BAT-BP: A new BAT based back-propagation algorithm for efficient data classification

Training neural networks particularly back propagation algorithm is a complex task of great importance in the field of supervised learning. One of the nature inspired meta-heuristic Bat algorithm is becoming a popular method in solving many complex optimization problems. Thus, this study investigates the use of Bat algorithm along with back-propagation neural network (BPNN) algorithm in-order to gain optimal weights to solve the local minima problem and also to enhance the convergence rate. This study intends to show the superiority (time performance and quality of solution) of the proposed meta-heuristic Bat-BP algorithm over other more standard neural network training algorithms. The performance of the proposed Bat-BP algorithm is then compared with Artificial Bee Colony using BPNN (ABC-BP), Artificial Bee Colony using Levenberg-Marquardt (ABC-LM) and BPNN algorithm. Classification datasets from UCI machine learning repository are used to train the network. The simulation results show that the efficiency of BPNN training process is highly enhanced when combined with BAT algorithm

Pre-dispersive near-infrared light sensing in non-destructively classifying the brix of intact pineapples

Exported fresh intact pineapples must fulfill the minimum internal quality requirement of 12 degree brix. Even though near-infrared (NIR) spectroscopic approaches are promising to non-destructively and rapidly assess the internal quality of intact pineapples, these approaches involve expensive and complex NIR spectroscopic instrumentation. Thus, this research evaluates the performance of a proposed pre-dispersive NIR light sensing approach in non-destructively classifying the Brix of pineapples using K-fold cross-validation, holdout validation, and sensitive analysis. First, the proposed pre-dispersive NIR sensing device that consisted of a light sensing element and five NIR light emitting diodes with peak wavelengths of 780, 850, 870, 910, and 940 nm, respectively, was developed. After that, the diffuse reflectance NIR light of intact pineapples was non-destructively acquired using the developed NIR sensing device before their Brix values were conventionally measured using a digital refractometer. Next, an artificial neural network (ANN) was trained and optimized to classify the Brix values of pineapples using the acquired NIR light. The results of the sensitivity analysis showed that either one wavelength that was near to the water absorbance or chlorophyll band was redundant in the classification. The performance of the trained ANN was tested using new pineapples with the optimal classification accuracy of 80.56%. This indicates that the proposed predispersive NIR light sensing approach coupled with the ANN is promising to be an alternative to non-destructively classifying the internal quality of fruits

Issues and trends in fashion education sustainability

United Nations (UN) has declared Sustainable Development Education (ESD) as an effort to achieve sustainable development goals (SDGs). Hence, it is encouraged that all parties globally, the government, the industry and education to implement a holistic and creative ESD approach to realize the goals of SDGs. ESD issues are also discussed in fashion education because the fashion industry is a contributor to environmental pollution. Thus, the values of sustainability in fashion education should be applied to students at an early stage so that students can understand different perspectives when faced with sustainability issues. In addition, students' attitudes and behaviour towards environmental issues are also at a moderate level. Students have been influenced by various influences such as mass media, ideologies and other social aspects. Thus, the objective of this study was to provide more information on issues in sustainable fashion education. The researcher also tried to channel some initiatives and the mechanism that can be used for the ESD practice approach in fashion education. The methodology used in this article was through the information review of previous studies. The information collected consists of articles of study and existing reports. The general and specific keywords were also used in the information search. Besides, the researcher also modified previous research results and combined various previous research findings to identify issues and trends of ESD in fashion education. From the findings, several parties play important roles in practising ESD in fashion education. The parties involved are educators, educational institutions, the government and the industry. In addition, various approaches can be done to promote ESD practices to students such as the application of mixed technology, the use of conceptual models in the fashion curriculum, the development of sustainable fashion courses, innovation in curriculum and pedagogy as well as collaborative activities between institutions and the industry. The findings also showed that several theories and models of previous studies discussed the significant relationship of factors influencing ESD practices. Those theories are Theory of Planned Behavior-TPB, Norm Activation Theory - NAT, Predictors of Environmental Behavior: PEB model and KAP Model

End point prediction of basic oxygen furnace (BOF) steelmaking based on improved bat-neural network

A mixed bat optimization algorithm based on chaos and differential evolution (CDEBA) is proposed for the endblow process of basic oxygen furnance (BOF) after sub-lance detection, and a prediction model based on BP neural network optimized by chaotic differential bat algorithm (CDEBA-NN) is presented. The simulation results show that the prediction model of carbon content achieves a hit rate of 94 % with the error range of 0,005 %, and 90 % for temperature with the error range of 15 °C, the accuracy is higher than the traditional neural network model, and then it verifies the effectiveness of the proposed model

An Efficient Fingerprint Identification using Neural Network and BAT Algorithm

The uniqueness, firmness, public recognition, and its minimum risk of intrusion made fingerprint is an expansively used personal authentication metrics. Fingerprint technology is a biometric technique used to distinguish persons based on their physical traits. Fingerprint based authentication schemes are becoming increasingly common and usage of these in fingerprint security schemes, made an objective to the attackers. The repute of the fingerprint image controls the sturdiness of a fingerprint authentication system. We intend for an effective method for fingerprint classification with the help of soft computing methods. The proposed classification scheme is classified into three phases. The first phase is preprocessing in which the fingerprint images are enhanced by employing median filters. After noise removal histogram equalization is achieved for augmenting the images. The second stage is the feature Extraction phase in which numerous image features such as Area, SURF, holo entropy, and SIFT features are extracted. The final phase is classification using hybrid Neural for classification of fingerprint as fake or original. The neural network is unified with BAT algorithm for optimizing the weight factor

An architecture for recycling intermediates in a column-store

Automatic recycling intermediate results to improve both query response time and throughput is a grand c

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy

A Hybrid of Artificial Bee Colony, Genetic Algorithm, and Neural Network for Diabetic Mellitus Diagnosing

Researchers widely have introduced the Artificial Bee Colony (ABC) as an optimization algorithm to deal with classification and prediction problems. ABC has been combined with different Artificial Intelligent (AI) techniques to obtain optimum performance indicators. This work introduces a hybrid of ABC, Genetic Algorithm (GA), and Back Propagation Neural Network (BPNN) in the application of classifying, and diagnosing Diabetic Mellitus (DM). The optimized algorithm is combined with a mutation technique of Genetic Algorithm (GA) to obtain the optimum set of training weights for a BPNN. The idea is to prove that weights’ initial index in their initialized set has an impact on the performance rate. Experiments are conducted in three different cases; standard BPNN alone, BPNN trained with ABC, and BPNN trained with the mutation based ABC. The work tests all three cases of optimization on two different datasets (Primary dataset, and Secondary dataset) of diabetic mellitus (DM). The primary dataset is built by this work through collecting 31 features of 501 DM patients in local hospitals. The secondary dataset is the Pima dataset. Results show that the BPNN trained with the mutation based ABC can produce better local solutions than the standard BPNN and BPNN trained in combination with ABC