10, నవంబర్ 2026, మంగళవారం

Crop Yield Prediction in Diverse Environmental Conditions Using a Hybrid Whale Optimization–AdaBoost Framework

 



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వీరిచే పోస్ట్ చేయబడింది వద్ద కామెంట్‌లు లేవు:

9, నవంబర్ 2026, సోమవారం

DATA IS CIPHERED , FIRST AUTHOR PROF RAM.A.DAYINABOYINA............................................more papers are in progress

Building a Mobile Price Prediction System Using Web Scraping and Machine Learning by amalgamate data engineering, data science, and e-commerce analytics.

 

Ramanjaneyulu DayinaboyinaProfessor,Dept of AIML, Chiral a , Andhra Pradesh, dramu2002@gmail.Com, India .1

Raman Kumar Mandal, Asst. prof CSE GNITC Hyderabad, email reffect1985@gmail.com, india .2

Ponnapalli Hemant, Asst. prof CSE, DRK institute of science and technology, ponnapallihemanth784@gmail.com, india .3

Nisha, Asst prof CSE, ,Chandigargh university punjab, mohali, india .4

Anjanadevi Dayinaboyina, Senior Software Engineer, Wipro Ltd, anjana11.mca@gmail.com, Chennai, India .5

 

Abstract

The internet retail market is a constantly evolving landscape. Consequently, both buyers and sellers require technology-driven advancements for the analysis of mobile phone pricing. Web scraping—a technique used to collect data from the web—is widely employed for this purpose. The process further incorporates data ................................................................................

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వీరిచే పోస్ట్ చేయబడింది వద్ద కామెంట్‌లు లేవు:

ESTIMATING CALORIES IN FOOD AND DRINKS USING NEURAL NETWORKS INVOLVES A MULTI-STEP COMPUTER VISION AND MACHINE LEARNING PIPELINE THAT TRANSFORMS RAW IMAGES INTO NUTRITIONAL DATA.

D.Ramanjaneyulu 1, Raman kumar mandal2 ,PriyaRanjan Raj3 ,K.Ravi Naik 4 , Anjanadevi Dayinaboyina.5

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వీరిచే పోస్ట్ చేయబడింది వద్ద కామెంట్‌లు లేవు:

8, నవంబర్ 2026, ఆదివారం

……………….thesis @ 280 pages The intersection of Human-Computer Interaction (HCI) and Software Engineering (SE) When these two disciplines merge, how they transform isolated code into intuitive, human-centred digital products. Professor ram.a dayinaboyina SPM ROYCE SE, PHD HCI BEN, DRIX WEB PROG,TECH

వీరిచే పోస్ట్ చేయబడింది వద్ద కామెంట్‌లు లేవు:

5, నవంబర్ 2026, గురువారం

infi around 1200 /-




వీరిచే పోస్ట్ చేయబడింది వద్ద కామెంట్‌లు లేవు:

2, నవంబర్ 2026, సోమవారం

Oh, the night is my world. City light painted girls. In the day, nothing matters. It's the night. professor ram.a.dayinaboyina NRI.......

వీరిచే పోస్ట్ చేయబడింది వద్ద కామెంట్‌లు లేవు:

slow learners............................Dr . ram.a.dayinaboyina.

 In NAAC (National Assessment and Accreditation Council) accreditation—specifically under Criterion 2 (Teaching-Learning and Evaluation, Metric 2.2.1: Catering to Student Diversity)—colleges and universities identify slow learners (or slow performers) to provide them with remedial coaching and structured support. 

Institutions typically identify slow learners using a combination of entry-level metrics, internal assessments, and teacher observations: 
1. Entry-Level & Baseline Metrics (At Admission / Year 1)
  • Qualifying Exam Scores: Comparing scores from the qualifying examination (such as 10+2 / HSC or entrance test marks) against institutional benchmarks.
  • Bridge Course Performance: Evaluating how students handle introductory or bridge courses designed to cover foundational gaps. 
2. Continuous Internal & University Evaluations
  • Internal Test Scores: Scoring below a set threshold (commonly less than 50% or less than 35% in internal class tests, mid-semester exams, or terminal exams).
  • University / Semester-End Exams (SEE): Failing or barely passing subjects in preceding university or semester-end examinations. 
3. Classroom & Behavioral Observations
  • Slow Pace of Understanding: Taking extra time to grasp core concepts and requiring continuous repetition. 
  • Participation & Engagement: Lacking motivation in class, struggling to follow multi-step instructions, or being unable to contribute to higher-order questions or discussions. 
  • Subject Teacher Feedback: Qualitative assessments and logs maintained by individual subject teachers after initial tests or unit evaluations. 
Action Taken After Identification (Required for NAAC)
Once identified, institutions must document and implement supportive interventions to showcase under Metric 2.2.1: 
  • Remedial Coaching: Extra classes or tutorial sessions focused on difficult subject codes.
  • Mentorship & Counseling: Regular guidance sessions by assigned faculty mentors.
  • Retests / Re-evaluations: Giving students a chance to improve and tracking whether they move out of the slow-learner category. 
You can review sample frameworks and institutional guidelines via the MIT University NAAC Guidelines or the 
If you are preparing a NAAC Self-Study Report (SSR) or file for Criterion 2, let me know:
  • What specific passing/threshold percentage your institution uses?
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