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A recent study conducted by researchers at Amrita Vishwa Vidyapeetham in Kerala, India, has shed light on the deceptive practices of predatory journals. The study analyzed three datasets of calls for papers from potential predatory journals and legitimate journals using text mining and programming language. The researchers found that most potential predatory journals use similar language and templates in their calls for papers, often using flattering and positive words that are rarely seen in emails from legitimate journals. Based on these findings, the researchers developed a toolkit for detecting unsolicited calls for papers from potential predatory journals. This research provides valuable insights and tools for distinguishing between predatory and legitimate journals, ultimately helping researchers deal with predatory journals more effectively. [Extracted from the article]

A recent research paper from Symbiosis International (Deemed University) in Pune, India, explores the adoption of digital branding among specialty eatery start-ups in a post-pandemic environment. The study examines the advantages and drawbacks of digital branding and investigates the intention of specialty eateries to adopt it. The research, which surveyed 231 small cafes and restaurants in Maharashtra's major cities, found that digital branding plays a crucial role in enhancing the performance of specialty eatery start-ups. The study also highlights the mediating effect of digital support and awareness on the adoption intention of digital branding. This quantitative research provides valuable insights for specialty eatery start-ups and related businesses to enhance their digital branding strategies. [Extracted from the article]

Researchers at Amrita Vishwa Vidyapeetham in India have conducted a study on sustainable food and agriculture. The study focuses on the use of neural networks to optimize and analyze agriculture data for improved farming practices. The researchers propose a back-propagation model with adaptive momentum estimation and the Adam optimizer, which achieves good prediction accuracy and convergence rate. The study concludes that this hybridization of neural networks with adaptive optimization methods outperforms other benchmark algorithms. The research has been peer-reviewed and published in the journal Soft Computing. [Extracted from the article]