Forest fires have become a major threat around the world, causing many negative impacts on human beings and forest ecosystems. Even though rapid climatic changes arising from high environmental pollution, greenhouse effects, etc. have caused this situation, a higher percentage of forest fires occur due to human activities. Therefore, to minimize the destruction caused by forest fires, the need to detect forest fires at their initial stage is needed. This paper proposes a model that can be used to detect forest fires at the initial stage using a wireless sensor network. Furthermore, to acquire more accurate fire detection, a machine learning regression model is proposed. Moreover, thorough attention is given to sensor node design and node placement in the forest to be fitted in the forest environment to minimize the damage and harmful effects caused by wild animals, weather conditions, etc. to the system.

The vision of this research paper is that the mobile phone is aware of its user's motion state and surroundings and modifies its behaviour especially the characteristics of Location-Based-Services based on this information. In the research it is implemented, and evaluated a methodology which can identify individual user, states. This learning is expected to occur online and not expected the requirement of any external supervision. The proposed system relies on hidden markov modelling and Log Likelihood Method. The implementation of the methodology is performed by first training the hidden markov model for the required number of states by an intended network trace. The log likelihood value of the data for each hidden markov model in the set is computed and the motion state is identified by choosing the hidden markov model that produced the highest value. The results of simulations carried out in WCDMA environment indicate that the proposed method is able to assist to create a meaningful user context model at various propagation conditions defined by both 3rd Generation Partnership Project (3GPP) and Wireless World Initiative New Radio (WINNER) propagation scenarios while only requiring a network trace without having an integrated sensor onboard cellular phone or any other wearable sensor device.

Commuters lose a lot of time and effort due to the inefficiency of traffic management. Although nowadays most of the processes are automated, it seems a speed violation detection is the least focused area apart from using speed guns which the RADAR may make mistakes and yet, doing such will benefit people by saving their time and let them escape from the troublesome situations. To address this issue, a real-time solution by fully automating the process of detecting the speed violation and the license plates of the offenders is proposed in this paper. A vehicle approaching a specific area will be automatically identified and tracked from a reference starting point. Within the covered range of the camera according to the traffic density present at that instance, the maximum speed for a vehicle is estimated and the vehicles that exceed the stipulated limit are identified as a violation. The core part of the proposed system is license plate recognition. To properly extract the license plate with the best view to proceed with the identification process is another problem that needs to be focused on. We utilized deep neural networks in a novel way for the aforesaid purpose. As these neural networks consist of numerous parameters, we utilized GPU for processing to gain smoothness in real-time. Using our novel segmentation free license plate identification method which utilizes object detection principle to fully capture the speed violation along with its offender. Numerous field trials proved that the proposed methodology provides far superior performance levels compared to the conventional systems and the other existing methodologies, which will certainly cater to the demanding requirements of Transportation 4.0.