The advancement of both the fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has enabled the development of effective automatic systems for analyzing human behavior. It is possible to recognize gestures, which are frequently used by people to communicate information non-verbally, by studying hand movements. So, the main contribution of this research is the collected dataset, which is taken from open-source videos of the relevant subjects that contain actions that depict confidence levels. The dataset contains high-quality frames with minimal bias and less noise. Secondly, we have chosen the domain of confidence determination during social issues such as interviews, discussions, or criminal investigations. Thirdly, the proposed model is a combination of two high-performing models, i.e., CNN (GoogLeNet) and LSTM. GoogLeNet is the state-of-the-art architecture for hand detection and gesture recognition. LSTM prevents the loss of information by keeping temporal data. So the combination of these two outperformed during the training and testing process. This study presents a method to recognize different categories of Self-Efficacy by performing multi-class classification based on the current situation of hand movements using visual data processing and feature extraction. The proposed architecture pre-processes the sequence of images collected from different scenarios, including humans, and their quality frames are extracted. These frames are then processed to extract and analyze the features regarding their body joints and hand position and classify them into four different classes related to efficacy, i.e., confidence, cooperation, confusion, and uncomfortable. The features are extracted using a combination framework of customized Convolutional Neural Network (CNN) layers with Long Short-Term Memory (LSTM) for feature extraction and classification. Remarkable results have been achieved from this study representing 90.48% accuracy with effective recognition of human body gestures through deep learning approaches.

Cloud computing is one of the leading technology in IT and computer science domain. Business IT infrastructures are equipping themselves with modern regime of clouds. In the presence of several opportunities, selection criteria decision becomes vital when there is no supporting information available. Global clouds also need evaluation and assessment from its users that what they think about and how new ones could make their selection as per their needs. Recommended systems were built to propose best services using customer's feedback, applying quality of service parameters, assigning scores, trust worthiness and clustering in different forms and models. These techniques did not record and use interrelationships between the services that is true impact of service utilization. In the proposed approach, service association factor calculates value of interrelations among services used by the end user. An intelligent leaning based recommendation system is developed for assisting users to select services on their respective preferences. This technique is evaluated on leading service providers and results show that learning base system performs well on all types of cloud models.

Wireless communication has become the primary means of electronic communication around the world. Particularly, we focus on the radio propagation conditions of a wireless sensor network (WSN), our case study, where common methods exist for measurements of WSN’s performance. However, these methods are expensive and not fully automated, and therefore they have severe drawbacks and limitations. Thus, this paper describes an automatic, simple, and fast-answer system for solving practical radio-communication problems by resorting to two branches of artificial intelligence: machine learning and knowledge-based systems (KBS). The non-incremental unsupervised learning module classifies a dataset into clusters that are then computed in order to induce concepts, which are specific features shared by every dataset within a cluster. These concepts serve to construct implication rules being the knowledge used by the KBS in charge of answering queries about WSN in radio propagation conditions. Furthermore, given that WSN is a dynamic environment, our system also incorporates an incremental supervised learning module in order to process a new received reduced dataset, and potentially to induce new concepts and update the knowledge base of implication rules. Such process is performed unchanging the inference engine module of our KBS. Finally, we provide illustrating examples of our approach’s abilities for query-answering, highlighting that its response time fulfills restricted real-world conditions thanks to the conception of polynomial-complexity algorithms on which our system runs, and which are among our contributions.

Generally, it is seen that the disable people most commonly blinds in our society are not encouraged to work with normal community due to lack of vision, communication, and understandings. Fortunately, this is no longer a big issue. By use of modern technology and tools we can develop flexible environments in which blinds work collaboratively with normal ones and share their knowledge with each other to produce a robust and worthy task. In this research, we solve the issue by proposing a platform for different categories of people (fully/partially impaired and normal). The locally/remotely distributed participants work together (online or offline) in a Web based co-authoring environment to produce a shared document. The system presents a simplified design of mechanisms to share, modify, and update document and their resources in a consistent and controlled way such that each group member can easily able to know the updates and be well aware about the current state of the common object. The main goal of the research is to make the handicap persons self-reliant, confident, and independent so that they can play their equal part in every group activity of the society. The approach is evaluated by means of experiments conducted with blinds and normal authors in a distributed environment.