Network-Computation in Neural Systems最新文献

HCAR-AM ground nut leaf net: Hybrid convolution-based adaptive ResNet with attention mechanism for detecting ground nut leaf diseases with adaptive segmentation. HCAR-AM 坚果叶网：基于混合卷积的自适应 ResNet，采用注意力机制，通过自适应分割检测坚花叶病。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-11-17 DOI: 10.1080/0954898X.2024.2424248

Annamalai Thiruvengadam Madhavi, Kamal Basha Rahimunnisa

{"title":"HCAR-AM ground nut leaf net: Hybrid convolution-based adaptive ResNet with attention mechanism for detecting ground nut leaf diseases with adaptive segmentation.","authors":"Annamalai Thiruvengadam Madhavi, Kamal Basha Rahimunnisa","doi":"10.1080/0954898X.2024.2424248","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2424248","url":null,"abstract":"Estimating the optimal answer is expensive for huge data resources that decrease the functionality of the system. To solve these issues, the latest groundnut leaf disorder identification model by deep learning techniques is implemented. The images are collected from traditional databases, and then they are given to the pre-processing stage. Then, relevant features are drawn out from the preprocessed images in two stages. In the first stage, the preprocessed image is segmented using adaptive TransResunet++, where the variables are tuned with the help of designed Hybrid Position of Beluga Whale and Cuttle Fish (HP-BWCF) and finally get the feature set 1 using Kaze Feature Points and Binary Descriptors. In the second stage, the same Kaze feature points and the binary descriptors are extracted from the preprocessed image separately, and then obtain feature set 2. Then, the extracted feature sets 1 and 2 are concatenated and given to the Hybrid Convolution-based Adaptive Resnet with Attention Mechanism (HCAR-AM) to detect the ground nut leaf diseases very effectively. The parameters from this HCAR-AM are tuned via the same HP-BWCF. The experimental outcome is analysed over various recently developed ground nut leaf disease detection approaches in accordance with various performance measures.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-41"},"PeriodicalIF":1.1,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Kruskal Szekeres generative adversarial network augmented deep autoencoder for colorectal cancer detection. 用于结直肠癌检测的 Kruskal Szekeres 生成对抗网络增强型深度自动编码器。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-11-16 DOI: 10.1080/0954898X.2024.2426580

Suresh Kumar Krishnamoorthy, Vanitha Cn

{"title":"Kruskal Szekeres generative adversarial network augmented deep autoencoder for colorectal cancer detection.","authors":"Suresh Kumar Krishnamoorthy, Vanitha Cn","doi":"10.1080/0954898X.2024.2426580","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2426580","url":null,"abstract":"Cancer involves abnormal cell growth, with types like intestinal and oesophageal cancer often diagnosed in advanced stages, making them hard to cure. Symptoms are like burning sensations in the stomach and swallowing difficulties are specified as colorectal cancer. Deep learning significantly impacts the medical image processing and diagnosis, offering potential improvements in accuracy and efficiency. The Kruskal Szekeres Generative Adversarial Network Augmented Deep Autoencoder (KSGANA-DA) is introduced for early colorectal cancer detection and it comprises two stages; Initial stage, data augmentation uses Affine Transform via Random Horizontal Rotation and Geometric Transform via Kruskal-Szekeres that coordinates to improve the training dataset diversity, boosting detection performance. The second stage, a Deep Autoencoder Anatomical Landmark-based Image Segmentation preserves edge pixel spatial locations, improving precision and recall for early boundary detection. Experiments validate KSGANA-DA performance and different existing methods are implemented into Python. The results of KSGANA-DA are to provide higher precision by 41%, recall by 7%, and lesser training time by 46% than compared to conventional methods.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-27"},"PeriodicalIF":1.1,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Can human brain connectivity explain verbal working memory? 人脑连通性能否解释言语工作记忆？

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-11-12 DOI: 10.1080/0954898X.2024.2421196

Maxime Carriere, Rosario Tomasello, Friedemann Pulvermüller

{"title":"Can human brain connectivity explain verbal working memory?","authors":"Maxime Carriere, Rosario Tomasello, Friedemann Pulvermüller","doi":"10.1080/0954898X.2024.2421196","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2421196","url":null,"abstract":"The ability of humans to store spoken words in verbal working memory and build extensive vocabularies is believed to stem from evolutionary changes in cortical connectivity across primate species. However, the underlying neurobiological mechanisms remain unclear. Why can humans acquire vast vocabularies, while non-human primates cannot? This study addresses this question using brain-constrained neural networks that realize between-species differences in cortical connectivity. It investigates how these structural differences support the formation of neural representations for spoken words and the emergence of verbal working memory, crucial for human vocabulary building. We develop comparative models of frontotemporal and occipital cortices, reflecting human and non-human primate neuroanatomy. Using meanfield and spiking neural networks, we simulate auditory word recognition and examine verbal working memory function. The \"human models\", characterized by denser inter-area connectivity in core language areas, produced larger cell assemblies than the \"monkey models\", with specific topographies reflecting semantic properties of the represented words. Crucially, longer-lasting reverberant neural activity was observed in human versus monkey architectures, compatible with robust verbal working memory, a necessary condition for vocabulary building. Our findings offer insights into the structural basis of human-specific symbol learning and verbal working memory, shedding light on humans' unique capacity for large vocabulary acquisition.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-42"},"PeriodicalIF":1.1,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142632807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic screening of retinal lesions for detecting diabetic retinopathy using adaptive multiscale MobileNet with abnormality segmentation from public dataset. 利用自适应多尺度 MobileNet 对公共数据集进行异常分割，自动筛查视网膜病变以检测糖尿病视网膜病变。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-11-09 DOI: 10.1080/0954898X.2024.2424242

Nandhini Selvaganapathy, Saravanan Siddhan, Parthasarathy Sundararajan, Sathiyaprasad Balasundaram

{"title":"Automatic screening of retinal lesions for detecting diabetic retinopathy using adaptive multiscale MobileNet with abnormality segmentation from public dataset.","authors":"Nandhini Selvaganapathy, Saravanan Siddhan, Parthasarathy Sundararajan, Sathiyaprasad Balasundaram","doi":"10.1080/0954898X.2024.2424242","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2424242","url":null,"abstract":"Owing to the epidemic growth of diabetes, ophthalmologists need to examine the huge fundus images for diagnosing the disease of Diabetic Retinopathy (DR). Without proper knowledge, people are too lethargic to detect the DR. Therefore, the early diagnosis system is requisite for treating ailments in the medical industry. Therefore, a novel deep model-based DR detection structure is recommended to fix the aforementioned difficulties. The developed deep model-based diabetic retinopathy detection process is performed adaptively. The DR detection process is imitated by garnering the images from benchmark sources. The gathered images are further preceded by the abnormality segmentation phase. Here, the Residual TransUNet with Enhanced loss function is used to employ the abnormality segmentation, and the loss function in this structure may be helpful to lessen the error in the segmentation procedure. Further, the segmented images are passed to the final phase of retinopathy detection. At this phase, the detection is carried out through the Adaptive Multiscale MobileNet. The variables in the AMMNet are optimized by the Adaptive Puzzle Optimization to obtain better detection performance. Finally, the effectiveness of the offered approach is confirmed by the experimentation procedure over various performance indices.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-33"},"PeriodicalIF":1.1,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142632789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0