Centroid Method Research Articles

MULTI-CRITERIA DECISION ANALYSIS USING COMPLEX PROPORTIONAL ASSESSMENTS AND RANK ORDER CENTROID METHODS IN THE SELECTION SYSTEM FOR TUTORING INSTITUTIONS

Tutoring can help increase students' self-confidence, reduce anxiety about tests or assignments, and overcome learning barriers. The large number of tutoring institutions that offer various programs makes parents or students have to be observant when choosing them. To choose a tutoring institution, parents or students must know all the profiles and programs of the institution to be selected. This creates a difficult and long time to come up with a choice. The purpose of this study is to use the Multi Criteria Decision Analysis (MCDA) approach through the Complex Proportional Assessment (COPRAS) method and the Rank Order Centroid (ROC) method to create a Decision Support System (DSS) that will make it easier to choose a tutoring institution. The ROC approach serves to determine the weight based on the order of importance of the criteria. The COPRAS method is used because this method takes utility into account by assessing the usefulness of each alternative. This research produced a web-based tutoring institution selection DSS that can provide alternative recommendations based on criteria determined by decision-makers. The results of system calculations and manual calculations do not show a different value, which shows that the system produces a valid COPRAS approach value. Based on the results of usability testing, the built DSS scored 89.17%; in other words, the system is feasible to use.

Groundwater Quality Assessment Based on the Random Forest Water Quality Index—Taking Karamay City as an Example

In the past few decades, global industrial development and population growth have led to a scarcity of water resources, making sustainable management of groundwater a global challenge. The Water Quality Index (WQI) serves as a comprehensive method for assessing water quality and can provide valuable recommendations at the water quality level, optimizing policies for groundwater management. However, the subjectivity and uncertainty of the traditional WQI have negative impacts on evaluation outcomes, particularly in determining indicator weights and selecting aggregation functions. The proposed water quality index for groundwater based on the random forest (RFWQI) model in this study addresses these issues. It selects water quality indicators based on the actual pollution situation in the study area, employs an advanced random forest model to rank water quality indicators, determines indicator weights using the rank centroid method, scores the indicators using a sub-index function designed for groundwater development, and compares the results of two commonly used aggregation functions to identify the optimal one. Based on the aggregated scores, the water quality at 137 monitoring sites is classified into five levels: “Excellent”, “Good”, “Medium”, “Poor”, or “Unacceptable”. Among the 11 water quality indicators (sodium, sulfate, chloride, bicarbonate, total dissolved solids, fluoride, boron, nitrate, pH, CODMn, and hardness), chloride was given the highest weight (0.236), followed by total dissolved solids (0.156), and sodium was given the lowest weight (0.008). The random forest model exhibits a good prediction capability before hyperparameter tuning (86% accuracy, RMSE of 0.378), and after grid search and five-fold cross-validation, the optimal hyperparameter combination is determined, further improving the performance of the random forest model (94% accuracy, F1-Score of 0.967, AUC of 0.91, RMSE of 0.232). For the newly developed groundwater sub-index function, interpolation is used to score each indicator, and after comparing two aggregation functions, the NSF aggregation function is selected as the most suitable for groundwater assessment. Overall, most of the groundwater in the study area was of poor quality (52.5% of low quality) and not suitable for drinking.

Fixed charge 4-dimensional transportation problem for breakable incompatible items with type-2 fuzzy random parameters under volume constraint

Real-life problems are full of uncertainties combined with impreciseness and randomness. Transportation problems become difficult when its parameters are type-2 fuzzy random type. This paper presents a 4-dimensional fixed charge transportation problem for incompatible and compatible breakable items with type-2 fuzzy random parameters under the availability, demand, conveyance’s weight and volume capacity constraint. The model is developed for usual transportation cost minimization objective, where transportation costs, fixed charges, availabilities, demands, conveyances’ weights and volume capacities are type-2 fuzzy random. A type-2 fuzzy random parameter is converted to type-1 fuzzy random one by critical value-based reduction method and then converted to a random parameter using two different methods-generalized credibility measure and centroid method. The random parameter is converted to crisp parameter using probability chance constraint method for constraints and expectation for objective. A detailed conversion procedure from type-2 fuzzy random to crisp is also presented. The reduced deterministic problem is solved by the Generalized Reduced Gradient method using Lingo 11.0 and numerically illustrated. A real-life example is introduced. Particular models are deduced for non-breakable items or different pairwise incompatible items. The sensitivity analyses against different generalized credibility levels of different parameters are presented for total transportation cost. The numerical results show the efficiency of the proposed critical value reduction-generalized credibility-chance constraint, and expectation method. The importance of uncertainty, multi-path and vehicles’ volume capacity in transportation problems is demonstrated.

A High-Accuracy Star Centroid Extraction Method Based on Kalman Filter for Multi-Exposure Imaging Star Sensors.

A multi-exposure imaging approach proposed in earlier studies is used to increase star sensors' attitude update rate by N times. Unfortunately, serious noises are also introduced in the star image due to multiple exposures. Therefore, a star centroid extraction method based on Kalman Filter is proposed in this paper. Firstly, star point prediction windows are generated based on centroids' kinematic model. Secondly, the classic centroid method is used to calculate the coarse centroids of the star points within the prediction windows. Lastly, the coarse centroids are, respectively, processed by each Kalman Filter to filter image noises, and thus fine centroids are obtained. Simulations are conducted to verify the Kalman-Filter-based estimation model. Under noises with zero mean and ±0.4, ±1.0, and ±2.5 pixel maximum deviations, the coordinate errors after filtering are reduced to about 37.5%, 26.3%, and 20.7% of the original ones, respectively. In addition, experiments are conducted to verify the star point prediction windows. Among 100 star images, the average proportion of the number of effective star point objects obtained by the star point prediction windows in the total object number of each star image is calculated as only 0.95%. Both the simulated and experimental results demonstrate the feasibility and effectiveness of the proposed method.

Process Optimization of Sea Buckthorn Fruit Powder Effervescent Tablets by Random Centroid Methodology Combined with Fuzzy Mathematical Sensory Evaluation

Solid beverages of effervescent tablets have good taste and portable features and are favored by consumers, but product quality and nutrition cannot meet the need of increasing nutritional requirements. Sea buckthorn fruit has a special flavor and nutrient-rich characteristics, but the related products of effervescent tablets have not been developed. In this paper, different additive contents (sea buckthorn fruit powder, erythritol, disintegrant, maltodextrin, polyvinylpyrrolidone (PVP)) were optimized using the random centroid method; the obtained effervescent effect sensory evaluation characteristics (appearance, beverage, appearance, taste, solubility) were used to establish a fuzzy mathematic model for sensory evaluation method of process optimizing; and the nutritional components and characteristics of optimized sea buckthorn powder effervescent tablets were compared to the ones of the commercial product. The results show that the optimal process conditions (47.7% sea buckthorn fruit powder, 1.3% erythritol, 1:1 disintegrant ratio, 2% maltodextrin and 2.9% PVP) were obtained according to the highest fuzzy comprehensive sensory score (87.76). Moreover, the optimized one contains a higher content of vitamin C (50.36 mg/100 g), carotenoids (10.18 mg/100 g), total phenols (11.52 GAE/g), and total flavonoids (28.46 mg RE/100 g), as well as a shorter disintegration time (10 s). The results indicate the RCO, combined with fuzzy mathematical sensory evaluation, is preferably suitable for effervescent tablet process optimization, and the quality indicators met the requirements of the effervescent tablet.

Isosceles Triangular and Isosceles Trapezoidal Membership Functions Using Centroid Method

Summary Since isosceles triangular and trapezoidal membership functions [4] are easy to manage, they were applied to various fuzzy approximate reasoning [10], [13], [14]. The centroids of isosceles triangular and trapezoidal membership functions are mentioned in this article [16], [9] and formalized in [11] and [12]. Some propositions of the composition mapping (f + · g, or f +* g using Mizar formalism, where f, g are a ne mappings), are proved following [3], [15]. Then different notations for the same isosceles triangular and trapezoidal membership function are formalized. We proved the agreement of the same function expressed with different parameters and formalized those centroids with parameters. In addition, various properties of membership functions on intervals where the endpoints of the domain are fixed and on general intervals are formalized in Mizar [1], [2]. Our formal development contains also some numerical results which can be potentially useful to encode either fuzzy numbers [7], or even fuzzy implications [5], [6] and extends the possibility of building hybrid rough-fuzzy approach in the future [8].

Analisis Dalam Pendukung Keputusan Seleksi Content Creator Mahasiswa Terbaik Menerapkan Metode EDAS dan ROC

One of the uses of technology is social media, which is an application that can convey information to many people. Around 49 percent of Indonesia's population are active users of social media because the use of social media as a means of promotion is considered easier and more efficient to get a wider target audience. In creating content on social media, a quality content creator is needed. The selection of student content creators at Budi Darma University in Medan is important to be able to get students who are creative and have the potential to become social media content creators. In selecting content creators, students at Budi Darma University have several predetermined criteria, namely the number of social media, the number of tiktok followers, the number of Instagram followers, content production per day, content creativity and content design updates. In the process of selecting student content creators, a system is needed that can speed up and simplify the process of selecting student content creators, namely the SPK (Decision Support System). In this study the EDAS (Evaluation Based on Distance from Average Solution) method and the Rank Oder Centroid (ROC) method used are expected to help the selection process for accepting student content creators. So the best alternative to become a student content creator lies in alternative A8 on behalf of Arsyillah with a score of 182001

Co-branding drivers between professional sport organizations and on-field sports apparel sponsors

PurposeThis study aims to investigate drivers of co-branding, and the relative strength of these drivers within the Persian Gulf Pro League (PGPL). The study examines sport sponsorship, and specifically the relationship between professional football teams and on-field apparel sponsors.Design/methodology/approachSixteen experts participated in semi-structured interviews and ranked the co-branding drivers. The expert opinions were organized into estimates and triangular fuzzy numbers were established before the Mamdani Fuzzy Inference System converted the fuzzy outputs into crisp output values using the Centroid method. Next, the rankings of the drivers by the same 16 participants were analyzed using the analytical hierarchy process (AHP).FindingsThe study identified four main factors with 19 indicators: brand management (i.e. enhancing brand value, utilizing the knowledge and experience of partner brands, brand position, brand identity, brand equity and brand image), partner relationships (i.e. satisfaction, mutual trust, commitment, common interest, product reliability and innovative strategies), marketing factors (i.e. marketing mix, market position, competitive advantage and entry into new domestic markets) and supporting factors (i.e. copyright, contracts and social media law). The AHP identified the most influential factors as marketing, partner relationships, brand management and support.Originality/valueBased on the study’s findings, the authors recommend that PGPL teams adopt a partnership mindset, seek alignment of values and recognize the plurality of stakeholders to a sponsorship and their relationships to each other. The study highlights the challenges of co-branding activities in a developing country where trademark laws are not well developed.

Sistem Pendukung Keputusan Rekomendasi Aplikasi Pembuat Kuis Edukasi Untuk Pembelajaran Menerapkan Metode OCRA dan Pembobotan ROC

Education quizzes are a form of interactive test or game designed to support the learning process in a fun and interactive way. Despite the availability of many applications for creating educational quizzes, users often face difficulties in selecting the ones that suit their learning needs and objectives. Therefore, this research suggests the use of a Decision Support System by implementing the OCRA and Weighted Rank Order Centroid (ROC) methods to recommend educational quiz maker applications. The ROC method is utilized to determine the weights or relative values of predefined criteria, thus facilitating the ranking process based on the importance of each criterion. Furthermore, the OCRA method is employed to analyze the operational competitiveness level of various alternative educational quiz maker applications. The research findings indicate that out of the 5 evaluated alternatives, ProProfs Quiz Maker scored the highest with a value of 1.515, making it the top choice for online learning media. Thus, the Decision Support System based on OCRA and ROC provides accurate recommendations for selecting educational quiz maker applications that align with learning needs and objectives.

PENENTUAN PERINGKAT PELANGGAN TERBAIK MENGGUNAKAN METODE RANK ORDER CENTROID DAN WEIGHTED PRODUCT (STUDI KASUS ONESNET)

This study addresses the problem faced by Onesnet in its environment, which is how to provide discounts or rewards to customers who have not received them before. This is due to the increasing competition in the internet installation service industry. The aim of this research is to determine the best customers in order to maintain customer loyalty in the increasingly competitive industry. The Weighted Product method is used as a decision-making technique by multiplying the ratings of each connected attribute, where the ratings of each attribute must be exponentiated with the corresponding attribute weight. The data used in this research comes from interviews and observations at Onesnet, which will be processed using the Rank Order Centroid method for weighting and Weighted Product method to evaluate and make a decision. The result of this study is the most loyal customer who becomes the best customer with a value of 0.14578.

Machine Intelligence Based Assessment of Nutritional Olfactory Features for Controlling Health Hazards Using Electronic Nose

Purpose: Purpose of this research is to carry out a machine learning intelligence based innovative method to determine quality of food which may be hazards to health if consumed by humans. This article detects human sickness by sensing nutrition that causes smells. Theoretical framework: In developing nations, people just focus on basic need of food rather than focusing on the quality and the nutritional values of food which are exhibiting hazards impact of unhealthy food on the lives of people. Many people are suffering from diabetics, cancers, cardiac problem, liver problems and stomach related health issues which are originated due to consumption of bad food. Consumers are satisfied with food quality, and more individuals are assessing it. Method/design/approach: As a methodology, an electronic nose uses chemical sensors to identify complicated odors. Standard technologies can detect gases from households, industries, and explosive materials. It cannot fulfill freshness requirements. Electronic noses, computer vision, and other sensory approaches may imitate human olfactory, taste, visual, and sensory qualities, both pleasantly and unpleasantly. Neural networks organize innovative artificial/mechanical intelligence systems to interpret fragrance recordings for human brain recognition. Inspired by human brain processing, we offer optimized feedback, centroid clustering, and self-organizing maps for machine learning systems to identify smell data. This work proposes a simulation technique based on benchmark datasets to achieve high type accuracy, precision, and recall for diverse scented records where additional information may be artificially/mechanically found. The centroid SOM research of olfaction involves investigating more physiologically and nutritionally feasible methods for mapping, understanding, and interpreting massive scent datasets for real-world applications. Results and conclusion: In all analyzed result and conclusion, the accuracy, precision, and recall of the clustering centroid with optimized feedback SOM are superior to the existing clustering approach. By simulating the data on different set of test and train data it has observed that Proposed (Cluster Centroid with SOM ) method is effective than the existing (Centroid) method. For example, 10% of test data existing method has 67.55% of accuracy and proposed method has 86.75% which is shown in result and conclusion section in details. Research implications: The research makes an effective contribution by demonstrating the potential and the need to adopt sustainable practices in the management of contemporary companies. Originality/value: The results and conclusion obtained in this research are unprecedented, innovative and relevant to the medico health community to avoid health diseases, in the context of reliability in social community suggest eating a fresh and pleasant food to avoid health diseases.

Decision System for Beneficiaries of the Family Hope Program Using the Rank Order Centroid Method and Weighted Product Method

Poor refers to a state of having little or insufficient material possessions or resources relative to others in a particular society or community. Many factors become the criteria of poverty. These factors identified and measured using the Rank Order Centroid (ROC) method and Weighted Product (WP) method to decide on prospective beneficiaries’ social assistance of Program Keluarga Harapan (PKH) or the Family Hope Program. There are 8 criteria used in this research; income of the head of the family, number of dependents, occupation, home ownership, vehicle ownership, condition of the house using Geographical Information system or Global Positioning System (GPS), and having received assistance. The purpose of this study is to facilitate the Samarinda City Social Service in providing appropriate assessments and decisions. The results indicate that the ROC and WP method was successfully applied in decision support systems for the best alternatives with a preference value of 0.000991677.

A Novel Range-Selective Generalized Weighted Centroid Method for Source Localization in Bearing-Only Sensor Networks

In this study, the source localization problem is investigated using a group of networked sensors if only bearing measurements are available. The existing bearing-only source localization methods are subjected to a critical problem, i.e., how to achieve a fine balance between the computational complexity and the positioning accuracy when the threshold effect is considered. A novel range-selective generalized weighted centroid localization (RSGWCL) estimator is proposed to address the above-mentioned problem. Without the threshold effect, the proposed RSGWCL method is capable of achieving comparable positioning accuracy to the maximum likelihood estimator (MLE). If the threshold effect is identified, the RSGWCL estimator still has a capability of providing reliable estimates and outperforms the MLE. The result of the simulation is analyzed, such that the performance of the proposed method can be verified.

Study of the Condition of Forest Fire Fighting Vehicles

The Forest Fire Fighting Vehicle (FFFV) is one of the most important pieces of equipment in direct firefighting; therefore, its maintenance is strategic to guarantee high levels of reliability. The history of interventions is essential to support the increase in the quality of maintenance, namely with regard to the specificity of each equipment, in its actual operating conditions. In the absence of previous information, it is important to resort to complementary tools that allow for overcoming this gap where usually the knowledge of maintenance held by professionals and users is structuring and very helpful. In this perspective, data were collected from several fire brigades. The analysis and decisions were possible using fuzzy logic, following the Mamdani model and the centroid method for the defuzzification phase. Subsequently, a Failure Modes, Effects and Criticality Analysis (FMECA) was carried out to identify which would be the most severe failures, the possible cause of each failure and the respective maintenance action. Through the results obtained, it was possible to identify a set of elements of the FFFV where maintenance should pay additional attention so that the vehicle guarantees the desired levels of reliability and propose a maintenance program with added value compared to what is currently practised.

Influence of Image Processing Method on Wavefront Reconstruction Accuracy of Large-Aperture Laser

In order to improve the wavefront reconstruction accuracy of a large-aperture laser, this paper proposed an adaptive window preprocessing algorithm based on the threshold center of gravity method (AW-TCoG). The effects of median filtering and mean filtering on spot image processing and wavefront reconstruction accuracy are simulated and analyzed. The results show that the mean filtering method has a better effect on noise elimination and can further improve the accuracy of wavefront reconstruction. In addition, the centroid detection errors of large-aperture laser wavefront reconstruction through the center of gravity (CoG), the threshold center of gravity (T-CoG), and the Windowing method were studied. The analysis shows that, due to the influence of noise, the wavefront reconstruction accuracy is poor when the CoG and Windowing methods are used to calculate centroid parameters, while the wavefront reconstruction accuracy of the threshold centroid method is better and can reach 0.2λ. When using the AW-TCoG proposed in this paper, the wavefront reconstruction accuracy can be maintained within 0.1λ for different incident wavefront RMS values and spot images with different signal-to-noise ratio (SNR) levels. Compared with the traditional threshold centroid method, the wavefront reconstruction accuracy of this method is significantly improved.

Failure Mode and Effect Analysis with a Fuzzy Logic Approach

Failure mode and effect analysis (FMEA) is one of the most used techniques in risk management due to its potential to solve multidisciplinary engineering problems. The role of experts is fundamental when developing the FMEA; they identify the failure modes by expressing their opinion based on their experience. A relevant aspect is a way in which the experts evaluate to obtain the indicator of the risk priority number (RPN), which is based on qualitative analysis and a table of criteria where they subjectively and intuitively determine the factor level (severity, occurrence, and detection) for each of the failures. With this, imprecision is present due to the interpretation that each one has regarding the failures. Therefore, this research proposes a fuzzy logic evaluation system with a solid mathematical basis that integrates these conditions of imprecision and uncertainty, thus offering a robust system capable of emulating the evaluation form of experts to support and improve decision making. One of the main contributions of this research is in the defuzzification stage, adjusting the centroid method and treating each set individually. With this, the RPN values approximate to the conventional technique were obtained. Simulations were carried out to test and determine the system’s best structure. The system was validated in a textile company in southern Guanajuato. The results demonstrate that the system reliably represents how experts perform risk assessment.

Implementation of Complex Proportional Assessment and Rank Order Centroid Methods for Selecting Delivery Services

Choosing the right delivery service partner is an important thing for companies to consider. This is because the selection of the right delivery service partner can minimize the risks involved. Generally, choosing a delivery partner service is done by looking at the profile of the freight forwarder's partner. It takes time to determine the right delivery service partner. This study aims to apply the Complex Proportional Assessment (COPRAS) and Rank Order Centroid (ROC) methods in a decision support system for selecting delivery service partners to make it easier to make the right decisions and meet needs. The ROC weighting method is used to determine the value of the criteria based on priority. Meanwhile, the COPRAS approach is used to determine the best solution based on an analysis of the existing options through alternative assessments by providing interval-based utility judgments. In the case study conducted, the best alternative was obtained, namely J&T Express with a score of 100, followed by JNE Express with a value of 92.09, SiCepat with a value of 91.89, Ninja Express with a value of 91.42. The COPRAS calculation results on the system developed with the manual calculation results show the same value, this means that the calculations on the system are valid. The usability scores, on the other hand, have an average value of 88.33% and are considered good

Improved interval type-2 fuzzy K-means clustering based on adaptive iterative center with new defuzzification method

Interval Type-2 Fuzzy K-means (IT2FKM) is an efficient clustering algorithm, which mainly focuses on further describes the uncertainty of the data sets by introducing interval type-2 fuzzy membership function. Since the estimation center obtained by two membership functions is an interval type-1 fuzzy set, it is necessary to use the defuzzification method to obtain a crisp cluster center. The existing IT2FKM algorithms adopt the generalized centroid method (GC) in the process of type-reduction and defuzzification for iterative clustering center. But unfortunately, this method simply uses a single numerical value 0.5 as the type-reduction coefficient to balance and synthesize for the left value and the right value of an interval cluster center, while ignoring the distribution of the clusters. This defect makes IT2FKM fail to update the optimal cluster center when facing with imbalanced clusters, which affect the accuracy in some cases. To address this failing, in this paper, some adverse effects caused by generalized centroid method when facing with imbalanced clusters are analyzed in detail. Thereafter, an improved type-reduction method of iterative cluster centers for IT2FKM clustering is proposed considering the imbalanced clustering distributions. On the basis of which, an enhanced IT2FKM algorithm is further developed. The enhanced IT2FKM algorithm has an average classification accuracy of 97.5% when faced with four datasets with different degrees imbalance, which is about 3.25% higher than other clustering algorithms. The effectiveness and superiority of the proposed algorithm are further demonstrated by some experimental analysis of UCI standard datasets.

APPLICATION OF THE SIMPLE ADDITIVE WEIGHTING (SAW) & RANK ORDER CENTROID (ROC) METHODS IN THE SELECTION OF OUTSTANDING STUDENTS AT SMK AL-HUDA SADANANYA

One of the obstacles to determining student achievement is that the teacher manually assesses student achievement seen from the high score of student report cards. By using this selection, it can still be felt that itl is lessl effective andl efficient andl not asl expected. Tol determine studentl achievement tol be morel accurate, ofl course, requiresl a decisionl support systeml (DSS) usingl a combinedl method, namelyl the Simplel Additive Weightingl (SAW) lmethod, which isl a weightedl addition lmethod. The basicl concept ofl the SAWl method isl to findl a weightedl sum ofl performance ratingsl for eachl alternative inl all attributesl and thel second methodl is tol use thel Rank Orderl Centroid (ROCl) method whichl is al method thatl can producel weighting ofl a numberl of criterial according tol the importancel of thel criteria lset. The authorl took 25l students dividedl from 5l students whol were rankedl in thel top 5l in eachl class ofl the 5l existing lclasses, then froml the 25l students wel processed againl with thel final resultl there werel 5 studentsl who excell with thel highest scorel namely Lilisl Napilah wasl ranked firstl with al score l0.957 followedl by Nal'il Fauzanl second withl a scorel of l0.935 thenl Dela Anandahl in thirdl place withl a scorel of l0.925 fourthl Agin Maulanal with al score ofl 0.882l and finallyl Delia Aprillizal with al score ofl 0. l860. The decisionl support systeml for selectingl outstanding studentsl at All Huda Sadananyal Vocational Schooll can bel developed intol a webl-based decisionl support systeml so thatl decision makingl can bel faster, precisel and accuratel to makel it easierl for thel school tol select outstandingl students. schoolsl to applyl for scholarships to tertiary institutions as well as for the distribution of labor.

Wrong Way Vehicle Detection in Single and Double Lane

Wrong-way driving is one of the primary causes of traffic jams and accidents globally. It is possible to identify vehicles going the wrong direction, which lessens accidents and traffic congestion. Surveillance footage has become an important source of data due to the accessibility of less priced cameras and the expanding use of real-time traffic management systems. In this paper, we propose a technique for automatically identifying automobiles moving against traffic. Our system uses the You Only Look Once (CNN) algorithm to recognize and track vehicles from video inputs and the centroid tracking method to determine each vehicle's orientation inside a given region of interest (ROI) in order to identify vehicles traveling in the wrong direction. It functions in three steps. The Deep sort tracking method is particularly good in detecting and tracking objects, and the centroid tracking technique can effectively monitor the direction of travel. Experiments with a variety of traffic films show that the suggested method can detect and identify wrong-way moving vehicles in a variety of lighting and weather scenarios. The interface of the system is quite simple and easy to use.