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Stakeholder satisfaction is the main motive of the software industry. Consumer and producer satisfaction means achieving a software-quality product. There are so many techniques, methods, and models are present for the software-quality prediction. Computational intelligence is also playing a crucial role in the prediction of quality characteristics. This paper gives a new optimal mathematical model for the prediction of the degree of stakeholder satisfaction (Q). Optimal models validate the real data using the relationship impacts of various quality attributes. It uses the equations of constraints. The optimal model gives the maximum and minimum values for Q. Constraints constitutes of software quality characteristics. In the given case study example, the idle value of Q is 30 but using optimal model it gives the maximum optimal value of Q = 22.788020075 for xLAB IT consulting services on their project In-Reg Molecule Registration using the MATLAB. It means if there is any change in the value of any software quality characteristics, then it will decrease the value of Q. It proves that the given result is an optimal solution. INDEX TERMS ISO/IEC 9126, software quality, degree of customer satisfaction, reliability, reusability.

The COVID-19 has resulted in one of the world’s most significant worldwide lock-downs, affecting human mental health. Therefore, emotion recognition is becoming one of the essential research areas among various world researchers. Treatment that is efficacious and diagnosed early for negative emotions is the only way to save people from mental health problems. Genetic programming, a very important research area of artificial intelligence, proves its potential in almost every field. Therefore, in this study, a genetic program-based feature selection (FSGP) technique is proposed. A fourteen-channel EEG device gives 70 features for the input brain signal; with the help of GP, all the irrelevant and redundant features are separated, and 32 relevant features are selected. The proposed model achieves a classification accuracy of 85% that outmatches other prior works.

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Software engineering is the process of developing software by utilizing applications of computer engineering. In the present day, predicting the reliability of the software system become a recent issue and an attractive issue for the research area in the field of software engineering. Different techniques have been applied to estimate and predict the reliability of a system. To make new software from the beginning is a difficult task. Component-Based Software Engineering (CBSE) helps in minimizing these efforts in making new software because it utilizes factors like reusability, component dependency, and component interaction that results in decreasing complexity of the system. Soft computing may be applied to estimate reliability. A new model is proposed to estimate the reliability of Component-based Software (CBS) using series and parallel reliability models and later on, the proposed component-based software reliability model is evaluated using two soft computing techniques-Fuzzy Logic and PSO. The experimental results conclude that the proposed reliability model has a lower error rate in predicting CBSE reliability as compared to reliability prediction utilizing fuzzy logic and PSO.