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Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The stipulation of expert advice for disease identification is also a challenge for the farmers. The traditional techniques adopted for plant disease detection require more human intervention, are unhandy for farmers, and have a high cost of deployment, operation, and maintenance. Therefore, there is a requirement for automating plant disease detection and classification. Deep learning and IoT-based solutions are proposed in the literature for plant disease detection and classification. However, there is a huge scope to develop low-cost systems by integrating these techniques for data collection, feature visualization, and disease detection. This research aims to develop the ‘Automatic and Intelligent Data Collector and Classifier’ framework by integrating IoT and deep learning. The framework automatically collects the imagery and parametric data from the pearl millet farmland at ICAR, Mysore, India. It automatically sends the collected data to the cloud server and the Raspberry Pi. The ‘Custom-Net’ model designed as a part of this research is deployed on the cloud server. It collaborates with the Raspberry Pi to precisely predict the blast and rust diseases in pearl millet. Moreover, the Grad-CAM is employed to visualize the features extracted by the ‘Custom-Net’. Furthermore, the impact of transfer learning on the ‘Custom-Net’ and state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 is shown in this manuscript. Based on the experimental results, and features visualization by Grad-CAM, it is observed that the ‘Custom-Net’ extracts the relevant features and the transfer learning improves the extraction of relevant features. Additionally, the ‘Custom-Net’ model reports a classification accuracy of 98.78% that is equivalent to state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19. Although the classification of ‘Custom-Net’ is comparable to state-of-the-art models, it is effective in reducing the training time by 86.67%. It makes the model more suitable for automating disease detection. This proves that the proposed model is effective in providing a low-cost and handy tool for farmers to improve crop yield and product quality.
Nidhi Kundu; Geeta Rani; Vijaypal Dhaka; Kalpit Gupta; Siddaiah Nayak; Sahil Verma; Muhammad Ijaz; Marcin Woźniak. IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet. Sensors 2021, 21, 5386 .
AMA StyleNidhi Kundu, Geeta Rani, Vijaypal Dhaka, Kalpit Gupta, Siddaiah Nayak, Sahil Verma, Muhammad Ijaz, Marcin Woźniak. IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet. Sensors. 2021; 21 (16):5386.
Chicago/Turabian StyleNidhi Kundu; Geeta Rani; Vijaypal Dhaka; Kalpit Gupta; Siddaiah Nayak; Sahil Verma; Muhammad Ijaz; Marcin Woźniak. 2021. "IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet." Sensors 21, no. 16: 5386.
Amidst the global pandemic and catastrophe created by ‘COVID-19’, every research institution and scientist are doing their best efforts to invent or find the vaccine or medicine for the disease. The objective of this research is to design and develop a deep learning-based multi-modal for the screening of COVID-19 using chest radiographs and genomic sequences. The modal is also effective in finding the degree of genomic similarity among the Severe Acute Respiratory Syndrome-Coronavirus 2 and other prevalent viruses such as Severe Acute Respiratory Syndrome-Coronavirus, Middle East Respiratory Syndrome-Coronavirus, Human Immunodeficiency Virus, and Human T-cell Leukaemia Virus. The experimental results on the datasets available at National Centre for Biotechnology Information, GitHub, and Kaggle repositories show that it is successful in detecting the genome of ‘SARS-CoV-2’ in the host genome with an accuracy of 99.27% and screening of chest radiographs into COVID-19, non-COVID pneumonia and healthy with a sensitivity of 95.47%. Thus, it may prove a useful tool for doctors to quickly classify the infected and non-infected genomes. It can also be useful in finding the most effective drug from the available drugs for the treatment of ‘COVID-19’.
Geeta Rani; Meet Ganpatlal Oza; Vijaypal Singh Dhaka; Nitesh Pradhan; Sahil Verma; Joel J. P. C. Rodrigues. Applying deep learning-based multi-modal for detection of coronavirus. Multimedia Systems 2021, 1 -12.
AMA StyleGeeta Rani, Meet Ganpatlal Oza, Vijaypal Singh Dhaka, Nitesh Pradhan, Sahil Verma, Joel J. P. C. Rodrigues. Applying deep learning-based multi-modal for detection of coronavirus. Multimedia Systems. 2021; ():1-12.
Chicago/Turabian StyleGeeta Rani; Meet Ganpatlal Oza; Vijaypal Singh Dhaka; Nitesh Pradhan; Sahil Verma; Joel J. P. C. Rodrigues. 2021. "Applying deep learning-based multi-modal for detection of coronavirus." Multimedia Systems , no. : 1-12.
In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects, faces, bones, handwritten digits, and traffic signs signify the importance of Convolutional Neural Networks in the real world. The effectiveness of Convolutional Neural Networks in image recognition motivates the researchers to extend its applications in the field of agriculture for recognition of plant species, yield management, weed detection, soil, and water management, fruit counting, diseases, and pest detection, evaluating the nutrient status of plants, and much more. The availability of voluminous research works in applying deep learning models in agriculture leads to difficulty in selecting a suitable model according to the type of dataset and experimental environment. In this manuscript, the authors present a survey of the existing literature in applying deep Convolutional Neural Networks to predict plant diseases from leaf images. This manuscript presents an exemplary comparison of the pre-processing techniques, Convolutional Neural Network models, frameworks, and optimization techniques applied to detect and classify plant diseases using leaf images as a data set. This manuscript also presents a survey of the datasets and performance metrics used to evaluate the efficacy of models. The manuscript highlights the advantages and disadvantages of different techniques and models proposed in the existing literature. This survey will ease the task of researchers working in the field of applying deep learning techniques for the identification and classification of plant leaf diseases.
Vijaypal Dhaka; Sangeeta Meena; Geeta Rani; Deepak Sinwar; Kavita Kavita; Muhammad Ijaz; Marcin Woźniak. A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases. Sensors 2021, 21, 4749 .
AMA StyleVijaypal Dhaka, Sangeeta Meena, Geeta Rani, Deepak Sinwar, Kavita Kavita, Muhammad Ijaz, Marcin Woźniak. A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases. Sensors. 2021; 21 (14):4749.
Chicago/Turabian StyleVijaypal Dhaka; Sangeeta Meena; Geeta Rani; Deepak Sinwar; Kavita Kavita; Muhammad Ijaz; Marcin Woźniak. 2021. "A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases." Sensors 21, no. 14: 4749.
Cardiovascular is one of the most critical diseases that affect persons very abominably. Coronary artery diseases (CAD) are one of the categories of cardiovascular diseases that cause a high death rate. So it is imperative to control these death rates by developing an advanced model of machine learning through which diseases can be detected at the premature stage. Due to the lack of enough facilities for tools like angiography it has become a substantial challenge for the health care organization to detect such diseases. If tools exist, then these are known for being expensive and also have numerous side effects. The main goal of this research is to enhance the accuracy of existing models using optimization techniques with machine learning techniques. Alizadeh-Sani CAD dataset has been used which consists of a total of 303 records with 56 attributes. The proposed model reported following values of precision (0.92), recall (0.92), and accuracy (0.93). This proves the efficacy of employing the optimization techniques with machine learning algorithms.
Savita; Ganga Sharma; Geeta Rani; Vijaypal Singh Dhaka. Efficient Predictive Modelling for Classification of Coronary Artery Diseases Using Machine Learning Approach. IOP Conference Series: Materials Science and Engineering 2021, 1099, 012068 .
AMA StyleSavita, Ganga Sharma, Geeta Rani, Vijaypal Singh Dhaka. Efficient Predictive Modelling for Classification of Coronary Artery Diseases Using Machine Learning Approach. IOP Conference Series: Materials Science and Engineering. 2021; 1099 (1):012068.
Chicago/Turabian StyleSavita; Ganga Sharma; Geeta Rani; Vijaypal Singh Dhaka. 2021. "Efficient Predictive Modelling for Classification of Coronary Artery Diseases Using Machine Learning Approach." IOP Conference Series: Materials Science and Engineering 1099, no. 1: 012068.
The objective of this research is to develop a convolutional neural network model ‘COVID‐Screen‐Net’ for multi‐class classification of chest X‐ray images into three classes viz. COVID‐19, bacterial pneumonia, and normal. The model performs the automatic feature extraction from X‐ray images and accurately identifies the features responsible for distinguishing the X‐ray images of different classes. It plots these features on the GradCam. The authors optimized the number of convolution and activation layers according to the size of the dataset. They also fine‐tuned the hyperparameters to minimize the computation time and to enhance the efficiency of the model. The performance of the model has been evaluated on the anonymous chest X‐ray images collected from hospitals and the dataset available on the web. The model attains an average accuracy of 97.71% and a maximum recall of 100%. The comparative analysis shows that the ‘COVID‐Screen‐Net’ outperforms the existing systems for screening of COVID‐19. The effectiveness of the model is validated by the radiology experts on the real‐time dataset. Therefore, it may prove a useful tool for quick and low‐cost mass screening of patients of COVID‐19. This tool may reduce the burden on health experts in the present situation of the Global Pandemic. The copyright of this tool is registered in the names of authors under the laws of Intellectual Property Rights in India with the registration number ‘SW‐13625/2020’.
Vijaypal Singh Dhaka; Geeta Rani; Meet Ganpatlal Oza; Tarushi Sharma; Ankit Misra. A deep learning model for mass screening of COVID ‐19. International Journal of Imaging Systems and Technology 2021, 31, 483 -498.
AMA StyleVijaypal Singh Dhaka, Geeta Rani, Meet Ganpatlal Oza, Tarushi Sharma, Ankit Misra. A deep learning model for mass screening of COVID ‐19. International Journal of Imaging Systems and Technology. 2021; 31 (2):483-498.
Chicago/Turabian StyleVijaypal Singh Dhaka; Geeta Rani; Meet Ganpatlal Oza; Tarushi Sharma; Ankit Misra. 2021. "A deep learning model for mass screening of COVID ‐19." International Journal of Imaging Systems and Technology 31, no. 2: 483-498.
Amidst the global pandemic and catastrophe created by ‘COVID-19’, every research institution and scientists are doing their best efforts to invent or find the vaccine or medicine for the disease. The objective of this research is to design and develop a deep learning model for finding the degree of similarity of the genome of the Severe Acute Respiratory Syndrome-Coronavirus 2 (‘SARS-CoV-2’) with a given genome. This research also aims at detecting the genome of ‘SARS-CoV-2’ in the host human beings. The experimental results on the dataset publicly available at National Centre for Biotechnology Information, show that the model is effective in predicting the similarity score of the genomic sequence of ‘SARS-CoV-2’ and other prevalent viruses such as Severe Acute Respiratory Syndrome-Coronavirus, Middle East Respiratory Syndrome Coronavirus, Human Immunodeficiency Virus, and Human T- cell Leukaemia Virus. This is successful in detecting the genome of ‘SARS-CoV-2’ in the host genome with an accuracy of 99.27%. It may prove a useful tool for doctors to quickly classify the infected and non-infected genomes. It can also be useful in finding the most effective drug from the available drugs for the treatment of ‘COVID-19’.
Geeta Rani; Meet Ganpatlal Oza; Vijaypal Singh Dhaka; Nitesh Pradhan; Sahil Verma; Joel J. P. C. Rodrigues. Applying Deep Learning for Genome Detection of Coronavirus. 2020, 1 .
AMA StyleGeeta Rani, Meet Ganpatlal Oza, Vijaypal Singh Dhaka, Nitesh Pradhan, Sahil Verma, Joel J. P. C. Rodrigues. Applying Deep Learning for Genome Detection of Coronavirus. . 2020; ():1.
Chicago/Turabian StyleGeeta Rani; Meet Ganpatlal Oza; Vijaypal Singh Dhaka; Nitesh Pradhan; Sahil Verma; Joel J. P. C. Rodrigues. 2020. "Applying Deep Learning for Genome Detection of Coronavirus." , no. : 1.
Imaging techniques such as X-ray, computerized tomography scan and magnetic resonance imaging are useful in the correct diagnosis of a disease or deformity in the organ. Two-dimensional imaging techniques such as X-ray give a clear picture of simple bone deformity but fail in visualizing multiple fractures in a bone. Moreover, these lack in providing a multi-angle view of a bone. Three-dimensional techniques such as computerized tomography scan and magnetic resonance imaging present a correct orientation of fracture geometry. Computerized tomography scan is a collection of multiple slices of an image. These slices provide a fair idea about a fracture but fail in the measurement of correct dimensions of a fractured fragment and to observe its geometry. It also exposes a patient with carcinogenic radiations. Magnetic resonance imaging induces a strong magnetic field. So, it becomes ineffective for organs containing metallic implants. The high cost of three-dimensional imaging techniques makes them inaccessible for economic weaker section of society. The limitations of two- and three-dimensional imaging techniques motivate researchers to propose an innovative machine learning model ‘CT slices to $3$-D convertor’ that accepts multiple slices of an image and yields a multi-dimensional view at all possible angles from 0 degree to 360 degree for an input image.
Nitesh Pradhan; Vijaypal Singh Dhaka; Geeta Rani; Himanshu Chaudhary. Machine Learning Model for Multi-View Visualization of Medical Images. The Computer Journal 2020, 1 .
AMA StyleNitesh Pradhan, Vijaypal Singh Dhaka, Geeta Rani, Himanshu Chaudhary. Machine Learning Model for Multi-View Visualization of Medical Images. The Computer Journal. 2020; ():1.
Chicago/Turabian StyleNitesh Pradhan; Vijaypal Singh Dhaka; Geeta Rani; Himanshu Chaudhary. 2020. "Machine Learning Model for Multi-View Visualization of Medical Images." The Computer Journal , no. : 1.
Aims: The manuscript aims at designing and developing a model for optimum contrast enhancement of an input image. The output image of model ensures the minimum noise, the maximum brightness and the maximum entropy preservation. Objectives: * To determine an optimal value of threshold by using the concept of entropy maximization for segmentation of all types of low contrast images. * To minimize the problem of over enhancement by using a combination of weighted distribution and weighted constrained model before applying histogram equalization process. * To provide an optimum contrast enhancement with minimum noise and undesirable visual artefacts. * To preserve the maximum entropy during the contrast enhancement process and providing detailed information recorded in an image. * To provide the maximum mean brightness preservation with better PSNR and contrast. * To effectively retain the natural appearance of an images. * To avoid all unnatural changes that occur in Cumulative Density Function. * To minimize the problems such as noise, blurring and intensity saturation artefacts. Methods: 1. Histogram Building. 2. Segmentation using Shannon’s Entropy Maximization. 3. Weighted Normalized Constrained Model. 4. Histogram Equalization. 5. Adaptive Gamma Correction Process. 6. Homomorphic Filtering. Results: Experimental results obtained by applying the proposed technique MEWCHE-AGC on the dataset of low contrast images, prove that MEWCHE-AGC preserves the maximum brightness, yields the maximum entropy, high value of PSNR and high contrast. This technique is also effective in retaining the natural appearance of an images. The comparative analysis of MEWCHE-AGC with existing techniques of contrast enhancement is an evidence for its better performance in both qualitative as well as quantitative aspects. Conclusion: The technique MEWCHE-AGC is suitable for enhancement of digital images with varying contrasts. Thus useful for extracting the detailed and precise information from an input image. Thus becomes useful in identification of a desired regions in an image.
Monika Agarwal; Geeta Rani; Shilpy Agarwal; Vijaypal Singh Dhaka. Sequential Model for Digital Image Contrast Enhancement. Recent Advances in Computer Science and Communications 2020, 13, 1 -14.
AMA StyleMonika Agarwal, Geeta Rani, Shilpy Agarwal, Vijaypal Singh Dhaka. Sequential Model for Digital Image Contrast Enhancement. Recent Advances in Computer Science and Communications. 2020; 13 ():1-14.
Chicago/Turabian StyleMonika Agarwal; Geeta Rani; Shilpy Agarwal; Vijaypal Singh Dhaka. 2020. "Sequential Model for Digital Image Contrast Enhancement." Recent Advances in Computer Science and Communications 13, no. : 1-14.
Since the last decade, there is a significant change in the procedure of medical diagnosis and treatment. Specifically, when internal tissues, organs such as heart, lungs, brain, kidneys and bones are the target regions, a doctor recommends ‘computerized tomography’ scan and/or magnetic resonance imaging to get a clear picture of the damaged portion of an organ or a bone. This is important for correct examination of the medical deformities such as bone fracture, arthritis, and brain tumor. It ensures prescription of the best possible treatment. But ‘computerized tomography’ scan exposes a patient to high ionizing radiation. These rays make a person more prone to cancer. Magnetic resonance imaging requires a strong magnetic field. Thus, it becomes impractical for patients with implants in their body. Moreover, the high cost makes the above-stated techniques unaffordable for low economy class of society. The above-mentioned challenges of ‘computerized tomography’ scan and magnetic resonance imaging motivate researchers to focus on developing a technique for conversion of 2-dimensional view of medical images into their corresponding multiple views. In this manuscript, the authors design and develop a deep learning model that makes an effective use of conditional generative adversarial network, an extension of generative adversarial network for the transformation of 2-dimensional views of human bone into the corresponding multiple views at different angles. The model will prove useful for both doctors and patients.
Nitesh Pradhan; Vijaypal Singh Dhaka; Geeta Rani; Himanshu Chaudhary. Transforming view of medical images using deep learning. Neural Computing and Applications 2020, 32, 15043 -15054.
AMA StyleNitesh Pradhan, Vijaypal Singh Dhaka, Geeta Rani, Himanshu Chaudhary. Transforming view of medical images using deep learning. Neural Computing and Applications. 2020; 32 (18):15043-15054.
Chicago/Turabian StyleNitesh Pradhan; Vijaypal Singh Dhaka; Geeta Rani; Himanshu Chaudhary. 2020. "Transforming view of medical images using deep learning." Neural Computing and Applications 32, no. 18: 15043-15054.
Magnetic resonance imaging (MRI) is a real assistant for doctors. It provides rich information about anatomy of human body for precise diagnosis of a diseases or disorder. But it is quite challenging to extract relevant information from low contrast and poor quality MRI images. Poor visual interpretation is a hindrance in correct diagnosis of a disease. This creates a strong need for contrast enhancement of MRI images. Study of existing literature shows that conventional techniques focus on intensity histogram equalization. These techniques face the problems of over enhancement, noise and unwanted artifacts. Moreover, these are incapable to yield the maximum entropy and brightness preservation. Thus ineffective in diagnosis of a defect/disease such as tumor. This motivates the authors to propose the contrast enhancement model namely optimized double threshold weighted constrained histogram equalization. The model is a pipelined approach that incorporates Otsu's double threshold method, particle swarm optimized weighted constrained model, histogram equalization, adaptive gamma correction, and Wiener filtering. This algorithm preserves all essential information recorded in an image by automatically selecting an appropriate value of threshold for image segmentation. The proposed model is effective in detecting tumor from enhanced MRI images.
Monika Agarwal; Geeta Rani; Vijaypal Singh Dhaka. Optimized contrast enhancement for tumor detection. International Journal of Imaging Systems and Technology 2020, 30, 687 -703.
AMA StyleMonika Agarwal, Geeta Rani, Vijaypal Singh Dhaka. Optimized contrast enhancement for tumor detection. International Journal of Imaging Systems and Technology. 2020; 30 (3):687-703.
Chicago/Turabian StyleMonika Agarwal; Geeta Rani; Vijaypal Singh Dhaka. 2020. "Optimized contrast enhancement for tumor detection." International Journal of Imaging Systems and Technology 30, no. 3: 687-703.
Real-time object detection and tracking is a vast, vibrant yet inconclusive area of computer vision. Automatic object detection and tracking are useful in surveillance, tracking systems used in security, mobile robots, medical therapy, driver assistance systems, and analysis of sports. Algorithms proposed in existing literature use color segmentation, edge tracking, shape detection for detection, and tracking of an object. The challenges such as tracking in dynamic environment and difficult tracking of multiple objects in multiple-camera environment and expensive computation restrict the implementation of these systems for solving real-world problems. This motivates us to develop a system that is efficient in real-time object detection and tracking. In this paper, authors develop the real-time object detection and tracking system using velocity control. Experimental results prove its efficacy in detection and tracking of simple as well as complex objects in both simple and complex backgrounds. The system is effective in detecting and tracking the co-occurrence of two objects. It clearly shows the impact of color dominance or shape dominance, self-shadow, and image of an object in a mirror.
Geeta Rani; Anita Jindal. Real-Time Object Detection and Tracking Using Velocity Control. Blockchain Technology and Innovations in Business Processes 2019, 767 -778.
AMA StyleGeeta Rani, Anita Jindal. Real-Time Object Detection and Tracking Using Velocity Control. Blockchain Technology and Innovations in Business Processes. 2019; ():767-778.
Chicago/Turabian StyleGeeta Rani; Anita Jindal. 2019. "Real-Time Object Detection and Tracking Using Velocity Control." Blockchain Technology and Innovations in Business Processes , no. : 767-778.
There is a boom in research works in the arena of devising new methodologies for indexing and accessing hidden web data available in databases. To exploit this hidden web data, users need to fill various search forms available on World Wide Web with appropriate values. For a common user with non technical background, this is quite difficult to find suitable values. Ontology provides a way to find these values. Ontology is useful in constructing, a semantic database that provides values for various fields of search form interfaces. Ontology based data extraction is efficient in dealing with large amount of information available on the World Wide Web. This information may be available in different formats such as Hypertext Mark UP Language, HTML, Web Ontology Language, OWL, Recognition Form Designer, RDF and Extensible Markup Language, XML files. This paper creates an ontology database which provides us relevant information about book domain. It presents relevant data before the user rather than giving vague and irrelevant results as traditional databases generate.
Mohini Goyal; Geeta Rani. Impact of Ontology on Databases. Communications in Computer and Information Science 2018, 48 -60.
AMA StyleMohini Goyal, Geeta Rani. Impact of Ontology on Databases. Communications in Computer and Information Science. 2018; ():48-60.
Chicago/Turabian StyleMohini Goyal; Geeta Rani. 2018. "Impact of Ontology on Databases." Communications in Computer and Information Science , no. : 48-60.
E-governance plays a pivotal role in the domain of online services by ensuring round the clock accessibility of a wide spectrum of services. However, the huge amount of uploaded information and a vacillating user base makes it rather difficult to access the desired information from the portal. This requires a system which intelligently presents a personalized user interface. A challenging requirement in designing such a system is classifying the diversified users on the basis of their web experience. Traditional web usage mining techniques have been used to cluster similar users primarily on the basis of their page access patterns. In this paper, we veer our attention towards the level of user experience by introducing three parameters namely, page switching behavior, page probing behavior and session count which predominantly decide the level of experience acquired by e-governance users. We make an innovative use of Rough Set Theory to derive a rule-based classification system using three reduct optimization algorithms namely, Johnson Algorithm, Genetic Algorithm and Basic Minimal classification method. In order to test our system, we classified the user base that is publically available in the CTI dataset into two categories. The Basic Minimal method reports the highest accuracy of 74.90% with five fold cross validation.
Geeta Rani; Shampa Chakraverty. A Rough Set Based Approach for Web User Profiling. Communications in Computer and Information Science 2018, 541 -553.
AMA StyleGeeta Rani, Shampa Chakraverty. A Rough Set Based Approach for Web User Profiling. Communications in Computer and Information Science. 2018; ():541-553.
Chicago/Turabian StyleGeeta Rani; Shampa Chakraverty. 2018. "A Rough Set Based Approach for Web User Profiling." Communications in Computer and Information Science , no. : 541-553.
The boom in the government services online has put a great difficulty before the users in selection of the desired web pages on the e-governance portal. This has increased the requirement of such a recommendation system which intelligently satisfies needs of a huge user base. The intelligent collaborative recommendation system proposed in this paper analyzes web logs using web usage mining techniques. It provides such an interface to the existing users where they set their preferences for personalized and collaborative recommendations. Experience of previous users is a cornerstone for recommending default set of pages to naïve users. The use of efficient data structure trie performs dual functionality. This clusters the similar users together which saves the efforts of applying a separate approach for categorizing users. In addition, it conveniently recommends the desired pages to the user. This system dynamically changes the support value of a pattern. This automates the promotion and demotion of a pattern to a group. The hashing technique efficiently finds pages of user interest, in the trie in O(1) time complexity.
Geeta Rani. Classifier cum recommender system for E-governance using collaborative trie. 2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN) 2017, 368 -373.
AMA StyleGeeta Rani. Classifier cum recommender system for E-governance using collaborative trie. 2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN). 2017; ():368-373.
Chicago/Turabian StyleGeeta Rani. 2017. "Classifier cum recommender system for E-governance using collaborative trie." 2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN) , no. : 368-373.