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Building A Student Knowledge Based Ontology Approach Using Web Ontology Language 5

Building A Student Knowledge Based Ontology Approach Using W...

引用

5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Reddy, A. Brahmananda Akhil, Karnam Anuradha, Alampally Shriya, Golla Bainee, Kumkum Prasad, R. Sai Giridhar Vnr Vignana Jyothi Institute Of Engineering And Technology Department Of Computer Science Engineering Hyderabad India

ISBN: (纸本)9798350330861

Education is rapidly progressing and it is very important to have a bridge between innovative skills and conceptual learning. Education will provide learning abilities through various modes in sense of cognitive abilities and applying knowledge to gain optimality. Apart from these skills it is very important to have good behaviour to enhance the quality of life. Currently children are facing a lot of issues with academics, emotional imbalance, anxiety and depression, mental illness and lack of physical fitness. To overcome the above issues a knowledge-based ontology approach is presented and proposed using Web Ontology Language (OWL). To achieve this, survey was conducted among students of age group 1823 to analyse their SWOT analysis, emotional stability of a student, intelligent quotient by understanding their personas, habitat in schooling, intermediate, graduation, sports and curricular activities. Surveys were circulated through google forms, face to face interaction were conducted and responses were noted. Based on the responses from the survey analysis, entities and objects are identified and relationships between them were inter linked. Ontologies are predominantly used to identify the relations between entities and objects, using OWL on platform protégé editor, for the identified entities relations were build. The generated output which are in the form of graphs are termed as knowledge-base are stored in the inference engine. SWRL was used to validate the proposed ontology to provide the optimal results. © 2023 IEEE.

关键词： Ontology

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Handwritten Prescription Recognition Using VGG Based Architecture with Bi-LSTM

Handwritten Prescription Recognition Using VGG Based Archite...

引用

2024 International Research Conference on Smart Computing and Systems engineering, SCSE 2024

作者： Kavinda, Isuru Fernando, Harinda Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe Sri Lanka

ISBN: (纸本)9798350375688

Illegible handwriting on medical prescriptions poses a significant challenge, often leading to the misinterpretation of drug names and dosages. This issue primarily stems from doctors' use of Latin abbreviations, driven by time constraints during patient consultations. The resultant discrepancies in prescription interpretation can potentially expose patients to incorrect medications and dosages, leading to adverse effects. In response to this concern, this study proposes a novel system integrating Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) models. Promising findings are reported in this study, achieving a loss of 0.4874 and an accuracy of 0.83 in the latest trial. The final model offers a potential solution to the persistent problem of illegible prescriptions, enhancing patient safety in the healthcare domain. © 2024 IEEE.

关键词： Long short-term memory

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ScriptNet: Federated Learning CNN's Enhancements in Handwritten English Recognition

ScriptNet: Federated Learning CNN's Enhancements in Handwrit...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Suryavanshi, Ankita Mehta, Shiva Gopal, Lisa Choudhary, Ankur Hariharan, Shanmugasundaram Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350361537

This research intends to apply Federated Learning (FL) with Convolutional Neural Networks (CNNs) to realize this goal, which is the accurate recognition and categorization of English handwritten scripts and to make a step in the direction of Handwritten English Script Recognition Systems (HESRS). The clients do this by horizontally storing local data, and then they group the data into five categories that reflect five levels of Complexity in English scripts. The investigation of real-world customer data provides encouraging findings: Client ms-1 obtained an efficacy of 80.91% precision and 80.98% recall, which yielded a finality of 80.92% and an accuracy of 92%. By the end of the whole process, ms clients ms-2 to ms-5 reached the best level very quickly, with ms-5 having the highest accuracy of 89.9% along with precision (89.96%), recall (89.99%) and precision (89.97%). The averages of the data sets that were obtained through the aggregation via average, the overall averages ranged from 0.7956 for mp-2 to 0.8997 for mp-5. Hence, the young averages had almost the same trend, and the micro averages were slightly higher. Therefore, such a rating implies a consensus in the personal judgment and performance rating. The sub-least micro average marks its extreme estimated 89.98% at mp-5, meaning superpower performance modelling with all sets of information. The individual macro averages I have achieved are between 80.94% and 89.97%. The group weighted averages stay at 81.04% to 89.97%, whereas those for micro averages from customer mp-1 to mp-5 place them at slightly higher values of 81% to 89.98%, respectively. © 2024 IEEE.

关键词： Federated learning

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Bert-Ner: A Transformer-Based Approach For Named Entity Recognition 15

Bert-Ner: A Transformer-Based Approach For Named Entity Reco...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kaur, Navjeet Saha, Ashish Swami, Makul Singh, Muskan Dalal, Ravi Apex Institute of Technology Chandigarh University Department of Computer Science and Engineering Punjab Mohali India

ISBN: (纸本)9798350370249

The NER ('Named Entity Recognition') technique picks up on named entities including people, places, companies etc., that are in the unstructured data in a new language processing technique. But, it arises a fundamental issue of the lack of localization that cannot be feasible. In spite of the achieved top performance of supervised NER methods in resource-rich domains, they usually too slow and not so accurate in domains with little labeled data. Here, the work present a new model known as BERT-NER which is an exceptionally deep learning approach built on the BERT ('Bidirectional Encoder Representations from Transformers') architecture as its pre-trained language model. BERT-NER introduces the BERT installation to the NER mission making the context extraction be on the dot. BERT-NER fuses the BERT encoder and token classification layers together, optimized using cross-entropy loss. The experiment on the CoNLL-2003 dataset showed that BERT-NER could be an attention grabber as the scores were 93.12% precision, 94.46% recall, 93.79% F1 score, and 98.51% accuracy marking it as one of the field's top results. The qualitative evaluation on the other hand, shows brilliance in systemic solutions across multiple domains. This project not only is about named entity recognition but focuses on creating broader domain in order to enhance the flexibility of the process. Through benefiting from transfer learning and initialized language models, BERT-NER increases the capability of the BERT model, in this manner extending a list of its applications in NER. © 2024 IEEE.

关键词： BERT Classification Named Entity Recognition (NER) Neural Networks Tokenization

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A Review on the Role of Microrobots and Nanorobots used in Healthcare Domain 6

A Review on the Role of Microrobots and Nanorobots used in H...

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6th Parul International Conference on engineering and technology, PICET 2024

作者： Kumar, Shravan Kumar, Praveen Kumar, Rajan Kumar, Mukesh Kumar, Dhiraj Kumar, Gulshan Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India

ISBN: (纸本)9798350369748

Advanced technology such as microrobots and nanorobots have the potential to completely transform the healthcare industry. These tiny robotic devices provide fine control for a range of biological applications since they operate at the micro and nanoscales. This review explores the various healthcare uses, fabrication ideas, and design concepts of microrobots and nanorobots. It specifically looks at their works in tissue engineering, minimally invasive surgery, and targeted drug administration. Aiming to harness the transformative impact of these emerging technologies in healthcare, research and practitioners can recive significant insights from the discussion of challenges and future directions in the sector. Design-wise, microrobots and nanorobots display a range of configurations suited to particular uses, such as payload delivery systems, sensing capabilities, and propulsion mechanisms. © 2024 IEEE.

关键词： Microrobots

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Multi-frame Depth Estimation for Autonomous Driving Using Motion Masking and Self-supervised Learning 1

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2nd International Conference on Intelligent Computing Systems and Applications, ICICSA 2023

作者： Singh, Om Biswas, Anupam Paul, Rajdeep Department of Mathematics National Institute of Technology Silchar India Department of Computer Science and Engineering National Institute of Technology Silchar India

ISBN: (数字)9789819754120

ISBN: (纸本)9789819754113

Fisheye cameras are often employed in automatic driving for 360∘ near-field sensing around the car. This paper suggests a multi-task visual perception network using rectified fisheye photographs and motion masking to assist the vehicle in detecting its surroundings. However, strong nonlinear distortions constitute a trade-off and necessitate more complex algorithms. Dense-depth supervision in practice has shown promise and may be able to address the problem, even though it can be difficult to offer accurate methods for self-supervised learning. The supervision signal from the neighbouring frames is used to train self-supervised depth estimation networks to forecast scene depth. Additionally, the information between the consecutive frames consists of moving objects which has relative motion between them with respect to the motion of the camera which will produce bias and must be tackled with motion masking annotations before feeding to the network. Also, for further ablation study, the proposed system is trained with the unstructured environments of roads that most developing countries have. Thus, we used AdasInd dataset along with WoodScape and Kitti dataset. Further, to measure the accuracy of the output from the depth pixel, a novel measure relative pixel depth alignment (RPDA) is proposed. Most monocular networks do not make use of this additional signal, ignoring crucial information that can enhance expected depth. Therefore, we propose multi-frame depth estimation from fisheye images using self-supervised learning, a dense adaptive depth estimation method that makes use of the sequential data accessible during test time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Pixels

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Burning Neural Networks with Harary Graphs for Optimal Connectivity Control

Burning Neural Networks with Harary Graphs for Optimal Conne...

引用

International Conference on Computational engineering, ICCE 2023

作者： Rajan, R. Sundara Kannan, V. Kamala Navaneetan, M. Department of Mathematics Hindustan Institute of Technology and Science Tamil Nadu Chennai India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai India

ISBN: (纸本)9783031777639

Embark on a journey into the neural landscape where fires ignite, spread, and reveal the secrets of connectivity. This groundbreaking exploration introduces "NeuroBurning," a novel approach that computes the burning number for Harary graphs, unraveling the minimum activation needed to set the entire neural network ablaze. From controlling disease spread to optimizing brain stimulation, this study delves into the computation of the burning number and its captivating applications in understanding the intricate web of connections within the brain. In this study, we have calculated the burning number for Harary graphs and explored the dynamics of neural connectivity by examining this concept within the framework of Harary graphs. Additionally, we have determined the burning number for pencil graphs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph neural networks

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Solar Irradiance Forecasting Using MATLAB Platform

Solar Irradiance Forecasting Using MATLAB Platform

引用

International Conference on Green Energy and Sustainable technology, ICGEST 2023

作者： Roga, Sukanta Bardhan, Shawli Das, Soumyabrata Uppara, Abhijith Manikanta, Suragani Ashok Mechanical Engineering Department Visvesvaraya National Institute of Technology Nagpur Nagpur India Department of Computer Science and Engineering Techno College of Engineering Agartala Agartala India Electrical Engineering Department Visvesvaraya National Institute of Technology Nagpur Nagpur India

ISBN: (纸本)9789819610112

The solar power industry has emerged as a significant player in the growing energy market. In recent years, its grid penetration has increased correspondingly. Short-term solar forecasting accuracy is difficult to come by. It is crucial for solar park scheduling. Solar generation technologies have seen tremendous expansion in the energy market over the last decade or two, owing to increased renewable energy penetration in electrical grid systems. The solar resource is highly changeable, owing to a variety of factors. As a result of climate conditions, different grid and plant-level issues arise. As a result, forecasting methods are essential for various periods. With great accuracy, models for numerical weather prediction (NWP) are used in this article. This method applies to solar forecasts for the day-ahead forecasting and it is assessed by forecasting global outcomes. Irradiance was measured horizontally at five key solar power facility locations in the subcontinent of India. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Solar irradiance

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Enhancing Flower Classification Through Deep Learning: A Comprehensive Investigation

Enhancing Flower Classification Through Deep Learning: A Com...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Singh, Rahul Rajora, Ricky Chauhan, Rahul Devliyal, Swati Rajora, Aarju Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Chandigarh Engineering College Department of Computer Science & Engineering Mohali Jhanjeri India

ISBN: (纸本)9798350394399

Flower categorisation is crucial in botanical study, environmental monitoring, and ecological preservation. The ability to classify flowers accurately and effectively has been possible because of the exponential rise of visual data and the development of advanced deep-learning algorithms. To further automate botanical species identification, this investigation of multiple deep-learning methods for classifying flowers is presented in this research paper. We examine the effectiveness of cutting-edge convolutional neural networks (CNNs) and suggest fresh methods to improve their accuracy, resilience, and scalability. Our experimental analyses cover a range of floral datasets, taking into account difficulties including changes in lighting, size, and viewpoint. The results give information on the relative effectiveness of various models and their advantages and disadvantages. This research is expected to enhance botany, promote environmental conservation efforts, and inspire the creation of cutting-edge picture recognition systems as we work to automate the recognition of flower species. © 2024 IEEE.

关键词： Image recognition

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Real-Time Face Mask Detection System for Public Safety Based on ResNet50 & YOLOV2 15

Real-Time Face Mask Detection System for Public Safety Based...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Tun, Nay Kyi Godwin Premi, M.S. Myanmar Institute of Information Technology Mandalay Myanmar Department of Electronic Computer and Engineering Satayabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798350370249

The covid epidemic started in 2019 and has become the most terrifying disease that has destroyed many people's family lives, and it is frightening as an event that cannot be forgotten for the whole country to this day. To prevent the disease, the WHO has issued warnings to keep hands clean and to wear a mask whenever people go out to prevent the spread of the disease. For those involved in policy implementation;it is very difficult to identify people without a mask or wearing it in an improper manner. Crowded shopping centers, markets, hospitals, we all know that it has become an important matter to monitor whether the mask is mandatory in the departmental areas. However, monitoring in real time has become a waste of manpower and money. Therefore, even today, we still see the development of deep learning-based technologies that can monitor whether a facemask Detection based on CNN is implemented as one of the Deep Learning methods. In this system, transfer learning and ResNet-50 is used for Face Mask Detection. The dataset is collected from CCTV in the university and also standard dataset (Kaggel) for the training and testing. The main contribution of the proposed work is DL based real-time system development using the proposed CNN for determining whether the people on CCTV is wearing mask, not wearing mask and improperly mask. This system will provide better result in many position. © 2024 IEEE.

关键词： Classification CNN Detection ResNet50 YOLO

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