Inventory prediction and management in Nigeria using market basket analysis associative rule mining: memetic algorithm based approach

Abstract

<p class="Text">A key challenge in businesses today is determining inventory level for each product (to be) sold to clients. A pre-knowledge will suppress inventory stock-up and help avert unnecessary demurrage. It will also avoid stock out and avert loss of clients to competition. Study aims to unveil customer’s behavior in purchasing goods and thus, predict a next time purchase as well as serve as decision support to determine the required amount of each goods inventory. Study is conducted for Delta Mall (Asaba and Warri branches) department store. We adapt the memetic algorithm on market basket dataset to examine buying behavior of customers, their preference and frequency at which goods are purchased in common (basket). Result shows some items placed in basket allow customers to purchase items of similar value, or best combined with the selected items due to shelf-placement via concept of feature drift. Model yields 21-rules for eight items obtained from data transaction mining dataset acquired from Delta Mall.</p>

Forging a deep learning neural network intrusion detection framework to curb the distributed denial of service attack

“…The auto-encoder is an unsupervised multi-layered neural network consisting both an encoder and a decoder network. Its encoder seeks to transform inputs data-points from a high unto a low-dimension via an encoding function fencoder as in (1) where x m is a data point, and h m is the encoding vector obtained. Conversely, its decoder network seeks to reconstruct the function using fdecoder as in (2) with x m as decoding vector from h m .…”

“…We employed the rectified linear unit (ReLU) activation function with 500-epochs (though optimal values were reached at 100, 300 and 500 epochs taking into account accuracy and time to train the model). There is no best practice in selecting the number of hidden layers/neurons therein and using more hidden layer(s) grants the model greater capability to perform more complex function on the data [1,2]. We seek minimum training error that will also result in the best fit, selecting the number of hidden layers (and neurons for each layer) was established via a trail-and-error method, and examining the results.…”

“…Technology seeks to better advance our living existence unto higher plains cum levels of sophistication with ease. Tremendous adoption and integration of the Internet has significantly advanced the use of data sharing programs that seeks to effectively disseminate data froom one user to another [1]. This adoption and integration has been attributed to its usage ease, ubiquity of its nature, low-cost of transaction and trust in communication channel-all of which continues to advance its popularity, adoption ease and usage.…”

Memetic algorithm for short messaging service spam filter using text normalization and semantic approach

“…Bayesian network design needs to consider the attributes, search algorithm and estimation algorithms. Thus, we use the hill-climber search algorithm with five parents used as the search algorithm for this network with simple estimator as an estimate on algorithm with threshold value "0.5" [38].…”

The Implementation of K-Means Clustering in Coal-fired Power Plant Performance

“…Even the model grows further, these approach has an ability to applied in industry [7]. For instance, the association has been applied largely for industry's knowledge discovery database, as applied in inventory management [8], market analysis [9] and also alarm system [10]. The supervised algorithm that required input and output dataset for training and testing has largely applied for sustainability performance [5] also demand forecasting [11].…”