Network-Computation in Neural Systems最新文献

In this study, a novel technique termed Quadratic Discriminant Feature Selected Broken Stick Regressive Deep Convolution Neural Learning Classification (QDFSBSRDCNLC) Technique is proposed for disease classification and hence yields prediction of turmeric crop. Initially, we gathered the images of turmeric crops with and without diseases. The images are collected from the turmeric research field at Bhavanisagar. Quadratic Discriminant Analysis (QDA) is utilized to select relevant features from a dataset, reducing dimensionality. In this paper, four models, named FCN8, PSP Net, MobileNetV3 (small), and Deep Lab V3 are chosen for semantic segmentation of disease in turmeric crops. Turmeric crop production predicts is an important part of modern agriculture, allowing farmers to make sensible choices and optimize resources. We can predict turmeric crop yields accurately by using modern data analysis approaches. Predictive models take into consideration variables such as weather, soil quality, and farming techniques. The experimental results demonstrated that MobileNetV3 (small) performed better than other established ones with the accuracy of 97.99%, IoU of 96.82%, and Coefficient of 97.80% for 50 epochs. The proposed QDFSBSRDCNLC Technique effectively classifies diseases and predicts the yield of turmeric crops, with MobileNetV3 (small) showing superior performance among the tested models.

In this paper, meningioma detection and segmentation method is proposed. This research work proposes an effective method to locate meningioma pictures through a novel CFCM classification approach. This proposed method consist of Non-Sub sampled Contourlet Transform decomposition module which decomposes the entire brain image into multi-scale sub-band images and then the heuristic and uniqueness features have been computed individually. Then, these heuristic and uniqueness features are trained and classified using Convolutional Fuzzy C Means (CFCM) classifier. This proposed method is applied on two independent brain imaging datasets. The proposed meningioma identification system stated in this work obtained 98.81% of Se, 98.83% of Sp, 99.04% of Acc, 99.12% of pr, and 99.14% of FIS on Nanfang University dataset brain images. The proposed meningioma identification system stated in this work obtained 98.92% of Se, 98.88% of Sp, 98.9% of Acc, 98.88% of pr, and 99.36% of FIS on the BRATS 2021 brain images. Finally, the tumour segmentation module is designed in VLSI, and it is simulated using Xilinx project navigator in this paper.

The existing approaches suffer from scalability and security issues while transmitting data. Blockchain is a recently emerged technology, and it is an emerging platform that allows secure transmission. A distributed design is required to address these issues and abide by security regulations. Blockchain has been recently introduced as an alternative solution to solve complex and challenging security issues while storing data. Thus, an intelligent blockchain-assisted IoT architecture is provided in this work to perform secure healthcare data transmission. The first aim of our model is to detect malware attacks in IoT networks. To detect the malware activities, the attack detection data was gathered, and it was fed as input to the Hybrid Adaptive Deep Learning Method. For further enhancement, the FUPOA performs the parameter tuning. A privacy preservation model is employed to secure healthcare data by generating the optimal key formation, in which the key is optimized using FUPOA. This secured data can be stored in the blockchain to increase data integrity and privacy. The optimal feature selection is done by the FUPOA approach. Further, the acquired optimal features are fed to the HADL-AM for predicting the data. The experimental analysis has been done and compared among different approaches.

Brain Tumor (BT) is an irregular growth of cells in the brain or in the tissues surrounding it. Detecting and predicting tumours is essential in today's world, yet managing these diseases poses a considerable challenge. Among the various modalities, Magnetic Resonance Imaging (MRI) has been extensively exploited for diagnosing tumours. The traditional methods for predicting survival are based on handcrafted features from MRI and clinical information, which is generally subjective and laborious. This paper devises a new method named, Deep Residual PyramidNet (DRP_Net) for BT classification and survival prediction. The input MRI image is primarily derived from the BraTS dataset. Then, image enhancement is done to improve the quality of images using homomorphic filtering. Next, deep joint segmentation is used to process the tumourtumour region segmentation. Consequently, Haar wavelet and Local Directional Number Pattern (LDNP) based feature extraction is mined. Afterward, BT classification is achieved through DRP_Net, which is a fusion of Deep Residual Network (DRN) and PyramidNet. At last, the survival prediction is accomplished by employing the Deep Recurrent Neural Network (DRNN). Furthermore, DRP_Net has attained superior performance with a True Negative Rate (TNR) of 91.99%, an accuracy of 90.18%, and True Positive Rate (TPR) of 91.08%.

Efficient data transmission in Wireless Sensor Networks (WSNs) is a critical challenge. Traditional routing protocols focus on energy efficiency but do not consider other factors that might degrade performance. This research proposes a novel Hybrid Beluga Whale-Coati Optimization (HBWCO) algorithm to address these issues, focusing on optimizing energy-efficient data transmission. In the proposed approach, initially, sensor nodes and field dimensions are initialized. Then, K-means clustering is applied to grouping nodes. The Deep Q-Net model is used to predict energy levels of nodes. CH is selected as per the node having higher energy. Multipath routing is performed through the HBWCO algorithm, which optimally selects the best routing paths by considering factors like reliability, residual energy, predicted energy, throughput, and traffic intensity. If link breakage occurs, a route maintenance phase is initiated using Source Link Breakage Warning (SLBW) message strategy to notify the source node about the issue of choosing another path. This work offers a comprehensive approach to enhancing energy efficiency in networks. The suggested HBWCO approach is in contrast to the traditional methods. The HBWCO approach has achieved the highest reliability of 0.948 and the highest throughput of 3496. Therefore, the HBWCO algorithm offers an effective solution for data transmission and routing reliability.

Insider threat is one of the most serious and frequent security risks facing various industries like governmental organizations, businesses, and institutions. Insider threat identification has a special combination of difficulties, including vastly unbalanced data, insufficient ground truth, and drifting and shifting behaviour. A user behaviour-based insider threat detection model utilizing a hybrid deep long short-term memory-random forest (LSTM-RF) model is developed to address these challenges. In this proposed insider threat detection model, the user log data is preprocessed to replace the missing value and to normalize the data to certain range. Then, these preprocessed data are provided as the input of the attribute selection process that mainly applies for selecting the essential attribute using Spearman's rank correlation coefficient. Then the deep hybrid LSTM-RF classifier to detect whether a system is affected by inside threat or not such as malware, authentication, phishing are fed to the selected features. Hybrid LSTM-RF method is implemented in python and achieved 96% accuracy, 90% precision, 90% specificity, 97% sensitivity, and 94% F1-score. During an attack, it can be easily detected inside the system attack.

This work plans to develop a biometric authentication model by the combination of multi-modal inputs like voice, fingerprint, and iris to provide high security. At first, the spectrogram images, the collected fingerprint, and the collected iris input were given to a Multi-scale Residual Attention Network (RAN) with Atrous Spatial Pyramid Pooling (ASPP) to extract the best values. These three features are then fed to optimal weighted feature fusion, where weight optimization from the features is done via the Enhanced Lichtenberg Algorithm (ELA). These features are fed into the decision-making stage, where the Dilated Adaptive Recurrent Neural Network is utilized to identify the individuals, where the parameters are optimized from RNN using ELA to improve the recognition performance. The simulation findings achieved from the developed multimodal authentication systems are validated using diverse algorithms over several efficacy metrics like accuracy, precision, sensitivity, F1-score, etc. From the result analysis, the ELA-DARNN-based user authentication system showed a higher accuracy of 96.01, and other models such as 90% than SVM, CNN, CNN-AlexNet, and Dil-ARNN given the accuracy to be 87.94, 89.88, 93.25, and 91.94. Therefore, the outcomes explored that the offered approach has attained elevated results and also effectively supports to reduction of data theft.

An accurate classification of brain tumour disease is an important function in diagnosing cancer disease. Several deep learning (DL) methods have been used to identify and categorize the tumour illness. Nevertheless, the better categorized result was not consistently obtained by the traditional DL procedures. Therefore, a superior answer to this problem is offered by the optimized DL approaches. Here, the brain tumour categorization (BTC) is done using the devised Hybrid Fruit Bee Optimization based Deep Convolution Neural Network (HFBO-based DCNN). Here, the noise in the image is removed through pre-processing using a Gaussian filter. Next, the feature extraction process is done using the SegNet and this helps to extract the relevant data from the input image. Then, the feature selection is done with the help of the HFBO algorithm. Additionally, the brain tumour classification is done by the Deep CNN, and the established HFBO algorithm is used to train the weight. The devised model is analysed using the testing accuracy, sensitivity, and specificity and produced the values of 0.926, 0.926, and 0.931, respectively.

Initially, a class of Caputo fractional-order bidirectional associative memory neural networks in two variables is developed, building upon the groundwork laid by delayed Caputo fractional system in one variable. Next, the Razumikhin-type uniform stability conditions, originally formulated for single-variable systems, are successfully extended to accommodate the complexities of delayed Caputo fractional systems in two variables. Leveraging this extension and employing a suitable Lyapunov function, the delay-dependent uniform stability criteria for the addressed fractional-order bidirectional associative memory neural networks are expressed in terms of linear matrix inequalities. Finally, the effectiveness and practicality of the theoretical findings are demonstrated through the application of two numerical examples, affirming the viability of the proposed approach.

Plant diseases significantly threaten food security by reducing the quantity and quality of agricultural products. This paper presents a deep learning approach for classifying foliar diseases in apple plants using the Tunicate Swarm Sine Cosine Algorithm-based Deep Residual Network (TSSCA-based DRN). Cluster heads in simulated Internet of Things (IoT) networks are selected by Fractional Lion Optimization (FLION), and images are pre-processed with a Gaussian filter and segmented using the DeepJoint model. The TSSCA, combining the Tunicate Swarm Algorithm (TSA) and Sine Cosine Algorithm (SCA), enhances the classifier's effectiveness. Moreover, Plant Pathology 2020 - FGVC7 dataset is used in this work. This dataset is designed for the classification of foliar diseases in apple trees. The TSSCA-based DRN outperforms other methods, achieving 97% accuracy, 94.666% specificity, 96.888% sensitivity, and 0.0442J maximal energy, with significant improvements over existing approaches. Additionally, the proposed model demonstrates superior accuracy, outperforming other methods by 8.97%, 6.58%, 2.07%, 1.71%, 1.14%, 1.07%, 0.93%, and 0.64% over Multidimensional Feature Compensation Residual neural network (MDFC - ResNet), Convolutional Neural Network (CNN), Multi-Context Fusion Network (MCFN), Advanced Segmented Dimension Extraction (ASDE), and DRN, fuzzy deep convolutional neural network (FCDCNN), ResNet9-SE, Capsule Neural Network (CapsNet), IoT-based scrutinizing model, and Multi-Model Fusion Network (MMF-Net).