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The expansion of the Internet of Things is expected to lead to the emergence of the Internet of Medical Things (IoMT), which will revolutionize the health-care industry (IoT). The Internet of Things (IoT) revolution is outpacing current human services thanks to its bright mechanical, economical, and social future. Security is essential because most patient information is housed on a cloud platform in the hospital. The security of medical images in the Internet of Things was investigated in this research using a new cryptographic model and optimization approaches. For the effective storage and safe transfer of patient data along with medical images, a separate framework is required. The key management and optimization will be chosen utilizing the Rivest–Shamir–Adleman-based Arnold map (RSA-AM), hostile orchestration (HO), and obstruction bloom breeding optimization (OBBO) to increase the encryption and decryption processes' level of security. The effectiveness of the suggested strategy is measured using peak signal-to-noise ratio (PSNR), entropy, mean square error (MSE), bit error rate (BER), structural similarity index (SSI), and correlation coefficient (CC). The investigation shows that the recommended approach provides greater security than other current systems. [ABSTRACT FROM AUTHOR]

In recent years, the high improvement in communication, Internet of Things (IoT) and cloud computing have begun complex questioning in security. Based on the development, cyberattacks can be increased since the present security techniques do not give optimal solutions. As a result, the authors of this paper created filter-based ensemble feature selection (FEFS) and employed a deep learning model (DLM) for cloud computing intrusion detection. Initially, the intrusion data were collected from the global datasets of KDDCup-99 and NSL-KDD. The data were utilized for validation of the proposed methodology. The collected database was utilized for feature selection to empower the intrusion prediction. The FEFS is a combination of three feature extraction processes: filter, wrapper and embedded algorithms. Based on the above feature extraction process, the essential features were selected for enabling the training process in the DLM. Finally, the classifier received the chosen features. The DLM is a combination of a recurrent neural network (RNN) and Tasmanian devil optimization (TDO). In the RNN, the optimal weighting parameter is selected with the assistance of the TDO. The proposed technique was implemented in MATLAB, and its effectiveness was assessed using performance metrics including sensitivity, F measure, precision, sensitivity, recall and accuracy. The proposed method was compared with the conventional techniques such as an RNN and deep neural network (DNN) and RNN–genetic algorithm (RNN-GA), respectively. [ABSTRACT FROM AUTHOR]

A MANET consists of a group of mobile nodes. In a MANET, scalability and mobility have a greater influence on routing performance. The clustering technique plays a vital role in enhancing the routing mechanism and improving the network lifetime of a large-scale network like a MANET. The clustering process will degrade network performance if the malicious node is chosen as the Cluster Leader (CL). Thus, the secure clustering process in a MANET is a very challenging task. To overcome this problem, the following key factors like Trust Value (TV), Residual Energy Level (REL), and Mobility (M) of the node are used as decision-making parameters to elect a Cluster Leader (CL). In this work, we have proposed a soft computing-based neuro-fuzzy model, ANFIS-based Energy-Efficient Secure Clustering Model (ANFIS-EESC), with a primary objective of forming energy-aware stable trust-based clustering in a MANET. Moreover, we have proposed two working novel algorithms: Weight-Based Trust Estimation (WBTE) algorithm and the Fuzzy-Based Clustering (FBC) algorithm. The primary objective of the WBTE algorithm is to measure the trustworthiness of the nodes and to mitigate the malicious nodes. Fuzzy-Based Clustering (FBC) algorithm is a fuzzy logic-based cluster formation algorithm. In our proposed work, each non-CL in the system applies the cluster density of CL and mobility for each CL node using the Mamdani Fuzzy Inference system, and makes the decision to join as a member with a CL that holds maximum value. Simulation results show that the proposed work enhances the network performance by electing a more stable trust-aware and energy-aware node as Cluster leader (CL). We compare the performance parameters of the proposed work, such as packet delivery rate, energy consumption, detection rate, and reaffiliation, with the existing work, Weighted Clustering Algorithm (WCA). The network lifetime is 39% greater in the proposed ANFIS-EESC model than in the other existing work, WCA. Moreover, ANFIS-EESC shows an enhancement of 22% to 32% in packet delivery ratio and 32% and 39% in throughput. From the above analysis, it has been proved that the proposed work gives a better performance in terms of reliability and stability when compared to the existing work, WCA. [ABSTRACT FROM AUTHOR]

The notion of predictive maintenance is perceived as a breakthrough in the manufacturing and other industrial sectors. The recent developments in this field can be attributed to the amalgamation of Artificial Intelligence- and Machine Learning (ML)-based solutions in predicting the health state of the machines. Most of the existing machine learning models are a hybridization of common ML algorithms that require extensive feature engineering. However, the real time deployment of these models demands a lower computational effort with higher accuracy. The proposed Multi-labeled Context-based Multilayered Bayesian Inferential (M-CMBI) predictive analytic classification framework is a novel approach that uses a cognitive approach by mimicking the brain's activity, termed MisMatch Negativity (MMN), to classify the faults. This adaptive model aims to classify the faults into multiple classes based on the estimated fault magnitude. This model is tested for efficacy on the Pump dataset which contains 52 items of raw sensor data to predict the class into normal, broken and recovering. Not all sensor data will contribute to the quality of prediction. Hence, the nature of the sensor data is analyzed using Exploratory Data Analysis (EDA) to prioritize the significance of the sensors and the faults are classified based on their fault magnitude. The results of the classification are validated on metrics such as accuracy, F1-Score, Precision and Recall against state of art techniques. Thus, the proposed model can yield promising results without time-consuming feature engineering and complex signal processing tasks, making it highly favorable to be deployed in real-time applications. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) is a network approach achieved by decoupling of the control and data planes. The control plane is logically centralized and the data plane is distributed across the network elements. The real-time network is in need of the incorporation of distributed controllers to maintain distributed state information of the traffic flows. Software-based solutions aid distributed SDN controllers to handle fluctuating network traffic and the controller's configurations are dynamically programmed in real time. In this study, SDN controllers were programmed with a stateful firewall application to provide firewall functionalities without the support of committed hardware. A stateful firewall filtered traffic based on the complete context of incoming packets; it continuously evaluated the entire context of traffic flows, looking for network entry rather than specific traffic flows. In addition, a flow-based scheduling module was implemented in the distributed controllers to improve network scalability. A network cluster was configured with three distributed controllers and we experimented with three independent network topologies. The performance of the proposed network model was evaluated by measuring and analyzing metrics such as network throughput (kbps), delay (ms) and network overhead (pkt/ms) for various combinations of controllers and topologies. The results of the analysis were determined using the mininet emulator. The findings of the performance evaluation indicate that the distributed SDN controllers performs better than a centralized controller. When comparing distributed SDN with two controllers and distributed SDN with three controllers the overall network throughput is increased by 64%, the delay is decreased by 43% and network overhead is reduced by 39%. [ABSTRACT FROM AUTHOR]

TiO2 electrode material is a poor choice for supercapacitor electrodes because it has low conductivity, poor cyclic stability, and a low capacitance value. It is inevitable to enhance electrode materials of this kind by increasing the surface area and combining high electronic conductivity materials. In the current research work, it was proposed to combine reduced graphene oxide (rGO) as it might provide a large surface area for intercalation and deintercalation, and also, it could establish the shorter paths to ion transfer, leading to a reduction in ionic resistance. The size, surface morphology, and crystalline structure of as-prepared rGO/TiO2 nanocomposites were studied using HRTEM, FESEM, and XRD, respectively. Using an electrochemical workstation, the capacitive behaviors of the rGO/TiO2 electrode materials were assessed with respect to scan rate and current density. The capacitances obtained through cyclic voltammetry and galvanostatic charge-discharge techniques were found to be higher when compared to TiO2 alone. Furthermore, the as-synthesized nanocomposites were able to achieve a higher energy density and better cycle stability. [ABSTRACT FROM AUTHOR]

Hadoop is a framework for storing and processing huge amounts of data. With HDFS, large data sets can be managed on commodity hardware. MapReduce is a programming model for processing vast amounts of data in parallel. Mapping and reducing can be performed by using the MapReduce programming framework. A very large amount of data is transferred from Mapper to Reducer without any filtering or recursion, resulting in overdrawn bandwidth. In this paper, we introduce an algorithm called Inner MAPping Combiner (IMapC) for the map phase. This algorithm in the Mapper combines the values of recurring keys. In order to test the efficiency of the algorithm, different approaches were tested. According to the test, MapReduce programs that are implemented with the Default Combiner (DC) of IMapC will be 70% more efficient than those that are implemented without one. To make computations significantly faster, this work can be combined with MapReduce. [ABSTRACT FROM AUTHOR]