Perspective of International Law on Maritime Dispute: Case Between Kenya and Somalia

The research analysed the maritime dispute between Kenya and Somalia under the International law perspective. Both states have been experiencing maritime disputes over maritime boundaries of more than 100,000 sq km of seabed in the waters of the Indian Ocean. They began to clash after Somalia accusing Kenya of illegally granting exploration rights to resources in the waters to multinational companies, Total and Eni. As Kenya declared, the waters of the East African Coast are one of the hottest oil exploration prospects in the world, and the contested area has hydrocarbon reserves. The research method is normative legal research. Accordingly, the nature of the research was descriptive-qualitative with data collection techniques by conducting a literature study. The research shows that maritime boundary dispute has worsened diplomatic relations between Kenya and Somalia. Prior to bringing the case to the International Court of Justice (ICJ), the two states agreed to resolve the dispute through bilateral negotiations. However, the case was still unsettled. Therefore, Somalia decided to bring the case before the Court

Diagnostic Classification of Cases of Canine Leishmaniasis Using Machine Learning

Proposal techniques that reduce financial costs in the diagnosis and treatment of animal diseases are welcome. This work uses some machine learning techniques to classify whether or not cases of canine visceral leishmaniasis are present by physical examinations. For validation of the method, four machine learning models were chosen: K-nearest neighbor, Naïve Bayes, support vector machine and logistic regression models. The tests were performed on three hundred and forty dogs, using eighteen characteristics of the animal and the ELISA (enzyme-linked immunosorbent assay) serological test as validation. Logistic regression achieved the best metrics: Accuracy of 75%, sensitivity of 84%, specificity of 67%, a positive likelihood ratio of 2.53 and a negative likelihood ratio of 0.23, showing a positive relationship in the evaluation between the true positives and rejecting the cases of false negatives