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The purpose of this paper is to present a Differential Immune Clone Clustering Algorithm (DICCA) to solve image segmentation.

DICCA combines immune clone selection and differential evolution, and two populations are used in the evolutionary process. Clone reproduction and selection, differential mutation, crossover and selection are adopted to evolve two populations, which can increase population diversity and avoid local optimum. After extracting the texture features of an image and encoding them with real numbers, DICCA is used to partition these features, and the final segmentation result is obtained.

This approach is applied to segment all sorts of images into homogeneous regions, including artificial synthetic texture images, natural images and remote sensing images, and the experimental results show the effectiveness of the proposed algorithm.

The method presented in this paper represents a new approach to solving clustering problems. The novel method applies the idea two populations are used in the evolutionary process. The proposed clustering algorithm is shown to be effective in solving image segmentation.

Gene selection is considered as the fundamental process in the bioinformatics field. The existing methodologies pertain to cancer classification are mostly clinical basis, and its diagnosis capability is limited. Nowadays, the significant problems of cancer diagnosis are solved by the utilization of gene expression data. The researchers have been introducing many possibilities to diagnose cancer appropriately and effectively. This paper aims to develop the cancer data classification using gene expression data.

The proposed classification model involves three main phases: “(1) Feature extraction, (2) Optimal Feature Selection and (3) Classification”. Initially, five benchmark gene expression datasets are collected. From the collected gene expression data, the feature extraction is performed. To diminish the length of the feature vectors, optimal feature selection is performed, for which a new meta-heuristic algorithm termed as quantum-inspired immune clone optimization algorithm (QICO) is used. Once the relevant features are selected, the classification is performed by a deep learning model called recurrent neural network (RNN). Finally, the experimental analysis reveals that the proposed QICO-based feature selection model outperforms the other heuristic-based feature selection and optimized RNN outperforms the other machine learning methods.

The proposed QICO-RNN is acquiring the best outcomes at any learning percentage. On considering the learning percentage 85, the accuracy of the proposed QICO-RNN was 3.2% excellent than RNN, 4.3% excellent than RF, 3.8% excellent than NB and 2.1% excellent than KNN for Dataset 1. For Dataset 2, at learning percentage 35, the accuracy of the proposed QICO-RNN was 13.3% exclusive than RNN, 8.9% exclusive than RF and 14.8% exclusive than NB and KNN. Hence, the developed QICO algorithm is performing well in classifying the cancer data using gene expression data accurately.

This paper introduces a new optimal feature selection model using QICO and QICO-based RNN for effective classification of cancer data using gene expression data. This is the first work that utilizes an optimal feature selection model using QICO and QICO-RNN for effective classification of cancer data using gene expression data.

This study aims to introduce an affective computing‐based method of identifying student understanding throughout a distance learning course.

The study proposed a learning emotion recognition model that included three phases: feature extraction and generation, feature subset selection and emotion recognition. Features are extracted from facial images and transform a given measument of facial expressions to a new set of features defining and computing by eigenvectors. Feature subset selection uses the immune memory clone algorithms to optimize the feature selection. Emotion recognition uses a classifier to build the connection between facial expression and learning emotion.

Experimental results using the basic expression of facial expression recognition research database, JAFFE, show that the proposed facial expression recognition method has high classification performance. The experiment results also show that the recognition of spontaneous facial expressions is effective in the synchronous distance learning courses.

The study shows that identifying student comprehension based on facial expression recognition in synchronous distance learning courses is feasible. This can help instrutors understand the student comprehension real time. So instructors can adapt their teaching materials and strategy to fit with the learning status of students.

This paper aims to investigate the scheduling and loading problems of tow trains for mixed-model assembly lines (MMALs). An in-plant milk-run delivery model has been formulated to minimize total line-side inventory for all stations over the planning horizon by specifying the departure time, parts quantity of each delivery and the destination station.

An immune clonal selection algorithm (ICSA) combined with neighborhood search (NS) and simulated annealing (SA) operators, which is called the NSICSA algorithm, is developed, possessing the global search ability of ICSA, the ability of SA for escaping local optimum and the deep search ability of NS to get better solutions.

The modifications have overcome the deficiency of insufficient local search and deepened the search depth of the original metaheuristic. Meanwhile, good approximate solutions are obtained in small-, medium- and large-scale instances. Furthermore, inventory peaks are in control according to computational results, proving the effectiveness of the mathematical model.

This study works out only if there is no breakdown of tow trains. The current work contributes to the in-plant milk-run delivery scheduling for MMALs, and it can be modified to deal with similar part feeding problems.

The capacity limit of line-side inventory for workstations as well as no stock-outs rules are taken into account, and the scheduling and loading problems are solved satisfactorily for the part distribution of MMALs.

This is a conceptual study to analyze the potential of enset-based culinary tourism for sustainable rural development and to obtain a place as a niche tourism market in South-Western Ethiopia. It assumed enset agro-biodiversity as the effect of ages of environment, genetic resources and cultural interaction as a distinctive regional image.

This an exploratory paper based on an in-depth interview, field observation and content analysis of documents. By means of in-depth interviews, the researchers managed to gather extended information from community elders and experts in culture and tourism offices selected based on a snowball technique. Besides, it has gone through systematic reviews of about 180 empirical and conceptual articles, books and conference papers with a critical reading of the content, identification of categories, examination and interpretation of ideas, to supplement the in-depth-interview. The thematic analysis applied to identify various ideas, concepts, categories and relationships to produce themes presented under discussion and results.

The study found enset-based culinary tourism not only improve the local economy and regional image, but also it would enhance conservation of traditional farming system, biodiversity, food heritages, genetic varieties and livestock. It also identified 18 enset food varieties compatible with the principle of balanced diets. Finally, the study advised rural development planners to consider enset-based culinary tourism so that it would revive lost food traditions and consumption patterns, enhance the regional heritage and destination branding.

The research is a conceptual study that lacked empirical investigation concerning the livelihood impact, gender implication and actual tourist data. Therefore, future research needs to focus on the aforementioned limitations.

This study addressed SW Ethiopia, which is the primary center of Ensete ventricosum, and argued that enset-based culinary tourism would help to build regional image and obtain a place as a niche rural tourism destination. It would also contribute to the conservation of food heritages, environmentally sustainable farming system, soil conservation, crop diversities and livestock population in addition to producing tourist experience. Moreover, it would encourage the revival of traditional consumption, reinvent lost food traditions and identities.

It was hoped that rural tourism would eventually improve the livelihood and enhance the capability of resilience. It is also expected to maintain the traditional social-economic structure based on the enset farm while fostering cultural development.

To the knowledge of the researchers there is no previous work on enset based-culinary tourism in Ethiopia and probably there is no published culinary tourism paper elsewhere.

This paper aims to demonstrate the principle and practical applications of hyperspectral object detection, carry out the problem we now face and the possible solution. Also some challenges in this field are discussed.

First, the paper summarized the current research status of the hyperspectral techniques. Then, the paper demonstrated the development of underwater hyperspectral techniques from three major aspects, which are UHI preprocess, unmixing and applications. Finally, the paper presents a conclusion of applications of hyperspectral imaging and future research directions.

Various methods and scenarios for underwater object detection with hyperspectral imaging are compared, which include preprocessing, unmixing and classification. A summary is made to demonstrate the application scope and results of different methods, which may play an important role in the application of underwater hyperspectral object detection in the future.

This paper introduced several methods of hyperspectral image process, give out the conclusion of the advantages and disadvantages of each method, then demonstrated the challenges we face and the possible way to deal with them.

The purpose of this paper is to present a new method to obtain robust solutions to electromagnetic optimization problems, solved with evolutional algorithms, which are insensitive to changes in design parameters such as spatial size, positioning and material constant.

Adjoint variable method is employed to evaluate the sensitivity of individuals in evolutional processes.

It is shown in the numerical examples, where the present method is applied to optimization of a superconducting energy storage system and C‐shape magnet, that robust solutions are actually obtained which are insensitive to deviations in spatial sizes.

Unlike usual optimization methods, the present method takes into account deviation in the design parameters due to production errors and long‐term changes. Moreover, the present method is limited to about twice the computational cost of non‐robust optimization methods.

Driven by sustainable production, mobile robots are introduced as a new clean-energy material handling tool for mixed-model assembly lines (MMALs), which reduces energy consumption and lineside inventory of workstations (LSI). Nevertheless, the previous part feeding scheduling method was designed for conventional material handling tools without considering the flexible spatial layout of the robotic mobile fulfillment system (RMFS). To fill this gap, this paper focuses on a greening mobile robot part feeding scheduling problem with Just-In-Time (JIT) considerations, where the layout and number of pods can be adjusted.

A novel hybrid-load pod (HL-pod) and mobile robot are proposed to carry out part feeding tasks between material supermarkets and assembly lines. A bi-objective mixed-integer programming model is formulated to minimize both total energy consumption and LSI, aligning with environmental and sustainable JIT goals. Due to the NP-hard nature of the proposed problem, a chaotic differential evolution algorithm for multi-objective optimization based on iterated local search (CDEMIL) algorithm is presented. The effectiveness of the proposed algorithm is verified by dealing with the HL-pod-based greening part feeding scheduling problem in different problem scales and compared to two benchmark algorithms. Managerial insights analyses are conducted to implement the HL-pod strategy.

The CDEMIL algorithm's ability to produce Pareto fronts for different problem scales confirms its effectiveness and feasibility. Computational results show that the proposed algorithm outperforms the other two compared algorithms regarding solution quality and convergence speed. Additionally, the results indicate that the HL-pod performs better than adopting a single type of pod.

This study proposes an innovative solution to the scheduling problem for efficient JIT part feeding using RMFS and HL-pods in automobile MMALs. It considers both the layout and number of pods, ensuring a sustainable and environmental-friendly approach to production.

None of the numerous food products that comprise the Italian food tradition can boast of business revitalisation as much as that which involved the wine industry in Italy and in the rest of the world in the last decade. This is not the appropriate moment to consider the reasons for this change, nor is it the right place to compare the industrial situations of that time with those present today. Rapidly covering the field of the extensive history of the wine business, it is sufficient to cite certain simplified facts in order to show how the end user of the product – the consumer – has dramatically changed his consumption history, which initiated the process of regeneration of the business, a process never before seen, in the world of agricultural industries. The companies in the vine‐growing and wine‐making business have been both the driving force and the beneficiaries of this state of affairs. Indeed, to have a clearer picture, a hypothetical external person, observing the wine business panorama today, would notice clear features and company models, that are unrelated to the historical past of the industry.

From humble beginnings, Casella Wines has become Australia's greatest wine producer. The purpose of this paper is to describe how the company has become so successful.

The paper comprises many quotes from John Castella, Managing Director of Casella Wines, among others, and covers various areas of the business, for example, foundation building, core enhancement strategy, product/market strategy, hiring policy, and brand building.

For Casella, real success is measured in terms of how proud the family is to make a contribution to wine making and to Australia, as the country of adoption for its post‐war Italian immigrant founders more than five decades ago. Above all, the winery is much today as it was then – all about sustaining family relationships, sharing good wines with good friends, and passing on wine making skills to the next generation so they can, in due time, carry on the family tradition.

This paper would make a useful, research‐informed teaching case, highlighting the phenomenal growth of the yellowtail brand and the family business that developed it.