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.

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.

Security is crucial all around. Access into institutions, examination centers, organizations or even estates ought to be controlled and closely monitored through a verification system. The method of authenticating a student for an examination has an obvious problem such as presentation of fake clearance card, impersonation and so on and the unethical manner associated with the examination is a grim issue that requires the stakeholders in academic area to seek for alternative means of authenticating student for examination, because the manual paper-based clearance process is fundamentally flawed. Sequel to that, a dependable and effective system is designed to tackle the issues of the convectional technique. The system will verify the understudy using fingerprint biometrics technique and generate a report which can serve as an attendance.