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Recent studies state that, for a person with autism spectrum disorder, learning and improvement is often seen in environments where technological tools are involved. A robot is an excellent tool to be used in therapy and teaching. It can transform teaching methods, not just in the classrooms but also in the in-house clinical practices. With the rapid advancement in deep learning techniques, robots became more capable of handling human behaviour. In this paper, we present a cost-efficient, socially designed robot called `Tinku’, developed to assist in teaching special needs children. `Tinku’ is low cost but is full of features and has the ability to produce human-like expressions. Its design is inspired by the widely accepted animated character `WALL-E’. Its capabilities include offline speech processing and computer vision—we used light object detection models, such as Yolo v3-tiny and single shot detector (SSD)—for obstacle avoidance, non-verbal communication, expressing emotions in an anthropomorphic way, etc. It uses an onboard deep learning technique to localize the objects in the scene and uses the information for semantic perception. We have developed several lessons for training using these features. A sample lesson about brushing is discussed to show the robot’s capabilities. Tinku is cute, and loaded with lots of features, and the management of all the processes is mind-blowing. It is developed in the supervision of clinical experts and its condition for application is taken care of. A small survey on the appearance is also discussed. More importantly, it is tested on small children for the acceptance of the technology and compatibility in terms of voice interaction. It helps autistic kids using state-of-the-art deep learning models. Autism Spectral disorders are being increasingly identified today’s world. The studies show that children are prone to interact with technology more comfortably than a with human instructor. To fulfil this demand, we presented a cost-effective solution in the form of a robot with some common lessons for the training of an autism-affected child.

In the present work, we propose a novel method utilizing only a decoder for generation of pseudo-examples, which has shown great success in image classification tasks. The proposed method is particularly constructive when the data are in a limited quantity used for semi-supervised learning (SSL) or few-shot learning (FSL). While most of the previous works have used an autoencoder to improve the classification performance for SSL, using a single autoencoder may generate confusing pseudo-examples that could degrade the classifier’s performance. On the other hand, various models that utilize encoder–decoder architecture for sample generation can significantly increase computational overhead. To address the issues mentioned above, we propose an efficient means of generating pseudo-examples by using only the generator (decoder) network separately for each class that has shown to be effective for both SSL and FSL. In our approach, the decoder is trained for each class sample using random noise, and multiple samples are generated using the trained decoder. Our generator-based approach outperforms previous state-of-the-art SSL and FSL approaches. In addition, we released the Urdu digits dataset consisting of 10,000 images, including 8000 training and 2000 test images collected through three different methods for purposes of diversity. Furthermore, we explored the effectiveness of our proposed method on the Urdu digits dataset by using both SSL and FSL, which demonstrated improvement of 3.04% and 1.50% in terms of average accuracy, respectively, illustrating the superiority of the proposed method compared to the current state-of-the-art models.

The competent software architecture plays a crucial role in the difficult task of big data processing for SQL and NoSQL databases. SQL databases were created to organize data and allow for horizontal expansion. NoSQL databases, on the other hand, support horizontal scalability and can efficiently process large amounts of unstructured data. Organizational needs determine which paradigm is appropriate, yet selecting the best option is not always easy. Differences in database design are what set SQL and NoSQL databases apart. Each NoSQL database type also consistently employs a mixed-model approach. Therefore, it is challenging for cloud users to transfer their data among different cloud storage services (CSPs). There are several different paradigms being monitored by the various cloud platforms (IaaS, PaaS, SaaS, and DBaaS). The purpose of this SLR is to examine the articles that address cloud data portability and interoperability, as well as the software architectures of SQL and NoSQL databases. Numerous studies comparing the capabilities of SQL and NoSQL of databases, particularly Oracle RDBMS and NoSQL Document Database (MongoDB), in terms of scale, performance, availability, consistency, and sharding, were presented as part of the state of the art. Research indicates that NoSQL databases, with their specifically tailored structures, may be the best option for big data analytics, while SQL databases are best suited for online transaction processing (OLTP) purposes.

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