Comparative assessment of differently randomized accelerated particle swarm optimization and squirrel search algorithms for selective harmonics elimination problem.

A random initialization of the search particles is a strong argument in favor of the deployment of nature-inspired metaheuristic algorithms when the knowledge of a good initial guess is lacked. This article analyses the impact of the type of randomization on the working of algorithms and the acquired solutions. In this study, five different types of randomizations are applied to the Accelerated Particle Swarm Optimization (APSO) and Squirrel Search Algorithm (SSA) during the initializations and proceedings of the search particles for selective harmonics elimination (SHE). The types of randomizations include exponential, normal, Rayleigh, uniform, and Weibull characteristics. The statistical analysis shows that the type of randomization does impact the working of optimization algorithms and the fittest value of the objective function.

An adapted model predictive control MPPT for validation of optimum GMPP tracking under partial shading conditions.

The energy generation efficiency of photovoltaic (PV) systems is compromised by partial shading conditions (PSCs) of solar irradiance with many maximum power points (MPPs) while tracking output power. Addressing this challenge in the PV system, this article proposes an adapted hybrid control algorithm that tracks the global maximum power point (GMPP) by preventing it from settling at different local maximum power points (LMPPs). The proposed scheme involves the deployment of a 3 × 3 multi-string PV array with a single modified boost converter model and an adapted perturb and observe-based model predictive control (APO-MPC) algorithm. In contrast to traditional strategies, this technique effectively extracts and stabilizes the output power by predicting upcoming future states through the computation of reference current. The boost converter regulates voltage and current levels of the whole PV array, while the proposed algorithm dynamically adjusts the converter's operation to track the GMPP by minimizing the cost function of MPC. Additionally, it reduces hardware costs by eliminating the need for an output current sensor, all while ensuring effective tracking across a variety of climatic profiles. The research illustrates the efficient validation of the proposed method with accurate and stable convergence towards the GMPP with minimal sensors, consequently reducing overall hardware expenses. Simulation and hardware-based outcomes reveal that this approach outperforms classical techniques in terms of both cost-effectiveness and power extraction efficiency, even under PSCs of constant, rapidly changing, and linearly changing irradiances.

Identification of Soil Types and Salinity Using MODIS Terra Data and Machine Learning Techniques in Multiple Regions of Pakistan.

Soil, a significant natural resource, plays a crucial role in supporting various ecosystems and serves as the foundation of Pakistan's economy due to its primary use in agriculture. Hence, timely monitoring of soil type and salinity is essential. However, traditional methods for identifying soil types and detecting salinity are time-consuming, requiring expert intervention and extensive laboratory experiments. The objective of this study is to propose a model that leverages MODIS Terra data to identify soil types and detect soil salinity. To achieve this, 195 soil samples were collected from Lahore, Kot Addu, and Kohat, dating from October 2022 to November 2022. Simultaneously, spectral data of the same regions were obtained to spatially map soil types and salinity of bare land. The spectral reflectance of band values, salinity indices, and vegetation indices were utilized to classify the soil types and predict soil salinity. To perform the classification and regression tasks, the study employed three popular techniques in the research community: Random Forest (RF), Ada Boost (AB), and Gradient Boosting (GB), along with Decision Tree (DT), K-Nearest Neighbor (KNN), and Extra Tree (ET). A 70-30 test train validation split was used for the implementation of these techniques. The efficacy of the multi-class classification models for soil types was evaluated using accuracy, precision, recall, and f1-score. On the other hand, the regression models' performances were evaluated and compared using R-squared (R2), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The results demonstrated that Random Forest outperformed other methods for both predicting soil types (accuracy = 65.38, precision = 0.60, recall = 0.57, and f1-score = 0.57) and predicting salinity (R2 = 0.90, MAE = 0.56, MSE = 0.98, RMSE = 0.97). Finally, the study designed a web portal to enable real-time prediction of soil types and salinity using these models. This web portal can be utilized by farmers and decision-makers to make informed decisions regarding soil, crop cultivation, and agricultural planning.

Learned-SBL-GAMP based hybrid precoders/combiners in millimeter wave massive MIMO systems.

In Millimeter-Wave (mm-Wave) massive Multiple-Input Multiple-Output (MIMO) systems, hybrid precoders/combiners must be designed to improve antenna gain and reduce hardware complexity. Sparse Bayesian learning via Expectation Maximization (SBL-EM) algorithm is not practically feasible for high signal dimensions because estimating sparse signals and designing optimal hybrid precoders/combiners using SBL-EM still provide high computational complexity for higher signal dimensions. To overcome the issues of high computational complexity along with making it suitable for larger data sets, in this paper, we propose Learned-Sparse Bayesian Learning with Generalized Approximate Message Passing algorithm (L-SBL-GAMP) algorithm for designing optimal hybrid precoders/combiners suitable for mmWave Massive MIMO systems. The L-SBL-GAMP algorithm is an extension of the SBL-GAMP algorithm that incorporates a Deep Neural Network (DNN) to improve the system performance. Based on the nature of the training data, the L-SBL-GAMP can design the optimal Hybrid precoders/combiners, which enhances the spectral efficiency of mmWave massive MIMO systems. The proposed L-SBL-GAMP algorithm reduces the iterations, training overhead, and computational complexity compared to the SBL-EM algorithm. The simulation results unveil that the proposed L-SBL-GAMP provides higher achievable rates, better accuracy, and low computational complexity compared to the existing algorithm, such as Orthogonal Matching Pursuit (OMP), Simultaneous Orthogonal Matching Pursuit (SOMP), SBL-EM and SBL-GAMP for mmWave massive MIMO architectures.

A Novel Monopole Ultra-Wide-Band Multiple-Input Multiple-Output Antenna with Triple-Notched Characteristics for Enhanced Wireless Communication and Portable Systems.

This study introduces a monopole 4 × 4 Ultra-Wide-Band (UWB) Multiple-Input Multiple-Output (MIMO) antenna system with a novel structure and outstanding performance. The proposed design has triple-notched characteristics due to CSRR etching and a C-shaped curve. The notching occurs in 4.5 GHz, 5.5 GHz, and 8.8 GHz frequencies in the C-band, WLAN band, and satellite network, respectively. Complementary Split-Ring Resonators (CSRR) are etched at the feed line and ground plane, and a C-shaped curve is used to reduce interference between the ultra-wide band and narrowband. The mutual coupling of CSRR enables the MIMO architecture to achieve high isolation and polarisation diversity. With prototype dimensions of (60.4 × 60.4) mm2, the proposed antenna design is small. The simulated and measured results show good agreement, indicating the effectiveness of the UWB-MIMO antenna for wireless communication and portable systems.

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence.

The Internet of Things (IoT) is a well-known technology that has a significant impact on many areas, including connections, work, healthcare, and the economy. IoT has the potential to improve life in a variety of contexts, from smart cities to classrooms, by automating tasks, increasing output, and decreasing anxiety. Cyberattacks and threats, on the other hand, have a significant impact on intelligent IoT applications. Many traditional techniques for protecting the IoT are now ineffective due to new dangers and vulnerabilities. To keep their security procedures, IoT systems of the future will need AI-efficient machine learning and deep learning. The capabilities of artificial intelligence, particularly machine and deep learning solutions, must be used if the next-generation IoT system is to have a continuously changing and up-to-date security system. IoT security intelligence is examined in this paper from every angle available. An innovative method for protecting IoT devices against a variety of cyberattacks is to use machine learning and deep learning to gain information from raw data. Finally, we discuss relevant research issues and potential next steps considering our findings. This article examines how machine learning and deep learning can be used to detect attack patterns in unstructured data and safeguard IoT devices. We discuss the challenges that researchers face, as well as potential future directions for this research area, considering these findings. Anyone with an interest in the IoT or cybersecurity can use this website's content as a technical resource and reference.

COVID-19 Detection Mechanism in Vehicles Using a Deep Extreme Machine Learning Approach.

COVID-19 is a rapidly spreading pandemic, and early detection is important to halting the spread of infection. Recently, the outbreak of this virus has severely affected people around the world with increasing death rates. The increased death rates are because of its spreading nature among people, mainly through physical interactions. Therefore, it is very important to control the spreading of the virus and detect people's symptoms during the initial stages so proper preventive measures can be taken in good time. In response to COVID-19, revolutionary automation such as deep learning, machine learning, image processing, and medical images such as chest radiography (CXR) and computed tomography (CT) have been developed in this environment. Currently, the coronavirus is identified via an RT-PCR test. Alternative solutions are required due to the lengthy moratorium period and the large number of false-negative estimations. To prevent the spreading of the virus, we propose the Vehicle-based COVID-19 Detection System to reveal the related symptoms of a person in the vehicles. Moreover, deep extreme machine learning is applied. The proposed system uses headaches, flu, fever, cough, chest pain, shortness of breath, tiredness, nasal congestion, diarrhea, breathing difficulty, and pneumonia. The symptoms are considered parameters to reveal the presence of COVID-19 in a person. Our proposed approach in Vehicles will make it easier for governments to perform COVID-19 tests timely in cities. Due to the ambiguous nature of symptoms in humans, we utilize fuzzy modeling for simulation. The suggested COVID-19 detection model achieved an accuracy of more than 90%.

Integration of federated learning with IoT for smart cities applications, challenges, and solutions.

In the past few years, privacy concerns have grown, making the financial models of businesses more vulnerable to attack. In many cases, it is hard to emphasize the importance of monitoring things in real-time with data from Internet of Things (IoT) devices. The people who make the IoT devices and those who use them face big problems when they try to use Artificial Intelligence (AI) techniques in real-world applications, where data must be collected and processed at a central location. Federated learning (FL) has made a decentralized, cooperative AI system that can be used by many IoT apps that use AI. It is possible because it can train AI on IoT devices that are spread out and do not need to share data. FL allows local models to be trained on local data and share their knowledge to improve a global model. Also, shared learning allows models from all over the world to be trained using data from all over the world. This article looks at the IoT in all of its forms, including "smart" businesses, "smart" cities, "smart" transportation, and "smart" healthcare. This study looks at the safety problems that the federated learning with IoT (FL-IoT) area has brought to market. This research is needed to explore because federated learning is a new technique, and a small amount of work is done on challenges faced during integration with IoT. This research also helps in the real world in such applications where encrypted data must be sent from one place to another. Researchers and graduate students are the audience of our article.

An Adaptive Heterogeneous Online Learning Ensemble Classifier for Nonstationary Environments.

In recent years, the prevalence of technological advances has led to an enormous and ever-increasing amount of data that are now commonly available in a streaming fashion. In such nonstationary environments, the underlying process generating the data stream is characterized by an intrinsic nonstationary or evolving or drifting phenomenon known as concept drift. Given the increasingly common applications whose data generation mechanisms are susceptible to change, the need for effective and efficient algorithms for learning from and adapting to evolving or drifting environments can hardly be overstated. In dynamic environments associated with concept drift, learning models are frequently updated to adapt to changes in the underlying probability distribution of the data. A lot of work in the area of learning in nonstationary environments focuses on updating the learning predictive model to optimize recovery from concept drift and convergence to new concepts by adjusting parameters and discarding poorly performing models while little effort has been dedicated to investigate what type of learning model is suitable at any given time for different types of concept drift. In this paper, we investigate the impact of heterogeneous online ensemble learning based on online model selection for predictive modeling in dynamic environments. We propose a novel heterogeneous ensemble approach based on online dynamic ensemble selection that accurately interchanges between different types of base models in an ensemble to enhance its predictive performance in nonstationary environments. The approach is known as Heterogeneous Dynamic Ensemble Selection based on Accuracy and Diversity (HDES-AD) and makes use of models generated by different base learners to increase diversity to circumvent problems associated with existing dynamic ensemble classifiers that may experience loss of diversity due to the exclusion of base learners generated by different base algorithms. The algorithm is evaluated on artificial and real-world datasets with well-known online homogeneous online ensemble approaches such as DDD, AFWE, and OAUE. The results show that HDES-AD performed significantly better than the other three homogeneous online ensemble approaches in nonstationary environments.

Power system events classification using genetic algorithm based feature weighting technique for support vector machine.

Currently, ensuring that power systems operate efficiently in stable and secure conditions has become a key challenge worldwide. Various unwanted events including injections and faults, especially within the generation and transmission domains are major causes of these instability menaces. The earlier operators can identify and accurately diagnose these unwanted events, the faster they can react and execute timely corrective measures to prevent large-scale blackouts and avoidable loss to lives and equipment. This paper presents a hybrid classification technique using support vector machine (SVM) with the evolutionary genetic algorithm (GA) model to detect and classify power system unwanted events in an accurate yet straightforward manner. In the proposed classification approach, the features of two large dimensional synchrophasor datasets are initially reduced using principal component analysis before they are weighted in their relevance and the dominant weights are heuristically identified using the genetic algorithm to boost classification results. Consequently, the weighted and dominant selected features by the GA are utilized to train the modelled linear SVM and radial basis function kernel SVM in classifying unwanted events. The performance of the proposed GA-SVM model was evaluated and compared with other models using key classification metrics. The high classification results from the proposed model validates the proposed method. The experimental results indicate that the proposed model can achieve an overall improvement in the classification rate of unwanted events in power systems and it showed that the application of the GA as the feature weighting tool offers significant improvement on classification performances.

A Survey on the Security of Low Power Wide Area Networks: Threats, Challenges, and Potential Solutions.

Low power wide area network (LPWAN) is among the fastest growing networks in Internet of Things (IoT) technologies. Owing to varieties of outstanding features which include long range communication and low power consumption, LPWANs are fast becoming the most widely deployed connectivity standards in IoT domain. However, this promising network are exposed to various security and privacy threats and challenges. For reliable connectivity within networks, the security and privacy challenges need to be effectively addressed with proper mitigation protocol in place. In this paper, a comprehensive review on the security feature of LPWAN is presented. The paper mainly focuses on analyzing LPWAN's key cybersecurity architecture and it present a significant emphasis on how the LPWAN is highly attractive to intruders and attackers. This paper aims at summarizing recent research works on key LPWAN security challenges such as replay attack, denial-of-service attack, worm hole attack, and eavesdropping attack, the effect of the attacks, and most importantly the various approaches proposed in the literature for the attacks' mitigation. The paper concludes by highlighting major research gaps and future directions for the successful deployment of LPWAN.

Smart Meter Data Collection Using Public Taxis.

The advent of wireless sensor networks (WSN) has opened up an array of applications. Due to the ad-hoc nature of WSN and the small size of wireless nodes, multiple system configurations are possible. In order to collect data from WSN, some systems utilize static nodes with a network setup that consists of multiple relays to facilitate the dissemination of data to a gateway. Other WSN architectures consist of a mixture of static and mobile nodes. Mobile nodes are able to collect data from the WSN when in close proximity to a static node. Such nodes are referred to as data mules. Data mules presents multiple advantages including the improvement of the network life as communication usually takes place via a single hop. In order to collect smart meter data, we propose the usage of mini-bus taxis carrying a data collector node as an alternative to traditional GSM models where data collected is directly uploaded from a data concentrator to a server. Using the vast network of mini-bus taxis in South Africa, data collection in areas lacking GSM network will be possible. This paper will attempt to present all the relevant parameters required for such data collection scheme to be successful.

System dynamics of active and passive postural changes: Insights from principal dynamic modes analysis of baroreflex loop.

The baroreflex being a key modulator of cardiovascular control ensures adequate blood pressure regulation under orthostatic stress which otherwise may cause severe hypotension. Contrary to conventional baroreflex sensitivity indices derived across a-priori traditional frequency bands, the present study is aimed at proposing new indices for the assessment of baroreflex drive which follows active (supine to stand-up) and passive (supine to head-up tilt) postural changes. To achieve this, a novel system identification approach of principal dynamic modes (PDM) was utilized to extract data-adaptive frequency components of closed-loop interactions between beat-to-beat interval and systolic blood pressure recorded from 10 healthy humans. We observed that the gain of low-pass global PDM of cardiac arm (:feedback reflex loop, mediated by pressure sensors to adjust heart rate in response to arterial blood pressure), and 0.2 Hz global PDM of mechanical arm (:feed-forward pathways, originating changes in arterial blood pressure in response to heart rate variations) may function as potential markers to distinguish active and passive orthostatic tests in healthy subjects.

Investigating Random Linear Coding from a Pricing Perspective.

In this paper, we study the implications of using a form of network coding known as Random Linear Coding (RLC) for unicast communications from an economic perspective by investigating a simple scenario, in which several network nodes, the users, download files from the Internet via another network node, the sender, and the receivers as users pay a certain price to the sender for this service. The mean packet delay for a transmission scheme with RLC is analyzed and applied into an optimal pricing model to characterize the optimal admission rate, price and revenue. The simulation results show that RLC achieves better performance in terms of both mean packet delay and revenue compared to the basic retransmission scheme.