An enhanced binary bat and Markov clustering algorithms to improve event detection for heterogeneous news text documents

Event Detection (ED) works on identifying events from various types of data. Building an ED model for news text documents greatly helps decision-makers in various disciplines in improving their strategies. However, identifying and summarizing events from such data is a non-trivial task due to the large volume of published heterogeneous news text documents. Such documents create a high-dimensional feature space that influences the overall performance of the baseline methods in ED model. To address such a problem, this research presents an enhanced ED model that includes improved methods for the crucial phases of the ED model such as Feature Selection (FS), ED, and summarization. This work focuses on the FS problem by automatically detecting events through a novel wrapper FS method based on Adapted Binary Bat Algorithm (ABBA) and Adapted Markov Clustering Algorithm (AMCL), termed ABBA-AMCL. These adaptive techniques were developed to overcome the premature convergence in BBA and fast convergence rate in MCL. Furthermore, this study proposes four summarizing methods to generate informative summaries. The enhanced ED model was tested on 10 benchmark datasets and 2 Facebook news datasets. The effectiveness of ABBA-AMCL was compared to 8 FS methods based on meta-heuristic algorithms and 6 graph-based ED methods. The empirical and statistical results proved that ABBAAMCL surpassed other methods on most datasets. The key representative features demonstrated that ABBA-AMCL method successfully detects real-world events from Facebook news datasets with 0.96 Precision and 1 Recall for dataset 11, while for dataset 12, the Precision is 1 and Recall is 0.76. To conclude, the novel ABBA-AMCL presented in this research has successfully bridged the research gap and resolved the curse of high dimensionality feature space for heterogeneous news text documents. Hence, the enhanced ED model can organize news documents into distinct events and provide policymakers with valuable information for decision making

Probabilistic and artificial intelligence modelling of drought and agricultural crop yield in Pakistan

Pakistan is a drought-prone, agricultural nation with hydro-meteorological imbalances that increase the scarcity of water resources, thus, constraining water availability and leading major risks to the agricultural productivity sector and food security. Rainfall and drought are imperative matters of consideration, both for hydrological and agricultural applications. The aim of this doctoral thesis is to advance new knowledge in designing hybridized probabilistic and artificial intelligence forecasts models for rainfall, drought and crop yield within the agricultural hubs in Pakistan. The choice of these study regions is a strategic decision, to focus on precision agriculture given the importance of rainfall and drought events on agricultural crops in socioeconomic activities of Pakistan. The outcomes of this PhD contribute to efficient modelling of seasonal rainfall, drought and crop yield to assist farmers and other stakeholders to promote more strategic decisions for better management of climate risk for agriculturalreliant nations

Using Bat Algorithm with Levy Walk to Solve Directing Orbits of Chaotic Systems

Part 1: Machine LearningInternational audienceBat algorithm is a novel swarm intelligent version by simulating the bat seeking behavior. However, due to the poor exploitation, the performance is not well. Therefore, a new variant named bat algorithm with Gaussian walk is proposed in which the original uniform sample manner is replaced by one Gaussian sample. In this paper, we want to further investigate the performance with Levy sampling manner. To test its performance, three other variants of bat algorithms are employed to compare, simulation results show our modification is superior to other algorithms