Critical Metrics Research Articles

A Predictive Approach for Lithium-Ion Battery SOH using LSTM Neural Networks Enhanced by Health Matrix Optimization

The State of Health (SOH) is a critical performance metric that characterizes the condition of lithium-ion batteries, directly influencing their lifespan and operational efficiency. In order to enhance the accuracy of SOH predictions for batteries and reduce operational risks, a novel approach has been introduced. This method, based on Health Matrix Optimization for Long Short Term Memory (LSTM) neural networks, aims to optimize the prediction process. Initially, the Spearman correlation coefficient method is employed to analyze the correlation of battery state data. Through the use of a heatmap, data points with strong correlations to SOH are identified, leading to the creation of a health feature matrix. This matrix is then utilized to fine-tune the hyperparameters of the LSTM neural network, resulting in refined approximations. Subsequently, by employing this optimized LSTM neural network, accurate predictions of the SOH for lithium-ion batteries are made. The results demonstrate a notable improvement in prediction accuracy by 35.71% and a significant increase in prediction speed by 35.5% when compared to traditional methods. This innovative approach proves to be effective in enhancing battery performance and longevity.

WiTUnet: A U-shaped architecture integrating CNN and Transformer for improved feature alignment and local information fusion

Low-dose computed tomography (LDCT) has emerged as the preferred technology for diagnostic medical imaging due to the potential health risks associated with X-ray radiation and conventional computed tomography (CT) techniques. While LDCT utilizes a lower radiation dose compared to standard CT, it results in increased image noise, which can impair the accuracy of diagnoses. To mitigate this issue, advanced deep learning-based LDCT denoising algorithms have been developed. These primarily utilize Convolutional Neural Networks (CNNs) or Transformer Networks and often employ the Unet architecture, which enhances image detail by integrating feature maps from the encoder and decoder via skip connections. However, existing methods focus excessively on the optimization of the encoder and decoder structures while overlooking potential enhancements to the Unet architecture itself. This oversight can be problematic due to significant differences in feature map characteristics between the encoder and decoder, where simple fusion strategies may hinder effective image reconstruction. In this paper, we introduce WiTUnet, a novel LDCT image denoising method that utilizes nested, dense skip pathway in place of traditional skip connections to improve feature integration. Additionally, to address the high computational demands of conventional Transformers on large images, WiTUnet incorporates a windowed Transformer structure that processes images in smaller, non-overlapping segments, significantly reducing computational load. Moreover, our approach includes a Local Image Perception Enhancement (LiPe) module within both the encoder and decoder to replace the standard multi-layer perceptron (MLP) in Transformers, thereby improving the capture and representation of local image features. Through extensive experimental comparisons, WiTUnet has demonstrated superior performance over existing methods in critical metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Root Mean Square Error (RMSE), significantly enhancing noise removal and image quality. The code is available on github https://github.com/woldier/WiTUNet.

MUCM-Net: a Mamba powered UCM-Net for skin lesion segmentation

Aim: Skin lesion segmentation is critical for early skin cancer detection. Challenges in automatic segmentation from dermoscopic images include variations in color, texture, and artifacts of indistinct lesion boundaries. This study aims to develop and evaluate MUCM-Net, a lightweight and efficient model for skin lesion segmentation, leveraging Mamba state-space models integrated with UCM-Net architecture optimized for mobile deployment and early skin cancer detection. Methods: MUCM-Net combines Convolutional Neural Networks (CNNs), multi-layer perceptions (MLPs), and Mamba elements into a hybrid feature learning module. Results: The model was trained and tested on the International Skin Imaging Collaboration (ISIC) 2017 and ISIC2018 datasets, consisting of 2,000 and 2,594 dermoscopic images, respectively. Critical metrics for evaluation included Dice Similarity Coefficient (DSC), sensitivity (SE), specificity (SP), and accuracy (ACC). The model’s computational efficiency was also assessed by measuring Giga Floating-point Operations Per Second (GFLOPS) and the number of parameters. MUCM-Net demonstrated superior performance in skin lesion segmentation with an average DSC of 0.91 on the ISIC2017 dataset and 0.89 on the ISIC2018 dataset, outperforming existing models. It achieved high SE (0.93), SP (0.95), and ACC (0.92) with low computational demands (0.055–0.064 GFLOPS). Conclusions: The model’s innovative Mamba-UCM layer significantly enhanced feature learning while maintaining efficiency that is suitable for mobile devices. MUCM-Net establishes a new standard in lightweight skin lesion segmentation, balancing exceptional ACC with efficient computational performance. Its ability to perform well on mobile devices makes it a scalable tool for early skin cancer detection in resource-limited settings. The open-source availability of MUCM-Net supports further research and collaboration, promoting advances in mobile health diagnostics and the fight against skin cancer. MUCM-Net source code will be posted on https://github.com/chunyuyuan/MUCM-Net.

Definition of Critical Metrics for Performance Evaluation and Multiphase Flow Modeling in an Alkaline Electrolyzer Using CFD

Gas evolution and flow patterns inside an alkaline electrolyzer cell strongly affect efficiency, although such effects have not been explored in detail to date. The present study aims to critically analyze the dependence of cell performance on the multiphase flow phenomena, defining some key metrics for its assessment using CFD. Six performance indicators, involving gas accumulation, bubble coverage, and flow uniformity, are applied to a 3D CFD model of an alkaline cathodic cell, and possible optimizations of the cell geometry are evaluated. The results demonstrate the complexity of defining the optimal indicator, which strictly depends on the case study and on the analysis at hand. For the cell analyzed herein, the parameters linked to the electrode volume fraction were indicated as the most influential on the cell efficiency, allowing us to define the best geometry case during the optimization. Furthermore, a sensitivity analysis was conducted, which showed that higher mass flow rates are generally preferable as they are linked to higher bubble removal. Higher current densities, allowing enhanced gas production, are instead associated with slightly lower efficiencies and stronger nonuniformity of the electrolyte flow inside the cell.

Missed detection probability evaluation for the RAIM based on worst-case fault magnitude searching

Missed detection probability is a critical metric for the integrity performance of receiver autonomous integrity monitoring (RAIM) in the presence of faults. The traditional missed detection probability evaluation method for RAIM is limited by impractical time consumption because of the absence of accurate searching interval for the magnitude of a worst-case fault. To address this issue, the searching interval for the magnitude of a worst-case fault is constructed by the combination of minimum detectable magnitude and minimum hazardous magnitude, and the searching interval adjustment is designed to avoid the absence of worst-case fault magnitude so that the maximum missed detection probability can be accurately evaluated. The simulation result indicates that the proposed method can achieve higher accuracy for the worst-case fault magnitude searching. Furthermore, the accuracy of worldwide evaluated missed detection rate can achieve an improvement of 57·66% at most by the proposed method for the different classical RAIM algorithms.

Feedback-based quantum algorithm inspired by counterdiabatic driving

In recent quantum algorithmic developments, a feedback-based approach has shown promise for preparing quantum many-body system ground states and solving combinatorial optimization problems. This method utilizes quantum Lyapunov control to iteratively construct quantum circuits. Here, we propose a substantial enhancement by implementing a protocol that uses ideas from quantum Lyapunov control and the counterdiabatic driving protocol, a key concept from quantum adiabaticity. Our approach introduces an additional control field inspired by counterdiabatic driving. We apply our algorithm to prepare ground states in one-dimensional quantum Ising spin chains. Comprehensive simulations demonstrate a remarkable acceleration in population transfer to low-energy states within a significantly reduced time frame compared to conventional feedback-based quantum algorithms. This acceleration translates to a reduced quantum circuit depth, a critical metric for potential quantum computer implementation. We validate our algorithm on the IBM cloud computer, highlighting its efficacy in expediting quantum computations for many-body systems and combinatorial optimization problems. Published by the American Physical Society 2024

Performance Evaluation of mmWave Communication for Real-Time UAV-Base Station Links

Introduction: In the context of modern communication networks, the increasing demand for high data rates, minimal latency, and improved reliability—especially in scenarios involving Unmanned Aerial Vehicles (UAVs)—calls for innovative solutions. Millimeter-wave (mmWave) communication emerges as a fitting choice due to its capacity to offer ample bandwidth and high data rates. Methods: This research paper presents a comprehensive study that compares the performance of mmWave communication links across four different frequencies (6, 28, 60, and 120 GHz) for UAV-to-Base Station (BS) communication. The investigation, conducted through simulations, adheres to atmospheric conditions such as rain and fog, different environments (urban and rural) scenarios. It evaluates the suitability of mmWave frequencies using critical performance metrics: latency, received power, Bit Error Rate (BER) and Signal-to-Interference plus Noise Ratio (SINR). These metrics play a fundamental role in assessing communication link quality. Results: Our findings reveal that leveraging higher mmWave frequencies—specifically 60 and 120 GHz—significantly enhances UAV-to-BS communication performance. Compared to lower frequencies, these higher bands exhibit reduced latency, higher data rates, and improved throughput. Consequently, mmWave frequencies beyond 60 GHz prove well-suited for UAV communication, facilitating efficient data exchange with minimal delay. Conclusion: Furthermore, the potential for reduced interference and enhanced reliability at these elevated frequencies makes them particularly advantageous in applications requiring seamless connectivity. Examples include UAV-assisted emergency response, surveillance, and remote sensing. These insights hold substantial implications for the design and deployment of UAV communication systems, highlighting mmWave technology’s transformative potential in the emerging era of aerial communication.

Energy-Saving Multi-Agent Deep Reinforcement Learning Algorithm for Drone Routing Problem.

With the rapid advancement of drone technology, the efficient distribution of drones has garnered significant attention. Central to this discourse is the energy consumption of drones, a critical metric for assessing energy-efficient distribution strategies. Accordingly, this study delves into the energy consumption factors affecting drone distribution. A primary challenge in drone distribution lies in devising optimal, energy-efficient routes for drones. However, traditional routing algorithms, predominantly heuristic-based, exhibit certain limitations. These algorithms often rely on heuristic rules and expert knowledge, which can constrain their ability to escape local optima. Motivated by these shortcomings, we propose a novel multi-agent deep reinforcement learning algorithm that integrates a drone energy consumption model, namely EMADRL. The EMADRL algorithm first formulates the drone routing problem within a multi-agent reinforcement learning framework. It subsequently designs a strategy network model comprising multiple agent networks, tailored to address the node adjacency and masking complexities typical of multi-depot vehicle routing problem. Training utilizes strategy gradient algorithms and attention mechanisms. Furthermore, local and sampling search strategies are introduced to enhance solution quality. Extensive experimentation demonstrates that EMADRL consistently achieves high-quality solutions swiftly. A comparative analysis against contemporary algorithms reveals EMADRL's superior energy efficiency, with average energy savings of 5.96% and maximum savings reaching 12.45%. Thus, this approach offers a promising new avenue for optimizing energy consumption in last-mile distribution scenarios.

Forecasting firm growth resumption post-stagnation

Our study forecasts the likelihood of firms resuming growth after periods of stagnation or declining sales. We employ machine learning methods, including Random Forest, LightGBM, and CatBoost, alongside logistic regression models. To address class imbalance, we incorporate oversampling techniques such as SMOTE, ADASYN, and SMOTEENN. We focus on two key indicators—Precision (predictive accuracy) and Recall (completeness of prediction)—to meet the needs of different investor groups. The performance of our models is evaluated using metrics such as accuracy, precision, recall, F-score, and RocAUC, with Venkatraman's test applied for model comparison. Our key findings reveal that CatBoost achieves a predictive accuracy of 65–67 %, significantly outperforming random firm selection, which yields only 13–17 % accuracy. The combination of the CatBoost method with the SMOTEENN technique notably enhances Recall values, reaching 58–63 %, a critical metric for large investors and policymakers. Our study offers a methodological approach to better understand and forecast the trajectories of firms engaged in open innovation.

Numerical optimization of inorganic p-Sb2Se3/n-ZrS2 heterojunction solar cells: achieving high efficiency through SCAPS-1D simulation

p-Sb2Se3 and n-ZrS2 materials show strong potential for cost-effective photovoltaic applications. This study presents a detailed numerical analysis of p-Sb2Se3/n-ZrS2 heterojunction solar cells using SCAPS-1D, focusing on how key parameters such as layer thickness, doping density, and bandgap have affected device performance. Critical photovoltaic metrics, such as built-in voltage (Vbi), carrier lifetime, depletion width (Wd), recombination rates, and photogenerated current, were examined. Our findings demonstrate that optimizing the p-Sb2Se3 absorber layer with a 1.0 eV bandgap, 5000 nm thickness, and doping density of 1020 cm−3 leads to a maximum efficiency of 32.14%, with a fill factor (FF) of 84.57%, short-circuit current density (Jsc) of 47.61 mA cm−2, and open-circuit voltage (Voc) of 0.792 V. For the ZrS2 buffer layer, the best performance was achieved with a 1.2 eV bandgap, 200 nm thickness, and doping density below 1 × 1020 cm−3. These optimized parameters significantly enhanced carrier separation and minimized recombination losses, leading to improved power conversion efficiency. In addition to theoretical optimization, this study emphasizes the practical potential of these materials for real-world applications. The combination of Sb2Se3 and ZrS2 offers a low-cost fabrication process suitable for scalable commercial solar cell production while maintaining high efficiency. These results underscore the viability of p-Sb2Se3/n-ZrS2 heterojunctions as promising candidates for next-generation clean energy solutions.

BCCHI-HCNN: Breast Cancer Classification from Histopathological Images Using Hybrid Deep CNN Models.

Breast cancer is the most common cancer in women globally, imposing a significant burden on global public health due to high death rates. Data from the World Health Organization show an alarming annual incidence of nearly 2.3 million new cases, drawing the attention of patients, healthcare professionals, and governments alike. Through the examination of histopathological pictures, this study aims to revolutionize the early and precise identification of breast cancer by utilizing the capabilities of a deep convolutional neural network (CNN)-based model. The model's performance is improved by including numerous classifiers, including support vector machine (SVM), decision tree, and K-nearest neighbors (KNN), using transfer learning techniques. The studies include evaluating two separate feature vectors, one with and one without principal component analysis (PCA). Extensive comparisons are made to measure the model's performance against current deep learning models, including critical metrics such as false positive rate, true positive rate, accuracy, precision, and recall. The data show that the SVM algorithm with PCA features achieves excellent speed and accuracy, with an amazing accuracy of 99.5%. Furthermore, although being somewhat slower than SVM, the decision tree model has the greatest accuracy of 99.4% without PCA. This study suggests a viable strategy for improving early breast cancer diagnosis, opening the path for more effective healthcare treatments and better patient outcomes.

FEM Investigation of the Roughness and Residual Stress of Diamond Burnished Surface

Characterization of surface integrity is possible with three critical metrics: microstructure, surface roughness, and residual stress. The latter two are discussed in this paper for low-alloyed aluminum material quality. Ball burnishing is a regularly used finishing procedure to improve surface roughness, shape accuracy, and fatigue life, taking advantage of the fact that it can favorably influence the variation in stress conditions in the material. The effect of burnishing is investigated using finite element simulation with DEFORM 2D software using the real surface roughness of the workpiece. The FEM model of the process is validated with experimental tests, the surface roughness is measured using an AltiSurf520 measuring device, and the residual stress is analyzed with a Stresstech Xstress 3000 G3R X-ray diffraction system (Stresstech, Vaajakoski, Finland). The results indicate that the burnishing process improves the surface roughness and stress conditions of AlCu6BiPb low-alloyed aluminum, and the study shows that there is good agreement between the FE and experimental results, further revealing the effect of the process parameters on the distribution of the compressive residual stress.

Development of A Radio Frequency Identification System for The Manufacturing Packing Process Via The DMAIC Approach

Quality has long been a critical metric for determining a company's competitiveness in the business world. The use of tools, methods, and concepts to improve and regulate product quality has been widespread. The purpose of this research is to solve the missing accessories issue that occurs during the packing process by using the Lean Six Sigma tool DMAIC (Define, Measure, Analyze, Improve, and Control). As part of the Lean Six Sigma quality effort, the DMAIC technique is frequently defined as a problem-solving methodology and a data-driven quality strategy. The actual root cause for these missing accessories is the lack of a proper validation process during the packing process. A brainstorming session was held to develop solutions to this problem, and one suggestion was to employ the radio frequency identification (RFID) validation system. This new implementation was set up during the kitting process by pasting RFID labels for all accessories. These accessories were validated back during the packing process to ensure no missing accessories after the operator sealed the carton box. After implementing the RFID system, the company managed to reduce the cost of manpower and parts that needed to be replaced in case of missing data. It also improves the efficiency of the business, as the addition of a validation RFID system avoids mistakes by detecting missing accessories before the product is delivered to the customer.

Towards AI driven surface roughness evaluation in manufacturing: a prospective study

AbstractIn the era of Industry 4.0 and the digital transformation of the manufacturing sector, this article explores the significant potential of machine learning (ML) and deep learning (DL) techniques in evaluating surface roughness—a critical metric of product quality. The integration of edge computing with current computational resources and intelligent sensors has revolutionized the application of AI-driven algorithms in smart manufacturing. It provides real-time data analysis and decision-making capabilities that were unattainable only a decade ago. The research effort intends to improve data-driven decision-making for product quality evaluation by leveraging data integration from manufacturing operations and surface quality measurements. Although a substantial amount of research has been conducted in the related fields, it is still difficult to comprehend and compile all the data on surface roughness research predictive assessment in the form of a process pipeline. This thorough systematic analysis examines scholarly articles published between 2014 and 2024 focusing on surface roughness assessment in precision manufacturing settings. The article is thoroughly classified based on the manufacturing processes, datasets, and ML models used, giving light on the present status, prominent approaches, and existing issues in this sector. A table summarizing the relevant works in this domain providing an easy access to the current trends have been provided. The article not only compiles essential findings and identifies research gaps and similarities in existing methodologies, but it also discusses future research directions and open issues in AI-aided surface roughness evaluation. The critical analysis of the literature reveals a scientific gaps which includes consistent development of benchmarked datasets and making the AI models more explainable using the state-of-the-art explainable AI (XAI) algorithms. The ultimate objective of the article is not only to provide a guide for the practitioners in either of the three domains of AI, manufacturing or surface metrology but also to pave the path for more robust, efficient, and accurate surface quality evaluation processes in production.

Dirac Eigenvalue Optimisation and Harmonic Maps to Complex Projective Spaces

Abstract Consider a Dirac operator on an oriented compact surface endowed with a Riemannian metric and spin structure. Provided the area and the conformal class are fixed, how small can the $k$-th positive Dirac eigenvalue be? This problem mirrors the maximization problem for the eigenvalues of the Laplacian, which is related to the study of harmonic maps into spheres. We uncover the connection between the critical metrics for Dirac eigenvalues and harmonic maps into complex projective spaces. Using this approach we show that for many conformal classes on a torus the first nonzero Dirac eigenvalue is minimised by the flat metric. We also present a new geometric proof of Bär’s theorem stating that the first nonzero Dirac eigenvalue on the sphere is minimised by the standard round metric.

Advanced Wind Power Forecasting

The popularity of LSTM networks in wind prediction is due to their inherent capabilities in handling the long-term dependencies of sequential data. This normally outperforms traditional methods like ARIMA. However, current work has illustrated that EMD-based approaches outperform LSTM models in many critical performance metrics. Although it is very powerful in gathering historical patterns due to the enhanced gating mechanisms, LSTM has a lot of challenges regarding computational efficiency and flexibility. EMD models overwhelmingly outperformed the LSTM model in computational time by probably about 20-25%, thereby solving a weakness that may be one of the intrinsic weaknesses of the LSTM model. In addition, the EMD models improved by about 2-4% in accuracy as compared to LSTM, thus evidencing better ability in the prediction of the wind data. On feature extraction, EMD did better, improving by 7% over LSTM. Moreover, EMD has better robustness and adaptability, with improvements of 6% and 4%, respectively. This introduction of EMD does bring a bit of an increase in model complexity, this disadvantage is rather insignificant when compared with the huge advantages it brings in efficiency and accuracy. While an LSTM network is very good at modeling a long-term relationship, it suffers from some problems in handling nonlinear interactions and continuous series data, thus its effectiveness may be limited in some cases of forecasting. In contrast, EMD has a clear advantage in real-time applications owing to enhanced efficiency and precision. From the results obtained in this study, it becomes easy to envision that the EMD methodology is un-equivocally better than LSTM in all the critical aspects, such as computational efficiency, accuracy in prediction, feature extraction, resilience, and adaptability when used individually. Results indicate that EMD has a more constructive methodology toward wind pattern predictions, beating LSTM on many measures of performance. The advantages presented should be maximized in future studies aimed at further improving and optimizing a wind-forecasting system, with special attention toward EMD's benefits in gaining more accurate and efficient forecasts.

Research on Broadband Measurement Method of Power System based on Wavelet Transform

This study delves into the exploration of broadband measurement techniques for power systems, utilizing wavelet transform as a foundational tool for signal analysis. The research rigorously evaluates the efficacy of several machine learning algorithms, namely Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Random Forest, in interpreting and analyzing broadband signals within power systems. Through a detailed analytical process, the performance of each algorithm is meticulously assessed based on several critical metrics: accuracy, precision, recall, and F1-score. The research investigates broadband measurement methods for power systems using wavelet transform and evaluates the performance of Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Random Forest. Results show SVM achieving an accuracy of 85%, precision of 86%, recall of 82%, and F1-score of 84%. ANN yields 82% accuracy, 84% precision, 78% recall, and 81% F1 score. KNN demonstrates 87% accuracy, 88% precision, 84% recall, and 86% F1 score. DT achieves 79% accuracy, 80% precision, 75% recall, and 77% F1 score. Overall, the study provides insights into machine learning algorithms’ effectiveness in broadband power system measurement.

Beyond the g-Value: A comparative study of solar control coated glass and spectrally selective glass in terms of building energy efficiency

The thermal efficiency of transparent envelopes is a key factor in building energy consumption and indoor thermal comfort, with the g-value being a critical metric for evaluating these effects. Solar control film glass and low-emissivity (Low-E) glass, recognized for their distinct advantages, are extensively used in construction. However, comprehensive research on their photothermal properties, especially regarding spectral and angular dependencies, remains limited. In this study, a meticulous field experiment was conducted under six distinct conditions during both winter and summer to examine the thermal performance between solar control coated glass (SCCG) and spectrally selective glass (SSG). The results show that, despite having similar heat transfer and solar heat gain coefficients, their indoor peak temperatures can differ by 1.2 °C to 5.6 °C under typical sunny conditions, and variations in cooling and heating energy consumption range from 6.9 % to 14.4 %. Further analysis reveals that the standard g-value overestimates the solar heat gain of SSG by approximately 10 %, with significant discrepancies in the direct transmission heat and secondary heat transfer of 17.8 % and 37.4 %, respectively. In contrast, the standard g-value is more suitable for SCCG, with a measured g-value that is only 1.2 % higher than the standard g-value. The spectral and angular dependencies of both glasses were also compared in this study, and SSG was found to have a strong spectral dependence and a higher angular dependence than SCCG. These findings indicate that SCCG is more beneficial for energy conservation in cold climates when the thermal parameters are identical. Moreover, the research underscores the limitations of current standards and calculation methods for evaluating solar heat gain in transparent envelopes, particularly for glasses with spectral selectivity.

Data-driven employee engagement: A pathway to superior customer service

This paper explores the significant correlation between employee engagement and customer service quality, emphasizing the role of data-driven strategies in enhancing organizational outcomes. The paper highlights the importance of using data analytics to understand and improve employee engagement by analyzing key theories that link engagement to customer satisfaction. It discusses the critical metrics used to measure engagement and how data-driven insights can inform HR strategies, leading to superior customer service. The paper also examines the implications of implementing these strategies, addressing potential challenges such as data privacy, misinterpretation, and cultural resistance. Finally, it suggests future research directions, particularly in integrating emerging technologies like AI and machine learning, to refine engagement strategies further and adapt them to diverse work environments. This review underscores the evolving role of data analytics in HR and its potential to transform employee engagement into a key driver of business success.

Enhancing sustainable healthcare practices through energy-efficient wireless body area networks

This paper explores how the integration and performance of Wireless Body Area Networks (WBANs) contribute to sustainable healthcare practices. WBANs play a crucial role in reducing the environmental footprint of healthcare systems by providing continuous monitoring that can prevent unnecessary hospitalizations and reduce the consumption of disposable medical supplies. Additionally, the design of WBANs using environmentally friendly materials, low-power devices, and recyclable components is discussed to optimize the sustainability of healthcare practices. To enhance infrastructure, services, and quality of life, new technologies have been integrated to improve WBANs. Patients' essential health data may be continuously and instantly collected thanks to these networks, and healthcare providers can receive the data right away for quick analysis and action. WBANs have healthcare benefits, but there are also major drawbacks. Technologies that can combine low power consumption, minimal delay, and high enough data rates are needed for WBANs in order to support a variety of medical applications. The effectiveness of the LoRaWAN and IEEE 802.15.6 technologies in WBAN is assessed in this paper. With the use of seven crucial metrics which are throughput, arrival rate, delay, energy consumption, residual energy, network lifetime, and packet delivery ratio (PDR), this paper seeks to study which technology is appropriate with WBANs using NS3 as a simulation program. With network parameters, 1000 seconds, and 50 nodes, the paper evaluated the performance of each technology. The results show that IEEE 802.15.6 is superior in terms of throughput, PDR, and arrival rate whereas LoRaWAN is superior in terms of energy consumption, residual energy, network lifetime, and delay.