Short Message Service (SMS) is a feature of a mobile phone that enable convenient and instant way of sending electronic messages between users. As SMS usage increases fraudulent text messages, known as spam, are becoming more common. Spam SMS may result in leaking personal information, invasion of privacy or accessing unauthorized data from mobile devices. Users of mobile phones can mistakingly give away personal information with the assumption that they are sharing it with the right recipients. This work propose a SMS spam detection method that combines convolutional neural network (CNN) and long short term memory (LSTM) deep learning algorithms. The CNN is used for feature extraction while the LSTM classifies the message. The SMS spam dataset, collected from online repository, is used to train the model. Word embeddings is used to vectorize the words in the message to make it suitable for the model. The result obtained from the implementation outperforms other machine learning algorithms with an accuracy of 99.77%.

Intelligent Transportation Systems (ITS) are built on top of self-organizing networks, known as Vehicular Ad hoc Networks (VANET). In VANET, each vehicle equipped with communication devices represents a node and is allowed to send and receive safety messages through wireless communication channels. These messages are either periodic (beacons) or event-driven. Beacons are transmitted periodically while the event-driven messages are generated when an abnormal condition or an imminent danger is detected. The event-driven messages should be delivered to neighbouring nodes with high reliability and limit time as a single delayed or lost message could result in loss of lives. In dense network, the periodic messages broadcast lead to broadcast storm/blind flooding problem in VANETs. It is very important to keep the communication channel free from congestion in order to ensure timely and reliable delivery of event-driven safety messages. This study presents a review of existing congestion control schemes for VANETs with the aim of discussing the contributions and drawbacks of the algorithms.

Vehicular ad hoc network (VANET) is a self-organized, multi-purpose, service-oriented communication network that enables communication between vehicles and between vehicles and roadside infrastructures for the purpose of exchanging messages. In a dense traffic scenario, the message traffic may generate a load higher than the available capacity of the transmission medium leading to channel congestion problem. This situation leads to a rise in packet loss rates and transmission delay. Some existing congestion control schemes adapt the transmission power, transmission rate, and contention window parameters by making comparison with neighboring values through classical logic. However, the approach does not consider points between two close parameter values. This work uses fuzzy logic to improve the adaptation process of the network contention window parameter. The proposed scheme achieved a 15% higher in-packet delivery ratio and 10ms faster transmission compared with related work in terms end-to-end delay.

Public transportation provides opportunities for mobility, access to basic services, work and studying. Generally, commuters often experience waiting at a transport terminal for transport controllers to get information about transport facilities (means of transportation). In order to reduce the waiting time at transport terminals, an automated enquiry system becomes necessary. The transport enquiry system not only provide bus details but also helps in travel planning and saving of enormous timing of the user, which would have been spent in waiting at the bus station. The aim of this research work is to develop a voice based automated transport enquiry system. The voice application is important to help people especially those with educational inefficiency and visual impairments, have access to the functionality of the transport enquiry system. A test case of FUTA shuttle system was used. The evaluation of the system performance is based on three metrics such as response time, efficiency and functionality.

Africa has long been a place for successful mineral and oil and gas exploration; however, the diversity of geology, technical innovation, and opportunity make it a unique place. A single special section cannot hope to capture the breadth of the geophysical activity going on today somewhere on the continent. But we hope that with this issue we at least make a dent in the subject by offering a good variety of papers from both hard rock and hydrocarbon exploration.