Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-Hop Routing Protocol

Wireless Sensor Networks (WSN) has evolved into a key technology for ubiquitous living and the domain of interest has remained active in research owing to its extensive range of applications. In spite of this, it is challenging to design energy-efficient WSN. The routing approaches are leveraged to reduce the utilization of energy and prolonging the lifespan of network. In order to solve the restricted energy problem, it is essential to reduce the energy utilization of data, transmitted from the routing protocol and improve network development. In this background, the current study proposes a novel Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-hop Routing Protocol (DEAOA-MHRP) for WSN. The aim of the proposed DEAOA-MHRP model is select the optimal routes to reach the destination in WSN. To accomplish this, DEAOA-MHRP model initially integrates the concepts of Different Evolution (DE) and Arithmetic Optimization Algorithms (AOA) to improve convergence rate and solution quality. Besides, the inclusion of DE in traditional AOA helps in overcoming local optima problems. In addition, the proposed DEAOA-MRP technique derives a fitness function comprising two input variables such as residual energy and distance. In order to ensure the energy efficient performance of DEAOA-MHRP model, a detailed comparative study was conducted and the results established its superior performance over recent approaches.     

Competitive Multi-Verse Optimization with Deep Learning Based Sleep Stage Classification

Sleep plays a vital role in optimum working of the brain and the body. Numerous people suffer from sleep-oriented illnesses like apnea, insomnia, etc. Sleep stage classification is a primary process in the quantitative examination of polysomnographic recording. Sleep stage scoring is mainly based on experts’ knowledge which is laborious and time consuming. Hence, it can be essential to design automated sleep stage classification model using machine learning (ML) and deep learning (DL) approaches. In this view, this study focuses on the design of Competitive Multi-verse Optimization with Deep Learning Based Sleep Stage Classification (CMVODL-SSC) model using Electroencephalogram (EEG) signals. The proposed CMVODL-SSC model intends to effectively categorize different sleep stages on EEG signals. Primarily, data pre-processing is performed to convert the actual data into useful format. Besides, a cascaded long short term memory (CLSTM) model is employed to perform classification process. At last, the CMVO algorithm is utilized for optimally tuning the hyperparameters involved in the CLSTM model. In order to report the enhancements of the CMVODL-SSC model, a wide range of simulations was carried out and the results ensured the better performance of the CMVODL-SSC model with average accuracy of 96.90%.     

Power and energy optimization with reduced complexity in different deployment scenarios of massive MIMO network

With the increase in the number of users, the role of massive MIMO has become more significant. But there is a significant increase in the power and energy consumption in the massive MIMO network for transmission, processing, and reception. Hence, the prior role is to reduce the power consumption and increase the energy efficiency of the network. In this paper, the work is done to reduce the power consumption, while maintaining the reduced complexity in the massive MIMO and small‐cell scenario, and to increase the energy efficiency, by optimizing the number of users and number of transmission antennas in the massive MIMO scenario. This paper has also found out the optimal values of the energy efficiency, number of transmission antennas, and number of users for a massive MIMO network in different deployment scenarios like indoor hotspot, ultradense, dense urban, urban, suburban, and rural areas in both single‐cell scenario and multicell scenario at the base station and user equipment side according to the ITU‐R M.2135 standard.     

Adaptive schemes for distributed web caching

In distributed web caching architectures, institutional proxies take advantage of their neighbors’ contents in order to reduce the number of requests forwarded to the server. Intuitively, the maximum benefit from this cooperation is expected when the proxies that exhibit similar requests are grouped together. The current practice is to follow a static and manual configuration of neighbors. Such an approach has a number of drawbacks: (i) static allocation may not determine the best neighbors, especially if global knowledge of the participating proxies is not available, (ii) a manual allocation places significant administrative burden, (iii) static schemes are insensitive to changes in access patterns, and (iv) they cannot deal with the introduction of new, potentially useful, proxies. In this paper, we propose a set of algorithms that allow proxies to independently explore the network for better neighbors and continuously update their configuration in an adaptive fashion. The simulation experiments illustrate that dynamic neighbor reconfiguration leads to significantly higher hit ratios compared to the static approach. Although some researchers in the past have recognized the need for adaptive caching, to the best of our knowledge this is the first study to propose concrete algorithms and evaluate their efficacy.     

Automated Deep Learning Based Melanoma Detection and Classification Using Biomedical Dermoscopic Images

Melanoma remains a serious illness which is a common form of skin cancer. Since the earlier detection of melanoma reduces the mortality rate, it is essential to design reliable and automated disease diagnosis model using dermoscopic images. The recent advances in deep learning (DL) models find useful to examine the medical image and make proper decisions. In this study, an automated deep learning based melanoma detection and classification (ADL-MDC) model is presented. The goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of melanoma. The ADL-MDC technique performs contrast enhancement and data augmentation at the initial stage. Besides, the k-means clustering technique is applied for the image segmentation process. In addition, Adagrad optimizer based Capsule Network (CapsNet) model is derived for effective feature extraction process. Lastly, crow search optimization (CSO) algorithm with sparse autoencoder (SAE) model is utilized for the melanoma classification process. The exploitation of the Adagrad and CSO algorithm helps to properly accomplish improved performance. A wide range of simulation analyses is carried out on benchmark datasets and the results are inspected under several aspects. The simulation results reported the enhanced performance of the ADL-MDC technique over the recent approaches.     

Quantum Artificial Intelligence Based Node Localization Technique for Wireless Networks

Artificial intelligence (AI) techniques have received significant attention among research communities in the field of networking, image processing, natural language processing, robotics, etc. At the same time, a major problem in wireless sensor networks (WSN) is node localization, which aims to identify the exact position of the sensor nodes (SN) using the known position of several anchor nodes. WSN comprises a massive number of SNs and records the position of the nodes, which becomes a tedious process. Besides, the SNs might be subjected to node mobility and the position alters with time. So, a precise node localization (NL) manner is required for determining the location of the SNs. In this view, this paper presents a new quantum bird migration optimizer-based NL (QBMA-NL) technique for WSN. The goal of the QBMA-NL approach is for determining the position of unknown nodes in the network by the use of anchor nodes. The QBMA-NL technique is mainly based on the mating behavior of bird species at the time of mating season. In addition, an objective function is derived based on the received signal strength indicator (RSSI) and Euclidean distance from the known to unknown SNs. For demonstrating the improved performance of the QBMA-NL technique, a wide range of simulations take place and the results reported the supreme performance over the recent NL techniques.     

On the Performance of Phase‐I Bivariate Dispersion Charts to Non‐Normality

A phase‐I study is generally used when population parameters are unknown. The performance of any phase‐II chart depends on the preciseness of the control limits obtained from the phase‐I analysis. The performance of phase‐I bivariate dispersion charts has mainly been investigated for bivariate normal distribution. However, this assumption is seldom fulfilled in reality. The current work develops and studies the performance of phase‐I |S| and |G| charts for monitoring the process dispersion of bivariate non‐normal distributions. The necessary control charting constants are determined for the bivariate non‐normal distributions at nominal false alarm probability (FAP₀). The performance of these charts is evaluated and compared in a situation when samples are generated by bivariate logistic, bivariate Laplace, bivariate exponential, or bivariate t₅ distribution. The analysis shows that the proper consideration to underlying bivariate distribution in the construction of phase‐I bivariate dispersion charts is very important to give a real picture of in or out of control process status. Copyright © 2016 John Wiley & Sons, Ltd.     

EDM of Ti-6Al-4V: Electrode and polarity selection for minimum tool wear rate and overcut

Titanium and its alloys especially Ti-6Al-4V are being widely used numerous application areas. In addition to have iconic properties, Ti-6Al-4V is considered as challenging material in machining perspective that is why it has captured global research focus. In this research, electric discharge machining of Ti-6Al-4V has been carried out by employing four different types of electrode materials (graphite, aluminum, copper, and brass) assigned with two alternate polarities (positive and negative). Selection of the most appropriate tool material and electrode polarity is the important aspect needed to be explored for this alloy. In addition to polarity, discharge current, and pulse time ratio have been considered as process variables owing to have their direct influence in electric discharge machining. Taguchi L9 array has been employed for each of the four electrodes with positive polarity and similarly L9 with negative polarity. Thus, a total number of 72 experiments have been conducted. Tool wear rate and overcut (OC) around the machined surfaces are the response characteristics to be investigated in order to achieve minimum amounts of both of these two responses. Selection of the most suitable tool with common tool polarity has been carried out meeting the decision criteria of minimum tool wear and minimum OC.     

An investigation on the optimum machinability of NiTi based shape memory alloy

In the present work, the machinability of nickel–titanium (Nitinol) shape memory alloy has been discussed. Nitinol is known as a difficult-to-machine alloy due to its high hardness, which requires a large amount of cutting force, resulting in high rate of tool wearing. Therefore, researchers have made an effort to ameliorate the machinability of this material to achieve a finer surface quality. The previous studies found that the cutting speed will remarkably influence the surface properties of machined nickel–titanium alloy in turning process. Tool wear and cutting force are at minimum values in a particular range of cutting speeds so that it leads to diminishing machining barriers such as burr formation and chip-breaking. Lower cutting force and consequently lower temperature and stresses in the machining process improve the mechanical properties as well as reducing hardness, distortion, and residual stress. The machining process was optimized by applying a numerical approach through ANSYS/LS-DYNA R15 software. The obtained results demonstrated the optimum cutting speed in the machining process, which are in good agreement with experiments.     

Porous Cobalt Phosphide Polyhedrons with Iron Doping as an Efficient Bifunctional Electrocatalyst

Iron (Fe)‐doped porous cobalt phosphide polyhedrons are designed and synthesized as an efficient bifunctional electrocatalyst for both hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The synthesis strategy involves one‐step route for doping foreign metallic element and forming porous cobalt phosphide polyhedrons. With varying doping levels of Fe, the optimized Fe‐doped porous cobalt phosphide polyhedron exhibits significantly enhanced HER and OER performances, including low onset overpotentials, large current densities, as well as small Tafel slopes and good electrochemical stability during HER and OER.