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Cognitive Radio (CR) technology has been highlighted as one of the most likely answers to the issue of spectrum shortage with the rise of fifth generation and beyond communication. Secondary users (SUs) in cognitive radio networks (CRN) must continuously monitor the spectrum to forecast channel occupancy by primary users (PUs) based on fundamental factors, such as location, time, and RF band. A hybrid deep learning model called LSTMMLP (Long Short-Term Memory-Multilayer Perceptron) is proposed to improve idle channel prediction probability thus reducing the overall sensing time by cognitive users during spectrum sensing. Performance evaluation for the proposed model is done in terms of prediction error and efficiency, the GSM-900 spectrum dataset demonstrates that LSTM-MLP performs better in terms of improved prediction accuracy compared to existing state-of-art prediction techniques. [ABSTRACT FROM AUTHOR]

Spectrum monitoring is one of the significant tasks required during the spectrum sharing process in cognitive radio networks (CRNs). Although spectrum monitoring is widely used to monitor the usage of allocated spectrum resources, this work focuses on detecting a primary user (PU) in the presence of secondary user (SU) signals. For signal classification, existing methods, including cooperative, noncooperative, and neural network-based models, are frequently used, but they are still inconsistent because they lack sensitivity and accuracy. A deep neural network model for intelligent wireless signal identification to perform spectrum monitoring is proposed to perform efficient sensing at low SNR (signal to noise ratio) and preserve hyperspectral image features. A hybrid deep learning model called SPECTRUMNET (spectrum sensing using deep neural network) is presented. It can quickly and accurately monitor the spectrum from spectrogram images by utilizing cyclostationary features and convolutional neural networks (CNN). The class imbalance issue is solved by uniformly spreading the samples throughout the classes using the oversampling method known as SMOTE (Synthetic Minority Oversampling Technique). The proposed model achieves a classification accuracy of 94.46% at a low SNR of −15 dB, which is an improvement over existing CNN models with minor trainable parameters. [ABSTRACT FROM AUTHOR]

A bacterial or bone infection in the feet causes diabetic foot infection (DFI), which results in reddish skin in the wound and surrounding area. DFI is the most prevalent and dangerous type of diabetic mellitus. It will mainly occur in people with heart disease, renal illness, or eye disease. The clinical signs and symptoms of local inflammation are used to diagnose diabetic foot infection. In assessing diabetic foot ulcers, the infection has significant clinical implications in predicting the likelihood of amputation. In this work, a diabetic foot infection network (DFINET) is proposed to assess infection and no infection from diabetic foot ulcer images. A DFINET consists of 22 layers with a unique parallel convolution layer with ReLU, a normalization layer, and a fully connected layer with a dropout connection. Experiments have shown that the DFINET, when combined with this technique and improved image augmentation, should yield promising results in infection recognition, with an accuracy of 91.98%, and a Matthews correlation coefficient of 0.84 on binary classification. Such enhancements to existing methods shows that the suggested approach can assist medical experts in automated detection of DFI. [ABSTRACT FROM AUTHOR]

INTRODUCTION Recently, the role of Artificial Intelligence plays a major role in the communication sector. As the revolution of spectrum and its standards is progressing towards 5G networks and beyond, there is a rapid innovation in design of 5G compatible gadgets in order to incorporate evolving wireless spectrum standards. Cognitive radio is an intelligent technology that can efficiently handle the radio spectrum usage. OBJECTIVES Researchers have been working since its inception to use this revolutionary technology in the management of the radio spectrum for both terrestrial and satellite communication. For 5G networks, research works focus on enabling efficient utilization of its features like extreme broadband, ultra-low latency communication, and ultra reliable connectivity for connected devices. CONCLUSION In this paper, energy efficient spectrum sensing schemes and challenges of 5G networks are explored and this review will assist any researcher/service provider/mobile communication sector to quickly select and apply relevant energy efficient spectrum sensing techniques using dynamic intelligent cognitive radio technology to incorporate either Co-operative, Non-Cooperative or Interference based techniques based on their application. to show how conventional energy efficient spectrum sensing techniques used in cognitive radio networks can be efficiently applied to 5G terrestrial applications. [ABSTRACT FROM AUTHOR]

This article uses cutting-edge deep learning technology to identify structural damage from images for a civil engineering application. The public infrastructures of the country are generally inspected physically by a visual evaluation by qualified inspectors. However, manual inspections are pretty time-consuming and often require too much labor. The number of experts capable of evaluating such structural damage is inadequate. As a result, computer vision-based techniques for automatic damage detection have been developed. This paper's civil infrastructure damages are classified into four damages of roads common in Indian highways and the concrete deterioration in the bridges. The convolutional neural network has become a standard tool for organizing and recognizing images. In this paper, an ensemble of three CNN models is proposed, and two are transfer learning-based models. The proposed ensemble transfer learning model provided a validation accuracy of 87.1%. [ABSTRACT FROM AUTHOR]

Traditional screening of cervical cancer type classification majorly depends on the pathologist's experience, which also has less accuracy. Colposcopy is a critical component of cervical cancer prevention. In conjunction with precancer screening and treatment, colposcopy has played an essential role in lowering the incidence and mortality from cervical cancer over the last 50 years. However, due to the increase in workload, vision screening causes misdiagnosis and low diagnostic efficiency. Medical image processing using the convolutional neural network (CNN) model shows its superiority for the classification of cervical cancer type in the field of deep learning. This paper proposes two deep learning CNN architectures to detect cervical cancer using the colposcopy images; one is the VGG19 (TL) model, and the other is CYENET. In the CNN architecture, VGG19 is adopted as a transfer learning for the studies. A new model is developed and termed as the Colposcopy Ensemble Network (CYENET) to classify cervical cancers from colposcopy images automatically. The accuracy, specificity, and sensitivity are estimated for the developed model. The classification accuracy for VGG19 was 73.3%. Relatively satisfied results are obtained for VGG19 (TL). From the kappa score of the VGG19 model, we can interpret that it comes under the category of moderate classification. The experimental results show that the proposed CYENET exhibited high sensitivity, specificity, and kappa scores of 92.4%, 96.2%, and 88%, respectively. The classification accuracy of the CYENET model is improved as 92.3%, which is 19% higher than the VGG19 (TL) model. [ABSTRACT FROM AUTHOR]

In recent years, the rapidly increasing vehicular population, driving environment and human factors has led to a lot of traffic accidents. If there exists a mechanism to detect obstructions in the road, and then relay the processed information back to the driver. He may be alerted about the impending danger. This paper proposes a vision-based monitoring algorithm to detect vehicles in a blind-spot area using three rear-view cameras. Three frames are stitched to obtain a single wide-range frame. The stitched frame eliminates a major portion of the vehicle's blind-spot. This system uses the YOLO algorithm to detect a vehicle in the detection window. The alarm signal is generated if the detected vehicle in the blind-spot crosses the hazardous zone. Vehicle data set with truck, bus, car, and motor-cycle is used to train the YOLO model. The processing speed of the system is improved while keeping performance degradation as minimal as possible. [ABSTRACT FROM AUTHOR]

In Mobile Ad Hoc NETwork (MANET), an attacker can easily make a server or a network resource unavailable to users by temporarily interrupting or suspending the services of a host connected to the Internet. So far, several secured routing protocols have been proposed for MANET. But all of them have certain disadvantages. Hence, security in MANET is still a challenging area. An Efficient Secure Enhanced Routing Protocol (ESERP) for MANET is proposed which is an attack resistant authentication mechanism. The problem definition is that the proposed protocol focuses on efficient security against protocol exploitation flooding attacks which is a part of Distributed Denial-of-Service (DDoS) attacks in MANET routing protocols. The ESERP protocol is used to address all the issues of protocol attacks in DDoS attacks in MANET for route establishment. The Protocol exploitation flooding attacks in DDoS includes SYN floods, fragmented packet attacks, Ping of Death, Smurf DDoS and more. Detailed simulation studies have confirmed the efficiency and effectiveness of ESERP. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy is a noninvasive wireless imaging technology that becomes increasingly popular in recent years. One of the major drawbacks of this technology is that it generates a large number of photos that must be analyzed by medical personnel, which takes time. Various research groups have proposed different image processing and machine learning techniques to classify gastrointestinal tract diseases in recent years. Traditional image processing algorithms and a data augmentation technique are combined with an adjusted pretrained deep convolutional neural network to classify diseases in the gastrointestinal tract from wireless endoscopy images in this research. We take advantage of pretrained models VGG16, ResNet-18, and GoogLeNet, a convolutional neural network (CNN) model with adjusted fully connected and output layers. The proposed models are validated with a dataset consisting of 6702 images of 8 classes. The VGG16 model achieved the highest results with 96.33% accuracy, 96.37% recall, 96.5% precision, and 96.5% F1-measure. Compared to other state-of-the-art models, the VGG16 model has the highest Matthews Correlation Coefficient value of 0.95 and Cohen's kappa score of 0.96. [ABSTRACT FROM AUTHOR]

In a Medical Wireless Network (MWN), sensors constantly monitor patient's physiological condition and movement. Inter-MWN communications are set up between the Patient Server and one or more Centralized Coordinators. However, MWNs require protocols with little energy consumption and the self-organizing attribute perceived in ad-hoc networks. The proposed Smart Routing Protocol (SRP) selects only the nodes with a higher residual energy and lower traffic density for routing. This approach enhances cooperation among the nodes of a Mobile Ad Hoc Network. Consequently, SRP produces better results than the existing protocols, namely Conditional Min-Max Battery Cost Routing, Min-Max Battery Cost Routing and AdHoc On-demand Distance Vector in terms of network parameters. The performance of the erstwhile schemes for routing protocols is evaluated using the network simulator Qualnet v 4.5.