Universities sometimes promote Assistant Professors directly to Professor (skipping Associate) or recruit directly to Professor roles by assessing outstanding research/teaching (UGC guidelines allow direct recruitment to Professor

Co,Pos...............

 

                

..................Operating System CO1 to CO6" refers to the typical Course Outcomes (COs) for an OS subject, covering foundational concepts (CO1), processes/scheduling (CO2, CO3), memory/file management (CO4, CO5), and advanced topics like Linux/mobile OS (CO6), explaining key OS functions from basics to practical implementations like < process scheduling, memory allocation, file systems, and security. 

Here's a breakdown of common COs for Operating Systems:

• CO1: Introduction to OS
  • Understanding basic concepts, history, functions, and types (batch, multiprogramming, real-time, distributed) of operating systems.
• CO2: Processes & CPU Scheduling
  • Learning about processes, threads, process coordination, and various CPU scheduling algorithms (FCFS, SJF, Priority, Round Robin).
• CO3: Process Synchronization & Deadlocks
  • Analyzing process synchronization mechanisms (semaphores, monitors) and techniques for handling deadlocks.
• CO4: Memory Management
  • Understanding memory allocation (contiguous, non-contiguous), virtual memory, paging, and segmentation.
• CO5: File Systems & I/O
  • Studying file system structures, disk scheduling, I/O management, and device management.
• CO6: Advanced Topics
  • Exploring security, protection, networking basics, distributed systems, real-time systems, or specific OSs like Linux/Android. 

These COs progress from foundational knowledge (CO1) to detailed analysis and application (CO4, CO5, CO6), guiding students through the core components of modern operating systems. 

Food and Beverage Calorie Prediction with Neural Networks...... total 12 pages..........."Under peer review" SpringerNature..........

 

Food and Beverage Calorie Prediction with Neural Networks

Raman kumar mandal^l   karingula Venudhar ²

Ramanjaneyulu Dayinaboyina Associate Professor , Dept of AIML , CMR College of  Engineering and Technology,

Kandlakoya medchal Hyderabad , Telangana, India

 

 

¹Assistant professor Department of IT, CMR College of  Engineering and Technology,

Kandlakoya medchal Hyderabad , Telangana, India

 

 

 

 

Abstract.This observe introduces a revolutionary approach for enhancing nutritional monitoring and selling healthful consuming behaviour via deep analyzing technology. The primary objective is to correctly come across several meals objects and estimate their calorie content cloth in actual-time, offering customers a smart and customized technique to nutritional tracking performed using Python, the gadget makes use of the cellular net architecture for food category and calorie estimation. The proposed gadget methods meals snap shots through a web based totally interface, figuring out gadgets hastily and supplying customers with a accurate calorie estimate. This machine empowers people with treasured insights into their nutritional behaviour, permitting them to song meals intake, set dietary dreams, and make knowledgeable picks. via way of leveraging deep studying for green and correct calorie estimation, this approach offers a transformative solution for nutritional control. With its excessive precision, real-time evaluation, and clever nutritional monitoring, this system has the ability to redefine how humans monitor and optimize their dietary conduct, fostering greater healthy lifestyles and average well-being.

 

Keywords:: Deep Learning, Food Calorie Estimation, Image Processing, CNN, Multi-Label Classification, Computer Vision

 

1       Introduction

Rapid advances in computer vision and deep learning have had a major impact on a wide range of areas including healthcare and nutrition the key uses are food detection and calorie estimation and it is estimated that a key role plays in intelligent nutrition monitoring accurate food identification and estimation of calorie content can help individuals make healthy nutrition decisions and promote a healthier lifestyle increased smartphone use and growing nutritional concerns have encouraged research into efficient and accurate calorie estimation systems these approaches are highly susceptible to variations in lighting conditions location and food presentation style furthermore many existing systems related to reporting on food categories are limited and lack actual calorie assessment skills this makes them ineffective for widespread use to address these challenges this study uses deep learning techniques particularly mobilenet architecture to improve the accuracy of food detection mobilenet is a lightweight and efficient model suitable for mobile applications .

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total 12 pages.

NOW A DAYS PROBLEM IS WITHOUT KNOWING UGC AICTE RULES................. MANY PEOPLE.not knowing............................

 

Mr.Ram.A.Dayinaboyina.........................

 

Mr ram.a.dayinaboyina..........

 

Mr.Ram.A.Dayinaboyina..............................