The role of an automatic licensed plate detection system (ALPDS) cannot be over-emphasized in the world today. The need for an automated system for vehicle license plate number recognition is important for security challenges. Therefore, this paper provides a smart system for vehicle license number recognition using Computer Vision. The system was trained using images of vehicles license numbers as training data. The training images were first annotated using the Visual Graphic Generator (VGG) annotation tool, after the annotation process, the trained images were pre-processed using the OpenCV library for conversion and masking of images. TesseractOCR was then used in extracting just texts from the images. The pre-processed and segmented images were then used in training the Mask R-CNN from a pre-trained weight. The result of the proposed system shows how the Mask R-CNN model was trained in ten training steps. The mask R-CNN model obtained accuracy and a loss value for each training step. The mask R-CNN model was evaluated using both training and test data. For the training and testing data, the Mask R-CNN was evaluated in terms of accuracy and loss. The evaluation was done using graphs. The results from the graph show that the Mask R-CNN had a better accuracy result in both training and testing data. The accuracy for training data was that of 95.25% and the accuracy for the testing data was 97.69%. For real-time vehicle license plate number recognition, we deployed our proposed model to the web. Here, we built a web application that allows real-time surveillance video.  Our model was tested on different vehicles in the car park. The result of the mask R-CNN on the test shows how the Mask R-CNN model was used in not just capturing and extracting the vehicle’s license plate number but predicting the characters that appeared on the vehicle’s license plate number. We also compared our proposed system with another existing system. The comparison was done in terms of accuracy, loss, and precision. The result of our proposed model gave us an accuracy of 97.69%, which is higher than the existing system (85%). This study can further be improved by using the Internet of Things in performing live video streaming and also providing a database system that will be storing the predicted vehicle numbers for cars that are detected.

Internet of Things is the interconnectivity between things, individuals and cloud administrations by means of web, which empowers new plans of action. Because of these exchanges, immense volumes of information are smartly created and is shipped off cloud-based server through web; the information is being handled and broken down, bringing about significant and convenient activities for observing the car parking. The serious issue that is arising currently at a worldwide scale and developing dramatically is the gridlock issue brought about by vehicles. A worldwide scale and developing dramatically is the gridlock issue brought about by vehicles. Among that, finding a better parking sparking space in urban areas has become a major problem with an increase of the numbers of vehicles on a daily bases. Therefore making it difficult in having a better and safe parking spot. The system proposes an intelligent smart parking system using computer vision and internet of things. The proposed system starts by acquiring a dataset. The dataset is made up images of various vehicles, which was collected from the faculty of science car park at the Rivers State University, Port Harcourt, Rivers State Nigeria. We proposed two methods for vehicle/parking slot detection. The first method is the use of convolution neural network algorithm which is used with a haar cascade classifier in detection of multiple vehicles in a single picture and video, and put rectangular boxes  on identified vehicles. This first method obtained an accuracy of 99.80%. In the second method, we made use of a Mask R-CNN, here we download a pre-trained model weights which was trained on a coco dataset to identify various objects in videos and pictures. The Mask R-CNN model was used to identify various vehicles by putting a bounding box on each of the vehicle detected, but one of the problem of the Mask R-CNN is that it quite slow in training, and it could not really detect all vehicles tested on a high quality high definition video. In summary our, trained model was able to detect vehicles and parking slot on high quality video and it consumes lesser graphic card.

The problems of privacy and security is becoming a major challenge when it comes to the distributed systems, federated machine learning system especially when data are been transmitted or learned on a network , this necessitated the reasons for this research work which is all about wireless federated machine learning process using a Raspberry Pi. The Raspberry Pi 4 is a single hardware board with built in Linux operating system. We used data set of names from nine (9) different languages and then develop a training model using recurrent neural network to train this names compare to the names in the existing language like French, Scottish to predict if the names are from any of this language, this is done wirelessly with the Wi-Fi network in a federated machine learning environment for experimental setup with PySft’s that is installed in the python environment. The system was able to predict that name from which the language it originate from, the methodology that is implore in the research work is the Rapid Application Development (RAD). The benefits of this system are to ensure privacy, reduces the computing power, ensure real time learning and most importantly it is cost effective.

Urbanization has significantly increased over the last two centuries. In year 1800 only 2% of people lived in cities, while in year 1900 this percent increased to 12%. Recent studies indicate that in year 2008 more than 50% of the world population lived in urban areas, with this percentage expected to reach 75% by year 2030. Urban land cover occupies only 2% or 3% of the earth surface, yet it has been recognized that urban growth is associated with many socioeconomic and environmental problems. For example, impervious surfaces that result from urbanization dramatically increase peak discharges associated with storm and snowmelt events, which in turn makes more likely downstream flooding as storm waters exceed stream channel capacities. Urbanization is a very important aspect in country’s development, that’s why this thesis presents urban growth development in Port Harcourt city. The thesis proposed a model in predicting the urban expansion in terms of population growth and land use expansion. The model was trained on a satellite imagery of greater Port Harcourt city. The satellite imagery covers Port Harcourt, Obio/Akpor, Eleme, Etchem, Oyibo, and Omumma local government area in Rivers state. The model was trained using group method of data handling. The result of the model shows a greater change in land use for grater Port Harcourt City, it also shows that by 2035, there will be an increase in pollution for about 5,449,213. The is to say that greater Port Harcourt city will have over 5 million population increase.

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