No abstract

Natural language processing (NLP) is widely used in multi‐media real‐time applications for understanding human interactions through computer aided‐analysis. NLP is common in auto‐filling, voice recognition, typo‐checking applications, and so forth. Multilingual NLP requires vast data processing and interaction recognition features for leveraging content retrieval precision. To strengthen this concept, a predictive typological content retrieval method is introduced in this article. The proposed method maximizes and relies on distributed transfer learning for training multilingual interactions with pitch and tone features. The phonetic pronunciation and the previous content‐based predictions are forwarded using knowledge transfer. This knowledge is modelled using the training data and precise contents identified in the previous processing instances. For this purpose, the auto‐fill and error correction data are augmented with the training and multilingual processing databases. Depending on the current prediction and previous content, the knowledge base is updated, and further training relies on this feature. Therefore, the proposed method accurately identifies the content across multilingual NLP models.

Today’s Indian cities are confronted with a wide range of problems due to social equity and urban housing policy failures, including rising populations, shifting family structures, increasing numbers of people living in informal communities and slums, inadequate urban infrastructure, growing environmental concerns, and an increase in migration. India has a severe housing shortage. A significant chasm separates housing demand from the supply. The discrepancy has led to the proliferation of urban slums, where millions of people are subjected to the lowest sanitation and hygiene standards. Housing policies in Indian cities include increasing taxes on unused or uninhabited land to fund the construction of low-cost dwellings, altering zoning laws to mandate that builders, and promoting higher densities of houses to spread out infrastructure costs among a larger population. Many people who have been the beneficiaries of land acquisition or other forms of forcible relocation are eligible for assistance through the Rehabilitation and Housing Resettlement Program. The proposed method included the city’s slums in the Advanced Smart Urban Missions (ASUM) planning process to overcome housing policy failures. A three-dimensional social equity framework with dispersion, identification, and protocol dimension aspects is applied to evaluate formal recovery plans’ objectives, priorities, and tactics. One of the most critical aspects of providing more housing alternatives for more people is ensuring everyone can afford it. Human rights, different perspectives, development initiatives in India, and policy inclusivity were also investigated. It makes suggestions for improving intelligent city policy that considers the needs of the city’s disadvantaged populations. This study looks into the problems that slum dwellers have with relocation and evictions and is limited to establishing various Smart Urban Missions. The research will help streamline the intelligent city development process sequentially, improving conditions for the urban poor and disadvantaged.