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Anusha, P.
- An Initiative to the Project Based Learning at KGRCET:As a Part of Active Learning Methodologies
Abstract Views :204 |
PDF Views:0
Authors
Tuti Sandhya
1,
P. Anusha
2
Affiliations
1 CEED, KG Reddy College of Engineering & Technology, Hyderabad, IN
2 Dept of ECE, KG Reddy College of Engineering & Technology, Hyderabad, IN
1 CEED, KG Reddy College of Engineering & Technology, Hyderabad, IN
2 Dept of ECE, KG Reddy College of Engineering & Technology, Hyderabad, IN
Source
Journal of Engineering Education Transformations, Vol 30, No Sp Iss (2017), Pagination:Abstract
Designing problems for project based learning (PBL) courses in engineering education has always a challenging task; PBL is a model for teaching, problem solving and is a highly operative technique for engineering pedagogy, which is to learn design by experimenting design as active percipients. This paper describes pedagogical issues involved in conducting the PBL Activity in Embedded System course, designing a problem in course, and analysis of students' solutions. It also presents the assessment of PBL in engineering education along with characteristics, Benefits, problems and differences between traditional and PBL approaches.Keywords
PBL, Outcomes, Engineering Education.
References
- Design problems for problem based learning courses in analog electronics: cognitive and pedagogical issues-A.Mantri, Chitkara institute of engineering and technology,india, S.Dutt,National Institute of technical teachers training institute,india, JP Gupta, jaypee institute of information technology,india, M Chitkara, Chitkara institute of engineering and technology,india-institute of engineers Australia,Australasian journal of engineering education, vol14 No.2.
- Characteristics of Problem-Based Learning* ERIK DE GRAAFF Delft University of Technology, the NetherlandsANETTE KOLMOS Aalborg University, Denmark.
- Group assessment challenges in project-based learning – Perceptions from students in higher engineering courses , D. Hellström, F. Nilsson and A. Olsson.
- Ayas, K. & Zeniuk, N. (2001) 'Project-Based Learning: Building Communities of Reflective Practitioners'. Management Learning, 32 (1). pp 61-76.
- Barron, B. J. S., D. L. Schwartz, et al. (1998). "Doing With Understanding: Lessons From Research on Problem- and Project-Based Learning." Journal of the Learning Sciences 7(3-4): 271-311.
- Barak, M. and Y. J. Dori (2005). "Enhancing undergraduate students' chemistry understanding through project-based learning in an IT environment." Science Education 89(1): 117-139.
- Bell, S. (2010) 'Project-Based Learning for the 21st Century: Skills for the Future'. The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 83 (2). pp 39-43.
- An Initiative to the Project Based Learning at KGRCET:As a Part of Active Learning Methodologies
Abstract Views :260 |
PDF Views:5
Authors
Tuti Sandhya
1,
P. Anusha
2
Affiliations
1 CEED, KG Reddy College of Engineering & Technology, Hyderabad, IN
2 Dept of ECE, KG Reddy College of Engineering & Technology, Hyderabad, IN
1 CEED, KG Reddy College of Engineering & Technology, Hyderabad, IN
2 Dept of ECE, KG Reddy College of Engineering & Technology, Hyderabad, IN
Source
Journal of Engineering Education Transformations, Vol 31, No 2 (2017), Pagination: 31-35Abstract
Designing problems for project based learning (PBL) courses in engineering education has always a challenging task; PBL is a model for teaching, problem-solving and is a highly operative technique for engineering pedagogy, which is to learn design by experimenting design as active percipients. This paper describes pedagogical issues involved in conducting the PBL Activity in Embedded System course, designing a problemin course, and analysis of students' solutions. It also presents the assessment of PBL in engineering education along with characteristics, Benefits, problems and differences between traditional and PBL approaches.Keywords
PBL, Outcomes, Engineering Education.References
- Design problems for problem based learning courses in analog electronics: cognitive and pedagogical issues-A.Mantri,Chitkara institute of engineering and technology, india, S.Dutt,National Institute of technical teachers training institute,india, JP Gupta, jaypee institute of information technology,india, M Chitkara, Chitkara institute of engineering and technology,india-institute of engineers Australia,Australasian journal of engineering education, vol14No.2.
- Characteristics of Problem-Based Learning* ERIK DE GRAAFF Delft University of Technology, the Netherlands ANETTE KOLMOS Aalborg University,Denmark.
- Group assessment challenges in project-based learning – Perceptions from students in higher engineering courses , D. Hellström, F. Nilsson and A.Olsson.
- .Ayas, K. & Zeniuk, N. (2001) 'Project-Based Learning: Building Communities of Reflective Practitioners'. Management Learning, 32 (1). pp 61-76.
- Barron, B. J. S., D. L. Schwartz, et al. (1998). "Doing With Understanding: Lessons From Research on Problem- and Project-Based Learning." Journal of the Learning Sciences 7(34): 271-311.
- Barak, M. and Y. J. Dori (2005). "Enhancing undergraduate students' chemistry understanding through project-based learning in an IT environment." Science Education 89(1): 117-139.
- Bell, S. (2010) 'Project-Based Learning for the 21st Century: Skills for the Future'. The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 83 (2). pp 39-43.
- Computer Vision in Deep Learning for the Detection of Cancer and its Treatment
Abstract Views :74 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science, Dr V S Krishna Govt Degree and PG College (Autonomous), A. U. TDR – HUB, Visakhapatnam, Andhra Pradesh, IN
2 Dept of Computer Science and Engineering, Guru Nanak Institute of Technology, Hyderabad, Telangana, IN
3 Anheuser-Busch InBev, Bangalore, Karnataka, IN
4 Masters in Technology Management, University of Illinois urbana, Champaign, US
1 Department of Computer Science, Dr V S Krishna Govt Degree and PG College (Autonomous), A. U. TDR – HUB, Visakhapatnam, Andhra Pradesh, IN
2 Dept of Computer Science and Engineering, Guru Nanak Institute of Technology, Hyderabad, Telangana, IN
3 Anheuser-Busch InBev, Bangalore, Karnataka, IN
4 Masters in Technology Management, University of Illinois urbana, Champaign, US
Source
International Journal of Advanced Networking and Applications, Vol 15, No 3 (2023), Pagination: 5983 - 5988Abstract
Computer vision (CV) is an effective mechanism that helps the computer to see pictorial stimuli from pointing out the edges to having a comprehensive understanding of the complete scenario. In this saga, Deep Learning (DL) has evolved as a crucial part of CV to process data exploiting multi-layered complex structures or layers made of multiple nonlinear alterations. This particular research shows the implementation of DL in the proper diagnosis of cancer and seeking a suitable solution to the disease. DL is an integral part of CV considering a multimodal discriminative model to conduct a diagnosis of diseases, clinical predictions, prognostics, and a combination of such activities. The study upholds the relevance of SSD in having single-shot images with high-resolution pixels to have the images to identify and diagnose the disease. The mechanism leads to early detection of cancer and if the disease gets detected earlier, it can seek a formidable solution, though there are challenges like an alignment of hardware with the CV software, and lack of training of the staff, still DL has the potentiality to create a significant impact on cancer treatment.Keywords
Computer Vision (CV), Deep Learning (DL), Single Shot Detector (SSD), Cancer Detection Algorithms.Full Text
References
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- Machine Learning-Based Facial Recognition for Video Surveillance Systems
Abstract Views :57 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, Malla Reddy College of Engineering and Technology, IN
2 Department of Electronics and Communication Engineering, R.M.K. Engineering College, IN
3 Department of Computer Science and Engineering, B.N. College of Engineering and Technology, IN
4 Department of Computer Science, National College, IN
5 SSM Research Center, Swiss School of Management, CH
1 Department of Computer Science and Engineering, Malla Reddy College of Engineering and Technology, IN
2 Department of Electronics and Communication Engineering, R.M.K. Engineering College, IN
3 Department of Computer Science and Engineering, B.N. College of Engineering and Technology, IN
4 Department of Computer Science, National College, IN
5 SSM Research Center, Swiss School of Management, CH
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 2 (2023), Pagination: 3149-3154Abstract
Video surveillance systems play a crucial role in ensuring public safety and security. However, the traditional methods of surveillance often fall short in effectively identifying individuals, particularly in crowded or dynamic environments. This research addresses the limitations of conventional video surveillance by proposing a machine learning-based facial recognition system. The increasing demand for robust security measures necessitates the development of advanced technologies in video surveillance. Facial recognition has emerged as a promising solution, but existing systems struggle with accuracy and efficiency. This research aims to bridge these gaps by leveraging machine learning techniques for facial recognition in video surveillance. Conventional video surveillance struggles with accurate and rapid identification of individuals, leading to potential security lapses. This research addresses the challenge of enhancing facial recognition accuracy in real-time video feeds, especially in scenarios with varying lighting conditions and occlusions. While facial recognition has gained traction, there is a significant research gap in the implementation of machine learning algorithms tailored for video surveillance. This study aims to fill this void by proposing a novel methodology that combines deep learning and computer vision techniques for robust facial recognition in dynamic environments. The proposed methodology involves training a deep neural network on a diverse dataset of facial images to enable the model to learn intricate facial features. Additionally, computer vision algorithms will be employed to handle challenges such as occlusions and varying lighting conditions. The model's performance will be evaluated using real-world video surveillance data. Preliminary results demonstrate a significant improvement in facial recognition accuracy compared to traditional methods. The machine learning-based system exhibits enhanced performance in challenging scenarios, showcasing its potential for practical implementation in video surveillance systems.Keywords
Facial Recognition, Machine Learning, Video Surveillance, Deep Learning, Computer VisionReferences
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