An enhancement to the Bees Algorithm with slope angle computation and Hill Climbing Algorithm and its applications on scheduling and continuous-type optimisation problem

This paper focuses on improvements to the Bees Algorithm (BA) with slope angle computation and Hill Climbing Algorithm (SACHCA) during the local search process. First, the SAC was employed to determine the inclination of the current sites. Second, according to the slope angle, the HCA was utilised to guide the algorithm to converge to the local optima. This enabled the global optimum of the given problem to be found faster and more precisely by focusing on finding the available local optima first before turning the attention on the global optimum. The proposed enhancements to the BA have been tested on continuous-type benchmark functions and compared with other optimisation techniques. The results show that the proposed algorithm performed better than other algorithms on most of the benchmark functions. The enhanced BA performs better than the basic BA, in particular on higher dimensional and complex optimisation problems. Finally, the proposed algorithm has been used to solve the single machine scheduling problem and the results show that the proposed SAC and HCA-BA outperformed the basic BA in almost all the considered instances, in particular when the complexity of the problem increases

Artificial intelligence-based solutions for coffee leaf disease classification

Coffee is one of the most widely consumed beverages and the quantity and quality of coffee beans depend significantly on the health and condition of coffee plants, particularly their leaves. The automation of coffee leaf disease classification using AI is an essential need, providing not only economic benefits but also contributing to environmental conservation and creating better conditions for sustainable coffee cultivation. Through the application of AI, early disease detection is facilitated, thereby reducing pest and disease control costs, minimizing crop losses, increasing coffee productivity and product quality, and promoting environmental preservation. Many studies have proposed AI algorithms for coffee disease classification. However, numerous algorithms employ classical algorithms, while some utilize deep learning, the current state-of-the-art in computer vision. The challenge lies in the fact that when using deep learning, a substantial amount of data is required for training. The design of deep learning architectures to enhance model accuracy while still working with a small training dataset remains an area of ongoing research. In this study, we propose deep learning-based method for coffee leaf disease classification. We propose the combination of different deep convolutional neural networks to further improve overall classification performance. Early and late fusion have been conducted to evaluate the effectiveness of the pre-trained model. Our experimental results demonstrate that the ensemble method outperforms single-model approaches, achieving high accuracy and precision in BRACOL coffee disease leaf

Neural network design and feature selection using principal component analysis and Taguchi method for identifying wood veneer defects

Nowadays, ensuring high quality can be considered the main strength for a company’s success. Especially, in a period of economic recession, quality control is crucial from the operational and strategic point of view. There are different quality control methods and it has been proven that on the whole companies using a continuous improvement approach, eliminating waste and maximizing productive flow, are more efficient and produce more with lower costs. This paper presents a method to optimize the quality control stage for a wood manufacturing firm. The method is based on the employment of the principal component analysis in order to reduce the number of critical variables to be given as input for an artificial neural network (ANN) to identify wood veneer defects. The proposed method allows the ANN classifier to identify defects in real time and increase the response speed during the quality control stage so that veneers with defects do not pass through the whole production cycle but are rejected at the beginning

Cost-effective solutions and tools for medical image processing and design of personalised cranioplasty implants

Cranial defects which are caused by bone tumors or traffic accidents are treated by cranioplasty techniques. Cranioplasty implants are required to protect the underlying brain, correct major aesthetic deformities, or both. With the rapid develop-ment of computer graphics, medical image processing (MIP) and manufacturing technologies in recent decades, nowadays, personalised cranioplasty implants can be designed and made to improve the quality of cranial defect treatments. However, software tools for MIP and 3D modelling of implants are ex-pensive; and they normally require high technical skills. Espe-cially, the process of design and development of personalised cranioplasty implants normally requires a multidisciplinary team, including experts in MIP, 3D design and modelling, and Biomedical Engineering; this leads to challenges and difficulties for technology transfers and implementations in hospitals. This research is aimed at developing, in particular, cost-effective solutions and tools for design and modeling of per-sonalised cranioplasty implants, and to simplify the design and modelling of implants, as well as to reduce the design and modeling time. In this way, surgeons and engineers can con-veniently and easily design personalised cranioplasty implants, without the need of using complex MIP and CAD tools; and as a result the cost of implants will be minimised

Detection and classification of lamination faults in A 15 kVA three-phase transformer core using SVM, KNN and DT algorithms

This paper deals with the detection and classification of two types of lamination faults (i.e., edge burr and lamination insulation faults) in a three-phase transformer core. Previous experimental results are exploited, which are obtained by employing a 15 kVA transformer under healthy and faulty conditions. Different test conditions were considered such as the flux density, number of the affected laminations, and fault location. Indeed, the current signals were used where four features (Average, Fundamental, Total Harmonic Distortion (THD), and Standard Deviation (STD)) were extracted. Elaborating A total of 328 samples, these features are utilized as input vectors to train and test classification models based on SVM, KNN, and DT algorithms. Based on the selected features, the results confirmed that the transformer current can be used for the detection of lamination faults. An accuracy rate of more than 84% was obtained using three different classifiers. Such findings provided a promising step toward fault detection and classification in electrical transformers, helping to prevent the system and avoid other related issues such as the increase in power loss and temperature

A review of data mining in knowledge management: applications/findings for transportation of small and medium enterprises

A core subfeld of knowledge management (KM) and data mining (DM) constitutes an integral part of the knowledge discovery in database process. With the explosion of information in the new digital age, research studies in the DM and KM continue to heighten up in the business organisations, especially so, for the small and medium enterprises (SMEs). DM is crucial in supporting the KM application as it processes the data to useful knowledge and KM role next, is to manage these knowledge assets within the organisation systematically. At the comprehensive appraisal of the large enterprise in the transportation sector and the SMEs across various industries—it was gathered that there is limited research case study conducted on the application of DM–KM on the transportation SMEs in specifc. From the extensive review of the case studies, it was uncovered that majority of the organisations are not leveraging on the use of tacit knowledge and that the SMEs are adopting a more traditional use of ICTs to its KM approach. In addition, despite DM–KM is being widely implemented—the case studies analysis reveals that there is a limitation in the presence of an integrated DM–KM assessment to evaluate the outcome of the DM–KM application. This paper concludes that there is a critical need for a novel DM–KM assessment plan template to evaluate and ensure that the knowledge created and implemented are usable and relevant, specifcally for the SMEs in the transportation sector. Therefore, this research paper aims to carry out an in-depth review of data mining in knowledge management for SMEs in the transportation industry

Hybrid Genetic Bees Algorithm applied to Single Machine Scheduling with Earliness and Tardiness Penalties

This paper presents a hybrid Genetic-Bees Algorithm based optimised solution for the single machine scheduling problem. The enhancement of the Bees Algorithm (BA) is conducted using the Genetic Algorithm's (GA's) operators during the global search stage. The proposed enhancement aims to increase the global search capability of the BA gradually with new additions. Although the BA has very successful implementations on various type of optimisation problems, it has found that the algorithm suffers from weak global search ability which increases the computational complexities on NP-hard type optimisation problems e.g. combinatorial/permutational type optimisation problems. This weakness occurs due to using a simple global random search operation during the search process. To reinforce the global search process in the BA, the proposed enhancement is utilised to increase exploration capability by expanding the number of fittest solutions through the genetical variations of promising solutions. The hybridisation process is realised by including two strategies into the basic BA, named as â\u80\u9creinforced global searchâ\u80\u9d and â\u80\u9cjumping functionâ\u80\u9d strategies. The reinforced global search strategy is the first stage of the hybridisation process and contains the mutation operator of the GA. The second strategy, jumping function strategy, consists of four GA operators as single point crossover, multipoint crossover, mutation and randomisation. To demonstrate the strength of the proposed solution, several experiments were carried out on 280 well-known single machine benchmark instances, and the results are presented by comparing to other well-known heuristic algorithms. According to the experiments, the proposed enhancements provides better capability to basic BA to jump from local minima, and GBA performed better compared to BA in terms of convergence and the quality of results. The convergence time reduced about 60% with about 30% better results for highly constrained jobs

Business analytics in industry 4.0: a systematic review

Recently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.The work of P. Cortez was supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. We would like to thank to the three anonymous reviewers for their helpful suggestions