Acceptable Error Rate Research Articles

Surface Roughness Evaluation of Blast-Cleaned Structural Steel Plates Considering Measurement Deviation

AbstractSurface roughness is an important characteristic of blast-cleaned structural steel plates. This is because the adhesion of the coating and the frictional grip of the bolted connections are related to surface roughness. Surface roughness is usually measured using a surface roughness meter. Because the roughness of blast-cleaned surfaces is not uniform, it varies from position to position. The surface roughness test results obtained from a broad area are more valuable than those obtained from a narrow area. Because the measured values contain deviations, they are measured multiple times and averaged to reduce the influence of deviations. However, to evaluate roughness, it is necessary to clarify the measurement conditions to obtain accurate results. Consider combining these sentences as follows: In this study, the surface roughness of the blast-cleaned structural steel sample was measured at multiple locations to obtain its distribution characteristics. During each sample measurement, the detection sensor of the surface roughness meter was lifted and placed on the steel sample to determine deviations from the actual results. The surface roughness distributions of five blast-cleaned samples were the same under the identical measurement conditions. This indicates that accurate results can be obtained by measuring representative samples, without the need for measuring all samples; actually, 17 measurements were adequate to obtain a mean surface roughness value with a 95% confidence level and a 5% acceptable error rate.

A-203 A Disconnect Between Detectable Patient Error Rates and QC Understanding

Abstract Background ISO 14971 defines “risk evaluation as the process of comparing the estimated risk against given risk criteria to determine the acceptability of the risk. Effective QC processes would detect if a stable acceptable error rate became unacceptable. Methods A survey was circulated through LinkedIn and Labvine. Forty respondents who included their credentials were included in the final analysis. Questions included: 1. What is the best choice for acceptable error rate when an analytical process is stable and in control, 2. If an analytical process “fails,” how many errors are you willing to report before the lab should stop reporting, and 3. “If an analytical process has a 5% stable error, how high would the error rate after failure need to become for a 1-2s rule to detect failure with one QC sample in a single QC run?” This interactive poster will allow viewers to cast their vote for Question 4 and then reveal an LJ chart depicting a stable error rate of 5% with a shift to show the number of 1-2s failures with their chosen acceptable post-failure error rate. Results During stable operation, only 8% wanted zero errors while 38% voted that they would allow up to 5% errors; After failure, 66% wanted to report zero errors and only 10% would allow 5% failure. 69% believed they would see a 1-2s failure in one run if the error rate rose to less than 50%; 19% believed it required a 100% error rate. Conclusions There is a strange disconnect where most believed it was acceptable to report more errors during stable operation than if the method failed. There is a widespread misunderstanding of the logic that connects management of acceptable patient risk with the capabilities of statistical QC.

A-202 Quality control design survey reveals 399 ways to do it wrong

Abstract Background The effectiveness of statistical quality control processes to detect unacceptable patient risk depends on the choices that laboratory professionals make for the design of quality control charts and acceptable patient risk. Recommended best practice consists of putting a meaningful recent mean and SD on the QC chart, selecting rules based on the method’s margin for error to unacceptable risk or Sigma value, and basing acceptable risk on clinical need. Methods A survey was circulated through LinkedIn and Labvine. Forty respondents who included their credentials were included in the final analysis. Respondents were asked to select the one most correct answer four questions regarding the parameters that govern the effectiveness of quality control processes. 1. The best source of the mean on the QC chart? (Four choices) 2. Best source of the SD on the QC chart? (Five choices) 3. Best way to select QC rules for each QC sample? (Six choices) 4. If an analytical process “fails,” we should stop reporting before [errors are reported.] (Five choices) A total of 400 potential patterns of QC selections were chosen. (4 * 5 * 5 * 4). Results When asked acceptable error rate if an analytical process “fails,” responses were More than 5% errors are reported - 11%. Any errors are reported - 66% It varies with clinical use of the test - 16% It varies with capabilities of our method - 8% While most respondents chose at least one of the recommended answers for QC chart design and rules, only three chose the best choice for all parameters. Conclusions Despite decades of recommendations of best practice for quality control, typical practice throughout the laboratory community in general does not reflect this. It is time to consider a new approach to teaching, validating and certifying competency in medical laboratory quality control.

Environmentally Friendly Synthesis of Gelatin Hydrogel Nanoparticles for Gastric Cancer Treatment, Bisphenol A Sensing and Nursing Applications: Fabrication, Characterization and ANN Modeling

This study presents a dual application approach for the environmentally friendly synthesis of gelatin hydrogel nanoparticles with potential applications in gastric cancer treatment, bisphenol A (BPA) sensing, and nursing. Gelatin hydrogel nanoparticles were synthesized using a green and freeze-drying method, avoiding the use of toxic chemicals and solvents. The nanoparticles showed excellent biocompatibility and promising potential for drug delivery system (DDS) in gastric cancer treatment. The controlled release of anticancer drugs from the gelatin nanoparticles was showed, highlighting their potential in targeted therapy. Additionally, the gelatin hydrogel nanoparticles were explored for BPA sensing. BPA is a widely used chemical known for its adverse effects on human health. The gelatin nanoparticles showed high selectivity and sensitivity towards BPA detection, making them suitable for environmental monitoring and health applications using scanning electron microscope (SEM). Also, in this study, an artificial neural network (ANN) was used to estimate the release of docetaxel (%) at 72 hours, the release of paclitaxel (%) at 72 hours, tensile strength with sample (wt%), and porosity (%) in broader ranges than the experimental samples. The environmentally friendly synthesis of gelatin hydrogel nanoparticles presented in this study offers a versatile platform with dual applications in gastric cancer treatment and sensing of harmful chemicals. The obtained results show the potential of these nanoparticles for innovative therapeutic and diagnostic strategies in healthcare and environmental monitoring. The study showed the development of sustainable and multifunctional nanomaterials for various biomedical applications. The modeling of the neural network predictions shows that increasing the sample (wt%) and porosity (%) leads to an increase in the release of docetaxel (%) at 72 hours, the release of paclitaxel (%) at 72 hours, and tensile strength. As porosity decreases, the release of docetaxel increases, and the release of paclitaxel and tensile strength also increase. Additionally, the prediction errors of the ANN in this study were evaluated using linear regression, showing acceptable error rates compared to the target results obtained from the experimental tests.

Visible light communication optical FBMC-OQAM PAPR reduction using FHT-PTR algorithm for beyond 5G system

Abstract This paper addresses the reduction of peak-to-average power ratio (PAPR) in optical filter bank multicarrier (FBMC) with offset quadrature amplitude modulation (OQAM) using a fast Hadamard transform (FHT) centered partial transmit sequence (PTS) algorithm. High PAPR in FBMC-OQAM systems can cause non-linear distortions in optical transmitters, such as LEDs, leading to signal degradation. The proposed FHT-PTS algorithm effectively mitigates this issue by dividing the data symbols into disjoint subsets, applying phase rotations, and performing inverse FHT to generate multiple candidate sequences. The sequence with the lowest PAPR is selected for transmission. This technique ensures significant PAPR reduction while maintaining acceptable bit error rate (BER) performance. Simulation results demonstrate that the FHT-PTS algorithm achieves substantial PAPR reduction gain of 3 dB–6 dB compared to conventional PTS and other existing methods. The power spectrum density (PSD) analysis shows that the spectral efficiency of the FBMC-OQAM system is preserved, with minimal out-of-band emissions. The FHT operations significantly reduce the complexity compared to traditional Fourier transform-based methods, making the algorithm more feasible for real-time applications.

Resilience, job satisfaction, occupational stress, and occupational accidents among healthcare professionals: A Bayesian network analysis

BACKGROUND: Occupational accidents remain a critical challenge for healthcare professionals. OBJECTIVE: In the present study, using the Bayesian network (BN) approach association among resilience, job satisfaction, stress, and occupational accidents among healthcare professionals is examined. METHODS: Data was gathered using several valid questionnaires. The BN approach was utilized to analyze the r5/31/2024ionships between the variables of the current study. The performance of BN analysis was evaluated using related indexes. RESULTS: In total, 300 healthcare professionals participated in this study. Results showed that almost 23% of healthcare professionals had experienced occupational accidents. Results of the sensitivity analysis demonstrated that job satisfaction had the most significant influence on occupational accidents in healthcare settings. The belief updating analysis results showed that by increasing job satisfaction and decreasing stress of healthcare professionals the occurrence of occupational accidents decreased 9.8% and 6.4%, respectively. Moreover, decreasing the stress of healthcare professionals can lead to an increase in the level of job satisfaction. Evaluation indexes showed that the performance of the developed BN was acceptable (error rate: 16.09). CONCLUSION: The Findings reveal that both job satisfaction and stress had a significant influence on occupational accidents in healthcare professionals. Moreover, by influencing job satisfaction and stress, resilience can indirectly affect occupational accidents.

Development of a Method for Automatic Matching of Unstructured Medical Data to ICD-10 Codes.

Inconsistent disease coding standards in medicine create hurdles in data exchange and analysis. This paper proposes a machine learning system to address this challenge. The system automatically matches unstructured medical text (doctor notes, complaints) to ICD-10 codes. It leverages a unique architecture featuring a training layer for model development and a knowledge base that captures relationships between symptoms and diseases. Experiments using data from a large medical research center demonstrated the system's effectiveness in disease classification prediction. Logistic regression emerged as the optimal model due to its superior processing speed, achieving an accuracy of 81.07% with acceptable error rates during high-load testing. This approach offers a promising solution to improve healthcare informatics by overcoming coding standard incompatibility and automating code prediction from unstructured medical text.

Examining the contact problem of a functionally graded layer supported by an elastic half‐plane with the analytical and numerical methods

This study offers a comparative study of the analytical and numerical methods for investigating a contact problem. The contact problem comprises a functionally graded layer supported by a half‐plane and loaded with a distributed load from the top surface. First, the analytical and numerical solutions to the problem are acquired by utilizing a theory of elasticity and finite element method, respectively. The problem is transformed into a system of integral equations in which the contact stress is an unknown function. The solution of the integral equation was achieved with Gauss–Jacobi integration formulation. The finite element model of the problem is created using ANSYS software, and the two‐dimensional analysis of the problem is performed. Results were obtained from the samples for different material properties and loading conditions. The distributed load width and non‐homogeneity parameters significantly impact on contact mechanics. The results indicate that the contact area and the contact stress obtained from finite element method (FEM) are close to the analytical results. As a result, acceptable error rates were obtained. Finally, this study provides evidence of a good agreement between the two methods.

Exploring the Influence of Dynamic Indicators in Urban Spaces on Residents’ Environmental Behavior: A Case Study in Shanghai Utilizing Mixed-Methods Approach and Artificial Neural Network (ANN) Modeling

The main aim of this article is to evaluate the impact of dynamic indicators associated with urban spaces on the environmental behavior of residents in Shanghai, China. With the city experiencing rapid urbanization and increasing environmental concerns, it is crucial to understand how the design and management of urban spaces can encourage pro-environmental attitudes and actions among the population. The study specifically focuses on dynamic indicators, namely vitality, accessibility, quality, and walkability, to develop a comprehensive understanding of the utilization and experience of urban spaces. The research outcomes will make valuable contributions towards Shanghai’s objectives of achieving environmental sustainability, while also providing insights that have broader relevance to sustainable urban development globally. As the built environment significantly influences energy consumption, resource utilization, pollution generation, and overall human wellbeing, attaining urban sustainability is of paramount importance. The findings of this study will aid in informing strategies and policies that promote sustainable practices, not only benefiting Shanghai but also serving as a valuable resource for urban development initiatives worldwide. Sustainable urban design principles, including compactness, density, mixed land use, greening, and walkability, have been associated with pro-environmental behaviors, including reduced reliance on automobiles, increased walking and cycling, and heightened environmental consciousness. Nevertheless, the relationship between the built environment and sustainability behaviors is intricate and is influenced by multiple factors. Consequently, further research is necessary to comprehend how specific spatial and temporal dynamics impact environmental behaviors within urban settings. In this study, an artificial neural network (ANN) was developed to estimate the quality and walkability of an area and environmental behaviors by considering the augmented vitality and accessibility factors. The ANN’s predictions demonstrate that higher levels of vitality and accessibility positively contribute to improved walkability and environmental behaviors. The accuracy of the ANN’s predictions was assessed using linear regression, which yielded acceptable error rates when compared with experimental results.

PENERAPAN ALGORITMA BACKPROPOGATION DALAM PREDIKSI JUMLAH PENDUDUK DI PROVINSI SUMATERA UTARA

Total Population is the total number of individuals living in an area or country at any given time.country at any given time. However, a large population has several negative effects of social instability, poverty, reduced quality of life, and increased unemployment.quality of life, and increased unemployment. This research discusses the application of the Backpropagation algorithm in predicting population in the province of North Sumatra province. The purpose of this research is to make predictions that can help local governments in development planning and more effective management of resources more effectively. The Backpropagation method is used in training of the artificial neural network model using historical data on the population population and the factors that influence the growth. The results of experimental results show that the resulting model is able to provide fairly accurate predictions with an acceptable error rate. This paper presents the results of research that aims to apply the Backpropagation algorithm in an effort to predict population in North Sumatra province from 2013-2022 using Microsoft Excel and Matlab version 2011b for data processing and analysis. version 2011b for data processing and analysis. The architecture uses three models, namely: 4-5-1, 4-10-1, 4-15-1. The most accurate architecture model is 3-15-1 model which has a Mean Squared Error (MSE) of 0.00000034 and 100% accuracy rate with time 00:05 at epoch 92.

Examining resting state functional connectivity and frequency power analysis in adults who stutter compared to adults who do not stutter.

Stuttering is a speech disorder characterized by impaired connections between brain regions involved in speech production. This study aimed to investigate functional connectivity and frequency power during rest in adults who stutter (AWS) compared to fluent adults (AWNS) in the dorsolateral prefrontal cortex (DLPFC), dorsolateral frontal cortex (DLFC), supplementary motor area (SMA), motor speech, angular gyrus (AG), and inferior temporal gyrus (ITG). Fifteen AWS (3 females, 12 males) and fifteen age- and sex-matched AWNS (3 females, 12 males) participated in this study. All participants were native Persian speakers. Stuttering severity in the AWS group was assessed using the Persian version of the Stuttering Severity Instrument Fourth Edition (SSI-4). Resting-state electroencephalography (EEG) was recorded for 5 min while participants sat comfortably with their eyes open. We analyzed frequency band power across various frequency bands and investigated functional connectivity within the specified speech region. Significant between-group differences were found in band powers including alpha, beta, delta, theta, and gamma, specifically in the premotor, SMA, motor speech, and frontal regions. AWS also showed increased coherence between the right motor speech region compared to controls. We demonstrate that the proposed hierarchical false discovery rate (FDR) method is the most effective for both simulations and experimental data. In the expected regions, this method revealed significant synchrony effects at an acceptable error rate of 5%. The results highlight disrupted functional connectivity in AWS at resting state, particularly in speech-related and associated areas. Given the complex neurological basis of developmental stuttering, robust neural markers are closely linked to this phenomenon. These markers include imbalanced activity within brain regions associated with speech and motor functions, coupled with impaired functional connectivity between these regions. The cortico-basal ganglia-thalamo-cortical system governs the dynamic interplay between cortical regions, with SMA as a key cortical site. It is hypothesized that the aberrant resting state functional connectivity will impact the language planning and motor execution necessary for fluent speech. Examining resting-state metrics as biomarkers could further elucidate the neural underpinnings of stuttering and guide intervention.

Photonic MMW Generation Using PM-DPMZM for Full Duplex 32×10 Gbps RoF-WDM System

Abstract This paper presents a study of a 32×10 Gbps Radio over Fiber and Wavelength Division Multiplexing (RoF-WDM) full-duplex system that uses Phase Modulators and a Dual-Port Mach-Zehnder Modulator (PMs-DPMZM) for bidirectional data transfer. The system employs Millimeter-Wave (MMW) signaling over optical fiber and focuses on selecting a technology that provides high transmission capacity per wavelength, improved spectral efficiency, and resistance against optical transmission impairments. The proposed method was validated using simulation results to confirm the efficiency of the proposed system in generating a 40 GHz signal and efficiently detecting and modulating the RF signals. The results demonstrate that the system exhibits strong resistance against dispersion, non-linear effects, and noise, delivering satisfactory performance for distances of up to 220 km. By analyzing the input power, the paper establishes a relationship between input power and signal quality, revealing that an optimal power of 0 dBm leads to an improved Quality Factor (QF) and reduced transmission errors. Furthermore, the evaluation of received optical power indicates the power level required to maintain an acceptable error rate, approximately -20.9690 dBm for downstream data transfer and -20.7245 dBm for upstream data transfer at the BER limit. The simulation performance also demonstrates the transmission efficiency achieved through a high Polarization Mode Dispersion (PMD) coefficient of up to 0.8. The analytical calculations conducted in this work provide valuable insights for optimizing and enhancing the performance of RoF-WDM networks.

Sistem Pengenalan Wajah pada Sistem KYC dengan Algoritma Local Binary Pattern Histogram

With the increasing internet usage post-pandemic, ensuring the security of a fintech application becomes imperative. Bangbeli implements KYC procedures using facial recognition technology and stringent security protocols to verify identities and safeguard users' personal data in compliance with Bank Indonesia regulations. Utilizing Haar Cascade Classifier, Local Binary Pattern Histogram, and histogram equalization, an API (Application Programming Interface) has been created for facial training and prediction. These methods were chosen for their credibility, achieving an 88% accuracy with 33 samples and 90% with 10 samples. This study focuses on constructing an API for mobile services at Bangbeli, achieving 87.5% accuracy, 81.25% precision, 87.5% recall, and a 25% error rate. The model demonstrates good performance in facial recognition, with an acceptable error rate. Although precision is slightly lower than recall, it suggests the model is more inclined to identify most positive data with some errors rather than discard potentially identifiable faces.

Joint Learning and Channel Coding for Error-Tolerant IoT Systems Based on Machine Learning

In several machine learning (ML)-based Internet of Things (IoT) systems, data is captured by IoT devices and then transmitted over a wireless channel for remote processing. Since noise often appears on the channel (so causing data corruption and consequently an incorrect ML result), channel protection must be provided to guarantee an acceptable error rate for the transmitted data, especially in safety-critical applications. An often-used protection technique employs error correction codes (ECCs); however, even with some improved designs, the power dissipation required by an ECC implementation may still not meet the strict requirements of hardware-constrained platforms. To address this issue, a “joint learning and channel coding” (JLCC) scheme is proposed in this paper. In such a scheme, the ML model is retrained using two methods to tolerate some channel errors, such that the system requires an ECC with significantly lower protection capability. Since ML training is executed remotely, JLCC achieves a significant power reduction for ECC without introducing any additional overhead to the IoT device. An electrocardiogram (ECG) system is taken as a case study to illustrate the proposed JLCC scheme and evaluate its effectiveness. A low-density parity-check (LDPC) code is employed for protection of the system with/without JLCC; its analysis and implementation are presented. Simulation results show that, when employing JLCC with the proposed two retraining methods, an average reduction of 29.15% and 34.82% in the dissipated power is achieved for the ECG sensor when compared to the original system.

Prediction of LDL in hypertriglyceridemic subjects using an innovative ensemble machine learning technique

Abstract Objectives Determining low-density lipoprotein (LDL) is a costly and time-consuming operation, but triglyceride value above 400 (TG>400) always requires LDL measurement. Obtaining a fast LDL forecast by accurate prediction can be valuable to experts. However, if a high error margin exists, LDL prediction can be critical and unusable. Our objective is LDL value and level prediction with an error less than low total acceptable error rate (% TEa). Methods Our present work used 6392 lab records to predict the patient LDL value using state-of-the-art Artificial Intelligence methods. The designed model, p-LDL-M, predicts LDL value and class with an overall average test score of 98.70 %, using custom, hyper-parameter-tuned Ensemble Machine Learning algorithm. Results The results show that using our innovative p-LDL-M is advisable for subjects with critical TG>400. Analysis proved that our model is positively affected by the Hopkins and Friedewald equations normally used for (TG≤400). The conclusion follows that the test score performance of p-LDL-M using only (TG>400) is 7.72 % inferior to the same p-LDL-M, using Hopkins and Friedewald supported data. In addition, the test score performance of the NIH-Equ-2 for (TG>400) is much inferior to p-LDL-M prediction results. Conclusions In conclusion, obtaining an accurate and fast LDL value and level forecast for people with (TG>400) using our innovative p-LDL-M is highly recommendable.

Prediction of compressive strength of brick columns confined with FRP, FRCM, and SRG system using GEP and ANN methods

This study assesses the strength capacity of brick columns under various confinement materials, including fiber-reinforced polymer (FRP), fiber-reinforced cementitious matrix (FRCM), and steel-reinforced grout (SRG) using gene expression programming (GEP) and artificial neural networks (ANN) models. To achieve this, a comprehensive database of masonry column test results from existing scientific literature is compiled. The models' performance is evaluated using statistical errors like the coefficient of linear correlation (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Additionally, sensitivity analysis is carried out to assess the significance of individual parameters in the models. The findings reveal that ANN predictions closely match empirical data, demonstrating a strong correlation coefficient of 0.95. The accuracy of the ANN approach is reasonably high, with only 26% of the predicted values deviating by more than 20% from the actual data. Based on the statistical analyses, the correlation coefficient between the actual and estimated data was 0.88, for GEP method. Also, the GEP model yields outcomes, with roughly 43% of the predicted values differing by 20–50% from the actual data. In a comparison of the two models, the ANN model outperforms the GEP model, displaying a 40% reduction in error when estimating the compressive strength of masonry columns. The data estimated by the GEP were sparser than those estimated by the ANN. Nevertheless, the GEP model still maintains an acceptable correlation coefficient and error rate, making it a viable choice for precise predictions. It offers a user-friendly formula and meets the needs of both customers and builders.

Analysis of System Capacity and Spectral Efficiency of Fixed-Grid Network

In this article, the performance of a fixed grid network is examined for various modulation formats to estimate the system's capacity and spectral efficiency. The optical In-phase Quadrature Modulator (IQM) structure is used to build a fixed grid network modulation, and the homodyne detection approach is used for the receiver. Data multiplexing is accomplished using the Polarization Division Multiplexed (PDM) technology. 100 Gbps, 150 Gbps, and 200 Gbps data rates are transmitted under these circumstances utilizing various modulation formats. Various pre-processing and signal recovery steps are explained by using modern digital signal processing systems. The achieved spectrum efficiencies for PM-QPSK, PM-8 QAM, and PM-16 QAM, respectively, were 2, 3, and 4 (bits/s)/Hz. Different modulation like PM-QPSK, PM-8-QAM, and PM-16-QAM each has system capacities of 8-9, 12-13.5, and 16-18 Tbps and it reaches transmission distances of 3000, 1300, and 700 kilometers with acceptable Bit Error Rate (BER≤ 2× 10-3 ) respectively. Peak optical power for received signal detection and full width at half maximum is noted for the different modulations under a fixed grind network.

Shallow Water Experiment of OFDM Underwater Acoustic Communications

The large variability of communication properties of underwater acoustic channels, and especially the strongly varying instantaneous conditions in shallow waters, is a challenge for the designers of underwater acoustic communication (UAC) systems. The use of phase modulated signals does not allow reliable data transmission through such a tough communication channel. However, orthogonal frequency-division multiplexing (OFDM), being a multi-carrier amplitude and phase modulation technique applied successfully in the latest standards of wireless communications, gives the chance of reliable communication with an acceptable error rate. This paper describes communication tests conducted with the use of a laboratory model of an OFDM data transmission system in a shallow water environment in Wdzydze Lake.

A novel strategy to guarantee a minimum indoor temperature in social housing buildings

Energy poverty, along with climate change, is becoming one of the most worrying issues in Europe. Many people live without a minimum thermal comfort in their homes because they are not able to afford high energy bills. In other words, the indoor temperature of many dwellings remains below the recommended temperature during long periods, particularly in winter. This is more frequent among low-income families, which are over-represented in ‘Spain's social housing. In this paper, we present a simple and novel procedure to manage heating energy consumption in order to guarantee a minimum indoor temperature in social housing dwellings. To test the procedure, a social housing building located in the Basque Country, in northern Spain, has been selected as the case study. The heating consumption and outdoor temperature have been monitored for three winters and a characteristic curve for the heating consumption has been derived using the methodology proposed. Thanks to this procedure, the indoor conditions of the dwellings have improved by 80.9%, helping to alleviate energy poverty. The results show that the method is reliable, since the heating consumption needed to guarantee a specific indoor temperature could be estimated with an acceptable error rate. In the end, several aspects of this case study are discussed, and conclusions that propose certain suggestions to energy policies are derived.

Challenges and Opportunities in QoS Enhancement for Internet-Based Multimedia Communication

Advancements in communication technologies have fueled the rapid expansion of the multimedia content delivery market, encompassing various media types such as images, audio, and video. Personal communication, computing, broadcasting, and entertainment heavily rely on multimedia content, which is transmitted through diverse communication and network technologies to reach a wide range of devices. QoS requirements for multimedia services are determined by parameters including bandwidth flexibility, low end-to-end delay, minimal delay variation, and acceptable error rates. These parameters are interconnected and vary depending on the type of multimedia application. However, the QoS needs of multimedia communications cannot be fully met by the internet's basic infrastructure. Mechanisms for ensuring QoS guarantees over the Internet have been developed through extensive research and development. This study examines developments, difficulties, and strategies for improving the transmission of multimedia content over the Internet, providing an overview of the methodologies and strategies used to satisfy QoS criteria in multimedia communications.