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With the rapid increase in communication technologies and smart devices, an enormous surge in data traffic has been observed. A huge amount of data gets generated every second by different applications, users, and devices. This rapid generation of data has created the need for solutions to analyze the change in data over time in unforeseen ways despite resource constraints.These unforeseeable changes in the underlying distribution of streaming data over time are identified as concept drifts. This paper presents a novel approach named ElStream that detects concept drift using ensemble and conventional machine learning techniques using both real and artificial data. ElStream utilizes the majority voting technique making only optimum classifier to vote for decision. Experiments were conducted to evaluate the performance of the proposed approach. According to experimental analysis, the ensemble learning approach provides a consistent performance for both artificial and real-world data sets. Experiments prove that the ElStream provides better accuracy of 12.49%, 11.98%, 10.06%, 1.2%, and 0.33% for PokerHand, LED, Random RBF, Electricity, and SEA dataset respectively, which is better as compared to previous state-ofthe-art studies and conventional machine learning algorithms.

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Distributed generation (DG) and capacitor bank (CB) allocation in distribution systems (DS) has the potential to enhance the overall system performance of radial distribution systems (RDS) using a multiobjective optimization technique. The benefits of CB and DG injection in the RDS greatly depend on selecting a suitable number of CBs/DGs and their volume along with the finest location. This work proposes applying a hybrid enhanced grey wolf optimizer and particle swarm optimization (EGWO-PSO) algorithm for optimal placement and sizing of DGs and CBs. EGWO is a metaheuristic optimization technique stimulated by grey wolves. On the other hand, PSO is a swarm-based metaheuristic optimization algorithm that finds the optimal solution to a problem through the movement of the particles. The advantages of both techniques are utilized to acquire mutual benefits, i.e., the exploration ability of the EGWO and the exploitation ability of the PSO. The proposed hybrid method has a high convergence speed and is not trapped in local optimal. Using this hybrid method, technical, economic, and environmental advantages are enhanced using multiobjective functions (MOF) such as minimizing active power losses, voltage deviation index (VDI), the total cost of electrical energy, and total emissions from generation sources and enhancing the voltage stability index (VSI). Six different operational cases are considered and carried out on two standard distribution systems, namely, IEEE 33- and 69-bus RDSs, to demonstrate the proposed scheme’s effectiveness extensively. The simulated results are compared with existing optimization algorithms. From the obtained results, it is observed that the proposed EGWO-PSO gives distinguished enhancements in multiobjective optimization of different conflicting objective functions and high-level performance with global optimal values.

Recently, vehicular ad hoc networks (VANETs) got much popularity and are now being considered as integral parts of the automobile industry. As a subclass of MANETs, the VANETs are being used in the intelligent transport system (ITS) to support passengers, vehicles, and facilities like road protection, including misadventure warnings and driver succor, along with other infotainment services. The advantages and comforts of VANETs are obvious; however, with the continuous progression in autonomous automobile technologies, VANETs are facing numerous security challenges including DoS, Sybil, impersonation, replay, and related attacks. This paper discusses the characteristics and security issues including attacks and threats at different protocol layers of the VANETs architecture. Moreover, the paper also surveys different countermeasures.

Life-threatening novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), also known as COVID-19, has engulfed the world and caused health and economic challenges. To control the spread of COVID-19, a mechanism is required to enforce physical distancing between people. This paper proposes a Blockchain-based framework that preserves patients' anonymity while tracing their contacts with the help of Bluetooth-enabled smartphones. We use a smartphone application to interact with the proposed blockchain framework for contact tracing of the general public using Bluetooth and to store the obtained data over the cloud, which is accessible to health departments and government agencies to perform necessary and timely actions (e.g., like quarantine the infected people moving around). Thus, the proposed framework helps people perform their regular business and day-to-day activities with a controlled mechanism that keeps them safe from infected and exposed people. The smartphone application is capable enough to check their COVID status after analyzing the symptoms quickly and observes (based on given symptoms) either this person is infected or not. As a result, the proposed Adaptive Neuro-Fuzzy Interference System (ANFIS) system predicts the COVID status, and K-Nearest Neighbor (KNN) enhances the accuracy rate to 95.9% compared to state-of-the-art results.

This paper introduces an innovative passive topology of bandpass (BP) negative group delay (NGD) electrical circuit implemented with symmetric H-tree network. The multi-port topology of BP NGD circuit is originally represented by a resistorless H-tree constituted by lumped LC-passive network. Until now, the NGD electronic circuits available in the literature are implemented by two-port circuits which are using either resistive elements or distributed microstrip topologies. In the present investigation, the feasibility of the modelling, design, fabrication and test of unfamiliar BP NGD original H-tree circuits. The main objective of the study is to identify analytically the existence of the BP NGD function with the resistorless H-tree circuit. Because of its simplicity and the analytical equation compactness, the uncommon approach of tensorial analysis of networks (TAN) is used in the paper. After the branch and mesh analyses, the impedance matrix of the resistorless H-tree is elaborated. Hence, the resistorless H-tree equivalent Smatrix is established by means of impedance to scattering matrix transform. The BP NGD analysis in function of the inductance and capacitance parameters constituting the H-tree circuit is presented. Then, the main steps of the BP NGD investigation are described. The existence of BP NGD behavior in function of the adequate transmission parameters is identified by the consideration of the canonical form. To validate the BP NGD behavior, a proof of concept (POC) of LC-network based resistorless and symmetrical tree prototype is designed, fabricated and measured. Comparison results between well-correlated TAN calculation, simulation and experimentation are discussed. INDEX TERMS Tensorial analysis of networks (TAN), Kron's formalism, Bandpass negative group delay (NGD), resistorless topology, lumped H-tree, symmetric multi-port circuit, S-parameter modeling, LCnetwork passive circuit.