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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.

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.

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.

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.

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.

Detailed analysis based on histogram stretching for image contrast adjustment has been performed. However unprocessed image includes unwanted noise which needs to be reduced for performing the stretching accurately. The level of noise reduction greatly effects the contrast adjustment and varies from images to images depending on the intensity of noise itself. This paper discusses about image contrast adjustment technique along with the algorithm. Results are analyzed corresponding to images of different quality based on histogram stretching considering different level for the noise reduction. General Terms Histogram stretching, Visual quality, Image contrast

Underwater object detection has a considerable impact in the field of ocean technology. This paper presents a techniqueto detect a submerged object based on the medium's spectral attenuation coefficient by applying Beer-Lambert Law. The value of this coefficient is a function of the medium's composition, depth of the object,and wavelength of incident light. Experiments were performed in a swimming pool using objects of different colors at different depths to validate the mathematical model for object detection.