Network-Computation in Neural Systems最新文献

With the development of medical imaging amenities, a rising quantity of data emerges in the present image processing that has led to gradually more burden for data transmission and storage. Image compression is a method of lessening the excess in images and symbolizing it in a short way that could permit more gainful exploitation of storage capacity and network bandwidth. This paper develops a new image compression model with steps like segmentation, encoding, and decoding. Initially, segmentation is carried out using Improved Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). This phase assists in ROI separation. Subsequently, compression occurs using Improved Huffman encoding. Also, in particular, the encoding parameters are optimally chosen via a new algorithm named Snake Updated BES Optimization (SU-BESO). In the last phase, decoding is done, during which, Huffman decoding as well as region fusion are carried out. Finally, the examination is done to prove the potential of the developed SU-BESO model.

Purpose: Big Data's extensive capabilities can aid in addressing the unpredictability of food supply caused by a variety of issues including soil degradation, climate change, water pollution, socio-cultural expansion, governmental laws, and market volatility. However, crop monitoring and classification are critical components of agricultural precision farming. This paper intends to propose a crop classification via a hybrid classification model.

Design: First, the input image dataset is subjected to the preprocessing stage to enhance the image dataset by removing noise and blurring the edges with the aid of Gaussian filtering. Second, the improved spider local image feature, median binary pattern and haralick texture features are extracted from the preprocessed image dataset by utilizing the map-reduce framework, to handle big data. Third, the hybrid classification model is proposed that involves two classifiers such as Bi-GRU and CNN.

Findings: The weights of both classifier Bi-GRU and CNN were tuned optimally by the proposed hybrid optimization BUBMO that combined both BMO and BWO. The greatest MCC obtained by the propose is 91.47%, whilst the traditional model scored the lowest MCC.

Originality: The accuracy and improved efficacy of the crop categorization are achieved by employing the suggested classification method.

Brain tumour classification plays a significant role in improving patient care, treatment planning, and enhancing the overall healthcare system's effectiveness. This article presents a ResNet framework optimized using Henry gas solubility optimization (HGSO) for the classification of brain tumours, resulting in improved classification performance in magnetic resonance images (MRI). Two variants of the deep residual neural network, namely ResNet-18 and ResNet-50, are trained on the MRI training dataset. The four critical hyperparameters of the ResNet model: momentum, initial learning rate, maximum epochs, and validation frequency are tuned to obtain optimal values using HGSO algorithm. Subsequently, the optimized ResNet model is evaluated using two separate databases: Database1, comprising four tumour classes, and Database2, with three tumour classes. The performance is assessed using accuracy, sensitivity, specificity, precision, and F-score. The highest classification accuracy of 0.9825 is attained using the proposed optimized ResNet-50 framework on Database1. Moreover, the Gradient-weighted Class Activation Mapping (GRAD-CAM) algorithm is utilized to enhance the understanding of deep neural networks by highlighting the regions that are influential in making a particular classification decision. Grad-CAM heatmaps confirm the model focuses on relevant tumour features, not image artefacts. This research enhances MRI brain tumour classification via deep learning optimization strategies.

The sentiment analysis is an essential component that enables automation of achieving insights from the information that is user generated. However, the difficulty of sentiment analysis is the lack of enough labelled data in the Natural Language Processing (NLP) sector. Thus, to evaluate these sentiments, multiple mechanisms have been utilized in the past decades. The deep learning-aided approaches are becoming very famous nowadays because of their better performances. To surmount such existing issues, an attention deep learning model is proposed using an improved heuristic approach. At first, the input text data is gathered from public resources. Further, it is followed by text pre-processing to prevent unrelated text data. Further, the obtained pre-processed text is fed into the Multiscale Feature Fusion-based Adaptive and Attention-based Convolution Neural Network (MFF-AACNet). In the developed system, the features are extracted from Bidirectional Encoder Representations from Transformers (BERT), Transformers, and word2vector. Furthermore, the resultant features are fused, and it is subjected to the MFF-AACNet, where the sentiment is analysed. The parameter tuning is done by an improved Fitness Opposition of Rat Swarm Optimizer (FORSO). Finally, the performance analysis was conducted for the implemented model. The proposed framework achieves higher accuracy compared to traditional methods.

The rapid growth of cloud computing has led to the widespread adoption of heterogeneous virtualized environments, offering scalable and flexible resources to meet diverse user demands. However, the increasing complexity and variability in workload characteristics pose significant challenges in optimizing energy consumption. Many scheduling algorithms have been suggested to address this. Therefore, a self-attention-based progressive generative adversarial network optimized with Dwarf Mongoose algorithm adopted Energy and Deadline Aware Scheduling in heterogeneous virtualized cloud computing (SAPGAN-DMA-DAS-HVCC) is proposed in this paper. Here, a self-attention based progressive generative adversarial network (SAPGAN) is proposed to schedule activities in a cloud environment with an objective function of makespan and energy consumption. Then Dwarf Mongoose algorithm is proposed to optimize the weight parameters of SAPGAN. Outcome of proposed approach SAPGAN-DMA-DAS-HVCC contains 32.77%, 34.83% and 35.76% higher right skewed makespan, 31.52%, 33.28% and 29.14% lower cost when analysed to the existing models, like task scheduling in heterogeneous cloud environment utilizing mean grey wolf optimization approach, energy and performance-efficient task scheduling in heterogeneous virtualized Energy and Performance Efficient Task Scheduling Algorithm, energy and make span aware scheduling of deadline sensitive tasks on the cloud environment, respectively.

Human Activity Recognition (HAR) systems are designed to continuously monitor human behaviour, mainly in the areas of entertainment and surveillance in intelligent home environments. In this manuscript, Human Activity Recognition utilizing optimized Attention Induced Multi head Convolutional Neural Network with Mobile Net V1 from Mobile Health Data (HAR-AMCNN-MNV1) is proposed. The input data is collected through MHEALTH and UCI HAR datasets. Neural Spectrospatial Filtering (NSF) is used for avoiding accurate labelling and reduces errors. Afterwards, Variational Density Peak Clustering Algorithm (VDPCA) is used for segmenting the data. Feature Extraction and Classification is done by Attention Induced Multi head Convolutional Neural Network with Mobile Net V1 (AMCNN-MNV1). AMCNN is used for extracting Hand-crafted features. AMCNN-MNV1 effectively classifies the human activities as Sitting and relaxing (Sit), Climbing stairs (CS), Walking (Walk), Standing still (Std), Waist bends forward (WBF), Frontal elevation of arms (FEA), Jogging (Jog), Knees bending (crouching) (KB), Cycling (Cycl), Lying down (Lay), Jump front & back (JFB) and Running (Run). Siberian Tiger Optimization Algorithm (STOA) is proposed to optimize the weight parameter of AMCNN-MNV1 classifier. The proposed method attains 21.19%, 23.45%, and 21.76% higher accuracy, 31.15%, 24.65% and 22.72% higher precision; 21.15%, 20.18%, and 21.28% higher recall evaluated to the existing methods.

Recent technical advancements enable omics-based biological study of molecules with very high throughput and low cost, such as genomic, proteomic, and microbionics'. To overcome this drawback, Omics Data Classification using Constitutive Artificial Neural Network Optimized with Single Candidate Optimizer (ODC-ZOA-CANN-SCO) is proposed in this manuscript. The input data is pre-processing by using Adaptive variational Bayesian filtering (AVBF) to replace missing values. The pre-processing data is fed to Zebra Optimization Algorithm (ZOA) for dimensionality reduction. Then, the Constitutive Artificial Neural Network (CANN) is employed to classify omics data. The weight parameter is optimized by Single Candidate Optimizer (SCO). The proposed ODC-ZOA-CANN-SCO method attains 25.36%, 21.04%, 22.18%, 26.90%, and 28.12% higher accuracy when analysed to the existing methods like multi-omics data integration utilizing adaptive graph learning and attention mode for patient categorization with biomarker identification (MOD-AGL-AM-PABI), deep learning method depending upon multi-omics data integration to create risk stratification prediction mode for skin cutaneous melanoma (DL-MODI-RSP-SCM), Deep belief network-base model for identifying Alzheimer's disease utilizing multi-omics data (DDN-DAD-MOD), hybrid cancer prediction depending upon multi-omics data and reinforcement learning state action reward state action (HCP-MOD-RL-SARSA), machine learning basis method under omics data including biological knowledge database for cancer clinical endpoint prediction (ML-ODBKD-CCEP) methods, respectively.

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.

This study introduces a deep self-organizing map neural network based on level-set (LS-SOM) for the customization of a shoe-last defined from plantar pressure imaging data. To alleviate the over-segmentation problem of images, which refers to segmenting images into more subcomponents, a domain-based segmentation model of plantar pressure images was constructed. The domain growth algorithm was subsequently modified by optimizing its parameters. A SOM with 10, 15, 20, and 30 hidden layers was compared and validated according to domain growth characteristics by using merging and splitting algorithms. Furthermore, we incorporated a level set segmentation method into the plantar pressure image algorithm to enhance its efficiency. Compared to the literature, this proposed method has significantly improved pixel accuracy, average cross-combination ratio, frequency-weighted cross-combination ratio, and boundary F1 index comparison. Using the proposed methods, shoe lasts can be designed optimally, and wearing comfort is enhanced, particularly for people with high blood pressure.

Skin melanin lesions are typically identified as tiny patches on the skin, which are impacted by melanocyte cell overgrowth. The number of people with skin cancer is increasing worldwide. Accurate and timely skin cancer identification is critical to reduce the mortality rates. An incorrect diagnosis can be fatal to the patient. To tackle these issues, this article proposes the Recurrent Prototypical Object Segmentation Network (RPO-SegNet) for the segmentation of skin lesions and a hybrid Deep Learning (DL) - based skin cancer classification. The RPO-SegNet is formed by integrating the Recurrent Prototypical Networks (RP-Net), and Object Segmentation Networks (O-SegNet). At first, the input image is taken from a database and forwarded to image pre-processing. Then, the segmentation of skin lesions is accomplished using the proposed RPO-SegNet. After the segmentation, feature extraction is accomplished. Finally, skin cancer classification and detection are accomplished by employing the Fuzzy-based Shepard Convolutional Maxout Network (FSCMN) by combining the Deep Maxout Network (DMN), and Shepard Convolutional Neural Network (ShCNN). The established RPO-SegNet+FSCMN attained improved accuracy, True Negative Rate (TNR), True Positive Rate (TPR), dice coefficient, Jaccard coefficient, and segmentation analysis of 91.985%, 92.735%, 93.485%, 90.902%, 90.164%, and 91.734%.