Pub Date : 2018-12-01DOI: 10.1109/ICCITECHN.2018.8631965
Sagar Dakua, Alamgir Kabir Rusad, N. Sakib, Md. Ahsan-Ul Kabir Shawon, Md Kafiul Islam
Recently, Human Machine Interface (HMI) has become an important part of medical technology where different bio-signals such as EOG, EMG, and EEG can be deployed to develop a closed-loop control system for physically disabled and elderly people to improve their quality of life. On the other hand, as the road and industrial accidents are increasing in countries like Bangladesh, more and more people are losing body parts and are not able to treat their condition properly due to the financial burden and lack in technological advancement. In this research, we have preliminarily designed, implemented and tested a low-cost EMG recording and monitoring system that can detect and process EMG signals from different kinds of muscle contraction. The recorded signals can be interfaced with the computer through Arduino UNO and then the EMG signals can be analyzed further in MATLAB platform. We have also developed an algorithm to detect muscle contraction and expansion from raw EMG recordings and then converted them into command signals that can control a robotic arm. In order to demonstrate the efficacy of the proposed system, lab experiments are performed by recording of EMG signals from 5 subjects by attaching electrodes on their shoulders and calculated the accuracy of detecting muscle contraction based on four ROC parameters, known as True Positives (TP) False Positives (FP), True Negatives (TN) and False Negatives (FN); which results in 83% accuracy on an average. Then based on the detected muscle contraction, we successfully demonstrated to control a robotic arm by opening and closing of its fingers which can later be replaced with a prosthetic arm for those disabled persons who lost their arms. This research will help not only to diagnose any neuromuscular disease by comparing it with any healthy subject's EMG signal, but also these EMG recordings can be processed and decoded to control prosthetic arm for those people who lost their arm but cannot afford commercially available expensive prosthetic systems in a developing country like Bangladesh.
{"title":"Towards Design and Implementation of a Low-Cost EMG Signal Recorder for Application in Prosthetic Arm Control for Developing Countries Like Bangladesh","authors":"Sagar Dakua, Alamgir Kabir Rusad, N. Sakib, Md. Ahsan-Ul Kabir Shawon, Md Kafiul Islam","doi":"10.1109/ICCITECHN.2018.8631965","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2018.8631965","url":null,"abstract":"Recently, Human Machine Interface (HMI) has become an important part of medical technology where different bio-signals such as EOG, EMG, and EEG can be deployed to develop a closed-loop control system for physically disabled and elderly people to improve their quality of life. On the other hand, as the road and industrial accidents are increasing in countries like Bangladesh, more and more people are losing body parts and are not able to treat their condition properly due to the financial burden and lack in technological advancement. In this research, we have preliminarily designed, implemented and tested a low-cost EMG recording and monitoring system that can detect and process EMG signals from different kinds of muscle contraction. The recorded signals can be interfaced with the computer through Arduino UNO and then the EMG signals can be analyzed further in MATLAB platform. We have also developed an algorithm to detect muscle contraction and expansion from raw EMG recordings and then converted them into command signals that can control a robotic arm. In order to demonstrate the efficacy of the proposed system, lab experiments are performed by recording of EMG signals from 5 subjects by attaching electrodes on their shoulders and calculated the accuracy of detecting muscle contraction based on four ROC parameters, known as True Positives (TP) False Positives (FP), True Negatives (TN) and False Negatives (FN); which results in 83% accuracy on an average. Then based on the detected muscle contraction, we successfully demonstrated to control a robotic arm by opening and closing of its fingers which can later be replaced with a prosthetic arm for those disabled persons who lost their arms. This research will help not only to diagnose any neuromuscular disease by comparing it with any healthy subject's EMG signal, but also these EMG recordings can be processed and decoded to control prosthetic arm for those people who lost their arm but cannot afford commercially available expensive prosthetic systems in a developing country like Bangladesh.","PeriodicalId":355984,"journal":{"name":"2018 21st International Conference of Computer and Information Technology (ICCIT)","volume":"14 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":"114860770","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.2018.8631913
Nishargo Nigar, Mohammed Nazim Uddin
Developing bad food habits at an early age is becoming a very concerning issue among parents. Unhealthy food consumption may lead to various diseases. In fact, this is the major reason behind children obesity issues. In this paper, we developed an Internet of Things enabled mHealth platform with transfer learning where we suggest a balanced and categorized food chart according to nutrition and children's meal timing.
{"title":"A mHealth Platform Using Transfer Learning and Internet of Things to Improve Children's Health Consciousness and Behavior","authors":"Nishargo Nigar, Mohammed Nazim Uddin","doi":"10.1109/ICCITECHN.2018.8631913","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2018.8631913","url":null,"abstract":"Developing bad food habits at an early age is becoming a very concerning issue among parents. Unhealthy food consumption may lead to various diseases. In fact, this is the major reason behind children obesity issues. In this paper, we developed an Internet of Things enabled mHealth platform with transfer learning where we suggest a balanced and categorized food chart according to nutrition and children's meal timing.","PeriodicalId":355984,"journal":{"name":"2018 21st International Conference of Computer and Information Technology (ICCIT)","volume":"118 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":"124194376","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.2018.8631933
Ahmed Ibrahim
This paper presents a design and development of a method to measure the Carbon monoxide (CO) and Carbon dioxide (CO2) in air using this instrument for remote monitoring system based on micro-controller. This embedded system is designed using the MQ-7 Carbon Monoxide (CO) gas sensor and the MQ-135 the air quality sensor. The pollutant materials contract with the sensors and numerical reading values of C02 and CO are calculated in particles per million (ppm) unit and then displayed on a display device. A buzzer is connected to the micro-controller as the poisonous gases reaches its safety limits, the buzzer turns on and alarms for danger. On the other hand, the whole system unit is portable.
{"title":"Carbon Dioxide and Carbon Monoxide Level Detector","authors":"Ahmed Ibrahim","doi":"10.1109/ICCITECHN.2018.8631933","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2018.8631933","url":null,"abstract":"This paper presents a design and development of a method to measure the Carbon monoxide (CO) and Carbon dioxide (CO2) in air using this instrument for remote monitoring system based on micro-controller. This embedded system is designed using the MQ-7 Carbon Monoxide (CO) gas sensor and the MQ-135 the air quality sensor. The pollutant materials contract with the sensors and numerical reading values of C02 and CO are calculated in particles per million (ppm) unit and then displayed on a display device. A buzzer is connected to the micro-controller as the poisonous gases reaches its safety limits, the buzzer turns on and alarms for danger. On the other hand, the whole system unit is portable.","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":"124302983","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.2018.8631923
Shaily Roy, Samiha Nanjiba, Amitabha Chakrabarty
Over the past few years, Bitcoin has been a topic of interest of many, from academic researchers to trade investors. Bitcoin is the first as well as the most popular cryptocurrency till date. Since its launch in 2009, it has become widely popular amongst various kinds of people for its trading system without the need of a third party and also due to high volatility of Bitcoin price. In this paper, we propose a suitable model that can predict the market price of Bitcoin best by applying a few statistical analysis. Our work is done on four year's bitcoin data from 2013 to 2017 based on time series approaches especially autoregressive integrated moving average (ARIMA) model and the work finally could acquire an accuracy of 90% for deciding volatility in weighted costs of bitcoin in the short run.
{"title":"Bitcoin Price Forecasting Using Time Series Analysis","authors":"Shaily Roy, Samiha Nanjiba, Amitabha Chakrabarty","doi":"10.1109/ICCITECHN.2018.8631923","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2018.8631923","url":null,"abstract":"Over the past few years, Bitcoin has been a topic of interest of many, from academic researchers to trade investors. Bitcoin is the first as well as the most popular cryptocurrency till date. Since its launch in 2009, it has become widely popular amongst various kinds of people for its trading system without the need of a third party and also due to high volatility of Bitcoin price. In this paper, we propose a suitable model that can predict the market price of Bitcoin best by applying a few statistical analysis. Our work is done on four year's bitcoin data from 2013 to 2017 based on time series approaches especially autoregressive integrated moving average (ARIMA) model and the work finally could acquire an accuracy of 90% for deciding volatility in weighted costs of bitcoin in the short run.","PeriodicalId":355984,"journal":{"name":"2018 21st International Conference of Computer and Information Technology (ICCIT)","volume":"10 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":"124438746","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.2018.8631959
Tajul Islam, M. Hashem
Fog computing is a modern research trend to carry cloud computing benefits to network edges. Edge data center (EDC) or fog devices are used to minimize the network congestion and latency by processing data flows and user demands in close real time. Edge data center (EDC) or fog devices are positioned between data sources or iot devices and cloud data center (CDC). Fog devices work as a semi-permanent storage and mainly provides location awareness. Moreover, for processing data in the EDC or fog devices, task scheduling is a vital issue because here generate a lots of data from IoT or sensor layer. In our proposed strategy, we have used EDC or fog infrastructure for providing real time services in latency sensitive applications. By using ford-fulkerson algorithm and priority based queue, we have done load balancing and task scheduling in fog devices or EDC based network fast processing these large amount of data or big data.
{"title":"Task Scheduling for Big Data Management in Fog Infrastructure","authors":"Tajul Islam, M. Hashem","doi":"10.1109/ICCITECHN.2018.8631959","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2018.8631959","url":null,"abstract":"Fog computing is a modern research trend to carry cloud computing benefits to network edges. Edge data center (EDC) or fog devices are used to minimize the network congestion and latency by processing data flows and user demands in close real time. Edge data center (EDC) or fog devices are positioned between data sources or iot devices and cloud data center (CDC). Fog devices work as a semi-permanent storage and mainly provides location awareness. Moreover, for processing data in the EDC or fog devices, task scheduling is a vital issue because here generate a lots of data from IoT or sensor layer. In our proposed strategy, we have used EDC or fog infrastructure for providing real time services in latency sensitive applications. By using ford-fulkerson algorithm and priority based queue, we have done load balancing and task scheduling in fog devices or EDC based network fast processing these large amount of data or big data.","PeriodicalId":355984,"journal":{"name":"2018 21st International Conference of Computer and Information Technology (ICCIT)","volume":"108 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":"125047509","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.2018.8631977
Hemayet Ahmed Chowdhury, Md. Azizul Haque Imon, Md Saiful Islam
Word Embeddings can be used by deep layers of neural networks to extract features from them to learn stylo-metric patterns of authors based on context and co-occurrence of the words in the field of Authorship Attribution. In this paper, we investigate the effects of different types of word embeddings in Authorship Attribution of Bengali Literature, specifically the skip-gram and continuous-bag-of-words(CBOW) models generated by Word2Vec and fastText along with the word vectors generated by Glove. We experiment with dense neural network models, such as the convolutional and recurrent neural networks and analyse how different word embedding models effect the performance of the classifiers and discuss their properties in this classification task of Authorship Attribution of Bengali Literature. The experiments are performed on a data set we prepared, consisting of 2400 on-line blog articles from 6 authors of recent times.
{"title":"A Comparative Analysis of Word Embedding Representations in Authorship Attribution of Bengali Literature","authors":"Hemayet Ahmed Chowdhury, Md. Azizul Haque Imon, Md Saiful Islam","doi":"10.1109/ICCITECHN.2018.8631977","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2018.8631977","url":null,"abstract":"Word Embeddings can be used by deep layers of neural networks to extract features from them to learn stylo-metric patterns of authors based on context and co-occurrence of the words in the field of Authorship Attribution. In this paper, we investigate the effects of different types of word embeddings in Authorship Attribution of Bengali Literature, specifically the skip-gram and continuous-bag-of-words(CBOW) models generated by Word2Vec and fastText along with the word vectors generated by Glove. We experiment with dense neural network models, such as the convolutional and recurrent neural networks and analyse how different word embedding models effect the performance of the classifiers and discuss their properties in this classification task of Authorship Attribution of Bengali Literature. The experiments are performed on a data set we prepared, consisting of 2400 on-line blog articles from 6 authors of recent times.","PeriodicalId":355984,"journal":{"name":"2018 21st International Conference of Computer and Information Technology (ICCIT)","volume":"158 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":"126919602","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.2018.8631931
S. A. Chowdhury, Firoj Alam, Naira Khan
Named Entity Recognition is one of the fundamental problems for Information Extraction and the task is to find the mentioned entities in text. Over the years there has been significant progress in Named Entity Recognition (NER) research for resource-rich languages such as English, Chinese, and Italian. Although, there are a number of studies for Bangla NER, however, most of these studies are conducted almost a decade ago and were focused on a single geographical location (i.e., India). Therefore, in this paper, we present a corpus annotated with seven named entities with a particular focus on Bangladeshi Bangla. It is a part of the development of the Bangla Content Annotation Bank (B-CAB). We also present baseline results, which can be useful for future research. For the baseline results, we employed word-level, POS, gazetteers and contextual features along with Conditional Random Fields (CRFs). Our study also includes the exploration of deep neural networks. Additionally, we investigated another large corpus from a different geographical location (i.e., India) and concluded on the importance of geographic-based NER for a language.
{"title":"Towards Bangla Named Entity Recognition","authors":"S. A. Chowdhury, Firoj Alam, Naira Khan","doi":"10.1109/ICCITECHN.2018.8631931","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2018.8631931","url":null,"abstract":"Named Entity Recognition is one of the fundamental problems for Information Extraction and the task is to find the mentioned entities in text. Over the years there has been significant progress in Named Entity Recognition (NER) research for resource-rich languages such as English, Chinese, and Italian. Although, there are a number of studies for Bangla NER, however, most of these studies are conducted almost a decade ago and were focused on a single geographical location (i.e., India). Therefore, in this paper, we present a corpus annotated with seven named entities with a particular focus on Bangladeshi Bangla. It is a part of the development of the Bangla Content Annotation Bank (B-CAB). We also present baseline results, which can be useful for future research. For the baseline results, we employed word-level, POS, gazetteers and contextual features along with Conditional Random Fields (CRFs). Our study also includes the exploration of deep neural networks. Additionally, we investigated another large corpus from a different geographical location (i.e., India) and concluded on the importance of geographic-based NER for a language.","PeriodicalId":355984,"journal":{"name":"2018 21st International Conference of Computer and Information Technology (ICCIT)","volume":"5 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":"125644224","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.2018.8631916
M. Amin, Khondaker Sazzadul Karim, Afrina Amin, Jing Hua Li
The primatial aim of the study is to ascertain the key motivating factors that are directly or indirectly swaying rural consumers' adoption and usage behavior of various ICT-enabled products and services available in Bangladesh. Using a self-administered questionnaire as a research instrument, this study analyzed one hundred and three rural consumers' data collected systematically from a village, locally known as Dollar Bazar, situated in Manikganj district. Result of the study revealed that the latent variables Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) had no significant influence on the Behavioral Intention to Use (BIU); subsequently, the two variables: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) identified as antecedents of the variable Self-efficacy(SE) which estimated a strong impact on the Behavioral Intention to Use (BIU) in a technology-mediated environment in Bangladesh.
{"title":"Preliminary Enquiry into the Adoption Behavior of K'Ts Enabled Products and Services at the Bottom of the Pyramid (BOP) in Bangladesh","authors":"M. Amin, Khondaker Sazzadul Karim, Afrina Amin, Jing Hua Li","doi":"10.1109/ICCITECHN.2018.8631916","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2018.8631916","url":null,"abstract":"The primatial aim of the study is to ascertain the key motivating factors that are directly or indirectly swaying rural consumers' adoption and usage behavior of various ICT-enabled products and services available in Bangladesh. Using a self-administered questionnaire as a research instrument, this study analyzed one hundred and three rural consumers' data collected systematically from a village, locally known as Dollar Bazar, situated in Manikganj district. Result of the study revealed that the latent variables Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) had no significant influence on the Behavioral Intention to Use (BIU); subsequently, the two variables: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) identified as antecedents of the variable Self-efficacy(SE) which estimated a strong impact on the Behavioral Intention to Use (BIU) in a technology-mediated environment in Bangladesh.","PeriodicalId":355984,"journal":{"name":"2018 21st International Conference of Computer and Information Technology (ICCIT)","volume":"142 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":"132520022","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.2018.8631947
Nafisa Nowshin, Zakia Sultana Ritu, Sabir Ismail
In this paper, we present a crowd-source based Bangla to English parallel corpus and evaluate its accuracy. A complete and informative corpus is necessary for any language for its development through automated process. A Bangla to English parallel corpus has importance in various multi-lingual applications and NLP research works. But there is still scarcity of a complete Bangla to English parallel corpus. In this paper we propose a large scale crowd-source method of construction of a Bangla to English parallel corpus through crowd-sourcing. We chose crowd-sourcing method to venture a new approach in corpus construction and evaluate human behavior pattern in doing so. The translations were collected form under graduate students of university to ensure strong language knowledge. A Bangla to English parallel corpus will help in comparing linguistic features of these languages. In this paper we present an initial dataset prepared via crowd-sourcing which will serve as a baseline for further analysis of crowd source based corpus. Our primary dataset is consists of 517 Bangla sentences and for every Bangla sentence, we collected 4 English sentences on an average and 2143 English sentences in total via crowd-sourcing. This data was collected over a period of 2 months and from 62 users. Finally we analyze the dataset and give some conclusive idea about further research.
{"title":"A Crowd-Source Based Corpus on Bangla to English Translation","authors":"Nafisa Nowshin, Zakia Sultana Ritu, Sabir Ismail","doi":"10.1109/ICCITECHN.2018.8631947","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2018.8631947","url":null,"abstract":"In this paper, we present a crowd-source based Bangla to English parallel corpus and evaluate its accuracy. A complete and informative corpus is necessary for any language for its development through automated process. A Bangla to English parallel corpus has importance in various multi-lingual applications and NLP research works. But there is still scarcity of a complete Bangla to English parallel corpus. In this paper we propose a large scale crowd-source method of construction of a Bangla to English parallel corpus through crowd-sourcing. We chose crowd-sourcing method to venture a new approach in corpus construction and evaluate human behavior pattern in doing so. The translations were collected form under graduate students of university to ensure strong language knowledge. A Bangla to English parallel corpus will help in comparing linguistic features of these languages. In this paper we present an initial dataset prepared via crowd-sourcing which will serve as a baseline for further analysis of crowd source based corpus. Our primary dataset is consists of 517 Bangla sentences and for every Bangla sentence, we collected 4 English sentences on an average and 2143 English sentences in total via crowd-sourcing. This data was collected over a period of 2 months and from 62 users. Finally we analyze the dataset and give some conclusive idea about further research.","PeriodicalId":355984,"journal":{"name":"2018 21st International Conference of Computer and Information Technology (ICCIT)","volume":"35 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":"115766646","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.2018.8631953
T. Ahammad, Uzzal Kumar Acharjee, M. Hasan
The rising demands of cloud computing tend to increase the energy consumption. So, a sustainable computing environment is essential for ensuring efficient resource allocation considering the quality of service (QoS). There are many approaches in the literature employing for minimizing energy use in cloud. Predicting workload is one of the most robust and promising tasks of energy-aware cloud computing. This paper presents a service-oriented model for determining future resources requirement by predicting cloud workloads. The model incorporates several key issues alongside with load predictor to establish an energy-effective cloud environment. The workload prediction is accomplished with Multilayer Perceptron (MLP) because of its better prediction quality than the most commonly used approaches. Moreover, an implementation architecture of the proposed model is suggested to achieve the goal of this paper.
{"title":"Energy-Effective Service-Oriented Cloud Resource Allocation Model Based on Workload Prediction","authors":"T. Ahammad, Uzzal Kumar Acharjee, M. Hasan","doi":"10.1109/ICCITECHN.2018.8631953","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2018.8631953","url":null,"abstract":"The rising demands of cloud computing tend to increase the energy consumption. So, a sustainable computing environment is essential for ensuring efficient resource allocation considering the quality of service (QoS). There are many approaches in the literature employing for minimizing energy use in cloud. Predicting workload is one of the most robust and promising tasks of energy-aware cloud computing. This paper presents a service-oriented model for determining future resources requirement by predicting cloud workloads. The model incorporates several key issues alongside with load predictor to establish an energy-effective cloud environment. The workload prediction is accomplished with Multilayer Perceptron (MLP) because of its better prediction quality than the most commonly used approaches. Moreover, an implementation architecture of the proposed model is suggested to achieve the goal of this paper.","PeriodicalId":355984,"journal":{"name":"2018 21st International Conference of Computer and Information Technology (ICCIT)","volume":"29 20","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120857360","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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