Deep learning techniques have gained significant importance among artificial intelligence techniques for any computing applications. Among them, deep convolutional neural networks (DCNNs) is one of the widely used deep learning networks for any practical applications. The accuracy is generally high and the manual feature extraction process is not necessary in these networks. However, the high accuracy is achieved at the cost of huge computational complexity. The complexity in DCNN is mainly due to: 1) increased number of layers between input and output layers and 2) two set of parameters (one set of filter coefficients and another set of weights) in the fully connected network need to be adjusted. In this paper, the second aspect is targeted to reduce the computational complexity of conventional DCNN. Suitable modifications are performed in the training algorithm to reduce the number of parameter adjustments. The weight adjustment process in the fully connected layer is completely eliminated in the proposed modified approach. Instead, a simple assignment process is used to find the weights of this fully connected layer. Thus, the computational complexity is significantly reduced in the proposed approach. The application of modified DCNN is explored in the context of magnetic resonance brain tumor image classification. Abnormal brain tumor images from four different classes are used in this paper. The experimental results show promising results for the proposed approach. INDEX TERMS Deep learning, convolutional neural network, brain images, image classification.

Analyzing the sentiments of people from social media content through text, speech, and images is becoming vital in a variety of applications. Many existing research studies on sentiment analysis rely on textual data, and similar to the sharing of text, users of social media share more photographs and videos. Compared to text, images are said to exhibit the sentiments in a much better way. So, there is an urge to build a sentiment analysis model based on images from social media. In our work, we employed different transfer learning models, including the VGG-19, ResNet50V2, and DenseNet-121 models, to perform sentiment analysis based on images. They were fine-tuned by freezing and unfreezing some of the layers, and their performance was boosted by applying regularization techniques. We used the Twitter-based images available in the Crowdflower dataset, which contains URLs of images with their sentiment polarities. Our work also presents a comparative analysis of these pre-trained models in the prediction of image sentiments on our dataset. The accuracies of our fine-tuned transfer learning models involving VGG-19, ResNet50V2, and DenseNet-121 are 0.73, 0.75, and 0.89, respectively. When compared to previous attempts at visual sentiment analysis, which used a variety of machine and deep learning techniques, our model had an improved accuracy by about 5% to 10%. According to the findings, the fine-tuned DenseNet-121 model outperformed the VGG-19 and ResNet50V2 models in image sentiment prediction.

Historical maps classification has become an important application in today’s scenario of everchanging land boundaries. Historical map changes include the change in boundaries of cities/states, vegetation regions, water bodies and so forth. Change detection in these regions are mainly carried out via satellite images. Hence, an extensive knowledge on satellite image processing is necessary for historical map classification applications. An exhaustive analysis on the merits and demerits of many satellite image processing methods are discussed in this paper. Though several computational methods are available, different methods perform differently for the various satellite image processing applications. Wrong selection of methods will lead to inferior results for a specific application. This work highlights the methods and the suitable satellite imaging methods associated with these applications. Several comparative analyses are also performed in this work to show the suitability of several methods. This work will help support the selection of innovative solutions for the different problems associated with satellite image processing applications.

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Integrating MOOCs in Blended Courses Authors: Carmen Holotescu, Gabriela Grosseck Numerous recent studies appreciate that "MOOCs bring an impetus of reform, research and innovation to the Academy" (DBIS, 2013). Even though MOOCs are usually developed and delivered as independent online courses, experiments to wrap formal university courses around existing MOOCs are reported by teachers and researchers in different articles (Bruff et al., 2013). This paper describes a different approach, in which the participation of students in different MOOCs was integrated in a blended course run on a social mobile LMS. The topics of MOOCs delivered on different platforms and having different characteristics were connected with the fall 2013 undergraduate course of Web Programming. The main parts of this study deal with: - The reasons to integrate MOOCs in the university course - How the course was designed, how the students' activities on different MOOCs platforms were assessed and integrated in the course scenario - The results of a survey that evaluates students experiences related to MOOCs: a number of MOOCs features were assessed (Holotescu, Grosseck and Cretu, 2013); also answers to a few problems are analysed: o Did the participation in MOOCs support students to clarify and expand the course issues? o What are students' suggestions for a more active participation in MOOCs? o Comparing the learning scenarios of MOOCs with the Web Programming blended course how the course and its virtual space can be improved? o Do the students consider that the MOOCs phenomenom is important for professional and personal development? The conclusions of the paper can be used by other teachers/instructors for integrating MOOCs in the courses they deliver/facilitate. References: Bruff, D. O., Fisher, D. H., McEwen, K. E., & Smith, B. E. (2013). Wrapping a MOOC: Student Perceptions of an Experiment in Blended Learning. Journal of Online Learning & Teaching, 9(2). https://my.vanderbilt.edu/douglasfisher/files/2013/06/JOLTPaperFinal6-9-2013.pdf Department for Business, Innovation and Skills, London. (2013). The Maturing of the MOOC. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/240193/13-1173-maturing-of-the-mooc.pdf Holotescu, C., Cretu, V., & Grosseck, G. (2013). MOOC'a Anatomy: Microblogging as the MOOC's Control Center. Conference Proceedings of "eLearning and Software for Education" (eLSE) (No. 02, pp. 312-319), Bucharest, April 2013. Hill, P. (2012). Online Educational Delivery Models: A Descriptive View. Educause Review, 47(6), 84-86. http://www.educause.edu/ir/library/pdf/ERM1263.pdf