Covid CT Net: A Transfer Learning Approach for Identifying Corona Virus from CT Scans
Smaranjit Ghose and Suhrid Datta, SRM Institute of Science and Technology, India
The pandemic of COVID-19 has been rapidly spreading across the globe since it first surfaced in the Wuhan province of China. Several governments are forced to have nationwide lockdowns due to the progressive increase in daily number of cases. The hospitals and other medical facilities are facing difficulties to cope with the overwhelming number of patients they can provide support due to the shortage in the number of required medical professionals and resources for meeting this demand. While the vaccine to cure this disease is still on the way, early diagnosis of patients and putting them in quarantine has become a cumbersome task too. In this study, we propose to build an Artificial Intelligence based system for classifying patients as Covid-19 positive or negative within a few seconds by using their chest CT Scans. We use a transfer learning approach to build our classifier model using a dataset obtained from openly available sources. This work is meant to assist medical professionals in saving hours of their time for the diagnosis of the Corona virus using chest radiographs and not intended to be the solo way of diagnosis.
Covid-19, Deep Learning, CT Scans, Deep Convolutional Neural Networks, computer tomography scans.
The Validity of the ICSID Arbitration Clauses Included in Investment Contracts Concluded via Smart Contracts
Elnur Karimov, PhD Candidate at the Institue of Social Sciences, Istanbul University, Istanbul, Turkey
This study has described the blockchain technology and its areas of application, the definition and form of smart contracts and their differences from traditional types of contracts. It has also discussed the validity of arbitration clauses included in smart contracts in the light of the International Convention on the Settlement of Investment Disputes (ICSID Convention) and other texts of the ICSID related to the investment arbitration, decisions of the ICSID tribunals and doctrine. Although it is hard to claim today that even the institutional arbitration centres such as the ICSID possess the required technical capacity to interact with smart contracts, this study has concluded that taking into account the level of flexibility of the condition of the 'consent in writing' interpreted in several ICSID tribunal decisions on the jurisdiction, it is possible in theory to conclude a valid arbitration agreement in the form of the smart contract which can be written in codes and self-executed. Besides, this study has revisited the ICSID model arbitration clauses and based on these clauses, suggested a new arbitration clause suitable to the smart contract.
Blockchain, Smart Contracts, ICSID Arbitration, Arbitration Clause.
Survey on Prediction of Diabetes using Classification Algorithms
Pawan Toralkar and Nagaraj Vernekar, Department of Computer Science and Engineering, Goa College of Engineering, India
The potential of Data mining methods can be used to benefit predictions on medical data. The focus of this research paper is to evaluate various data mining methods used in prediction of diabetes.Diabetes mellitus,commonly known as diabetes is a group of diseases which results in high sugar level in the blood which may have a drastic effect such macro vascular and micro vascular complications. Diabetes diagnosed by traditional method such as physical and chemical test may result in inaccurate outputs. To overcome this limitation we make the prediction of disease using a different Data Mining algorithm.
Diabetes mellitus, Normalization, Clustering, Classification, K-mean, Decision Tree, SVM.