Communities globally experience devastating effects, high monetary loss and loss of lives due to incidents of flood and other hazards. Inadequate information and awareness of flood hazard make the management of flood risks arduous and challenging. This paper proposes a hybridized analytic approach via unsupervised and supervised learning methodologies, for the discovery of pieces of knowledge, clustering and prediction of flood severity levels (FSL). A two-staged unsupervised learning based on [Formula: see text]-means and self-organizing maps (SOM) was performed on the unlabeled flood dataset. [Formula: see text]-means based on silhouette criterion discovered top three representatives of the optimal numbers of clusters inherent in the flood dataset. Experts’ judgment favored four clusters, while Squared Euclidean distance was the best performing distance measure. SOM provided cluster visuals of the input attributes within the four different groups and transformed the dataset into a labeled one. A 5-layered Adaptive Neuro Fuzzy Inference System (ANFIS) driven by hybrid learning algorithm was applied to classify and predict FSL. ANFIS optimized by Genetic Algorithm (GA) produced root mean squared error (RMSE) of 0.323 and Error Standard Deviation of 0.408 while Particle Swarm Optimized ANFIS model produced 0.288 as the RMSE, depicting 11% improvement when compared with GA optimized model. The result shows significant improvement in the classification and prediction of flood risks using single ML tool.
The complexity and the dynamism of oil spillages make it difficult for planners and responders to produce robust plans towards their management. There is need for an understanding of the nature, sources, impact and responses required to prevent or control their occurrence. This paper develops an intelligent hybrid system driven by Sugeno-Type Adaptive Neuro Fuzzy Inference System (ANFIS) for the identification, extraction and classification of oil spillage risk patterns. Dataset consisting of 1008 records was used for training, validation and testing of the system. Result of sensitivity analysis shows that Cause, Location and Type of spilled oil have cumulative significance of 85.1%. Optimal weights of Neural Network (NN) were determined via Genetic Algorithm with hybrid encoding scheme. The Mean Squared Error (MSE) of NN training is 0.2405. NN training, validation and testing results yielded R > 0.839 in all cases indicating a strong linear relationship between each output and target data. Rule pruning was performed with support (15%) and confidence (10%) minimum thresholds and antecedent-size of 3. The performance of the ANFIS was evaluated with eight different types of membership functions (MFs) and two learning algorithms. The model with triangular MF gave the best performance among all other given models while hybrid-learning algorithm performed better than back propagation algorithm. The ANFIS model reported in the paper adopted triangular MF and hybrid learning algorithm for the predication and classification of oil spillage risk patterns. Average training and testing MSE of the model is 0.414315 and 0.221402 respectively. The knowledge mining results show that ANFIS based systems provide satisfactory results in the prediction and classification of oil spillage risk patterns.
Mobile agent is becoming an emerging tool for monitoring and managing computer networks. Its usefulness in this regard emanates from its ability to communicate with other agents and devices, and navigate a computer network to collect data and take actions autonomously. In this research, an investigation of the use of an agent-based system to monitor the software tools on the nodes of a computer network is carried out. The proposed framework adopts a multi-agent system approach combining a static server agent with a mobile monitor agent which move around and extract data from each node via the server agent. The system was tested in a computer network environment which is characterized by a Windows NT. The programming and mobility infrastructure is the C#, an object-oriented and multifunctional programming scheme. The performance of the proposed agentbased system and Remote MONitoring (RMON) system are simulated and the results obtained show the cost of service, query time and delay overhead is lower in the agent-based system than that of RMON.
This paper reports the findings from
The use of fingerprint for human identity management has been on the rise lately. Reasons adduced for this include its high level of uniqueness, availability, consistency and universality. The task of human identity management based on fingerprint technology involves a number of processes which include enrolment, enhancement, feature and singular points detection and extraction and pattern matching. Detection and extraction of genuine and reliable feature and singular points are paramount for reliable pattern matching. The limitations of some existing fingerprint singular point detection algorithms include inaccurate detection and failure with some fingerprint pattern and poor quality images. In this paper, a modified Poincare Index fingerprint singular point detection algorithm is proposed for resolving these limitations. The results of the experimental study of the new algorithm on Dataset DB1 of FVC2000 standard fingerprint database show that the new algorithm reliably and adequately detected singular points from fingerprints of all patterns and qualities.
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