Tajbia Karim

Diabetic retinopathy is known to be a hazardous eye disease which can cause vision loss or blindness in long run. Recently, this disease has spread dangerously among people who are suffering from diabetes. In this paper, a diabetic retinopathy identification technique has been proposed by utilizing rear end eye image i.e., fundus image obtained from the retina of the patient. This method involves digital image processing on fundus image. Micro-aneurysm, which is considered as first stage of diabetic retinopathy has been diagnosed using MATLAB Neural Network Pattern Recognition Tool (NPRTOOL). The proposed method has been compared to two other existing methods- Naive Bayes method and SVM classifier method. In this research, NPRTOOL tool has been used for calculation and experimental work. Significant improvement has been found in terms of sensitivity and accuracy compared to the aforementioned existing methods.

This research represents a design of an electrical water vehicle by using the freely available renewable energy source known as wind. The main objective of this design is to develop a wind renewable power station inside a water vehicle. To achieve this goal vertical axis wind turbine, permanent magnet motor/generator, dc motor/generator, boost converter have been used. As shipping industries are facing a major problem of reducing the fuel dependency and carbon-dioxide emissions, this design will limit the fuel consumptions and decrease the carbon-dioxide emissions.

Handwritten character recognition is a very tough task in case of complex shaped alphabet set like Bangla script. As optical character recognition (OCR) has a huge application in mobile devices, model needs to be suitable for mobile applications. Many researches have been performed in this arena but none of them achieved satisfactory accuracy or could not detect more than 200 characters. MobileNet is a state of art (convolutional neural network) CNN architecture which is designed for mobile devices as it requires less computing power. In this paper, we used MobileNet for handwritten character recognition. It has achieved 96.46% accuracy in recognizing 231 classes (171 compound, 50 basic and 10 numerals), 96.17% accuracy in 171 compound character classes, 98.37% accuracy in 50 basic character classes and 99.56% accuracy in 10 numeral character classes.

With the increasing demand of energy, the energy production is not that much sufficient and that’s why it has become an important issue to make accurate prediction of energy consumption for efficient management of energy. Hence appropriate demand side forecasting has a great economical worth. Objective of our paper is to render representations of a suitable time series forecasting model using autoregressive integrated moving average (ARIMA) and Holt Winters model for the energy consumption of Ohio/Kentucky and also predict the accuracy considering different periods (daily, weekly, monthly). We apply these two models and observe that Holt Winters model outperforms ARIMA model in each (daily, weekly and monthly observations) of the cases. We also make a comparison among few other existing analyses of time series forecasting and find out that the mean absolute percentage error (MASE) of Holt Winters model is least considering the monthly data.

In the online world, especially in the social media platform most of us write without much regard to correct spelling and grammar. The spelling mistakes are much larger in proportion when it comes to Bangla language. In our paper, we presented a method for error detection and correction in Bangla words' spellings. Our system could detect a misspelled Bangla word and provide two following services-suggesting correct spellings for the word and correcting the word. We had used Norvig's algorithm for the purpose but instead of using probabilities of the words to prepare the suggestions and corrections, we had used Jaro-Winkler distance. The previous works done in this field for Bangla language are either very slow or offers less accuracy. Our system successfully achieved a 97% accuracy when evaluated with 1000 Bangla words.

This paper represents design of Speech Generating Device (SGD) to help mute, deaf and paralyzed people communicate with others. Speech generation is controlled by physical gesture and using the gestures particular voice commands are played in a loudspeaker. To recognize physical gestures Flex Sensors, Accelerometer, Gyroscope and other Inertial Measurement Unit (IMU) sensors can be used. Accelerometer sensor is the most economic one which has been used for our purpose.

Individuals with hearing and speaking impairment communicate using sign language. The movement of hand, body and expressions of face are the means by which the people, who are unable to hear and speak, can communicate. Bangla sign alphabets are formed with one or two hand movements. There are some features which differentiates the signs. To detect and recognize the signs, analyzing its shape and comparing its features is necessary. This paper aims to propose a model and build a computer systemthat can recognize Bangla Sign Lanugage alphabets and translate them to corresponding Bangla letters by means of deep convolutional neural network (CNN). CNN has been introduced in this model in form of a pre-trained model called "MobileNet" which produced an average accuracy of 95.71% in recognizing 36 Bangla Sign Language alphabets. Keywords: Accuracy Bangla sign language (BSL) CNN Convolution MobileNet This is an open access article under the CC BY-SA license.

Every year thousands of people lose their lives due to traffic accidents. Road accidents, especially the ones on the highways are the most fatal ones. Accidents not only cut peoples' lives short but also cause intense financial loss to the country. Many people who survive disastrous accidents are often left with critical injury or paralysis. Particularly in Bangladesh majority of the drivers are minimally educated. They do not have sufficient knowledge and tend to ignore the traffic rules often. As a result the roads are filled with careless drivers. Consequently most of the accidents on the highways and city roads occur due to the lack of awareness of the drivers. Additionally, many of the roads are poorly lit which makes it difficult to drive in unfavorable weather. An autonomous lane detection system can play an important role as a solution to the problem by assisting the driver in seeing the lane clearly. It can also generate warning to the driver in case of an unintentional...