'Kill Two Million of Them': Institutionalised Hate Speech, Impunity and 21st Century Atrocities in India

Hate speech and incitement have been instrumental in atrocity crimes that have occurred in India, even prior to its independence. These atrocities include targeted killings of minorities based on religious and ethnic identity, and demonstrate persistent features of systematic, orchestrated violence that is fuelled by a Hindu nationalist ideology. This ideology is routinely promulgated at the highest levels of political leadership. This article traces both the historical and institutional character of hate speech and incitement in India to understand its repeated manifestation over time. Through case studies of recent violence, it considers the implications of new legal developments, technology and the covid-19 pandemic on the character and dynamic of hate speech, incitement and atrocity violence in India. It considers key reforms and areas for accountability on which the international community could engage the government and civil society in India on the issue of hate speech and incitement to promote atrocity prevention at the domestic level. © 2023 Cecilia Jacob and Mujeeb Kanth.

Deep learning algorithm for classification and prediction of COVID-19 Pneumonia using CT scan images

COVID-19 is a precarious and life-killing disease. However, early identification and treatment can reduce the risk of losing people's life. Although CT radiography is one of the finest imaging methods within the medical field, it's difficult for doctors to categorize the COVID-19 in CT scans. So, computer-Aided diagnoses are often helpful for doctors to understand them accurately without consuming more time. Deep learning has been validated as a well-liked and effective method in many areas of medical imaging diagnostics. The convolutional neural networks CNN were designed to classify COVID-19 disease and Pneumonia. These networks are realistic for CT scans by classifying scanned images with some modifications of the lung nodules. The behavior pattern of the pulmonary nodule is disintegrated into the CNN so that unlike network layers is often wont to secure the appearances of the pulmonary nodules with different sizes. The CNN is proficient in capturing the image structure from its feature maps. Added improvements are available within the areas where the abnormalities lie, possibly over the utilization of Convolutional learning algorithms, and therefore the flexibility of an automatic discovery system. © 2022 Author(s).