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In this study, a flexible and fully printable four element patch antenna array bent on a cylindrical-shaped surface for main beam steering is proposed. The individual phases of the microstrip patches in the array are varied by using novel planar phase shifters constituted of microstrip composite right/left-handed (CRLH) lines ingrained with microparticles. The reinforcement of newly introduced microparticles in the CRLH transmission line structure makes it a variable phase shifter, which is fabricated on a flexible substrate in completely planar and conformal configurations. The magnetic particles-based CRLH-TL phase shifter (MP-CRLH) consists of multiple cavities in the substrate filled with micron-sized silver-coated ferrite particles. These particles in the cavity are magnetically aligned by a small magnet placed beneath the cavity. A particular phase shift can be achieved by activating particles in multiple cavities in the MP-CLRH phase shifter. The optimum locations of the cavities partially filled with magnetic particles for a particular phase shift have been explored after extensive 3D simulations in the frequency domain using CST Studio Suite. The main advantage of using the MP-CRLH phase shifter is that it can be conformed to non-planar surfaces, such as cylindrical-shaped in this scenario. As compared to conventional IC/discrete components-based phase shifters, the working of the novel MP-CRLH phase shifter is independent of external SMT and lumped circuit components for its biasing. The four unit-cell cascaded MP-CRLH phase shifter is then integrated with a four-element patch array on a cylindrical-shaped surface with radii of 30 cm and 10 cm. The main pattern of the array has been successfully scanned at 0°, −15°, and +30° using the integrated MP-CRLH phase shifter at.2.45 GHz. The full-wave simulation results are validated with the measured radiation patterns in a fully anechoic chamber facility. The conformal phased array integrated with MP-CRLH phase shifters on a flexible substrate can be used for applications like on-body communication systems, wearable devices, flexible electronics, and aircraft communication systems.

Cloud computing (CC) is a popular distributed computing technology that is virtualized, scalable, and provides resources and services on demand through subscription-based models. Cloud Service Providers (CSPs) deliver these services, primarily catering to a vast range of business and scientific applications. CC is used to evaluate large-scale scientific applications, often implemented as scientific workflows (SWs). This paper highlights the wide range of applications using CC, including image processing, data mining, and healthcare systems, all of which require efficient and accessible cloud services. These applications often have strict deadlines and consume significant energy, emphasizing the need for low-energy resource utilization. The paper proposes a Replication-based Resource Provisioning and Constrained-aware Task Scheduling (R2PCT) Framework for Cloud Workflows to address these challenges. The framework aims to manage resources efficiently in a multilevel environment and schedule tasks using replication-based fault-tolerant techniques while considering deadline and budget constraints. To evaluate its effectiveness, simulations were conducted on WorkflowSim for Montage and CyberShake workflows. The proposed R2PCT strategy performs better than the existing state-of-the-art strategy, reducing execution time by an average of 52%, minimizing execution cost by 34%, and decreasing energy consumption by 35% compared to the existing R-DEAR strategy for Montage SWs. For CyberShake workflows, the proposed R2PCT strategy performs better than existing methods, reducing execution time by an average of 59%, minimizing execution cost by 38%, and decreasing energy consumption by 60% compared to the existing R-DEAR strategy.

Over the past few years, there has been a notable surge in the integration of Blockchain technology into supply chain management systems. This integration holds the promise of enhanced transparency, security, and efficiency in monitoring the movement of goods and services. This study presents a novel approach aimed at fortifying privacy and accuracy within blockchain-based supply chain management systems. The methodology integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) units with secure multi-party computation (MPC) and differential privacy techniques as a hybrid model. The objective is to safeguard the confidentiality of transaction data while enabling precise detection of media tampering. Performance evaluation revolves around three key aspects: accuracy, privacy preservation, and computational efficiency. In terms of accuracy assessment, the proposed hybrid approach is benchmarked against traditional machine learning algorithms including Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and Random Forest. Results indicate superior performance, with the proposed hybrid method achieving an accuracy of 0.95, outperforming conventional algorithms. Precision, recall, and F1-score metrics further confirm the effectiveness of the approach in accurately identifying media tampering instances. Privacy preservation capabilities are evaluated through differential privacy techniques, revealing the method’s ability to inject controlled noise into the data to protect individual privacy. Results demonstrate varying levels of privacy preservation across different settings, highlighting the trade-off between privacy and data utility. Computational efficiency is also scrutinized, considering the additional overhead introduced by privacy preservation mechanisms and secure MPC protocols. While there is a slight increase in computational time, the proposed approach maintains reasonable training and inference times, ensuring practical applicability in real-world scenarios.

Accurate and efficient demand forecasting is essential to grid stability, supply, and management in today’s electricity markets. Due to the complex pattern of electric power demand time series, it is challenging to model them directly. Therefore, this research proposes a novel time series ensemble approach to forecast electric power demand in the Peruvian electricity market one month ahead. This approach treats the first preprocessed electricity demand time series for missing values, variance stabilization, normalization, stationarity, and seasonality issues. Secondly, six single time series and three of their proposed ensemble models forecast the clean demand time series. The results indicate that the proposed time ensemble approach is an efficient and precise one-month-ahead forecast for electricity demand in the Peruvian electricity market. Additionally, the final best ensemble forecasting model within the proposed ensemble time series forecasting approach obtained the smallest average accuracy errors, performing statistically significantly better than those mentioned in the best-proposed models in the literature. Lastly, while numerous global studies have been conducted from various perspectives, no analysis has been undertaken using an ensemble learning approach to forecast electric power demand in the Peruvian electricity market.

In order to predict and fill in the gaps in categorical datasets, this research looked into the use of machine learning algorithms. The emphasis was on ensemble models constructed using the Error Correction Output Codes (ECOC) framework, including models based on SVM and KNN as well as a hybrid classifier that combines models based on SVM, KNN, and MLP. Three diverse datasets—the CPU, Hypothyroid, and Breast Cancer datasets—were employed to validate these algorithms. Results indicated that these machine learning techniques provided substantial performance in predicting and completing missing data, with the effectiveness varying based on the specific dataset and missing data pattern. Compared to solo models, ensemble models that made use of the ECOC framework significantly improved prediction accuracy and robustness. Deep learning for missing data imputation has obstacles despite these encouraging results, including the requirement for large amounts of labeled data and the possibility of over-fitting. Subsequent research endeavors ought to evaluate the feasibility and efficacy of deep learning algorithms in the context of the imputation of missing data.

Quad-copters continue to be an area of active research due to their extensive applications in both civilian and military domains. In this paper, we present an advanced approach to enhance the attitude control of Quad-copters, focusing on trajectory tracking performance. We introduce a state-of-the-art Conditioned Adaptive Barrier Function Integral Terminal Sliding Mode Controller (CABFIT-SMC) for precise attitude control. To optimize the control law parameters effectively, we introduce the Redfox algorithm, a newly developed optimization technique inspired by the intelligence of red foxes in hunting and decision-making. The paper includes an in-depth comparative analysis of the Redfox-optimized CABFIT-SMC with the previously researched quantum particle swarm optimization (QPSO) algorithm, presented in our earlier work. The evaluation involves comparing graphs and tables for six different performance measures. These include mean absolute percentage error, root mean square error, integral square error, integral absolute error, integral time absolute error, and integral time square error. We confirm the stability of the system using Lyapunov stability analysis. To test how well the controller works, we use a challenging 3D-helical trajectory. This helps us see if the optimized controllers perform consistently and effectively. Furthermore, we validate the controllers using a Controller-in-Loop setup, demonstrating their effectiveness under realistic operating conditions. Our results demonstrate the CABFIT-SMC Redfox optimized outperforms QPSO-optimized CABFIT-SMC across all performance metrics, solidifying its effectiveness for precise attitude tracking of Quad-copter systems. The proposed approach contributes to improved maneuverability and control precision, with potential applications in various practical scenarios.

Companies and organizations aim to improve the performance of their business processes to stay competitive. Recently, researchers have shown significant interest in process mining, particularly its ability to extract accurate information from process-related data. Process enhancement is a crucial aspect of process mining, involving the extraction of information from the actual process event log to extend or improve existing processes. Enhancement can be classified into two types: extension and repair. This paper specifically focuses on the repair type of enhancement. Information systems commonly encounter logging errors or exhibit special behaviors that introduce noise into the event log. In this research, we investigate the process mining model in the presence of noise in the event log. We propose a method for repairing event logs by decomposing them into sub-logs and eliminating the noisy behavior within these sub-logs using covering probability. The repaired sub-logs are then integrated into the original event log at the appropriate location. Additionally, we propose a probabilistic method that considers the frequency of occurrence for activities in a given situation. This method allows for the removal of noisy and abnormal behavior from the event log, providing an overall perspective on the process. To validate our approach, we generate artificial event logs with the presence of noisy behavior using the ProM framework. By using the RapidMiner-based ProM Extension, we generate a test set to illustrate how various types of noisy behavior in an event log can be identified and repaired. Our findings clearly demonstrate that repairing the event log improves the performance of a process mining model.

Manage Pressure Drilling (MPD) presents a pioneering methodology for enhancing control over well operations and drilling processes, furnishing a proactive approach to mitigate risks stemming from fluctuating downhole pressures. Through the meticulous regulation of drilling fluid via a choke valve, MPD adeptly manages fluid pressure dynamics, effectively addressing challenges posed by undesirable pressure fluctuations. These fluctuations, often instigated by the vertical oscillations of the drill string due to wave disturbances, known as Heave Motion, can jeopardize the integrity of the well. MPD thus serves as a robust contingency against weather-induced delays, optimizing operational efficiency and bolstering the effectiveness of offshore drilling endeavors. This paper introduces various nonlinear control techniques, notably Sliding Mode Control (SMC) and Super-Twisting Sliding Mode Control (STSMC), with a specific emphasis on their application. The central objective is to utilize these controllers to effectively manage the choke valve and mitigate pressure fluctuations within the well. The controller gains underwent detailed refinement using a genetic algorithm, with a focus on minimizing the integral time absolute error (ITAE). The mathematical analysis involves using Lyapunov stability criteria to confirm that the introduced controllers are asymptotically stable. Additionally, comprehensive performance comparisons among the proposed control strategies were meticulously conducted using MATLAB/Simulink simulations. Moving beyond simulation, real-time validation involved an experimental setup integrating the capabilities of the C2000 Delfino with the MCU F28379D Launchpad.

Connecting smart industrial components to computer networks revolutionizes business operations. However, in the Industrial Internet of Things (IIoT), the sharing of data has bandwidth, computational, and privacy issues. Researchers presented cloud computing and fine-grained access control to overcome these challenges. However, traditional centralized computing systems involve single points of failure. To mitigate these challenges, we have proposed a secure and incentive-based data-sharing framework for IIoT systems using blockchain technology. We leverage blockchain due to its ability to provide secure and tamper-resistant data storage and sharing as participants store their data on a distributed ledger (DL), preventing unauthorized access. A security protocol is designed that utilizes the properties of elliptic curve cryptography (ECC). Moreover, Shapley value is employed to calculate revenue and distribute it fairly. To perform the formal security evaluation, we have conducted extensive simulations using the Automated Validation of Internet Security Protocols and Applications (AVISPA) and Scyther protocol simulation tools, which demonstrated that our protocol is robust against various adversarial attacks. The experimental results show that the proposed incentive distribution framework demonstrated fairness in the distribution of revenue among participants.

In the era of digital communication, social media platforms have experienced exponential growth, becoming primary channels for information exchange. However, this surge has also amplified the rapid spread of hate speech, prompting extensive research efforts for effective mitigation. These efforts have prominently featured advanced natural language processing techniques, particularly emphasizing deep learning methods that have shown promising outcomes. This article presents a novel approach to address this pressing issue, combining a comprehensive dataset of 18 sources. It includes 0.45 million comments sourced from various digital platforms spanning different time frames. There were two models utilized to address the diversity in the data and leverage distinct strengths found within deep learning frameworks: CNN and BiLSTM with an attention mechanism. These models were tailored to handle specific subsets of the data, allowing for a more targeted approach. The unique outputs from both models were then fused into a unified model. This methodology outperformed recent models, showcasing enhanced generalization capabilities even when tested on the largest and most diverse dataset. Our model achieved an impressive accuracy of 89%, while maintaining a high precision of 0.88 and recall of 0.91.

A compact fork-shaped MIMO antenna system with a 2 × 2 arrangement with four elements is presented. The MIMO elements are arranged orthogonally to achieve a small overall size of 36 × 28 mm2 and a wide bandwidth for 5G mm-wave applications. MIMO elements are positioned 4 mm from each corner of the substrate to achieve compact size and minimize coupling. To improve the isolation of the proposed MIMO system, a metamaterial slab is inserted in the middle of the substrate and between radiating elements of the MIMO antenna system, which improves isolation by 10 dB within the whole operating band and achieves maximum isolation of 65 dB at 34.5 GHz. The proposed MIMO system operates in the Ka-band frequency range of 22–50 GHz with isolation greater than 30 dB and efficiency above 80% across the entire frequency spectrum for 5G communication. Additionally, the performance parameters of MIMO are examined, including diversity gain (DG) and envelope correlation coefficient (ECC), and it is found that they meet the required standards of DG approximately equal to 10 and ECC

Multi-objective energy optimization is pivotal for reliable and secure power system operation. However, multi-objective energy optimization is challenging due to interdependent and conflicting objectives. Thus, a multi-objective optimization model is needed to cater to conflicting objectives. On this note, a multi-objective optimization model is developed, where a non-dominated genetic sorting algorithm is employed to optimize objectives pollution emission, operation cost, and loss of load expectation (LOLE) considering renewable energy sources (RES). RES, like wind and solar, are intermittent and uncertain, which are modelled using a beta probability density function (PDF). The developed method’s effectiveness and applicability are analyzed by implementing it on the 30-bus system, and the results are compared for two cases. Findings reveal that the developed multi-objective optimization model minimizes operation cost, pollution emission, and LOLE by 59%, 7%, and 2.67%, respectively, compared to existing models.

Demand response (DR) shaves peak energy consumption and drives energy conservation to ensure reliable operation of power grid. With the emergence of the smart power grid (SPG), DR has become increasingly popular and highly contributes to energy optimization. On this note, in this work, DR is adopted for scheduling home appliances to reduce utility bill payment, peak to average demand ratio (PADR), and discomfort. First, home appliances are classified into two categories according to time and power flexibility: time-flexible and power-flexible. Secondly, the demand-side users power usage scheduling problem is modelled as per the user priority and modes of operation considering demand and supply. Finally, the energy consumption scheduler (ECS) is developed to adjust the time and power of both types of appliances under different operation modes to acquire desired tradeoff between utility bills payment and discomfort, and PADR and discomfort. Simulation results depict that employing the proposed ECS benefits demand-side users by minimizing their utility bills payment, PADR, and achieving the desired tradeoff between utility bill payment and discomfort, and PADR and discomfort. Results illustrate that developed model reduced utility bill payment and alleviated PADR without compromising comfort by 28% and 21%, respectively, compared to without scheduling case.

This article presents a compact ultra-wideband (UWB) circular monopole antenna with a frequency-selective surface (FSS) for gain enhancement. The proposed antenna has a circular patch with circular cuts at the edges and is excited by a microstrip feed. The bottom plane is truncated and further modified by two triangular cuts at the sides and one rectangular cut in the middle to improve the radiation characteristics of the UWB antenna. The antenna is designed on an FR-4 substrate with a thickness of 1.6 mm, a relative permittivity of 4.3, and planner dimensions of 30 mm × 30 mm. To improve the proposed antenna’s gain, an FSS is designed that consists of periodic unit cells of metal printed on the upper layer of an FR-4 substrate with dimensions of 0.11λ × 0.11λ at the lowest operating frequency of 3.3 GHz. The FSS shows a very low transmission coefficient and linearly reducing reflection phase with increasing frequency over a frequency range of 3.3–10.8 GHz. The gain of the proposed antenna is increased from 3 dB to 8.1 dB at 9 GHz by placing the antenna on the FSS. Moreover, the prototype of the proposed antenna is fabricated, and the experimental results are measured, which show close agreements with simulated results. The FSS-based antenna has directional radiation patterns, making it a potential candidate for ground-penetrating radar and UWB applications.

The concept of smart cities is unique where the intelligent information technologies have used to form a heterogeneous with the help of edge and fog networks. Edge Computing (EC) has gained attention due to its distributive nature that brings computing resources closer to user devices for fast data communications. These networks are vulnerable to a variety of cyber threats due open network design and architecture and a lack of trusted computing resources. The Denial of Service (DoS) and Sybil attacks are common in these networks and cause of services degradation. Whereas in a Sybil attack, a node illegitimately claims multiple identities and escalate and launch more attacks in the networks. Identification and detection of malicious nodes and evaluating their trustworthiness is a challenging task. This paper proposes a novel Assertive Trust-based Efficient Technique (ATBET) for edge-based smart city networks by calculating the direct, indirect, penalized and cumulative trust. The trust is evaluated by using packet drop rate and the penalized over the transmission paths. The proposed solution detects the attacks and eliminates the malicious or selfish nodes from edge-based networks. The experiment results are conducted in simulation and compared proposed solution with state or the art solutions in terms of trustworthiness level, latency, packet delivery ratio, and network life span.

This work presents a novel and highly compact broadband Yagi antenna structure in multilayer configuration for Ku band applications. The antenna is formed by stacking two identical Yagi elements which are fed in-phase with a single strip-based transmission line. Metalized vias has been used to connect the ground planes and to launch the signal in the driver element of both Yagi elements. The design has multiple advantages of providing large gain, large impedance bandwidth, high front-to-back ratio along with low radiation leakage. The unique broadside coupling introduced in this topology causes constructive interference of near fields in the H-plane thus causing wider HPBW. Detailed optimization processes along with physical insight have been provided for a better understanding of the working principle. The design has been extended into a $1 \times 4$ antenna array formation in the E-plane to enhance the boresight gain. The array thus formed offers a fractional bandwidth of 26.6% over the frequency range from 16.51 GHz to 21 GHz. It produces a gain of 11.83 dBi with a front-to-back ratio of 33.2 dB. The HPBW in the H-plane is measured to be 94.7° which allows this antenna system to be used in cellular communication, scanning, and surveillance system.

The dominance-based rough set approach is crucial to the advancement of rough set theory. It gives a more thorough and adaptable framework for knowledge acquisition, information analysis, and DM. It is a means of expressing discrepancies resulting from the examination of the domains with specified preference rankings of the characteristics. This work seeks to extend the Rough set approach utilizing dominance relationships to a Pythagorean fuzzy setting. The lower and upper approximations of Pythagorean fuzzy dominance-based rough set are determined by using the constructive technique. Next, we examine the basic characteristics for the rough estimations relying on the Pythagorean fuzzy dominance. By combining Approximate Distribution Reductions with a Pythagorean fuzzy dominance-based rough set, reductions are prescribed in four distinct manners. Additionally, the discernibility matrices and theorems connected to these reductions are produced. Such findings are all Pythagorean fuzzy generalizations or extensions of the conventional rough set method relying on dominance. Finally, the conceptual ideas are supported with a numerical example.

A large number of schemes have been proposed to deal with location privacy preservation in Vehicular Ad Hoc Networks (VANETs). One of the most popular ways to preserve location privacy is pseudonym changing, which includes mix-zone, silent zone, mix-context zone and trigger-based schemes. These schemes rely on changing pseudonyms after specific intervals to preserve location privacy of vehicles. Since pseudonym changing schemes are efficient in areas where traffic is dense, most of the pseudonym changing schemes require vehicles to be a part of dense traffic in order to change a pseudonym. Although pseudonym changing schemes are efficient in terms of location privacy preservation, a huge drawback is that these schemes are best suitable in dense traffic areas. Another drawback is that these schemes do not prevent from colluding attacks. In this paper, we propose a scheme, called LPSA (Location Privacy in Sparse Areas) to overcome this limitation. We modify the pseudonym changing scheme so as to preserve location privacy irrespective of the traffic density. This makes LPSA suitable for both dense and sparse traffic areas. For location privacy preservation in dense areas, we utilize pseudonym changing in mix-context zones. To preserve location privacy in sparse areas, we modify pseudonym changing scheme. We use differential privacy to preserve privacy of the data shared by the vehicles. Noise is added in raw beacon message attributes using Local Differential Privacy (LDP) to get perturbed messages. Multiple perturbed messages are transmitted to confuse an adversary in sparse traffic areas. LPSA also provides protection against colluding attacks. Our results show that LPSA provides better traceability, average anonymity set size, pseudonym change per trace and average confusion in both sparse and dense traffic areas as compared to the recent literature.

Researchers in the domain of unmanned aerial vehicles (UAVs) have recently shown a great deal of interest in the quadcopter domain due to its wide variety of applications. In addition to their military applications, quadcopters are now widely used in the civilian sector. This paper presents optimized controllers for attitude and altitude control of a quadcopter system. A novel conditioned adaptive barrier function integral terminal sliding mode controller (CABFIT-SMC) is designed to address the trajectory tracking problem of the quadcopter, and a sliding mode control (SMC) is implemented for comparative analysis. Four optimization algorithms (i.e., Ant Colony Optimization, Artificial Bee Colony, Particle Swarm Optimization, and Genetic Algorithm) have been used to optimize the proposed control laws. The quadcopter’s non-linear model is formulated through the Lagrange formalism in MATLAB ODE-45, incorporating gyroscopic moments and aerodynamic effects. A Lyapunov stability analysis is carried out to verify the system’s asymptotic stability. A graphical and tabular comparative analysis is provided for all optimized control laws. Five performance indexes, including mean absolute percentage error, root mean square error, integral square error, integral absolute error, and integral time absolute error, are used to determine the best control law. The proposed optimized controllers are evaluated for performance and consistency using a 3D-helical complex trajectory. Based on the rigorous performance evaluation, it has been demonstrated that the CABFIT-SMC controller optimized with the ABC algorithm achieved the most superior results, with the lowest MAPE value of 13.245, RMSE value of 0.0043, and transient response characteristics yielding the quickest rise and settling times. The commendable performance of the ABC-optimized CABFIT-SMC further reinforces its potential as a robust controller for precise attitude, altitude, heading, and position tracking in quadcopter systems.

Although the concepts of imprecision are captured by both rough sets and intuitionistic fuzzy sets, investigations combining these two ideas and their applications in incompletely ordered information systems are scarce. The semantics of many kinds of missing information still lacks consensus on a global level. Rule extraction is also an important task in a sort of decision system in which condition attributes are treated as intuitionistic fuzzy values and decision attributes are crisp ones. The main goal of this paper is to address semantic issues related to incomplete information. This paper contributes to the following aspects: First, four types of incomplete information are classified (i.e., “do-not-care value”, “partially-known value”, “class-specific value” and “non-applicable value”), and then a complete information system is introduced using novel semantics, followed by a ranking approach to create each object’s neighborhood using intuitionistic fuzzy values for condition attributes. Further, a dominance-based intuitionistic fuzzy decision table is proposed. Second, the lower and upper approximation sets of an object and crisp classes validated by decision attributes are determined by comparing their relationships. Third, the rule extraction approach is developed using the discernibility matrix and discernibility function to collect knowledge from existing dominance intuitionistic fuzzy decision tables. Finally, the provided approach is used for the estimation of inflation rates in LDCs with inadequate data.

Lung cancer is the most common cause of cancer deaths worldwide. Early detection is crucial for successful treatment and increasing patient survival rates. Artificial intelligence techniques can play a significant role in the early detection of lung cancer. Various methods based on machine learning and deep learning approaches are used to detect lung cancer. This research works aims to develop automated methods to accurately identify and classify lung cancer in CT scans by using computational intelligence techniques. The process typically involves lobe segmentation, extracting candidate nodules, and classifying nodules as either cancer or non-cancer. The proposed lung cancer classification uses modified U-Net based lobe segmentation and nodule detection model consisting of three phases. The first phase segments lobe using CT slice and predicted mask using modified U-Net architecture and the second phase extracts candidate nodule using predicted mask and label employing modified U-Net architecture. Finally, the third phase is based on modified AlexNet, and a support vector machine is applied to classify candidate nodules into cancer and non-cancer. The experimental results of the proposed methodology for lobe segmentation, candidate nodule extraction, and classification of lung cancer have shown promising results on the publicly available LUAN16 dataset. The modified AlexNet-SVM classification model achieves 97.98% of accuracy, 98.84% of sensitivity, 97.47% of specificity, 97.53% of precision, and 97.70% of F1 for the classification of lung cancer.

A dominant statistical method, in which the best combination of factors’ levels are predicted by analyzing a few representative combinations of factors’ levels, named as orthogonal experimental design (OED). The OED is an effective approach for analyzing the effect of multi-levels factors simultaneously and it works on orthogonal learning (OL) strategy. An evolutionary programming based heuristic method has two contradictory features–exploration and exploitation, balancing in these features have significant impact on its optimization performance. We have applied an OED based auxiliary search strategy for enhancing performance of the bird swarm algorithm (BSA) by improving its exploitation search ability. It is a challenging task to keep balance among two contradictory features–exploration and exploitation of a heuristic approach, while addressing optimal power flow (OPF) problems in power systems. In this research study, we have proposed improved BSA (IBSA) for solving the OPF problems in thermal power systems. We have conducted a study of the OPF problems with objective functions-reducing electricity generation cost, emission pollution, and active power loss to measure the efficiency of proposed IBSA. In this work, we have utilized five benchmark functions and solved OPF problems using three IEEE test systems including IEEE-30 bus system, IEEE-57 bus system, and IEEE-118 bus system to verify stability, effectiveness, and performance of proposed IBSA. The statistical and simulation results have indicated that the proposed IBSA has better convergence, efficiency, and robustness features than the original BSA as well as other heuristic approaches. It is observed that lowest electricity generation cost 800.3975 $\$ $ /h on IEEE-30 bus system, 41663.5500 $\$ $ /h on IEEE-57 bus system, and 134941.0367 $\$ $ /h on IEEE-118 bus system have been achieved using proposed IBSA to address the OPF problems. Furthermore, in transmission lines of the power system network minimum active power loss 16.2869MW has been observed by conducting a case study on the IEEE 118-bus system based on the proposed IBSA approach.

The present study predicts the energy savings, carbon emissions reduction and economic benefits for Pakistan through energy efficiency improvements for room air conditioners under different policy scenarios. The simulation model assumes that room air conditioner stock will increase from 2.7 million for year 2020 to 4.9 million for year 2030. A market average energy efficiency ratio of 2.95 is assumed for year 2020. The impact of improvement in energy efficiency ratio is analyzed for business as usual and four other different scenarios: continuous improvement (5-years and 2-years cycles) and accelerated improvement (5-years and 2-years cycles). The model dynamically incorporates the retiring of old stock and addition of new stock every year. Our results show that annual national electricity consumption for this sector is projected to increase from 7.3 TWh in 2020 to 12.6 TWh in 2030 under the business as usual scenario. The cumulative energy consumption and carbon emissions are 103.5 TWh and 63.1 MtCO 2. However, implementing energy efficiency policies can result in electricity savings of 11.6 TWh over the ten years or savings nearly 7 million barrels of oil import. Policy recommendations aimed at promoting energy labeling, minimum energy performance standards, regulatory policies, and further country-specific studies for variety of appliances are provided.

In many electronic applications, the dielectric and structural properties of reinforced composites are vital. In this research work, the influence of fiber proportion on the properties of a silica fiber/epoxy (SFE) composite was investigated. The structure, morphology, dielectric constant and loss factor, mechanical properties, and thermal stability were determined. The increase of wt.% of silica fiber (SiO2 (f)) x = 30 to 90, reduced the dielectric constant (εr) and dielectric loss (δ) of the SFE composite from their original values to 18.9% and 48.5%, lowering local charge displacement towards the applied electric field. The SFE composite showed higher mechanical properties with the increase in SiO2 (f), x = 30 to 80, the tensile strength (UTS) was raised from 91.6 MPa to 155.7 MPa, the compression strength (UCS) was increased from 261.1 MPa to 409.6 MPa and the flexural strength was enhanced from 192.3 MPa to 311.9 MPa. Upon further addition of SiO2 (f) to the composite, i.e., x = 90, the mechanical properties were reduced a little, but the dielectric properties were not changed. Increasing SiO2 (f) improved the thermal stability as weight loss was found to be 69% (x = 30) and 24% (x = 90), and average moisture absorption was found to be 1.1 to 1.8%. A silica fiber/epoxy composite, for microelectronics, can be made from a low-cost fiber, and its dielectric properties as well as its mechanical and thermal stability can be tuned or improved by varying fiber fractions.

One of the primary jobs of a software project manager is to assign available resources to software development tasks in such a way that results in a high-quality product at a low cost. Software Project Scheduling (SPS) allocates the most appropriate human resource to project activities at the right time to reduce software project failure risks and minimize project makespan. In literature, the SPS problem is referred to as the Multiple Resource-Constrained Project Scheduling Problem (MRCPSP). The MRCPSP assigns human resources with multiple skills and proficiency levels to various project activities. Human abilities can be distinguished into technical/hard and non-technical/soft skills. The former describes the skills related to technology, tools, etc. While the latter deals with the skills related to the personality, such as being introvert, extrovert, sensing, etc. Recent studies have shown that some tasks may require specific soft skills. Moreover, the efficiency and productivity of the assigned resource significantly reduce if the soft skill requirements are ignored during task allocation. Ultimately, the development process might end up in lower-quality software products with higher development costs; worst case, the project may even fail. Several MRCPSP-based SPS approaches have been designed to reduce the development costs of software projects. These mechanisms consider the hard skills of a human resource with different proficiency levels. However, they overlook the soft skills required leading to the inefficiency of the allocated human resources. This will increase the project makespan and may cause higher development costs or even project failures. Therefore, to fill this gap, we propose Multi-Skill Resource Constrained and Personality Traits based Project Scheduling (MSRCPPS) considering the soft skills as well as the technical skills of a human resource during SPS. The main objective is to minimize software project makespan. Finally, the effectiveness of our proposed approach is evaluated against existing state-of-the-art using extensive simulations.

This work presents an eight element array antenna with single layer frequency selective surface (FSS) to obtain high gain. The eight elements are fed by single port. The FSS consists of 14 $\times $ 6 unit cells with one unit cell size is 5 $\times $ 5 mm2 having wideband behavior. The antenna uses Rogers RT Duroid 5880 substrate and giving very wide bandwidth from 20 GHz to 65 GHz, covering millimeter wave 5G bands (including 28 GHz, 38 GHz and 60 GHz). The designed FSS is showing stop band transmission characteristics below −10 dB threshold from 25 GHz to 42 GHz and 59 GHz to 61 GHz. The eight element antenna integrated with the FSS reflector, which results an improvement in the gain level from 12 dB to 15 dB at 28 GHz, from 10 dB to 12 dB at 38 GHz, and from 9.5 to 11 dB at 60 GHz. The dimensions of the antenna are 65 $\times $ 27 $\times $ 0.857 mm3. The proposed antenna shows stable gain and directional radiation patterns. The simulation findings are experimentally confirmed, by testing the fabricated prototypes of the proposed antenna system.

A software-defined vehicular network is made up of an IoT (Internet of Things) based vehicular ad-hoc network and a software-defined network. For better communication in IoT based vehicle networks, researchers are now working on the VANET (Vehicular Ad-hoc Network) to increase the overall system performance. To maximize the VANET ad-hoc network’s information application performance and reliability, edge computing has gained the attention of researchers. In current research, cloud computing is used for message related task execution, which increases the response time. We propose a Software-defined Fault Tolerance and QoS-Aware (Quality of Service) IoT-Based Vehicular Networks Using Edge Computing Secured by Blockchain to reduce overall communication delay, message failure fault tolerance, and secure service provisioning for VANET ad-hoc networks in this article. We proposed heuristic algorithms to solve the above mentioned problems of response delay, message failure, fault tolerance, and security provided by the Blockchain. The proposed model gets vehicle messages through SDN (Software defined network) nodes, which are placed on nearby edge servers, and the edge servers are validated by the blockchain to provide secure services to vehicles. The SDN controller, which exists on an edge server, which is placed on the road side to overcome communication delays, receives different messages from the vehicles and divides these messages in to two different categories. The message division is performed by the edge server by judging the time line, size, and emergency situation. SDN controller organized these messages and forwarded them to their destination. After the message is delivered to its destination, a fault tolerance mechanism checks their acknowledgements. If the message delivery fails, the fault tolerance algorithm will resend the failure message. The proposed model is implemented using a custom simulator and compared with the latest VANET based QoS and fault tolerance models. The result shows the performance of the proposed model, which decreased the overall message communication delay by 55% of the normal and emergency messages by using the edge server SDN controller. Furthermore, the proposed model reduces the execution time, security risk, and message failure ratio by using the edge server, cloud server and blockchain infrastructure.

Risk prediction is the most sensitive and critical activity in the Software Development Life Cycle (SDLC). It might determine whether the project succeeds or fails. To increase the success probability of a software project, the risk should be predicted at the early stages. This study proposed a novel model based on the requirement risk dataset to predict software requirement risks using Tree-Family -Machine-Learning (TF-ML) approaches. Moreover, the proposed model is compared with the state-of-the-art models to determine the best-suited methodology based on the nature of the dataset. These strategies are assessed and evaluated using a variety of metrics. The findings of this study may be reused as a baseline for future studies and research, allowing the results of any proposed approach, model, or framework to be benchmarked and easily checked.

A widespread mistrust towards the traditional voting system has made democratic voting in any country very critical. People have seen their fundamental rights being violated. Other digital voting systems have been challenged due to a lack of transparency. Most voting systems are not transparent enough; this makes it very difficult for the government to gain voters’ trust. The reason behind the failure of the traditional and current digital voting system is that it can be easily exploited. The primary objective is to resolve problems of the traditional and digital voting system, which include any kind of mishap or injustice during the process of voting. Blockchain technology can be used in the voting system to have a fair election and reduce injustice. The physical voting systems have many flaws in it as well as the digital voting systems are not perfect enough to be implemented on large scale. This appraises the need for a solution to secure the democratic rights of the people. This article presents a platform based on modern technology blockchain that provides maximum transparency and reliability of the system to build a trustful relationship between voters and election authorities. The proposed platform provides a framework that can be implemented to conduct voting activity digitally through blockchain without involving any physical polling stations. Our proposed framework supports a scalable blockchain, by using flexible consensus algorithms. The Chain Security Algorithm applied in the voting system makes the voting transaction more secure. Smart contracts provide a secure connection between the user and the network while executing a transaction in the chain. The security of the blockchain based voting system has also been discussed. Additionally, encryption of transactions using cryptographic hash and prevention of attack 51% on the blockchain has also been elaborated. Furthermore, the methodology for carrying out blockchain transactions during the process of voting has been elaborated using Blockchain Finally, the performance evaluation of the proposed system shows that the system can be implemented in a large-scale population.

A massive research corpus is generated in this epoch based on some previously established concepts or findings. For the acknowledgment of the base knowledge, researchers perform citations. Citations are the key considerations used in finding the different research measures, such as ranking the institutions, researchers, countries, computing the impact factor of journals, allocating research funds, etc. But in calculating these critical measures, citations are treated equally. However, researchers have argued that all citations can never be equally influential. Therefore, researchers have proposed other techniques to identify the important content-based, meta-data-based, and bibliographic-based citations. However, the produced results by the state-of-the-art still need to be improved. In this research work, we proposed an approach based on two primary modules, 1) The section-wise citation count and 2) Sentiment based analysis of citation sentences. The first technique is based on extracting the different sections of the research articles and performing citation count. We applied Neural Network and Multiple Regression on section-wise citations for automatic weight assignment. The citation sentences were extracted in the second approach, and sentiment analysis was used for sentences. Citations were classified with Support Vector Machine, Multilayer Perceptron, and Random Forest. F-measure, Recall, and Precision were considered to evaluate the results, compared with the state-of-the-art results. The value of precision with the proposed approach was enhanced to 0.94.

Brick kiln sector in South Asia contributes to nearly quarter of the global bricks production. Coal is the primary fuel used by this industry and its inefficient combustion process results in harmful pollutants contributing to deteriorating air quality, health problems in local communities, and greenhouse gas emissions. The aim of this study is to investigate the impact of cleaner technologies and adoption pathways to achieve environment-friendly and energy-efficient production of bricks in Pakistan during 2019–2050. The study incorporates various timelines for (Zig-Zag Kilns) ZZKs and Vertical Shaft Brick Kilns (VSBKs) adoption, and the phasing out of conventional, Fixed Chimney Bull’s Trench Kiln (FCBTKs). Low Emissions Analysis Platform (LEAP) based numerical calculations are performed to simulate brick production from 2019–2050 for seven different scenarios incorporating an assimilated technology mix of FCBTKs, ZZKs, and VSBKs. Emissions of carbon dioxide, carbon monoxide, black carbon, methane, non-methane volatile organic compounds, particulate matter, sulfur oxides and nitrogen oxides are considered in this study. The swiftness of energy demand, and emissions reductions are greatly impacted by the elimination of the conventional FCBTKs. By adopting clearner brick manufacturing, 365.5 million tons (21.8%) of GHG emissions reduction can be achieved. The annual social cost of using FCBTKs increases from 1.02 billion $ in 2019 to 1.88 billion $ in 2050. Cumulative fuel saving potential of retrofitting to ZZKs is 157 million tons (17.7%) and it offers nearly 0.92 billion $ (51%) savings in annual social cost for year 2050. Our study finds that early adoption of ZZKs would benefit more than delayed adoption of VSBKs. However, transition to ZZK/ VSBK alone would not be sufficient to achieve net-zero carbon emissions. Carbon neutral fuels such as biomass and refuse-derived fuel (RDF) appear as an attractive option for deep decarbonization of this sector.

Accurate detection and classification of breast cancer is a critical task in medical imaging due to the complexity of breast tissues. Due to automatic feature extraction ability, deep learning methods have been successfully applied in different areas, especially in the field of medical imaging. In this study, a novel patch-based deep learning method called Pa-DBN-BC is proposed to detect and classify breast cancer on histopathology images using the Deep Belief Network (DBN). Features are extracted through an unsupervised pre-training and supervised fine-tuning phase. The network automatically extracts features from image patches. Logistic regression is used to classify the patches from histopathology images. The features extracted from the patches are fed to the model as input and the model presents the result as a probability matrix as either a positive sample (cancer) or a negative sample (background). The proposed model is trained and tested on the whole slide histopathology image dataset having images from four different data cohorts and achieved an accuracy of 86%. Consequently, the proposed method is better than the traditional ones, as it automatically learns the best possible features and experimental results show that the model outperformed the previously proposed deep learning methods.

Wireless communication systems play a very crucial role in modern society for entertainment, business, commercial, health and safety applications. These systems keep evolving from one generation to next generation and currently we are seeing deployment of fifth generation (5G) wireless systems around the world. Academics and industries are already discussing beyond 5G wireless systems which will be sixth generation (6G) of the evolution. One of the main and key components of 6G systems will be the use of Artificial Intelligence (AI) and Machine Learning (ML) for such wireless networks. Every component and building block of a wireless system that we currently are familiar with from our knowledge of wireless technologies up to 5G, such as physical, network and application layers, will involve one or another AI/ML techniques. This overview paper, presents an up-to-date review of future wireless system concepts such as 6G and role of ML techniques in these future wireless systems. In particular, we present a conceptual model for 6G and show the use and role of ML techniques in each layer of the model. We review some classical and contemporary ML techniques such as supervised and un-supervised learning, Reinforcement Learning (RL), Deep Learning (DL) and Federated Learning (FL) in the context of wireless communication systems. We conclude the paper with some future applications and research challenges in the area of ML and AI for 6G networks.

Lack of insulin production by pancreas causes high blood glucose level (BGL) in the diabetic patients. For their treatment, manual insulin intake is possible only during the day timings but not feasible during the night when the patient is sleeping. Artificial pancreas (AP) is used for the automatic regulation of BGL by continuous injection of insulin. The nonlinear Bergman's Minimal Model (BMM) considers fixed meal disturbance which may actually vary continuously during medication due to meal intake or by doing exercise. This variation has been taken into account by the Extended Bergman's Minimal Model (EBMM). In this paper, two nonlinear: Terminal Synergetic and State Feedback Linearization based controllers have been proposed for AP to regulate BGL using EBMM. Asymptotic stability of the proposed controllers has been proved using Lyapunov theory. Comparison of the proposed controllers with each other and that with PID controller has been done using MATLAB/Simulink. White noise has been added as the disturbance to further analyze the output performance of the proposed controllers. The Terminal Synergetic controller which performs better than others, has also been implemented on the data of six Type 1 diabetic patients available in the literature.

The public awareness about global warming, emission of green-house gases and depletion of natural resources like oil and natural gas, are the main factors due to which fuel cell hybrid electric vehicles (FHEVs) have attained importance in automotive industry. Hard driving conditions like steep areas, slippery roads and rough terrains boost up the nonlinearities present in vehicle’s model. The considered unified mathematical model of FHEV is based on fuel cell as a primary source, ultracapacitor and battery as storage units as well as the induction motor dynamics. The variations in parameters like resistance, capacitance, inductance and the nonlinearities of the dynamical system have also been considered. Three adaptation based nonlinear controllers namely adaptive terminal sliding mode, adaptive terminal synergetic and adaptive synergetic controllers have been proposed for the regulation of DC bus voltage along with speed tracking when subjected to European extra urban driving cycle. Lyapunov stability theory has been used to ensure global asymptotic stability of the system. Proposed controllers have been simulated on MATLAB/Simulink, where their comparison has been presented with each other and with recently proposed nonlinear controllers in the literature. Furthermore, ATSMC has further been implemented on real-time microcontroller hardware in the loop setup. The experimental results show that it provides better performance.

The prediction and determination of thermal response for the metallic parts is a very crucial step in the design of power transformers. This paper presents a comparative analysis of different thermal models for predicting the hotspot temperature and top oil temperature of power transformers. Also, a new thermal model is proposed for the monitoring of transformer operation which is capable of identifying the hotspot temperature and the top oil temperature by taking into account the ambient temperature and the load variation with respect to real time. The model is experimentally validated and compared with the actual field data. It is found that results obtained from the proposed model and the actual field data are in good agreement.

3D Stacked Integrated Circuits (SICs) offer a promising way to cope with the technology scaling; however, the test access requirements are highly complicated due to increased transistor density and a limited number of test channels. Moreover, although the vertical interconnects in 3D SIC are capable of high-speed data transfer, the overall test speed is restricted by scan-chains that are not optimized for timing. Reduced Pin-Count Testing (RPCT) has been effectively used under these scenarios. In particular, Time Division Multiplexing (TDM) allows full utilization of interconnect bandwidth while providing low scan frequencies supported by the scan chains. However, these methods rely on Uni-Directional Signaling (UDS), in which a chip terminal (pin or a TSV) can either be used to transmit or receive data at a given time. This requires that at least two chip terminals are available at every die interface (Tester-Die or Die-Die) to form a single test channel. In this paper, we propose Simultaneous Bi-Directional Signaling (SBS), which allows a chip terminal to be used simultaneously to send and receive data, thus forming a test channel using one pin instead of two. We demonstrate how SBS can be used in conjunction with TDM to achieve reduced pin count testing while using only half the number of pins compared to conventional TDM based methods, consuming only 22.6% additional power. Alternatively, the advantage could be manifested as a test time reduction by utilizing all available test channels, allowing more parallelism and test time reduction down to half compared to UDS-based TDM. Experiments using 45nm technology suggest that the proposed method can operate at up to 1.2 GHz test clock for a stack of 3-dies, whereas for higher frequencies, a binary-weighted transmitter is proposed capable of up to 2.46 GHz test clock.

Brain Tumor is an unwanted mass when cells grow abnormally. It puts pressure on certain areas of the brain which dangerously affects its functioning. For severe tumors like malignant, surgery is not a good option because they do not have definite boundaries and are closely attached with the other healthy tissues of the brain. So chemotherapy is preferable in this case to avoid damaging the healthy cells. The primary intention of this research is to suggest a controller for drug injection so that the tumor cells may reach the desired reference value of zero. In this paper, variable structure based nonlinear control algorithms: sliding mode control, integral sliding mode control, double integral sliding mode control and super-twisting sliding mode controllers have been proposed to reduce the tumor cells, maintain a safe number of healthy cells, keep the immune cells above a certain value and ensure suitable amount of chemotherapy drug. Lyapunov based stability theory has been used to prove stability of SMC, ISMC, DISMC and ST-SMC. MATLAB/Simulink environment has been used to analyze the performance of the proposed controllers on the basis of chattering, rate of convergence, undershoot/overshoot etc. and on the basis of these results drug through most suitable controller is advised. Among the proposed controllers, due to its better convergence, reduced chattering and less amount of drug used, ST-SMC is suggested for the chemotherapy of brain tumor.

This paper presents a health analysis technique for transformer winding insulation through thermal monitoring and Fast Fourier Transform (FFT) power spectrum. A novel thermal model for the Kraft paper insulation of transformer is proposed by using the transformer’s top-oil and winding hot-spot temperature models. The relationship between the temperature rise of oil inside the transformer tank and the winding insulation degradation are considered by utilizing the data-sets and daily load cycles of a 10/13 MVA, 132/11 kV, 50 Hz, ONAF grid power transformer. The model based on IEEE Guide for loading mineral-oil-immersed transformers is developed in Simulink. The hotspot temperature rise from the thermal model is used as a reference to analyze the winding insulation degradation in the form of high frequency partial discharges (PDs) upon the output parameters of the transformer. Using data analysis techniques, a correlation is presented between the load cycles and the hot-spot temperature through which the health status of the transformer winding insulation is estimated. Moreover, the high frequency transients were detected using the Fast Fourier Transform (FFT) spectrum analyzer tool in MATLAB. The preliminary study shows that high frequency PDs are detected for the overheated and deteriorated state of the winding insulation. The results show that the proposed technique is feasible for the health analysis of power transformers and successfully predicted the deterioration of the transformer winding insulation.

The evolution of technology has increased the consumption of electric power locally and globally which lead to a dramatic increase in demand for electric power. Electricity consumption rate in different forms at home and commercially increased. Sometimes, it affects household appliances due to the raised demands based on conditions of load shedding, electricity shortfall, and emergencies. It includes sudden electricity breakdown due to heavy rainfall or storm. This study investigates the adaptation of an optimal solution for the usage of energy to meet a revolutionary change. A technique is proposed that will address the issues regarding electric power shortfall and emergencies caused by the sudden breakthrough of electricity. The proposed technique automates the appliances in three main ways, a) locally automation, b) web-based, c) app-based automation. Using a microcontroller, appliances are locally controlled. By using a web page or application, the appliances are controlled remotely. This work helps in saving energy by automatic or manual switching of appliances on and off according to need through the web or apps. Experimental results show that the average accuracy of the system for local scenarios is 88.71, for web-based scenarios is 88.55 and for app-based is 88.56 respectively. It also handles emergency situations by calculating the load and perform smart switching, so that devices may remain safe even in abnormal situations like low or high voltages.

Breast cancer is often a fatal disease that has a substantial impact on the female mortality rate. Rapidly spreading breast cancer is due to the abnormal growth of malignant cells in the breast. Early detection of breast cancer can increase treatment opportunities and patient survival rates. Various screening methods with computer-aided detection systems have been developed for the effective diagnosis and treatment of breast cancer. Image data plays an important role in the medical and health industry. Features are extracted from image datasets through deep learning, as deep learning techniques extract features more accurately and rapidly than other existing methods. Deep learning effectively assists existing methods, such as mammogram screening and biopsy, in examining and diagnosing breast cancer. This paper proposes an Internet of Medical Things (IoMT) cloud-based model for the intelligent prediction of breast cancer stages. The proposed model is employed to detect breast cancer and its stages. The experimental results demonstrate 98.86% and 97.81% accuracy for the training and validation phases, respectively. In addition, they demonstrate accuracies of 99.69%, 99.32%, 98.96%, and 99.32% for detecting ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma. The results of the proposed intelligent prediction of breast cancer stages empowered with the deep learning (IPBCS-DL) model exhibits higher accuracy than existing state-of-the-art methods, indicating its potential to lower the breast cancer mortality rate.

In literature, proposed approaches mostly focused on household appliances scheduling for reducing consumers’ electricity bills, peak-to-average ratio, electricity usage in peak load hours, and enhancing user comfort level. The scheduling of smart home deployed energy resources recently became a critical issue on demand side due to a higher share of renewable energy sources. In this paper, a new hybrid genetic-based harmony search (HGHS) approach has been proposed for modeling the home energy management system, which contributes to minimizing consumers’ electricity bills and electricity usage during peak load hours by scheduling both household appliances and smart home deployed energy resources. We have comparatively evaluated the optimization results obtained from the proposed HGHS and other approaches. The experimental results confirmed the superiority of HGHS over genetic algorithm (GA) and harmony search algorithm (HSA). The proposed HGHS scheduling approach outperformed more efficiently than HSA and GA. The electricity usage cost for completing one-day operation of household appliances was limited to 1305.7 cents, 953.65 cents, and 569.44 cents in the proposed scheduling approach for case I, case II, and case III, respectively and was observed as lower than other approaches. The electricity consumption cost was reduced upto 23.125%, 43.87% and 66.44% in case I, case II, and case III, respectively using proposed scheduling approach as compared to an unscheduled load scenario. Moreover, the electrical peak load was limited to 3.07 kW, 2.9478 kW, and 1.9 kW during the proposed HGHS scheduling approach and was reported as lower than other approaches.

In recent studies, emphasis has been placed on optimal power flow (OPF) problems in traditional thermal, wind, and solar energy sources-based hybrid power systems. Various metaheuristic algorithms have been proposed to find optimal solutions to the OPF problems in the hybrid power system. The OPF, due to the quadratic nature of its primary objective function, is a nonlinear, nonconvex, and quadratic optimization problem. In this study, we have proposed a bio-inspired bird swarm algorithm (BSA) to find an optimal solution to the OPF problem in the hybrid power system because it performs well in the case of optimizing the well-known Rastrigin quadratic benchmark function. In this study, uncertainty of utility load demand and stochastic electricity output from renewable energy resources (RESs) including wind and solar are incorporated into the hybrid power system for achieving accuracy in operations and planning of the system. We have used a modified IEEE-30 bus test system to verify and measure the performance of BSA and a comparison is made with well-known evolutionary metaheuristic algorithms. The proposed BSA consistently achieves more accurate and stable results than other metaheuristic algorithms. Simulation-based optimization results have shown the superiority of BSA approach to solve the OPF problems by satisfying all constraints and minimum power generation cost 863.121 $\$ $ /h is achieved in case study 1. Simulation-based experiment results have indicated that by imposing the carbon tax ( $ ton/h$ ) the power generation from RESs was increased. In case study 2, the proposed BSA approach has also outperformed and minimum electricity cost 890.728 $\$ $ /h is achieved as compared to other algorithms.

Developer forums are essential for software engineers to solve their problems with the assistance of experts on such forums. However, sometimes the solutions (answers) of a problem are not satisfactory or challenging to select the potential answer. Information seekers usually browse all the answers within the question thread to get the potential answer. The manual selection of correct answers is a tedious and time-consuming task. In this paper, we propose an automatic classification approach to predict the correct answers for developer forums. We first extract the metadata and combination of Q/A for each thread of the developer community (Stack Overflow). Then, the natural language processing techniques are applied to preprocess the Q/A combinations of the given dataset. After that, a keyword ranking algorithm is leveraged to extract keywords and their ranking scores for each Q/A combination. Based on keywords and their ranking scores for each Q/A combination, a keywords-based feature vector is constructed. Subsequently, word embedding is leveraged to convert each preprocessed Q/A combination into a text-based feature vector. Finally, we pass the metadata, keywords-based features, and text-based features to the ensemble deep learning model for training to predict correct answers. The results of 10-fold cross-validation specify that the proposed approach is accurate and surpasses the state-of-the-art. On average, it improves the accuracy, precision, recall, and f-measure up to 1.72%, 24.96%, 6.57%, and 16.62%, respectively.

Human Acquired Immune Deficiency Syndrome or Human Immunodeficiency Virus (HIV) Infection is a disease caused by a lentivirus known as the HIV virus. It alters the human immune system making him/her vulnerable to diseases and infections. The susceptibility worsens if the syndrome is not controlled through proper drug injection. In the literature, the researchers have proposed different control methodologies of drug injection so that the infected cells may reach the desired reference value. In this paper, nonlinear control algorithms based on the integral backstepping approach and synergetic control have been proposed to reduce steady-state error for robust tracking of infected helper T-cells to a set reference level for a deterministic model of HIV virus. The model is based on the mass balance of helper T-cells and viral load. System stability analysis has been proved with the help of a suitable Lyapunov-based theory. The viral load has been suppressed to zero. Both the numerical analysis and the simulations have been performed to validate the performance of proposed controllers. The proposed controllers have been compared with each other, with generic backstepping and backstepping embedded with sliding mode control techniques. They have been used to check the effect of efficiency of the drug on the control input. The simulations have been performed on MATLAB/Simulink.

This paper presents a printed patch antenna design to achieve frequency and bandwidth reconfigurability. Two RF PIN diodes are simultaneously operated to achieve the multi-reconfigurability operation. The patch is inspired from a circular loop design. The basic structure of loop is altered, and PIN diodes are integrated into the patch. The antenna operates in dual band configuration at 3.42 and 8.02 GHz in the diodes ‘OFF’ state, whereas the antenna switches to triple band operation at 2.21, 4.85, and 10.19 GHz in the diodes ‘ON’ state. Moreover, the antenna also exhibits an increased bandwidth from 7.54 to 12 GHz in the diodes ‘ON’ state, as compared to a narrow bandwidth from 7.71 to 8.48 GHz in the diodes ‘OFF’ state. The proposed antenna structure is implemented and fabricated using FR4 epoxy substrate of relative permittivity 4.4, and thickness 1.6 mm. Implemented design exhibits measured gains of 3.06 dBi, 2.81 dBi, and 2.92 dBi at 2.21, 4.85, and 10.19 GHz in the PIN diodes ‘ON’ state, respectively, while in the PIN diodes ‘OFF’ state, at 3.42 GHz the gain is 3.03 dBi and at 8.02 GHz the gain is 3.37 dBi. Overall, simulation results agree well with the measured results.

Brain tumor is a deadly disease and its classification is a challenging task for radiologists because of the heterogeneous nature of the tumor cells. Recently, computer-aided diagnosis-based systems have promised, as an assistive technology, to diagnose the brain tumor, through magnetic resonance imaging (MRI). In recent applications of pre-trained models, normally features are extracted from bottom layers which are different from natural images to medical images. To overcome this problem, this study proposes a method of multi-level features extraction and concatenation for early diagnosis of brain tumor. Two pre-trained deep learning models i.e. Inception-v3 and DensNet201 make this model valid. With the help of these two models, two different scenarios of brain tumor detection and its classification were evaluated. First, the features from different Inception modules were extracted from pre-trained Inception-v3 model and concatenated these features for brain tumor classification. Then, these features were passed to softmax classifier to classify the brain tumor. Second, pre-trained DensNet201 was used to extract features from various DensNet blocks. Then, these features were concatenated and passed to softmax classifier to classify the brain tumor. Both scenarios were evaluated with the help of three-class brain tumor dataset that is available publicly. The proposed method produced 99.34 %, and 99.51% testing accuracies respectively with Inception-v3 and DensNet201 on testing samples and achieved highest performance in the detection of brain tumor. As results indicated, the proposed method based on features concatenation using pre-trained models outperformed as compared to existing state-of-the-art deep learning and machine learning based methods for brain tumor classification.

Exponential decrease in oil and natural gas resources, increasing global warming issues and insufficiency of fossil fuels has shifted the focus to fuel cell hybrid electric vehicles (FHEVs). FHEV model used in this work consists of fuel cell, ultracapacitor and battery. Non-linearities present in the vehicle model dominate because of extreme driving conditions like rough terrains, slippery roads or hilly areas. Behavior of components like energy sources, induction motors and power processing blocks deviate significantly from their normal behavior when driving in highly demanding situations. To tackle these shortcomings, non-linear controllers are preferred because of their efficiency. In literature, different controllers have been proposed for either the energy sources or the induction motor separately, whereas this research work focuses on a unified hybrid electric vehicle (HEV) model to simultaneously control the energy sources and the induction motor. The model used is a complete representation of electric system of FHEV and increases the performance of the vehicle. This unified model provides improved DC bus voltage regulation along with speed tracking when subjected to European extra urban drive cycle (EUDC). In this work, Robust Integral Backstepping and Robust Backstepping controllers have been designed. Lyapunov based analysis ensures the global stability of the system. Performance of proposed controllers is validated in MATLAB/Simulink environment. A comparative analysis is also given to illustrate the importance of the unified model proposed in this work.

The recommender systems predict and calculate user preferences for recommendations. However, such predictions and calculations are neither accurate nor viable in the context of smart TV due to the reasons that it is a lean-back, non-personalized device, and normally enjoyed in groups. Hence, group recommendations have utmost importance, specifically from the perspectives of watching smart TV. The existing group recommendation techniques predict the individual's preferences and then create a virtual group profile for recommendations. However, identifying and satisfying every group member is challenging. Numerous techniques have been proposed, such as face detection and recognition systems, but these systems lead to security and privacy issues. This paper proposes a smart TV-based recommender system that aims to identify the individuals and group members from their “age,” “gender,” and “number” information to overcome the biases that occur due to predictions and estimations of user's preferences. The study proposes a novel formula and age-gender matrix for generating anonymous, consolidated, and secure profiles, including group profiles on a smart TV. This study further proposes a novel method for finding a dominant character in a group by utilizing the user's ratings. Results show that the group decision has a significant impact on supplying social metadata, such as ratings, comments, etc., which in turn improve recommendation results. For materializing the proposed work, smart TV's processing, storage, and camera are utilized. The prototypical implementation has been tested and analyzed with improved recommendation results and viewer(s) satisfaction.

As the usage of wireless technology grows, there are evermore demands on the antennas that support these platforms. This need has led to the design of unique antennas with improved bandwidth, agile frequency capabilities, compact size and greater efficiencies. In part though, the trade-off for such capabilities is antenna complexity. This paper presents a new technique for simplifying the method of changing the operation of a printed antenna using micron-sized silver coated particles that respond to magneto-static fields. More specifically, a structure consisting of a low-loss dielectric material with a cylindrical cavity containing micro-sized particles is developed. The overall size of the dielectric material is 1.5 mm × 1.5 mm × 0.5 mm and the cavity has a diameter of 0.9 mm. Furthermore, the top and bottom of the cavity with the micron-sized particles is capped with copper foil. Then, to manipulate the enclosed particles, a static magnet is placed near the structure. The enclosed particles columnize and orientate in the direction of the field-lines, connecting the top and bottom copper foil plates. To disconnect the plates then, the field is simply removed and the columns collapse. Macroscopically, the structure has the behavior of a switch. The structures presented in this work are denoted as Magneto-static Field Responsive Structures (MRSs). The MRSs have an additional benefit of not requiring a direct connection to a biasing circuit. This is very useful because there are many antenna designs that make it difficult to embed biasing circuitry to reconfigure printed antennas using MEMS and PIN diodes, for example. Finally, a new frequency reconfigurable Electromagnetic Band Gap (EBG) antenna is presented. This design is unique because the complex layout does not allow for traditional biasing circuitry and the operation is changed using the new MRSs presented in this paper.