Accuracy Of System Research Articles

Molecular Docking and ADMET Analysis Strategy-Based Stability Indicating RP-HPLC-PDA Method Development and Validation of Toremifene.

The purpose of this research is to develop an analytical method and validate it according to ICH guidelines for the estimation of Toremifene by RP-HPLC-PDA with molecular docking and ADMET analysis. From molecular docking, it came to know the receptor affinity specifically to estrogen receptors (ERα and ERβ), which are responsible for cancer therapy. ADMET analyses secure its therapeutic potential as well safety of the drug. An isocratic method has developed by RP-HPLC-PDA (AGILENT 1100) with symmetry of 100 mm x 4.6 mm x 5 μm particle size C18 column and optimise mobile phase is methanol: 0.1% OPA (orthophosphoric acid) water ratio of 43:57% v/v. Under different conditions like acidic, alkaline, oxidative, and neutral environments, toremifene was tested for degradation. The developed method is validated in accordance with ICH guidelines. A calibration curve with an r2 value of 0.9987 has been prepared across the range of 10 to 50 μg/ml with five standard dilutions. The retention time of the drug is 5.575 minutes. The validation results are system suitability (%RSD-0.76), inter-day precision (%RSD 0.14-0.29), intraday precision (%RSD 0.08-0.34), accuracy (%RSD 0.16-0.96), and robustness (%RSD 0.16-0.35). In different intended conditions, four peaks are in 1 N HCl, two peaks in 1 N NaOH, three peaks in 10% H2O2 (1hr), and one peak in neutral. Toremifene, a Selective Estrogen Receptor Modulator (SERM), Drug pharmacokinetic properties and receptor binding affinity results are helpful in designing the analytical method. Developing the RP-HPLC-PDA method is found to be novel, simple and precise. It could be used for testing toremifene in bulk and pharmaceutical tablet dosage forms in quality control, as well as stability tests.

Calculation and measurement of trapping stiffness in femtosecond optical tweezers.

Due to the characteristics of ultra-short pulse width and ultra-high peak power, femtosecond pulse laser can effectively induce nonlinear optical effects in trapped objects. As a result, it holds great value in the fields of micro and nano manipulation, microfluidics, and cell biology. However, the nonlinear optical effects on the stiffness of femtosecond optical traps remain unclear. Calibration of trap stiffness is crucial for accurately measuring forces and manipulating small particles. In this paper, we compare the stiffness between femtosecond optical traps and continuous wave optical traps. Experimental results demonstrate that the stiffness of the femtosecond optical trap in the splitting direction is greater than that in other directions and the stiffness of the continuous wave optical trap under the same laser power condition. Additionally, as the laser power increases, the stiffnesses of both the femtosecond optical trap and the continuous wave optical trap gradually increases. In contrast to a linear increase of the continuous wave optical trap, the stiffness of the femtosecond optical trap exhibits an exponential rise with increasing laser power. This research provides guidance and reference for improving the force measurement accuracy of femtosecond optical tweezer system.

Fractional-Order PID Control of Permanent Magnet Synchronous Motors Based on Based on Improved Squirrel Algorithm

An adaptive differential squirrel algorithm fractional order PID controller is proposed to address the problems of instability and poor accuracy of Permanent Magnet Synchronous Motor PID control systems. In order to improve the global optimisation of the squirrel search algorithm, the crossover operation of the difference algorithm was combined, adaptive predation probability factor, dynamic crossover probability and Gaussian distribution perturbation operation are added to the squirrel search algorithm to improve its exploration and exploitation capability. In the comparative experiment of PMSM speed control, ADESA fractional-order PID was contrasted with conventional PID and other intelligent optimization algorithms. The results indicate that the PMSM control utilizing fractional-order PID through ADESA demonstrated minimal overshoot, the fastest response time, and superior anti-disturbance capabilities compared to PID and other intelligent optimization algorithms. This significantly enhanced the control performance of the permanent magnet synchronous motor.

Multi-objective SHM sensor path optimisation for damage detection in large composite stiffened panels

This work proposes a novel methodology for the automatic multi-objective optimisation of sensor paths in structural health monitoring (SHM) sensor networks using archived multi-objective simulated annealing. Using all of the sensor paths within a sensor network may not always be beneficial during damage detection. Many sensor paths may experience significant signal noise, attenuation, and wave mode conversion due to the presence of features, such as stiffeners, and hence impair the detection accuracy of the overall system. Many paths will also contribute little to the overall coverage level or damage detection accuracy of the network and can be ignored, reducing complexity. Knowing which paths to include, and which to exclude, can require significant prior expert knowledge, which may not always be available. Furthermore, even when expert knowledge is considered, the optimum path selection might not be achieved. Therefore, this work proposes a novel automatic procedure for optimising the sensor paths of an SHM sensor network to maximise coverage level, maximise damage detection accuracy and minimise the overall signal noise in the network due to geometric features. This procedure was tested on a real-world large composite stiffened panel with many geometric features in the form of frames and stiffeners. Compared to using all of the available sensor pairs, the optimised network exhibits superior performance in terms of detection accuracy and overall noise. It was also found to provide very similar performance, in terms of coverage level and overall signal noise, to a sensor path network designed based on prior expert knowledge but provided up to 35% higher damage detection accuracy. As a result, the novel procedure proposed in this work has the capability to design high-performing SHM sensor path networks for structures with complex geometries but without the need for prior expert knowledge, making SHM more accessible to the engineering community.

SISTEM EVALUASI PELANGGARAN LALU LINTAS NTMC POLRI DENGAN ALGORITMA FUZZY LOGIC BERBASIS PYTHON

This research aims to build a traffic violation system using a fuzzy logic algorithm based on the Python language. By utilizing input components to receive traffic violation data and output components to produce traffic understanding level values. This method is used to process data based on traffic violations, system test results show the system's accuracy. To confirm the system's capabilities in traffic violations provides a baseline assessment of the level of accurate understanding of police law. The implications of this system show its potential as an effective detection tool for measuring existing legal violations from various levels of a person's understanding, especially in the legal field. Taking this research into account could provide a strong foundation for the development of similar systems in a variety of future comprehension evaluation contexts

SISTEM EVALUASI PELANGGARAN LALU LINTAS NTMC POLRI DENGAN ALGORITMA FUZZY LOGIC BERBASIS PYTHON

This research aims to build a traffic violation system using a fuzzy logic algorithm based on the Python language. By utilizing input components to receive traffic violation data and output components to produce traffic understanding level values. This method is used to process data based on traffic violations, system test results show the system's accuracy. To confirm the system's capabilities in traffic violations provides a baseline assessment of the level of accurate understanding of police law. The implications of this system show its potential as an effective detection tool for measuring existing legal violations from various levels of a person's understanding, especially in the legal field. Taking this research into account could provide a strong foundation for the development of similar systems in a variety of future comprehension evaluation contexts

An Expert System for Indian Sign Language Recognition Using Spatial Attention–based Feature and Temporal Feature

Sign Language (SL) is the only means of communication for the hearing-impaired people. Normal people have difficulty understanding SL, resulting in a communication barrier between hearing impaired people and hearing community. However, the Sign Language Recognition System (SLRS) has helped to bridge the communication gap. Many SLRs are proposed for recognizing SL; however, a limited number of works are reported for Indian Sign Language (ISL). Most of the existing SLRS focus on global features other than the Region of Interest (ROI). Focusing more on the hand region and extracting local features from the ROI improves system accuracy. The attention mechanism is a widely used technique for emphasizing the ROI. However, only a few SLRS used the attention method. They employed the Convolution Block Attention Module and temporal attention but Spatial Attention (SA) is not utilized in previous SLRS. Therefore, a novel SA based SLRS named Spatial Attention-based Sign Language Recognition Module (SASLRM) is proposed to recognize ISL words for emergency situations. SASLRM recognizes ISL words by combining convolution features from a pretrained VGG-19 model and attention features from a SA module. The proposed model accomplished an average accuracy of 95.627% on the ISL dataset. The proposed SASLRM is further validated on LSA64, WLASL, and Cambridge Hand Gesture Recognition datasets where, the proposed model reached an accuracy of 97.84%, 98.86%, and 98.22%, respectively. The results indicate the effectiveness of the proposed SLRS in comparison with the existing SLRS.

IoT-Machine Learning-Based Structural Health Monitoring System for Detection of Cracks in Bridges

The deteriorating condition of infrastructure, particularly bridges, poses significant challenges to public safety and necessitates the development of advanced monitoring systems for early detection of structural defects. This paper proposes a novel approach by integrating Internet of Things (IoT) technology with machine learning (ML) algorithms for real-time structural health monitoring, focusing on the detection of cracks in bridges. The proposed system employs a network of IoT sensors strategically deployed on the bridge structure to collect diverse data related to environmental conditions, strain, and vibrations. These sensors provide a continuous stream of data, creating a comprehensive dataset for analysis. Machine learning algorithms, specifically designed for anomaly detection, are applied to this dataset to identify patterns indicative of potential structural issues. This system is designed and implemented using ML and IoT. The excitation results shows that the designed system accuracy and efficiency of crack detection is improved and further enhance overall structural resilience. Furthermore, the integration of a real-time alerting mechanism allows for immediate notification of detected anomalies.

The Effective Fragment Molecular Orbital Method: Achieving High Scalability and Accuracy for Large Systems.

The effective fragment molecular orbital (EFMO) method has been developed to predict the total energy of a very large molecular system accurately (with respect to the underlying quantum mechanical method) and efficiently by taking advantage of the locality of strong chemical interactions and employing a two-level hierarchical parallelism. The accuracy of the EFMO method is partly attributed to the accurate and robust intermolecular interaction prediction between distant fragments, in particular, the many-body polarization and dispersion effects, which require the generation of static and dynamic polarizability tensors by solving the coupled perturbed Hartree-Fock (CPHF) and time-dependent HF (TDHF) equations, respectively. Solving the CPHF and TDHF equations is the main EFMO computational bottleneck due to the inefficient (serial) and I/O-intensive implementation of the CPHF and TDHF solvers. In this work, the efficiency and scalability of the EFMO method are significantly improved with a new CPU memory-based implementation for solving the CPHF and TDHF equations that are parallelized by either message passing interface (MPI) or hybrid MPI/OpenMP. The accuracy of the EFMO method is demonstrated for both covalently bonded systems and noncovalently bound molecular clusters by systematically examining the effects of basis sets and a key distance-related cutoff parameter, Rcut. Rcut determines whether a fragment pair (dimer) is treated by the chosen ab initio method or calculated using the effective fragment potential (EFP) method (separated dimers). Decreasing the value of Rcut increases the number of separated (EFP) dimers, thereby decreasing the computational effort. It is demonstrated that excellent accuracy (<1 kcal/mol error per fragment) can be achieved when using a sufficiently large basis set with diffuse functions coupled with a small Rcut value. With the new parallel implementation, the total EFMO wall time is substantially reduced, especially with a high number of MPI ranks. Given a sufficient workload, nearly ideal strong scaling is achieved for the CPHF and TDHF parts of the calculation. For the first time, EFMO calculations with the inclusion of long-range polarization and dispersion interactions on a hydrated mesoporous silica nanoparticle with explicit water solvent molecules (more than 15k atoms) are achieved on a massively parallel supercomputer using nearly 1000 physical nodes. In addition, EFMO calculations on the carbinolamine formation step of an amine-catalyzed aldol reaction at the nanoscale with explicit solvent effects are presented.

Structured invariant subspace and decomposition of systems with time delays and uncertainties

AbstractThis article discusses invariant subspaces of a matrix with a given partition structure. The existence of a nontrivial structured invariant subspace is equivalent to the possibility of decomposing the associated system with multiple feedback blocks such that the feedback operators are subject to a given constraint. The formulation is especially useful in the stability analysis of time‐delay systems using the Lyapunov–Krasovskii functional approach where computational efficiency is essential in order to achieve accuracy for large scale systems. The set of all structured invariant subspaces are obtained (thus all possible decompositions are obtained as a result) for the coupled differential‐difference equations (DDE) associated with the DDE of retarded and neutral types, as well as systems with a time‐varying delay. It was shown that the known ad hoc methods of reducing the dimensions of delay channels can be considered as special cases of decomposition where one subsystem has trivial dynamics. The reduction of computational cost is demonstrated by a numerical example. For the general case, a recursive procedure is developed to obtain the set of all structured invariant subspaces. Based on this procedure, a method is presented to obtain a nontrivial structured invariant subspace that considers computational efficiency and increased possibility of terminating in a finite number of steps.

Chaotic model and control of an atmospheric convective system coupled with large-scale circulation

Large-scale weather systems affect or determine the generation, development and movement of small- and medium-scale convective weather systems, and the understanding of their influence mechanism is of great significance for the prediction and forecasting of small- and medium-scale convective systems. From the perspective of dynamics, based on the Lorenz–Stenflo model describing convective systems and the Lorenz84 model describing low-order atmospheric circulation, we constructed a chaotic model of an atmospheric convective system considering large-scale circulation, analyzed the influence of the system parameters on the convective system, and designed a state feedback controller to adjust the convective motion. For the constructed convective weather system model, the existence and boundedness of its solution were analyzed, the Hamiltonian quantity was solved, the dynamic characteristics of the Lyapunov exponent, bifurcation diagram, attractor phase diagram and other methods were used to analyze its dynamic characteristics, the physical significance of its dynamic behavior was discussed, and the influence mechanism of the system parameters on convective weather was given. Additionally, the temperature difference between the upper and lower air currents is changed by the state feedback method, which provides a feasible scheme for adjusting the intensity of convective motion. The simulation experiments show that the chaotic model can explain and reveal the complex dynamic behavior of large-scale weather circulation in small- and medium-scale convective systems, which is of great significance for improving the prediction accuracy of convective systems in local areas, and feedback control can obtain the desired convective motion state.

A novel method for electric vehicle insulation detection based on the extended Kalman filter algorithm

Addressing the critical need for enhanced safety in the burgeoning electric vehicle market, this study presents a novel insulation detection method based on the Extended Kalman Filter (EKF) algorithm. To resolve the conflict between the response speed and detection accuracy of the insulation detection system caused by Y-capacitors, the Levenberg–Marquardt (L-M) algorithm is employed to effectively estimate the parameters of the feedback voltage model developed for an unbalanced electrical bridge. The state equation of the insulation testing system has been constructed, and the EKF algorithm is applied innovatively to monitor insulation resistance and Y-capacitance, demonstrating superior anti-interference capabilities. Simulation experiments have underscored the significant contributions of the L-M algorithm in expanding the detection scope of the system. Bench tests confirmed the ability of the approach to monitor changes in insulation resistance and Y-capacitance rapidly and accurately. Under normal conditions, the maximum relative error for insulation resistance measurement is 1.56%, with a response time of 1.5 s.

Robot control system based on deep learning and RPA

Robots can help people complete repetitive and high-risk tasks, such as industrial production, medical care, environmental monitoring, etc. The control system of robots is the key to their ability to complete tasks, and studying robot control systems is of great significance. This article used Convolutional Neural Network (CNN) and Robotic Process Automation (RPA) technologies to optimize and train the robot control system and constructed a robot control system. This article conducts perception and decision-making experiments and execution experiments in the experimental section. According to the experimental results, it can be concluded that the average image recognition accuracy of the robot control system in perception and decision-making experiments was 94.62% . The average decision accuracy was 87.5%, and the average time efficiency was 176 seconds. During the execution of the experiment, the deviation of the motion trajectory shall not exceed 5 cm, and the oscillation amplitude shall not exceed 6°; the distance from the obstacle shall not exceed 20 cm, and the movement speed shall not exceed 0.6 m/s; the operating time shall not exceed 25 hours, and the number of faults shall not exceed 0.2 times per hour, all within the normal range. The robot control system based on Deep Learning (DL) and RPA has broad application prospects and research value, which would bring new opportunities and challenges to the development and application of robot technology.

MMKG-PAR: Multi-Modal Knowledge Graphs-Based Personalized Attraction Recommendation

As the tourism industry rapidly develops, providing personalized attraction recommendations has become a hot research area. Knowledge graphs, with their rich semantic information and entity relationships, not only enhance the accuracy and personalization of recommendation systems but also energize the sustainable development of the tourism industry. Current research mainly focuses on single-modal knowledge modeling, limiting the in-depth understanding of complex entity characteristics and relationships. To address this challenge, this paper proposes a multi-modal knowledge graphs-based personalized attraction recommendation (MMKG-PAR) model. We utilized data from the “Travel Yunnan” app, along with users’ historical interaction data, to construct a collaborative multi-modal knowledge graph for Yunnan tourist attractions, which includes various forms such as images and text. Then, we employed advanced feature extraction methods to extract useful features from multi-modal data (images and text), and these were used as entity attributes to enhance the representation of entity nodes. To more effectively process graph-structured data and capture the complex relationships between nodes, our model incorporated graph neural networks and introduced an attention mechanism for mining and inferring higher-order information about entities. Additionally, MMKG-PAR introduced a dynamic time-weighted strategy for representing users, effectively capturing and precisely describing the dynamics of user behavior. Experimental results demonstrate that MMKG-PAR surpasses existing methods in personalized recommendations, providing significant support for the continuous development and innovation in the tourism industry.

Research on a Visual/Ultra-Wideband Tightly Coupled Fusion Localization Algorithm.

In the autonomous navigation of mobile robots, precise positioning is crucial. In forest environments with weak satellite signals or in sites disturbed by complex environments, satellite positioning accuracy has difficulty in meeting the requirements of autonomous navigation positioning accuracy for robots. This article proposes a vision SLAM/UWB tightly coupled localization method and designs a UWB non-line-of-sight error identification method using the displacement increment of the visual odometer. It utilizes the displacement increment of visual output and UWB ranging information as measurement values and applies the extended Kalman filtering algorithm for data fusion. This study utilized the constructed experimental platform to collect images and ultra-wideband ranging data in outdoor environments and experimentally validated the combined positioning method. The experimental results show that the algorithm outperforms individual UWB or loosely coupled combination positioning methods in terms of positioning accuracy. It effectively eliminates non-line-of-sight errors in UWB, improving the accuracy and stability of the combined positioning system.

Immune Checkpoint Inhibition in Pediatric Oncology Patients: A Single-Institution Experience

Immunotherapy has emerged as a promising treatment approach in oncology, as it is specifically designed to boost the strength and accuracy of the immune system, allowing it to target tumor cells but spare non-tumor tissue. This treatment not only demonstrates potential for improved clinical outcomes but may also be associated with fewer adverse effects compared to traditional therapies. Despite its early success, the application of immunotherapy has largely been limited to adult cancer patients, with slow adoption noted in the treatment of pediatric cancer patients. Our objective is to demonstrate a single institution’s experience with immunotherapy in pediatric cancer patients and to discuss the use of these treatment modalities in this unique patient population. We performed a retrospective chart review and identified patients who received immune checkpoint inhibitors (ICIs) and/or underwent immunohistochemistry (IHC) testing for programmed death ligand 1 (PD-L1), quantification of tumor mutational burden (TMB), and classification of microsatellite instability (MSI) status. In total, we identified seven pediatric cancer patients who received therapy with ICIs. Four of these patients demonstrated positive PD-L1 expression, high TMB, and/or MSI-high status. These patients were treated with nivolumab alone or in combination with ipilimumab or brentuximab. The diagnoses included: multifocal epithelioid and spindle cell hemangioma (n = 1); metastatic melanoma (n = 2); histiocytic sarcoma (n = 1); rectal adenocarcinoma in the setting of constitutional mismatch repair deficiency syndrome (CMMRD) (n = 1); and Hodgkin lymphoma (n = 2). The patients received between four and nineteen cycles of immunotherapy. Immunotherapy-related adverse events included: mild allergic reaction; prodromal symptoms; anemia; neutropenia; transaminitis; endocrinopathies; and self-limiting neuritis. Of the seven patients, three are still being treated with immunotherapy (the patients with rectal adenocarcinoma, metastatic melanoma, and multifocal epithelioid and spindle cell hemangioma) with positive treatment responses observed on imaging, one is being treated with other modalities (the patient with Hodgkin lymphoma), two have achieved remission (the patients with metastatic melanoma and Hodgkin lymphoma), and one has relapsed (the patient with histiocytic sarcoma). The three patients who completed their immunotherapy regimens have been followed for 1 month, 4 months, and 10 months, respectively. This report of a single-institution experience with immunotherapy in pediatric cancer patients highlights the positive impact immunotherapy can have, especially when utilized to treat relapsed/refractory malignancies, as tumor regression or stabilization of disease burden was achieved in six of the patients described (CR = 2; PR = 4). Further research is needed to accurately identify pediatric oncology patients who could benefit from immunotherapy.

Applying self-attention model to learn both Empirical Risk Minimization and Invariant Risk Minimization for multimedia recommendation

Multimedia recommendation systems have many applications in our daily life. However, how accurately capture a customer's preference is an issue that is difficult to deal with. The proposed Invariant Risk Minimization (IRM) and Empirical Risk Minimization (ERM) are ways to learn a customer's preference. Still, both frameworks show some limitations: although ERM performs excellently in a single environment, it fails to generalize well when faced with multiple and new domains. On the other hand, IRM learns invariant features across heterogeneous environments, but it lacks theoretical guarantees and performs less effectively where the invariants are unclear. This paper proposes an ERM and IRM Optimized Rating Framework (EIOR) as our final recommender model with direct rating scores. The EIOR enhances the accuracy and functionality of the multimedia recommendation systems by utilizing self-attention mechanisms to combine IRM and ERM with adjusted attention weights. Specifically, IRM learns invariant parts across different environments, while ERM learns variant parts. With self-attention, we can adaptively allocate attention weights for the two pieces and seek the optimal pair of attention weights based on the loss function. We demonstrate EIOR on a cutting-edge recommender model UltraGCN and use the open multimedia dataset of TikTok to finish all the experiments. The results validate the effectiveness of EIOR by comparing purely operating invariant representations alone with the framework of IRM.

Adaptive Fuzzy Sliding Mode Control and Dynamic Modeling of Flap Wheel Polishing Force Control System

Polishing force is one of the key process parameters in the polishing process of blisk blades, and its control accuracy will affect the surface quality and processing accuracy of the workpiece. The contact mechanism between the polishing surface and flap wheel was analyzed, and the calculation model of the polishing force and nonlinear dynamic model of the polishing force control system was established. Considering the influence of friction characteristics, parameter perturbation, and nonlinear dead zone on the control accuracy of the polishing force system, an adaptive fuzzy sliding mode controller (AFSMC) was designed. AFSMC uses a fuzzy system to adaptively approximate the nonlinear function terms in the sliding mode control law, adopts an exponential approach law in the switching control part of the sliding mode control (SMC), and designs the adaptive law for adjustable parameters in the fuzzy system based on the Lyapunov Theorem. Simulation and experimental results show that the designed AFSMC has a fast dynamic response, strong anti-interference ability, and high control accuracy, and it can reduce SMC high-frequency chatter. Polishing experiments show that compared with traditional PID, AFSMC can improve the form and position accuracy of the blade by 42% and reduce the surface roughness by 50%.

Research on surface roughness detection and prediction of ti-6Al-4v titanium alloy based on multi-feature fusion

Considering that titanium alloy is a critical aviation resource, ensuring its machining quality is of significant importance. Surface roughness remains a key parameter for surface quality inspection. This article introduces a multi-sensor titanium alloy milling monitoring system aimed at accurately monitoring surface quality during titanium alloy processing. Principal component analysis is conducted on three-dimensional milling force and vibration. We propose a multi-objective cutting parameter optimization procedure to simultaneously optimize multiple cutting parameters to improve surface roughness and tool life. Considering the high dependence of visual measurement on the light source, we strive to mitigate this limitation and improve prediction accuracy by considering one-dimensional and two-dimensional feature values. We establish a multidimensional signal feature surface roughness prediction system based on milling parameters, milling vibration, milling force, and texture image features. Using particle swarm algorithm and machine learning models, the undetermined parameters in the prediction system are obtained. The results show that the prediction accuracy of the multi-signal feature fusion surface roughness prediction system is 99.12%, with a mean square error of less than 0.01. The research can provide some theoretical guidance for the accurate monitoring of the surface quality of titanium alloy processing.

Advancements in Computer Vision Applications for Traffic Surveillance Systems

With the increasing need for efficient traffic surveillance systems, computer vision emerges as a promising technology to address various challenges in this domain. This paper aims to provide an overview of recent advancements in the application of computer vision in traffic surveillance. It commences by discussing fundamental concepts of computer vision and its role in traffic monitoring, followed by an exploration of various techniques and algorithms used in computer vision systems, including object detection, tracking, and classification. Furthermore, it investigates the integration of computer vision with other technologies such as machine learning and deep learning, emphasizing their impact on enhancing the accuracy and efficiency of traffic surveillance systems. The review explores specific applications of computer vision in traffic surveillance, such as traffic flow monitoring, anomaly detection, and license plate recognition. Additionally, it addresses challenges and limitations associated with the implementation of computer vision in real-world traffic scenarios. Synthesizing existing literature, case studies, and research findings, this review offers a comprehensive insight into the current state-of-the-art in the application of computer vision for traffic surveillance systems. The paper concludes with a discussion of future directions and potential research opportunities in this rapidly evolving field.