The wide acceptability of Advanced Encryption Standard (AES) as the most efficient of all of the symmetric cryptographic techniques has further opened it up to more attacks. Efforts that were aimed at securing information while using AES is still being undermined by the activities of attackers This has further necessitated the need for researchers to come up with ways of enhancing the strength of AES. This article presents an enhanced AES algorithm that was achieved by modifying its SubBytes and ShiftRows transformations. The SubBytes transformation is modified to be round key dependent, while the ShiftRows transformation is randomized. The rationale behind the modification is to make the two transformations round key dependent, so that a single bit change in the key will produce a significant change in the cipher text. The conventional and modified AES algorithms are both implemented and evaluated in terms avalanche effect and execution time. The modified AES algorithm achieved an avalanche effect of 57.81% as compared to 50.78 recorded with the conventional AES. However, with 16, 32, 64, and 128 plain text bytes, the modified AES recorded an execution time of 0.18, 0.31, 0.46, and 0.59 ms, respectively. This is slightly higher than the results obtained with the conventional AES. Though a slightly higher execution time in milliseconds was recorded with the modified AES, the improved encryption and decryption strength via the avalanche effects measured is a desirable feat.

Agriculture has become the bedrock of some growing economy in the world but the discovery of crude oil and other resources in a developing country like Nigeria has led to the extreme decline in the practice. Many youths now have either low or no interested in agriculture and the majority of the people that are actively practicing are the older generation. This study revisits the impact of education on students interest in agriculture as a professional career. The study was carried out in an agriculture-based uni-versity with a state of the art equipment. Our findings show that 64% of agricul-ture students who participated in this survey are willing to pursue agriculture-related careers. Likewise, there is a significant relationship between the student’s gender and farm ownership, with 84% of male students likelier to own farms. Also, our results have shown that students in higher levels have more interests in agriculture compared to students at lower levels and this invariably increases the possibilities of their pursuing agriculture-related careers or businesses.

In this work, we designed and developed a Virtual Reality guided tour mobile app for Landmark University farms, LF-ViT. We were motivated by the need to circumvent the problem of bio-security caused by incessant visit to the farm by visitors, tourists or customers. The guided tour was implemented using the storytelling technique. Other technical details of the design and implementation process are discussed

Machine learning techniques such as deep learning methods have produced promising results in medical images analysis. This work proposes a user-friendly system that utilizes deep learning techniques for detecting and diagnosing diseases using medical images. This includes the design of CAD-based project that can reduce human factor-related errors while performing manual screening of medical images. The system accepts medical images as input and performs segmentation of the images. Segmentation process analyzes and identifies the region of interest (ROI) of diseases from medical images. Analyzing and segmentation of medical images has assisted in the diagnosis and monitoring of some diseases. Diseases such as skin cancer, age-related fovea degeneration, diabetic retinopathy, glaucoma, hypertension, arteriosclerosis, and choroidal neovascularization can be effectively managed by the analysis of skin lesion and retinal vessels images. The proposed system was evaluated on diseases such as diabetic retinopathy from retina images and skin cancer from dermoscopic images.

The development of an accident detection system is a crucial step towards improving emergency response times, saving lives and achieving the ambitious projection of the United Nations General Assembly to drastically reduce the global fatality rate of road traffic crashes by half by the year 2030. It is also cardinal to the attainment of the United Nation's SDG 11 goal of making cities and human settlements inclusive, safe, resilient and sustainable. In this study we present a preliminary development of an AI-powered Accident Alert System (AI-PaaS). The system has four modules namely, sensors module, detection module, registration module and messaging module. The detection module is powered by sensing technology and the Hidden Markov Model to intelligently and correctly detect that an accidents sound. The MPU 6050 containing both accelerometer and gyroscope is also integrated to detect any sharp variation in the acceleration and angular vis-a-vis a predefined threshold value. Once an accident has been detected, the messaging module is triggered to communicate first responders and the victims' pre-registered kin. Preliminary results are presented. The system can potentially reduce road accident fatality by providing accurate and timely location-based information to emergency service providers.