PND-Net: plant nutrition deficiency and disease classification using graph convolutional network

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: July 5, 2024

Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified detected at early stages. Hence, continuous health monitoring of is very crucial handling stress. The deep learning methods have proven its superior performances in the automated detection deficiencies from visual symptoms leaves. This article proposes a new method disease classification using graph convolutional network (GNN), added upon base neural (CNN). Sometimes, global feature descriptor might fail to capture vital region diseased leaf, which causes inaccurate disease. To address this issue, regional holistic aggregation. In work, region-based summarization multi-scales explored spatial pyramidal pooling discriminative representation. Furthermore, GCN developed capacitate finer details classifying insufficiency nutrients. proposed method, called Plant Nutrition Deficiency Disease Network (PND-Net), has been evaluated on two public datasets deficiency, four backbone CNNs. best PND-Net as follows: (a) 90.00% Banana 90.54% Coffee deficiency; (b) 96.18% Potato 84.30% PlantDoc Xception backbone. additional experiments carried out generalization, achieved state-of-the-art datasets, namely Breast Cancer Histopathology Image Classification (BreakHis 40

Language: Английский

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Small size CNN (CAS-CNN), and modified MobileNetV2 (CAS-MODMOBNET) to identify cashew nut and fruit diseases

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: April 2, 2024

Language: Английский

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Sugar detection in adulterated honey using hyper-spectral imaging with stacking generalization method

Food Chemistry, Journal Year: 2024, Volume and Issue: 450, P. 139322 - 139322

Published: April 9, 2024

Language: Английский

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Machine learning based technique to predict the water adulterant in milk using portable near infrared spectroscopy

Journal of Food Composition and Analysis, Journal Year: 2024, Volume and Issue: 131, P. 106270 - 106270

Published: April 22, 2024

Language: Английский

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Isolated Video-Based Sign Language Recognition Using a Hybrid CNN-LSTM Framework Based on Attention Mechanism

Electronics, Journal Year: 2024, Volume and Issue: 13(7), P. 1229 - 1229

Published: March 26, 2024

Sign language is a complex that uses hand gestures, body movements, and facial expressions majorly used by the deaf community. recognition (SLR) popular research domain as it provides an efficient reliable solution to bridge communication gap between people who are hard of hearing those with good hearing. Recognizing isolated sign words from video challenging area in computer vision. This paper proposes hybrid SLR framework combines convolutional neural network (CNN) attention-based long-short-term memory (LSTM) network. We MobileNetV2 backbone model due its lightweight structure, which reduces complexity architecture for deriving meaningful features frame sequence. The spatial fed LSTM optimized attention mechanism select significant gesture cues frames focus on salient sequential data. proposed method evaluated benchmark WLASL dataset 100 classes based precision, recall, F1-score, 5-fold cross-validation metrics. Our methodology acquired average accuracy 84.65%. experiment results illustrate our performed effectively computationally efficiently compared other state-of-the-art methods.

Language: Английский

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Lung Cancer Classification Using Deep Learning Hybrid Model

Advances in medical technologies and clinical practice book series, Journal Year: 2024, Volume and Issue: unknown, P. 207 - 223

Published: March 11, 2024

Abnormal growths in the lungs caused by disease. The classification of CT scans is accomplished applying machine learning strategies. Classification methods based on deep learning, such as support vector machines, can categorize a wide variety image datasets and produce segmentation results highest caliber. In this work, we suggested method for feature extraction from images altering SVM CNN then hybrid model resulting those modifications (NNSVLC). For investigation, Kaggle dataset will be utilized. proposed was found to accurate 91.7% time, determined experiments.

Language: Английский

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Multimodal breast cancer hybrid explainable computer-aided diagnosis using medical mammograms and ultrasound Images

Journal of Applied Biomedicine, Journal Year: 2024, Volume and Issue: 44(3), P. 731 - 758

Published: July 1, 2024

Language: Английский

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Advanced Android Malware Detection: Merging Deep Learning and XGBoost Techniques

Bilişim Teknolojileri Dergisi, Journal Year: 2025, Volume and Issue: 18(1), P. 45 - 61

Published: Jan. 31, 2025

The increasing importance of Android devices in our lives brings with it the need to secure personal information stored on these devices, such as contact details, documents, location data, and browser data. These are often targeted by attacks malware designed steal this In response, work takes a novel approach detection integrating deep learning traditional machine algorithms. An extensive experimental study was conducted using DroidCollector network traffic analysis dataset. Eight different methods analysed for classification. first phase, experiments were both original stabilised datasets most effective identified. second best performing combined XGBoost This hybrid increased classification success 3-4%. highest F1 accuracy values obtained after 150 epochs training BiLSTM+XGBoost 95.12% 99.33% respectively. results highlight superiority combining techniques over individual models significantly improve accuracy. integrated method provides very important strategy developing high-performance various applications.

Language: Английский

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Improving breast cancer classification in fine-grain ultrasound images through feature discrimination and a transfer learning approach

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 106, P. 107690 - 107690

Published: Feb. 20, 2025

Language: Английский

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RADYOMİK ÖZELLİKLER VE MAKİNE ÖĞRENMESİ TEKNİKLERİYLE MEME TÜMÖRLERİNİN SINIFLANDIRILMASI

Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, Journal Year: 2025, Volume and Issue: 28(1), P. 38 - 50

Published: March 3, 2025

Meme kanseri, dünya genelinde kadınlar arasında en sık görülen kanser türüdür ve erken teşhis, tedavi başarısını önemli ölçüde artırmaktadır. Bu çalışmada, meme ultrason görüntülerinden iyi huylu kötü tümörleri sınıflandırmak amacıyla radyomik özellikler makine öğrenmesi teknikleri kullanılmıştır. Çalışmada, halka açık BUSI veri seti Sadece olarak etiketlenmiş görüntüler sınıflandırmada kullanılmış olup, normal etiketli çalışmaya dahil edilmemiştir. yaklaşım, modelin iki sınıf arasındaki ayrımı yüksek doğrulukla yapmasına odaklanmıştır. Veri setindeki dengesizlik, tümörlerin görüntülerinin y ekseninde aynalanarak artırılmasıyla giderilmiştir. PyRadiomics kütüphanesi ile çıkarılan 123 özellik arasından, önem skoru korelasyon matrisi kullanılarak 40 seçilmiştir. Sınıflandırma aşamasında XGBoost, Gradient Boosting, AdaBoost, SVM, Random Forest Decision Tree algoritmaları uygulanmış, doğruluk oranı (%98.13) Boosting algoritması elde edilmiştir.

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