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<p><span lang="EN-US">Today during ‘Covid-20’, people are more inclined towards online shopping. In general practice, analysis of browsing history and customer’s micro behaviour against online shopping habits have been used for future suggestions. Due to this, the predictions made were suffereing from over-similarity problem and the user was unable to find any novelty in the recommended items. Observing these issues, e-shopping quality can be enhanced by adding a factor other than similarity. The current research suggests and advertise those products which belongs to a person’s region. For this research work the data has been collected on the basis of area-wise, like, country-based seggregation. Here the considered dataset belongs to country, ‘India’, its culture, its handicraft and its citizens. Datasets and their combinations based on multiple attributes are input for the proposed predictive system. In this paper, existing data is also considered for collecting customers demographic details which is further mapped with the area-wise dataset. Also, a framework has been proposed which uses database and user query as input for its predictive system in order to generate default suggestions for the user other than the submitted query also.</span></p>

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With the emerging era of E‐commerce and online shopping, people are also in a habit to receive default product recommendations on the web pages that they access. Google is already providing such suggestions. Till now recommendations were made only based on previous sentiments or feedback or ratings, but this research has improved the product recommendation method by including one more parameter for the same. This article represents two parameters for making predictions of product allocation to a new customer. These parameters are ratings given by the existing users for that particular product and the region to which the new customer belongs. Following these parameters, a prediction model and an algorithm, Improved_Collab_Similarity, have been implemented. The dataset has been developed where India as a country along with all its States has been considered for products which are popular for their creation based on regional and ancient skills of the people belonging to that area. Results for the mentioned prediction model have been discussed in this article where generally precision increases with the increase in a number of products but at some points, it does not increase when a smaller number of that product was purchased by the customers.

Objectives: Radiologic technologists (RTs) are typically exposed to low doses of radiations for longer periods, which have a health risk over many organs and tissues. Resistant tissues like nerves have shown neuropathic changes due to acute high-dose radiation exposure in the form of radiation therapy but the effect of low-dose chronic radiation exposure over peripheral nerves in RTs has been studied scantily. Materials and Methods: Nerve conduction parameters were recorded from 30 RTs and 30 age- and sex-matched healthy individuals who were not exposed to radiation. Motor nerve conduction study (NCS) of bilateral median, ulnar, radial, common peroneal and tibial nerves and sensory NCS of bilateral median, ulnar and radial nerves were recorded and compared. Results: Significant changes were observed in the form of reduction in motor and sensory nerve conduction velocity (P < 0.05) in all the examined nerves. Sensory nerve action potential (SNAP) amplitudes were reduced and latencies were prolonged significantly (P < 0.05) in all the examined sensory nerves. We also found reduced compound muscle action potential amplitude (significant in ulnar, radial, common peroneal and tibial nerves) along with prolonged motor distal latencies (significant in median, ulnar and tibial nerves) among RTs compared to healthy individuals. Conclusion: Chronic low-dose exposure of ionising radiation causes sub-clinical neuropathies affecting both sensory and motor nerves.