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Colorization of Grayscale Images Using Neural Network Without User Intervention

Colorization of Grayscale Images Using Neural Network Withou...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Vinish Kumar Santosh Verma Gaurav Raj Department of Computer Science and Engineering (AI&ML) RKGIT Ghaziabad UP India Course Mentor-Geeksforgeeks Noida UP India

The task of colorizing grayscale images has been performed manually for a long time. Recently, neural networks have found their use in this domain. In this project we propose a system that takes a grayscale image as an input and produces a colored image of the same. The technique implemented in this project does not require complicated human inputs or image processing techniques, but rather training of the neural network much like a human mind. Where manual colorization methods can take months to color a single image, neural networks, once trained, are much more efficient in terms of time consumption and also comparable levels of accuracy can be achieved which depends upon training of the neural network. The Neural network works on hit and trial method; at first it makes a random prediction for a pixel, calculates the error and then tries to reduce it as much as possible by the method of backpropagation. The classifier, trained using backpropagation of information, predicts the color of a pixel based on the grayscale value of its own as well as the surrounding pixels in the grayscale image.

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Face Detection in Video Live Stream Using Deep Learning 2

Face Detection in Video Live Stream Using Deep Learning

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Amrita Kumar, Rajiv Joshi, Shivani Raj, Daksh Centre for Cyber Security and Cryptology Computer Science & Engineering Sharda School of Engineering & Technology Sharda University U.P. Greater Noida India G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Nvidia Ai Learning & Research Center U.P. Greater Noida India G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering U.P. Greater Noida India Galgotias College of Engineering and Technology Department of Computer Science and Engineering U.P. Greater Noida India

ISBN: (纸本)9798350371055

With the increasing popularity of live streaming platforms, there is an imperative need for robust methods for identifying individuals in real-time. The proposed system utilizes Convolutional Neural Networks (CNN) computer vision algorithms to identify faces in live streams and match them against a pre-existing database of known individuals. Additionally, the system employs deep learning and supervised learning techniques along with various types of CNN to achieve robust facial recognition in diverse lighting and environmental conditions. The system's accuracy is evaluated using various performance metrics, including precision, recall, and F1-score. The results demonstrate that the proposed system achieves high accuracy in person identification during real-time live streams. This system holds potential applications in a variety of domains, including security, entertainment, and social media. © 2024 IEEE.

关键词： computer vision Convolutional neural networks (CNN) deep learning F1-score Live streaming precision recall supervised learning

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A Smart Governance Architecture for Integrating Heterogeneous IoT Solutions within the Framework of Smart Cities

A Smart Governance Architecture for Integrating Heterogeneou...

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2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Madhurya, J.A. Padhi, Manoj Kumar Kondala Rao, P. Awasthy, Mohan Pareek, Piyush Kumar Gitam School of Technology Gitam University Department of Cse Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Hyderabad Institute of Technology and Management Department of Ece Hyderabad India Bharati Vidyapeeth Peeth Deemed to Be University Department of Engineering and Technology Navi Mumbai India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350301595

The Internet of Things (IoT) is a network of networked devices that links commonplace things to the Internet. The present IT infrastructure for managing IoT technologies lacks the necessary enterprise networking approach, which necessitates municipal collaboration. This is due to the compartmentalization of smart city app functionality. To integrate heterogeneous Internet of Things solutions within the framework of smart cities, we offer a smart governance architecture that integrates power needs, security constraints, and scalability components. This will promote cross-sector contacts at all levels and contribute to the accessibility of smart cities. This research proposal suggests a multi-level governance model for the distributed administration of IoT subsystems as a possible way to solve the problem of IoT systems in smart city applications that don't work together. As an important part of smart governance, the proposed architecture includes things like a one-of-a-kind model, open-source software, and digital objects. The goal of this research is to run a proof-of-concept simulation to test the suggested infrastructure. A lot of thought will be put into the solution for multi-level governance, as well as security and the resolution of digital objects with security constraints. The proposed infrastructure is meant to make it possible for different Internet of Things technologies and agents to work well together by providing security, repeatability, scalability, and interoperability. © 2023 IEEE.

关键词： Smart city

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Leveraging Large Language Models for Biomedical Knowledge Graph Construction and Querying: An Advanced NLP Approach 25

Leveraging Large Language Models for Biomedical Knowledge Gr...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Suramya Jadhav Suki Perumal Yasmin Tadavi Bikshita Dash Srinivasan Parthiban Computer Engineering Department Pune Institute of Computer Technology Pune India Department of CSE (AI & ML) PES University Bangalore India Department of Data Science and Engineering Indian Institute of Science Education and Research Bhopal India Aarupadai Veedu Medical College & Hospital Centre for AI in Medicine Vinayaka Missions Research Foundation Puducherry India

ISBN: (纸本)9798400713316

This paper introduces a novel methodology for constructing a comprehensive biomedical knowledge graph by applying advanced Natural Language Processing (NLP) techniques. By leveraging Large Language Models (LLMs) and a multifaceted prompt engineering approach, we effectively perform Named Entity Recognition (NER) and Relation Extraction (RE) on biomedical literature, targeting entities such as diseases, drugs, proteins, procedures, and symptoms. Our methodology incorporates eight distinct prompt engineering strategies for NER and a standardized approach for RE, facilitating the extraction of intricate inter-entity relationships. The resulting knowledge graph amalgamates diverse data sources into a unified framework, enabling efficient querying, visualization, and analysis of biomedical information. Furthermore, we present an innovative query processing pipeline that integrates GPT-3.5 turbo with the knowledge graph, allowing users to interact with the graph through natural language. This integrated system empowers the discovery of novel correlations, accelerating scientific research and fostering interdisciplinary collaboration. This represents a substantial contribution to the field of biomedical knowledge graph construction, offering a robust platform for accelerating scientific discovery and informing clinical decision-making.

关键词： knowledge graphs

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Natural Language Processing of Text-Based Metrics for Image Captioning 1

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International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2021

作者： Sengan, Sudhakar Sagar, P. Vidya Saravanan, N.P. Amarendra, K. Subburaj, Arjun Maheswari, S. Rangasamy, Rajasekar Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation A.P. Vaddeswaram India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai638060 India Tranxit Technology Solutions Pvt. Ltd. Tamil Nadu Chennai India Department of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering-AI & ML School Engineering Malla Reddy University Hyderabad India

ISBN: (数字)9789811900112

ISBN: (纸本)9789811900105

A rapidly increasing interest has been focused on natural language processing and computer vision study by automatically producing descriptive sentences for images in present trends. Image processing is a success factor, especially involving somaticized image knowledge and creating reliable, appropriately organized explanation phrases. In this paper, a dynamic program that uses the VCG16 platform to develop image descriptions and a long short-term memory (LSTM) to contain appropriate words with generated text keywords successfully is proposed. This paper determines the NLP method’s usefulness using Flickr8 K-statistical models and demonstrates that their model delivers accurate performance compared to the Bleu metric. The Bleu metric is an automated system for measuring a machine translation’s performance by monitoring the effectiveness of text transformed from natural to other languages. The results have been justified with images that satisfy the NLP algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Domain Aware Multi-task Pretraining of 3D Swin Transformer for T1-Weighted Brain MRI 17th

Domain Aware Multi-task Pretraining of 3D Swin Transformer ...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Kim, Jonghun Kim, Mansu Park, Hyunjin Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of Center for Neuroscience Imaging Research Institute for Basic Science Suwon Korea Republic of AI Graduate School Gwangju Institute of Science and Technology Gwangju Korea Republic of

ISBN: (纸本)9789819609000

The scarcity of annotated medical images is a major bottleneck in developing learning models for medical image analysis. Hence, recent studies have focused on pretrained models with fewer annotation requirements that can be fine-tuned for various downstream tasks. However, existing approaches are mainly 3D adaptions of 2D approaches ill-suited for 3D medical imaging data. Motivated by this gap, we propose novel domain-aware multi-task learning tasks to pretrain a 3D Swin Transformer for brain magnetic resonance imaging (MRI). Our method considers the domain knowledge in brain MRI by incorporating brain anatomy and morphology as well as standard pretext tasks adapted for 3D imaging in a contrastive learning setting. We pretrain our model using large-scale brain MRI data of 13,687 samples spanning several large-scale databases. Our method outperforms existing supervised and self-supervised methods in three downstream tasks of Alzheimer’s disease classification, Parkinson’s disease classification, and age prediction tasks. The ablation study of the proposed pretext tasks shows the effectiveness of our pretext tasks. Our code is available at ***/jongdory/DAMT. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neurodegenerative diseases

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Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction 39

Disconnected Emerging Knowledge Graph Oriented Inductive Lin...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Zhang, Yufeng Wang, Weiqing Yin, Hongzhi Zhao, Pengpeng Chen, Wei Zhao, Lei Soochow University School of Computer Science and Technology China Monash University Department of Data Science and Ai Australia The University of Queensland School of Information Technology and Electrical Engineering Australia

ISBN: (纸本)9798350322279

Inductive link prediction (ILP) is to predict links for unseen entities in emerging knowledge graphs (KGs), considering the evolving nature of KGs. A more challenging scenario is that emerging KGs consist of only unseen entities without any edge connected to original KGs, called as disconnected emerging KGs (DEKGs). Existing studies for DEKGs only focus on predicting enclosing links, i.e., predicting links inside the emerging KG. The bridging links, which carry the evolutionary information from the original KG to DEKG, have not been investigated by previous work so far. To fill in the gap, we propose a novel model entitled DEKG-ILP (Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction) that consists of the following two components. (1) The module CLRM (Contrastive Learning-based Relation-specific Feature Modeling) is developed to extract global relation-based semantic features that are shared between original KGs and DEKGs with a novel sampling strategy. (2) The module GSM (GNN-based Subgraph Modeling) is proposed to extract the local subgraph topological information around each link in KGs. The extensive experiments conducted on several benchmark datasets demonstrate that DEKG-ILP has obvious performance improvements compared with state-of-the-art methods for both enclosing and bridging link prediction. © 2023 IEEE.

关键词： Knowledge graph

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From Generalist to Specialist: A Survey of Large Language Models for Chemistry 31

From Generalist to Specialist: A Survey of Large Language Mo...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Han, Yang Wan, Ziping Chen, Lu Yu, Kai Chen, Xin X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence SJTU AI Institute Shanghai Jiao Tong University Shanghai China Suzhou Laboratory Suzhou China

ISBN: (纸本)9798891761964

Large Language Models (LLMs) have significantly transformed our daily life and established a new paradigm in natural language processing (NLP). However, the predominant pretraining of LLMs on extensive web-based texts remains insufficient for advanced scientific discovery, particularly in chemistry. The scarcity of specialized chemistry data, coupled with the complexity of multi-modal data such as 2D graph, 3D structure and spectrum, present distinct challenges. Although several studies have reviewed Pretrained Language Models (PLMs) in chemistry, there is a conspicuous absence of a systematic survey specifically focused on chemistry-oriented LLMs. In this paper, we outline methodologies for incorporating domain-specific chemistry knowledge and multi-modal information into LLMs, we also conceptualize chemistry LLMs as agents using chemistry tools and investigate their potential to accelerate scientific research. Additionally, we conclude the existing benchmarks to evaluate chemistry ability of LLMs. Finally, we critically examine the current challenges and identify promising directions for future research. Through this comprehensive survey, we aim to assist researchers in staying at the forefront of developments in chemistry LLMs and to inspire innovative applications in the field. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Enhanced Large Language Models

Enhanced Large Language Models

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International Conference on Inventive Computation Technologies (ICICT)

作者： J.R.V. Jeny G. Bharath Babu S. Jaganmohan Reddy N. Muthukumaran K. Abhiram Department of Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science Nalgonda Telangana Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

This work studies ways for improving the effectiveness of a language model in creating and understanding dialogue summaries. Two primary approaches are investigated, in the Complete Fine-Tuning step, a pre-trained FLAN-T5 model is fine-tuned using a dataset of dialog-summary pairs. The dataset is pre-processed to provide explicit instructions to the model, and the fine-tuning process involves training the entire model on the dataset. Qualitative and quantitative evaluations are then conducted to assess the effectiveness of the fine-tuned model in generating summaries. In the PEFT phase, a more computationally efficient method is employed, utilizing Low-Rank Adaptation (LoRA) to train a new layer/parameter adapter while keeping the underlying FLAN-T5 model frozen. This approach significantly reduces computational resources compared to Complete Fine-Tuning while maintaining comparable performance. The resulting model, equipped with the PEFT adapter, is evaluated qualitatively and quantitatively to measure its summarization capabilities. Both approaches sheds light on the compromises between computing efficiency and model performance.

关键词： Training Measurement Adaptation models Generative ai Computational modeling Natural languages Information retrieval

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Glioma Detection using Improved Artificial Neural Network in MRI Images 2

Glioma Detection using Improved Artificial Neural Network in...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Nandihal, Priya Shetty S, Vijaya Guha, Tapas Pareek, Piyush Kumar Global Academy of Technology Department of AI/ML Bengaluru India Nitte Meenakshi Institute of Technology Department of Computer Science Engineering Bangalore India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

The physical identification of tumors may be a laborious and time-consuming process for medical professionals because of the complex nature of the tumor and the noise involution that can occur in magnetic resonance (MR) imaging information. Therefore, determining the location of the tumor at an earlier stage is quite important. The medical scan may track and prognosticate the uncontrolled proliferation of cancer pretentious regions at different levels in order to deliver a felicitous diagnosis at an early time. This is accomplished via the utilisation of segmentation in conjunction with relegation procedures. In order to recognize the tissues of a brain tumor, segmentation of the picture obtained from the MRI is a crucial and challenging step. So, the proposed work includes the tumor segmentation process using Region Growth Algorithm with Gray-Level-Run-Length-Matrix and Centre-Symmetric-Local-Binary-Patterns texture feature extraction process. The segmented images undergo feature extraction process with higher level of accuracy. The performance metrics are measured using accuracy, sensitivity and specificity. The proposed work has 0.97% sensitivity, 0.85% specificity and 99.80% accuracy. © 2022 IEEE.

关键词： Tumors

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