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Breast cancer (BC) is the most widely found disease among women in the world. The early detection of BC can frequently lessen the mortality rate as well as progress the probability of providing proper treatment. Hence, this paper focuses on devising the Exponential Honey Badger Optimization-based Deep Covolutional Neural Network (EHBO-based DCNN) for early identification of BC in the Internet of Things (IoT). Here, the Honey Badger Optimization (HBO) and Exponential Weighted Moving Average (EWMA) algorithms have been combined to create the EHBO. The EHBO is created to transfer the acquired medical data to the base station (BS) by choosing the best cluster heads to categorize the BC. Then, the statistical and texture features are extracted. Further, data augmentation is performed. Finally, the BC classification is done by DCNN. Thus, the observational outcome reveals that the EHBO-based DCNN algorithm attained outstanding performance concerning the testing accuracy, sensitivity, and specificity of 0.9051, 0.8971, and 0.9029, correspondingly. The accuracy of the proposed method is 7.23%, 6.62%, 5.39%, and 3.45% higher than the methods, such as multi-layer perceptron (MLP) classifier, deep learning, support vector machine (SVM), and ensemble-based classifier. [ABSTRACT FROM AUTHOR]

Summary: In the present epoch of computing, the world has changed from older conventional print media to social platform channels. Fake news articles have the prospects to handle the opinions of the public and so may harm human groupings. Therefore, it is necessary to explore the authenticity and credibility of the news flash being shared on the internet community. Hence, this research paper devises an efficient and robust fake news detection model, named Exponential Chimp Optimization Algorithm (EChOA)‐based Deep Neuro‐Fuzzy Network (DNFN) for detecting fake news. The introduced model utilizes a MapReduce framework that includes the mapper and reducer phases for processing big data for detecting fake news. First phase of processing is the Mapper work, in which every input used in the database is processed and creates an intermediate key‐value pair. In the reducer phase, the fusion of features is performed by arranging the features with the help of computing the optimal parameter and Rand similarity coefficient using a Deep Q Network (DQN). Here, the detection of fake news is obtained by DNFN, and the DNFN is done using implemented EChOA. The EChOA‐based DNFN effectively generates robust and effective fake news detection performance by choosing the optimal feature subsets through feature fusion. The EChOA is designed by integrating the Exponential Weighted Moving Average (EWMA) and Chimp Optimization Algorithm (ChOA). Moreover, the EChOA‐based DNFN method outperformed various former fake news detection approaches and attains the highest performance based on the testing accuracy is 0.909, sensitivity is 0.937, and specificity is 0.891 using the FakeNewsNet dataset. [ABSTRACT FROM AUTHOR]

The study of opinions conveyed in a text format is examined using sentiment analysis. Analyzing the sentiment plays a fundamental part in the sentiment orientation of reviews and extracting necessary information. Numerous existing sentiment classification approaches are designed with lesser classification performance. Hence, it is vital to design a sentiment classification model that poses the capability for classifying the opinions expressed in review data. An effective sentiment classification method, named exponential poor–rich optimization-based gated recurrent unit, is introduced in this research for classifying the sentiment using reviews. The developed exponential poor–rich optimization algorithm is newly derived by the merging of poor–rich optimization and exponential weighted moving average and poor–rich optimization. With a deep residual network, necessary features are extracted, and then, feature fusion is done by RV coefficient. The process of classification of sentiment is completed using a gated recurrent unit classifier. The training of the gated recurrent unit classifier is done by the exponential poor–rich optimization algorithm. The devised technique outperformed various existing sentiment classification approaches and attained better performance by achieving a higher true positive rate of 0.934, true negative rate of 0.901, and accuracy of 0.912. [ABSTRACT FROM AUTHOR]

In this paper, human step length is estimated based on the wireless channel properties and the received signal strength indicator (RSSI) method. The path loss between two ankles, called the on-ankle path loss, is converted from the RSSI, which is measured by our developed wearable hardware in indoor and outdoor ambulation scenarios. The human walking step length is estimated by a reliable range of RSSI values. The upper threshold and the lower threshold of this range are determined experimentally. This paper advances our previous step length measurement technique by proposing a novel exponential weighted moving average (EWMA) algorithm to update the upper and lower thresholds, and thus the step length estimation, recursively. The EWMA algorithm allows our measurement technique to process each shorter subset of the dataset, called a time window, and estimate the step length, rather than having to process the whole dataset at a time. The step length is periodically updated on the fly when the time window is "sliding" forwards. Thus, the EWMA algorithm facilitates the step length estimation in real-time. The impact of the EWMA parameter is analysed, and the optimal parameter is discovered for different experimental scenarios. Our experiments show that the EWMA algorithm could achieve comparable accuracy as our previously proposed technique with errors as small as 3.02% and 0.30% for the indoor and outdoor scenarios, respectively, while the processing time required to output an estimation of the step length could be significantly shortened by 53.96% and 60% for the indoor walking and outdoor walking, respectively. [ABSTRACT FROM AUTHOR]

Finding good relevant documents for query optimization is a well-known difficulty in the field of document retrieval. This paper develops a novel approach, named Exponential Aquila Optimizer (EAO)-based Deep Fuzzy Clustering for retrieving the documents. The proposed technique effectively finds the relevant documents and tries to understand the relationship among the documents and queries in terms of the significance of documents for query optimization. Here, the Deep Fuzzy Clustering is employed for performing cluster-based inverted indexing where the Training procedure of Deep Fuzzy Clustering is done using the developed optimization algorithm, named EAO. Meanwhile, the developed EAO is newly designed by the incorporation of EWMA and AO. In addition, complex query matching is done using the Tversky index for the user-based queries, such as multigram queries and semantic queries. On the other hand, the RV coefficient is accomplished for performing query optimization for relevant document retrieval. The proposed technique achieves better performance in terms of the performance metrics, like precision, recall, and F-measure with the maximum precision of 1, maximum recall of 0.956, and maximum F-measure of 0.977, respectively. [ABSTRACT FROM AUTHOR]

Traditionally, in using an auxiliary variable for quality control applications, the response and auxiliary variables are often assumed to follow a bivariate normal distribution. Given that the response variable and an auxiliary variable are linearly related, a novel process monitoring method is proposed to scrutinize the shifts of the model parameter when the response and auxiliary variables follow the smallest extreme value distributions. Utilizing the estimates of the model parameters as input variables, auxiliary information based (AIB) multivariate sign exponential weighted moving average (AIB-MSEWMA) control chart is proposed. Because the AIB-MSEWMA control chart doesn't require the multivariate normality distribution assumption for the estimators of model parameters, the AIB-MSEWMA control chart is more reliable than the competitors of the AIB-Hotelling T2 and AIB multivariate EWMA control charts in terms of the false alarm rate. Finally, the operation of the AIB-MSEWMA control chart is illustrated through a regenerated data set of the car brake system. [ABSTRACT FROM AUTHOR]

In this paper, human step length is estimated based on the wireless channel properties and the received signal strength indicator (RSSI) method. The path loss between two ankles, called the on-ankle path loss, is converted from the RSSI, which is measured by our developed wearable hardware in indoor and outdoor ambulation scenarios. The human walking step length is estimated by a reliable range of RSSI values. The upper threshold and the lower threshold of this range are determined experimentally. This paper advances our previous step length measurement technique by proposing a novel exponential weighted moving average (EWMA) algorithm to update the upper and lower thresholds, and thus the step length estimation, recursively. The EWMA algorithm allows our measurement technique to process each shorter subset of the dataset, called a time window, and estimate the step length, rather than having to process the whole dataset at a time. The step length is periodically updated on the fly when the time window is “sliding” forwards. Thus, the EWMA algorithm facilitates the step length estimation in real-time. The impact of the EWMA parameter is analysed, and the optimal parameter is discovered for different experimental scenarios. Our experiments show that the EWMA algorithm could achieve comparable accuracy as our previously proposed technique with errors as small as 3.02% and 0.30% for the indoor and outdoor scenarios, respectively, while the processing time required to output an estimation of the step length could be significantly shortened by 53.96% and 60% for the indoor walking and outdoor walking, respectively.

An Intrabody Nanonetwork (IBNN) is constituted by nanoscale devices that are implanted inside the human body for monitoring of physiological parameters for disease diagnosis and treatment purposes. The extraordinary accuracy and precision of these nanoscale devices in cellular level disease diagnosis and drug delivery are envisioned to advance the traditional healthcare system. However, the feature constraints of these nanoscale devices, such as inadequate energy resources, topology-unawareness, and limited computational power, challenges the development of energy-efficient routing protocol for IBNNs. The presented work concentrates on the primary limitations and responsibilities of IBNNs and designs a routing protocol that incorporates characteristics of Exponential Weighted Moving Average (EWMA) Based Opportunistic Data Transmission (EWMA-ODT) and Artificial Colony Algorithm Based Query Response Transmission (ABC-QRT) approaches for efficiently handling the routing challenges of IBNNs. In EWMA-ODT, the moving Nano Biosensors (NBSs) employ the EWMA method attributes to aggregate detected data by assigning high weightage to the recent detected information. Later, the aggregated data is transmitted to the Nano Router (NR) when the direct data transmission opportunity is available, the reception of aggregated briefs NR about the condition of the network after the last successful interaction with minimum energy consumption. Whereas, the ABC-QRT approach introduces the ABC algorithm for the selection of those optimal NBSs that have maximum fitness value for satisfying the data transmission demand of the external healthcare system with minimal traffic overhead. The simulation results validate that the joint contribution of these approaches enhances IBNNs lifetime and reduces end-to-end delay as compared to the flooding scheme.

Summary: The major aim of data center (DC) is to optimize the three parameters, viz., energy consumption, resource utilization, and quality of service (QoS). Our objective is to balance these three parameters by managing the virtual machine (VM) efficiently. Therefore, this paper presents a holistic view of VM consolidation and explains its constituent subprocesses: (i) host overload detection, (ii) VM selection for migration from the overloaded host, and (iii) VM placement for provisioning selected VMs on a new set of hosts. Later, this paper attempts to optimize the parameters mentioned above by proposing a host overload detection algorithm based on exponential weighted moving average (EWMA) and its variants. The performance of the proposed EWMA is compared with the state‐of‐the‐art algorithms based on (i) local regression, (ii) median absolute deviation, and (iii) interquartile range. The efficacy of the proposed EWMA is evaluated in combination with four different existing VM selection policies: (i) minimum migration time, (ii) minimum utilization, (iii) maximum correlation, and (iv) random selection. The evaluation metrics comprise energy consumption, VM migration count from the overloaded hosts representing the resource utilization, service level agreement (SLA) violations describing the QoS, and the average execution time. Simulation experiments are carried out on the CloudSim simulator using PlanetLab real cloud trace as workload. The proposed EWMA consumes 18.33% less energy, causes 47.81% less VM migration, makes 9.91% less SLA violation, and takes 43.44% less average‐time for the execution of the entire workload as compared to state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

Fault detection is essential for monitoring of various biological processes and becomes even more important in that context. This paper, therefore, presents an enhanced tool that merges state estimation with fault detection (FD) methods to improve monitoring of biological processes. The proposed technique, so-called particle filter (PF)-based maximum double adaptive exponential weighted moving average (EWMA) chart, involves two steps. First, the states of the biological processes are estimated using the PF method. In the second step, the faults are detected using the maximum double adaptive EWMA chart. The proposed method is based on the maximum of the absolute values of the EWMA statistics, one monitoring adaptively the variance and the other controlling the mean. The FD performance is studied utilising a wastewater treatment model. The detection performances are assessed in terms of missed detection rate, false alarm rate, detection speed, sensibility to fault sizes and robustness to noise levels. [ABSTRACT FROM AUTHOR]

In mobile ad hoc networks, cooperation among active mobile nodes is considered to play a vital role in reliable transmission of data. But, the selfish mobile nodes present in an ad hoc environment refuse to forward neighbouring nodes’ packet for conserving its own energy. This intentional selfish behaviour drastically reduces the degree of cooperation maintained between the mobile nodes. Hence, a need arises for devising an effective mechanism which incorporates both energy efficiency and reputation into account for mitigating selfish behaviour in MANETs. In this paper, we propose an Exponential Reliability Coefficient based reputation Mechanism (ERCRM) which isolates the selfish nodes from the routing path based on Exponential Reliability Coefficient (ExRC). This reliability coefficient manipulated through exponential failure rate based on moving average method highlights the most recent past behaviour of the mobile nodes for quantifying its genuineness. From the simulation results, it is evident that, the proposed ERCRM approach outperforms the existing Packet Conservation Monitoring Algorithm (PCMA) and Spilt Half Reliability Coefficient based Mathematical Model (SHRCM) in terms of performance evaluation metrics such as packet delivery ratio, throughput, total overhead and control overhead. Further, this ERCRM mechanism has a successful rate of 28% in isolating the selfish nodes from the routing path. Furthermore, it also aids in framing the exponential threshold point of detection as 0.4, where a maximum number of selfish nodes are identified when compared to the existing models available in the literature.

Abstract This paper presents a hybrid method for the detection of distributed denial-of-service (DDoS) attacks that combines feature-based and volume-based detection. Our approach is based on an exponential moving average algorithm for decision-making, applied to both entropy and packet number time series. The approach has been tested by performing a controlled DDoS experiment in a real academic network. The network setup and test scenarios including both high-rate and low-rate attacks are described in the paper. The performance of the proposed method is compared to the performance of two methods that are already known in the literature. One is based on the counting of SYN packets and is used for detection of SYN flood attacks, while the other is based on a CUSUM algorithm applied to the entropy time series. The results show the advantage of our approach compared to methods that are based on either entropy or number of packets only. [ABSTRACT FROM AUTHOR]

With the advancement in the science and technology, cloud computing has become a recent trend in environment with immense requirement of infrastructure and resources. Load balancing of cloud computing environments is an important matter of concern. The migration of the overloaded virtual machines (VMs) to the underloaded VM with optimized resource utilization is the effective way of the load balancing. In this paper, a new VM migration algorithm for the load balancing in the cloud is proposed. The migration algorithm proposed (EGSA-VMM) is based on exponential gravitational search algorithm which is the integration of gravitational search algorithm and exponential weighted moving average theory. In our approach, the migration is done based on the migration cost and QoS. The experimentation of proposed EGSA-based VM migration algorithm is compared with ACO and GSA. The simulation of experiments shows that the proposed EGSA-VMM algorithm achieves load balancing and reasonable resource utilization, which outperforms existing migration strategies in terms of number of VM migrations and number of SLA violations. [ABSTRACT FROM AUTHOR]

• The input of depression detection is both MRI image and speech signal. • The image and signal features are extracted from the input. • The proposedExpAVOAis the concatenation of AVOA and EWMA. • This technique achieved the testing accuracy of 0.945. Depression is defined as a psychiatric disorder, which has many adverse effects on the person, family, and society. In this paper, the multimodalities are utilized as an input for depression detection that includes both speech signal and brain MRI for better detection. Here, the median filter and Gaussian filter are utilized to remove the clamour exist in MRI and speech signal, respectively. State of depression is classified from both MRI and speech signal using a Deep Maxout Network (DMN), where the weight of the deep learning network is trained by a novel approach of Exponential African Vulture Optimization Algorithm (ExpAVOA). The ExpAVOA is formed by the merging of the Exponentially Weighted Moving Average (EWMA) and African Vultures Optimization Algorithm (AVOA). Finally, the classified outcomes of the MRI and speech signals are fused by the RV coefficient. Thus, the classified outcome is either a healthy person or a depressed person. The unique optimal deep learning technique attained maximum testing accuracy, sensitivity, and specificity of 0.959, 0.942, and 0.968 respectively. [ABSTRACT FROM AUTHOR]

The smoothness and dynamic nature of the exploitation are significantly influenced by Mobile Ad hoc Networks (MANETs). The wireless and active environments, on the other hand, are vulnerable to a range of threats. Enhancing the reliable intrusion detection model is crucial to safeguarding the MANET from various malicious attacks. In this paper, an effective intrusion detection method for MANET is presented based on a Deep Neuro Fuzzy Network based on Exponential-Henry Gas Solubility Optimization (EHGSO). The newly designed EHGSO algorithm is used to select the optimal routes in the early phases of safe routing. The fitness metrics that define this approach include energy, distance, neighbourhood quality, and link quality. The proposed EHGSO combines the Henry Gas Solubility Optimization (HGSO) with Exponential Weighted Moving Average (EWMA). The second phase, in which the conveyed data packets are altered and the Knowledge discovery in databases (KDD) features are extracted, starts the intrusion detection phase at the base station. Following the extraction of the KDD features, data augmentation is performed. The Deep Neuro Fuzzy Network is trained using the suggested EHGSO method before performing intrusion detection. The proposed method demonstrates higher performance when compared to all other existing technologies. In without attacks scenario, the values achieved by the proposed method considering the metrics, such as energy, throughput, packet drop, jitter, Performance and Development Review (PDR), precision, and recall is 0.342 J, 134975 kbps, 4.123, 0.086, 95.877%, 0.950, and 0.924, respectively. [ABSTRACT FROM AUTHOR]