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This review is focused on video flame and smoke based fire detection algorithms for both indoor and outdoor environments. It analyzes and discusses them in a taxonomical manner for the last two decades. These are mainly based on handcraft features with or without classifiers and deep learning approaches. The separate treatment is provided for detecting flames and smoke. Their static and dynamic characteristics are elaborated for the handcraft feature approach. The blending of the obtained features from these characteristics is the focus of most of the research and these concepts are analyzed critically. A fusion of both visible and thermal images leading to multi-fusion and multimodal approaches have conversed. It is a step towards obtaining accurate detection results and how the handcraft feature approach tackles the problems of flame and smoke detection, as well as their weaknesses are discussed which are still not solved. Some of these weaknesses can be tackled by developing a technology based on artificial intelligence named deep-learning. Its taxonomical literature study with a focus on the flame and smoke detection is presented. The strengths and weaknesses of this approach are discussed with possible solutions. The latest trend in literature which focuses on the hybrid approach utilizing both handcraft feature, and deep learning approaches is discussed. This approach aims to minimize the weaknesses still present in the current systems.

In this paper, our aim was to the control the Rotary crane by using Fuzzy logic. For this one must obtain the mathematical model of the crane. After obtaining the model, the dynamics of the crane is identified. The Rotary crane is to be controlled in such a manner to increase the system speed without compromising the stability and safety of the whole system. Also the load is transferred to desired location accurately. The main advantage of Fuzzy based control is that no parameter identification is required as compare to other control strategies available. There are some applications where the conventional control strategies cannot provide the desired response up to certain level. But the fuzzy logic based control is capable of enhance the response, since it has learning capability which helps to create the accurate rules for each separate conditions. Also this fuzzy based control will reduce oscillations of payload to prevent hazards for people and equipments in the work place, industries.

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