CSE - Artificial Intelligence & Machine Learning
KOMMURI PRATAP REDDY INSTITUTE OF TECHNOLOGY
About Department of CSE - Artificial Intelligence & Machine Learning
The Department of Computer Science And Engineering was established in the year 2008. Experienced professors guide the department aiming at educating and training students with sound knowledge and awareness in the fields of Computers, Communication, and Information Technology.
The major goal of the Department of Computer Science And Engineering is to produce highly knowledgeable, competent and resourceful young engineers who can perform well in a wide variety of job profiles. To achieve this, curriculum provides a strong foundation in both the analytic, computing and technological aspects of Computer Science Engineering.
It also provides ample opportunities to students to work on mini-projects, develop communication skills, explore internship opportunities in industry and world-class universities and take part in national and international design contests.
The Department of CSE also organizes Workshops, Expert Talks, Project Expo, Poster Presentation competitions for the students. The department has established the Remote Centre in Supported with IIT Bombay.
Core Companies offering Computer Science And Engineering IMCS group , Madhees Techno Pvt Ltd, Proto Tech Solutions, SIG, ERT Technologies, Multiplier, Stealth Technologies, BYJU’s, Aliens Developers Pvt Ltd.
Vision of the Department
To produce excellent standard, quality education of professionals by imparting cognitive learning environment, ethical, research and industrial orientation to become pioneering Data Scientists
Mission of the Department
- Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge by using structured and unstructured data.
- Data science which depend on the application. More recently, full-featured, end-to-end platforms have been developed and heavily used for data science and machine learning.
- To facilitate the programming skills by imparting the qualitative technicality in theoretical and pragmatically approaches.
- Enabling students to get expertise in critical skills with data science education and facilitate socially responsive research and innovation.
Faculty
S.No. | Name of the Faculty | Qualification | Designation | JNTU ID | Details |
| 1. | Dr T KISHANRAO | PhD | Professor | 7314-220704-170801 | View |
| 2. | POWN KAMARAJAPANDIAN | PhD | Professor | 6264-221201-143335 | View |
| 3. | P SHANTHI KUMARI | M.Tech | Asst.Professor | 9823-200307-161006 | View |
| 4. | J SWATHI | M.Tech | Asst.Professor | 7043-220121-123311 | View |
| 5. | P SOUJANYA | M.Tech | Asst.Professor | 1749-150422-130013 | View |
| 6. | MD PASHA | M.Tech | Asst.Professor | 6374-210819-145155 | View |
| 7. | T DEEPIKA | M.Tech | Asst.Professor | 6260-220328-143855 | View |
| 8. | B RAJESH REDDY | M.Tech | Asst.Professor | 9294-220331-112428 | View |
| 9. | V HARSHITHA | M.Tech | Asst.Professor | 3906-161222-134435 | View |
| 10. | A HAREESHA | M.Tech | Asst.Professor | 8123-161227-115221 | View |
| 11. | B SHOBARANI | M.Tech | Asst.Professor | 3892-230119-153526 | View |
| 12. | A ASHOK KUMAR | M.Tech | Asst.Professor | 6649-150411-155740 | View |
| 13. | N ANUSHA | M.Tech | Asst.Professor | 7551-230605-191859 | View |
| 14. | INDIRA SAMALA | M.Tech | Asst.Professor | 84150406-132500 | View |
| 15. | HEMALATHA P | M.Tech | Asst.Professor | 3708-230602-105537 | View |
| 16. | K SWATHI | M.Tech | Asst.Professor | 6683-170125-124551 | View |
| 17. | R DINESH KUMAR | M.Tech | Asst.Professor | 5772-230321-114439 | View |
| 18. | A ASHARANI | M.Tech | Asst.Professor | 6585-230618-123011 | View |
| 19. | T RAVINDER | M.Tech | Asst.Professor | 5610-230804-120723 | View |
| 20. | P MANEMMA | M.Tech | Asst.Professor | 0187-150419-123425 | View |
| 21. | V SONY | M.Tech | Asst.Professor | 1819-240217-164140 | View |
| 22. | A AMARESHWAR | M.Tech | Asst.Professor | 34150407-111157 | View |
| 23. | I VASANTHA KUMARI | M.Tech | Asst.Professor | 9190-220705-171631 | View |
| 24. | CHEKKILLA NAVYA | M.Tech | Asst.Professor | 9009-250311-155656 | View |
| 25. | S CHANDRASHEKAR | M.Tech | Asst.Professor | 3796-150506-173342 | View |
| 26. | SAMREEN | M.Tech | Asst.Professor | 2820-211102-125433 | View |
Course Outcomes
Course Outcomes are statements that describe significant and essential learning that the Students have achieved, and can reliably demonstrate at the end of a course. A Course outcome makes clear the intended result of the learning rather than what form the instruction will take. A good course outcome states what a student will know or be able to do at the end of instruction. It focuses on student performance. Other synonyms are learning outcome or Course learning outcome.
The advantages of CourseOutcomes:
Benefits for the course and module designer
In terms of course and module design, the use of explicit course outcome statements can help ensure consistency of delivery across modules or programs. They can aid curriculum design by clarifying areas of overlap between existing modules, program and qualifications.
Benefits for quality assurance and standards
Quality assurance benefits from the adoption of learning outcomes via the resulting increase in transparency and better comparability of standards between and within qualifications.
Benefits for Students and employers
Students benefit from a comprehensive set of statements of exactly what they will be able to achieve after successful study. Course outcomes provide Students with clear information that can help them with their choice of module/unit/program/qualification to study and can lead to more effective learning.
Benefits for national and international educational transparency
Internationally, Course outcomes contribute to the mobility of students by facilitating the recognition of their qualifications and improving the transparency of qualifications and thus simplifying credit transfer.
Course Outcomes for the Department of Computer Science and Engineering
Laboratories
| ACADEMIC LABS | COMPUTING FACILITIES WITHIN THE DEPARTMENT | ||||||
|---|---|---|---|---|---|---|---|
| Name of Academic Lab | Room No | Major Equipment | No.of Systems | Printers | Other if any | ||
| 1 | Python Programming lab | B203/A | computer systems | 36 | 1 | UPS,LinuxServer,Internet facility | |
| 2 | Database Lab | B203/B | computer systems | 36 | 1 | Projector, Internet facility | |
| 3 | Data Structures Lab | B204 | computer systems | 36 | 1 | Internet facility | |
| 4 | Java Programming Lab | B306 | computer systems | 36 | 1 | Internet facility | |
| 5 | IT Workshop Lab | B308 | computer systems | 36 | 1 | Internet facility | |
| 6 | Operating Systems Lab | B309 | computer systems | 36 | 1 | Internet facility | |
| 7 | Operating Systems Lab | B203/C | computer systems | 36 | 1 | Internet facility | |
| 8 | Projects Lab | A002 | computer systems | 36 | 1 | Internet facility | |
LIST OF PROJECT LAB
| Sno | Name of the Lab | Room No | No of Projects Done | |
|---|---|---|---|---|
| Major | Mini | |||
| 1 | Project Lab | A002 | 09 | 09 |
Industrial visit
| S.no | Name of the industry visited | Date of Visit |
| 1 | L&T Metro | 23-01-2019 |
| 2 | BSNL Regional Telecom Training Centre | 23-07-2018 |
| 3 | BSNL-Regional Telecom Training Centre | 23-1-2018 |
| 4 | Doordarshan Kendra | 11-08-2017 |
| 5 | Doordarshan Kendra, Ramanthapur | 06-09-2016 |
| 6 | T-Hub | 18-02-2017 |
Course file
Course File Contents
Sr. No. | Contents |
1 | Institute V & M , Department V&M, PEO’s, PO’s, PSO’s |
2 | University Syllabus |
3 | Course Outcomes |
4 | CO-PO mapping with Justification |
5 | Is Syllabus Changes Listed? |
6 | Gaps Identified during Mapping if any |
7 | Topics beyond syllabus |
8 | Evidence of (Seminar/ Guest lecture/ Workshop, etc.) conducted for fulfilment of Gap |
9 | Revised CO-PO Mapping if any |
10 | Student Customization based on previous year/ semester result |
11 | Student Customization based on Mid-I & Mid-II |
12 | Course outcome assessment sheet |
13 | Lecture notes |
14 | PPT’s, Videos (in CD), Self Learning Material |
15 | Web references |
16 | Charts |
17 | Assignments |
18 | Tutorial evidence |
19 | Unit wise Question bank |
20 | Is Gate Question bank present? |
21 | Mid 1- Question papers |
22 | Mid 1 – Question paper – Key |
23 | Mid 1 – Question paper – Scheme of Evaluation |
24 | Mid 2- Question papers |
25 | Mid 2 – Question paper – Key |
26 | Mid 2 – Question paper – Scheme of Evaluation |
27 | University Question papers (Last three years) |
28 | Remedial Classes |
29 | Result Analysis (After Completion of course ) |
30 | Student Feedback Analysis |
31 | Lesson plan |
32 | Time table |
33 | Department Calendar |
34 | University Calendar |
35 | Attendance Register -Teacher Log updated with signature of faculty and HOD |
36 | Internal, Assignment Marks entry in Attendance Register |
37 | Sample Answer Sheets |
38 | Sample Assignment Sheets |
39 | Sample Tutorial Sheets |
40 | Audited by IQAC |
Signature of the faculty
Sponsored Research
| Sno | Name of the Faculty | Academic year | Project Title | Approximate. Cost | Sponsored Company | Status | Duration |
| 1 | Dr. Dilip Kumar Mahapatra | 2015-16 | Novel Recommender system model for online social voting using matrix factorization and nearest neighbor approach(Data Mining) | 4.55 Lakhs | SRK Travels | Completed | 1 Year |
| Dr.K. Jagan Mohan | |||||||
| 2 | Dr. SaratChadraNayak | 2016-17 | A supervised joint topic modeling for sentiment analysis in one go(Data Mining) | 5.75 Lakhs | SAK Informatics | Completed | 2 Years |
| A.Venugopal | |||||||
| 3 | Dr.D..Eswar | 2017-18 | Archiving Efficient and secure data acquisition for cloud supported internet of | 7.00 Lakhs | SAK Informatics | Completed | 2 Years |
| R.KrishnaNayak | |||||||
| 4 | Dr. Marlene Grace Verghese | 2017-18 | A network based spam detection framework for reviews in online social media(Inform ation forensics and security) | 6.10 Lakhs | HACKBOATS | In progress | 3 Years |
| K. Suparna | |||||||
| 5 | Dr. D.Eshwar | 2018-19 | Vehicle detection and classification | 7.25 Lakhs | SAK INFORMATICS | In progress | 3 Years |
| Dr. Marlene Grace Verghese | |||||||
| 6 | Dr. D.Eshwar | 2018-19 | Vehicle detection and classification | 7.75 Lakhs | SAK INFORMATICS | In progress | 3 Years |
| Dr. Marlene Grace Verghese |
Consultancy Projects : A.Y:2015-16:
| Sno | Name of the Faculty | Academic year | Project Title | Approximate. Cost | Sponsored Company | Status | Duration |
| 1 | Dr.Dileep Kumar | 2015-16 | Content Development | 4 Lakhs | HEBEON TECHNOLOGIES | Completed | 1 Year |
| Dr.Dileep Kumar |
2016-17:
| Sno | Name of the Faculty | Academic year | Project Title | Approximate. Cost | Sponsored Company | Status | Duration |
| 1 | Dr.k.kiran Kumar | 2016-17 | Design and Develop of websites, office automation | 2.9 Lakhs | SRS Technologies | Completed | 1 Year |
| Dr.k.kiran Kumarr | |||||||
| 2 | K.Suparna | Content Development | 5 Lakhs | Completed | Completed | 2 Years | |
| K.Suparna |
A Y: 2017-18
| Sno | Name of the Faculty | Academic year | Project Title | Approximate. Cost | Sponsored Company | Status | Duration |
| 1 | Dr.Sarath Chandra Nayak | 2017-18 | Content Development | 5.4 Lakhs | HEBEON Technologies | Completed | 2 Years |
| K.BalaThipuraSundari | |||||||
| 2 | A.Prakash | office automation and front office automation | 4 Lakhs | MahadweeepPharma | Completed | 2 Years | |
| R.Krishnanayak | |||||||
| 3 | A.Prakash | Bus Tracker application | 6.7 Lakhs | SRK Travels | On Going | 3 Years | |
| B.Ramesh |
2018-19:
| Sno | Name of the Faculty | Academic year | Project Title | Approximate. Cost | Sponsored Company | Status | Duration |
| 1 | Dr.Marlene Grace |
2018-19 |
Third Party Project Development | 3.85 Lakhs | Code Kindle | On Going | 2 Years |
| G.Saritha | |||||||
| 2 | G.Saritha | Content Development | 6.95 Lakhs | Learn IT Hub | On Going | 3 Years | |
| A.Venugopal | |||||||
| 3 | K.BalaThripurasundari | Driver Safety Index Using Integrated Computing system | 8 Lakhs | SRK Travels | On Going | 3 Years | |
| K.Suparna |
Product Development:
| Sno | Faculty Name | Tittle | Student 1 | Student 2 | Student 3 | Student 4 | |
| 1 | 2015-2016 | Dr.Dilip Kumar Mahapatra | Dr.Dilip Kumar Mahapatra | Sneha ReddySneha Reddy | AnkitRai | Gayatri | Rahul Singh |
| 2 | 2016-2017 | K.Suparna | Rock paper scissors | K.BindhuPriya | M.Leela | Shaik AbdulSahilMajeed | Manoj Kumar |
| 3 | 2016-2017 | Dr.K.Kiran Kumar | Space Fighter | V. Anusha Reddy | Y. Shravya | B.Anil | B.Sravan |
| 4 | 2017-2018 | Dr.K.Kiran Kumar | E-Commerce Application | VikramPreek | Nawaz | Shankar | Pooja Reddy |
| 5 | 2017-2018 | G.Saritha | Space Fighter | CH.Rajesh | NishaKumari | G.Bhanava | Dwarakanath |
| 6 | 2018-2019 | Dr.D.Eshwar | Network pen testing project | Harivamshi | Lakshman | Kavitha | Anikrutha |
| 7 | 2018-2019 | Dr.D.Marlene Grace | Block chain real time voting | VikramPareek | Shanka | Swetha | Pranavi |
| 8 | 2018-2019 | Dr.D.Eshwar | A distributed publisher driven secure data sharing for information | Sushmitha | K.BindhuPriya | Ch.Samhith | Ch.Samhith |
Internal Quality of Question Paper
A sample of mid semester question paper is specified below.
Academic Year: 2017-18
Scheme for Weak and Advanced Learners
The department has a well defined process of monitoring, guiding and assisting the students and to identify them as weak or bright students and providing them necessary support in improving their performance.
Process of identification of students as weak/ bright students:
- Appropriate care has taken by the faculty in monitoring the performance of a student in the class, attendance, assignment, mid semester and external examination results.
- The mentors regularly conduct meeting with the students and identify weak and bright students.
- The mentors regularly conduct meeting with the students and identify weak and bright students.
- Faculty will analyze and identify the weak students and bright students based on their previous performance before the start of the semester.
- After the 3rd week of classes based on the observation and previous semester result the identification of weak and bright students will be done.
Scheme
Students who got below 60% or less than or equal to 14/25 marks in mid semester are considered as weak students, Students who scored above 72 % or more than 18/25 Marks are considered as bright students.