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共 71279 条记录，以下是91-100 订阅

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Deep Learning Based Model to Recommend Safe Route Navigation System 2

Deep Learning Based Model to Recommend Safe Route Navigation...

引用

2nd international conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Garg, Sourav Mittal, Aashu Sathiyasuntharam, V. Department of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (纸本)9798331530389

The level of crime rates in practically every part of the world has risen to unprecedented levels. These are tough times that call for extreme methods to protect individuals, particularly those who must travel to unknown and known locations on a daily basis. Regardless of the mode of transportation used by the victim walking, driving one's own car, using a public transportation vehicle, autorickshaws, or taxis the majority of these crimes happen when the victim is traveling. This research suggests a Deep Learning Based Model To Recommend Safe Route Navigation System that shows the user a safe path shown on maps and based on the area's historical criminal history. Our approach is implemented in a hierarchical manner: a Decision Network is used to determine the user-specific attributes in the first place, and then that same Decision Network is used to activate the creation of safe routes via Geospatial Data Analysis. Users can view the navigated routes of the effective system as a colour coded map. Acknowledging that the real consequences which our research strive for should produce, we validated our model with specific data of San Francisco city for demonstrative purposes. © 2025 ieee.

关键词： Deep learning

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Distributed-computing based versatile healthcare services framework for diagnostic markers 2nd

Distributed-computing based versatile healthcare services fr...

引用

2nd international conference on Next-Generation Computing and information Systems, ICNGCIS 2023

作者： Chunduri, Venkata Posinasetty, Bhargavi Son, Mukesh Byeon, Haewon Department of Mathematics & Computer Science Indiana State University United States The University of Southern Mississippi HattiesburgMS United States Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Pune Tathawade India Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of

ISBN: (纸本)9781032738659

Recently, there has been a boom in imaginative medical services technologies that have enhanced the delivery of medical care data. Human-to-human and gadget-to-gadget networks are crucial in people’s lives and function 24 hours a day, seven days a week. MobileCloud-Computing becomes a crucial tool in this regard. Given the tremendous improvements in the Internet of Things, such a combination has become a major worry. This study developed a framework that included two essential application components: flexible usage and a server request. The server application records patient data while a Counterfeit Neural Network (CNN) module distinguishes between the two stroke subtypes. Similarly, as a framework for stroke patients, our model ensures accessibility, security, and adaptability by utilising the Stroke dataset for CNN computation and the Multilayer Perceptron Algorithm (MLP), which has been completed without precedent for working with large amounts of data in this extension. © 2025 The Author(s).

关键词： Hospital data processing

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Augmented Reality in Education: Enhancing Learning Experiences 2

Augmented Reality in Education: Enhancing Learning Experienc...

引用

2nd international conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Sharma, Rakshit Gupta, Naman Choudhary, Rohan Budhiraja, Aastha School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (纸本)9798331530389

The integration of Augmented Reality (AR) into learning environments is still an area of significant challenge in light of increasingly dynamic technological landscapes. Whereas the traditional methods of learning do not appeal much to digitally native learners, the existing gap in terms of the systematic improvement of outcomes lies in the understanding of immersive technologies. This paper combines both quantitative assessments of learning and qualitative phenomenological analyses from three different educational settings: schools at the secondary level, institutions of higher learning, and professional training centers. Through an effective intervention involving 742 participants using cutting-edge AR learning platforms, our study has revealed significant cognitive and engagement-related improvements. The groups experiencing AR-enhanced curricula showed a 37% increase in knowledge retention, as well as 42% higher engagement rates, along with significantly improved spatial reasoning abilities. Qualitative data further support the idea that motivation improves, as participants indicated better conceptual understanding and greater independence in learning. The findings indicate that AR is transformative in educational practice, making it simple to bring theoretical knowledge and experiential learning together. Here, the merging of digital information with the students' physical learning environment is not merely a technological intervention, but a fundamental redesign of pedagogical approaches that promises a more interactive, personalized, and immersive education paradigm. © 2025 ieee.

关键词： Contrastive Learning

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KrishiAI: Architecture for Harnessing Capabilities of LLMs for Delivery of Accurate Cultivation information to Farmers 2nd

KrishiAI: Architecture for Harnessing Capabilities of LLMs f...

引用

2nd international conference on Agriculture-Centric Computation

作者： Kumar, Mohit Wilson, Shyama Goel, Neeraj Ginige, Athula Indian Inst Technol Ropar India Western Sydney Univ Penrith NSW Australia

ISBN: (纸本)9783031744396;9783031744402

With the constantly evolving agricultural environment, farmers need timely information relevant to their context to make informed decisions. Although several digital solutions have been introduced to address this requirement, these disregard farmers' context in providing information, including information quality. To address this gap, we developed a chatbot in this study incorporating the latest advances in computing techniques. We created an architecture to harness current Large Language Model (LLM) capabilities, including knowledge modeling, to deliver context-specific information to users through questions and answers. The context was established from previous interactions in the form of a dialogue. Neo4J graph database was used to create the knowledge base, ensuring efficient storage and retrieval of contextual information. LLM was used to convert user queries into Cypher queries and to transform knowledge base responses into natural language. A sanitiser module was developed to verify the accuracy of the Cypher queries generated through LLM, significantly enhancing the system's overall reliability. The primary evaluation proved that transitioning from a traditional document-based format to a question-and-answering format and then to a dialogue-based format significantly enhances farmers' ability to access the desired information effectively and promptly.

关键词： LLM Knowledge base Question and Answering GPT

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Machine learning-based approaches for the diagnosis and detection of brain stroke 2nd

Machine learning-based approaches for the diagnosis and dete...

引用

2nd international conference on Next-Generation Computing and information Systems, ICNGCIS 2023

作者： Panchal, Trupti Ramasubramanian, Sugayaalini Kumar, Yogesh Department of Mechanical Engineering Pandit Deendayal Energy University Gujarat Gandhinagar India Department of Computer Science and Engineering Pandit Deendayal Energy University Gujarat Gandhinagar India

ISBN: (纸本)9781032738659

This paper presents an in-depth analysis on brain stroke prediction using machine learning based approaches for prediction and classification. We take on a wide range of data sources, including demographic data, medical records, lifestyle conditions, and clinical measures for a prediction model. We managed to determine significant patterns and risk factors related to stroke owing to the dataset utilised in this study. Feature selection techniques are utilised to identify the most relevant predictors, enhancing the efficiency and interpretability of the model. In addition, we study alternative data preparation methods, such as managing missing values, outlier identification, and feature scaling, to ensure data quality and model resilience. © 2025 The Author(s).

关键词： Forecasting

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The Symphony of Style: An Extensive Examination of Determination Mechanisms in Deep Learning-Inspired Outfit Suggestions 2

The Symphony of Style: An Extensive Examination of Determina...

引用

2nd international conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Guru Prasad, M.S. Bhosagi, Ravatappa A. Madhusudhana, K. Prithviraj Chandrappa, S. Kumar, Vikash Dehradun India School of Computing and Information Technology Reva University Bengaluru India Dept. of Electronics & Communication Engineering SDM Institute of Technology Ujire India NMAMIT Mangalore India Bengaluru India Department of Computer Application Graphic Era Deemed University Dehradun India

ISBN: (纸本)9798331530389

In the field of the fashion industry, the introduction of deep learning technology has taken an innovative revolution by providing assistance in the construction of intellectual systems that are intelligent to deliver personalized ensemble recommendations. In this research work, an innovative ensemble recommendation structure called Deep Learning VGG16 (Visual Geometry Group 16) recommender is presented. The proposed method allows a deep learning model VGG16 to understand complex patterns and developments resulting from a vast group of fashion images. In order to increase the user experience and greater participation in the well-established fashion industry, the system offers personalized ensemble ideas. The important, core and fundamental component of the system is VGG16, consisting of 13 convolutional layers and 3 fully connected layers, which is used to support the analysis of fashion images. A cutting-edge convolutional neural network (CNN) model is improved over a large and diverse collection of fashion images. Model will extract advanced high-level information and statistics with the capability to distinguish particulars in colour patterns, clothing combinations, and stylistic elements. Using this process of development, the system becomes better at identifying user preferences and fashion trends. © 2025 ieee.

关键词： Convolutional neural networks

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Medical BCFL: A blockchain-enabled federated learning architecture for secure healthcare data sharing 24

Medical BCFL: A blockchain-enabled federated learning archit...

引用

2nd international conference on Artificial Intelligence, Systems and Network Security, AISNS 2024

作者： Dong, Qianyu Liu, Jiazheng Jiang, Yuchen School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Guangdong Shenzhen China School of Computer Science and Technology DongHua University Shanghai China

ISBN: (纸本)9798400711237

In response to the growing demand for secure healthcare systems, this paper disaplys a blockchain-enabled federated learning architecture called Medical BCFL. The architecture aims to address privacy concerns and facilitate secure healthcare data sharing by combining the decentralized nature of blockchain with the collaborative learning model of federated learning. First, a blockchain system is built through smart contracts to record model updates and participant reputations for each round of communication. Next, a federated learning system is built to ensure privacy protection during collaborative model training and encourage data sharing through a reward mechanism. Ultimately, a loosely coupled architecture is used to integrate the blockchain with the federated learning system, where model training takes place in the federated learning system and intermediate information is synchronized to the blockchain. The proposed method achieves over 92% accuracy in MNIST dataset in an adversarial setting of 40% malicious clients and 8% accuracy loss in a real-world BloodMNIST dataset with the same proportion of malicious clients. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Evaluating Generative Models: A Review of Challenges and Opportunities in Assessment of Generative Adversarial Networks 2

Evaluating Generative Models: A Review of Challenges and Opp...

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2nd international conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Shah, Akash Rakesh, Nitin Gulhane, Monali Agrawal, Pratik Kaur, Mandeep Dixit, Saurav Pune India Nagpur Campus Nagpur India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India

ISBN: (纸本)9798331530389

Generative adversarial networks (GANs) have achieved remarkable success in image synthesis but evaluating their quality remains an open challenge. Existing automated metrics like FID are unable to fully capture generation performance across both objective fidelity and subjective aesthetic attributes important to human perception. This paper conducts an extensive review of literature on GAN evaluation methodology. Key limitations of current approaches are analyzed through a discussion of popular metrics and their inability to predict human judgment comprehensively. To address this, a novel hybrid evaluation model is proposed that combines automated scores with human ratings to provide a more balanced assessment. A pilot study demonstrates the hybrid metric can predict human response more accurately. However, additional validation is required. The review emphasizes the need for evaluation systems integrating richer human input to better optimize models according to user priorities. Interactive frameworks and techniques from computational creativity may help advance assessment towards comprehensively benchmarking generative capacity and artistic exploration. © 2025 ieee.

关键词： Generative adversarial networks

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Enhanced Coffee Bean Defect Detection Using Super-Resolution GANs and Deep Learning 2

Enhanced Coffee Bean Defect Detection Using Super-Resolution...

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2nd international conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Prabu, S. Thanigaivelu, P.S. Poonkodi, S. Anand, M. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur TamilNadu Chennai603203 India

ISBN: (纸本)9798331530389

Image Super-Resolution (SR) is a key challenge in computer vision, focusing on enhancing low-resolution images while preserving details. This paper presents a novel method using Generative Adversarial Networks (GANs) for improving image quality, specifically targeting coffee bean defect detection. The proposed SR-GAN model includes a generator that upscales low-resolution inputs and a discriminator that differentiates between generated high-resolution images and real ones. Trained on a coffee bean dataset, the model optimizes loss functions to balance perceptual quality and fidelity, capturing subtle defects like cracks and discolorations. Enhanced images are processed by two Convolutional Neural Networks (CNNs): one identifies broad defect categories, such as defective black beans, brown beans, broken beans and non-defective high-quality beans. This layered approach ensures more precise defect identification and classification, overcoming limitations of previous methods that struggled with subtle defects. Performance evaluations show that SR-GAN and CNN significantly outperforms existing models in resolution enhancement and classification accuracy, offering a robust solution for improving quality control in coffee production. © 2025 ieee.

关键词： Convolutional neural networks

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Employing Vision Transformer for Monocular Depth Estimation 2

Employing Vision Transformer for Monocular Depth Estimation

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2nd international conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Chaudhary, Anubhav Tripathi, Dev Brat Mishra, Harsh Pathak, Prasoon Dixit, Ashish Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

Monocular depth estimation (MDE) is an important task in computer vision, it enables a range of applications like robotic navigation, augmented reality and also used in surgical guidance. This paper shows the use of ViTs for MDE and focuses on the Depth Anything V2(DA-V2) model. DAV2 model leverages a teacher-student training approach and uses the synthetic and unlabeled real images which are designed to enhance the accuracy of depth estimation. DA-V2 is fine-tuned on EndoSLAM dataset and contains challenging visual scenes from endoscopic images of the stomach, colon and small intestine to check the adaptability of the model to medical depth estimation. Our experiment shows that the fine-tuned DA-V2 outperforms all the conventional models like Endo-SfMLearner and achieves reductions in root mean squared error(RMSE) and absolute relative error, across all anatomical datasets. DA-V2 achieves the RMSE values 1.245, 1.028, and 2.021 for the stomach, colon, and intestine datasets, respectively as compared to the SfMLearner's 6.387, 6.657, and 14.787. The findings show that ViTs are capable of achieving higher accuracy and generalization in monocular depth estimation tasks. This study highlights the effectiveness of DA-V2 and Vision Transformers in Monocular Depth Estimation and opens up possibilities for further model optimization. © 2025 ieee.

关键词： Convolutional neural networks

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