkarsh Sinha; Ramesh Chandra Poonia. A statistical analysis of COVID-19 using Gaussian and probabilistic model. Journal of Interdisciplinary Mathematics 2020, 24, 19 -32.
AMA StyleSoumya Ranjan Nayak, Vaibhav Arora, Utkarsh Sinha, Ramesh Chandra Poonia. A statistical analysis of COVID-19 using Gaussian and probabilistic model. Journal of Interdisciplinary Mathematics. 2020; 24 (1):19-32.
Chicago/Turabian StyleSoumya Ranjan Nayak; Vaibhav Arora; Utkarsh Sinha; Ramesh Chandra Poonia. 2020. "A statistical analysis of COVID-19 using Gaussian and probabilistic model." Journal of Interdisciplinary Mathematics 24, no. 1: 19-32.
The emergence of Coronavirus Disease 2019 (COVID-19) in early December 2019 has caused immense damage to health and global well-being. Currently, there are approximately five million confirmed cases and the novel virus is still spreading rapidly all over the world. Many hospitals across the globe are not yet equipped with an adequate amount of testing kits and the manual Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test is time-consuming and troublesome. It is hence very important to design an automated and early diagnosis system which can provide fast decision and greatly reduce the diagnosis error. The chest X-ray images along with emerging Artificial Intelligence (AI) methodologies, in particular Deep Learning (DL) algorithms have recently become a worthy choice for early COVID-19 screening. This paper proposes a DL assisted automated method using X-ray images for early diagnosis of COVID-19 infection. We evaluate the effectiveness of eight pre-trained Convolutional Neural Network (CNN) models such as AlexNet, VGG-16, GoogleNet, MobileNet-V2, SqueezeNet, ResNet-34, ResNet-50 and Inception-V3 for classification of COVID-19 from normal cases. Also, comparative analyses have been made among these models by considering several important factors such as batch size, learning rate, number of epochs, and type of optimizers with an aim to find the best suited model. The models have been validated on publicly available chest X-ray images and the best performance is obtained by ResNet-34 with an accuracy of 98.33%. This study will be useful for researchers to think for the design of more effective CNN based models for early COVID-19 detection.
Soumya Ranjan Nayak; Deepak Ranjan Nayak; Utkarsh Sinha; Vaibhav Arora; Ram Bilas Pachori. Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study. Biomedical Signal Processing and Control 2020, 64, 102365 -102365.
AMA StyleSoumya Ranjan Nayak, Deepak Ranjan Nayak, Utkarsh Sinha, Vaibhav Arora, Ram Bilas Pachori. Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study. Biomedical Signal Processing and Control. 2020; 64 ():102365-102365.
Chicago/Turabian StyleSoumya Ranjan Nayak; Deepak Ranjan Nayak; Utkarsh Sinha; Vaibhav Arora; Ram Bilas Pachori. 2020. "Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study." Biomedical Signal Processing and Control 64, no. : 102365-102365.
Transportation system (vehicle communication) plays a major role in today’s scenario. Detection of vehicle number plate exactly in blurry conditions was the most challenging issue found in the last three decades. Although many intensive studies were undertaken, none addressed this problem exhaustively. Various methods are introduced by several researchers for detecting the vehicle number from the vehicle number plate images. The purpose of this study was to investigate this current issue by implementing an edge-based approach on the basis of quantitative combination of Canny, Morphological and Sobel methods for the accurate detection of vehicle number in blurry conditions. The experimental results demonstrated that the proposed scheme outperforms its counterparts in terms of Sobel, Prewitt, Roberts, Laplacian of Gaussian (LoG), Morphological and Canny methods in all aspects with higher peak signal to noise ratio (PSNR) and signal to noise ratio (SNR) values. Hence, the proposed hybrid scheme is better and robust and results in accurate estimation of vehicle number from the blurry vehicle number plate (BVNP) images for the given datasets.
Kalyan Kumar Jena; Soumya Ranjan Nayak; Sasmita Mishra; Sarojananda Mishra. Vehicle Number Plate Detection: An Edge Image Based Approach. Advances in Intelligent Systems and Computing 2020, 24 -34.
AMA StyleKalyan Kumar Jena, Soumya Ranjan Nayak, Sasmita Mishra, Sarojananda Mishra. Vehicle Number Plate Detection: An Edge Image Based Approach. Advances in Intelligent Systems and Computing. 2020; ():24-34.
Chicago/Turabian StyleKalyan Kumar Jena; Soumya Ranjan Nayak; Sasmita Mishra; Sarojananda Mishra. 2020. "Vehicle Number Plate Detection: An Edge Image Based Approach." Advances in Intelligent Systems and Computing , no. : 24-34.
Muthukumaran Malarvel; Soumya Ranjan Nayak. Edge and region segmentation in high-resolution aerial images using improved kernel density estimation: A hybrid approach. Journal of Intelligent & Fuzzy Systems 2020, 39, 543 -560.
AMA StyleMuthukumaran Malarvel, Soumya Ranjan Nayak. Edge and region segmentation in high-resolution aerial images using improved kernel density estimation: A hybrid approach. Journal of Intelligent & Fuzzy Systems. 2020; 39 (1):543-560.
Chicago/Turabian StyleMuthukumaran Malarvel; Soumya Ranjan Nayak. 2020. "Edge and region segmentation in high-resolution aerial images using improved kernel density estimation: A hybrid approach." Journal of Intelligent & Fuzzy Systems 39, no. 1: 543-560.
In this current digital age of world, character recognition (CR) has been done through various machine learning algorithms. And it considered to be one the most challenging segment of pattern recognition. In addition to the above context, offline handwritten character is the most challenging one as compared with the printed one. Despite various algorithms that were harnessed on various handwritten scripts, it can be possible to have more feasibility solution and high recognition rate. Here, in this paper, we have focused on the handwritten numerals of Odia and Bangla scripts. To overcome the ambiguities that arise in handwritten, one has been resolved using the Convolutional Neural Network (CNN). Here we have suggested a state‐of‐the‐art CNN‐based approach for recognition of multiple handwritten numerals of both the scripts and clearly shown how effectively it has been used for evaluating the discriminate features from the original image and later leads to report high recognition rate. At the simulation level, we have listed up variance nature of the individual's images, and through CNN, a high recognition rate is achieved, which is quite helpful in building the automatic recognition system for handwritten numerals to have solution for real‐time problems.
Abhisek Sethy; Prashanta Kumar Patra; Soumya Ranjan Nayak. Offline Handwritten Numeral Recognition Using Convolution Neural Network. Machine Vision Inspection Systems 2020, 197 -212.
AMA StyleAbhisek Sethy, Prashanta Kumar Patra, Soumya Ranjan Nayak. Offline Handwritten Numeral Recognition Using Convolution Neural Network. Machine Vision Inspection Systems. 2020; ():197-212.
Chicago/Turabian StyleAbhisek Sethy; Prashanta Kumar Patra; Soumya Ranjan Nayak. 2020. "Offline Handwritten Numeral Recognition Using Convolution Neural Network." Machine Vision Inspection Systems , no. : 197-212.
In this work, we are solving the major problem of reducing the time complexity of searching a string in huge corpus by using GPU as our computational environment (utilizing GPGPU and CUDA as programming platform) and Knuth–Morris–Pratt (KMP) and BMH (Boyer–Moore–Horspool) as string matching algorithms. String matching is a widely used technique in current research interest of various application areas such as bioinformatics, network intrusion detection, and computer virus scan. Although data are memorized in various ways, text remains the main form to exchange information. This is particularly evident in literature or linguistics where data are composed of huge corpus and dictionaries. These analytics are required in computer science where a large amount of data is stored in linear files. To search a particular string from these huge corpus takes more time in traditional CPU’s and this is a major problem.
M. Musthafa Baig; S. Sivakumar; Soumya Ranjan Nayak. Optimizing Performance of Text Searching Using CPU and GPUs. Advances in Intelligent Systems and Computing 2020, 141 -150.
AMA StyleM. Musthafa Baig, S. Sivakumar, Soumya Ranjan Nayak. Optimizing Performance of Text Searching Using CPU and GPUs. Advances in Intelligent Systems and Computing. 2020; ():141-150.
Chicago/Turabian StyleM. Musthafa Baig; S. Sivakumar; Soumya Ranjan Nayak. 2020. "Optimizing Performance of Text Searching Using CPU and GPUs." Advances in Intelligent Systems and Computing , no. : 141-150.
Arpita Roy; Shaik Razia; Nikhat Parveen; Arumbaka Srinivasa Rao; Soumya Ranjan Nayak; Ramesh Chandra Poonia. Fuzzy rule based intelligent system for user authentication based on user behaviour. Journal of Discrete Mathematical Sciences and Cryptography 2020, 23, 409 -417.
AMA StyleArpita Roy, Shaik Razia, Nikhat Parveen, Arumbaka Srinivasa Rao, Soumya Ranjan Nayak, Ramesh Chandra Poonia. Fuzzy rule based intelligent system for user authentication based on user behaviour. Journal of Discrete Mathematical Sciences and Cryptography. 2020; 23 (2):409-417.
Chicago/Turabian StyleArpita Roy; Shaik Razia; Nikhat Parveen; Arumbaka Srinivasa Rao; Soumya Ranjan Nayak; Ramesh Chandra Poonia. 2020. "Fuzzy rule based intelligent system for user authentication based on user behaviour." Journal of Discrete Mathematical Sciences and Cryptography 23, no. 2: 409-417.
People prefer new technology by using online social media as a communication channels to express their suicidal thoughts. Primary identification and detection are viewed as an effective approach to avoid suicidal attempt and suicidal ideation-two basic hazards causing effective suicide. This paper exhibits different techniques to comprehend suicidal ideation through online user contents in particularly by considering twitter data for past last two years as an objective of early detection by means of sentiment analysis and supervised leaning methods. Analysing the text descriptions and users language exposes rich knowledge that can be utilized as a primary cautioning system for suicidal detection. To identify tweets exhibiting suicidal ideation, several features are extracted and a set of features are proposed for training the model over the dataset by using ensemble and baseline classifiers. Based on the outcome of baseline classifier; improved ensemble random forest (RF) algorithm achieved an accuracy of 0.99% compared to other classification methods for suicidal prediction with tweets containing suicidal thought is better when compared to the existing system. Such experimentation and monitoring may help individual and population-wide prevention by counseling and informing to suicidal research and policy. The experimental analysis expresses the feasibility of the methodology used by providing a benchmark for suicidal detection on online social network: Twitter.
E. Rajesh Kumar; K.V.S.N. Rama Rao; Soumya Ranjan Nayak; Ramesh Chandra. Suicidal ideation prediction in twitter data using machine learning techniques. Journal of Interdisciplinary Mathematics 2020, 23, 117 -125.
AMA StyleE. Rajesh Kumar, K.V.S.N. Rama Rao, Soumya Ranjan Nayak, Ramesh Chandra. Suicidal ideation prediction in twitter data using machine learning techniques. Journal of Interdisciplinary Mathematics. 2020; 23 (1):117-125.
Chicago/Turabian StyleE. Rajesh Kumar; K.V.S.N. Rama Rao; Soumya Ranjan Nayak; Ramesh Chandra. 2020. "Suicidal ideation prediction in twitter data using machine learning techniques." Journal of Interdisciplinary Mathematics 23, no. 1: 117-125.
The chapter focuses on application of digital image processing and deep learning for analyzing the occurrence of malaria from the medical reports. This approach is helpful in quick identification of the disease from the preliminary tests which are carried out in a person affected by malaria. The combination of deep learning has made the process much advanced as the convolutional neural network is able to gain deeper insights from the medical images of the person. Since traditional methods are not able to detect malaria properly and quickly, by means of convolutional neural networks, the early detection of malaria has been possible, and thus, this process will open a new door in the world of medical science.
Rasmita Lenka; Koustav Dutta; Ashimananda Khandual; Soumya Ranjan Nayak. Bio-Medical Image Processing. Genetic Algorithms and Applications for Stock Trading Optimization 2020, 158 -169.
AMA StyleRasmita Lenka, Koustav Dutta, Ashimananda Khandual, Soumya Ranjan Nayak. Bio-Medical Image Processing. Genetic Algorithms and Applications for Stock Trading Optimization. 2020; ():158-169.
Chicago/Turabian StyleRasmita Lenka; Koustav Dutta; Ashimananda Khandual; Soumya Ranjan Nayak. 2020. "Bio-Medical Image Processing." Genetic Algorithms and Applications for Stock Trading Optimization , no. : 158-169.
This chapter describes a novel method to enhance degraded nighttime images by dehazing and color correction method. In the first part of this chapter, the authors focus on filtering process for low illumination images. Secondly, they propose an efficient dehazing model for removing haziness Thirdly, a color correction method proposed for color consistency approach. Removing nighttime haze technique is an important and necessary procedure to avoid ill-condition visibility of human eyes. Scattering and color distortion are two major problems of distortion in case of hazy image. To increase the visibility of the scene, the authors compute the preprocessing using WLS filter. Then the airlight component for the non-uniform illumination presents in nighttime scenes is improved by using a modified well-known dark-channel prior algorithm for removing nighttime haze, and then it uses α-automatic color equalization as post-processing for color correction over the entire image for getting a better enhanced output image free from haze with improved color constancy.
Rasmita Lenka; Asimananda Khandual; Koustav Dutta; Soumya Ranjan Nayak. Image Enhancement. Genetic Algorithms and Applications for Stock Trading Optimization 2020, 211 -223.
AMA StyleRasmita Lenka, Asimananda Khandual, Koustav Dutta, Soumya Ranjan Nayak. Image Enhancement. Genetic Algorithms and Applications for Stock Trading Optimization. 2020; ():211-223.
Chicago/Turabian StyleRasmita Lenka; Asimananda Khandual; Koustav Dutta; Soumya Ranjan Nayak. 2020. "Image Enhancement." Genetic Algorithms and Applications for Stock Trading Optimization , no. : 211-223.
A noteworthy application pertaining to the photonic memory is disclosed in the present research. The suggested application is made with the help of silicon monoxide based on three-dimensional photonic structures. Further, the principle of operational mechanism deals with the analysis of absorption, reflection, and transmission where the photonic bandgap analysis of the proposed 3D structure is a backend of the same. Moreover, the signal of 1000 nm is considered as input one where the signal of 400 nm is chosen as an excited one to realize the above said memory application. Finally, the outcomes of the current research would be a worthwhile element for future application and it could be utilized in the optical computer system.
Iraj S. Amiri; Soumya Ranjan Nayak; Sanjay Kumar Sahu; G. Palai. Realization of 3D memory for optical computer: A new paragon to future photonics. Optik 2019, 203, 163914 .
AMA StyleIraj S. Amiri, Soumya Ranjan Nayak, Sanjay Kumar Sahu, G. Palai. Realization of 3D memory for optical computer: A new paragon to future photonics. Optik. 2019; 203 ():163914.
Chicago/Turabian StyleIraj S. Amiri; Soumya Ranjan Nayak; Sanjay Kumar Sahu; G. Palai. 2019. "Realization of 3D memory for optical computer: A new paragon to future photonics." Optik 203, no. : 163914.
The chip to chip communication pertaining to ultra-low power-based very large integrated device is addressed in this paper using photonic integrated circuits (PIC) where photonic circuits comprise nanoscale based light sources, one-dimensional waveguide, and photodetector. The principle of realization of transportation of signal through PIC relies on a different type of losses including diffraction, reflection, scattering, dispersion, and absorption. Moreover, the operational mechanism deals with the low potential signal of 0.5 V to 1.0 V for said communication. The reason for choosing such low potential is that different works related to industrial research are being operated nowadays to develop an efficient VLSI device.The current research has certain hegemony related to miniaturization size and high efficiency as compared to others. To overall, the present communication shows a new paradigm to optical VLSI for near future application in the field of photonics.
I.S. Amiri; G. Palai; Jafar A. Alzubi; Soumya Ranjan Nayak. Chip to chip communication through the photonic integrated circuit: A new paradigm to optical VLSI. Optik 2019, 202, 163588 .
AMA StyleI.S. Amiri, G. Palai, Jafar A. Alzubi, Soumya Ranjan Nayak. Chip to chip communication through the photonic integrated circuit: A new paradigm to optical VLSI. Optik. 2019; 202 ():163588.
Chicago/Turabian StyleI.S. Amiri; G. Palai; Jafar A. Alzubi; Soumya Ranjan Nayak. 2019. "Chip to chip communication through the photonic integrated circuit: A new paradigm to optical VLSI." Optik 202, no. : 163588.
I.S. Amiri; G. Palai; S K Tripathy; S.R. Nayak. Realisation of all photonic logic gates using plasmonic-based photonic structure through bandgap analysis. Optik 2019, 194, 1 .
AMA StyleI.S. Amiri, G. Palai, S K Tripathy, S.R. Nayak. Realisation of all photonic logic gates using plasmonic-based photonic structure through bandgap analysis. Optik. 2019; 194 ():1.
Chicago/Turabian StyleI.S. Amiri; G. Palai; S K Tripathy; S.R. Nayak. 2019. "Realisation of all photonic logic gates using plasmonic-based photonic structure through bandgap analysis." Optik 194, no. : 1.