Advances in Computational Intelligence Techniques-Based Multi-Intersection Querying Theory for Efficient QoS in the Next Generation Internet of Things.

An environment of physically linked, technologically networked things that can be found online is known as the "Internet of Things." With the use of various devices connected to a network that allows data transfer between these devices, this includes the creation of intelligent communications and computational environments, such as intelligent homes, smart transportation systems, and intelligent FinTech. A variety of learning and optimization methods form the foundation of computational intelligence. Therefore, including new learning techniques such as opposition-based learning, optimization strategies, and reinforcement learning is the key growing trend for the next generation of IoT applications. In this study, a collaborative control system based on multiagent reinforcement learning with intelligent sensors for variable-guidance sections at various junctions is proposed. In the future generation of Internet of Things (IoT) applications, this study provides a multi-intersection variable steering lane-appropriate control approach that uses intelligent sensors to reduce traffic congestion at many junctions. Since the multi-intersection scene's complicated traffic flow cannot be accommodated by the conventional variable steering lane management approach. The priority experience replay algorithm is also included to improve the efficiency of the transition sequence's use in the experience replay pool and speed up the algorithm's convergence for effective quality of service in the upcoming IoT applications. The experimental investigation demonstrates that the multi-intersection variable steering lane with intelligent sensors is an appropriate control mechanism, successfully reducing queue length and delay time. The effectiveness of waiting times and other indicators is superior to that of other control methods, which efficiently coordinate the strategy switching of variable steerable lanes and enhance the traffic capacity of the road network under multiple intersections for effective quality of service in the upcoming IoT applications.

Blockchain Framework for Secure COVID-19 Pandemic Data Handling and Protection.

COVID-19 pandemic caused global epidemic infections, which is one of the most severe infections in human medical history. In the absence of proper medications and vaccines, handling the pandemic has been challenging for governments and major health facilities. Additionally, tracing COVID-19 cases and handling data generated from the pandemic are also extremely challenging. Data privacy access and collection are also a challenge when handling COVID-19 data. Blockchain technology provides various features such as decentralization, anonymity, cryptographic security, smart contracts, and a distributed framework that allows users and entities to handle COVID-19 data better. Since the outbreak has made the moral crisis in the clinical and administrative centers worse than any other that has resulted in the decline in the supply of the exact information, however, it is vital to provide fast and accurate insight into the situation. As a result of all these concerns, this study emphasizes the need for COVID-19 data processing to acquire aspects such as data security, data integrity, real-time data handling, and data management to provide patients with all benefits from which they had been denied owing to misinformation. Hence, the management of COVID-19 data through the use of the blockchain framework is crucial. Therefore, this paper illustrates how blockchain technology can be implemented in the COVID-19 data handling process. The paper also proposes a framework with three main layers: data collection layer; data access and privacy layer; and data storage layer.

Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network.

Prostate cancer is one of the most common cancers in men worldwide, second only to lung cancer. The most common method used in diagnosing prostate cancer is the microscopic observation of stained biopsies by a pathologist and the Gleason score of the tissue microarray images. However, scoring prostate cancer tissue microarrays by pathologists using Gleason mode under many tissue microarray images is time-consuming, susceptible to subjective factors between different observers, and has low reproducibility. We have used the two most common technologies, deep learning, and computer vision, in this research, as the development of deep learning and computer vision has made pathology computer-aided diagnosis systems more objective and repeatable. Furthermore, the U-Net network, which is used in our study, is the most extensively used network in medical image segmentation. Unlike the classifiers used in previous studies, a region segmentation model based on an improved U-Net network is proposed in our research, which fuses deep and shallow layers through densely connected blocks. At the same time, the features of each scale are supervised. As an outcome of the research, the network parameters can be reduced, the computational efficiency can be improved, and the method's effectiveness is verified on a fully annotated dataset.

Appropriate Supervised Machine Learning Techniques for Mesothelioma Detection and Cure.

Mesothelioma is a dangerous, violent cancer, which forms a protecting layer around inner tissues such as the lungs, stomach, and heart. We investigate numerous AI methodologies and consider the exact DM conclusion outcomes in this study, which focuses on DM determination. K-nearest neighborhood, linear-discriminant analysis, Naive Bayes, decision-tree, random forest, support vector machine, and logistic regression analyses have been used in clinical decision support systems in the detection of mesothelioma. To test the accuracy of the evaluated categorizers, the researchers used a dataset of 350 instances with 35 highlights and six execution measures. LDA, NB, KNN, SVM, DT, LogR, and RF have precisions of 65%, 70%, 92%, 100%, 100%, 100%, and 100%, correspondingly. In count, the calculated complication of individual approaches has been evaluated. Every process is chosen on the basis of its characterization, exactness, and calculated complications. SVM, DT, LogR, and RF outclass the others and, unexpectedly, earlier research.

Light Weighted Healthcare CNN Model to Detect Prostate Cancer on Multiparametric MRI.

The SEMRCNN model is proposed for autonomously extracting prostate cancer locations from regions of multiparametric magnetic resonance imaging (MP-MRI). Feature maps are explored in order to provide fine segmentation based on the candidate regions. Two parallel convolutional networks retrieve these maps of apparent diffusion coefficient (ADC) and T2W images, which are then integrated to use the complimentary information in MP-MRI. By utilizing extrusion and excitation blocks, it is feasible to automatically increase the number of relevant features in the fusion feature map. The aim of this study is to study the current scenario of the SE Mask-RCNN and deep convolutional network segmentation model that can automatically identify prostate cancer in the MP-MRI prostatic region. Experiments are conducted using 140 instances. SEMRCNN segmentation of prostate cancer lesions has a Dice coefficient of 0.654, a sensitivity of 0.695, a specificity of 0.970, and a positive predictive value of 0.685. SEMRCNN outperforms other models like as V net, Resnet50-U-net, Mask-RCNN, and U network model for prostate cancer MP-MRI segmentation. This approach accomplishes fine segmentation of lesions by recognizing and finding potential locations of prostate cancer lesions, eliminating interference from surrounding areas, and improving the learning of the lesions' features.

Identification of End-User Economical Relationship Graph Using Lightweight Blockchain-Based BERT Model.

Current methods for extracting information from user resumes do not work well with unstructured user resumes in economic announcements, and they do not work well with documents that have the same users in them. Unstructured user information is turned into structured user information templates in this study. It also proposes a way to build person relationship graphs in the field of economics. First, the lightweight blockchain-based BERT model (B-BERT) is trained. The learned B-BERT pretraining model is then utilized to get the event instance vector, categorize it appropriately, and populate the hierarchical user information templates with accurate user characteristics. The aim of this research is that it has investigated the approach of creating character connection graphs in the Chinese financial system and suggests a framework for doing so in the economic sector. Furthermore, the relationship between users is found through the filled-in user information template, and a graph of user relationships is made. This is how it works: finally, the experiment is checked by filling in a manually annotated dataset. In tests, the method can be used to get text information from unstructured economic user resumes and build a relationship map of people in the financial field. The experimental results show that the proposed approach is capable of efficiently retrieving information from unstructured financial personnel resume text and generating a character relationship graph in the economic sphere.

Classification of EEG Signal-Based Encephalon Magnetic Signs for Identification of Epilepsy-Based Neurological Disorder.

Magnetoencephalography (MEG) is now widely used in clinical examinations and medical research in many fields. Resting-state magnetoencephalography-based brain network analysis can be used to study the physiological or pathological mechanisms of the brain. Furthermore, magnetoencephalography analysis has a significant reference value for the diagnosis of epilepsy. The scope of the proposed research is that this research demonstrates how to locate spikes in the phase locking functional brain connectivity network of the Desikan-Killiany brain region division using a neural network approach. It also improves detection accuracy and reduces missed and false detection rates. The automatic classification of epilepsy encephalomagnetic signals can make timely judgments on the patient's condition, which is of tremendous clinical significance. The existing literature's research on the automatic type of epilepsy EEG signals is relatively sufficient, but the research on epilepsy EEG signals is relatively weak. A full-band machine learning automatic discrimination method of epilepsy brain magnetic spikes based on the brain functional connection network is proposed. The four classifiers are comprehensively compared. The classifier with the best effect is selected, and the discrimination accuracy can reach 93.8%. Therefore, this method has a good application prospect in automatically identifying and labeling epileptic spikes in magnetoencephalography.