This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today's world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system will first perform image pre-processing stages to prepare data for training using a convolutional neural network. After this processing, the input document is segmented using line, word and character segmentation. The proposed system get the accuracy during the character segmentation up to 86%. Then these segmented characters are sent to a convolutional neural network for their recognition. The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset. The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%, and for validation that accuracy slightly decreases with 90.42%.
The development of socially interactive agents emerged as a challenging task due to paradigm shift from behavioral to motivational based agents. In current nascent scenario, machines may have their own motives and goals like humans, which subsequently enhance their capability to improve their communication during any social interaction. Moreover single general purpose learning is not applicable in every social interaction. These conditions improve attention and perception based subjective learning mechanism for agents to make better cooperation with human during any social interaction. In this paper, we propose a cognitive model of attention driven preconscious perception with subjective learning and social-human interaction for machines to improve their communications.
Cloud computing is providing IT services to its customer based on Service level agreements (SLAs). It is important for cloud service providers to provide reliable Quality of service (QoS) and to maintain SLAs accountability. Cloud service providers need to predict possible service violations before the emergence of an issue to perform remedial actions for it. Cloud users' major concerns; the factors for service reliability are based on response time, accessibility, availability, and speed. In this paper, we, therefore, experiment with the parallel mutant-Particle swarm optimization (PSO) for the detection and predictions of QoS violations in terms of response time, speed, accessibility, and availability. This paper also compares Simple-PSO and Parallel Mutant-PSO. In simulation results, it is observed that the proposed Parallel Mutant-PSO solution for cloud QoS violation prediction achieves 94% accuracy which is many accurate results and is computationally the fastest technique in comparison of conventional PSO technique.
Purpose: The goal of this research was to investigate the difficulties that deaf parents have when it comes to the academic performance of their hearing children. Methodology: Case studies based on qualitative paradigms were used for this study. Participants in the research were parents who were deaf themselves but whose children had normal hearing. The sample comprises of 14 deaf couples from two divisions of Punjab—Lahore and Gujranwala. The data collection method consisted of a self-developed interview schedule with open-ended questions. Thematic analysis, a qualitative method, was used to analyze the data. Findings: Deaf parents have to deal with a number of obstacles, the most significant of which are communication barriers, attitudes they confront, and misunderstandings held by the community as a whole, all of which limit their capacity to participate in their children's academic lives who have normal hearing. Implications: It was suggested to the various stakeholders that they should be required to play their respective contributing roles in the process of reducing the constraints that restrict the engagement of parents in the academics of their children on a consistent basis, beginning with the most fundamental level.
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