Revenue generation has a positive and significant effect on the growth of any business. The existing work process in Nigerian Airspace Management Agency (NAMA) billing system creates the loophole for revenue deficit. The need to enhance the billing system of this organization prompted the need for this study. Primary data were collected from NAMA station at Muritala International Airport and its corporate headquarters. The data include the process and time of attending to airlines or their agents (which includes arrival time, service time, departure time, Waiting time etc.), Six months financial statement of NAMA MMIA, Debt profile details for debtors and number of airlines attended to per day. These data formed the input for developing the SimPy model to bring together process based discrete events. Validation was done by comparing the simulated results with the real results using statistical t-test at p less than 0.05 (p<0.05). 15 airlines were considered for 5 months (assuming 30 days per month), making an average of 6 landings per day per route which represents 3 flights within a route per day. The rate is ₦7,000 per one-hour flight per route. The simulation results reveal that with the proposed model ₦23,948,044 was generated as against ₦12,486,680 of the existing system. This represents 191.788% improvement. This result is for Murhitala Muhammed International Airport (local wing) only. The python simulation package (SimPy) used, introduced approval levels for the various department, thereby ensuring no departments' function was undermined. This model is useful for Airspace management, in that it reduces deficit without any negative impact on the safety and service delivery of airlines.

With competition intensifying across service-oriented business, customer satisfaction is the name of the game. If customers did not perceive the orgnisation well, the company will definitely run out of business. This paper analysed the data collected from customer service unit of a particular bank using Jupyter note -a framework under Python programming language to know customers need and to improve customer satisfaction. From the analysis, it was discovered that there were six (6) purposes of customers' visitation to the banking hall i.e six services were rendered by the customer service unit of the bank, they are: account authentication; account list; authenticate user; balance check; fund transfer and register customer. All services except Balance Check and Fund Transfer are at their peak in the morning by 8am. Also, user authentication has highest queue length of 450 customers in the morning (8:00am). It was also discovered that customers call for services in the early part of the day and keeps decreasing until the break period when customers will be able to visit bank to make their transactions. Among all the services rendered by the bank, account authentication has the highest average queue length, followed by fund transfer, customers registration, user authentication, balance check and account listing with values 141. 000, 131.600, 104.100, 103.500, 96.625 and 51.500 respectively for the month of April. This study was able to learn how customers really feel about the services rendered by the bank and the bank also has been able to know the level of the customers satisfaction, how to improve on the services render and where the immediate focus need to be in order to accelerate the bank growth because satisfied customers will buy more, stay longer and share their positive experience.

One of the distinguishing features of an individual is handwriting and it has been established that everyone has unique handwriting differing from one another. This unique feature evolves with time and is influenced by a variety of factors such as gender, physical and mental health, and age among others. Also, the recent development in using individual peculiar features for forensic investigations in banks and other allied institutions either for signature verification or identification spelled the need to develop a smart system that can predict the age range with offline handwriting recognition. It is on this background that this research employed an optimized deep learning technique comprising of Gravitational Search Algorithm and Convolutional Neural Network (CNN-GSA) for offline handwriting age range prediction. A local database was populated with samples of the signature captured with a digital camera (5 megapixels), the CNN was employed for feature extraction, GSA was utilized to select optimal CNN parameters used for classification while the combined CNNGSA was utilized for an offline handwritten-based age prediction system. The performance evaluation of the approach proposed was done using sensitivity, specificity, precision, false positive rate, recognition accuracy, and processing time for all the variants, while the superiority of the system developed was ascertained by comparing it with the original CNN.