IJRST- International Journal of Research in Science & Technology

IJRST is Devoted to Quality

IJRST remains loyal to the provision of quality research for our researchers. The purpose of the journal is to publish quality articles to the humankind. Our ambition is to help IJRST become a global leader in the research of Science and Technology. The paper is reviewed by our team of reviewers, thus, guaranteeing publication of quality research paper
IJRST is Dedicated to Research

The purpose of research is to inform action. Thus, your study should seek to contextualize its findings within the larger body of research. Research must always be high quality in order to produce knowledge that is applicable outside of the research setting with implications that go beyond the group that has participated in the research
IJRST is Committed to Innovation

Research and experimental development comprises creative work undertaken on a systematic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications. we have the right people who can go the extra mile to bring an innovation towards implementation and production.

Volume: 8 Issue: 4

S.No.	Article Title & Authors	Page No	Download
1.	SOCIAL NETWORKS WITH NATURAL LEARNING PROCESSING- EMPLOYBILITY OF KEY INFLUENCERS LEXICAL, SYNTACTIC AND SEMANTIC IN ENHANCING BUSINESS INTELLIGENCE Karan Gupta	(1-8)
2.	MACHINE LEARNING IN DATA NORMALISATION-EMPLYBILITY OF SUPERVISED CLASSIFICATION TECHNIQUES IN CONTENT CATEGORISATION USING MACHINE LEARNING ALGORITHMS FOR ENHANCING UNWANTED SOCIAL NETWORKING DATA Sahil Kapoor	(9-17)
3.	FOUNDATION EXTRACTION - DETERMING THE DECLARATION OF THE VOYAGING VELOCITY THROUGH MINIMAL GRAPH CUTS SEGMENTATION IN ENHANCING CONSEQUENT PROTEST RECOGNITION SYSTEMS Divyashi Agrawal	(18-29)
4.	IOT: ENHANCING THE APPLICABILITY TO ANTICIPATE THE CONTROL METRICS IN AN AMBIENCE Karan Sablok	(30-38)
5.	CLOUD FILE SHARING AND DATA SECURITY THREATS â€“ EXPLORING THE EMPLOYABILITY OF GRAPH-BASED UNSUPERVISED LEARNING IN DETECTING AND SAFEGUARDING CLOUD FILES Harshit Yadav	(39-44)
6.	MACHINE LEARNING IN NETWORK LOAD BALANCE: EMPLOYABILITY OF NEUTRAL NETWORK IN EFFICACIOUS USE OF ADAPTABLE MULTIPATH ROUTING APPROACH Rishit Garg	(45-49)