Emotion recognition is an important aspect of human interaction, and this ability of humans to interpret emotions based on facial expressions is a basic element for effective communication. Machine... Show More

Emotion recognition is an important aspect of human interaction, and this ability of humans to interpret emotions based on facial expressions is a basic element for effective communication. Machine learning can help automate this complicated task with the help of feature engineering. This work proposes some pipelines trained on the JAFFE dataset using feature extraction methods, namely principal component analysis (PCA) and local binary pattern (LBP) combined with Fisher discriminant ratio (FDR) as a feature selection method. In order to build a classification scheme capable of successfully identifying face images related to the six universal emotions and neutral expression, all possible combinations have been empirically analyzed. In the final model, PCA combined with FDR has been used on the support vector machine classifier with a linear kernel. The results obtained are encouraging and this work may also prove important for disciplines other than computer science such as for management purposes.

Supply chain management is the broad range of activities required to plan, control, and execute the flow of a product. As a less corruptible and more automated alternative to traditional databases... Show More

Supply chain management is the broad range of activities required to plan, control, and execute the flow of a product. As a less corruptible and more automated alternative to traditional databases, blockchains are well suited to the complicated record-keeping. However distributed database management system is a centralized software system; the blockchain technology can overcome the problem of synchronization between multiple databases; it also ensures that integrity problems are solved. In the proposed model, Ethereum blockchain is used to solve a few major supply chain problems to manage a distributed database. The model has incorporated techniques to predict the rise and fall of the demand for the medicine in the market by using machine learning algorithms such as linear regression and LSTM; also, the trend predicted by both the models has been compared. The result shows that while using linear regression the predicted trend is not very accurate and cannot trace the actual trend closely whereas BLSTM has performed well in predicting the trends of time series data.

To scale back the energy consumption, this paper proposed three algorithms: The first one is identifying the load balancing factors and redistribute the load. The second one is finding out the most... Show More

To scale back the energy consumption, this paper proposed three algorithms: The first one is identifying the load balancing factors and redistribute the load. The second one is finding out the most suitable server to assigning the task to the server, achieved by most efficient first fit algorithm (MEFFA), and the third algorithm is processing the task in the server in an efficient way by energy efficient virtual round robin (EEVRR) scheduling algorithm with FAT tree topology architecture. This EEVRR algorithm improves the quality of service via sending the task scheduling performance and cutting the delay in cloud data centers. It increases the energy efficiency by achieving the quality of service (QOS).

Service-oriented architecture is a widely accepted service used for supporting consolidation and integration functions under an enterprise system which are complex in nature but with an intelligent... Show More

Service-oriented architecture is a widely accepted service used for supporting consolidation and integration functions under an enterprise system which are complex in nature but with an intelligent framework which helps in integrating the services in an optimistic and dynamic manner for getting the task done. For any service-oriented architecture-based application, its services are the main components, as it requires service compositions for answering various requests. There exist many possible service compositions for completing a task. To find an optimum composition from those dynamically present during run time is another crucial aspect for the success of this architecture. The present research elaborated a novel idea for optimal composition of services in SOA or any other service-based system. This paper covers a case study along with the outcomes of the experiment which indicates the efficiency and validity of the proposed technique.

The role of artificial intelligence (AI) is becoming increasingly important in the field of banking and finance. It has come a long way, and the trend is likely to continue for some time in the... Show More

The role of artificial intelligence (AI) is becoming increasingly important in the field of banking and finance. It has come a long way, and the trend is likely to continue for some time in the future as well. This research study reviews the role of artificial intelligence and use of technology in the finance and banking industry and how AI has changed the way the banks and financial institutions do their business. Customer engagement is one of the most critical parts of the finance and banking industry. This research proposes an artificial intelligence and natural language processing (NLP)-based chatbot model for advising the customers of Islamic banking and finance. Presently, the proposed chatbot is the first chatbot that will help the Islamic finance and banking customers to interact in real time and get Islamic financial advice based on the principles of Sharia related to individual's financial needs.