The losses of developing countries from landmines accidents are very large. Thus, the need for new techniques to improve the efficiency of Landmines tracking systems is evident. In the recent years, many of research efforts have been directed to develop new and improved landmine detection methods. However, the increased costs of improving these methods led to drive up their prices. Thus they will not be available to the general public. The aim of this paper is to find a cheap and an effective method to help people for protecting and warning them from landmines risk during practiced their daily lives. In this context, this paper presents the design and development of framework for a Wireless Intelligent Landmines Tracking System (IWLTS) using mobile phone based on GPS and fuzzy logic. Proposed framework is really very helpful for the users who living near mine affected areas to track their children and themselves through Smart phones from landmines risk.

The integration of artificial intelligence techniques is becoming necessary for environmental risk assessment systems and decision-making, particularly under the limitations of individual intelligence techniques. A comprehensive architecture, called Environmental Risk Assessment Architecture (ERAA), was developed to capitalise the strengths of intelligent computing techniques and compensate for the limitations of individual intelligence techniques. This architecture is based on the combination of three well-known techniques, namely, artificial neural networks, fuzzy logic and genetic algorithm. The proposed architecture is implemented in the form of two models, namely, the neuro-fuzzy risk assessment model and the safe path selection model. Fuzzy arithmetic operations on fuzzy numbers and artificial neural networks with a back-propagation learning algorithm were used to represent the structure of the neuro-fuzzy risk assessment model, whereas genetic algorithms were used to develop the safe path selection model. Two methods were used to validate the proposed architecture, that is, the analytical method was used to validate the neuro-fuzzy risk assessment model and the safe path selection model, whereas the experimental method was used to evaluate the prototype. Results of the neuro-fuzzy risk assessment model were compared with the results obtained using individual intelligence techniques, such as the Mamdani and Sugeno models. By contrast, the results of the safe path selection model were compared with the results obtained using Dijkstra's algorithm and the Floyd-Warshall algorithm. The results obtained using the neuro-fuzzy risk assessment model show that the model exhibits a satisfactory performance in environmental risk assessment and an improvement in results with a difference rate of up to 10.8% compared with the Mamdani and Sugeno models. By contrast, the running time of the safe path selection model (96 µs) is shorter than the running times of Dijkstra's algorithm and the Floyd- Warshall algorithm (150 µs and 184 µs, respectively). The architecture proposed in this research provides the opportunity for combining intelligent computing techniques in a comprehensive architecture. Thus, the proposed architecture can be applied by developers of environmental risk assessment as a tool for developing applications on tracking and environmental risk assessment systems.

During the World War I ,II over fifty countries in the world today have been inherited a legacy of antipersonnel landmines and unexploded ordnance (UXO) which represents a major threat to lives, and hinders reconstruction and development efforts. Landmines have specific properties that make it harder to detect .Therefore; these properties lead landmine detectors to become more complex. Many examples can be found to address the increasing complexity of Landmines detection; unfortunately, these new techniques are high of cost and need experts to deal with it. Many developing countries face financial difficulties to get advanced technologies for detection landmines such as Robotic systems, this due to their high cost, use and maintenance difficulties which makes them unaffordable to these countries. The safety of operators, transportability, ease of maintenance and operation are the most factors that must take into consideration to improve the applicability and effectiveness of landmines tracking systems.The aim of the study is to proposed architecture of Intelligent Wireless Landmines Tracking System (IWLTS) with new decision model based on fuzzy logic. To find an affordable, light and easy to use alternative which meet users’ needs to protect and warn them from the risk of landmines during practice their lives, we suggested the design and development of Fuzzy Inference Model for IWLTS using Smart Phone. Fuzzy model require three step which are definitions of Linguistic Variable and fuzzy sets, determine fuzzy rules and the process of Fuzzy Inference. Designed Fuzzy Inference Model gives both: Landmine risk value in percentage and alert to avoid that risk.