Alzheimer’s disease(AD)is a neurodevelopmental impairment that results in a person’s behavior,thinking,and memory loss.Themost common symptoms ofADare losingmemory and early aging.In addition to these,there are seve...Alzheimer’s disease(AD)is a neurodevelopmental impairment that results in a person’s behavior,thinking,and memory loss.Themost common symptoms ofADare losingmemory and early aging.In addition to these,there are several serious impacts ofAD.However,the impact ofADcanbemitigatedby early-stagedetection though it cannot be cured permanently.Early-stage detection is the most challenging task for controlling and mitigating the impact of AD.The study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to address the issue.To build a predictive model,open-source data was collected where five stages of images of AD were available as Cognitive Normal(CN),Early Mild Cognitive Impairment(EMCI),Mild Cognitive Impairment(MCI),Late Mild Cognitive Impairment(LMCI),and AD.Every stage of AD is considered as a class,and then the dataset was divided into three parts binary class,three class,and five class.In this research,we applied different preprocessing steps with augmentation techniques to efficiently identifyAD.It integrates a random oversampling technique to handle the imbalance problem from target classes,mitigating the model overfitting and biases.Then three machine learning classifiers,such as random forest(RF),K-Nearest neighbor(KNN),and support vector machine(SVM),and two deep learning methods,such as convolutional neuronal network(CNN)and artificial neural network(ANN)were applied on these datasets.After analyzing the performance of the used models and the datasets,it is found that CNN with binary class outperformed 88.20%accuracy.The result of the study indicates that the model is highly potential to detect AD in the initial phase.展开更多
Cervical Cancer(CC)is a rapidly growing disease among women throughout the world,especially in developed and developing countries.For this many women have died.Fortunately,it is curable if it can be diagnosed and dete...Cervical Cancer(CC)is a rapidly growing disease among women throughout the world,especially in developed and developing countries.For this many women have died.Fortunately,it is curable if it can be diagnosed and detected at an early stage and taken proper treatment.But the high cost,awareness,highly equipped diagnosis environment,and availability of screening tests is a major barrier to participating in screening or clinical test diagnoses to detect CC at an early stage.To solve this issue,the study focuses on building a deep learning-based automated system to diagnose CC in the early stage using cervix cell images.The system is designed using the YOLOv5(You Only Look Once Version 5)model,which is a deep learning method.To build the model,cervical cancer pap-smear test image datasets were collected from an open-source repository and these were labeled and preprocessed.Then the YOLOv5 models were applied to the labeled dataset to train the model.Four versions of the YOLOv5 model were applied in this study to find the best fit model for building the automated system to diagnose CC at an early stage.All of the model’s variations performed admirably.The model can effectively detect cervical cancerous cell,according to the findings of the experiments.In the medical field,our study will be quite useful.It can be a good option for radiologists and help them make the best selections possible.展开更多
Cardiovascular diseases are the most common cause of death worldwide over the last few decades in the developed as well as underdeveloped and developing countries. Early detection of cardiac diseases and continuous su...Cardiovascular diseases are the most common cause of death worldwide over the last few decades in the developed as well as underdeveloped and developing countries. Early detection of cardiac diseases and continuous supervision of clinicians can reduce the mortality rate. However, accurate detection of heart diseases in all cases and consultation of a patient for 24 hours by a doctor is not available since it requires more sapience, time and expertise. In this?study, a tentative design of a cloud-based heart disease prediction system had been proposed to detect impending heart disease using Machine learning techniques. For the accurate detection of the heart disease, an efficient machine learning technique should be used which had been derived from a distinctive analysis among several machine learning algorithms in a Java Based Open Access Data Mining Platform, WEKA. The proposed algorithm was validated using two widely used open-access database, where 10-fold cross-validation is applied in order to analyze the performance of heart disease detection. An accuracy level of 97.53% accuracy was found from the SVM algorithm along with sensitivity and specificity of 97.50% and 94.94%respectively. Moreover, to monitor the heart disease patient round-the-clock by his/her caretaker/doctor, a real-time patient monitoring system was developed and presented using Arduino, capable of sensing some real-time parameters such as body temperature, blood pressure, humidity, heartbeat. The developed system can transmit the recorded data to a central server which are updated every 10 seconds. As a result, the doctors can visualize the patient’s real-time sensor data by using the application and start live video streaming if instant medication is required. Another important feature of the proposed system was that as soon as any real-time parameter of the patient exceeds the threshold, the prescribed doctor is notified at once through GSM technology.展开更多
This work focuses on a brief discussion of new concepts of using smartphone sensors for 3D painting in virtual or augmented reality. Motivation of this research comes from the idea of using different types of sensors ...This work focuses on a brief discussion of new concepts of using smartphone sensors for 3D painting in virtual or augmented reality. Motivation of this research comes from the idea of using different types of sensors which exist in our smartphones such as accelerometer, gyroscope, magnetometer etc. to track the position for painting in virtual reality, like Google Tilt Brush, but cost effectively. Research studies till date on estimating position and localization and tracking have been thoroughly reviewed to find the appropriate algorithm which will provide accurate result with minimum drift error. Sensor fusion, Inertial Measurement Unit (IMU), MEMS inertial sensor, Kalman filter based global translational localization systems are studied. It is observed, prevailing approaches consist issues such as stability, random bias drift, noisy acceleration output, position estimation error, robustness or accuracy, cost effectiveness etc. Moreover, issues with motions that do not follow laws of physics, bandwidth, restrictive nature of assumptions, scale optimization for large space are noticed as well. Advantages of such smartphone sensor based position estimation approaches include, less memory demand, very fast operation, making them well suited for real time problems and embedded systems. Being independent of the size of the system, they can work effectively for high dimensional systems as well. Through study of these approaches it is observed, extended Kalman filter gives the highest accuracy with reduced requirement of excess hardware during tracking. It renders better and faster result when used in accelerometer sensor. With the aid of various software, error accuracy can be increased further as well.展开更多
This paper presents a new operational strategy for a large-scale wind farm (WF) which is composed of both fixed speed wind turbines with squirrel cage induction generators (FSWT-SCIGs) and variable speed wind turbines...This paper presents a new operational strategy for a large-scale wind farm (WF) which is composed of both fixed speed wind turbines with squirrel cage induction generators (FSWT-SCIGs) and variable speed wind turbines with permanent magnet synchronous generators (VSWT-PMSGs). FSWT-SCIGs suffer greatly from meeting the requirements of fault ride through (FRT), because they are largely dependent on reactive power. Integration of flexible ac transmission system (FACTS) devices is a solution to overcome that problem, though it definitely increases the overall cost. Therefore, in this paper, a new method is proposed to stabilize FSWT-SCIGs by using VSWT-PMSGs in a WF. This is achieved by injecting the reactive power to the grid during fault condition by controlling the grid side converter (GSC) of PMSG. The conventional proportional-integral (PI)-based cascaded controller is usually used for GSC which can inject small amount of reactive power during fault period. Thus, it cannot stabilize larger rating of SCIG. In this paper, a suitable fuzzy logic controller (FLC) is proposed in the cascaded controller of GSC of PMSG in order to increase reactive power injection and thus improve the FRT capability of WF during voltage dip situation due to severe network fault. To evaluate the proposed controller performance, simulation analyses are performed on a modified IEEE nine-bus system. Simulation results clearly show that the proposed method can be a cost-effective solution which can effectively stabilize the larger rating of SCIG compared to conventional PI based control strategy.展开更多
<span style="font-family:Verdana;">Develop</span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;&qu...<span style="font-family:Verdana;">Develop</span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ment</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> of renewable energy (RE) and mitigation of carbon dioxide, as the two largest climate action initiatives are the most challenging factors for new generation green data center (GDC). Reduction of conventional electricity consumption as well as cost of electricity (COE) with preferred quality</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">of service (QoS) has been recognized as the interesting research topic in Information and Communication Technology (ICT) sector. Moreover, it becomes challenging to design a large-scale sustainable GDC with standalone RE supply. This paper gives spotlight on hybrid energy supply solution for the GDC to reduce grid electricity usage and minimum net system cost. The proposed framework includes RE source such as solar photovoltaic, wind turbine and non-renewable energy sources as Disel Generator (DG) and Battery. A hybrid optimization model is designed using HOMER software for cost assessment and energy evaluation to validate the effectiveness of the suggested scheme focusing on eco-friendly implication.</span></span></span>展开更多
Growing energy demand,diminishing fossil fuel reserves and geopolitical tensions are serious concerns for any country’s energy strategy and security.These factors have a greater impact on developing countries,as many...Growing energy demand,diminishing fossil fuel reserves and geopolitical tensions are serious concerns for any country’s energy strategy and security.These factors have a greater impact on developing countries,as many of them rely largely on traditional energy resources.Cleaner energy generation is the viable alternative for mitigating these problems,as well as achieving energy independ-ence and tackling climate change.The article discusses planning and design optimization of a residential community microgrid based on multiple renewable resources.In particular,the design and techno-economic assessment of a grid-tied hybrid microgrid for meeting the electricity demand of an alluvial region,Urir Char,located in southern Bangladesh,was addressed.Hybrid Optimization of Multiple Energy Resources is used for the evaluation and it is supplemented by a fuzzy-logic-based load profile design strategy.In addition to the analysis,a predictive load-shifting-based demand management is also introduced.Several cases were considered for the studies and,after considering several criteria,a grid-tied system comprising a photovoltaic array,wind turbine and energy storage system was found to be the best fit for powering the loads.The suggested system reduces the life-cycle cost by 18.3%,the levelized cost of energy by 61.9%and emissions by 77.2%when compared with the grid-only option.Along with the microgrid design,cooking emissions and energy categorization were also discussed.展开更多
The coronavirus disease that outbreak in 2019 has caused various health issues.According to the WHO,the first positive case was detected in Bangladesh on 7th March 2020,but while writing this paper in June 2021,the to...The coronavirus disease that outbreak in 2019 has caused various health issues.According to the WHO,the first positive case was detected in Bangladesh on 7th March 2020,but while writing this paper in June 2021,the total confirmed,recovered,and death cases were 826922,766266 and 13118,respectively.Due to the emergence of COVID-19 in Bangladesh,the country is facing a major public health crisis.Unfortunately,the country does not have a comprehensive health policy to address this issue.This makes it hard to predict how the pandemic will affect the population.Machine learning techniques can help us detect the disease's spread.To predict the trend,parameters,risks,and to take preventive measure in Bangladesh;this work utilized the Recurrent Neural Networks based Deep Learning methodologies like LongShort-Term Memory.Here,we aim to predict the epidemic's progression for a period of more than a year under various scenarios in Bangladesh.We extracted the data for daily confirmed,recovered,and death cases from March 2020 to August 2021.The obtained Root Mean Square Error(RMSE)values of confirmed,recovered,and death cases indicates that our result is more accurate than other contemporary techniques.This study indicates that the LSTM model could be used effectively in predicting contagious diseases.The obtained results could help in explaining the seriousness of the situation,also mayhelp the authorities to take precautionary steps to control the situation.展开更多
基金funded in part by the Natural Sciences and Engineering Research Council of Canada(NSERC)through Project Number:IFP22UQU4170008DSR0056.
文摘Alzheimer’s disease(AD)is a neurodevelopmental impairment that results in a person’s behavior,thinking,and memory loss.Themost common symptoms ofADare losingmemory and early aging.In addition to these,there are several serious impacts ofAD.However,the impact ofADcanbemitigatedby early-stagedetection though it cannot be cured permanently.Early-stage detection is the most challenging task for controlling and mitigating the impact of AD.The study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to address the issue.To build a predictive model,open-source data was collected where five stages of images of AD were available as Cognitive Normal(CN),Early Mild Cognitive Impairment(EMCI),Mild Cognitive Impairment(MCI),Late Mild Cognitive Impairment(LMCI),and AD.Every stage of AD is considered as a class,and then the dataset was divided into three parts binary class,three class,and five class.In this research,we applied different preprocessing steps with augmentation techniques to efficiently identifyAD.It integrates a random oversampling technique to handle the imbalance problem from target classes,mitigating the model overfitting and biases.Then three machine learning classifiers,such as random forest(RF),K-Nearest neighbor(KNN),and support vector machine(SVM),and two deep learning methods,such as convolutional neuronal network(CNN)and artificial neural network(ANN)were applied on these datasets.After analyzing the performance of the used models and the datasets,it is found that CNN with binary class outperformed 88.20%accuracy.The result of the study indicates that the model is highly potential to detect AD in the initial phase.
基金The project funding number is 22UQU4170008DSR07the Natural Sciences and Engineering Research Council of Canada(NSERC).
文摘Cervical Cancer(CC)is a rapidly growing disease among women throughout the world,especially in developed and developing countries.For this many women have died.Fortunately,it is curable if it can be diagnosed and detected at an early stage and taken proper treatment.But the high cost,awareness,highly equipped diagnosis environment,and availability of screening tests is a major barrier to participating in screening or clinical test diagnoses to detect CC at an early stage.To solve this issue,the study focuses on building a deep learning-based automated system to diagnose CC in the early stage using cervix cell images.The system is designed using the YOLOv5(You Only Look Once Version 5)model,which is a deep learning method.To build the model,cervical cancer pap-smear test image datasets were collected from an open-source repository and these were labeled and preprocessed.Then the YOLOv5 models were applied to the labeled dataset to train the model.Four versions of the YOLOv5 model were applied in this study to find the best fit model for building the automated system to diagnose CC at an early stage.All of the model’s variations performed admirably.The model can effectively detect cervical cancerous cell,according to the findings of the experiments.In the medical field,our study will be quite useful.It can be a good option for radiologists and help them make the best selections possible.
文摘Cardiovascular diseases are the most common cause of death worldwide over the last few decades in the developed as well as underdeveloped and developing countries. Early detection of cardiac diseases and continuous supervision of clinicians can reduce the mortality rate. However, accurate detection of heart diseases in all cases and consultation of a patient for 24 hours by a doctor is not available since it requires more sapience, time and expertise. In this?study, a tentative design of a cloud-based heart disease prediction system had been proposed to detect impending heart disease using Machine learning techniques. For the accurate detection of the heart disease, an efficient machine learning technique should be used which had been derived from a distinctive analysis among several machine learning algorithms in a Java Based Open Access Data Mining Platform, WEKA. The proposed algorithm was validated using two widely used open-access database, where 10-fold cross-validation is applied in order to analyze the performance of heart disease detection. An accuracy level of 97.53% accuracy was found from the SVM algorithm along with sensitivity and specificity of 97.50% and 94.94%respectively. Moreover, to monitor the heart disease patient round-the-clock by his/her caretaker/doctor, a real-time patient monitoring system was developed and presented using Arduino, capable of sensing some real-time parameters such as body temperature, blood pressure, humidity, heartbeat. The developed system can transmit the recorded data to a central server which are updated every 10 seconds. As a result, the doctors can visualize the patient’s real-time sensor data by using the application and start live video streaming if instant medication is required. Another important feature of the proposed system was that as soon as any real-time parameter of the patient exceeds the threshold, the prescribed doctor is notified at once through GSM technology.
文摘This work focuses on a brief discussion of new concepts of using smartphone sensors for 3D painting in virtual or augmented reality. Motivation of this research comes from the idea of using different types of sensors which exist in our smartphones such as accelerometer, gyroscope, magnetometer etc. to track the position for painting in virtual reality, like Google Tilt Brush, but cost effectively. Research studies till date on estimating position and localization and tracking have been thoroughly reviewed to find the appropriate algorithm which will provide accurate result with minimum drift error. Sensor fusion, Inertial Measurement Unit (IMU), MEMS inertial sensor, Kalman filter based global translational localization systems are studied. It is observed, prevailing approaches consist issues such as stability, random bias drift, noisy acceleration output, position estimation error, robustness or accuracy, cost effectiveness etc. Moreover, issues with motions that do not follow laws of physics, bandwidth, restrictive nature of assumptions, scale optimization for large space are noticed as well. Advantages of such smartphone sensor based position estimation approaches include, less memory demand, very fast operation, making them well suited for real time problems and embedded systems. Being independent of the size of the system, they can work effectively for high dimensional systems as well. Through study of these approaches it is observed, extended Kalman filter gives the highest accuracy with reduced requirement of excess hardware during tracking. It renders better and faster result when used in accelerometer sensor. With the aid of various software, error accuracy can be increased further as well.
文摘This paper presents a new operational strategy for a large-scale wind farm (WF) which is composed of both fixed speed wind turbines with squirrel cage induction generators (FSWT-SCIGs) and variable speed wind turbines with permanent magnet synchronous generators (VSWT-PMSGs). FSWT-SCIGs suffer greatly from meeting the requirements of fault ride through (FRT), because they are largely dependent on reactive power. Integration of flexible ac transmission system (FACTS) devices is a solution to overcome that problem, though it definitely increases the overall cost. Therefore, in this paper, a new method is proposed to stabilize FSWT-SCIGs by using VSWT-PMSGs in a WF. This is achieved by injecting the reactive power to the grid during fault condition by controlling the grid side converter (GSC) of PMSG. The conventional proportional-integral (PI)-based cascaded controller is usually used for GSC which can inject small amount of reactive power during fault period. Thus, it cannot stabilize larger rating of SCIG. In this paper, a suitable fuzzy logic controller (FLC) is proposed in the cascaded controller of GSC of PMSG in order to increase reactive power injection and thus improve the FRT capability of WF during voltage dip situation due to severe network fault. To evaluate the proposed controller performance, simulation analyses are performed on a modified IEEE nine-bus system. Simulation results clearly show that the proposed method can be a cost-effective solution which can effectively stabilize the larger rating of SCIG compared to conventional PI based control strategy.
文摘<span style="font-family:Verdana;">Develop</span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ment</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> of renewable energy (RE) and mitigation of carbon dioxide, as the two largest climate action initiatives are the most challenging factors for new generation green data center (GDC). Reduction of conventional electricity consumption as well as cost of electricity (COE) with preferred quality</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">of service (QoS) has been recognized as the interesting research topic in Information and Communication Technology (ICT) sector. Moreover, it becomes challenging to design a large-scale sustainable GDC with standalone RE supply. This paper gives spotlight on hybrid energy supply solution for the GDC to reduce grid electricity usage and minimum net system cost. The proposed framework includes RE source such as solar photovoltaic, wind turbine and non-renewable energy sources as Disel Generator (DG) and Battery. A hybrid optimization model is designed using HOMER software for cost assessment and energy evaluation to validate the effectiveness of the suggested scheme focusing on eco-friendly implication.</span></span></span>
基金The data were obtained from the National Aeronautics and Space Administration(NASA)Langley Research Center Prediction of Worldwide Energy Resource(POWER)Project funded through the NASA Earth Science/Applied Science Program.The data were obtained from the POWER Project’s Hourly 2.0.0 version on 11 November 2022.
文摘Growing energy demand,diminishing fossil fuel reserves and geopolitical tensions are serious concerns for any country’s energy strategy and security.These factors have a greater impact on developing countries,as many of them rely largely on traditional energy resources.Cleaner energy generation is the viable alternative for mitigating these problems,as well as achieving energy independ-ence and tackling climate change.The article discusses planning and design optimization of a residential community microgrid based on multiple renewable resources.In particular,the design and techno-economic assessment of a grid-tied hybrid microgrid for meeting the electricity demand of an alluvial region,Urir Char,located in southern Bangladesh,was addressed.Hybrid Optimization of Multiple Energy Resources is used for the evaluation and it is supplemented by a fuzzy-logic-based load profile design strategy.In addition to the analysis,a predictive load-shifting-based demand management is also introduced.Several cases were considered for the studies and,after considering several criteria,a grid-tied system comprising a photovoltaic array,wind turbine and energy storage system was found to be the best fit for powering the loads.The suggested system reduces the life-cycle cost by 18.3%,the levelized cost of energy by 61.9%and emissions by 77.2%when compared with the grid-only option.Along with the microgrid design,cooking emissions and energy categorization were also discussed.
文摘The coronavirus disease that outbreak in 2019 has caused various health issues.According to the WHO,the first positive case was detected in Bangladesh on 7th March 2020,but while writing this paper in June 2021,the total confirmed,recovered,and death cases were 826922,766266 and 13118,respectively.Due to the emergence of COVID-19 in Bangladesh,the country is facing a major public health crisis.Unfortunately,the country does not have a comprehensive health policy to address this issue.This makes it hard to predict how the pandemic will affect the population.Machine learning techniques can help us detect the disease's spread.To predict the trend,parameters,risks,and to take preventive measure in Bangladesh;this work utilized the Recurrent Neural Networks based Deep Learning methodologies like LongShort-Term Memory.Here,we aim to predict the epidemic's progression for a period of more than a year under various scenarios in Bangladesh.We extracted the data for daily confirmed,recovered,and death cases from March 2020 to August 2021.The obtained Root Mean Square Error(RMSE)values of confirmed,recovered,and death cases indicates that our result is more accurate than other contemporary techniques.This study indicates that the LSTM model could be used effectively in predicting contagious diseases.The obtained results could help in explaining the seriousness of the situation,also mayhelp the authorities to take precautionary steps to control the situation.
