Recent Advances in Electrical & Electronic Engineering - Volume 18, Issue 9, 2025

The air quality of any area depends upon the various PMs (particulate matter) and hazardous gases present in the air. Low-cost PM sensors and gas sensors are present in different target places to monitor the air quality, read the environmental data, and transmit it to local servers through the IoT device. The low-cost sensor is not reliable due to its low sensing capacity; therefore, the read data is calibrated with the meteorological data presented by the nearby meteorological Centre of that particular area. The calibrated reading data sent to the server could be analyzed through some Machine Learning (ML) models. The ML models help to predict the risk of asthma in a particular area. The risk of asthma is directly related to the air quality of the surroundings. It is observed that the air quality of the industrial area is much worse than the non-industrial belt. Air quality monitoring of industrial areas is always a challenging task due to the ununiformed pollution in some segregated places around the industry, emitting pollutants mostly from chimneys. The air quality of any area depends upon the PM (PM), i.e., PM2.5 and PM10.0, as well as the gasses like NO2(Nitrogen Dioxide), NH3 (Ammonia), SO2(Sulfur dioxide), CO(Carbon monoxide), O3(Ozone) and Benzene. These are the most hazardous gases generally emitted by common heavy industries like iron and steel. In this article, the researchers considered the industrial belt of the Asansol-Durgapur region of West Bengal, India, and predicted the risk of asthma attacks for the test dataset. The experiment was carried out on 10 different supervised machine learning (SML) models as well as semi-supervised machine learning (SSML) models. The SML models have been further refined through hyper-parameter tuning, and better results have been obtained in the case of some ML models. The result has been compared with the existing literature considering the same external environment from where the meteorological data was collected, and similar ML models have been used. The research outperformed the existing literature, which is depicted in the result and analysis section of the article.

The study evaluated ML models, both supervised and semi-supervised, to assess pollution levels. Relevant features were selected while less relevant ones were discarded. Accuracy levels of different ML algorithms werecompared in the results. The most effective model for an IoT system was chosen to maximize accuracy. In semi-supervised learning, feature selection followed supervised learning, but testing was akin to unsupervised learning. Results were compared with supervised learning data, enhancing reliability.

The result employing various classifiers werepresented across tables after the independent parameter Ozone was removed. Following the output of several classifiers, the results were verified using the k-fold validation method, with k being set to 5 or 10, accordingly. Tables display the best outcome, which is indicated in bold characters.

This study focused on predicting asthma risk in the Asansol-Durgapur industrial belt, India, using low-cost PM and gas sensors. Data calibration with meteorological inputs enhanced accuracy. ML models predicted risk and were refined through hyper-parameter tuning. Comparative analysis showed superior performance, emphasizing the importance of precise air quality monitoring. While offering a robust framework for future research, the study’s limitation lies in its area-specific dataset.

Minimally invasive interventional surgical robots are used as delivery tools to assist healthcare professionals with medical devices such as micro guidewires and microcatheters during surgical treatment of cardiovascular and cerebrovascular obstruction, thus reducing surgical fatigue and radiation for healthcare professionals.

In this paper, a new vascular interventional surgical robot with multiple degrees of freedom is designed, its mechanism principle and movement mode is discussed in detail, and a prototype prototype is fabricated to make it simulate and reproduce the doctor's hand movements.

Through Abaqus simulation and platform experiments, the performance of the surgical robot is evaluated and analyzed from the perspectives of micro guidewire delivery displacement accuracy and radial clamping force for its high accuracy, high reliability, and safety.

The results demonstrate that its surgical robot meets the requirements of providing vessel wall overload protection while the microcatheter delivery positioning accuracy error is less than 1 mm.

Human health is significantly compromised by air pollution, especially by local air quality. The majority of our society spends their lives in a confined geographical location, which if subjected to air pollution can expose them to long-term air contamination. It is also possible that poor air quality can pose serious health risks, especially to susceptible individuals thereby impacting their lifestyle. Air quality can be improved with appropriate plantation, but they are underutilized. Various air purification devices have been developed in response to the ever-increasing air pollution level.

However, artificial means of air purification are not very viable in terms of cost, accessibility to society, and reliable tools to purify air. This research integrates traditional solutions with modern technology to counter air purification by selectively using plant species and placing them in desired locations suitable for urban settings. The study aims to measure the constituents of various air pollutants spanning across regions to identify and accumulate pollution data using IoT-based smart devices, remit, and feed this information to cloud-based storage for further processing. In addition, advanced predictive intelligence is utilized to determine the plant species that can suffice the need for air purification through organic means in a given geographical zone resulting in enhancement of Air Quality (AQ), with minimal cost, prolonged shelf life, future proof and non-detrimental consequences.

Implementation outcome gives a promising outcome. Accurate readings of various air pollutants are aggregated. Suitable trees are identified to tackle these pollutants and their absorbing capacity is determined. Various predictive methods are employed and the random forest model recorded the best results. The sensory units of the model successfully captured the pollutant data and any major fluctuations were reported. The prediction pipeline recorded a mean precision, recall, and f-score value of about 0.95, 0.92, and 0.94 respectively while the mean accuracy of 0.965 was also noted. The observed training and validation accuracy with our model were 0.96 and 0.93 respectively.

Hence, the proposed ‘AeroGlan’ model may be locally applied as an air pollutants monitoring device and also to suggest suitable plant species required to counter air contamination in that locality.

The pre-stage deflection is a key aspect of the deflector jet tube electro-hydraulic servo valve, and its performance not only affects the pressure gain, flow gain, and other important parameters of the servo valve, but also affects the dynamic and static characteristics of the servo valve and electro-hydraulic servo system.

This paper aimed to outline the working principle of the pre-stage deflector jet tube type electro-hydraulic servo valve, and review and analyze its research performance development in five aspects.

The research progress in the key indexes of servo valve performance at different stages, the mechanism and phenomenon of the erosion and wear of the pre-stage, the mechanism and characteristics of the pre-stage cavitation, the structural improvement and parameter optimization of the pre-stage, as well as the pre-stage drive mode, has been summarized.

It has been found that although a large number of scholars at home and abroad have improved the structure and driving mode of the front stage of the deflecting jet tubular electro-hydraulic servo valve and optimized the parameters to improve the performance of the deflecting jet tubular electro-hydraulic servo valve, the internal flow field of the front stage is complex, prone to erosion and cavitation and many complex phenomena, and there are still many aspects to be improved and innovated.

This paper has thus summarized the defects of the front-end stage, and put forward three future innovations of miniaturization, intelligence, and multifunction, to contribute innovative ideas to the design of the front-end stage of the deflection jet pipe electro-hydraulic servo valve in the future.

WSNs are composed of various tiny low battery power Sensor Nodes (SNs). It is required to sense the dynamic network environments, which may fluctuate due to external factors or the system model itself. Furthermore, sensory data is transmitted over the communication channel to the destination. Integration of Artificial Neural Network (ANN) in WSNs provides the facility to learn SNs and networks from past experiences and make the predictions based on them. The computational ability of ANN may help to choose an efficient route for data transfer in WSNs. Also, it is able to improve the performance of the network w.r.t power consumption, latency, throughput, etc. This work explores an efficient way to manage the energy of the SNs using an ANN-based routing approach for the selection of Cluster Head (CH).

In this work, an ANN-based and algorithmic approach has been applied to select the CH. Various popular routing strategies such as LEACH-C, TEEN, SEP & DEEC have been used for simulation. LEACH-C and TEEN belong to the homogeneous category, while SEP and DEEC are categorized as heterogeneous. ANN based CH selection strategy has been proposed and compared with the other routing techniques.

ANN based CH selection strategy has been simulated and compared. Results revealed that CH election based on ANN will enhance the network lifetime. It is clear from the simulation results that ANN-DEEC is much more stable than protocols of the same category.

ANN-DEEC outperforms five times more than LEACH-C, 2.5 times more than TEEN and SEP, twice DEEC and 10-20% more than EDDEC and Advanced-DEEC. ANN-DEEC transmits 71021 packets to BS more than any other protocol.

In order to solve the problems of single influencing factors and inconsistency of subjective experience of multiple experts under different working conditions in the evaluation of existing cross-river transmission corridor schemes, a cross-river transmission corridor scheme evaluation method based on hierarchical analysis has been proposed in this work.

Firstly, six evaluation indices, including planning and important facility areas, environmentally sensitive points, geological conditions, hydrological conditions, safety and economy, have been established to construct a system for assessing the impact factors of cross-river transmission corridors. Then, the hierarchical analysis method has been combined with the improved grey correlation analysis to synthesize the weight judgement values of multiple experts, construct a unified scoring system for the weights of qualitative and quantitative indexes, effectively improve the stability of the indexes' weights, and realize the scientific assessment of the cross-river transmission channel scheme. Finally, the engineering scheme of a cross-river transmission channel has been evaluated.

Through practical engineering analysis, the selected cross-river transmission channel using this method has been found to be consistent with the actual project.

The results have shown that the method could effectively achieve the evaluation of various cross-river transmission channel schemes by comprehensively considering a variety of factors in the selection of cross-river corridors for transmission lines and effectively protecting the empirical roles of different experts.

The stable, safe, and reliable operation of the electric actuator valve system is crucial in industrial production. However, its faults can lead to production interruptions, equipment damage, and increased costs. Therefore, detecting and analyzing the causes of fault modes is crucial for exploring methods to reduce their occurrence and ensuring the normal operation of electric actuators.

Taking the IQ20-F14-A electric actuator from Rotork, UK, as a reference, this paper analyzes the causes of the phase loss fault mode from three aspects: software, circuitry, and external conditions. By analyzing and experimentally testing the power supply information extraction circuit, identifying the extraction signal related to phase information, and further comprehensively listing the factors that lead to phase loss faults, the scope is narrowed down through analysis and practical verification.

After investigating potential factors related to component design, circuit design, and connection issues within the circuit, causing a decay of a certain phase voltage on the power line to below the threshold leads to the optocoupler and information-extracting transistors entering a cutoff state, resulting in a phase loss alarm. Additionally, the continuous superposition of typical interference pulse trains on the input voltage for 9 seconds or more also triggers a phase loss alarm.

The fault mode mainly revolves around software, the decay of external supply phase voltage, and the interference of external phase voltage.

Visible spectrum iris recognition is an essential component of biometric identification systems since it offers robust security measures. This approach makes use of Transformer Networks, which are well-known for their powerful attention mechanisms.

In this study, the novel AHE-TAM method proposed for iris recognition in the visible spectrum was presented, and the application of Transformer Networks was investigated. When compared to earlier methods, AHE-TAM offers significant improvements in terms of precision, safety, and efficiency of computing. Through the utilization of attention mechanisms, the model can adapt to intricate details on the fly, thereby surpassing the performance of AHE-CNN, AHE-TransformerNet, and AHE-AM by an impressive margin of 1% on average. AHE-TAM also has improved security because it reduces the average FAR by 8% and the FRR by 7%.

This results in a lower overall risk. ROC AUC values have improved by 4%, which is a significant improvement that highlights the improved discriminatory power. Conclusion: The use of AHE-TAM results in a reduction of the computational processing times by an average of 13%.

In the pursuit of mitigating greenhouse gas emissions, the adoption of clean energy production has emerged as an imperative strategy. Thin film CdTe photovoltaic (PV) modules have become widely popular in commercial applications owing to their remarkable attributes, such as superior absorption capabilities, direct band gap material, and single junction operational efficiency. CdTe PV modules have secured a prominent status within the photovoltaic technology landscape by demonstrating a favourable band gap optimisation, better temperature coefficient, and enhanced energy yield.

This research presents an exploration of PV resistance modelling via a numerical methodology. A comprehensive CdTe PV module model is developed in the Matlab Simulink environment. Furthermore, the study explores the detailed performance of a 110 W thin film CdTe PV module. Critical parameters such as open circuit voltage, short circuit current, and maximum power are subjected to meticulous validation against data from established commercial photovoltaic modules. The efficiency and fill factor of the CdTe PV module are judiciously analysed with the changing weather condition.

Impressively, the analysis highlights the model’s exceptional precision under the considered conditions, particularly in the context of maximum power, where the relative error remains under 0.1%. Remarkably aligning with reference module parameters, the characteristic PV parameters exhibit remarkable closeness, with an overall relative error of less than 1.63% for all PV parameters under standard test conditions.

Moreover, a statistical measure named coefficient of determination ‘R²’ is found very near to 1 for all characteristics described and it satisfies the actual nature of the characteristics curve.

In grid-connected operation control, the Brushless Doubly-Fed Reluctance Generator (BDFRG) faces issues with strong parameter coupling and weak disturbance rejection.

This paper proposes a direct power control strategy with a novel integral sliding mode controller. By analyzing the correlation between the voltages on the stator control winding side and the active/reactive power, a direct power control model is derived from the d-q rotating coordinate system, achieving decoupling control of active and reactive power. An integral sliding surface, along with a smoothing function, is introduced to improve the switching behavior as the system approaches the sliding surface. Stability ranges for the parameters Kd and Kq are determined by constructing a Lyapunov function.

Results from simulations and hardware-in-the-loop (HIL) experiments demonstrate that the direct power control strategy with a novel integral sliding mode controller reduces chattering and improves the static and dynamic performance of the system, compared to conventional sliding mode control strategy.

The proposed direct power control strategy not only addresses the chattering issues during sliding mode switching but also ensures system stability and efficiency through optimized parameter adjustment.

The coupling of multiple heterogeneous energy sources in an integrated energy system has led to difficulties in coordinating the optimal scheduling of various energy sources. As a typical cyber-physical system, the development scale of an integrated energy system is limited by the communication bandwidth.

A coordinated optimal scheduling method for integrated energy system based on finite-time event-triggered consensus algorithm is proposed in this paper to achieve the optimal operation of an integrated energy system and lower the burden on the communication network.

In this paper, the optimal scheduling model of integrated energy system is established, and the finite-time consensus algorithm is applied to solve the model, so that the operating costs of various energy sources can reach the optimal solution within a finite time. Then, a discrete system communication scheme is established so that neighbor nodes exchange state information only at the triggering instants. The stability of the system is analyzed using the Lyapunov stability theory, and it is verified that the system does not exhibit the Zeno phenomenon. Finally, the effectiveness of the proposed optimal scheduling method is verified by case analysis.

The results show that the method can achieve the optimal operation of integrated energy system and effectively reduce the number of communications between neighbor nodes, lowering the burden on the communication network.

An integrated energy system composed of electric-heating-gas-cooling is given to verify the feasibility and effectiveness of the proposed method.

Extreme weather conditions exert a considerable influence on power load, leading to increasingly erratic fluctuations. Consequently, the dependable and precise forecasting of power load assumes paramount importance in power system planning.

Given the inadequacy of traditional forecasting approaches in handling long-term series load forecasting, this paper introduces a short-term power load forecasting model rooted in VNCMD-TiDE, aiming to enhance forecasting precision.

Initially, the XGBoost algorithm is employed to perform nonlinear coupling analysis between load and meteorological data, identifying crucial features. Following this, the VNCMD method is utilized to handle the nonlinear and non-stationary load data, decomposing it into multiple components with distinct frequencies. Building upon this decomposition, a TiDE-Bayesian model is constructed, wherein the decomposed components serve as inputs for prediction. Simultaneously, Bayesian optimization is leveraged to fine-tune hyperparameters. Ultimately, the prediction outcomes of each component are amalgamated to derive the final prediction.

The proposed model's performance is assessed through comparison with traditional machine learning models, such as LSTM. Achieving a noteworthy reduction in Root Mean Square Error (RMSE) by 4.14 underscores its exceptional predictive prowess.

Through the analysis of actual power load data in a specific location, the model proposed in this article demonstrates superior prediction accuracy, particularly evident during extreme weather conditions like snow and rain.

GaN HEMTs with field plates have emerged as a boon for the high power along with high voltage application domains. The work in this paper is based on developing a numerical model to study and analyze the device performance of the GaN HEMT device by including the field plate structure. The study is based on considering both the conventional and the proposed GaN HEMT device incorporated with a field plate structure and comparing their performances under various biasing conditions.

The study involves initially designing a conventional GaN HEMT device without a field plate. Next, a GaN HEMT device of the same dimensions but including a field plate is designed. The field plate length is varied between 0.5 µm and 4 µm to determine the optimal length. The design and simulations of both devices were performed using the SILVACO ATLAS TCAD tool. The impact of a field-plate on the device performance is also studied in the work. On the basis of the DC and RF characteristics, the device performance of both the proposed conventional and GaN HEMT with a field plate structure was compared. The different S-parameters were also plotted for the devices, which can be implemented in our future work for impedance matching, calculating different power-related parameters and stability parameters.

The increment in the breakdown voltage from 95V (conventional GaN HEMT) to 630V showcases the beneficial characteristics of the proposed GaN device with a field plate. The Id –Vgs curve and transconductance curve of both devices were plotted and compared. The drain current and maximum transconductance exhibited by the conventional HEMT is 838mA/mm at a Vgs of 2V and 120mS/mm, respectively, while the drain current and maximum transconductance exhibited by the proposed GaN HEMT, including field-plate, are 814mA/mm at a Vgs of 2V and 118mS/mm respectively. Thus, it can be reported that including the field-plate does not affect the DC performance of the device. The parasitic capacitances and the frequency parameters were plotted for both devices. It is observed that the field-plated device exhibits higher parasitic capacitances in comparison to the conventional GaN HEMT structure. This is further reflected in the frequency performances as the cut-off frequency and maximum frequency reported for the proposed field-plated device are 8GHz and 30GHz, respectively while the conventional GaN device reports a cut-off frequency and maximum frequency of 15GHz and 44GHz, respectively. The delay analysis reports an intrinsic time delay of 23ps for the field-plated and 19ps for the non-field-plated GaN HEMT device. Thus, it can be stated that the field plate affected the RF performance of the device. The S-parameters were also plotted for both the devices.

The work concludes that including a field-plate structure leads to breakdown voltage enhancement, which in turn will help achieve high power performance and withstand high voltages. However, this benefit comes at the cost of compromising the RF performance of the device. This issue can be minimized to a large extent by proper optimization of the device structure. Hence based on the requirement and the application area of the device, it should be designed and optimized accordingly.

Integration of cloud components in a wireless mesh network of Wireless-Optical Broadband Access Network (WOBAN) contributes to enhancing network performance.

This study aims to deploy the minimum cloudlets at the optimum location and to offload excess traffic from overloaded cloudlets to underloaded ones.

The objective of this study is to optimize cloudlet positioning and traffic offloading for cost-effective deployment, better resource utilization, reduced blocking probability, and lower delays.

The proposed methodology introduces a Cluster-Based Heuristic Approach (CBHA) for efficient cloudlet placement along with a traffic offloading mechanism using a Customized Donkey-Smuggler Optimization (CDSO) to enhance the overall network performance.

Simulations show the effectiveness of the proposed approach for resource utilization, blocking probability, delay, and cost.

The problem in position optimization of the cloudlet, along with traffic offloading, is solved using the proposed approaches to get better network performance in a cost-efficient manner.

Deep learning models often face challenges in achieving optimal accuracy when classifying multiclass datasets, particularly when the dataset size is limited. This study introduces Contrast Based Learning (CBL), a novel data augmentation technique designed to address data scarcity.

CBL innovatively concatenates multiple images and uses contrast learning to generate enriched datasets that exhibit a higher diversity of complex features. By focusing on the contrasts between various images, this method enhances the model's ability to learn nuanced features, thereby improving generalization and reducing overfitting.

Unlike traditional data augmentation methods, which rely on basic transformations, CBL dynamically concatenates images from different classes, creating complex inputs that provides the model with a more comprehensive training dataset. Experimental results show that CBL significantly improves classification accuracy and outperforms state-of-the-art methods across multiple small-scale multiclass datasets.

The findings highlight the robustness of CBL in addressing data limitations, demonstrating its potential to advance the classification performance of deep learning models.

Patients’ electronic health records (EHRs) stored in the cloud are more likely to suffer from data breaches. Data security risks arise from EHRs rely on centralised databases, as this approach places the responsibility for data protection on the user. Indeed, it leaves data security to a single organisation, leaving it open to intrusion by its employees. Maintaining the privacy and integrity of patient information is of utmost importance when interacting with and integrating with EHR systems.

Therefore, in order to find the best blockchain framework for trustworthy electronic health records (EHRs), a precise method for comparing the effects of several current blockchain systems is required. Data from electronic health record applications is encrypted and saved in the cloud using IPFS off-chain as part of this study, following a design that is consistent with patient data management. To secure electronic health record data, the system uses a hierarchical interdependency approach for block construction and framework design.

This research is focused on finding a solution to the issue of privacy leaking in data sharing. To ensure the privacy of user’s data, the healthcare system utilises a multilevel authentication mechanism and an encryption technique that is based on hierarchical interdependency attributes. Smart contracts have been used to establish hierarchical access control for various Internet of Things systems, guaranteeing control over data access. This paper describes about improve the system's scalability, transaction latency, and throughput by implementing a modified proof-of-stake (POS) security protocol in healthcare application 5.0. This helps to secure users' privacy and security.

In comparison to more conventional blockchain solutions, the experimental results demonstrate improvements in efficiency and security in terms of variables such as block size, upload time, transaction range, and evaluation metrics such as transaction delay.

With the advancement in Alzheimer's disease (AD) brain, cells start to deteriorate, which eventually creates physical dependency and mental instability that interferes with daily living. Presently, this disease is immedicable. Therefore, the only suitable treatment is early detection and prevention.

Although many studies have investigated the usefulness of deep learning in AD detection, relatively few have focused on the necessary image preprocessing processes, which are essential to any computer-aided diagnostic system. Furthermore, an optimal classification strategy that takes into account a diverse handful of prominent features is required.

This paper focuses on improving MRI-based AD detection by incorporating image enhancement approaches and deep hybrid learning into a fused framework to harness the power of multiple Deep Learning (DL) architectures and Machine Learning (ML) classifiers. The deep features extracted from three heterogeneous CNN architectures, namely, VGG16, DensetNet169, and MobileNetV1, are fused to produce a more informative and discriminative hybrid feature. Furthermore, the mRMR approach was used to optimise the acquired features, followed by classification via a stack of multiple ML classifiers to predict the target class.

The proposed architecture based on feature fusion strategy and ensemble learning resulted in 99.53%(Accuracy), 99.73%(Precision),99.70%(Recall), and 99.72% (F1 score). The presented model outperformed individual deep CNN architectures.

Lastly, we present a sobol-based sensitivity analysis that illustrates the concentration of the presented technique upon significant regions of the image and can assist medical professionals in decoding the decisions. The presented technique exeplifies the potency and constancy of categorizing Alzheimer's disease.