Crime against women, a never-ending issue is a sad reality that demands focused attention. The occurrence of crimes in different states in India varies a lot. Multi-Criteria Decision Making (MCDM) method, called TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is applied on real occurrences of crime to develop women vulnerability index (WVI). This index measures the susceptibility of women to crime in any region of India. This marks the first instance of applying MCDM technique (TOPSIS) to derive such an index for crime against women. The index will equip the law enforcing agencies and various NGOs to assess the susceptibility of Indian women in different regions and take appropriate action for mitigation of such crimes to create a safe environment for women. We find that states like Mizoram, Nagaland, Sikkim in northeast India and Lakshadweep Islands in southern India have very low values of the index and are the safest places for women. On the other hand, Uttar Pradesh, Delhi, Haryana, Rajasthan, and Bihar are Indian states where women are most susceptible to crime having very high values of WVI.

The Industrial Internet of Things (IIoT) represents a significant advancemenst in the manufacturing and engineering sectors, integrating advanced sensors, communication technologies, and data analytics to create intelligent, interconnected systems tailored for industrial environments. Unlike general IoT, IIoT is designed for robustness, reliability, and real-time operational efficiency, enabling seamless machine communication and real-time data collection and analysis. This supports predictive maintenance, optimized production processes, and overall improved efficiency. This paper provides an overview of IIoT, detailing its definition, scope, and historical evolution from traditional industrial automation to modern smart factories. It highlights the benefits of IIoT, such as enhanced operational efficiency, cost savings, and improved product quality, while also addressing challenges like cybersecurity risks, interoperability issues, and high initial investment costs. By examining these aspects through real-world examples and citations, the paper elucidates the profound impact of IIoT on industrial processes, offering valuable insights for industry practitioners and policymakers.

Cultivating rice is crucial in India to meet demands of a growing population. In order to improve crop yield, it's essential to address factors like diseases caused by bacteria, fungi, and viruses. Detecting and managing these diseases is vital, and one effective approach is employing rice plant disease detection methods. Deep learning techniques, known for their ability to analyse data, are used for disease identification in plants. This work explores various deep learning approaches for detecting rice plant disease. Deep learning, particularly in computer vision, has shown significant progress in detecting plant diseases. The study compares the effectiveness deep learning mechanisms, demonstrating superior performance of deep learning models. Utilizing deep learning can help prevent major crop losses by detecting leaf diseases through image analysis.