Network-Computation in Neural Systems最新文献

The 5th generation (5 G) network is required to meet the growing demand for fast data speeds and the expanding number of customers. Apart from offering higher speeds, 5 G will be employed in other industries such as the Internet of Things, broadcast services, and so on. Energy efficiency, scalability, resiliency, interoperability, and high data rate/low delay are the primary requirements and obstacles of 5 G cellular networks. Due to IEEE 802.11p's constraints, such as limited coverage, inability to handle dense vehicle networks, signal congestion, and connectivity outages, efficient data distribution is a big challenge (MAC contention problem). In this research, vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) and vehicle-to-pedestrian (V2P) services are used to overcome bandwidth constraints in very dense network communications from cellular tool to everything (C-V2X). Clustering is done through multi-layered multi-access edge clustering, which helps reduce vehicle contention. Fuzzy logic and Q-learning and intelligence are used for a multi-hop route selection system. The proposed protocol adjusts the number of cluster-head nodes using a Q-learning algorithm, allowing it to quickly adapt to a range of scenarios with varying bandwidths and vehicle densities.

As the number of patients increases, physicians are dealing with more and more cases of degenerative spine pathologies on a daily basis. To reduce the workload of healthcare professionals, we propose a modified Swin-UNet network model. Firstly, the Swin Transformer Blocks are improved using a residual post-normalization and scaling cosine attention mechanism, which makes the training process of the model more stable and improves the accuracy. Secondly, we use the log-space continuous position biasing method instead of the bicubic interpolation position biasing method. This method solves the problem of performance loss caused by the large difference between the resolution of the pretraining image and the resolution of the spine image. Finally, we introduce a segmentation smooth module (SSM) at the decoder stage. The SSM effectively reduces redundancy, and enhances the segmentation edge processing to improve the model's segmentation accuracy. To validate the proposed method, we conducted experiments on a real dataset provided by hospitals. The average segmentation accuracy is no less than 95%. The experimental results demonstrate the superiority of the proposed method over the original model and other models of the same type in segmenting the spinous processes of the vertebrae and the posterior arch of the spine.

Plant diseases are rising nowadays. Plant diseases lead to high economic losses. Internet of Things (IoT) technology has found its application in various sectors. This led to the introduction of smart farming, in which IoT has been utilized to help identify the exact spot of the diseased affected region on the leaf from the vast farmland in a well-organized and automated manner. Thus, the main focus of this task is the introduction of a novel plant disease detection model that relies on IoT technology. The collected images are given to the Image Transmission phase. Here, the encryption task is performed by employing the Advanced Encryption Standard (AES) and also the decrypted plant images are fed to the pre-processing stage. The Mask Regions with Convolutional Neural Networks (R-CNN) are used to segment the pre-processed images. Then, the segmented images are given to the detection phase in which the Adaptive Dense Hybrid Convolution Network with Attention Mechanism (ADHCN-AM) approach is utilized to perform the detection of plant disease. From the ADHCN-AM, the final detected plant disease outcomes are obtained. Throughout the entire validation, the offered model shows 95% enhancement in terms of MCC showcasing its effectiveness over the existing approaches.

Retinal haemorrhage stands as an early indicator of diabetic retinopathy, necessitating accurate detection for timely diagnosis. Addressing this need, this study proposes an enhanced machine-based diagnostic test for diabetic retinopathy through an updated UNet framework, adept at scrutinizing fundus images for signs of retinal haemorrhages. The customized UNet underwent GPU training using the IDRiD database, validated against the publicly available DIARETDB1 and IDRiD datasets. Emphasizing the complexity of segmentation, the study employed preprocessing techniques, augmenting image quality and data integrity. Subsequently, the trained neural network showcased a remarkable performance boost, accurately identifying haemorrhage regions with 80% sensitivity, 99.6% specificity, and 98.6% accuracy. The experimental findings solidify the network's reliability, showcasing potential to alleviate ophthalmologists' workload significantly. Notably, achieving an Intersection over Union (IoU) of 76.61% and a Dice coefficient of 86.51% underscores the system's competence. The study's outcomes signify substantial enhancements in diagnosing critical diabetic retinal conditions, promising profound improvements in diagnostic accuracy and efficiency, thereby marking a significant advancement in automated retinal haemorrhage detection for diabetic retinopathy.

Brain tumour (BT) is a dangerous neurological disorder produced by abnormal cell growth within the skull or brain. Nowadays, the death rate of people with BT is linearly growing. The finding of tumours at an early stage is crucial for giving treatment to patients, which improves the survival rate of patients. Hence, the BT classification (BTC) is done in this research using magnetic resonance imaging (MRI) images. In this research, the input MRI image is pre-processed using a non-local means (NLM) filter that denoises the input image. For attaining the effective classified result, the tumour area from the MRI image is segmented by the SegNet model. Furthermore, the BTC is accomplished by the LeNet model whose weight is optimized by the Golden Teacher Learning Optimization Algorithm (GTLO) such that the classified output produced by the LeNet model is Gliomas, Meningiomas, and Pituitary tumours. The experimental outcome displays that the GTLO-LeNet achieved an Accuracy of 0.896, Negative Predictive value (NPV) of 0.907, Positive Predictive value (PPV) of 0.821, True Negative Rate (TNR) of 0.880, and True Positive Rate (TPR) of 0.888.

Our approach includes picture preprocessing, feature extraction utilizing the SqueezeNet model, hyperparameter optimisation utilising the Equilibrium Optimizer (EO) algorithm, and classification utilising a Stacked Autoencoder (SAE) model. Each of these processes is carried out in a series of separate steps. During the image preprocessing stage, contrast limited adaptive histogram equalisations (CLAHE) is utilized to improve the contrasts, and Adaptive Bilateral Filtering (ABF) to get rid of any noise that may be present. The SqueezeNet paradigm is utilized to obtain relevant characteristics from the pictures that have been preprocessed, and the EO technique is utilized to fine-tune the hyperparameters. Finally, the SAE model categorises the diseases that affect the grape leaf. The simulation analysis of the EODTL-GLDC technique tested New Plant Diseases Datasets and the results were inspected in many prospects. The results demonstrate that this model outperforms other deep learning techniques and methods that are more often related to machine learning. Specifically, this technique was able to attain a precision of 96.31% on the testing datasets and 96.88% on the training data set that was split 80:20. These results offer more proof that the suggested strategy is successful in automating the detection and categorization of grape leaf diseases.

Nowadays, wireless sensor networks (WSN) have gained huge attention worldwide due to their wide applications in different domains. The limited amount of energy resources is considered as the main limitations of WSN, which generally affect the network life time. Hence, a dynamic clustering and routing model is designed to resolve this issue. In this research work, a deep-learning model is employed for the prediction of energy and an optimization algorithmic technique is designed for the determination of optimal routes. Initially, the dynamic cluster WSN is simulated using energy, mobility, trust, and Link Life Time (LLT) models. The deep neuro-fuzzy network (DNFN) is utilized for the prediction of residual energy of nodes and the cluster workloads are dynamically balanced by the dynamic clustering of data using a fuzzy system. The designed Flamingo Jellyfish Search Optimization (FJSO) model is used for tuning the weights of the fuzzy system by considering different fitness parameters. Moreover, routing is performed using FJSO model which is used for the identification of optimal path to transmit data. In addition, the experimentation is done using MATLAB tool and the results proved that the designed FJSO model attained maximum of 0.657J energy, a minimum of 0.739 m distance, 0.649 s delay, 0.849 trust, and 0.885 Mbps throughput.

In the diagnosis of cardiac disorders Heart sound has a major role, and early detection is crucial to safeguard the patients. Computerized strategies of heart sound classification advocate intensive and more exact results in a quick and better manner. Using a hybrid optimization-controlled deep learning strategy this paper proposed an automatic heart sound classification module. The parameter tuning of the Deep Neural Network (DNN) classifier in a satisfactory manner is the importance of this research which depends on the Hybrid Sneaky optimization algorithm. The developed sneaky optimization algorithm inherits the traits of questing and societal search agents. Moreover, input data from the Phonocardiogram (PCG) database undergoes the process of feature extraction which extract the important features, like statistical, Heart Rate Variability (HRV), and to enhance the performance of this model, the features of Mel frequency Cepstral coefficients (MFCC) are assisted. The developed Sneaky optimization-based DNN classifier's performance is determined in respect of the metrics, namely precision, accuracy, specificity, and sensitivity, which are around 97%, 96.98%, 97%, and 96.9%, respectively.

Leaf infection detection and diagnosis at an earlier stage can improve agricultural output and reduce monetary costs. An inaccurate segmentation may degrade the accuracy of disease classification due to some different and complex leaf diseases. Also, the disease's adhesion and dimension can overlap, causing partial under-segmentation. Therefore, a novel robust Deep Encoder-Decoder Cascaded Network (DEDCNet) model is proposed in this manuscript for leaf image segmentation that precisely segments the diseased leaf spots and differentiates similar diseases. This model is comprised of an Infected Spot Recognition Network and an Infected Spot Segmentation Network. Initially, ISRN is designed by integrating cascaded CNN with a Feature Pyramid Pooling layer to identify the infected leaf spot and avoid an impact of background details. After that, the ISSN developed using an encoder-decoder network, which uses a multi-scale dilated convolution kernel to precisely segment the infected leaf spot. Moreover, the resultant leaf segments are provided to the pre-learned CNN models to learn texture features followed by the SVM algorithm to categorize leaf disease classes. The ODEDCNet delivers exceptional performance on both the Betel Leaf Image and PlantVillage datasets. On the Betel Leaf Image dataset, it achieves an accuracy of 94.89%, with high precision (94.35%), recall (94.77%), and F-score (94.56%), while maintaining low under-segmentation (6.2%) and over-segmentation rates (2.8%). It also achieves a remarkable Dice coefficient of 0.9822, all in just 0.10 seconds. On the PlantVillage dataset, the ODEDCNet outperforms other existing models with an accuracy of 96.5%, demonstrating high precision (96.61%), recall (96.5%), and F-score (96.56%). It excels in reducing under-segmentation to just 3.12% and over-segmentation to 2.56%. Furthermore, it achieves a Dice coefficient of 0.9834 in a mere 0.09 seconds. It evident for the greater efficiency on both segmentation and categorization of leaf diseases contrasted with the existing models.