An improved moth flame optimization algorithm based on rough sets for tomato diseases detection

Plant diseases is one of the major bottlenecks in agricultural production that have bad effects on the economic of any country. Automatic detection of such disease could minimize these effects. Features selection is a usual pre-processing step used for automatic disease detection systems. It is an important process for detecting and eliminating noisy, irrelevant, and redundant data. Thus, it could lead to improve the detection performance. In this paper, an improved moth-flame approach to automatically detect tomato diseases was proposed. The moth-flame fitness function depends on the rough sets dependency degree and it takes into a consideration the number of selected features. The proposed algorithm used both of the power of exploration of the moth flame and the high performance of rough sets for the feature selection task to find the set of features maximizing the classification accuracy which was evaluated using the support vector machine (SVM). The performance of the MFORSFS algorithm was evaluated using many benchmark datasets taken from UCI machine learning data repository and then compared with feature selection approaches based on Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) with rough sets. The proposed algorithm was then used in a real-life problem, detecting tomato diseases (Powdery mildew and early blight) where a real dataset of tomato disease were manually built and a tomato disease detection approach was proposed and evaluated using this dataset. The experimental results showed that the proposed algorithm was efficient in terms of Recall, Precision, Accuracy and F-Score, as long as feature size reduction and execution time

Technology as Problem-Solving Procedures and Technology as Input-Output Relations: Some Perspectives on the Theory of Production

In this work, inspired by Winter [2006], in fact of vintage 1968, we discuss the relation between three dierent levels of analysis of technologies, namely as (i) bodies of problem-solving knowledge, (ii) organizational procedures, and (iii) input-output relations. We begin by arguing that the "primitive" levels of investigation, "where the action is", are those which concern knowledge and organizational procedures, while in most respects the I/O representation is just an ex post, derived, one. Next, we outline what we consider to be important advances in the understanding of productive knowledge and of the nature and behaviors of business organizations which to a good extent embody such a knowledge. Finally, we explore some implications of such "procedural" view of technologies in terms of input-output relations (of which standard production functions are a particular instantiation). We do that with the help of some pieces of evidence, drawing both upon incumbent literature and our own elaboration on micro longitudinal data on the Italian industry.Theory of Production, Organizational Routines, Problem-solving Knowledge, Production Function, Micro-heterogeneity

Innovative coordination of agribusiness chains and networks

To facilitate scientifically grounded innovative forms of strategic network coordination, this paper integrates two major bodies of literature on competitive advantage. The two bodies of literature are the industry-oriented outside-in approach, and the competence-oriented inside-out approach, here homogenized along the dimensions of degrees of firm embeddedness, respectively, the broadness of shared resource bases. The elements detailed are interfirm relationships, resource bases, network governance instruments, coordination mechanisms, the impact of events on network structures, and the active mobilisation of actors and resource. Thereby, the paper is able to detail 5 generic types of business networks. Next, it relates 21 network governance instruments to type of partnerships (binding vs loosening), forms of interaction (cooperative vs opportunistic). The realized reduction of network complexity enhances conceptual transparency and increases the instrumental usage of this research for effective network coordination by businesses. An integrated case illustrates the usefulness of the various concepts and the coherency of the different elements

Enhancing the Efficiency of a Decision Support System through the Clustering of Complex Rule-Based Knowledge Bases and Modification of the Inference Algorithm

Decision support systems founded on rule-based knowledge representation should be equipped with rule management mechanisms. Effective exploration of new knowledge in every domain of human life requires new algorithms of knowledge organization and a thorough search of the created data structures. In this work, the author introduces an optimization of both the knowledge base structure and the inference algorithm. Hence, a new, hierarchically organized knowledge base structure is proposed as it draws on the cluster analysis method and a new forward-chaining inference algorithm which searches only the so-called representatives of rule clusters. Making use of the similarity approach, the algorithm tries to discover new facts (new knowledge) from rules and facts already known. The author defines and analyses four various representative generation methods for rule clusters. Experimental results contain the analysis of the impact of the proposed methods on the efficiency of a decision support system with such knowledge representation. In order to do this, four representative generation methods and various types of clustering parameters (similarity measure, clustering methods, etc.) were examined. As can be seen, the proposed modification of both the structure of knowledge base and the inference algorithm has yielded satisfactory results

Technology and the economy

Overview of economics of innovatio