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| 1 | CNN-RNN based method for license plate recognition显示文摘 | Palaiahnakote Shivakumara Dongqi Tang Maryam Asadzadehkaljahi Tong Lu Umapada Pal Mohammad Hossein Anisi | 2018 | CAAI Transactions on Intelligence Technology2018，3，3： | 3 |
| 2 | Generation of biogas from coffee-pulp and cow-dung co-digestion:Infrared studies of postcombustion emissions显示文摘 | Grisel Corro Laura Paniagua Umapada Pal | 2013 | Energy Conversion and Management2013，74，： | 1 |
| 3 | Multi-gradient-direction based deep learning model for arecanut disease identification显示文摘Arecanut disease identification is a challenging problem in the field of image processing.In this work,we present a new combination of multi-gradient-direction and deep con-volutional neural networks for arecanut disease identification,namely,rot,split and rot-split.Due to the effect of the disease,there are chances of losing vital details in the images.To enhance the fine details in the images affected by diseases,we explore multi-Sobel directional masks for convolving with the input image,which results in enhanced images.The proposed method extracts arecanut as foreground from the enhanced images using Otsu thresholding.Further,the features are extracted for foreground information for disease identification by exploring the ResNet architecture.The advantage of the proposed approach is that it identifies the diseased images from the healthy arecanut images.Experimental results on the dataset of four classes(healthy,rot,split and rot-split)show that the proposed model is superior in terms of classification rate. | S.B.Mallikarjuna Palaiahnakote Shivakumara Vijeta Khare M.Basavanna Umapada Pal B.Poornima | 2022 | CAAI Transactions on Intelligence Technology2022，7，2： | 1 |
| 4 | Symmetry features for license plate classification显示文摘 | Karpuravalli Srinivas Raghunandan Palaiahnakote Shivakumara Lolika Padmanabhan Govindaraju Hemantha Kumar Tong Lu Umapada Pal | 2018 | CAAI Transactions on Intelligence Technology2018，3，3： | 0 |
| 5 | Graphology based handwritten character analysis for human behaviour identification显示文摘Graphology-based handwriting analysis to identify human behavior,irrespective of applications,is interesting.Unlike existing methods that use characters,words and sentences for behavioural analysis with human intervention,we propose an automatic method by analysing a few handwritten English lowercase characters from a to z to identify person behaviours.The proposed method extracts structural features,such as loops,slants,cursive,straight lines,stroke thickness,contour shapes,aspect ratio and other geometrical properties,from different zones of isolated character images to derive the hypothesis based on a dictionary of Graphological rules.The derived hypothesis has the ability to categorise the personal,positive,and negative social aspects of an individual.To evaluate the proposed method,an automatic system is developed which accepts characters from a to z written by different individuals across different genders and age groups.This automatic privacy projected system is available on the website(http://gffzza59f2e0d25434e2ahnxqxqp5nfpvc6cbw.ffgz.tsg.suse.edu.cn).For quantitative evaluation of the proposed method,several people are requested to use the system to check their characteristics with the system automatic response based on his/her handwriting by choosing to agree or disagree options.The automatic system receives 5300 responses from the users,for which,the proposed method achieves 86.70%accuracy. | Subhankar Ghosh Palaiahnakote Shivakumara Prasun Roy Umapada Pal Tong Lu | 2020 | CAAI Transactions on Intelligence Technology2020，5，1： | 0 |
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