Network-Computation in Neural Systems最新文献

Breast cancer is the foremost cause of mortality among females. Early diagnosis of a disease is necessary to avoid breast cancer by reducing the death rate and offering a better life to the individuals. Therefore, this work proposes a Parallel Convolutional SpinalNet (PConv-SpinalNet) for the efficient detection of breast cancer using mammogram images. At first, the input image is pre-processed using the Gabor filter. The tumour segmentation is conducted using LadderNet. Then, the segmented tumour samples are augmented using Image manipulation, Image erasing, and Image mix techniques. After that, the essential features, like CNN features, Texton, Local Gabor binary patterns (LGBP), scale-invariant feature transform (SIFT), and Local Monotonic Pattern (LMP) with discrete cosine transform (DCT) are extracted in the feature extraction phase. Finally, the detection of breast cancer is performed using PConv-SpinalNet. PConv-SpinalNet is developed by an integration of Parallel Convolutional Neural Networks (PCNN) and SpinalNet. The evaluation results show that PConv-SpinalNet accomplished a superior range of accuracy as 88.5%, True Positive Rate (TPR) as 89.7%, True Negative Rate (TNR) as 90.7%, Positive Predictive Value (PPV) as 91.3%, and Negative Predictive Value (NPV) as 92.5%.

Resource optimization and workload balancing in cloud computing environments necessitate efficient management of resources to minimize energy wastage and SLA (Service Level Agreement) violations. The existing scheduling techniques often face challenges with dynamic resource allocations and lead to inefficient job completion rates and container utilizations. Hence, this framework has been proposed to establish HUNHODRL, a newly-minted DRL-based framework that aims to improve container orchestration and workload allocation. The evaluation of this framework was done against HUNDRL, Bi-GGCN, and CNN methods comparatively under two sets of workloads with datasets on CPU, Memory, and Disk I/O utilization metrics. The model optimizes scheduling choices in HUNHODRL through a combination of destination host capacity vector and active job utilization matrix. The experimental results show that HUNHODRL outperforms existing models in container creation rate, job completion rate, SLA violation reduction, and energy efficiency. It facilitates increased container creation efficiency without increasing the energy costs of VM deployments. This method dynamically adapts itself and modifies the scheduling strategy to optimize performance amid varying workloads, thus establishing its scalability and robustness. A comparative analysis has demonstrated higher job completion rates against CNN, Bi-GGCNN, and HUNDRL, establishing the potential of DRL-based resource allocation. The significant gain in cloud resource utilization and energy-efficient task execution makes HUNHODRL and its suitable solution for next-generation cloud computing infrastructure.

Non-Orthogonal Multiple Access (NOMA) is the successive multiple-access methodologies for modern communication devices. Energy Efficiency (EE) is suggested in the NOMA system. In dynamic network conditions, the consideration of NOMA shows high computational complexity that minimizes the EE to degrade the system performance. This research suggested EE for the Multi-Carrier NOMA (MC-NOMA) models by optimization algorithm. The main scope of this research tends to improve the EE by Hybrid of Sewing Training and Lemur Optimization for optimizing the system parameters. The improvement made in this developed HSTLO algorithm can provide significant impact on MC-NOMA system, which it renders better user capacity while effectively optimizing the system parameters. Moreover, the Dilated Dense Recurrent Neural Network (DDRNN) model is developed. Employing the improvement in the deep learning model for the MC-NOMA system could effectively manage and enhance the system performance. Considering the DDRNN model can leverage to provide better generalization outcomes in different network scenarios that ensures to provide fast and reliable solutions compared to existing methods. Addressing the energy consumption problems in this research study will be analysed to show the advancement in MC-NOMA system that help to enhance the system performance.

A crucial role in many security and surveillance applications is crowd anomaly detection, where seeing unusual activity helps avert possible threats or interruptions. For precise anomaly identification, current models might not successfully incorporate spatial and temporal features. To overcome these drawbacks, a novel Crowd Anomaly Detection based on Opposition Behavior Learning updated Chimp Optimization Algorithm (CAD-OBLChoA) is proposed in this research to enhance the detection of abnormal crowd behaviours in dynamic environments. In this research, bilateral filtering is used for smoothening the image and reducing noise for preprocessing phase. For object detection, a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM)-based bounding box approach is used. Then, features like Colour features, Shape features, and Improved Texture features are extracted. Finally, the anomalies get detected based on the trained extracted feature set in the system. For this, an optimized CNN is used, where training is done by the OBLChoA scheme via tuning the optimal weights. The proposed CAD-OBLChoA scheme achieved a higher specificity of about 0.924 and 0.931 in the 90% training data for datasets 1 and 2. This approach could significantly improve crowd monitoring and security, enabling faster identification of potential threats or emergencies.

The neurodegenerative disorder called Parkinson's disease (PD) is one of the most common diseases now a day. In this research, PD is detected and severity classification is done using the proposed Jaccard LeNet (JLeNet) with the help of voice signal in the IoT environment. Here, the IoT simulation is done. Initially, from which voice signal is collected and the routing process is done by the proposed Chimp Wild Geese Algorithm (ChWGA). This ChWGA is the combination of the Wild Geese Algorithm (WGA) and Chimp Optimization Algorithm (ChOA). Finally, at Base Station (BS), PD is detected and classified. The input voice signal is fed for pre-processing conducted by an adaptive Kalman filter. Following this, feature extraction and feature selection are conducted, where Harmonic mean similarity helps in feature selection. Here, PD is detected using JLeNet, which is the hybridization of LeNet with the Jaccard similarity measure. In this work, routing metrics of energy and delay are superior and recorded with the values of 0.309 J and 0.434 ms for the ChWGA. Moreover, the proposed method attains an Accuracy of 0.910, True Positive Rate (TPR) of 0.903, and True Negative Rate (TNR) of 0.918.

PD is a progressive neurodegenerative disorder that leads to gradual motor impairments. Early detection is critical for slowing the disease's progression and providing patients access to timely therapies. However, accurately detecting PD in its early stages remains challenging. This study aims to develop an optimized deep learning model for PD classification using voice signals and hand-drawn spiral images, leveraging a ZFNet-LHO-DRN. The proposed model first preprocesses the input voice signal using a Gaussian filter to remove noise. Features are then extracted from the preprocessed signal and passed to ZFNet to generate output-1. For the hand-drawn spiral image, preprocessing is performed with a bilateral filter, followed by image augmentation. Here also, the features are extracted and forwarded to DRN to form output-2. Both classifiers are trained using the LHO algorithm. Finally, from the output-1 and output-2, the best one is selected based on the majority voting. The ZFNet-LHO-DRN model demonstrated excellent performance by achieving a premium accuracy of 89.8%, a NPV of 89.7%, a PPV of 89.7%, a TNR of 89.3%, and a TPR of 90.1%. The model's high accuracy and performance indicate its potential as a valuable tool for assisting in the early diagnosis of PD.

The proper study and administration of biodiversity relies heavily on accurate plant species identification. To determine a plant's species by manual identification, experts use a series of keys based on measurements of various plant features. The manual procedure, however, is tiresome and lengthy. Recently, advancements in technology have prompted the need for more effective approaches to satisfy species identification standards, such as the creation of digital-image-processing and template tools. There are significant obstacles to fully automating the recognition of plant species, despite the many current research on the topic. In this work, the leaf classification was performed using the ontological relationship between the leaf features and their classes. This relationship was identified by using the swarm intelligence techniques called particle swarm and cuckoo search algorithm. Finally, these features were trained using the traditional machine learning algorithm regression neural network. To increase the effectiveness of the ontology, the machine learning approach results were combined with the deep learning approach called RESNET50 using association rule. The proposed ontology model produced an identification accuracy of 98.8% for GRNN model, 99% accuracy for RESNET model and 99.9% for the combined model for 15 types of medicinal leaf sets.

This paper proposes a Tangent Rat Swarm Optimization (TRSO)-DenseNet for the detection of skin cancer to reduce the severity rate of cancer. Initially, the input image is pre-processed by employing a linear smoothing filter. The pre-processed image is transferred to skin lesion segmentation, where Mask-RCNN is utilized for segmenting the skin lesion. Then, image augmentation is performed using techniques such as vertical shifting, horizontal shifting, random rotation, brightness adjustment, blurring, and cropping. The augmented image is then fed into the feature extraction phase to identify statistical features, Haralick texture features, Convolutional Neural Network (CNN) features, Local Ternary Pattern (LTP), Histogram of Oriented Gradients (HOG), and Local Vector Pattern (LVP). Finally, the extracted features are fed into the skin cancer detection phase, where DenseNet is used to detect skin cancer. Here, DenseNet is structurally optimized by TRSO, which has the combination of the Tangent Search Algorithm (TSA) and Rat Swarm Optimizer (RSO). The TRSO-DenseNet model is implemented using MATLAB tool and analayzsed using the Society for Imaging Informatics in Medicine-International Skin Imaging Collaboration's (SIIM-ISIC) Melanoma Classification dataset. The Proposed model for skin cancer detection attained superior performance with an accuracy of 94.63%, TPR of 91.51%, and TNR of 92.46%.

Security has become crucial as Internet of Things (IoT) applications proliferate. IoT vulnerabilities are widespread, as demonstrated by a recent distributed denial-of-service (DDoS) assault, which many IoT devices unintentionally assisted with. IoT device management may be done safely with the help of the new software-defined anything (SDx) paradigm. In this study, a five-phase SDN design will be equipped with a detection and mitigation system of DDoS attack. Data cleaning is a method of pre-processing raw data that is crucial to the flow of information. The suitable features are chosen from the retrieved features using the augmented chi-square method. A deep two-layer architecture with four classifiers is utilized to characterize the attack's detection stage. Using the recently created hybrid optimization method known as the MUAE approach, the weight of the QNN is adjusted. Until the optimized QNN detects an attacker, regular data routing occurs. In that scenario, control is passed along to the mitigation of attacks step. For training rates of 60, 70, 80, and 90, the predicted accuracy of the model is 94.273%, 94.860%, 94.93%, and 96.02%. Finally, the decided system is verified against traditional ways to demonstrate its superiority in both mitigation and attack detection.

Alzheimer's disease (AD) is a severe neurological disorder that leads to irreversible memory loss. In the previous research, the early-stage Alzheimer's often presents with subtle memory issues that are difficult to differentiate from normal age-related changes. This research designed a novel detection model called the Zeiler and Fergus Quantum Dilated Convolutional Neural Network (ZF-QDCNN) for AD detection using Magnetic Resonance Imaging (MRI). Initially, the input MRI images are taken from a specific dataset, which is pre-processed using a Gaussian filter. Then, the brain area segmentation is performed by utilizing the Channel-wise Feature Pyramid Network for Medicine (CFPNet-M). After segmentation, relevant features are extracted, and the classification of AD is performed using the ZF-QDCNN, which is the integration of the Zeiler and Fergus Network (ZFNet) with the Quantum Dilated Convolutional Neural Network (QDCNN). Moreover, the ZF-QDCNN model demonstrated promising performance, achieving an accuracy of 91.7%, a sensitivity of 90.7%, a specificity of 92.7%, and a f-measure of 91.8% in detecting AD. Additionally, the proposed ZF-QDCNN model effectively identifies and classifies Alzheimer's disease in MRI images, highlighting its potential as a valuable tool for early diagnosis and management of the condition.