Pub Date : 2018-12-01DOI: 10.1109/ICCITECHN.2018.8631922
Bayezid Islam, F. Mahmud, A. Hossain
Facial expressions have been studied extensively for the analysis of human sentiment properly. A human emotion recognition system through recognizing human facial expression is proposed in this paper. After preprocessing, segmentation of the facial expression regions is done in a unique yet effective and easy way to segment the left eye, right eye, nose, mouth properly from the facial region. 2D Gabor filter is used for the extraction of features from the expression regions. For reducing the dimension of the extracted features, downsampling and Principal Component Analysis (PCA) is used. For carrying out the classification task multiclass Support Vector Machine (SVM) is used for its ability to handle complex problems in high dimensional spaces. Three publicly available facial expression dataset was used to evaluate the performance of the proposed system. Finally, performance on these datasets by the proposed method is compared to previously attained performance by different methods which indicate that the proposed method attains state-of-the-art performance.
{"title":"Facial Expression Region Segmentation Based Approach to Emotion Recognition Using 2D Gabor Filter and Multiclass Support Vector Machine","authors":"Bayezid Islam, F. Mahmud, A. Hossain","doi":"10.1109/ICCITECHN.2018.8631922","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2018.8631922","url":null,"abstract":"Facial expressions have been studied extensively for the analysis of human sentiment properly. A human emotion recognition system through recognizing human facial expression is proposed in this paper. After preprocessing, segmentation of the facial expression regions is done in a unique yet effective and easy way to segment the left eye, right eye, nose, mouth properly from the facial region. 2D Gabor filter is used for the extraction of features from the expression regions. For reducing the dimension of the extracted features, downsampling and Principal Component Analysis (PCA) is used. For carrying out the classification task multiclass Support Vector Machine (SVM) is used for its ability to handle complex problems in high dimensional spaces. Three publicly available facial expression dataset was used to evaluate the performance of the proposed system. Finally, performance on these datasets by the proposed method is compared to previously attained performance by different methods which indicate that the proposed method attains state-of-the-art performance.","PeriodicalId":355984,"journal":{"name":"2018 21st International Conference of Computer and Information Technology (ICCIT)","volume":"272 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122757234","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}
Pub Date : 2018-12-01DOI: 10.1109/ICCITECHN.2018.8631914
Md. Eftekhar Alam, M. A. Kader, Shamima Akter Proma, Sanchita Sharma
The noticeboard is a primary thing in any institution or organization to disperse information among the stakeholders. In the busy and fast moving world today, conventional sticking paper notice system is time-consuming and not suitable for quick sharing of information. This paper represents a smart electronic remote noticing system where an authorized accountable person can share information in the notice board anytime from his office room or any places in the world having the cellular network. In the proposed system, notice can be sent in two ways. The user can update notice from his office room either by voice or text message via a smartphone using Bluetooth and RF communication within 1-kilometer distance. In this way, the user sent notice using his own local wireless network and should not pay money to any operator. Another way to update notice by sending SMS using mobile network when the user stays outside of his office room. In this way, the user has to pay SMS charge to the mobile operator. The notices sent by the user are scrolled in a 32X8 LED matrix display. The system can show current notice with two previous notices. It also gives a notification by a buzzer when a new notice is received. This smart notice board can make noticing system of an organization much simple, fast and cost-effective.
{"title":"Development of a Voice and SMS Controlled Dot Matrix Display Based Smart Noticing System with RF Transceiver and GSM Modem","authors":"Md. Eftekhar Alam, M. A. Kader, Shamima Akter Proma, Sanchita Sharma","doi":"10.1109/ICCITECHN.2018.8631914","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2018.8631914","url":null,"abstract":"The noticeboard is a primary thing in any institution or organization to disperse information among the stakeholders. In the busy and fast moving world today, conventional sticking paper notice system is time-consuming and not suitable for quick sharing of information. This paper represents a smart electronic remote noticing system where an authorized accountable person can share information in the notice board anytime from his office room or any places in the world having the cellular network. In the proposed system, notice can be sent in two ways. The user can update notice from his office room either by voice or text message via a smartphone using Bluetooth and RF communication within 1-kilometer distance. In this way, the user sent notice using his own local wireless network and should not pay money to any operator. Another way to update notice by sending SMS using mobile network when the user stays outside of his office room. In this way, the user has to pay SMS charge to the mobile operator. The notices sent by the user are scrolled in a 32X8 LED matrix display. The system can show current notice with two previous notices. It also gives a notification by a buzzer when a new notice is received. This smart notice board can make noticing system of an organization much simple, fast and cost-effective.","PeriodicalId":355984,"journal":{"name":"2018 21st International Conference of Computer and Information Technology (ICCIT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125955851","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}
Pub Date : 2018-12-01DOI: 10.1109/ICCITECHN.2018.8631958
Mifta Sintaha, M. Mostakim
Regardless of the demography, social media has become an integral part of our everyday lives. Nowadays, it is the most popular platform people use for staying connected with their friends and family. As a consequence, the likelihood and growth of cyber threats have increased rapidly. To mitigate this situation, we proposed a system that can detect cyber crimes such as blackmail, fraud, impersonation, spam etc. from the social media network Twitter. This type of study can help people to detect early threats and possible criminal activity and the types of accounts to stay alert of in real time thereby, creating a more secure social media experience. Our main goal is to compare various sentiment analysis approaches for detecting bullying or threats from social media. We used two supervised machine learning algorithms to form a comparison and determine which among the two gives out the highest accuracy in order for us to decide how to detect cyberbullying activity on the Internet and be alert of threats in both the real and virtual world.
{"title":"An Empirical Study and Analysis of the Machine Learning Algorithms Used in Detecting Cyberbullying in Social Media","authors":"Mifta Sintaha, M. Mostakim","doi":"10.1109/ICCITECHN.2018.8631958","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2018.8631958","url":null,"abstract":"Regardless of the demography, social media has become an integral part of our everyday lives. Nowadays, it is the most popular platform people use for staying connected with their friends and family. As a consequence, the likelihood and growth of cyber threats have increased rapidly. To mitigate this situation, we proposed a system that can detect cyber crimes such as blackmail, fraud, impersonation, spam etc. from the social media network Twitter. This type of study can help people to detect early threats and possible criminal activity and the types of accounts to stay alert of in real time thereby, creating a more secure social media experience. Our main goal is to compare various sentiment analysis approaches for detecting bullying or threats from social media. We used two supervised machine learning algorithms to form a comparison and determine which among the two gives out the highest accuracy in order for us to decide how to detect cyberbullying activity on the Internet and be alert of threats in both the real and virtual world.","PeriodicalId":355984,"journal":{"name":"2018 21st International Conference of Computer and Information Technology (ICCIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130131305","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}
Data Mining is the technique of analyzing large amount of data to determine the relation among large dataset. In this paper, we are discussing a new method for document classification. Usually Document classification has been done by using classifier algorithms. Naïve Bayes classifier is frequently used for classification which provides more accurate result for larger dataset. The usage of information gain with naïve Bayes classifier reduces the length of branches by selecting maximum gain which produces more accurate result in classification. We propose a new methodology of assigning weights using information gain with naïve Bayes classifier. The performance of naive Bayes learning with weighted gain increases accuracy than any other traditional methods using naïve Bayes. The experimental result indicates that the proposed system could improve the performance of naïve Bayes significantly.
{"title":"Iterative Feature Selection Using Information Gain & Naïve Bayes for Document Classification","authors":"Chowdhury Mofizur Rahman, Lameya Afroze, Naznin Sultana Refath, Nafin Shawon","doi":"10.1109/ICCITECHN.2018.8631971","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2018.8631971","url":null,"abstract":"Data Mining is the technique of analyzing large amount of data to determine the relation among large dataset. In this paper, we are discussing a new method for document classification. Usually Document classification has been done by using classifier algorithms. Naïve Bayes classifier is frequently used for classification which provides more accurate result for larger dataset. The usage of information gain with naïve Bayes classifier reduces the length of branches by selecting maximum gain which produces more accurate result in classification. We propose a new methodology of assigning weights using information gain with naïve Bayes classifier. The performance of naive Bayes learning with weighted gain increases accuracy than any other traditional methods using naïve Bayes. The experimental result indicates that the proposed system could improve the performance of naïve Bayes significantly.","PeriodicalId":355984,"journal":{"name":"2018 21st International Conference of Computer and Information Technology (ICCIT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130218905","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}
Pub Date : 2018-12-01DOI: 10.1109/ICCITECHN.2018.8631951
S. Zobaed, Md. Enamul Haque, Shahidullah Kaiser, R. Hussain
Cloud services are widely deployed to store and process big data. Organizations who deal with big data, especially large document set, prefer utilizing cloud services for storage and computational efficiency. However, for processing large text corpus, an inefficient data processing is computationally expensive for real-time systems. In addition, efficient memory utilization is important to cluster big data including large text corpus. Clustering of the large text corpus is an important component of various document retrieval systems such as PubMed1. To address these challenges, in this paper, we present NoCS2 (Number of Cluster and Seed Selection) for efficient topic-based clustering from unstructured big data in the cloud. NoCS2 relies on computing and storage services in the cloud server. Traditional clustering solutions for text dataset consider a fixed number of clusters irrespective of the dataset size and characteristics such as science and technology. Alternatively, our solution dynamically determines the appropriate $k$ number of clusters based on the characteristics of the dataset. Particularly, we use precomputed matrix trace as the number of clusters for a dataset that represents the total number of keywords using vector representation. Then, we build $k$ clusters using topic-based similarity among keywords. Finally, we compare our proposed method with two state-of-the-art clustering methods. Empirical results demonstrate that the average closeness score of NoCS2 is better than other methods for large and sparse datasets.
云服务被广泛用于存储和处理大数据。处理大数据，特别是大型文档集的组织更喜欢使用云服务来存储和计算效率。然而，对于处理大型文本语料库，低效的数据处理对于实时系统来说是非常昂贵的。此外，高效的内存利用对于包括大型文本语料库在内的大数据的聚类也很重要。大型文本语料库的聚类是各种文档检索系统(如PubMed1)的重要组成部分。为了应对这些挑战，在本文中，我们提出了NoCS2 (Number of Cluster and Seed Selection)，用于从云中的非结构化大数据中高效地进行基于主题的聚类。NoCS2依赖于云服务器中的计算和存储服务。传统的文本数据聚类解决方案考虑固定数量的聚类，而不考虑数据集的大小和科学技术等特征。或者，我们的解决方案根据数据集的特征动态确定适当的$k$簇数。特别是，我们使用预先计算的矩阵跟踪作为数据集的簇数，该数据集使用向量表示来表示关键字的总数。然后，我们使用关键词之间基于主题的相似度构建$k$聚类。最后，我们将我们提出的方法与两种最先进的聚类方法进行了比较。实证结果表明，对于大型稀疏数据集，NoCS2的平均接近度得分优于其他方法。
{"title":"NoCS2: Topic-Based Clustering of Big Data Text Corpus in the Cloud","authors":"S. Zobaed, Md. Enamul Haque, Shahidullah Kaiser, R. Hussain","doi":"10.1109/ICCITECHN.2018.8631951","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2018.8631951","url":null,"abstract":"Cloud services are widely deployed to store and process big data. Organizations who deal with big data, especially large document set, prefer utilizing cloud services for storage and computational efficiency. However, for processing large text corpus, an inefficient data processing is computationally expensive for real-time systems. In addition, efficient memory utilization is important to cluster big data including large text corpus. Clustering of the large text corpus is an important component of various document retrieval systems such as PubMed1. To address these challenges, in this paper, we present NoCS2 (Number of Cluster and Seed Selection) for efficient topic-based clustering from unstructured big data in the cloud. NoCS2 relies on computing and storage services in the cloud server. Traditional clustering solutions for text dataset consider a fixed number of clusters irrespective of the dataset size and characteristics such as science and technology. Alternatively, our solution dynamically determines the appropriate $k$ number of clusters based on the characteristics of the dataset. Particularly, we use precomputed matrix trace as the number of clusters for a dataset that represents the total number of keywords using vector representation. Then, we build $k$ clusters using topic-based similarity among keywords. Finally, we compare our proposed method with two state-of-the-art clustering methods. Empirical results demonstrate that the average closeness score of NoCS2 is better than other methods for large and sparse datasets.","PeriodicalId":355984,"journal":{"name":"2018 21st International Conference of Computer and Information Technology (ICCIT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133788078","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}
Pub Date : 2018-12-01DOI: 10.1109/ICCITECHN.2018.8631907
Ashadullah Shawon, S. T. Zuhori, F. Mahmud, Md. Jamil-Ur Rahman
Website classification is a convenient starting point for building an intelligent web browser and social networking sites that can understand the favorite categories of a user and also detect adult or harmful websites perfectly. Classifying the web sites using the information of the Uniform Resource Locator (URL) is an important and fast technique. A perfect result is needed for URL classification to make it usable in the real world applications. So we have proposed an improved approach for URL classification that is able to provide a better result. We have introduced the word-based multiple n-gram models for efficient feature extraction and multinomial distribution for Naive Bayes classifier under the Random Search pipeline for hyperparameter optimization that finds the best parameters of the URL features. The experimental result of our research is compared with the result of previous research works and we have shown a better result than the existing result. Our experimental result provides 88.77% in recall and 87.63% in F1-Score which is the best performance so far.
{"title":"Website Classification Using Word Based Multiple N -Gram Models and Random Search Oriented Feature Parameters","authors":"Ashadullah Shawon, S. T. Zuhori, F. Mahmud, Md. Jamil-Ur Rahman","doi":"10.1109/ICCITECHN.2018.8631907","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2018.8631907","url":null,"abstract":"Website classification is a convenient starting point for building an intelligent web browser and social networking sites that can understand the favorite categories of a user and also detect adult or harmful websites perfectly. Classifying the web sites using the information of the Uniform Resource Locator (URL) is an important and fast technique. A perfect result is needed for URL classification to make it usable in the real world applications. So we have proposed an improved approach for URL classification that is able to provide a better result. We have introduced the word-based multiple n-gram models for efficient feature extraction and multinomial distribution for Naive Bayes classifier under the Random Search pipeline for hyperparameter optimization that finds the best parameters of the URL features. The experimental result of our research is compared with the result of previous research works and we have shown a better result than the existing result. Our experimental result provides 88.77% in recall and 87.63% in F1-Score which is the best performance so far.","PeriodicalId":355984,"journal":{"name":"2018 21st International Conference of Computer and Information Technology (ICCIT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114767428","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}
Irrigation is the process of applying appropriate amount of water to crop fields at needed interim. Irrigation is an exigent part of cultivation of rice. Although, the overall yield of rice paddy predominantly depends on proper irrigation, irrigation process in developing countries like Bangladesh is still backdated. This study proposes an Arduino/GSM based remotely controlled power efficient smart irrigation system for crops that need to be immersed in water during its growing period. It will ensure the proper irrigation of a field by monitoring water level of the paddy field, providing feedback to farmers and giving farmers option to control the water motor via SMS. This study is expected to improve the overall production of AMAN and BORO rice of Barishal region by automating the traditional irrigation system. Also it will provide a more sophisticated irrigation system for similar types of crops.
{"title":"Power Efficient Distant Controlled Smart Irrigation System for AMAN and BORO Rice","authors":"Rahat Hossain Faisal, Chandrika Saha, Md. Hasibul Hasan, Palash Kumar Kundu","doi":"10.1109/ICCITECHN.2018.8631927","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2018.8631927","url":null,"abstract":"Irrigation is the process of applying appropriate amount of water to crop fields at needed interim. Irrigation is an exigent part of cultivation of rice. Although, the overall yield of rice paddy predominantly depends on proper irrigation, irrigation process in developing countries like Bangladesh is still backdated. This study proposes an Arduino/GSM based remotely controlled power efficient smart irrigation system for crops that need to be immersed in water during its growing period. It will ensure the proper irrigation of a field by monitoring water level of the paddy field, providing feedback to farmers and giving farmers option to control the water motor via SMS. This study is expected to improve the overall production of AMAN and BORO rice of Barishal region by automating the traditional irrigation system. Also it will provide a more sophisticated irrigation system for similar types of crops.","PeriodicalId":355984,"journal":{"name":"2018 21st International Conference of Computer and Information Technology (ICCIT)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115105405","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}
Pub Date : 2018-12-01DOI: 10.1109/ICCITECHN.2018.8631936
Md Mosharaf Hossain, Thomas M. Hines, S. Ghafoor, Ryan J. Marshall, Muzakhir S. Amanzholov, R. Kannan
The symmetric rank-k update (SYRK) is a level-3 BLAS routine commonly used by many Data Mining/Machine Learning(DM/ML) algorithms such as regression, dimensionality reduction algorithms like PCA, matrix factorization and k-mean clustering. This paper presents a comprehensive analysis of the SYRK routine under popular dense linear algebra libraries such as OpenBLAS, Intel MKL, and BLIS particularly focusing on edge cases of dense matrices (thin or fat shapes) that are common in DM/ML applications. Our work identifies some performance issues of the SYRK routine in multi-threaded shared memory environments for edge cases and discuss matrix dependent modifications for performance improvement.
{"title":"Performance Issues of SYRK Implementations in Shared Memory Environments for Edge Cases","authors":"Md Mosharaf Hossain, Thomas M. Hines, S. Ghafoor, Ryan J. Marshall, Muzakhir S. Amanzholov, R. Kannan","doi":"10.1109/ICCITECHN.2018.8631936","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2018.8631936","url":null,"abstract":"The symmetric rank-k update (SYRK) is a level-3 BLAS routine commonly used by many Data Mining/Machine Learning(DM/ML) algorithms such as regression, dimensionality reduction algorithms like PCA, matrix factorization and k-mean clustering. This paper presents a comprehensive analysis of the SYRK routine under popular dense linear algebra libraries such as OpenBLAS, Intel MKL, and BLIS particularly focusing on edge cases of dense matrices (thin or fat shapes) that are common in DM/ML applications. Our work identifies some performance issues of the SYRK routine in multi-threaded shared memory environments for edge cases and discuss matrix dependent modifications for performance improvement.","PeriodicalId":355984,"journal":{"name":"2018 21st International Conference of Computer and Information Technology (ICCIT)","volume":"250 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124760354","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}
In this paper, we investigate a fully operational mobile platform rover “Mars Rover UIU” which can perform as human assistant in traversing and analyzing the surface of planet Mars and complete the assigned scientific analysis and Testing. The rover is designed with modified rocker-bogie, SMPS technology-based power system, a robotic Arm with 5 degrees of freedom and is controlled using Microsoft Xbox 360 controller. On June 2017 in the 11th annual University Rover Challenge organized by The Mars Society at Utah, USA, the team successfully placed 36th position out of 82 registered teams from 13 countries. “Mars Rover UIU” can perform various tasks such as Scientific Analysis, Extreme Retrieval and Delivery, Equipment Servicing and Autonomous traversal of a variety of terrains (sandy, rocky, vertical drops, steep slopes). It can examine existence of microbial life by studying geological context such as evidence of water flow, present minerals and soil structure. It can assist astronauts by retrieving and delivering objects to destinations marked by GPS coordinates and service equipment by connecting carabineers, flipping switches, pushing buttons, pouring fuel, screwing etc. This paper represents a synopsis of the mechanism design, software architecture and technologies implemented in “Mars Rover UIU” along with its potentiality.
{"title":"Design and Implementation of the Next Generation Mars Rover","authors":"Thajid Ibna Rouf Uday, Nazib Ahmad, Amit Ghosh, Junaed Jahin, Md. Mosfiqur Rahman, Md Ifraham Iqbal, Md. Toriqul Islam, Faizah Farzana, Md. Mizanur Rahman, Gma Ehsan Ur Rahman, Farhana Sharmin Tithi","doi":"10.1109/ICCITECHN.2018.8631928","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2018.8631928","url":null,"abstract":"In this paper, we investigate a fully operational mobile platform rover “Mars Rover UIU” which can perform as human assistant in traversing and analyzing the surface of planet Mars and complete the assigned scientific analysis and Testing. The rover is designed with modified rocker-bogie, SMPS technology-based power system, a robotic Arm with 5 degrees of freedom and is controlled using Microsoft Xbox 360 controller. On June 2017 in the 11th annual University Rover Challenge organized by The Mars Society at Utah, USA, the team successfully placed 36th position out of 82 registered teams from 13 countries. “Mars Rover UIU” can perform various tasks such as Scientific Analysis, Extreme Retrieval and Delivery, Equipment Servicing and Autonomous traversal of a variety of terrains (sandy, rocky, vertical drops, steep slopes). It can examine existence of microbial life by studying geological context such as evidence of water flow, present minerals and soil structure. It can assist astronauts by retrieving and delivering objects to destinations marked by GPS coordinates and service equipment by connecting carabineers, flipping switches, pushing buttons, pouring fuel, screwing etc. This paper represents a synopsis of the mechanism design, software architecture and technologies implemented in “Mars Rover UIU” along with its potentiality.","PeriodicalId":355984,"journal":{"name":"2018 21st International Conference of Computer and Information Technology (ICCIT)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128562897","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}
Pub Date : 2018-12-01DOI: 10.1109/ICCITECHN.2018.8631938
M. M. Rahman, Md Faisal Kabir, M. N. Huda
Machine translation means automatic translation which is performed using computer software. There are several approaches to machine translation, some of them need extensive linguistic knowledge while others require enormous statistical calculations. This paper presents a hybrid method, integrating corpus based approach and statistical approach for translating Bengali sentences into English with the help of N-gram language model. The corpus based method finds the corresponding target language translation of sentence fragments, selecting the best match text from the bilingual corpus to acquire knowledge while the N-gram model rearranges the sentence constituents to get an accurate translation without employing external linguistic rules. A variety of Bengali sentences, including various structures and verb tenses are considered to translate through the new system. The performance of the proposed system is evaluated in terms of adequacy, fluency, WER, and BLEU score. The assessment scores are compared with other conventional approaches as well as with Google Translate, a well-known free machine translation service by Google. It has been found that experimental results of the work provide higher scores over Google Translate and other methods with less computational cost.
{"title":"A Corpus Based N-gram Hybrid Approach of Bengali to English Machine Translation","authors":"M. M. Rahman, Md Faisal Kabir, M. N. Huda","doi":"10.1109/ICCITECHN.2018.8631938","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2018.8631938","url":null,"abstract":"Machine translation means automatic translation which is performed using computer software. There are several approaches to machine translation, some of them need extensive linguistic knowledge while others require enormous statistical calculations. This paper presents a hybrid method, integrating corpus based approach and statistical approach for translating Bengali sentences into English with the help of N-gram language model. The corpus based method finds the corresponding target language translation of sentence fragments, selecting the best match text from the bilingual corpus to acquire knowledge while the N-gram model rearranges the sentence constituents to get an accurate translation without employing external linguistic rules. A variety of Bengali sentences, including various structures and verb tenses are considered to translate through the new system. The performance of the proposed system is evaluated in terms of adequacy, fluency, WER, and BLEU score. The assessment scores are compared with other conventional approaches as well as with Google Translate, a well-known free machine translation service by Google. It has been found that experimental results of the work provide higher scores over Google Translate and other methods with less computational cost.","PeriodicalId":355984,"journal":{"name":"2018 21st International Conference of Computer and Information Technology (ICCIT)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127159891","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}
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