In the healthcare research community, Internet of Medical Things (IoMT) is transforming the healthcare system into the world of the future internet. In IoMT enabled Computer aided diagnosis (CAD) system, the Health-related information is stored via the internet, and supportive data is provided to the patients. The development of various smart devices is interconnected via the internet, which helps the patient to communicate with a medical expert using IoMT based remote healthcare system for various life threatening diseases, e.g., brain tumors. The brain tumor is one of the most dreadful diseases ever known to human beings. Often, the tumors are predecessors to cancers. The survival rates for these diseases are very low. So, early detection and classification of tumors can save a lot of lives. IoMT enabled CAD system plays a vital role in solving these problems. Deep learning, a new domain in Machine Learning, has attracted a lot of attention in the last few years. The concept of Convolutional Neural Networks (CNNs) has been widely used in this field. In this paper, we have classified brain tumors into three classes, namely glioma, meningioma and pituitary, using transfer learning model. The features of the brain MRI images are extracted using a pre-trained CNN, i.e. GoogLeNet. The features are then classified using classifiers such as softmax, Support Vector Machine (SVM), and K-Nearest Neighbor (K-NN). The proposed model is trained and tested on CE-MRI Figshare dataset. Further, Harvard medical repository dataset images are also considered for the experimental purpose to classify four types of tumors, and the results are compared with the other state-of-the-art models. Performance measures such as accuracy, precision, recall, specificity, and F1 score are examined to evaluate the performances of the proposed model.

The birth of beyond 5G (B5G) and emerge of 6G has made personal and industrial operations more reliable, efficient, and profitable, accelerating the development of the next-generation Internet of Things (IoT). We know, one of the most important key performance indicators in 6G is smart network architecture, and in massive IoT applications, energy efficient ubiquity networks rely mainly on the intelligence and automation for industrial applications. This paper addresses the energy consumption problem with a massive IoT system model with dynamic network architecture or clustering using a multi-agent system (MAS) for industrial 6G applications. The work uses distributed artificial intelligence (DAI) to cluster the sensor nodes in the system to find the main node and predict its location. The work initially uses the back-propagation neural network (BPNN) and convolutional neural network (CNN), which are respectively introduced for optimization. Furthermore, the work analyze the correlation of mutual clusters to allocate resources to individual nodes in each cluster efficiently. The simulation results show that the proposed method reduces the waste of resources caused by redundant data, improves the energy efficiency of the whole network, along with information preservation.

Stock market volatility has a significant impact on many economic and financial activities in the world. Forecasting stock price movement plays an important role in setting an investment strategy or determining the right timing for trading. However, stock price movements are noisy, nonlinear, and chaotic. It is difficult to forecast stock trends for improving return on investment. Here, we proposed a novel improved particle swarm optimization (IPSO) and long-short term memory (LSTM) hybrid model for stock price forecasting. An adaptive mutation factor was used as a parameter for model optimization to avoid premature convergence to a local optimum. Furthermore, we presented a nonlinear approach to improve the inertia weight of the particle swarm optimization and then used the IPSO model to optimize the hyperparameters of LSTM. The experimental results showed that the proposed model outperformed the other related baseline models: support-vector regression, LSTM and PSO-LSTM on the Australian stock market index. These results indicated the proposed model possesses high reliability and good forecasting capability.

A high-gain circularly polarized (CP) filtering stacked patch antenna with independently controllable radiation nulls is designed without an additional filtering circuit/network in this letter. The lower radiation patch consists of a square outer loop and a three-quarter circular inner ring to realize CP radiation, with the inner ring connected to two adjacent edges of the outer loop. In addition, the upper stacked patch provides improved impedance matching and CP characteristics within the passband. Furthermore, a U-slot and square annular slot are etched on the lower and upper patch to generate two radiation nulls and obtain a filtering characteristic in the lower and upper stopband, respectively. A prototype is fabricated and measured, and the measured results show that the proposed antenna can provide an impedance bandwidth of 15.2% (1.83-2.13 GHz), an axial ratio bandwidth of 8.2% (1.87-2.03 GHz), and a gain higher than 7.0 dBi.

Resource allocation in the Internet of Things (IoT) applications for Wireless Sensor Networks (WSNs) is a challenging problem that requires tasks processing from the appropriate sensor nodes without compromising the Quality-of-Service (QoS). Due to heterogeneity in sensors, the inter-cluster and intra-cluster cooperative communication between sensor nodes hinders the overall resource allocation of the network in terms of energy consumption and response time. Therefore, this paper establishes a multi-agent clustering WSN model, i.e., Adaptive Distributed Artificial Intelligence (ADAI) technique with a hierarchical resource allocation strategy to address the issue of resource allocation in these types of network. For the inter-cluster power allocation, we are considering QoS and energy consumption factors with DAI. Moreover, for intra-cluster resource allocation, this paper introduces Adaptive Particle Swarm Optimization (APSO), which uses its objective functions as the node distance and respective energy loads. The mathematical analysis and simulation results validate the propose method in terms of energy consumption and response time of the network.

In this letter, an eight-element broadband multiple-input multiple-output (MIMO) antenna array with a simple structure based on multiple modes is presented, which covers 3.3–6 GHz for fifth-generation (5G) smartphone. Double coupled-fed loop modes and a slot mode are well excited by the feeding strip connecting with the tuning branch. The broadband antenna is well designed without loading any lumped elements, thanks to the tuning branch on the side frames. Furthermore, the polarization diversity technique is exploited to improve isolation. As a result, the MIMO system operates in the 3.3-6 GHz (S ₁₁ < -6 dB) with desirable isolation higher than 12.1 dB, which satisfies the coverage of the 5G communication bands: n77/n78/n79 and 5G WLAN. Besides, acceptable efficiency and envelope correlation coefficients (ECCs) of lower than 0.2 can also be obtained, which demonstrates that the presented MIMO array has potential for the mobile communication of 5G smartphones.