Market basket analysis using artificial neural network

Market Basket Analysis (MBA) is a crucial technique used in the field of data mining to understand consumer purchasing patterns. The importance of advertisement in an MBA has been widely acknowledged as a key factor in influencing consumer behavior. With the advent of technology, MBA is undergoing a paradigm shift, with new tools and techniques being developed to improve its accuracy and efficiency. This research paper focuses on the various techniques used in MBA, the role of advertisement in MBA, and the impact of technology on the improvement of MBA. The paper discusses the various algorithms and data mining techniques used in MBA and their advantages and disadvantages. Additionally, it analyzes the importance of advertisement in MBA, its impact on consumer behavior, and the role of technology in enhancing MBA. The paper concludes by highlighting the potential of technology in revolutionizing the MBA field, providing more accurate and efficient results, and ultimately improving business outcomes.

این مطالعه به منظور بررسی پیش بینی شاخص های عملکرد تولیدی در مرغان تخم گذار با استفاده از شبکه های عصبی مصنوعی و رگرسیون غیرخطی چندگانه انجام شد. بررسی بر روی اطلاعات چهار دوره متوالی پرورش در یک واحد پرورش مرغ تخم گذار صورت گرفت. روش های داده-کاوی شامل رگرسیون خطی و غیر خطی، شبکه عصبی پرسپترون سه لایه، شبکه عصبی پرسپترون چهار لایه و شبکه عصبی با تابع پایه ای شعاعی بود. در این مدل ها از متغیرهای سن گله، میزان خوراک مصرفی و فصل تولید به عنوان متغیر پیشگو و شاخص های عملکرد تولیدی شامل درصد تخم-گذاری، وزن توده ای تخم مرغ تولیدی و ضریب تبدیل غذایی به عنوان متغیر پاسخ استفاده شد. نتایج نهایی رگرسیون های خطی نشان داد که برای تمامی متغیرهای وابسته مورد مطالعه متغیر مستقل سن گله معنی دار می باشد. بنابراین رگرسیون غیر خطی شاخص های عملکرد تولیدی در مقابل سن برای مقایسه با شبکه های عصبی مختلف مورد بررسی قرار گرفت و برای مقایسه کلیه مدل ها از ضریب تعیین (R2) و میانگین قدر مطلق خطا (MAE) استفاده شد. نتایج نشان داد بین شبکه های عصبی مصنوعی مختلف مورد مطالعه، شبکه با تابع پایه ای شعاعی بهتر از سایر مدل های در پیش‌بینی شاخص های عملکرد تولیدی مرغان تخم گذار عمل می کند.

This paper discusses the in near–real time processing of Global Navigation Satellite An alternative approach based on using Vertical Electrical Sounding (VES) measurements and Artificial Neural Network (ANN) technique is newly proposed for computing the hydraulic conductivity K and the transmissivity T of an aquifer. VES measurements in the locations, where available water samples exist are required in such an approach, in order to train a neural network with fitting capability to evaluate both the hydraulic conductivity and transmissivity. The hydraulic conductivity and transmissivity are thereafter extrapolated by the use of trained neural network, even in the VES points where no water samples exist. This approach is practiced and tested in the Khanasser valley, Northern Syria, where the hydraulic conductivity and the transmissivity of the Quaternary aquifer is computed. We find an acceptable agreement between the hydraulic conductivity values obtained by the new approach and those obtained by the pumping test, which range between 0.864 and 8.64 m/day.

This study predicts the moisture ratio history data of dehydrating yam slices from partial data using Artificial Neural Network (ANN) techniques. The moisture ratio history data at 65 oC, 75 oC, 85 oC, and 95 oC were recorded for the dehydration of 1.5 mm, 3.0 mm, and 4.5 mm thick yam slices in a Refractance Window Dryer. The Artificial Neural Network within MATLAB software (v. 8.5), using the Levenberg-Marquardt back-propagation algorithm, was trained with some of the data. After training, the Neural Network software predicted the moisture ratio of the primary variables not used in training. The predicted and experimental values were compared. The results showed that, the Artificial Neural Network (ANN) model using the Levenberg-Marquardt back-propagation training algorithm could accurately predict the experimental results not used in training., the predicted and observed data values fitted each other with correlation coefficient (R2) values of 0.97, 0.99 and 0.99, respectively, for the three-process condition considered. The high R2 establishes a strong correlation between the experimental and predicted values. This work is essential as it establishes that Artificial Neural Network (ANN) techniques, using the Levenberg-Marquardt back-propagation training algorithm, can predict food samples moisture ratios of in a drying process when data is incomplete.

The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data

Market basket analysis (MBA) aims to discover purchasing patterns and item associations from customer transaction data. A major drawback of current techniques for MBA is a lack of quantitative metrics to measure the real value associated with basket items. This paper addresses this gap by deriving a practical game-theoretic measure for MBA based on the Shapley value of cooperative games, which we call Shapley value index for MBA (SIMBA). The SIMBA of an item represents the average revenue it earns, including its influence on the revenue earned from sales of other items. A significant challenge when applying Shapley value-inspired approaches in practical domains is the exponential complexity of Shapley value computation. However, for the MBA domain, we show that SIMBA admits a scalable exact computation method that does not require sampling or other approximations. Specifically, a characteristic function for the MBA game is constructed so that the transaction dataset input corresponds to the game’s Harsanyi dividends. The relationship between Harsanyi dividends and the Shapley value is then exploited to efficiently compute SIMBA. This approach scales linearly in the number of transactions, making SIMBA a feasible approach for quantitative MBA. SIMBA can be used to screen conventional MBA techniques, such as association rules, to identify significant rules based on the items’ cross-selling capacity. This combination of existing MBA methods and SIMBA will generate rules based not only on frequency of co-occurrence, but also on the significance of the items. We demonstrate the working of the algorithm by analyzing openly available transaction data from an online retail store. To the best of our knowledge, this is the first time Shapley value is used in this way to solve market basket analyses of a practical size.

Attempts are being made to enhance the drive performance by intelligent control using fuzzy logic (FL) and neural network techniques. One of the frequently discussed applications of artificial intelligence in control is the replacement of a standard proportional plus integral (PI) speed controller with an FL or artificial neural network (ANN) speed controller. Regardless of all the work, it appears that a thorough comparison of the drive behavior under PI, FL, and ANN speed control is necessary. This article attempts to compare PI, fuzzy, and ANN controllers that are implemented in an embedded system for closed-loop speed control of DC drive fed by a buck-type DC–DC power converter. The PI controller is designed based on the small signal modeling of the system. The PI-like fuzzy controller structure is considered for comparison. Two ANN controllers are designed. One controller uses training data obtained from the simulation of a fuzzy controller and the other uses training data from the simulation of a PI controller. The performance of the controllers is studied for a variety of operating conditions, such as step change in speed command and step change in load torque. The parameters selected for the comparison are the steady-state error and the rise time of the response. It is shown that ANN speed controllers provide a superior speed response in terms of rise time and the steady-state error compared to PI and FL controllers. This advantage arises from the fact that the neural network has the property of generalization and the control surface of the neural controller is smooth. The designed neural network controller is simple, with three neurons only, and so it is best suited for embedded system implementation. It is also found that the ANN controller trained with the training data from a PI controller has a better response compared to the ANN controller trained with data from a fuzzy controller.

Market basket analysis may be a technique identify the association between couple’s products purchased jointly and identify cases. Scene an occurrence is when two or more things happening. Market basket analysis makes rules if-then, scenario for instance, if an item purchased a and b will likely. bought items rule is probability in nature or, in other words, they are available from the incident. In observation the frequency is that the proportion of a basket of things which will be employed interesting. Rule, price, product placement and various varieties of cross-selling strategy. In order to create it easier to grasp, think in terms of market research handbasket in a very supermarket. Market basket analysis taking data on the transactions containing an inventory of all items that bought by customers in a very purchase. The technique is ascertaining the relations which product bought on other products. This relationship then wont to construct the containing the if-then items purchased. Market basket analysis also called a rule association or analysis affinity, learning may be a technique data processing can be used in numerous fields, as education, marketing, informatics and science. The aim is to include retailer’s awareness of buyers’ actions in order for the retailers with knowledge about the actions of purchasers, which will allow the tailor to make the right choices. All of the numerous market basket algorithms available to try to do. Algorithms, whose current work is on statics and which did not catch data changes in time, but algorithms not only intended to mine static information, but also provided new ways of calculating modifications on paper-listed association processing rules, and provide new algorithms that could stimulate market engagement and encourage up-selling.

Forecasts of future events are required in many of the activities associated with the planning and operation of the components of a water resource system. For the hydrologic component, there is a need for both short- and long-term forecasts of river flow events in order to optimize the system or to plan for future expansion or reduction. This paper presents the comparison of different artificial neural network (ANN) techniques in short- and long-term continuous and intermittent daily streamflow forecasting. The studies in modelling the intermittent series are quite limited because of the complexity of fitting models in to these series. The available conventional models necessitate the adjustment of numerous parameters for calibration. Three different ANN techniques, namely, feed-forward back propagation (FFBP), generalized regression neural networks, and radial basis function-based neural networks (RBF) are applied to continuous and intermittent river flow data of two Turkish rivers for short-range and long-range forecasting studies. The k-fold partitioning method is employed for preparing the ANN training data successfully. In general, the forecasting performance of RBF is found to be superior to the other two ANN techniques and a time series model in terms of the selected performance criteria. It was observed that the FFBP method had some drawbacks such as a local minima problem and negative flow generation.

The potential of multiple linear regression (MLR) and artificial neural network (ANN) techniques in predicting transient water levels over a groundwater basin were compared. MLR and ANN modeling was carried out at 17 sites in Japan, considering all significant inputs: rainfall, ambient temperature, river stage, 11 seasonal dummy variables, and influential lags of rainfall, ambient temperature, river stage and groundwater level. Seventeen site-specific ANN models were developed, using multi-layer feed-forward neural networks trained with Levenberg-Marquardt backpropagation algorithms. The performance of the models was evaluated using statistical and graphical indicators. Comparison of the goodness-of-fit statistics of the MLR models with those of the ANN models indicated that there is better agreement between the ANN-predicted groundwater levels and the observed groundwater levels at all the sites, compared to the MLR. This finding was supported by the graphical indicators and the residual analysis. Thus, it is concluded that the ANN technique is superior to the MLR technique in predicting spatio-temporal distribution of groundwater levels in a basin. However, considering the practical advantages of the MLR technique, it is recommended as an alternative and cost-effective groundwater modeling tool.

Sustainable development (SD) has recently emerged as a critical axis in industrial strategic management and debate. Apart from the scarcity of resources, the influence of economic difficulties on human life and society has increased the relevance of SD. However, estimating and analyzing the SD of any business are still in its infancy. Thus, the SD of the sports goods industry is assessed using a multilayer artificial perceptron neural network (MLP) technique employing eight distinct characteristics connected to production efficiency, international and internal environment, export, and domestic development by using Statistica software. To discover an ideal model for SD in the sports goods business, stepwise models with varied input parameters were built. The identity activation function was used to create and train an error propagation algorithm-based network. The results of various eight network models revealed that, among other models, the accuracy of our fifth model, which incorporates production efficiency, international and internal environment, strategic management, and domestic distribution organization, gives the best answer. This model has the lowest root-mean-square error (RMSE) = 0.002 and the greatest R square = 0.922, demonstrating its efficacy and effectiveness in terms of SD. Based on these findings, it is possible to conclude that using artificial neural networks is beneficial in determining an optimal solution for SD and other similar problems since this tool is important and would be utilized for forecasting and decision-making in any business.

Market basket analysis has been an elementary part of quantitative decision support in retail marketing for many years and it is regularly cited as a prime application area of data mining. In this paper two competitive neural network approaches are presented and discussed with respect to their suitability for purchase interdependence analysis on the product category level. Particular attention is paid to the user-oriented representation or visualisation of cross-category dependences. Both approaches are applied to point of sales scanner data provided by a German retail chain to check how far they are able to uncover presumed purchase interdependences.

This research project focuses on the design and development of a web-based Point of Sale (POS) application that incorporates advanced analytical techniques, specifically Market Basket Analysis with the Apriori algorithm and the Association Rule method. The primary objective of this web-based POS system is to empower retailers in managing their sales transactions efficiently and gaining valuable insights into customer purchasing behavior. The web-based POS application is multifaceted, offering features such as sales transaction recording, product inventory management, and customer data tracking. What sets it apart is the integration of the Apriori algorithm and Association Rule method, which enable the system to analyze and understand customer purchasing patterns. It identifies strong product associations and establishes rules that support intelligent decision-making for businesses. The advantages of Market Basket Analysis are substantial. Retailers can identify relevant purchase patterns, such as frequently co-purchased products or cross-selling opportunities. This information can be used to enhance marketing strategies, optimize product placement in stores, and create bundled product offerings, ultimately boosting sales and revenue. By analyzing transaction data and recognizing patterns, retailers can streamline their operations, minimize wastage, and allocate resources more effectively. In summary, this research project showcases the transformative potential of integrating Market Basket Analysis, the Apriori algorithm, and the Association Rule method into web-based POS systems. By doing so, retailers can enhance operational efficiency, boost sales, and improve customer satisfaction, ultimately leading to more successful and competitive businesses in the retail sector.

In this paper, Artificial Neural Network (ANN) technique is implemented to improve speed and torque control of a separately excited DC machine drive. The speed and torque sensorless scheme based on ANN is estimated adaptively. The proposed controller is designed to estimate rotor speed and mechanical load torque as a Model Reference Adaptive System (MRAS) method for DC machine. The DC drive system consists of four quadrant DC/DC chopper with MOSFET transistors, ANN, logic gates and routing circuits. The DC drive circuit is designed, evaluated and modeled by Matlab/Simulink in the forward and reverse operation modes as a motor and generator, respectively. The DC drive system is simulated at different speed values (±1200 rpm) and mechanical torque (±7 N.m) in steady state and dynamic conditions. The simulation results illustrate the effectiveness of the proposed controller without speed or torque sensors.

This paper defines a protocol for geometric description and classification of white eggs of chickens using computer vision (CV) and artificial neural network (ANN) techniques. For this, 60 eggs were selected and their dimensions, volumes and surface areas measured using pachymeter, immersion method and mathematical expressions available in the literature, respectively, to compose the reference values. Subsequently, the eggs were imaged with RGB camera and the digital images analyzed using dedicated algorithm. The estimated geometric parameters were compared with the witness values, using descriptive statistics and t test, and used in ANN training for automatic classification of eggs. Perceptron was the algorithm adopted, different configurations were evaluated in the ANN and 20 digital images of white eggs available in the literature used in the classification validation. Prediction of the height, width, height/width, volume and surface area of the eggs using CV showed high correlation and low average square error with the reference values. The best egg classification results occurred with an ANN with three neurons and data of height, width and surface area as input parameters. The proposed protocol has proved feasible for the geometric description and classification of white eggs of Brazilian agro-industries.