Pub Date : 2020-06-05DOI: 10.1109/TENSYMP50017.2020.9230673
Md. Rakibul Islam, Mohiudding Ahmad, Md. Shahin Hossain, Muhammad Muinul Islam, Sk. Farid Uddin Ahmed
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.
{"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}
Pub Date : 2020-06-05DOI: 10.1109/TENSYMP50017.2020.9230804
Md. Kamrul Hasan, Md. M. Rahman, M. Anower, M. Rana, A. Paul, Kisalaya Chakrabatri
In this paper, a simple geometric structured Photonic crystal fiber (PCF) based temperature sensor is proposed and analyzed theoretically. The designed sensor considered polydimethylsiloxane (PDMS) as a temperature dependent analyte to sense the variation of temperature with its surroundings. To enhance the sensitivity and avoid corrosion due to oxidation, gold (Au) film is used as plasmonic material. While analyzing the performance of the sensor, the finite element method (FEM) is utilized. Also, performance characterization is done altering the design parameters, e.g., pitch, air-holes diameter, and thickness of the gold layer. The results reveal a maximum possible spectral sensitivity of 4.67 nm/°C, with the detection range 30 °C to 90 °C. The sensor also exhibits a standard FOM valuing of 0.05838 /°C and a resolution of 3 × 10−2 °C. Considering simple structure and excellent spectral sensitivity, the proposed sensor can be applied in myriad fields to measure the temperature.
{"title":"Design and Analysis of Plasmonic Temperature Sensor Utilizing Photonic Crystal Fiber","authors":"Md. Kamrul Hasan, Md. M. Rahman, M. Anower, M. Rana, A. Paul, Kisalaya Chakrabatri","doi":"10.1109/TENSYMP50017.2020.9230804","DOIUrl":"https://doi.org/10.1109/TENSYMP50017.2020.9230804","url":null,"abstract":"In this paper, a simple geometric structured Photonic crystal fiber (PCF) based temperature sensor is proposed and analyzed theoretically. The designed sensor considered polydimethylsiloxane (PDMS) as a temperature dependent analyte to sense the variation of temperature with its surroundings. To enhance the sensitivity and avoid corrosion due to oxidation, gold (Au) film is used as plasmonic material. While analyzing the performance of the sensor, the finite element method (FEM) is utilized. Also, performance characterization is done altering the design parameters, e.g., pitch, air-holes diameter, and thickness of the gold layer. The results reveal a maximum possible spectral sensitivity of 4.67 nm/°C, with the detection range 30 °C to 90 °C. The sensor also exhibits a standard FOM valuing of 0.05838 /°C and a resolution of 3 × 10−2 °C. Considering simple structure and excellent spectral sensitivity, the proposed sensor can be applied in myriad fields to measure the temperature.","PeriodicalId":6721,"journal":{"name":"2020 IEEE Region 10 Symposium (TENSYMP)","volume":"313 ","pages":"1189-1192"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91456671","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 : 2020-06-05DOI: 10.1109/TENSYMP50017.2020.9230871
Masud Rana Basunia, Ismot Ara Pervin, Md. Al Mahmud, S. Saha, M. Arifuzzaman
The highest invading cancer among the women is breast cancer. Early detection of breast cancer is the higher chance of the patient being treated. In this study, we have proposed an ensemble method named stacking classifier which combines multiple classification techniques and efficaciously classifies the benign and malignant tumor. “Wisconsin Diagnosis Breast Cancer” dataset culled from the UC Irvine Machine Learning Repository has been used for our experiment. We applied different classification techniques over the dataset and tuned their parameters to improve accuracy. We chose the three best classifiers for our proposed method. Generally, our proposed Stacking classifier combined the results of those best classifiers using meta classifier and provided 97.20% accuracy for breast cancer prediction. Performance of different data mining approaches have been evaluated rigorously through different evaluation metrics.
{"title":"On Predicting and Analyzing Breast Cancer using Data Mining Approach","authors":"Masud Rana Basunia, Ismot Ara Pervin, Md. Al Mahmud, S. Saha, M. Arifuzzaman","doi":"10.1109/TENSYMP50017.2020.9230871","DOIUrl":"https://doi.org/10.1109/TENSYMP50017.2020.9230871","url":null,"abstract":"The highest invading cancer among the women is breast cancer. Early detection of breast cancer is the higher chance of the patient being treated. In this study, we have proposed an ensemble method named stacking classifier which combines multiple classification techniques and efficaciously classifies the benign and malignant tumor. “Wisconsin Diagnosis Breast Cancer” dataset culled from the UC Irvine Machine Learning Repository has been used for our experiment. We applied different classification techniques over the dataset and tuned their parameters to improve accuracy. We chose the three best classifiers for our proposed method. Generally, our proposed Stacking classifier combined the results of those best classifiers using meta classifier and provided 97.20% accuracy for breast cancer prediction. Performance of different data mining approaches have been evaluated rigorously through different evaluation metrics.","PeriodicalId":6721,"journal":{"name":"2020 IEEE Region 10 Symposium (TENSYMP)","volume":"1 1","pages":"1257-1260"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87535485","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 : 2020-06-05DOI: 10.1109/TENSYMP50017.2020.9230899
Shaikh Rezwan Rafid Ahmad, Samee Mohammad Sayeed, Zaziba Ahmed, Nusayer Masud Siddique, M. Parvez
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.
{"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}
Pub Date : 2020-06-05DOI: 10.1109/TENSYMP50017.2020.9230778
Md. Bahar Ullah
This paper describes CPU Based YOLO, a real time object detection model to run on Non-GPU computers that may facilitate the users of low configuration computer. There are a lot of well improved algorithms for object detection such as YOLO, Faster R-CNN, Fast R-CNN, R-CNN, Mask R-CNN, R-FCN, SSD, RetinaNet etc. YOLO is a Deep Neural Network algorithm for object detection which is most fast and accurate than most other algorithms. YOLO is designed for GPU based computers which should have above 12GB Graphics Card. In our model, we optimize YOLO with OpenCV such a way that real time object detection can be possible on CPU based Computers. Our model detects object from video in 10.12 – 16.29 FPS and with 80-99% confidence on several Non –GPU computers. CPU Based YOLO achieves 31.05% mAP.
{"title":"CPU Based YOLO: A Real Time Object Detection Algorithm","authors":"Md. Bahar Ullah","doi":"10.1109/TENSYMP50017.2020.9230778","DOIUrl":"https://doi.org/10.1109/TENSYMP50017.2020.9230778","url":null,"abstract":"This paper describes CPU Based YOLO, a real time object detection model to run on Non-GPU computers that may facilitate the users of low configuration computer. There are a lot of well improved algorithms for object detection such as YOLO, Faster R-CNN, Fast R-CNN, R-CNN, Mask R-CNN, R-FCN, SSD, RetinaNet etc. YOLO is a Deep Neural Network algorithm for object detection which is most fast and accurate than most other algorithms. YOLO is designed for GPU based computers which should have above 12GB Graphics Card. In our model, we optimize YOLO with OpenCV such a way that real time object detection can be possible on CPU based Computers. Our model detects object from video in 10.12 – 16.29 FPS and with 80-99% confidence on several Non –GPU computers. CPU Based YOLO achieves 31.05% mAP.","PeriodicalId":6721,"journal":{"name":"2020 IEEE Region 10 Symposium (TENSYMP)","volume":"1 1","pages":"552-555"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90403702","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 : 2020-06-05DOI: 10.1109/TENSYMP50017.2020.9231008
F. Shah, F. Ahmed, Sajib Kumar Saha Joy, Sifat Ahmed, Samir Sadek, Rimon Shil, M. H. Kabir
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.
{"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}
Pub Date : 2020-06-05DOI: 10.1109/TENSYMP50017.2020.9230906
M. Rahman, Shailee Mitra, M. Motalab, T. Rakib
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.
{"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}
Pub Date : 2020-06-05DOI: 10.1109/TENSYMP50017.2020.9230833
Md. Rezanul Haque, Saurav Das, Mohammad Rejwan Uddin, Md Saiful Islam Leon, M. Razzak
This paper presents the design and evaluates the system performance of one-kilowatt capacity asynchronous and synchronous buck converter based solar-powered charging systems for battery-driven electric vehicles. The dc motor-operated three-wheeler rickshaw was taken for testing the systems, where a battery bank containing four series-connected sub-colloid storage type batteries of each with a capacity of 12V, 120Ah has been used. PSIM simulation software has been used to evaluate the performances of these two types of battery charging systems. Hardware prototypes of these two types of charging systems have also been made and an experimental testbed comprising a 48V battery bank of 100Ah capacity with a charging current of 6A was performed. The experimental results have also been evaluated and compared.
{"title":"Performance Evaluation of 1kW Asynchronous and Synchronous Buck Converter-based Solar-powered Battery Charging System for Electric Vehicles","authors":"Md. Rezanul Haque, Saurav Das, Mohammad Rejwan Uddin, Md Saiful Islam Leon, M. Razzak","doi":"10.1109/TENSYMP50017.2020.9230833","DOIUrl":"https://doi.org/10.1109/TENSYMP50017.2020.9230833","url":null,"abstract":"This paper presents the design and evaluates the system performance of one-kilowatt capacity asynchronous and synchronous buck converter based solar-powered charging systems for battery-driven electric vehicles. The dc motor-operated three-wheeler rickshaw was taken for testing the systems, where a battery bank containing four series-connected sub-colloid storage type batteries of each with a capacity of 12V, 120Ah has been used. PSIM simulation software has been used to evaluate the performances of these two types of battery charging systems. Hardware prototypes of these two types of charging systems have also been made and an experimental testbed comprising a 48V battery bank of 100Ah capacity with a charging current of 6A was performed. The experimental results have also been evaluated and compared.","PeriodicalId":6721,"journal":{"name":"2020 IEEE Region 10 Symposium (TENSYMP)","volume":"34 1","pages":"770-773"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84985851","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 : 2020-06-05DOI: 10.1109/tensymp50017.2020.9230979
Sunanda Das, Sajal Basak Partha, Kazi Nasim Imtiaz Hasan
Sentence generation serves the process of predicting relevant words in a specific sequence. The purpose of this research is to come up with a method for generating sentences while maintaining proper grammatical structure. Here, we have implemented a sentence generation system based on Long Short-Term Memory (LSTM) architecture. Our system generally follows the basics of word embedding where words from the dataset get tokenized and turned into vector forms. These vectors are then processed and passed through a Long Short-Term Memory layer. Successive words get generated from the system after each iteration. This process winds up generating relevant words to form a sentence or a passage. The results of the system are pretty convincing compared to different existing methods.
{"title":"Sentence Generation using LSTM Based Deep Learning","authors":"Sunanda Das, Sajal Basak Partha, Kazi Nasim Imtiaz Hasan","doi":"10.1109/tensymp50017.2020.9230979","DOIUrl":"https://doi.org/10.1109/tensymp50017.2020.9230979","url":null,"abstract":"Sentence generation serves the process of predicting relevant words in a specific sequence. The purpose of this research is to come up with a method for generating sentences while maintaining proper grammatical structure. Here, we have implemented a sentence generation system based on Long Short-Term Memory (LSTM) architecture. Our system generally follows the basics of word embedding where words from the dataset get tokenized and turned into vector forms. These vectors are then processed and passed through a Long Short-Term Memory layer. Successive words get generated from the system after each iteration. This process winds up generating relevant words to form a sentence or a passage. The results of the system are pretty convincing compared to different existing methods.","PeriodicalId":6721,"journal":{"name":"2020 IEEE Region 10 Symposium (TENSYMP)","volume":"70 1","pages":"1070-1073"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90579928","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 : 2020-06-05DOI: 10.1109/TENSYMP50017.2020.9230771
J. T. Raj
The commercial use of neural networks has been greatly curbed by data privacy concerns. As long as the accumulation and use of private data is regarded necessary for integrating neural networks into products, consumers will be reluctant to use or allow access to any deep learning integrated product and producers will be equally deterred from leveraging deep learning for performance improvement. Federated learning was first introduced as a solution to this conundrum in a 2016 paper published by Google titled Communication-Efficient Learning of Deep Networks from Decentralized Data [1]. In this study, we examine how the performance of a decentralized image classifier compares to that of a centralized one. The performance of an image classifier trained across ten devices was compared to a model built with the same architecture but trained centrally on one corpus of training data. The outcome demonstrates that the decentralized model compares quite well to the centrally trained classifier in terms of accuracy, precision and recall.
{"title":"Building Decentralized Image Classifiers with Federated Learning","authors":"J. T. Raj","doi":"10.1109/TENSYMP50017.2020.9230771","DOIUrl":"https://doi.org/10.1109/TENSYMP50017.2020.9230771","url":null,"abstract":"The commercial use of neural networks has been greatly curbed by data privacy concerns. As long as the accumulation and use of private data is regarded necessary for integrating neural networks into products, consumers will be reluctant to use or allow access to any deep learning integrated product and producers will be equally deterred from leveraging deep learning for performance improvement. Federated learning was first introduced as a solution to this conundrum in a 2016 paper published by Google titled Communication-Efficient Learning of Deep Networks from Decentralized Data [1]. In this study, we examine how the performance of a decentralized image classifier compares to that of a centralized one. The performance of an image classifier trained across ten devices was compared to a model built with the same architecture but trained centrally on one corpus of training data. The outcome demonstrates that the decentralized model compares quite well to the centrally trained classifier in terms of accuracy, precision and recall.","PeriodicalId":6721,"journal":{"name":"2020 IEEE Region 10 Symposium (TENSYMP)","volume":"48 1 1","pages":"489-494"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90919064","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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