All 5 helps him in getting his position back, but later they came to know about so many twists and turns in story and find Kubda as their main enemy. He told them that he is the chief of underworld (Syndicate) named Hingle Corbett, but someone else has taken that position after hurting him badly, and making him a "kubda" person. Here after so many twists and turns, we come to know that all 5 are in Jail and by help of a man "Kubda", they were able to run from it. All 5 killed Angaarak in revenge, but this was part of the story of the first book, as per provided flashback. Let me tell you about the story, There are 5 friends - Dhaamu aka Dharam, Palli aka Pallavi, Gullu aka Gopal, Chandu aka Chamunda and Sophie aka Sophiya, who were called "5 Jism 1 Jaan (5 body 1 life)", they are such close friends, but they killed one man named Angaarak, who as per told by someone, killed Dhaamu's parents. It is a suspense thriller, and its main characters only survives just for these two books unlike Surendra Mohan Pathak's famous characters. The book goes for 302 pages, and was published by Tulsi Paper Books, Meerut. I somehow got this book, and just bought it. This book is written by Ved Prakash Sharma, and as per the list provided it's his 137th novel but it's a sequel, a sequel to book released before it named "Jurm Ki Maa" (Mother of Crime), which I haven't read since I don't know about it. Tackling ways to re-use, harmonize, and improve our existing and future secondary care mental health data, leveraging advanced analytics such as NLP is worth the effort in an attempt to fill the data gap on social and behavioural contributors to mental health conditions and will be necessary to fulfill all of the domains needed to inform personalized interventions.So, I completed one more Hindi novel, this time its of Ved Prakash Sharma, and its named "Kubda", which in english means, a man with a humpback. Advancements in NLP offer new opportunities in the exploitation of unstructured text from secondary care EHR data particularly given that clinical notes and attachments are available in large volumes of patients and are more routinely completed by clinicians. The utility of other non-clinical fields reported semi-consistently (e.g., ethnicity and marital status) is entirely dependent on treating them appropriately in analyses, quantifying the many reasons behind missingness in consideration of selection biases. Most structured non-clinical data fields within secondary care mental health EHR data have too much missing data for adequate use.
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