Hi! I am Sohag Mollik. A computer science student graduated with a CGPA of 3.60 out of 4.00. I am a competitive programmer who enjoys solving algorithmic and data structure problems, passionate about competitive programming, and enjoying new challenges. I am an enthusiastic learner and hardworking person looking forward to developing my career in the Software industry.
Education
Govt. Wazed Memorial Secondary High School, Mollahat
Secondary School Certificate, Science
GPA: 4.89 out of 5.00 [2014-2016]
Govt. Bangabandhu College, Gopalganj
Higher Secondary School Certificate, Science
GPA: 4.00 out of 5.00 [2016-2018]
Jashore University Of Science and Technology, Jashore
Bachelor’s Degree of Computer Science
CGPA: 3.60 out of 4.00 [2018-2022]
Skill
- C, C++, python
- Problem-Solving
- OOP(C#)
- HTML, CSS, JavaScript
- php, Laravel
- MySQL
- GitHub
- Algorithm, Data structure
- Teamwork, Observation, Communication
Achievement
- Solved around 1200 mathematical, algorithmic, and data structure-based problems in different online judges. Link: [StopStalk] [Leetcode] [Codeforces]
- Participated in approximately 130 programming contests, both online and offline combined.
- ICPC Dhaka Preliminary Round, 2021 308th position between 1747 teams of different university.
- Khulna University IUPC 17th position between 32 teams of different university. [Show credential]
- Problem Solving (Basic) Certificate - HackerRank. [Show credential]
- ICPC Asia Dhaka Preliminary-2022. [Show credential]
- THE CASE 2.0 - IEEE SB CUET. [Show credential]
- Coding Problem Solving - Ostad. [Show credential]
Project
Food Recommendation System for Diabetes Patients. [GitHub Link]
Food recommendation system which suggests recipes based on user preferences. Used PHP, Javascript, MySQL Database, and Laravel frameworks. Empowering individuals with diabetes, this platform enables patients to effortlessly select their preferences from a comprehensive list and seamlessly update their personalized choices.
Research
Personalized Dietary Guidance: An Automated Machine Learning Approach with Diverse Datasets to Healthy Meal Recommendations for Hypertension Patients.
This paper proposed a recommendation system that recommends food for people with hypertension. The proposed methodology enabled the exploration of diverse datasets (Bangladeshi and Foreign country recipe datasets).
The work is mainly distributed into two parts:
Used fuzzy-wuzzy and cosine similarity techniques to map similarities between the user’s choices and the meal ingredients and names that filter with the nutritionist’s suggested and restricted food ingredients and used an artificial neural network for recommendation food. The accuracy of the artificial neural network model is 93% for the Bangladeshi recipe and 93.5% for the Foreign country recipe dataset. Based on the study, the system has helped users to control and reduce their hypertension.
Activities
JUST ACM LABORATORY. This is the Closed Group For Competitive Programming, only for JUST students.
- Conducting training classes on Basic C, C++ programming, Data Structures, and Algorithms for our juniors.
- Connected with the intra-department programming contest.
- Answered different programming problems of Social media in different programming groups.