2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)最新文献_第5页

Using Boosting Approaches to Detect Spam Reviews 使用增强方法检测垃圾评论

2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) Pub Date : 2019-05-01 DOI: 10.1109/ICASERT.2019.8934467

Sifat Ahmed, Faisal Muhammad

引用次数: 4

Offshore Wind Energy Estimation in the Bay of Bengal with Satellite Wind Measurement 利用卫星风测量估算孟加拉湾海上风能

2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) Pub Date : 2019-05-01 DOI: 10.1109/ICASERT.2019.8934915

Navila Rahman Nadi, F. Bingöl, Merete Badger

{"title":"Offshore Wind Energy Estimation in the Bay of Bengal with Satellite Wind Measurement","authors":"Navila Rahman Nadi, F. Bingöl, Merete Badger","doi":"10.1109/ICASERT.2019.8934915","DOIUrl":"https://doi.org/10.1109/ICASERT.2019.8934915","url":null,"abstract":"The objective of this paper is to obtain appropriate offshore location in the Bay of Bengal, Bangladesh for further development of wind energy. Through analyzing the previous published works, no offshore wind energy estimation has been found related to the Bay of Bengal. Therefore, this study can be claimed as the first footstep towards offshore wind energy analysis for this region. Generally, it is difficult to find offshore wind data relative to the wind turbine hub heights, thus a starting point is necessary to identify the possible wind power density of the region. In such scenario, Synthetic Aperture radars (SAR) have proven useful in previous studies. In this study, SAR based dataset- ENVISAT ASAR has been used for Wind Atlas generation of the Bay of Bengal. Furthermore, a comparative study has been performed with Global Wind Atlas (GWA) to determine a potential offshore wind farm production in a reasonable location at the bay. The annual energy production of that offshore windfarm has been analyzed by combining SAR, GWA and ASCAT datasets. Through ASAR based Wind Atlas and GWA comparison, some differences have been found where there are less samples from the ASAR datasets. Thus, Weibull statistical analysis are performed to have a better Weibull fitting and accurate estimation of Annual Energy production (AEP). The study summarizes that, satellite datasets can be a very useful method to detect potential zone if compared with any long time statistical result and bathymetry data together.","PeriodicalId":6613,"journal":{"name":"2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)","volume":"15 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89414813","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: 0

Question Bank Similarity Searching System (QB3S) Using NLP and Information Retrieval Technique 基于自然语言处理和信息检索技术的题库相似度搜索系统(QB3S

2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) Pub Date : 2019-05-01 DOI: 10.1109/ICASERT.2019.8934449

Md. Raihan Mia, A. S. M. Latiful Hoque

{"title":"Question Bank Similarity Searching System (QB3S) Using NLP and Information Retrieval Technique","authors":"Md. Raihan Mia, A. S. M. Latiful Hoque","doi":"10.1109/ICASERT.2019.8934449","DOIUrl":"https://doi.org/10.1109/ICASERT.2019.8934449","url":null,"abstract":"Problem Based e-learning(PBeL) in bangla language is one of the most progressing areas of the use of ICT in education. Question Bank(QB) is the main component of any PBeL system. Searching similarity in the complex structure of QB is a challenging task in the development of PBeL system. We have been developed an efficient Question Bank Similarity Searching System(QB3S) to find similar questions, handle duplicate question and rank search result of a query input based on NLP and Information Retrieval techniques. QB3S has four modules: bangla documents processing, question structure analysis and clustered indexing by B+ tree , word-net construction and Information retrieval module. Lexical analysis, stemming by finite automata rules and stopwords removing have been used for bangla document processing. The most challenging procedures of QB3S were Analyzing the structure of data for clustered indexing in the sorted sequential file of the QB database with a B+ tree data structure and improved TF-IDF algorithm with weighted functionality. A Word-net has been used for handling synonyms. Vector Space Model(VSM) has been designed from the value of TF-IDF weighted matrix. By using cosine similarity product rule, we have been Calculated the similarity value between the query input and all mcq of DB from VSM. QB3S has been evaluated in some experimental dataset to find results by imposing different test cases. The accuracy of searching performance which has found to be satisfactory.","PeriodicalId":6613,"journal":{"name":"2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)","volume":"25 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89439561","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: 3

Enhancement of Sensitivity for Surface Plasmon Resonance Biosensor with Higher Detection Accuracy and Quality Factor 提高表面等离子体共振生物传感器检测精度和质量因子的灵敏度

2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) Pub Date : 2019-05-01 DOI: 10.1109/ICASERT.2019.8934602

Mohammad Hasibul Hasan Hasib, Jannati Nabiha Nur, Kamrun Nahar Shushama, I. Rahaman, M. Rana, M. A. Al Mahfuz

引用次数: 2

Feature Selection and Comparative Analysis of the Supervised Learning Model for Hyperspectral Image Classification 高光谱图像分类的监督学习模型特征选择与比较分析

2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) Pub Date : 2019-05-01 DOI: 10.1109/ICASERT.2019.8934653

Abu Sayeed, Md. Ali Hossain, Md. Rabiul Islam

{"title":"Feature Selection and Comparative Analysis of the Supervised Learning Model for Hyperspectral Image Classification","authors":"Abu Sayeed, Md. Ali Hossain, Md. Rabiul Islam","doi":"10.1109/ICASERT.2019.8934653","DOIUrl":"https://doi.org/10.1109/ICASERT.2019.8934653","url":null,"abstract":"In remote sensing image classification, really it is an intimidating when kernel supervised learning approaches stands in need of adequate amount of training samples. Often there is a vital problem for definition and acquisition of reference data. For Hyperspectral image classification, improved spectral information is required to make it suitable for ground object identification. In this paper, Support Vector Machine with RBF kernel (KSVM) and the spectral angle mapper (SAM) are used for performance comparison in terms of classification accuracy in Hyperspectral image classification. Kernel support vector machine is more preferable for the mastery to generalize better hyperplane when limited availability of training samples and separate the classes competently in a new dimension feature space. Experiments are performed on NASA Airborne Visible Infrared Spectrometer (AVIRIS) image and it shows KSVM outperforms SAM and obtains the highest accuracy. Due to more well-conditioned against the outliers, KSVM significantly reduced the classification complexities than SAM.","PeriodicalId":6613,"journal":{"name":"2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)","volume":"93 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85851751","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: 0

Epileptic Seizure Detection and Classification using Support Vector Machine from Scalp EEG Signal 基于头皮脑电信号的支持向量机癫痫发作检测与分类

2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) Pub Date : 2019-05-01 DOI: 10.1109/ICASERT.2019.8934478

Sania Zahan, M. Islam

{"title":"Epileptic Seizure Detection and Classification using Support Vector Machine from Scalp EEG Signal","authors":"Sania Zahan, M. Islam","doi":"10.1109/ICASERT.2019.8934478","DOIUrl":"https://doi.org/10.1109/ICASERT.2019.8934478","url":null,"abstract":"Epilepsy is a neurological disorder originating from brain cells that can affect harshly on patients’ life. Imbalance in electrical signals in the neuron cells results in involuntary partial or full body movements or other physiological symptoms. Seizure attack is unpredictable and during its occurrence patient may lose control which can cause serious injury even death. Medical facilities like medication or surgery can be done to improve living condition and life expectancy of patients. For these measures to be beneficial early and correct detection of epilepsy is crucial. However detection from scalp EEG is tough due to the presence of artifacts, the state of the brain and the frequency of seizure occurrence. Hence this study proposes a reliable model of detection system. A zero phase bandpass butterworth filter is used to extract only the EEG signal eliminating all physiological and device artifacts. Frequency distribution of brain signal in both interictal and ictal state differs from that in normal person. So statistical measurements that correctly maps these changes are used to classify the dataset. For classification, a nonlinear support vector machine is used on two sets of dataset combination. Performance of detecting epileptic signal even in the interictal state is promising for use in medical applications.","PeriodicalId":6613,"journal":{"name":"2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)","volume":"1 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78627725","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

An Intelligent Assistive Tool for Alzheimer’s Patient 阿尔茨海默病患者的智能辅助工具

2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) Pub Date : 2019-05-01 DOI: 10.1109/ICASERT.2019.8934542

Kazi Shahrukh Omar, F. Rabbi, Afia Anjum, Tahrima Oannahary, Rezaul Karim Rizvi, Diana Shahrin, Tasmiah Tamzid Anannya, Sanjida Nasreen Tumpa, MMahboob Karim, Muhammad Nazrul Islam

{"title":"An Intelligent Assistive Tool for Alzheimer’s Patient","authors":"Kazi Shahrukh Omar, F. Rabbi, Afia Anjum, Tahrima Oannahary, Rezaul Karim Rizvi, Diana Shahrin, Tasmiah Tamzid Anannya, Sanjida Nasreen Tumpa, MMahboob Karim, Muhammad Nazrul Islam","doi":"10.1109/ICASERT.2019.8934542","DOIUrl":"https://doi.org/10.1109/ICASERT.2019.8934542","url":null,"abstract":"Alzheimer’s Disease (AD) is a chronic neurodegenerative disease that causes to develop dementia. Alzheimer’s patients find it hard to remember recent events, reason and even to recognize people they know. As the disease advances, symptoms can include difficulty with language, disorientation including getting lost, mood swings, loss of motivation, lack of self-awareness and overall behavior. Though a limited number of IT based solutions exist to provide support for Alzheimer’s patients, but most of these provide very isolated services either for the patients or for the caregivers. The objective of this research is to propose an assistive tool for Alzheimer’s patients and their caregivers to provide support like health monitoring, assist to find lost items, provide reminder to take medicine and assist to monitor patient’s location. A light-weighted evaluation study was carried out with 15 participants. The evaluation study showed that the proposed system was effective and usable for the patients and their caregivers.","PeriodicalId":6613,"journal":{"name":"2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)","volume":"28 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80122294","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

A Spatiotemporal Analysis and Forecasting of Electricity Generation-Mix in Bangladesh 孟加拉国发电结构的时空分析与预测

2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) Pub Date : 2019-05-01 DOI: 10.1109/ICASERT.2019.8934479

Sujoy Barua, Anik Nath, Fahim Shahriyar, N. Mohammad

引用次数: 2

Robust Control of Terminal Voltage of an Isolated Electric Power Generating Unit 隔离发电机组端电压的鲁棒控制

2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) Pub Date : 2019-05-01 DOI: 10.1109/ICASERT.2019.8934583

Fariya Tabassum, M. S. Rana

引用次数: 1

Pathological Voice Classification Using Deep Learning 基于深度学习的病理语音分类

2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) Pub Date : 2019-05-01 DOI: 10.1109/ICASERT.2019.8934514

Shuvendu Roy, Md. Ijaj Sayim, M. Akhand

引用次数: 5