Detection and Classification of License Plate by Neural Network Classifier Surekha Chalnewad, Arati Manjaramkar Idciot 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things Proceedings, 2023 A license plate is alphanumeric rectangular plate. It is fixed on the vehicle and used for identification of the vehicle. Generally, huge numbers of vehicles move-on the road which is the major issue of concern in identifying the vehicle(s) owner, registration place of vehicle, address, etc. The automatic license plate detection is one of the solutions for such kind of problems. There are numerous methodologies available for license plate detection, but certain factors like speed of vehicles, language used on license plate, non-uniform letter effects on license plate, etc. makes the task of recognition difficult. The license plate detection system has many applications like payment of parking fees; toll fee on the highway; traffic monitoring system; border security system; signal system, etc. This research work proposes a novel license plate detection technique with the extension of Sobel mask. In proposed system, first step is acquisition of image. Second step is to detect the vehicle from the acquired image. In third step, segmentation of license plate from vehicle image is done. Finally, neural network classifier is used to classify the vehicle(s) license plate. The proposed system gives promising, robust, and efficient results for license plate detection. Proposed system achieves accuracy of 98% is achieved in detecting the license plate.
Automatic Detection of Lung Diseases Using CNN and SVM Saba Siddiqua Sadique Ahemed Siddiqui, Rimsha Taskeen Siddi Habib Hyder, Arati Manjaramkar, Megha Jonnalagedda 2023 3rd International Conference on Intelligent Technologies Conit 2023, 2023 Lung diseases are becoming common everywhere in the world. These contain diseases like pneumonia, emphysema, tuberculosis, etc. which come under the category of Chronic Obstructive Pulmonary Diseases (COPDs). Other than these lung related diseases, a very popular recent lung disease is COVID-19. This work proposes an approach for detecting and classifying various lung related diseases with chest X-ray images using transfer learning concept, with MobileNet architecture as a feature extractor followed by Support Vector Machine (SVM). The images of chest X-ray are used to classify healthy individuals from patients suffering with various lung diseases like COVID-19, pneumonia, tuberculosis, and emphysema. In this study, a dataset of 2500 chest X-ray images of COVID-19, pneumonia, emphysema, tuberculosis infected, and normal images are used. The proposed method was able to achieve high accuracy 98.5% and F1-score of 99.8%.
Automated Red Lesion Detection: An Overview Arati Manjaramkar, Manesh Kokare Advances in Intelligent Systems and Computing, 2020 Incidences of diabetes are increasing worldwide. An eye complication associated with uncontrolled diabetes is diabetic retinopathy. If not treated, diabetic retinopathy can be vision threatening. The microaneurysms and dot hemorrhages are the only prominent clinically observable symptoms of DR. Their timely detection can help ophthalmologists in treating abnormalities efficiently and limit the disease severity. So detecting red lesions in early stage has become an indispensable task today. This paper gives an overview of earlier proposed algorithms and methods. It also compares these algorithms based on their performance for supporting the researchers by providing the gist of these algorithms. The standard retinal image databases are also compared and discussed.
Fruit disease classification and identification using image processing Shaikh Rakhshinda Nahid M. Ayyub, Aarti Manjramkar Proceedings of the 3rd International Conference on Computing Methodologies and Communication Iccmc 2019, 2019 Fruit Industry is the largest industry of India. Due to lack of maintenance, inappropriate manual inspection the fruit Disease causes huge losses in yield, quality and quantity. Manual inspection is tedious and time consuming process. An image processing approach is proposed for apple fruit disease identification and categorization using different color, texture and shape feature combination. The basic steps of the proposed approach are image segmentation, extraction of features (color, texture and shape), feature combination and finally apple disease identified and classified using multi-class support vector machine into diseased or normal class. Our proposed technique experimentally verified and validated. The accuracy of the proposed approach is achieved up to 96%.
MrFIM: A MapReduce Approach for Frequent Itemset Mining in Big Data Abdul Rahman, Arati Manjaramkar 2018 4th International Conference for Convergence in Technology I2ct 2018, 2018 Nowadays simultaneous extraction algorithms for finding common element sets does not allows concurrent execution, load steadiness, data allocation, and recovery mechanism on huge clusters. Therefore, we propose a Simultaneous Common Elements Extraction algorithm (SCEE) using MapReduce. To accomplish reduce storage and to prevent constructing traditional pattern bases, SCEE algorithm integrates the frequent items ultrametric tree, instead of traditional FP-trees. In this algorithm, the number of MapReduce tasks are three, designed to finish the whole extracting job. The mappers of third MapReduce job individually perform the decomposition operation on element sets, while the reducer will complete the grouping process by building ultrametric trees, and extraction of these trees distinctly. We also implemented an additional MapReduce job to find the top-K items from these frequent itemsets, we called this MapReduce job as an Aggregate Function. We designed the algorithm on cluster of computers. We demonstrate that the SCEE algorithm on the cluster is subtle to data allocation and dimension, since element sets with dissimilar length have different decomposition and building expenses. The real-world data for extensive experimentations prove that our proposed technique is effective and extendable.
Statistical Geometrical Features for Microaneurysm Detection Arati Manjaramkar, Manesh Kokare Journal of Digital Imaging, 2018 Automated microaneurysm (MA) detection is still an open challenge due to its small size and similarity with blood vessels. In this paper, we present a novel method which is simple, efficient, and real-time for segmenting and detecting MA in color fundus images (CFI). To do this, a novel set of features based on statistics of geometrical properties of connected regions, that can easily discriminate lesion and non-lesion pixels are used. For large-scale evaluation proposed method is validated on DIARETDB1, ROC, STARE, and MESSIDOR dataset. It proves robust with respect to different image characteristics and camera settings. The best performance was achieved on per-image evaluation on DIARETDB1 dataset with sensitivity of 88.09 at 92.65% specificity which is quite encouraging for clinical use.
Connected component clustering based hemorrhage detection in color fundus images Arati Manjaramkar, , Manesh Kokare, and International Journal of Intelligent Engineering and Systems, 2018 Damage of retina due to diabetes is termed diabetic retinopathy. Hemorrhages and Microaneurysms are the first clinically visible symptoms of diabetic retinopathy. Detecting and treating diabetic retinopathy early can prevent vision loss. Accurate segmentation of retinal hemorrhage in color fundus image (CFI) has become a challenging task today; as retinal hemorrhages have varied size, shape and texture. We propose a connected component clustering method based on maximally stable extremal regions (MSER) for detecting many occurrences of hemorrhages with different shape and size in a fundus image. Proposed method has three main steps: firstly hemorrhage candidate generation, second is feature extraction and finally third step is hemorrhage detection. We have is evaluated our method on the DIARETDB1 and MESSIDOR dataset and experimental results show that the proposed system outperforms other state-of-the-art methods in detecting large and vessel connected hemorrhages. The proposed method achieves image level sensitivity, specificity of 96.45, 97.64 and lesion level sensitivity, specificity of 94.89, 98.9 respectively.
Android-Based Malaria Detection Using Deep Learning RTSH Hyder, SSSA Siddiqui, M Jonnalagedda, A Manjaramkar International Conference on Data Science and Applications, 361-374 , 2023 2023
Automatic detection of lung diseases using CNN and SVM SSSA Siddiqui, RTSH Hyder, A Manjaramkar, M Jonnalagedda 2023 3rd International Conference on Intelligent Technologies (CONIT), 1-5 , 2023 2023 Citations: 3
Detection and classification of license plate by neural network classifier S Chalnewad, A Manjaramkar 2023 International Conference on Intelligent Data Communication Technologies … , 2023 2023 Citations: 2
A Web-based Intelligent Report e-learning System Using Linear Regression BS Uttkarsh Kabde, Arati Manjaramkar Journal of Information and Computational Science 11 (10 - 2021), 262 - 276 , 2021 2021
Fashion classification and object detection using CNN S Itkare, A Manjaramkar Information and Communication Technology for Competitive Strategies (ICTCS … , 2021 2021 Citations: 8
Iris Classification Based on K-SVD Dictionary Learning Algorithm AP Arati Manjaramkar International Journal of Creative Research Thoughts (IJCRT) 9 (6), 557-562 , 2021 2021
Brand Review Prediction using User Sentiments RS Arati Manjaramkar International Journal of Scientific Research in Engineering and Management … , 2021 2021
Automated red lesion detection: an overview A Manjaramkar, M Kokare Advanced Computing and Intelligent Engineering: Proceedings of ICACIE 2018 … , 2020 2020 Citations: 4
Fruit disease classification and identification using image processing SRNM Ayyub, A Manjramkar 2019 3rd International Conference on Computing Methodologies and … , 2019 2019 Citations: 33
MrFIM: A MapReduce Approach for Frequent Itemset Mining in Big Data A Rahman, A Manjaramkar 2018 4th International Conference for Convergence in Technology (I2CT), 1-5 , 2018 2018
Salient Object Detection for Synthetic Dataset A Aswar, A Manjaramkar International Conference on ISMAC in Computational Vision and Bio … , 2018 2018 Citations: 1
Statistical geometrical features for microaneurysm detection A Manjaramkar, M Kokare Journal of digital imaging 31 (2), 224-234 , 2018 2018 Citations: 28
Connected component clustering based hemorrhage detection in color fundus images A Manjaramkar, M Kokare International Journal of Intelligent Engineering and Systems 11 (2), 143-151 , 2018 2018 Citations: 17
Frequent Itemset Mining Algorithms: A Survey AM Abdul Rahman Journal of Emerging Technologies and Innovative Research 5 (3), 959-965 , 2018 2018
Decision trees for microaneurysms detection in color fundus images A Manjaramkar, M Kokare 2017 International conference on innovations in green energy and healthcare … , 2017 2017 Citations: 4
DEPTA: An efficient technique for web data extraction and alignment A Manjaramkar, RL Lokhande 2016 International Conference on Advances in Computing, Communications and … , 2016 2016 Citations: 7
Generalized classification rules for entity identification US Bhoskar, A Manjaramkar 2016 5th International Conference on Reliability, Infocom Technologies and … , 2016 2016
Cooperative bait detection scheme to prevent collaborative blackhole or grayhole attacks by malicious nodes in MANETs PR Dumne, A Manjaramkar 2016 5th International Conference on Reliability, Infocom Technologies and … , 2016 2016 Citations: 19
A rule based expert system for microaneurysm detection in digital fundus images A Manjaramkar, M Kokare 2016 International conference on computational techniques in information and … , 2016 2016 Citations: 11
Parallel face Detection and Recognition on GPU SJ Bhutekar, AK Manjaramkar 2014 Citations: 30
MOST CITED SCHOLAR PUBLICATIONS
Fruit disease classification and identification using image processing SRNM Ayyub, A Manjramkar 2019 3rd International Conference on Computing Methodologies and … , 2019 2019 Citations: 33
Parallel face Detection and Recognition on GPU SJ Bhutekar, AK Manjaramkar 2014 Citations: 30
Statistical geometrical features for microaneurysm detection A Manjaramkar, M Kokare Journal of digital imaging 31 (2), 224-234 , 2018 2018 Citations: 28
Cooperative bait detection scheme to prevent collaborative blackhole or grayhole attacks by malicious nodes in MANETs PR Dumne, A Manjaramkar 2016 5th International Conference on Reliability, Infocom Technologies and … , 2016 2016 Citations: 19
Connected component clustering based hemorrhage detection in color fundus images A Manjaramkar, M Kokare International Journal of Intelligent Engineering and Systems 11 (2), 143-151 , 2018 2018 Citations: 17
A rule based expert system for microaneurysm detection in digital fundus images A Manjaramkar, M Kokare 2016 International conference on computational techniques in information and … , 2016 2016 Citations: 11
A survey on SQL injection attack, detection and prevention techniques AM Shegokar, AK Manjaramkar Int. J. Comput. Sci. Inf. Technol 5 (2), 2553-2555 , 2014 2014 Citations: 9
Fashion classification and object detection using CNN S Itkare, A Manjaramkar Information and Communication Technology for Competitive Strategies (ICTCS … , 2021 2021 Citations: 8
DEPTA: An efficient technique for web data extraction and alignment A Manjaramkar, RL Lokhande 2016 International Conference on Advances in Computing, Communications and … , 2016 2016 Citations: 7
Automated red lesion detection: an overview A Manjaramkar, M Kokare Advanced Computing and Intelligent Engineering: Proceedings of ICACIE 2018 … , 2020 2020 Citations: 4
Decision trees for microaneurysms detection in color fundus images A Manjaramkar, M Kokare 2017 International conference on innovations in green energy and healthcare … , 2017 2017 Citations: 4
Automatic detection of lung diseases using CNN and SVM SSSA Siddiqui, RTSH Hyder, A Manjaramkar, M Jonnalagedda 2023 3rd International Conference on Intelligent Technologies (CONIT), 1-5 , 2023 2023 Citations: 3
Detection and classification of license plate by neural network classifier S Chalnewad, A Manjaramkar 2023 International Conference on Intelligent Data Communication Technologies … , 2023 2023 Citations: 2
Salient Object Detection for Synthetic Dataset A Aswar, A Manjaramkar International Conference on ISMAC in Computational Vision and Bio … , 2018 2018 Citations: 1
Android-Based Malaria Detection Using Deep Learning RTSH Hyder, SSSA Siddiqui, M Jonnalagedda, A Manjaramkar International Conference on Data Science and Applications, 361-374 , 2023 2023
A Web-based Intelligent Report e-learning System Using Linear Regression BS Uttkarsh Kabde, Arati Manjaramkar Journal of Information and Computational Science 11 (10 - 2021), 262 - 276 , 2021 2021
Iris Classification Based on K-SVD Dictionary Learning Algorithm AP Arati Manjaramkar International Journal of Creative Research Thoughts (IJCRT) 9 (6), 557-562 , 2021 2021
Brand Review Prediction using User Sentiments RS Arati Manjaramkar International Journal of Scientific Research in Engineering and Management … , 2021 2021
MrFIM: A MapReduce Approach for Frequent Itemset Mining in Big Data A Rahman, A Manjaramkar 2018 4th International Conference for Convergence in Technology (I2CT), 1-5 , 2018 2018
Frequent Itemset Mining Algorithms: A Survey AM Abdul Rahman Journal of Emerging Technologies and Innovative Research 5 (3), 959-965 , 2018 2018
