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Abstract Artificial intelligence-driven Chatbots, especially large language models (LLMs) like GPT-4, represent significant progress in digital education. These models excel in mimicking human-like text and transforming learning and teaching methods. This study examines the development, application, and impact of LLMs in education. It highlights their role in automating instructional tasks and promoting personalized learning experiences. Despite integration concerns and ethical debates, LLMs showcase the potential of AI to improve educational practices. Our research concludes that LLMs offer transformative opportunities for education. However, their incorporation requires careful ethical considerations, data privacy measures, and a balance between human educators and AI technologies. The findings suggest strategies for integrating LLMs into educational frameworks to enhance learning outcomes while preserving educational integrity.

Abstract Diabetes is a growing health concern in developing countries, causing considerable mortality rates. While machine learning (ML) approaches have been widely used to improve early detection and treatment, several studies have shown low classification accuracies due to overfitting, underfitting, and data noise. This research employs parallel and sequential ensemble ML approaches paired with feature selection techniques to boost classification accuracy. The Pima India Diabetes Data from the UCI ML Repository served as the dataset. Data preprocessing included cleaning the dataset by replacing missing values with column means and selecting highly correlated features using forward and backward selection methods. The dataset was split into two parts: training (70%), and testing (30%). Python was used for classification in Jupyter Notebook, and there were two design phases. The first phase utilized J48, Classification and Regression Tree (CART), and Decision Stump (DS) to create a random forest model. The second phase employed the same algorithms alongside sequential ensemble methods—XG Boost, AdaBoostM1, and Gradient Boosting—using an average voting algorithm for binary classification. Evaluation revealed that XG Boost, AdaBoostM1, and Gradient Boosting achieved classification accuracies of 100%, with performance metrics including F1 score, MCC, Precision, Recall, AUC-ROC, and AUC-PR all equal to 1.00, indicating reliable predictions of diabetes presence. Researchers and practitioners can leverage the predictive model developed in this work to make quick predictions of diabetes mellitus, which could save many lives.

Abstract As higher education faces technological advancement and environmental imperatives, AI becomes a key instrument for revolutionizing instructional methods and institutional operations. AI can improve educational outcomes, resource management, and long-term sustainability in higher education, according to this study. The research uses case studies and best practices to show how AI-driven innovations can minimize environmental impact, enhance energy efficiency, and customize learning, creating a more sustainable and inclusive academic environment. The document discusses AI ethics, including data privacy, algorithmic prejudice, and the digital divide. It emphasizes the need for strong ethical frameworks to use AI ethically and make decisions with transparency and fairness. The study also emphasizes the need for robust institutional rules and infrastructure to promote ethical AI integration, protecting student privacy and supporting fair access to AI technologies. The research also shows how AI-driven curriculum-building tools can educate students for future sustainability concerns and stimulate research innovation. The prospects and difficulties of AI in higher education are critically examined, including its potential to change traditional educational roles, improve academic performance, and maintain institutional profitability. Actionable recommendations for educators, politicians, and institutional leaders contribute to the education sustainability conversation. Focusing on AI and sustainability creates the framework for a future where technology and environmental stewardship are intimately connected, ensuring that higher education institutions can prosper in a fast-changing world.

Abstract A significant advancement in artificial intelligence is the development of large language models (LLMs). Despite opposition and explicit bans by some authorities, LLMs continue to play a transformative role, particularly in education, by improving language understanding and generation capabilities. This study explores LLMs’ types, history, and training processes, alongside their application in education, including digital and higher education settings. A novel theoretical framework is proposed to guide the integration of LLMs into education, addressing key challenges such as personalization, ethical concerns, and adaptability. Furthermore, the study presents practical case studies and solutions to barriers, such as data privacy and bias, offering insights into their role in enhancing the teaching–learning process. By providing a systematic analysis and proposing a structured framework, this study advances current knowledge and highlights the significant potential of LLMs in revolutionizing education.

Abstract This article discusses Blockchain and Generative AI in healthcare, including their uses, difficulties, and solutions. Blockchain technology improves EHR security, privacy, and interoperability, while smart contracts streamline supply chain management and administrative procedures. Blockchain verifies and secures IoT data, improving medical care and treatment, according to case studies. Generative AI systems like ChatGPT have transformed healthcare by personalizing therapy, diagnostics, and predictive analytics. AI systems can examine massive databases to diagnose diseases early, anticipate dangers, and personalize therapies. By providing timely information, boosting treatment adherence, and giving continuous support, AI-powered virtual health assistants have enhanced patient involvement. Generative AI has additionally enhanced medical research and drug development, cutting the time and expense of introducing new medicines. Generative AI and Blockchain provide safe patient data storage, high-quality AI training datasets, and efficient healthcare operations. Scalability, energy usage, and interoperability issues remain. Scalable Blockchain designs and standardized data integration and exchange protocols are suggested by this study. These technologies could improve medical research and therapy by making them safer, more effective, and more individualized.

Abstract One of the green, clean, and environment-friendly sources of energy is wind energy. For the assessment of wind energy potential, the parameters of the probability distribution function (PDF), i.e., Weibull distribution (WD), that fits well with the wind speed data must be known. In this research, we proposed a novel optimized energy pattern factor method (NOEPFM) based on the trust-region-dogleg algorithm and applied it to wind speed data of four cities of the Southern region of Punjab, Pakistan, to determine WD parameters, i.e., shape k and scale c parameters. In order to authenticate the practicability of the proposed NOEPFM, it is compared with the other existing energy pattern factor (EPF)-based methods such as the energy pattern factor method (EPFM), Sathyajith’s EPFM (EPFMS), and novel EPFM (NEPFM). The performance of NOEPFM is measured in terms of five goodness-of-fit indices, namely root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), coefficient of efficiency (CoE), and maximum absolute error (MaxAE). Numerical results reveal that the NOEPFM method was the best fit compared to the other EPFMs for all the considered wind speed datasets. This justifies the workability of the proposed NOEPFM and can serve as an enhanced approach for calculating wind power potential.

Abstract Sustainable agriculture is being transformed by drone-IoT integration, improving precision, efficiency, and sustainability. This study examines the pros and downsides of using various technologies to handle connectivity, data management, and power consumption issues. We assess existing integration methods, such as multispectral imaging, real-time IoT monitoring, and machine learning-driven predictive analytics, to gain actionable insights into soil health, crop conditions, and pest control. We also explore regulatory frameworks and technical constraints, including data security and affordability that prevent widespread use. Research shows that drone IoT solutions improve agricultural output, resource consumption, and farm efficiency, but cost and infrastructure hurdles limit availability, especially for smallholder farmers. These findings show that supporting regulatory frameworks and economical technology solutions are needed to increase adoption. Advances in agricultural autonomous decision-making could increase food security and sustainable farming worldwide.

Abstract This study evaluates and differentiates five advanced machine learning models—LSTM, GRU, CNN-LSTM, Random Forest, and SVR—aimed at precisely estimating solar and wind power generation to enhance renewable energy forecasting. LSTM achieved a remarkable Mean Squared Error (MSE) of 0.010 and R2 score of 0.90, highlighting its proficiency in capturing intricate temporal relationships. GRU closely followed, demonstrating potential as a viable option due to its remarkable combination of computational efficiency and accuracy (MSE = 0.015, R2 = 0.88). In datasets abundant in spatial correlations, the CNN-LSTM hybrid demonstrated its utility by providing novel insights into spatial–temporal patterns; nonetheless, it lagged considerably in accuracy, with a mean squared error (MSE) of 0.020 and a R2 of 0.87. Conversely, traditional models demonstrated a reliable albeit less dynamic ability to elucidate the complexities of renewable energy data; for instance, Random Forest exhibited a mean squared error (MSE) of 0.025, while Support Vector Regression (SVR) recorded an MSE of 0.030. The results affirm that deep learning architectures, particularly LSTM, offer a transformative method for renewable energy forecasting, hence enhancing accuracy and reliability in energy management systems.

Abstract In smart homes, industrial automation, healthcare, agriculture, and environmental monitoring, IoT antenna systems improve communication efficiency and dependability. IoT antenna systems affect network performance and connection by affecting gain, directivity, bandwidth, efficiency, and impedance matching. Dipole, patch, spiral, metamaterial-based, and other antenna types are tested in IoT settings to identify their applicability, benefits, and downsides. Current antenna technology has challenges with frequency, bandwidth, size, weight, material choices, and energy efficiency, requiring new solutions. According to the study, interference control, power consumption, and dynamic IoT adaptation research are inadequate. Metamaterials, nanomaterials, and 3D printing may circumvent these antenna design limitations. AI and machine learning can improve antenna design real-time optimization and performance in complex settings. The paper explores how standards and regulatory frameworks affect IoT antenna system development to ensure future designs meet a fast-growing market. For the growing range of IoT applications, this research suggests more flexible and reconfigurable antennas that can function across numerous frequency bands. The report emphasizes antenna material and design innovation to improve durability, cut costs, and scale manufacturing. This research tackles these key elements to enable the next generation of antenna systems to meet IoT technology's expanding needs and increase networked devices' functionality, efficiency, and integration across industries. This comprehensive approach helps identify current trends and concerns and prepares for future IoT antenna system advancements, enabling smarter, more connected, and more efficient technologies.

Abstract An accurate renewable energy output forecast is essential for energy efficiency and power system stability. Long Short-Term Memory(LSTM), Bidirectional LSTM(BiLSTM), Gated Recurrent Unit(GRU), and Convolutional Neural Network-LSTM(CNN-LSTM) Deep Neural Network (DNN) topologies are tested for solar and wind power production forecasting in this study. ARIMA was compared to the models. This study offers a unique architecture for Deep Neural Networks (DNNs) that are specifically tailored for renewable energy forecasting, optimizing accuracy by advanced hyperparameter tuning and the incorporation of essential meteorological and temporal variables. The optimized LSTM model outperformed others, with MAE (0.08765), MSE (0.00876), RMSE (0.09363), MAPE (3.8765), and R2 (0.99234) values. The GRU, CNN-LSTM, and BiLSTM models predicted well. Meteorological and time-based factors enhanced model accuracy. The addition of sun and wind data improved its prediction. The results show that advanced deep neural network (DNN) models can predict renewable energy, highlighting the importance of carefully selecting characteristics and fine-tuning the model. This work improves renewable energy estimates to promote a more reliable and environmentally sustainable electricity system.

Abstract The proliferation of Internet of Things (IoT) devices generates vast amounts of data, traditionally stored, processed, and analyzed using centralized systems, making them susceptible to attacks. Blockchain offers a solution by storing and securing IoT data in a distributed manner. However, the low performance and poor scalability of blockchain technology pose significant challenges for its application in IoT networks. The primary obstacle is the distributed consensus protocol, while ensuring data transparency, integrity, and immutability in a decentralized and untrusted circumstances which often compromises scalability. To address this issue, this paper introduces the use of the Delegated Proof of Stake (DPoS) consensus algorithm and sharding techniques to enhance scalability in blockchain-based IoT networks. Experimental results indicate that system throughput increases synchronously with the test load. Our findings reveal a tradeoff between throughput, latency, and up-downstream time on the Inter Planetary File System (IPFS). Given the critical importance of latency and throughput in IoT networks, the results demonstrate that DPoS offers high throughput, parallel processing, and robust security while efficiently scaling the network. Furthermore, at a test load of 500 Transactions Per Second (TPS), the system achieves a maximum throughput of approximately 11.094 ms. However, when the test load exceeds 2000 TPS, the total processing time for transactions extends to 11.205 ms. This method is particularly suitable for constrained IoT networks. Compared to previous edge computing-based approaches, our scheme demonstrates superior throughput performance.

Abstract The atmosphere’s fine articulate Matter (PM2.5) poses various health-related risks. Even though multiple efforts have been made to lower the emissions of these substances, the mortality rate is continuously increasing, requiring immediate inclination of the scientific community towards the design and development of advanced predictive models. Conventional statistical approaches have become dormant due to their limitations in capturing the innate relationships between the pollutants, particularly for predicting PM2.5 concentrations. In contrast, machine and deep learning techniques have shown great potential for forecasting air quality, providing more accuracy than its predecessor techniques. The present study investigates the utilization of hybrid approaches by integrating machine learning models with deep learning models to improve the prediction capabilities of PM2.5 concentration. It uses datasets from the World Air Quality Index (WAQI) and the State of Global Air (SOGA) to analyze the performance of the models on both the daily and annual data, respectively. This ensures the model’s effectiveness on a diversified dataset. The present study implements Random Forest (RF), Polynomial Regression (PR), XGBoost, and Extra Tree Regressor (ETR) coupled with Fully Connected Neural Network (FCNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) for obtaining optimized results. Finally, after a thorough investigation, the hybrid PR model coupled with FCNN (PR-FCNN) is found to be the best model with improved R-squared (R2) values, portraying its potential for predicting PM2.5 concentration accurately. Based on the experimentation, the preset study recommends implementing hybrid approaches, offering better predictive accuracy in forecasting air pollutants, especially PM2.5.

Abstract Background In today’s digital age, various diseases drastically reduce people’s quality of life. Arthritis is one amongst the most common and debilitating maladies. Osteoarthritis affects several joints, including the hands, knees, spine, and hips. This study focuses on the medical disorder underlying Knee Osteoarthritis (KOA) which severely impairs people’s quality of life. KOA is characterised by restricted mobility, stiffness, and terrible pain and can be caused by a range of factors such as ageing, obesity, and traumas. This degenerative disorder leads to progressive wear and tear of the knee joint. Methods To combat arthritis in the kneecap, this study employs a 12-layer Convolutional Neural Network (CNN) to reach deep learning capabilities. A collection of data from the Osteoarthritis Initiative (OAI) is used to classify KOA. Through the use of medical image processing; the study ascertains whether an individual has this ailment. A sophisticated CNN architecture created especially for binary classification and KOA severity utilising deep learning algorithms is the main component of this work. Results The cross-entropy loss function is an important component of the model’s laborious design that classifies data into two groups. The remaining section uses the Kellgren-Lawrence (KL) grade to classify the disease’s severity. In the binary classification, the proposed algorithm outperforms previous methods with an accuracy rate of 92.3%, and in the multiclassification, its accuracy rate is 78.4% which is superior to the previous findings. Conclusion Looking ahead, the research broadens the scope of this work by gathering information from various sources and using these methods on a wider range of datasets and situations. The potential for major advancements in the field of osteoarthritis detection and classification is highlighted by this forward-looking approach. Furthermore, this method reduces the intervention of medical practitioners and ultimately results in accurate diagnosis. Clinical trial number Not applicable.

Abstract The incorporation of Artificial Intelligence (AI) into the fields of Neurosurgery and Neurology has transformed the landscape of the healthcare industry. The present study describes seven dimensions of AI that have transformed the way of providing care, diagnosing, and treating patients. It has exhibited unparalleled accuracy in analyzing complex medical imaging data and expediting precise diagnoses of neurological conditions. It has also enabled personalized treatment plans by harnessing patient-specific data and genetic information, promising more effective therapies. For instance, AI-powered surgical robots have brought precision and remote capabilities to neurosurgical procedures, reducing human error. In AI, machine learning models predict disease progression, optimizing resource allocation and patient care, whereas wearable devices with AI provide continuous neurological monitoring, and enable early intervention for chronic conditions. It has also accelerated drug discovery by analyzing vast datasets, potentially leading to breakthrough therapies. Chatbots and virtual assistants powered by AI, enhance patient engagement and adherence to treatment plans. It holds promise in further personalization of care, augmented decision-making, earlier intervention, and the development of groundbreaking treatments. The present study mainly focuses on the incorporation of blockchain technology and provides a reasonable understanding of the associated issues and challenges along with its solutions. It will allow AI and healthcare professionals to advance the field and contribute towards the improvement of an individual's well-being when facing neurological challenges.

This study aims to address the urban vehicle parking issues by proposing a solution using Automatic Number Plate Recognition (ANPR) through image processing and a sensor-based hardware system. Integrating these technologies forms a Smart Parking Management System (SPMS) to automate parking processes and enhance the parking experience. The study aims to create an efficient system that eliminates manual vehicle registration and optimizes space utilization. ANPR and IoT-based sensors help users identify the available slots and pay only for the actual parking duration, which will help to minimize the fixed billing rates. The proposed ANPR system processes vehicle number plates at entry, ensuring seamless identification and eliminating manual registration. IoT sensors monitor real-time slot occupancy, transmitting data to a web admin panel. This panel provides insights such as entry and exit times, total parking duration, and billing costs, facilitating efficient management and remote monitoring. The ANPR-based SPMS reduces reliance on manual processes, streamlining entry procedures. By dynamically assessing slot availability through IoT sensors, users can locate unoccupied spaces quickly, which enhances user convenience. The web admin panel allows administrators to monitor the system remotely, ensuring smooth operations and maintaining accurate records. This study introduces a comprehensive solution to urban parking challenges by integrating ANPR and IoT technologies. The SPMS improves efficiency, reduces human resource needs, and enhances user experience with flexible billing based on actual duration. The combination of hardware and software provides a foundation for effective urban parking management.

Abstract Free space optical (FSO) systems offer an attractive and cost-effective solution for providing communication services in remote regions, as they allow secure transmission without the need for licensing and with lower deployment costs. However, the performance of FSO systems can be significantly impacted by atmospheric turbulences, creating considerable challenges to their deployment. To meet the expanding bandwidth requirements in optical networks, dense wavelength division multiplexing (DWDM) has emerged as a viable option. The development of a 400-Gbps DWDM-FSO system with advanced modulation formats is the subject of this paper. To ensure efficient energy conservation in such a system, power consumption needs to be minimized while maintaining performance level; this calls for optimization of different components within the system. The system is made up of 10 channels and each channel can transmit data at 40 Gbps. Various modulation schemes like carrier-suppressed return-to-zero, modified duo binary return-to-zero, differential phase shift keying, and duo binary return-to-zero are studied for their impact on system performance parameters Q-factor and bit error rate (BER) in C-band around 1550 nm wavelengths. The assessment is also extended to the effects that changing FSO length, input power, and data rate have on these two parameters as well as an evaluation regarding how differing atmospheric conditions influence the FSO system’s effectiveness.

Satellite communication has reached a turning point in transmitting data quickly and receiving signals clearly in faraway places. This paper presents a metamaterial-based antenna for distant and military applications that overcomes restrictions and performs unmatched. This project explores metamaterials to create a unique antenna. Expect to be astounded by its power to bend electromagnetic waves, miniaturize, and change our relationship with the universe. The feeding mechanism of this antenna is a modified co-planar waveguide. At 5.6 GHz, the proposed antenna unit cell measures 0.076λ0×0.2λ0, making it small thanks to its MTM characteristic. CP radiation is made when two orthogonally polarized modes are stimulated at the same time and two composite right- and left-handed transmission line unit cells are placed orthogonally. Reduced size, increased bandwidth, and improved Learn how this discovery opens new doors for high-resolution photography, broadband internet, and space exploration. One nanostructure that makes up a metamaterial is the Split Ring Resonator (SRR). SRR dimensions must be smaller than the resonance wavelength, making them crucial for near-infrared and optical responses. This effort examined nanoscale SRR characteristics in the infrared and visible ranges. SRRs composed of aluminum (Al) and gold (Au) were produced on silicon and silica substrates using electron beam lithography (EBL). The proposed structure consists of a microstrip-line-supplied SRR, a parasitic patch that is perpendicular to the ground plane, and two via holes connected by two split rings that feed the patch indirectly. Choosing the right material parameter for all such antennas depends on the planned structure. Fabrication and testing of the antenna matched simulations. Dimensions: 23.7 mm × 16.2 mm × 1.6 mm; substrate dielectric constant: 4.4. Experimental data are detailed and compared to analytical calculations. Simulation results show that metamaterials have negative permittivity and permeability in a certain frequency range, which can be predicted by an analytical method. Simulations indicated eight BRS, WiMAX, radar, and mobile phone resonance frequencies. Radiation is dipole-like in the omnidirectional in the H-plane and E-plane. 10 dB return loss and 3 dB axial ratio bandwidths are 38.6.7% and 8.1%, respectively.

Abstract This paper proposes a total fusion of blockchain and AI tech for tomorrow’s viable, rich in diversity and transparent fisheries. It outlines the main goal of tackling overfishing challenges due to lack of transparency and biodiversity depletion in the fisheries sector. With the use of blockchain technology, we can ensure that all fishery products are safely traced from their harvest up to when they get to the market— at the same time, AI algorithms are used in monitoring fish populations and predicting them plus decision-making processes which should be enhanced thus promoting bio-diversity and ensuring sustainability of fish stocks. Results show promise on using both technologies together: improving sustainability plus transparency in fisheries which would promote more fish biodiversity, while others including using an artificial intelligence system have not been confirmed yet by observations. The conclusion underscores the transformative nature of these technologies as having great implications towards fisheries management; this implies that there is a need for future observational studies aimed at validating such other findings.

Abstract This study explores integrating blockchain technology into the Internet of Medical Things (IoMT) to address security and privacy challenges. Blockchain’s transparency, confidentiality, and decentralization offer significant potential benefits in the healthcare domain. The research examines various blockchain components, layers, and protocols, highlighting their role in IoMT. It also explores IoMT applications, security challenges, and methods for integrating blockchain to enhance security. Blockchain integration can be vital in securing and managing this data while preserving patient privacy. It also opens up new possibilities in healthcare, medical research, and data management. The results provide a practical approach to handling a large amount of data from IoMT devices. This strategy makes effective use of data resource fragmentation and encryption techniques. It is essential to have well-defined standards and norms, especially in the healthcare sector, where upholding safety and protecting the confidentiality of information are critical. These results illustrate that it is essential to follow standards like HIPAA, and blockchain technology can help ensure these criteria are met. Furthermore, the study explores the potential benefits of blockchain technology for enhancing inter-system communication in the healthcare industry while maintaining patient privacy protection. The results highlight the effectiveness of blockchain’s consistency and cryptographic techniques in combining identity management and healthcare data protection, protecting patient privacy and data integrity. Blockchain is an unchangeable distributed ledger system. In short, the paper provides important insights into how blockchain technology may transform the healthcare industry by effectively addressing significant challenges and generating legal, safe, and interoperable solutions. Researchers, doctors, and graduate students are the audience for our paper.

Abstract This article proposes a dual mode dual-polarized antenna configuration for IRNSS and fifth generation (5G) applications, operating at a frequency of 3.5 GHz based on characteristic mode analysis (CMA), and aims to provide broadband dual-polarized functionality. The original design of the antenna is a traditional patch antenna, and its dual-polarized features are determined using characteristic mode analysis. The full-wave method is used to stimulate both orthogonal modes using a 50 Ω coaxial input line at 3.5 GHz. In this design, the circular patch has been extended into an elliptical patch through a process of mode separation. The circular patch exhibits resonance at a frequency of 2.5 GHz, whereas the extended elliptical radiator demonstrates two resonance modes at 2.5 GHz and 3.5 GHz. The operational mechanism is elucidated by modal analysis and characteristic angle. This antenna operates on two different frequencies at the 2.5 GHz IRNSS band with horizontal polarization and the 3.5 GHz 5G service with vertical polarization. The maximum gain achieved with these frequency ranges is 5.31 dBi and 4.72 dBi, respectively. A ring resonator is chosen to improve the axial ratio and impedance bandwidth of the suggested prototype. The antenna's ground plane is shaped like a rectangle and features a V-shaped slot in the radiating patch. The antenna's physical footprint is 50 mm × 50 mm × 1.6 mm and an FR4 dielectric substrate serves as its foundation. Through its interaction with a PIN diode, the diode modifies the polarization of the antenna. The antenna functions as a right-handed circular polarization (RHCP), when the diode is operational. The bandwidth from 4.3 to 7.5 GHz is covered. On the other hand, it generates linear polarization (LP) between 4.2 and 5.3 GHz. The experimental antenna is evaluated and examined for its performance characteristics. The simulations are carried out utilizing the CST simulator. A prototype antenna has been manufactured and its performance has been validated against simulated findings.

Abstract Direct AC–AC converters are strong candidates in the power converting system to regulate grid voltage against the perturbation in the line voltage and to acquire frequency regulation at discrete step levels in variable speed drivers for industrial systems. All such applications require the inverted and non-inverted form of the input voltage across the output with voltage-regulating capabilities. The required value of the output frequency is gained with the proper arrangement of the number of positive and negative pulses of the input voltage across the output terminals. The period of each such pulse for low-frequency operation is almost the same as the half period of the input grid or utility voltage. These output pulses are generated by converting the positive and negative input half cycles in noninverting and inverting forms as per requirement. There is no control complication to generate control signals used to adjust the load frequency as the operating period of the switching devices is normally greater than the period of the source voltage. However, high-frequency pulse width modulated (PWM) control signals are used to regulate the output voltage. The size of the inductor and capacitor is inversely related to the value of the switching frequency. Similarly, the ripple contents of voltage and currents in these filtering components are also inversely linked with PWM frequency. These constraints motivate the circuit designer to select high PWM frequency. However, the alignment of the high-frequency control input with the variation in the input source voltage is a big challenge for a design engineer as the switching period of a high-frequency signal normally lies in the microsecond. It is also required to operate some high-frequency devices for various half cycles of the source voltage, creating control complications as the polarities of the half cycles are continuously changing. This requires at least the generation of two high-frequency signals for different intervals. The interruption of the filtering inductor current is a big source of high voltage surges in circuits where the high-frequency transistors operate in a complementary way. This may be due to internal defects in the switching transistors or some unnecessary inherent delay in their control signals. In this research work, a simplified AC–AC converter is developed that does not need alignment of high-frequency control with the polarity of the source voltage. With this approach, high-frequency signals can be generated with the help of any analog or digital control system. By applying this technique, only one high-frequency control signal is generated and applied in AC circuits, as in a DC converter, without applying a highly sensitive polarity sensing circuit. So, controlling complications is drastically simplified. The circuit and configuration always avoid the current interruption problem of filtering the inductor. The proposed control and circuit topology are tested both in computer-based simulation and practically developed circuits. The results obtained from these platforms endorse the effectiveness and validation of the proposed work.

Abstract Biofiltration is a method of pollution management that utilizes a bioreactor containing live material to absorb and destroy pollutants biologically. In this paper, we investigate mathematical models of biofiltration for mixing volatile organic compounds (VOCs) for instance hydrophilic (methanol) and hydrophobic ( $$\alpha$$ α -pinene). The system of nonlinear diffusion equations describes the Michaelis-Menten kinetics of the enzymic chemical reaction. These models represent the chemical oxidation in the gas phase and mass transmission within the air-biofilm junction. Furthermore, for the numerical study of the saturation of $$\alpha$$ α -pinene and methanol in the biofilm and gas state, we have developed an efficient supervised machine learning algorithm based on the architecture of Elman neural networks (ENN). Moreover, the Levenberg-Marquardt (LM) optimization paradigm is used to find the parameters/ neurons involved in the ENN architecture. The approximation to a solutions found by the ENN-LM technique for methanol saturation and $$\alpha$$ α -pinene under variations in different physical parameters are allegorized with the numerical results computed by state-of-the-art techniques. The graphical and statistical illustration of indications of performance relative to the terms of absolute errors, mean absolute deviations, computational complexity, and mean square error validates that our results perfectly describe the real-life situation and can further be used for problems arising in chemical engineering.

Abstract Gait recognition has become an increasingly promising area of research in the search for noninvasive and effective methods of person identification. Its potential applications in security systems and medical diagnosis make it an exciting field with wide-ranging implications. However, precisely recognizing and assessing gait patterns is difficult, particularly in changing situations or from multiple perspectives. In this study, we utilized the widely used CASIA-B dataset to observe the performance of our proposed gait recognition model, with the aim of addressing some of the existing limitations in this field. Fifty individuals are randomly selected from the dataset, and the resulting data are split evenly for training and testing purposes. We begin by excerpting features from gait photos using two well-known deep learning networks, MobileNetV1 and Xception. We then combined these features and reduced their dimensionality via principal component analysis (PCA) to improve the model's performance. We subsequently assessed the model using two distinct classifiers: a random forest and a one against all support vector machine (OaA-SVM). The findings indicate that the OaA-SVM classifier manifests superior performance compared to the others, with a mean accuracy of 98.77% over eleven different viewing angles. This study is conducive to the development of effective gait recognition algorithms that can be applied to heighten people’s security and promote their well-being.

This research article proposes a security-enhanced watermarking method for medical images using the Radon and Slantlet transforms. The first step involves transforming the cover image from the spatial domain to the Radon domain. This transformation involves rotation, scaling, and translation through the Radon transform, which alters the locations of concealed bits. As a result, identifying the embedded data poses a considerable challenge. The embedded data cannot be identified without employing the inverse Radon transform. Subsequently, the Radon-transformed image is converted to the frequency domain using the Slantlet transform. Secret bits are incorporated into frequency coefficients during this phase through the pixel pair mapping approach. The final watermarked image is generated by inserting side information into the robust watermarked image. Simulation experiments are carried out to evaluate the imperceptibility of watermarks in medical images, employing metrics such as PSNR and SSIM. The results indicate high imperceptibility, with PSNR values exceeding 45 dB and SSIM values surpassing 0.95 for all tested images. Furthermore, the proposed method's robustness and reversibility are assessed by exposing watermarked images to various attacks. Performance is measured through the BER and NCC. Experimental findings reveal a BER of 0.2 % for the watermarked information, indicating strong resilience against attacks. Additionally, the NCC is determined to be 0.96, highlighting a high level of reversibility in extracting embedded data.

The problem of brain tumor classification (BTC) has been approached with several methods and uses different features obtained from MRI brain scans. However, they suffer from achieving higher performance in BTC and produce poor performance with a higher false ratio. A convolutional neural network (CNN) based on BTC and a survival analysis model based on GDD (growth distribution depth) are presented. Initially, an adaptive median filter (AMF) is used to preprocess the MRI images in order to lower the amount of noise in the images. Secondly, in order to calculate the GDD value, the texture, shape, and gradient characteristics are extracted. Third, CNN is used to train the retrieved features based on the labels that were found. In the classification, the GDD features extracted are used to measure TSF (Tumor Support Factor) in each of them. The neurons of the network measure the value of tumor weight (TW) to perform classification. Additionally, the technique evaluates a patient’s survival and calculates the survival rate based on the TSF value of the growth characteristic. The multi-layer perceptron allows the computation of TW and supports the efficient performance of classification. The proposed method improves tumor classification performance by up to 97%.

Detection of aerial activities, including drones and birds, has practical implications for automating bird surveys and developing radar systems for aerial object collision detection. Convolutional neural networks (CNNs) have been extensively utilized for image recognition and classification tasks, albeit prior research predominantly focuses on single-class 'drone' classification. However, a gap persists in achieving high accuracy for multi-class classification. To address the limitations of traditional CNNs, such as vanishing gradients and the necessity for numerous layers, this study introduces a novel model termed ''MobVGG.” This model combines the architectures of MobileNetV2 and VGG16 to accurately classify images as either 'bird' or 'drone'. The dataset comprises 4212 images for each category of 'bird' and 'drone'. The stringent methodology was applied for dataset preparation and model training to ensure the reliability of the findings. Comparative analysis with previous research demonstrates that the proposed MobVGG model, trained on both 'bird' and 'drone' images, achieves superior accuracy (96 %) compared to benchmark studies. Our paper targets researchers and graduate students as its primary audience.

Abstract 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.

Abstract The proliferation of smart conurbations entails an efficient system design for managing all the crowds in public places. Multitude controlling procedures are carried out for controlling compact areas where more number of peoples is present at several groups. Therefore for controlling purpose the proposed method aims to design a pictorial representation using Internet of Things (IoT). The process is carried out by taking images and then organizing it using switching techniques in the presence of square boxes where entire populace is identified on real time experimentations. For processing and controlling the occurrence a separate architecture is designed with analytical equivalences where all data set is stored in cloud platform. Further the incorporation of system model is carried out using Switching Based Algorithm (SBA) which adds more number of columns even for high population cases. In order to verify the effectiveness of proposed model five scenarios are considered with performance evaluation metrics for SBA and all the test results provides best optimal results. Moreover the projected model is improved with an average percentage of 83 as compared to existing models.

Diagnosing and treating newborn seizures accurately and promptly is crucial for providing the best possible care for these patients. For the purpose of intelligently identifying newborn seizures, this work introduced a unique method that uses spectral and spatial graph neural networks (SSGNNs) optimized with the Aquila algorithm. Using electroencephalogram (EEG) recordings, the suggested methodology takes advantage of the complex spatial and spectral characteristics of infant brain activity. Spatial and spectral GNNs were used to extract significant spatiotemporal patterns suggestive of seizure episodes by organizing the brain activity data as a graph, with nodes representing various brain regions and edges signifying functional relationships. By combining spectral and spatial data, the depiction of newborn brain dynamics was improved and made it possible to distinguish between seizure and non-seizure phases with greater accuracy. Moreover, the introduction of the Aquila algorithm improved the GNNs' performance in seizure identification tasks by streamlining the training process. A large dataset of EEG recordings from newborns with and without seizures was used to assess the effectiveness of the suggested method. Higher accuracy, sensitivity, and specificity in seizure detection were achieved in the experimental results, which showed greater performance when compared to conventional methods. This work offered an automated, data-driven method for identifying newborn seizures, which is a major development in the treatment of newborns. By combining spectral and spatial GNNs and optimizing the results using the Aquila method, it is possible to enhance seizure detection accuracy and potentially prevent neurological consequences in affected children by intervening early. This method has the potential to completely change the way neonatal care is provided by giving medical professionals a strong tool for accurate and prompt seizure monitoring in neonatal intensive care units (NICU).

Abstract Patient-focused healthcare applications are important to patients because they offer a range of advantages that add value and improve the overall healthcare experience. The 5G networks, along with Mobile Edge Computing (MEC), can greatly transform healthcare applications, which in turn improves patient care. MEC plays an important role in the healthcare of patients by bringing computing resources to the edge of the network. It becomes part of an IoT system within healthcare that brings data closer to the core, speeds up decision-making, lowers latency, and improves the overall quality of care. While the usage of MEC and 5G networks is beneficial for healthcare purposes, there are some issues and difficulties that should be solved for the efficient introduction of this technological pair into healthcare. One of the critical issues that blockchain technology can help to overcome is the challenge faced by MEC in realizing the most potential applications involving IoT medical devices. This article presents a comprehensive literature review on IoT-based healthcare devices, which provide real-time solutions to patients, and discusses some major contributions made by MEC and 5G in the healthcare industry. The paper also discusses some of the limitations that 5G and MEC networks have in the IoT medical devices area, especially in the field of decentralized computing solutions. For this reason, the readership intended for this article is not only researchers but also graduate students.

Abstract The proposed work contains three major contribution, such as smart data collection, optimized training algorithm and integrating Bayesian approach with split learning to make privacy of the patent data. By integrating consumer electronics device such as wearable devices, and the Internet of Things (IoT) taking THz image, perform EM algorithm as training, used newly proposed slit learning method the technology promises enhanced imaging depth and improved tissue contrast, thereby enabling early and accurate disease detection the breast cancer disease. In our hybrid algorithm, the breast cancer model achieves an accuracy of 97.5 percent over 100 epochs, surpassing the less accurate old models which required a higher number of epochs, such as 165.

Abstract The research addresses energy consumption, latency, and network reliability challenges in wireless sensor network communication, especially in military security applications. A multi-agent context-aware model employing the belief-desire-intention (BDI) reasoning mechanism is proposed. This model utilizes a semantic knowledge-based intelligent reasoning network to monitor suspicious activities within a prohibited zone, generating alerts. Additionally, a BDI intelligent multi-level data transmission routing algorithm is proposed to optimize energy consumption constraints and enhance energy-awareness among nodes. The energy optimization analysis involves the Energy Percent Dataset, showcasing the efficiency of four wireless sensor network techniques (E-FEERP, GTEB, HHO-UCRA, EEIMWSN) in maintaining high energy levels. E-FEERP consistently exhibits superior energy efficiency (93 to 98%), emphasizing its effectiveness. The Energy Consumption Dataset provides insights into the joule measurements of energy consumption for each technique, highlighting their diverse energy efficiency characteristics. Latency measurements are presented for four techniques within a fixed transmission range of 5000 m. E-FEERP demonstrates latency ranging from 3.0 to 4.0 s, while multi-hop latency values range from 2.7 to 2.9 s. These values provide valuable insights into the performance characteristics of each technique under specified conditions. The Packet Delivery Ratio (PDR) dataset reveals the consistent performance of the techniques in maintaining successful packet delivery within the specified transmission range. E-FEERP achieves PDR values between 89.5 and 92.3%, demonstrating its reliability. The Packet Received Data further illustrates the efficiency of each technique in receiving transmitted packets. Moreover the network lifetime results show E-FEERP consistently improving from 2550 s to round 925. GTEB and HHO-UCRA exhibit fluctuations around 3100 and 3600 s, indicating variable performance. In contrast, EEIMWSN consistently improves from round 1250 to 4500 s.

Abstract This paper presents a study investigating the performance of functionally graded material (FGM) annular fins in heat transfer applications. An annular fin is a circular or annular structure used to improve heat transfer in various systems such as heat exchangers, electronic cooling systems, and power generation equipment. The main objective of this study is to analyze the efficiency of the ring fin in terms of heat transfer and temperature distribution. The fin surfaces are exposed to convection and radiation to dissipate heat. A supervised machine learning method was used to study the heat transfer characteristics and temperature distribution in the annular fin. In particular, a feedback architecture with the BFGS Quasi-Newton training algorithm (trainbfg) was used to analyze the solutions of the mathematical model governing the problem. This approach allows an in-depth study of the performance of fins, taking into account various physical parameters that affect its performance. To ensure the accuracy of the obtained solutions, a comparative analysis was performed using guided machine learning. The results were compared with those obtained by conventional methods such as the homotopy perturbation method, the finite difference method, and the Runge–Kutta method. In addition, a thorough statistical analysis was performed to confirm the reliability of the solutions. The results of this study provide valuable information on the behavior and performance of annular fins made from functionally graded materials. These findings contribute to the design and optimization of heat transfer systems, enabling better heat management and efficient use of available space.

Abstract 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.

A serious eye condition called cataracts can cause blindness. Early and accurate cataract detection is the most effective method for reducing risk and averting blindness. The optic nerve head is harmed by the neurodegenerative condition known as glaucoma. Machine learning and deep learning systems for glaucoma and cataract detection have recently received much attention in research. The automatic detection of these diseases also depends on deep learning transfer learning platforms like VeggNet, ResNet, and MobilNet. The authors proposed MobileNetV1 and MobileNetV2 based on an optimized architecture building lightweight deep neural networks using depth-wise separable convolutions. The experiments used publicly available data sets with both cataract & normal and glaucoma & normal images, and the results showed that the proposed model had the highest accuracy compared to the other models.

Antennas with higher gain and efficiency deliver superior performance across a wide frequency range. Achieving these characteristics at high frequencies while keeping a compact size necessitates sophisticated design approaches. This research presents a substrate-integrated waveguide (SIW) cavity-backed slotted patch antenna (SPA) tailored for the 28 GHz and 34 GHz frequency bands. Additionally, a linear tapered slot antenna is designed with a compact profile of 27.5 mm × 7.5 mm × 0.254 mm. The SIWs are implemented using vias on the outer profile of the antenna, and circular and rectangular slots are etched on the radiating surface. The goal of optimizing the antenna geometry is to enhance return loss within the desired frequency bandwidth, which means the Genetic Algorithm (GA) will determine the optimal antenna shape to achieve lower return loss than the original design within this bandwidth. The antenna exhibits dual resonance at 28 GHz and 38 GHz in the millimeter-wave range, providing an impedance bandwidth of 211 MHz (27.72 GHz–27.94 GHz) at 28 GHz and 127 MHz (37.88 GHz–37.98 GHz) centered at 38 GHz. The proposed antenna demonstrates gains of 8.04 dBi and 9.72 dBi at these operating bands. A prototype of the antenna is fabricated on RT/duroid 5880 and its characteristics are measured. The overall VSWR of the antenna ranges from 1 to 2, with a radiation efficiency of 94 %. The proposed antenna achieves dual-band performance with increased directivity and stable gain, exhibiting enhanced electric field distribution, radiation patterns, and reflection coefficient (S11), all of which contribute to a comprehensive understanding of the antenna's performance. This study compares the designed antenna's performance to that of the fabricated prototype. The proposed antenna is ideal for 5G applications due to its small size, broad spectral coverage, and excellent gain.

The concept of the Internet of Things (IoT) threat surface refers to the overall susceptibility of smart devices to potential security risks. This vulnerability includes the combined impact of security weaknesses, gaps in protective measures, and potential vulnerabilities within the device OS, installed libraries, and applications, as well as the infrastructure involved. This comprises both identified and unforeseen risks that could potentially compromise the device’s integrity, data, logs, and hosted applications. By minimizing the extent to which the device’s components are exposed, it becomes possible to reduce the vulnerabilities inherent in the device, thereby decreasing its overall threat surface area. This research introduces an innovative framework for assessing Smart IoT cameras within the ecosystem. This framework involves the identification and categorization of webcam devices, followed by an analysis of potential threats based on various exposure indicators present within each layer. Subsequently, this information is used to determine the possible paths through which a device might be compromised, allowing for the evaluation of severity and both maturity levels. The authors present metrics that aid in reevaluating and recalibrating the security levels, considering the discovered threat surface elements. These refined metrics offer a fresh perspective on security, offering valuable insights for stakeholders who are engaged in the development, deployment, and evaluation of the security aspects of such devices.

Persistence behavior is a tactic advanced adversaries use to maintain unauthorized access and control of compromised assets over extended periods. Organizations can efficiently detect persistent adversaries and reduce the growing risks posed by highly skilled cyber threats by embracing creative techniques and utilizing sophisticated tools. By taking a proactive stance, businesses may increase their entire cybersecurity posture by anticipating and mitigating possible risks before they escalate. Security analysts perform thorough investigations and extract meaningful insights from large datasets with greater technical advantage by using Elasticsearch in conjunction with a variety of linguistic tools. This research presents a novel methodology for proactive threat intelligence to identify and mitigate advanced adversaries that use persistent behaviors. The authors designed and set up an Elasticsearch-based advanced Security Information and Event Management platform to offer a proactive threat-hunting strategy. This enables comprehensive analysis and detection by integrating Lucene, Kibana, and domain-specific languages. The goal of this research is to locate hidden advanced enemies who exhibit persistent behavior during cyberattacks. The framework can help improve the organization’s resilience to identify and respond to threats by closely examining activities like boot or logon auto-start execution in registry keys, tampering with system processes and services, and unauthorized creation of local accounts on compromised assets. This study emphasizes proactive actions over reactive reactions, which advances danger detection techniques. This technical study provides security practitioners seeking to improve defenses against new advanced attacks to stay ahead in a dynamic threat landscape.

In developing countries, smart grids are nonexistent, and electricity theft significantly hampers power supply. This research introduces a lightweight deep-learning model using monthly customer readings as input data. By employing careful direct and indirect feature engineering techniques, including Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), UMAP (Uniform Manifold Approximation and Projection), and resampling methods such as Random-Under-Sampler (RUS), Synthetic Minority Over-sampling Technique (SMOTE), and Random-Over-Sampler (ROS), an effective solution is proposed. Previous studies indicate that models achieve high precision, recall, and F1 score for the non-theft (0) class, but perform poorly, even achieving 0 %, for the theft (1) class. Through parameter tuning and employing Random-Over-Sampler (ROS), significant improvements in accuracy, precision (89 %), recall (94 %), and F1 score (91 %) for the theft (1) class are achieved. The results demonstrate that the proposed model outperforms existing methods, showcasing its efficacy in detecting electricity theft in non-smart grid environments.

Abstract Motivated by the imperative demand for design integration and miniaturization within the terahertz (THz) spectrum, this study presents an innovative solution to the challenges associated with singular functionality, limited application scope, and intricate structures prevalent in conventional metasurfaces. The proposed multifunctional tunable metasurface leverages a hybridized grapheme–metal structure, addressing critical limitations in existing designs. Comprising three distinct layers, namely a graphene–gold resonance layer, a Topas dielectric layer, and a bottom gold film reflective layer, this terahertz metasurface exhibits multifunctionality that is both polarization and incident-angle independent. The metasurface demonstrates a broadband circular dichroism (CD) function when subjected to incident circularly polarized waves. In contrast, under linear incidence, the proposed design achieves functionalities encompassing linear dichroism (LD) and polarization conversion. Remarkably, graphene's chemical potential and the incident light’s state can be manipulated to tune each functional aspect's intensity finely. The proposed tunable multifaceted metasurface showcases significant referential importance within the terahertz spectrum, mainly contributing to advancing CD metamirrors, chiral photodetectors, polarization digital imaging systems, and intelligent switches.

Abstract Soil health is essential for whirling stale soil into rich coffee-growing land. By keeping healthy soil, coffee producers may improve plant growth, leaf health, buds, cherry and bean quality, and yield. Traditional soil monitoring is tedious, time-consuming, and error-prone. Enhancing the monitoring system using AI-based IoT technologies for quick and precise changes. Integrated soil fertility control system to optimize soil health, maximize efficiency, promote sustainability, and prevent crop threads using real-time data analysis to turn infertile land into fertile land. The RNN-IoT approach uses IoT sensors in the coffee plantation to collect real-time data on soil temperature, moisture, pH, nutrient levels, weather, CO2 levels, EC, TDS, and historical data. Data transmission using a wireless cloud platform. Testing and training using recurrent neural networks (RNNs) and gated recurrent units gathered data for predicting soil conditions and crop hazards. Researchers are carrying out detailed qualitative testing to evaluate the proposed RNN-IoT approach. Utilize counterfactual recommendations for developing alternative strategies for irrigation, fertilization, fertilizer regulation, and crop management, taking into account the existing soil conditions, forecasts, and historical data. The accuracy is evaluated by comparing it to other deep learning algorithms. The utilization of the RNN-IoT methodology for soil health monitoring enhances both efficiency and accuracy in comparison to conventional soil monitoring methods. Minimized the ecological impact by minimizing water and fertilizer utilization. Enhanced farmer decision-making and data accessibility with a mobile application that provides real-time data, AI-generated suggestions, and the ability to detect possible crop hazards for swift action.

Abstract Design closure and parameter optimisation are crucial in creating cutting-edge antennas. Antenna performance can be improved by fine-tuning preliminary designs created using theoretical considerations and rough dimension adjustment via supervised parameter sweeps. This paper introduces a frequency reconfigurable antenna design that can operate at 28/38 GHz frequencies to meet FCC and Ofcom standards for 5G applications and in the 18 GHz frequency band for K-band radar applications. A PIN diode is used in this design to configure multiple frequency bands. The antenna has a modified rectangular patch-like structure and two optimised plugins on either side. The study that is being presented focuses on maximising the parameters that are subject to optimisation, including length (Ls), width (Ws), strip line width (W1), and height (ht), where the antenna characteristic parameters such as directivity is tuned by a hybrid optimisation scheme called Elephant Clan Updated Grey Wolf Algorithm (ECU-GWA). Here, the performance of gain and directivity are optimally attained by considering parameters such as length, width, ground plane length, width, height, and feed offsets X and Y. The bandwidth of the proposed antenna at − 10 dB is 0.8 GHz, 1.94 GHz, and 7.92 GHz, respectively, at frequencies 18.5 GHz, 28.1 GHz, and 38.1 GHz. Also, according to the simulation results, in the 18 GHz, 28 GHz, and 38 GHz frequencies S11, the return loss is − 60.81 dB, − 56.31 dB, and − 14.19 dB, respectively. The proposed frequency reconfigurable antenna simulation results achieve gains of 4.41 dBi, 6.33 dBi, and 7.70 dBi at 18.5 GHz, 28.1 GHz, and 38.1 GHz, respectively. Also, a microstrip quarter-wave monopole antenna with an ellipsoidal-shaped complementary split-ring resonator-electromagnetic bandgap structure (ECSRR-EBG) structure has been designed based on a genetic algorithm having resonating at 2.9 GHz, 4.7 GHz, 6 GHz for WLAN applications. The gain of the suggested ECSRR metamaterial and EBG periodic structure, with and without the ECCSRR bow-tie antenna. This is done both in the lab and with numbers. The measured result shows that the ECSRR metamaterial boosts gain by 5.2 dBi at 5.9 GHz. At 5.57 GHz, the two-element MIMO antenna achieves its lowest ECC of 0.00081.

The exponential increase in the Internet of Things (IoT) has affected the cloud computing with increase transmission latency and network overhead for real-time applications. Cloud-fog computing paradigm tackle these limitations by moving computational services closer to the network edge i.e., fog nodes, enhancing the speed of real-time applications. This architecture, with its dynamic computing environment and diverse IoT devices and tasks, demands a reliable and energy-efficient communication network. Joint optimization of computation offloading and task scheduling is a primary challenge, as it involves offloading tasks to optimal computational resources and scheduling them in an efficient order for operational efficacy. While offloading tasks to fog nodes reduces delay but raises energy utilization, offloading them to cloud servers reduces energy usage but raises computational costs and latency. Additionally, inefficient order of task execution (executing lower priority jobs before higher priority tasks) can disrupt system stability and reliability. Therefore, an effective joint optimal computation offloading and task scheduling strategy is essential. To this end, we propose a Multi-objective Arithmetic Optimization-based joint computation offloading and task scheduling algorithm, aiming to minimize energy consumption and transmission latency. Extensive simulations in MATLAB demonstrate the efficacy of the proposed algorithm in terms of designated optimization objectives.

Because of its versatility across various applications, Blockchain has emerged as a technology garnering significant interest. It has effectively addressed the challenge of transitioning from a low-trust, centralized ledger maintained by a single third-party to a high-trust, decentralized structure maintained by multiple entities, often referred to as validating nodes. Consequently, numerous blockchain systems have arisen for a multitude of purposes. Nevertheless, a considerable number of these blockchain systems are plagued by significant deficiencies concerning their performance and security. These issues have to be rectified before the realization of a widespread adoption. An essential element within any blockchain system is its foundational consensus algorithm, a crucial determinant of both its performance and security attributes. Consequently, to tackle the shortcomings observed in various blockchain systems, the hardware implementation of a series of established and innovative consensus algorithms was carried out as part of this work. This paper aims to compare and analyze the different consensus methods in blockchain, namely PoS (Proof of Stake), PoW (Proof of Work) and PoA (Proof of Authority) using VHDL (Very High-Speed Integrated Circuit Hardware Description Language). Each of these methods has unique characteristics that influence the validation of transactions and the addition of blocks to the blockchain. In this context, we aim to demonstrate the importance of optimizing consensus execution time via IPs (Intellectual Property) in VHDL. We also evaluate their impact on security, scalability and performance for IoT applications.

In this manuscript, gain improvement is observed for an ultra-wideband (UWB) antenna. The proposed arrangement is optimally designed for high gain wideband communications. This arrangement comprises a low profile and easily circuit integrated antenna covering spectrum ranges from 3.1 GHz to 10.6 GHz for low power UWB communications. Extended impedance improvement is also achieved by employing a co-planar waveguide (CPW) feeding mechanism incorporating a castle shaped profile and wider radiation element. Moreover, a periodic structure based single layered frequency selective surface (FSS) for gain improvement is also suggested. This split center resonator (SCR) FSS is designed and employed owing to a smaller footprint and stable performance. The performance of this antenna-FSS arrangement is analyzed by varying the inter-element spacing of SCR, distance between SCR layer and the proposed CPW fed antenna. The simulations and measurements validate at least 5 dBi of gain improvement while offering wideband impedance matching and stable radiation performance. This improved performance and low-profile design makes the proposed arrangement suitable for modern high gain UWB communication services.

Energy efficiency and safety are two essential factors that play a significant role in operating a wireless sensor network. However, it is claimed that these two factors are naturally conflicting. The level of electrical consumption required by a security system is directly proportional to its degree of complexity. Wireless sensor networks require additional security measures above the capabilities of conventional network security protocols, such as encryption and key management. The potential application of machine learning techniques to address network security concerns is frequently discussed. These devices will have complete artificial intelligence capabilities, enabling them to understand their environment and respond. During the training phase, machine-learning systems may face challenges due to the large amount of data required and the complex nature of the training procedure. The main objective of the article is to know about different machine learning algorithms that are used to solve the security issues of wireless sensor networks. This study also focuses on the use of wireless sensor networks in different fields. Furthermore, this study also focuses on different Machine learning algorithms that are used to secure wireless sensor networks. Moreover, this study also addresses issues of adapting machine learning algorithms to accommodate the sensors’ functionalities in the network configuration. Furthermore, this article also focuses on open issues in this field that must be solved.

Software defect prediction plays a crucial role in enhancing software quality while achieving cost savings in testing. Its primary objective is to identify and send only defective modules to the testing stage. This research introduces an intelligent ensemble-based software defect prediction model that combines diverse classifiers. The proposed model employs a two-stage prediction process to detect defective modules. In the first stage, four supervised machine learning algorithms are employed: Random Forest, Support Vector Machine, Naïve Bayes, and Artificial Neural Network. These algorithms are optimized through iterative parameter optimization to achieve the highest accuracy possible. In the second stage, the predictive accuracy of the individual classifiers is integrated into a voting ensemble to make the final predictions. This ensemble approach further improves the accuracy and reliability of the defect predictions. Seven historical defect datasets from the NASA MDP repository, namely CM1, JM1, MC2, MW1, PC1, PC3, and PC4, were utilized to implement and evaluate the proposed defect prediction system. The results demonstrate that each dataset’s proposed intelligent system achieved remarkable accuracy, outperforming twenty state-of-the-art defect prediction techniques, including base classifiers and ensemble methods.

Purpose: This study investigates the impact of activity-based learning and the utilization of ChatGPT on students' academic performance within the educational framework. Objectives: The study aims to assess the effectiveness of activity-based learning in comparison to traditional methods, while also evaluating the potential benefits and drawbacks of integrating ChatGPT as an educational tool. Methods: The study employs a comparative approach, analyzing the outcomes of students exposed to activity-based learning versus those using conventional methods. Additionally, the study examines the usage of ChatGPT in education through surveys and trials to determine its contribution to personalized feedback, interactive learning, and innovative teaching methods. Results: The findings reveal that activity-based learning enhances students' engagement, motivation, and critical thinking skills. Students participating in activity-based learning demonstrate improved academic achievement, which is attributed to their active involvement and practical application of knowledge. Similarly, the integration of ChatGPT offers novel avenues for interactive learning and individualized assistance, fostering students' understanding and exploration of complex concepts. Conclusion: In conclusion, activity-based learning proves to be a student-centered approach that enhances learning outcomes by fostering active participation and practical engagement. The utilization of ChatGPT in education showcases its potential to enhance educational experiences through interactive conversations and innovative teaching methodologies, despite considerations regarding potential limitations and ethical implications.

In this article, a dual-mode, dual-polarized antenna designed using characteristic mode analysis (CMA) is described. An elliptical-shaped patch radiator is chosen with double slits on its minor axis. This design is based on mode separation from the circular patch into the elliptical patch. The suggested antenna geometry has a footprint of 60 mm × 60 mm × 1.6 mm. To design and fabricate the antenna, an FR-4 substrate with a relative permittivity of 4.3 is used, along with copper sheets 0.035 mm thick for the ground plane and the radiating plane. The circular patch has the resonating mode at 1.8 GHz, whereas the elliptical radiator gives different resonant modes at 1.8 GHz and 3.5 GHz. An orthogonal mode is excited with a 50-Ω coaxial feed line at 3.5 GHz by applying a full-wave approach. The antenna gives a −10dB bandwidth of 51 MHz (1.77–1.82 GHz) centered at 1.8 GHz and a bandwidth of 210 MHz (3.37–3.58 GHz) centered at 3.5 GHz. The working principle is explained through modal analysis and characteristic angles. This dual-band antenna covers a 1.8 GHz GSM band with horizontal polarization and a 3.5 GHz 5G service with vertical polarization. Peak gain attained with these bands is 5.9 dBi and 7.1 dBi, respectively. A CST full-wave simulator is used for the simulations. As a result of the antenna, radiation is stable and enhanced. Compared to measured results, simulation results are close to reality. The characteristic mode analysis (CMA) provides an in-depth look into different operating modes on the antenna in contrast with the conventional method, which relies on the simulated current distribution to verify functionality.

Immersive technology, especially virtual reality (VR), transforms education. It offers immersive and interactive learning experiences. This study presents a systematic review focusing on VR’s integration with educational theories in higher education. The review evaluates the literature on VR applications combined with pedagogical frameworks. It aims to identify effective strategies for enhancing educational experiences through VR. The process involved analyzing studies about VR and educational theories, focusing on methodologies, outcomes, and effectiveness. Findings show that VR improves learning outcomes when aligned with theories such as constructivism, experiential learning, and collaborative learning. These integrations offer personalized, immersive, and interactive learning experiences. The study highlights the importance of incorporating educational principles into VR application development. It suggests a promising direction for future research and implementation in education. This approach aims to maximize VR’s pedagogical value, enhancing learning outcomes across educational settings.