Computer Vision and Deep Learning Transforming Image Recognition and Beyond

International Journal of Computer Science and Information Technology, Journal Year: 2024, Volume and Issue: 2(1), P. 45 - 51

Published: March 6, 2024

Computer vision is a cutting-edge information processing technology that seeks to mimic the human visual nervous system. Its primary aim emulate psychological processes of interpret and depict objective scenery. This revolutionary field encompasses wide range applications, including life sciences, medical diagnosis, military operations, scientific research, many others. At heart computer lies theoretical core, which includes deep learning, image recognition, target detection, tracking These elements combine enable computers process, analyze, understand images, allowing for classification objects based on various patterns One standout advantages learning techniques, when compared traditional methods, their ability automatically learn adapt specific features required given problem. adaptive nature networks has opened up new possibilities paved way remarkable breakthroughs in vision. paper examines practical application convolutional neural (CNNs) elucidates advancements artificial intelligence within recognition. It does so by showcasing tangible benefits functionalities these technologies.

Language: Английский

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Comprehensive implementation of TextCNN for enhanced collaboration between natural language processing and system recommendation

Published: June 13, 2024

Natural Language Processing (NLP) constitutes a pivotal domain of artificial intelligence focused on enabling computers to comprehend, process, and generate human language. Text classification, fundamental NLP task, aims categorize text into predefined classes. In recent years, deep learning has emerged as dominant force across various research domains become staple technology within NLP, particularly in classification tasks. Unlike numerical visual data, processing underscores the need for nuanced capabilities. Traditional methodologies typically involve preprocessing textual data annotating samples manually derive effective feature presentations using classical machine algorithms. This paper delves current landscape applications specifically three core areas: representation, sequence modeling, knowledge representation. Furthermore, it explores advancements synergies facilitated by natural language realm while also addressing challenges posed adversarial techniques intext generation, semantic parsing. An empirical investigation tasks demonstrates efficacy interactive integration training, tandem with TextCNN, underscoring role these augmenting refining methodologies.

Language: Английский

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Enhancing Computer Digital Signal Processing through the Utilization of RNN Sequence Algorithms

International Journal of Computer Science and Information Technology, Journal Year: 2023, Volume and Issue: 1(1), P. 60 - 68

Published: Dec. 30, 2023

With the increase in computing power and availability of large amounts data, deep learning techniques, especially convolutional neural networks (CNNS) recurrent (RNNS), have become important tools for processing complex signals. These methods show excellent performance speech recognition, image processing, natural language so on. In this paper, we explore application network (RNN) sequence algorithms field computer digital signal highlighting current artificial intelligence techniques their capabilities solving problems. First, paper reviews basic principles development RNN algorithms, advances these advanced technologies made simulating way human brain processes information. The practical effect algorithm are demonstrated through experimental data. By comparing with traditional demonstrate efficiency accuracy signals, such as recognition noisy environments real-time video data processing. not only effectiveness RNNS field, but also highlight unique advantages when dealing high-dimensional Through empirical studies, aims to provide researchers engineers an in-depth understanding potential applications, looks forward future direction technology field.

Language: Английский

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Exploring New Frontiers of Deep Learning in Legal Practice: A Case Study of Large Language Models

International Journal of Computer Science and Information Technology, Journal Year: 2023, Volume and Issue: 1(1), P. 131 - 138

Published: Dec. 30, 2023

The LLM wave brought by ChatGPT has swept various vertical fields. Medical, finance, finance and other fields have gradually had their own exclusive large models, such as BloombergGPT, herbal medicine, Huatu, ChatMed, etc. In the legal field, we seen LawGPT Lawyers-LLAMA two preliminary open source models. People often think that using domain-specific knowledge to fine-tune model, you can get satisfactory results, but because of its inherent requirements for accuracy, simply fine-tuning with some dialogue data is not enough support needs real scenarios. Therefore, language model obviously most active field AI at present, source/closed models continue emerge, new research papers emerge in an endless stream, practitioners, how truly understand capabilities limitations apply model? present long been worth thinking deeply. this paper, propose a law called ChatLaw.

Language: Английский

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Towards Accurate and Reliable Fault Diagnosis in PV Systems: Techniques, Challenges, and Future Directions

Process Safety and Environmental Protection, Journal Year: 2025, Volume and Issue: unknown, P. 107217 - 107217

Published: April 1, 2025

Language: Английский

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AI Empowered of Advancements in Microbial and Tumor Cell Image Labeling for Enhanced Medical Insights

Journal of Theory and Practice of Engineering Science, Journal Year: 2024, Volume and Issue: 4(03), P. 21 - 27

Published: March 19, 2024

The traditional diagnosis model of tumor pathology depends on the experience doctor, which is inevitably subjective. With rapid development modern medical imaging, imaging technology has formed a system composed UI, CT, CR, DR, MRI, PET, PET-CT, digital subtraction angiography and PACS by single ordinary X-ray angiography. continuous enrichment changed from "auxiliary examination means" to most important clinical differential method in medicine. application scan sections clinicopathology, computer artificial intelligence (AI) assisted developed rapidly analysis tissue images. This paper summarizes progress AI pathology, describes exploration field quantitative histopathological molecular markers closely related treatment recent years, so as provide useful reference for intelligent model.

Language: Английский

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Demystifying Machine Learning

Saudi Journal of Engineering and Technology, Journal Year: 2024, Volume and Issue: 9(07), P. 299 - 303

Published: July 9, 2024

This paper delves into the rapidly evolving domain of Artificial Intelligence (AI), with a particular focus on Machine Learning (ML), dynamic and influential subset AI. It explores how ML empowers computers to learn from data, identify patterns, make decisions minimal human intervention. The manuscript examines broad utility across various real-world scenarios, emphasizing its critical role in enabling organizations evolve maintain competitive edge fast-paced technological landscape. discusses necessity for adopt new ways working embrace opportunities presented by AI remain viable global, online marketplace. reviews evolution ML, evaluates advantages disadvantages, contemplates future directions could lead willing integrate this powerful technology. overarching theme is transformative potential reshaping organizational strategies operations more interconnected intelligent future.

Language: Английский

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Research on radon concentration measurement value correction based on FASTLOF and NPSO-BP neural network model

Radiation Measurements, Journal Year: 2024, Volume and Issue: 177, P. 107257 - 107257

Published: July 26, 2024

Language: Английский

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OLD AND NEW TECHNOLOGIES OF LEARNING DEVELOPMENT IN MODERN CONDITIONS

Published: Feb. 12, 2024

ПРОДУКТИВНІСТЬ ЛІСІВ ФІЛІЇ «

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Explainable Multi-Label Classification Framework for Behavioral Health Based on Domain Concepts

2021 IEEE International Conference on Big Data (Big Data), Journal Year: 2024, Volume and Issue: unknown, P. 6528 - 6537

Published: Dec. 15, 2024

Language: Английский

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