2018 Conference on Information and Communication Technology (CICT)最新文献_第3页

Homomorphically Rectified Tile-wise Equalized Adaptive Gamma Correction for Histopathological Color Image Enhancement 组织病理彩色图像增强的同态校正平铺均匀自适应伽玛校正

2018 Conference on Information and Communication Technology (CICT) Pub Date : 2018-10-01 DOI: 10.1109/INFOCOMTECH.2018.8722364

Rangu Silpasai, Himanshu Singh, Anil Kumar, L. Balyan

{"title":"Homomorphically Rectified Tile-wise Equalized Adaptive Gamma Correction for Histopathological Color Image Enhancement","authors":"Rangu Silpasai, Himanshu Singh, Anil Kumar, L. Balyan","doi":"10.1109/INFOCOMTECH.2018.8722364","DOIUrl":"https://doi.org/10.1109/INFOCOMTECH.2018.8722364","url":null,"abstract":"In this paper, an efficient approach for quality improvement of histopathology slides’ images is suggested, along with proper luminance restoration and color-correction. Here, the extracted luminance channel is initially tile-wise equalized and an efficiently clipped image is acquired for evaluation of the required gamma value set for imparting adaptive boosting for the proper harvesting of the entire intensity span. The core contribution is to harvest the highly adaptive, pixel-wise, homomorphic color-correction ratio over the entire three-dimensional color image. In addition, this correction is introduced by intensity level-wise gamma correction. Again, it can be understood that the corresponding gamma value set is derived from a tile-wise uniformly equalized channel. Hence, this multi-level adaptiveness leads to a new strategy to attain the required objective. Rigorous experimentation is executed by employing the performance evaluation through standard quality measures and comparison with pre-existing recently proposed and highly appreciated quality enhancement approaches.","PeriodicalId":175757,"journal":{"name":"2018 Conference on Information and Communication Technology (CICT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121064810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

CICT 2018 Author Index CICT 2018作者索引

2018 Conference on Information and Communication Technology (CICT) Pub Date : 2018-10-01 DOI: 10.1109/infocomtech.2018.8721764

引用次数: 0

Tackling Toxic Online Communication with Recurrent Capsule Networks 用循环胶囊网络解决有毒的在线交流

2018 Conference on Information and Communication Technology (CICT) Pub Date : 2018-10-01 DOI: 10.1109/INFOCOMTECH.2018.8722433

Soham Deshmukh, Rahul Rade

{"title":"Tackling Toxic Online Communication with Recurrent Capsule Networks","authors":"Soham Deshmukh, Rahul Rade","doi":"10.1109/INFOCOMTECH.2018.8722433","DOIUrl":"https://doi.org/10.1109/INFOCOMTECH.2018.8722433","url":null,"abstract":"Internet has provided everyone a platform to productively exchange ideas, learn new things and have meaningful conversation. To make online interactions fruitful it is necessary the user feels comfortable with sharing information without the menace of online hate which includes insults, personal attacks, identity hate, threats and so on. The first step to combating this problem would be the identification of such online behaviour. Framing the problem as text classification, we present a novel and versatile model in this paper which employs Recurrent Neural Network and Capsule network as its backbone and captures contextual information to a larger extent when learning word representations in the text. A series of experiments are conducted on Wikipedia’s talk page edits provided by Jigsaw in Kaggle’s toxic comment classification challenge. The experimental results show that the proposed model outperforms other traditional state-of-the-art models on the dataset, thereby proving the effectiveness of capsule networks for multi-label text classification. The superior performance of architecture is also confirmed by results obtained on traditional benchmark datasets such as AG News, IMDB Large Movie Review and Yelp Reviews data.","PeriodicalId":175757,"journal":{"name":"2018 Conference on Information and Communication Technology (CICT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126343985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

A vehicle number plate recognition system using region-of-interest based filtering method 基于兴趣区域滤波方法的车牌识别系统

2018 Conference on Information and Communication Technology (CICT) Pub Date : 2018-10-01 DOI: 10.1109/INFOCOMTECH.2018.8722345

Rajib Ghosh, Suraj Thakre, Prabhat Kumar

引用次数: 6

Reinforcement Learning for Robots with special reference to the Inverse kinematics solutions 机器人的强化学习，特别是运动学逆解

2018 Conference on Information and Communication Technology (CICT) Pub Date : 2018-10-01 DOI: 10.1109/INFOCOMTECH.2018.8722399

Priya Shukla, G. Nandi

引用次数: 1

A 3.3dB Noise Figure,60-mW CMOS Receiver Front End for 865-867 MHz Band 865-867 MHz频段60mw CMOS接收机前端3.3dB噪声图

2018 Conference on Information and Communication Technology (CICT) Pub Date : 2018-10-01 DOI: 10.1109/INFOCOMTECH.2018.8722363

Awdhesh Pandey, Ankita Verma, P. K. Misra

引用次数: 2

Path Planning for Direct Energy Deposition with Collaborative Robots: A Review 协同机器人直接能量沉积路径规划研究进展

2018 Conference on Information and Communication Technology (CICT) Pub Date : 2018-10-01 DOI: 10.1109/INFOCOMTECH.2018.8722362

M. Manoharan, S. Kumaraguru

引用次数: 6

ANN-Cuckoo Optimization Technique to Predict Software Cost Estimation 预测软件成本估算的ANN-Cuckoo优化技术

2018 Conference on Information and Communication Technology (CICT) Pub Date : 2018-10-01 DOI: 10.1109/infocomtech.2018.8722380

Vishnu Sai Desai, R. Mohanty

{"title":"ANN-Cuckoo Optimization Technique to Predict Software Cost Estimation","authors":"Vishnu Sai Desai, R. Mohanty","doi":"10.1109/infocomtech.2018.8722380","DOIUrl":"https://doi.org/10.1109/infocomtech.2018.8722380","url":null,"abstract":"Software cost is of the most complex and vital aspect in consideration when software is in its development stages. To determine the amount of time, effort and resources required to complete the project successfully translate to Software Cost Estimation (SCE). Thus far, many models have been suggested such as Fuzzy Logic, Neural Networks, Support Vector Machines, Ant Colony Optimization, Genetic Algorithms, Decision Trees, Case-Based Reasoning and Soft Computing Techniques. Such computational models have contributed to a large extent in this arena. Yet, there still lies immense scope to apply optimization methods. Neural Networks are the most utilized techniques in software cost estimation by researchers. In this paper, we propose the use of a new model, i.e. Artificial Neural Networks (ANN) trained using Cuckoo Optimization Algorithm (COA) to predict Software Cost Estimation. The key goal is to exhibit use of a novel learning procedure for ANN to better predict SCE. The proposed model is verified with the ISBSG dataset and results are compared with existing models. The results shown are in terms of Root Mean Squared Error (RMSE) and Mean Magnitude of Relative Error (MMRE).","PeriodicalId":175757,"journal":{"name":"2018 Conference on Information and Communication Technology (CICT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131201090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

DCR-HMM: Depression detection based on Content Rating using Hidden Markov Model DCR-HMM:基于隐马尔可夫模型的内容评级抑郁检测

2018 Conference on Information and Communication Technology (CICT) Pub Date : 2018-10-01 DOI: 10.1109/INFOCOMTECH.2018.8722410

Haroon Ansari, Aditya Vijayvergia, Krishan Kumar

{"title":"DCR-HMM: Depression detection based on Content Rating using Hidden Markov Model","authors":"Haroon Ansari, Aditya Vijayvergia, Krishan Kumar","doi":"10.1109/INFOCOMTECH.2018.8722410","DOIUrl":"https://doi.org/10.1109/INFOCOMTECH.2018.8722410","url":null,"abstract":"Depression is a mental health issue that once used to be experienced by grandparents and great-grandparents, is so common today even among the youth. Over the years, methods have been developed to counter this mental health issue. But the biggest problem that is being faced is people find it hard to admit that they are suffering from depression. Although many people admit it and take measures, a majority of people find it hard to admit, especially students. Depression is common nowadays among the student but several social issues like peer pressure, gossips etc. make it hard for them to accept that they are suffering from depression. In our paper, we have introduced a novel approach to detect depression based on the content rating by the subject using Hidden Markov Model (HMM). A series of content is provided to the subject and based on whether the subject reacts to it skip it, we predict whether the subject is depressed or not. The experiments demonstrate that the proposed DCR-HMM model leads to very outcomes. Based on the acceptance of the state of mind by tested individuals, our model has acquired an accuracy of 95.6% when tested on 450 individuals.","PeriodicalId":175757,"journal":{"name":"2018 Conference on Information and Communication Technology (CICT)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131433037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

Handwritten Text Recognition in Bank Cheques 银行支票的手写文字识别

2018 Conference on Information and Communication Technology (CICT) Pub Date : 2018-10-01 DOI: 10.1109/INFOCOMTECH.2018.8722420

Rajib Ghosh, Chinmaya Panda, Prabhat Kumar

{"title":"Handwritten Text Recognition in Bank Cheques","authors":"Rajib Ghosh, Chinmaya Panda, Prabhat Kumar","doi":"10.1109/INFOCOMTECH.2018.8722420","DOIUrl":"https://doi.org/10.1109/INFOCOMTECH.2018.8722420","url":null,"abstract":"In spite of rapid evolution of digital technologies, a huge number of applications still rely on the use of paper based medium. This is especially true for processing of bank cheques. The pre-printed account number and cheque number might be easily readable and processed automatically. However, the handwritten texts in cheque are usually processed manually involving important time and cost. An attempt has been made in this paper to develop a bank cheque handwritten text recognition (BCHWTR) system for cheques of Indian banks by recognizing the handwritten characters present in the ’payee name’, ’courtesy amount (both in words and figures)’ and ’date’ fields by using image processing techniques on handwritten cheque images. Images of bank cheques are fed as input to the proposed system. There are four stages in the proposed system: cropping the image at a specific location; segmentation of handwritten lines, words and characters; feature extraction from individual characters and digits using Histogram of Oriented Gradients (HOG) method and Grey Level Co-occurrence Matrix (GLCM) texture features; recognition of isolated characters and digits using the Support Vector Machine (SVM) based classification process that ensures correct recognition. The performance of present BCHWTR system is evaluated on a self-generated dataset of bank cheques and it has provided a promising result.","PeriodicalId":175757,"journal":{"name":"2018 Conference on Information and Communication Technology (CICT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128330388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9