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Akademický článek

Implementation of AlexNet and Xception Architectures for Disease Detection in Orange Plants

Publikováno v: Journal of Applied Informatics and Computing, Vol 8, Iss 2, Pp 574-579 (2024)

Oranges are one of Indonesia's primary horticultural commodities, with production increasing each year. However, pest and disease infestations often go undetected, leading to significant reductions in crop yields. This study implements Convolutional

Externí odkaz: https://doaj.org/article/8cc9f94c5d0c49d7b42a953ee966e337

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Akademický článek

Rapid bacterial identification through volatile organic compound analysis and deep learning

Autor: Bowen Yan, Lin Zeng, Yanyi Lu, Min Li, Weiping Lu, Bangfu Zhou, Qinghua He

Publikováno v: BMC Bioinformatics, Vol 25, Iss 1, Pp 1-21 (2024)

Abstract Background The increasing antimicrobial resistance caused by the improper use of antibiotics poses a significant challenge to humanity. Rapid and accurate identification of microbial species in clinical settings is crucial for precise medica

Externí odkaz: https://doaj.org/article/05e29acde26741d4b4a69ee6fdcca7e2

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Akademický článek

Hybrid deep models for parallel feature extraction and enhanced emotion state classification

Autor: Sivasankaran Pichandi, Gomathy Balasubramanian, Venkatesh Chakrapani

Publikováno v: Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024)

Abstract Emotions play a vital role in recognizing a person’s thoughts and vary significantly with stress levels. Emotion and stress classification have gained considerable attention in robotics and artificial intelligence applications. While numer

Externí odkaz: https://doaj.org/article/b79b6e8ac8814a8694797071555aa7ea

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Akademický článek

A wavelet CNN with appropriate feed-allocation and PSO optimized activations for diabetic retinopathy grading

Autor: Chandrasekaran Raja, Santhosh Krishna B V, Balaji Loganathan, Sanjay Kumar Suman, L. Bhagyalakshmi, Mubarak Alrashoud, Jayant Giri, T. Sathish

Publikováno v: Automatika, Vol 65, Iss 4, Pp 1593-1605 (2024)

This work modifies the architecture of conventional CNN with the integration of Multi-resolution Analysis (MRA) in a CNN framework for Diabetic Retinopathy (DR) diagnosis and grading. Here, the HF sub-bands are subjected to optimized activations and

Externí odkaz: https://doaj.org/article/f6c7f939f119483ba7a026ce51e7be70

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Akademický článek

Real-time monitoring of urban roadway health: Utilizing GPR techniques for the early detection and classification of subsurface cavity diseases

Autor: Rohit Shrestha, Zhang Zhihou

Publikováno v: Discover Geoscience, Vol 2, Iss 1, Pp 1-20 (2024)

Abstract The effectiveness of ground penetrating radar (GPR) in identifying and categorizing diseases that occur underground beneath the surfaces of urban roads is investigated in this study. Both 2D and 3D forward modeling use simulation with the Gp

Externí odkaz: https://doaj.org/article/73ad61be1e6c41d8907f6b035aa5a396

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Akademický článek

Prediction of android ransomware with deep learning model using hybrid cryptography

Autor: K. R. Kalphana, S. Aanjankumar, M. Surya, M. S. Ramadevi, K. R. Ramela, T Anitha, N. Nagaprasad, Ramaswamy Krishnaraj

Publikováno v: Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)

Abstract In recent times, the number of malware on Android mobile phones has been growing, and a new kind of malware is Android ransomware. This research aims to address the emerging concerns about Android ransomware in the mobile sector. Previous st

Externí odkaz: https://doaj.org/article/61a32008e85c45d4add14571712ffbf8

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Akademický článek

Ứng dụng trí tuệ nhân tạo trong phân loại nguy cơ ung thư dựa trên hình ảnh X-quang tuyến vú

Autor: Nguyễn Chí Ngôn, Lê Đoàn Kết, Võ Văn Kha, Nguyễn Vĩnh Phong, Huỳnh Phương Anh, Đặng Phi Hùng

Publikováno v: Tạp chí Khoa học Đại học Cần Thơ, Vol 60, Iss 6 (2024)

Ung thư vú ở phụ nữ là loại ung thư phổ biến ở hầu hết các nước trên thế giới trong đó có Việt Nam. Sớm phát hiện và chẩn đoán chính xác là yếu tố quyết định quan trọng trong việc điều

Externí odkaz: https://doaj.org/article/a1d0321ce76f415b90fe854e8db2711b

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