Edugator is a browser-based, AI-enabled tool designed to help instructors of introductory computing courses create and deliver interactive educational content. It streamlines the content authoring process by incorpora...
详细信息
ISBN:
(纸本)9798400705328
Edugator is a browser-based, AI-enabled tool designed to help instructors of introductory computing courses create and deliver interactive educational content. It streamlines the content authoring process by incorporating generative AI models into both the creation and delivery stages. Instructors can create bespoke interactive computing lessons and programming problems by providing a prompt and a few clicks. They can also author templates and test cases in programming languages such as C++, Java, C, and Python. Additionally, instructors can validate programming problems by running them against an auto-generated solution, allowing them to refine the problems before releasing it to students, preventing misinformation or ambiguity. Students can complete lessons and solve programming problems in a browser-based text editor receiving immediate feedback. They can also interact with a large language model-powered AI chatbot that scaffolds a student on how to approach the problem without giving out solutions. Edugator is built using modern web frameworks and the goal of the tool is to accelerate the adoption of automated assessment tools by minimizing the challenges instructors face with such tools. It also supports Learning Tools Interoperability (LTI), allowing seamless integration with learning management systems (LMS). The demo will provide an overview of Edugator's features, including authoring programming problems and lessons using AI or remixing existing problems obtained from test banks, LTI integration, and AI-chatbot. More information about the tool can be found at https://***/ and https://***/edugatorlabs/resources
Using blockchain for student career prediction offers key benefits, including enhanced data security and privacy through encryption and immutability, ensuring personal information protection. It provides transparency ...
详细信息
ISBN:
(数字)9798331530389
ISBN:
(纸本)9798331530396
Using blockchain for student career prediction offers key benefits, including enhanced data security and privacy through encryption and immutability, ensuring personal information protection. It provides transparency and trust with a tamper-proof record of achievements and skills, fostering confidence among employers and educational institutions. Decentralization eliminates the need for a central authority, reducing risks of data loss or manipulation. Additionally, blockchain enables accurate traceability of student progress, ensuring accountability in career prediction data. Using machine learning to develop a prediction model for student career prediction offers substantial benefits by enabling personalized, data-driven insights that can significantly enhance career guidance. With this vast amount of data from various sources, identifying complex patterns and correlations that may not be evident through traditional analysis. This allows for highly accurate, individualized career recommendations, reducing biases and ensuring fair evaluations. Moreover, this model will continuously improve over time, adapting to new data and refining predictions, ultimately leading to more informed and effective career planning for students. Applying machine learning algorithms on student data stored on blockchain will provide exact information about students including their strengths and weaknesses both. This will lead to a better prediction and effecting planning.
The integration of renewable energy resources has made power system management increasingly complex. DRL is a potential solution to optimize power system operations, but it requires significant time and resources duri...
详细信息
ISBN:
(数字)9798350352528
ISBN:
(纸本)9798350352535
The integration of renewable energy resources has made power system management increasingly complex. DRL is a potential solution to optimize power system operations, but it requires significant time and resources during training. The control policies developed using DRL are specific to a single grid and require retraining from scratch for other grids. Training the DRL model from scratch is computationally expensive. This paper proposes a novel TL with a DRL framework to optimize VV C across different grids. This framework significantly reduces training time and improves VVC control performance by fine-tuning pre-trained DRL models for various grids. We developed a policy reuse classifier that transfers the knowledge from the IEEE-123 Bus system to the IEEE-13 Bus system. We performed an impact analysis to determine the effectiveness of TL. Our results show that TL improves the VVC control policy by 69.51 %, achieves faster convergence, and reduces the training time by 98.14%.
Animal biometric pattern is very much essential for individual identification. Nowadays as the Iris pattern of any individual animal is stable and unique in nature. Amphibians exhibit diverse color and geometrical iri...
详细信息
The main reason for a large portion of deaths related to cancer is caused by gastric cancer which is the worst form of disease. Endoscopy is the test that can identify this disease at its early stage which can result ...
详细信息
ISBN:
(数字)9798331519582
ISBN:
(纸本)9798331519599
The main reason for a large portion of deaths related to cancer is caused by gastric cancer which is the worst form of disease. Endoscopy is the test that can identify this disease at its early stage which can result in patient survival rates. This study tries to enhance the processing of gastroscopic images by using an enhanced Deep learning (DL) model to boost the accuracy of stomach cancer. Pre-processing frequently involves resizing and bilaterally filtering images to increase clarity and reduce noise. Gamma correction is then used to enhance contrast and make the images simpler to read. To function the model more consistently approaches like zooming, scaling, and rotation are applied to improve the images. The model was trained and evaluated in this research utilizing the Kvasir-SEG dataset, which contains thorough annotations for gastroscopic images. To gauge the outcomes, two important metrics were employed: Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). The processed photos show a very little decrease in quality, with Peak Signal-to-Noise Ratio PSNR values ranging from 50.65 to 52.94. The remarkably low Mean Squared Error (MSE) values (ranging from 0.0023 to 0.0029) show that the treated images are still fairly close to the originals. Based on the available data, this approach could significantly enhance the early detection of stomach cancer, allowing doctors to enhance and expedite patient care. In the future, an improved DL model will be used for lesion segmentation.
Various satellite images are used for scientific purposes; however, their availability is limited. To solve this problem, data augmentation is used. It is a widely used method to decrease model overfitting by increasi...
详细信息
ISBN:
(数字)9798331519582
ISBN:
(纸本)9798331519599
Various satellite images are used for scientific purposes; however, their availability is limited. To solve this problem, data augmentation is used. It is a widely used method to decrease model overfitting by increasing the number of training examples. Our goal is to increase the existing dataset while maintaining its credibility. For this, we have used stable diffusion to increase the dataset, followed by a vision transformer for image categorization. Vision transformers, in contrast to traditional techniques, divide images into patches and allow spatial connections, allowing data augmentation to expand the dataset via stable diffusion. This work provides insights into the technology architecture and training process and establishes a strong foundation for upcoming developments in the field by demonstrating the impact of ViT on satellite imagery interpretation coupled with stable diffusion. The efficiency of this model has been established as it achieved an accuracy of 99.16% in the SAT 6 dataset.
Gene Co-Expression Network (GCN) Analysis is fundamental for understanding gene-gene interactions and cellular processes. A co-expressed gene pair may exhibit patterns such as absolute, alternate, shifting, scaling, a...
详细信息
Road network extraction from satellite imagery plays a vital role in applications like autonomous navigation and urban development. This paper presents DFGNet, an advanced hybrid architecture designed to enhance the p...
详细信息
ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
Road network extraction from satellite imagery plays a vital role in applications like autonomous navigation and urban development. This paper presents DFGNet, an advanced hybrid architecture designed to enhance the precision and scalability of road segmentation tasks. DFGNet combines the strengths of D-LinkNet and FuseNet while introducing a novel Global Fusion Module (GFM) for effective feature integration. The encoder utilizes hybrid blocks with dilated convolutions to capture features across multiple scales, while residual connections ensure efficient feature propagation. In the decoder, the GFM optimally aligns spatial and feature information from the encoder, leading to superior segmentation accuracy. Additional refinement processes, including noise suppression and vectorization, further enhance the quality of extracted road networks. Evaluations on real-world remote sensing datasets highlight that DFGNet achieves competitive performance with an F1-score of 0.96 and an IoU of 0.79, surpassing several state-of-the-art approaches. This research offers a practical and robust solution for high-resolution road extraction, advancing the capabilities of remote sensing and geospatial analysis.
暂无评论