Papers by Priyadarshini Pattanaik
The goal of this paper is to tackle the challenge of estimating motion in sequences of 3D point c... more The goal of this paper is to tackle the challenge of estimating motion in sequences of 3D point clouds that feature the movement of the knee joint's 3D positions and color attributes. Kinematics and morphology (form) are two important factors in determining the features of flexion and extension. Joints are crucial parts of the linear motion system. Precise estimation of both moments and shape is required to comprehend the functionality of joint surfaces (e.g., the knee). The diagnosis of knee pathologies and treatment of chronic joint diseases such as Osteoarthritis requires an accurate understanding of the in vivo biomechanics of the human knee. However, measuring kinematics in human patients is challenging. The dynamic monitoring of knee motions, whereby generates a realistic bone model that includes and excludes cartilage, can be used to create a novel measurement technique for knee investigations. Such morph kinematic modeling offers the chance to analyze the knee's kine...
International Journal of Advanced Computer Science and Applications
The novel human Corona disease (COVID-19) is a pulmonary sickness brought on by an extraordinaril... more The novel human Corona disease (COVID-19) is a pulmonary sickness brought on by an extraordinarily outrageous respiratory condition crown 2. (SARS-CoV-2). Chest radiography imaging has a significant role in the screening, early diagnosis, and follow-up of the suspected individuals due to the effects of COVID-19 on pneumonic-sensitive tissue. It also has a severe impact on the economy as a whole. If positive patients are identified early, the spread of the pandemic illness can be slowed. To determine whether people are at risk for illnesses, a COVID-19 infection prediction is critical. This paper categorizes chest CT samples of COVID-19 affected patients. The two-stage proposed deep learning technique produces spatial function from images, so it is a very expeditious manner for image category hassle. Extensive experiments are drawn by considering the benchmark chest-Computed Tomography (chest-CT) image datasets. Comparative evaluation reveals that our proposed method outperforms amongst other 20 different existing pre-trained models. The test outcomes constitute that our proposed model achieved the best rating of 97.6%, 0.964, 0.964, and 0.982 concerning the accuracy, precision, recall, specificity, and F1score, respectively.
2022 Trends in Electrical, Electronics, Computer Engineering Conference (TEECCON)
DEStech Transactions on Engineering and Technology Research
The traditional technology of preparation of charge materials does not include an intellectual co... more The traditional technology of preparation of charge materials does not include an intellectual component and is conducted only on the basis of an evaluation of the results of an industrial experiment. This study has the following purpose: increasing the efficiency of the steelmaking process in large capacity arc furnace on the basis of implementation a new decision-making system about the composition of charge materials. Authors proposed a diagram of an interactive intelligent builder which is capable to solve problems of optimizing the composition of charge materials in the presence of several different targets.
Computational Intelligence in Software Modeling, Feb 21, 2022
Intelligent Systems Reference Library, 2020
International Journal of Advanced Intelligence Paradigms, 2021
Journal of King Saud University - Computer and Information Sciences, 2020
Abstract Automatic segmentation of erythrocytes in microscopic blood smear phone images is a crit... more Abstract Automatic segmentation of erythrocytes in microscopic blood smear phone images is a critical step to visualize and identify malaria using machine learning technologies. However, it still remains a challenging problem due to the scarcity of experts, low image qualities, slow manual and inefficient quality of diagnosis. To handle these issues to some extent, we proposed an effective multi-magnification deep residual neural network (MM-ResNet), where we fully automatically classify the microscopic blood smear images as either infected/ non-infected at multiple magnifications. We have experimentally evaluated our approach by using it to train more efficient variants of different compact deep convolutional neural networks (CNN), evaluated on phone datasets. The MM-ResNet end-to-end framework shows similar or superior accuracy than the baseline architectures, as measured by GPU timings on the publicly available microscopic blood smear phone images. This approach is the first application of a MM-ResNet for malaria-infected erythrocyte identification in microscopic blood smear images.
IOSR Journal of Engineering, 2014
Cloud Computing is a vast infrastructural and rising pool which provides huge storage of data in ... more Cloud Computing is a vast infrastructural and rising pool which provides huge storage of data in one sphere. Organizations nowadays are in the marathon of equipping the whole system in a cloud form. In this paper we are concern for providing a greater security to the cloud data from data mining attacks. Time complexity and security issue is taken into consideration. Data security and privacy protection issues are associated with cloud computing have been enhanced and modernised in this paper. We have proposed an architecture comprising of various components and network designs which provides a high level of security to the data. The rate of growth of cloud computing is so fast that it has already put its head in the mortar and is spread as the most durable untiring technology now. As cloud computing is a store house of data which might get affected by data mining attacks so we have launched this architecture.
Journal of King Saud University - Computer and Information Sciences, 2021
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB), 2019
The focus of this paper is towards comparing the computational paradigms of two unsupervised data... more The focus of this paper is towards comparing the computational paradigms of two unsupervised data reduction techniques, namely Auto encoder and Self-organizing Maps. The domain of inquiry in this paper is for automatic malaria identification from blood smear images, which has a great relevance in healthcare informatics and requires a good treatment for the patients. Extensive experiments are performed using the microscopically thick blood smear image datasets. Our results reveal that the deep-learning-based Auto encoder technique is better than the Self-organizing Maps technique in terms of accuracy of 87.5%. The Auto encoder technique is computationally efficient, which may further facilitate its malaria identification in the clinical routine.
An electronic tender (e-tender) system is a system in which selling, buying and providing contrac... more An electronic tender (e-tender) system is a system in which selling, buying and providing contract by the government with the help of online software. In this system the tender data is recorded, stored and processed primarily as digital information. In the modern world e-tender system is increasing speedily and the popularity of e-tender system need quality and security. In this paper a ‘KBETS’ is proposed which provides user to participating in tender nevertheless of the geographic locations and without worrying about security.
The main objective of the higher educational organization is to provide high quality and necessar... more The main objective of the higher educational organization is to provide high quality and necessary education to its students. The two goals of data mining in Indian education system is to analyze and enhance the chronicle way of recent educational data mining advances development; the second is to preserve, organize and discuss the content of the result which is produced by a data mining approach. The use of various data mining techniques such as random forest, decision tree, etc in Indian education processes will help to improve students' performance and provide a broad decision management skill in selection of courses as per their retention rate. This paper focuses on the model representation for analyzing the different data mining techniques in an Indian education system. Also the paper reviews a comparative study of ID3, K-Means, Naïve Bayes, Random Forest algorithm. In this paper, we have proposed the approach of Random Forest to predict the career decision for the 12 passi...
Sensors (Basel, Switzerland), 2020
The herpesvirus, polyomavirus, papillomavirus, and retrovirus families are associated with breast... more The herpesvirus, polyomavirus, papillomavirus, and retrovirus families are associated with breast cancer. More effort is needed to assess the role of these viruses in the detection and diagnosis of breast cancer cases in women. The aim of this paper is to propose an efficient segmentation and classification system in the Mammography Image Analysis Society (MIAS) images of medical images. Segmentation became challenging for medical images because they are not illuminated in the correct way. The role of segmentation is essential in concern with detecting syndromes in human. This research work is on the segmentation of medical images based on intuitionistic possibilistic fuzzy c-mean (IPFCM) clustering. Intuitionist fuzzy c-mean (IFCM) and possibilistic fuzzy c-mean (PFCM) algorithms are hybridised to deal with problems of fuzzy c-mean. The introduced clustering methodology, in this article, retains the positive points of PFCM which helps to overcome the problem of the coincident clust...
The infected red blood cell pixel count in thin blood smear image plays a vital role in malaria p... more The infected red blood cell pixel count in thin blood smear image plays a vital role in malaria parasite detection analysis. This paper proposes three stage object detection procedure of computer vision with Kernel-based detection and Kalman filtering process to detect malaria parasite. The use of Kernel based detection with exact pixel information makes the proposed procedure capable of accurately detecting and localizing the target infected by malaria parasites in thin blood smear images. The experiment is conducted on several microscopically preliminary screened benchmark gold standard diagnosis datasets of blood smear images, each 300×300 pixels of Plasmodium falciparum in thin blood smear images. The 300×300 size images were split into overlapping patches, each of size 50×50 pixels. The experimental results on the malaria blood smear image datasets demonstrate the effectiveness of the proposed method over the existing computer vision algorithms. The novelty of the work lies in ...
We compare Deep Convolutional Neural Networks (DCNN) frameworks, namely AlexNet and VGGNet, for t... more We compare Deep Convolutional Neural Networks (DCNN) frameworks, namely AlexNet and VGGNet, for the classification of healthy and malaria-infected cells in large, grayscale, low quality and low resolution microscopic images, in the case only a small training set is available. Experimental results deliver promising results on the path to quick, automatic and precise classification in unstrained images.
Wireless sensor network (WSN) is an integral part of Internet of Things (IoT). The sensor nodes i... more Wireless sensor network (WSN) is an integral part of Internet of Things (IoT). The sensor nodes in WSN generate large sensing data which is disseminated to intelligent servers using multiple wireless networks. This large data is prone to attacks from malicious nodes which become part of the network, and it is difficult to find these adversaries. The work in this paper presents a mechanism to detect adversaries for the IEEE 802.15.4 standard which is a central medium access protocol used in WSN-based IoT applications. The collisions and exhaustion attacks are detected based on a soft decision-based algorithm. In case the QoS of the network is compromised due to large data traffic, the proposed protocol adaptively varies the duty cycle of the IEEE 802.15.4. Simulation results show that the proposed intrusion detection and adaptive duty cycle algorithm improves the energy efficiency of a WSN with a reduced network delay.
International Journal of Healthcare Information Systems and Informatics
The genus Plasmodium parasite causes malaria infection. Fast detection and accurate diagnosis of ... more The genus Plasmodium parasite causes malaria infection. Fast detection and accurate diagnosis of infected and non-infected malaria erythrocytes from microscopic blood smear images open the door to effective assistance and patient-specific treatment. This article presents a comparative experimental analysis of visual detection of infected erythrocytes malaria parasites via the most efficient morphological techniques from gold standard blood smear images. In this article, twelve different widely-used morphological algorithms are evaluated followed by a random forest classifier for detecting infected erythrocytes based on their performance vis-a-vis microscopic blood smear images. Accurate detection of infected malaria erythrocytes is done using the two ranges of blood smear image datasets with varying malaria parasite density. Finally, compared to 11 morphological techniques in terms of accuracy, sensitivity, and specificity, the qualitative assessment of experimental results unveil t...
We compare Deep Convolutional Neural Networks (DCNN) frameworks, namely AlexNet and VGGNet, for t... more We compare Deep Convolutional Neural Networks (DCNN) frameworks, namely AlexNet and VGGNet, for the classification of healthy and malaria-infected cells in large, grayscale, low quality and low resolution microscopic images, in the case only a small training set is available. Experimental results deliver promising results on the path to quick, automatic and precise classification in unstained images.
International Journal of Advanced Intelligence Paradigms
Uploads
Papers by Priyadarshini Pattanaik