2020 IEEE Region 10 Symposium (TENSYMP)最新文献_第9页

Designing and Prototyping of an Electromechanical Ventilator based on Double CAM operation Integrated with Telemedicine Application 基于双凸轮操作与远程医疗应用集成的机电呼吸机设计与样机

2020 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2020-06-05 DOI: 10.1109/TENSYMP50017.2020.9230673

Md. Rakibul Islam, Mohiudding Ahmad, Md. Shahin Hossain, Muhammad Muinul Islam, Sk. Farid Uddin Ahmed

{"title":"Designing and Prototyping of an Electromechanical Ventilator based on Double CAM operation Integrated with Telemedicine Application","authors":"Md. Rakibul Islam, Mohiudding Ahmad, Md. Shahin Hossain, Muhammad Muinul Islam, Sk. Farid Uddin Ahmed","doi":"10.1109/TENSYMP50017.2020.9230673","DOIUrl":"https://doi.org/10.1109/TENSYMP50017.2020.9230673","url":null,"abstract":"In this paper, we proposed to design a new model of mechanical ventilator based on the Ambu bag automation for the patient who is unable to take breath normally. Here we have automated an Ambu bag for air supply whose inlet is connected with an oxygen cylinder and environmental air and outlet is connected to lung patient. The project device includes a robotic operator which can operate an Ambu bag continuously by compressing and decompressing it. The robotic operator is a Computer-Aided Manufacturing (CAM) arm that is controlled by a single microcontroller for operating on the Ambu bag from outside. It has a great advantage of using a single adult Ambu bag to deliver necessary air to all aged lung patients by setting different controlling modes with respect to age with reducing the necessity of pediatric Ambu bag and infant Ambu bag. By considering all of the physiological parameters, we have added three modes namely Adult mode, Pediatric mode, and Child mode. Each mode is included by different respiratory rate and tidal volume to be friendly with their corresponding subject. The proposed device can detect the air pressure and temperature from the Ambu bag outlet to make feedback for preventing the lung harm of the patient and display the parameters using an LCD. All medical data can be transferred via a communication protocol to an Android or iOS phone for telemedicine purposes in real-time. The overall system is portable, small in size (45cm×25cm×35cm), low weighted, time-efficient, and cost-effective. There is no need for training or the study of an operator about the proposed system to handle the device for the benefit of automation of the device.","PeriodicalId":6721,"journal":{"name":"2020 IEEE Region 10 Symposium (TENSYMP)","volume":"22 5","pages":"300-303"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91418539","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

Design and Analysis of Plasmonic Temperature Sensor Utilizing Photonic Crystal Fiber 光子晶体光纤等离子体温度传感器的设计与分析

2020 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2020-06-05 DOI: 10.1109/TENSYMP50017.2020.9230804

Md. Kamrul Hasan, Md. M. Rahman, M. Anower, M. Rana, A. Paul, Kisalaya Chakrabatri

引用次数: 1

On Predicting and Analyzing Breast Cancer using Data Mining Approach 基于数据挖掘方法的乳腺癌预测与分析

2020 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2020-06-05 DOI: 10.1109/TENSYMP50017.2020.9230871

Masud Rana Basunia, Ismot Ara Pervin, Md. Al Mahmud, S. Saha, M. Arifuzzaman

引用次数: 9

Prediction of Epileptic Seizures using Support Vector Machine and Regularization 基于支持向量机和正则化的癫痫发作预测

2020 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2020-06-05 DOI: 10.1109/TENSYMP50017.2020.9230899

Shaikh Rezwan Rafid Ahmad, Samee Mohammad Sayeed, Zaziba Ahmed, Nusayer Masud Siddique, M. Parvez

{"title":"Prediction of Epileptic Seizures using Support Vector Machine and Regularization","authors":"Shaikh Rezwan Rafid Ahmad, Samee Mohammad Sayeed, Zaziba Ahmed, Nusayer Masud Siddique, M. Parvez","doi":"10.1109/TENSYMP50017.2020.9230899","DOIUrl":"https://doi.org/10.1109/TENSYMP50017.2020.9230899","url":null,"abstract":"Epilepsy is a neurological disorder that causes abnormal behavior and recurrent seizures due to unusual brain activity. This study has attempted to predict seizures in epileptic patients through the process of feature extraction from EEG signals during preictal/ictal and interictal periods, classification and regularization. EEG signals from various parts of the brain from 10 epileptic patients are considered. Fast Fourier Transform (FFT) is used to determine the three features-the phase angle, the amplitude and the power spectral density of the signals. To classify the signals, these features are then used along with Support Vector Machine (SVM) as the classifier. Furthermore, regularization is used to make better predictions i.e. increase prediction accuracy and decrease the rate of false alarm. Finally, the proposed approach is tested on CHB-MIT Scalp EEG data set and it is able to predict epileptic seizures 25 minutes on average before the onset of the seizure with 100% accuracy and a low false-alarm rate of 0.46 per hour. This study intends to contribute to the development of better and advanced seizure predicting devices in the medical field.","PeriodicalId":6721,"journal":{"name":"2020 IEEE Region 10 Symposium (TENSYMP)","volume":"12 1","pages":"1217-1220"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87619477","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: 4

CPU Based YOLO: A Real Time Object Detection Algorithm 基于CPU的YOLO:一种实时目标检测算法

2020 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2020-06-05 DOI: 10.1109/TENSYMP50017.2020.9230778

Md. Bahar Ullah

引用次数: 33

Early Depression Detection from Social Network Using Deep Learning Techniques 使用深度学习技术从社交网络中检测早期抑郁症

2020 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2020-06-05 DOI: 10.1109/TENSYMP50017.2020.9231008

F. Shah, F. Ahmed, Sajib Kumar Saha Joy, Sifat Ahmed, Samir Sadek, Rimon Shil, M. H. Kabir

{"title":"Early Depression Detection from Social Network Using Deep Learning Techniques","authors":"F. Shah, F. Ahmed, Sajib Kumar Saha Joy, Sifat Ahmed, Samir Sadek, Rimon Shil, M. H. Kabir","doi":"10.1109/TENSYMP50017.2020.9231008","DOIUrl":"https://doi.org/10.1109/TENSYMP50017.2020.9231008","url":null,"abstract":"Depression is a psychological disorder that affects over three hundred million humans worldwide. A person who is depressed suffers from anxiety in day-to-day life, which affects that person in the relationship with their family and friends, leading to different diseases and in the worst-case death by suicide. With the growth of the social network, most of the people share their emotion, their feelings, their thoughts in social media. If their depression can be detected early by analyzing their post, then by taking necessary steps, a person can be saved from depression-related diseases or in the best case he can be saved from committing suicide. In this research work, a hybrid model has been proposed that can detect depression by analyzing user's textual posts. Deep learning algorithms were trained using the training data and then performance has been evaluated on the test data of the dataset of reddit which was published for the pilot piece of work, Early Detection of Depression in CLEF eRisk 2017. In particular, Bidirectional Long Short Term Memory (BiLSTM) with different word embedding techniques and metadata features were proposed which gave good results.","PeriodicalId":6721,"journal":{"name":"2020 IEEE Region 10 Symposium (TENSYMP)","volume":"47 1","pages":"823-826"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85741998","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: 31

Investigation on the Temperature and Size Dependent Mechanical Properties and Failure Behavior of Zinc Blende (ZB) Gallium Nitride (GaN) Semiconducting Nanowire 闪锌矿(ZB)氮化镓(GaN)半导体纳米线的温度和尺寸相关力学性能和失效行为研究

2020 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2020-06-05 DOI: 10.1109/TENSYMP50017.2020.9230906

M. Rahman, Shailee Mitra, M. Motalab, T. Rakib

{"title":"Investigation on the Temperature and Size Dependent Mechanical Properties and Failure Behavior of Zinc Blende (ZB) Gallium Nitride (GaN) Semiconducting Nanowire","authors":"M. Rahman, Shailee Mitra, M. Motalab, T. Rakib","doi":"10.1109/TENSYMP50017.2020.9230906","DOIUrl":"https://doi.org/10.1109/TENSYMP50017.2020.9230906","url":null,"abstract":"The mechanical properties of Gallium Nitride (GaN) nanowire has drawn considerable attention of researchers due to its application as electronic and semiconducting material. It has been successfully deployed in LEDs, transistors, Radars, Li-Fi communication system and many other electronic devices. In this research work, Molecular Dynamics simulations have been performed to explore the temperature-dependent mechanical properties of Zinc-Blende (ZB) GaN nanowire for tensile simulation. Stillinger-Weber (SW) potential has been employed to define the inter-atomic interactions between atoms in the GaN crystal. The temperature has been varied from 100K-600K and corresponding mechanical properties have been reported. To explore the nanowire size effect on the mechanical properties, the cross-sectional area of the nanowire has been varied for the temperature of 300K. Investigations suggest that increment of temperature results in the failure of GaN nanowire at a lower value of stress 37.96 GPa to 30.06 GPa and corresponding Young's Modulus decreases as well. We have calculated ultimate tensile stress and Young's modulus as 36.2 GPa and 189.3 GPa respectively at 300K for 13.37 nm2GaN nanowire. Our simulations results show that size has a significant effect on ultimate tensile stress and Young's Modulus of GaN nanowire. It has been found that as cross-sectional area increases both ultimate tensile stress and Young's modulus increases. Finally, the fracture behavior of GaN nanowire has also been reported from the atomistic simulation results. It has been found that 13.37 nm2GaN nanowire failed by creating a fracture plane along <111> direction of the nanowire axis and indicates the brittle nature of GaN nanowire.","PeriodicalId":6721,"journal":{"name":"2020 IEEE Region 10 Symposium (TENSYMP)","volume":"21 1","pages":"22-25"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91176038","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

Email Spam Detection using Bidirectional Long Short Term Memory with Convolutional Neural Network 基于双向长短期记忆的卷积神经网络垃圾邮件检测

2020 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2020-06-05 DOI: 10.1109/TENSYMP50017.2020.9230769

Sefat E Rahman, Shofi Ullah

{"title":"Email Spam Detection using Bidirectional Long Short Term Memory with Convolutional Neural Network","authors":"Sefat E Rahman, Shofi Ullah","doi":"10.1109/TENSYMP50017.2020.9230769","DOIUrl":"https://doi.org/10.1109/TENSYMP50017.2020.9230769","url":null,"abstract":"Communication over email in this era of Internet has become very popular on account of its being cheap and easy to use for messaging and sharing important information to others. But spam messages often times make large volume of unwanted messages in the users inbox and it also wastes the resources as well as valuable time of the users. Therefore, in order to identify the message whether it is spam or ham, an efficient and accurate technique is required. In this paper, we propose a new model for detecting spam messages based on the sentiment analysis of the textual data of the email body. We incorporate Word-Embeddings and Bidirectional LSTM network to analyze the sentimental and sequential properties of texts. Furthermore, we speed up the training time and extract higher level text features for Bi-LSTM network using Convolution Neural Network. We involve two datasets namely lingspam dataset and spam text message classification dataset and adopt recall, precision and f-score for comparing and evaluating the performance of our proposed approach. Our model achieves improved performance of accuracy about 98-99%. Apart from this, we demonstrate our model outperforms not only to some popular machine learning classifiers but also to state of the art approaches for detecting spam messages and hence, proves the superiority by itself.","PeriodicalId":6721,"journal":{"name":"2020 IEEE Region 10 Symposium (TENSYMP)","volume":"8 1","pages":"1307-1311"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73088120","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: 8

Predicting the Possibility of Being Malignant Tumor based on Physical Symptoms using IoT 利用物联网根据身体症状预测恶性肿瘤的可能性

2020 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2020-06-05 DOI: 10.1109/TENSYMP50017.2020.9230941

Md. Lizur Rahman, Sadat Hasan Shehab, Zareen Hasna Chowdhury, Avishake Kumar Datta

引用次数: 1

Impact Analysis of Cyber Attack under Stable State of Power System: Voltage Stability 电力系统稳定状态下网络攻击的影响分析:电压稳定

2020 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2020-06-05 DOI: 10.1109/TENSYMP50017.2020.9230732

Aniruddha Agrawal, Dallang M. Momin, Donnagratia Syndor, Shaik Affijulla

引用次数: 0