Information and communications technologies (ICTs) have spread rapidly in the developing world. There has been considerable interest in the potential role ICTs, particularly mobile phones, have begun to play in the marketing of agricultural outputs in these countries. In this paper, we discuss the potential impacts ICTs may have on welfare, both in terms of potential efficiency gains (via improved arbitrage), and welfare transfers among agents in the supply chain (via reduced informational asymmetries and market power). We also review the recent empirical evidence for such effects.

Information, efficiency and welfare in agricultural markets

OneClass SVM is a popular method for unsupervised anomaly detection. As many other methods, it suffers from the \textit{black box} problem: it is difficult to justify, in an intuitive and simple manner, why the decision frontier is identifying data points as anomalous or non anomalous. Such type of problem is being widely addressed for supervised models. However, it is still an uncharted area for unsupervised learning. In this paper, we evaluate some of the most important rule extraction techniques over OneClass SVM models, as well as presenting alternative designs for some of those XAI algorithms. Together with that, we propose algorithms to compute metrics related with XAI regarding the "comprehensivility", "representativeness", "stability" and "diversity" of the rules extracted. We evaluate our proposals with different datasets, including real-world data coming from industry. With this, our proposal contributes to extend Explainable AI techniques to unsupervised machine learning models.

Rule Extraction in Unsupervised Anomaly Detection for Model Explainability: Application to OneClass SVM

OneClass SVM is a popular method for unsupervised anomaly detection. As many other methods, it suffers from the black box problem: it is difficult to justify, in an intuitive and simple manner, why the decision frontier is identifying data points as anomalous or non anomalous. This problem is being widely addressed for supervised models. However, it is still an uncharted area for unsupervised learning. In this paper, we evaluate several rule extraction techniques over OneClass SVM models, while presenting alternative designs for some of those algorithms. Furthermore, we propose algorithms for computing metrics related to eXplainable Artificial Intelligence (XAI) regarding the “comprehensibility”, “representativeness”, “stability” and “diversity” of the extracted rules. We evaluate our proposals with different data sets, including real-world data coming from industry. Consequently, our proposal contributes to extending XAI techniques to unsupervised machine learning models.

Rule extraction in unsupervised anomaly detection for model explainability: Application to OneClass SVM

Underwater Acoustic Target Classification (UATC) is an important field in underwater acoustic signal processing. Developing a robust, efficient and high-performance classifier is challenging due to the complex ocean environment, to the great variety of underwater acoustic target classes, and to the ambiguity that arises because of similar classes. Furthermore, in the case of only one class target data is available, the classification model has to be learned from one class samples. In this paper, we proposed a hybrid method combining auditory spectrum and support vector data description ensemble for One Class Underwater Acoustic Target Classification (OCUTAC). The proposed new method consists of 3 stages: (1) Feature extraction method which is based on auditory spectrum for underwater acoustic signal processing. (2) Auditory Spectrum Features extracted from different training datasets which obtained via bagging method are used to construct different individual SVDD classifiers. (3) Individual SVDD classifiers are combined into a single module using a majority voting approach to make a final decision. Experimental results with real data show that the proposed hybrid method is effective for One Class Underwater Acoustic Target Classification.

Classification of Underwater Acoustic Target Using Auditory Spectrum Feature and SVDD Ensemble

https://iwaponline.com/wst/article-pdf/doi/10.2166/wst.2023.089/1188873/wst2023089.pdf

The improvement of wavelet-based multilinear regression for suspended sediment load modeling by considering the physiographic characteristics of the watershed.

One-Class Support Vector Machines (OCSVM) is widely applied for those classification problems where one of the classes of data is completely not present or not properly sampled However, the knowledge presented by OCSVM is not interpretable by the human analyst To ameliorate this problem there is a need for providing explanation aids to OCSVM classification decisions Recently, several rule extraction methods were evolved to provide explanation ability to Support Vector Machines (SVM) and Artificial Neural Networks (ANN) Motivated from these methods this paper proposes a new hybrid rule extraction method for OCSVM, which is not widely studied yet Proposed hybrid is composed with Support Vector Data Description (SVDD) and RIPPER rule learning The viability of the proposed hybrid is tested over three benchmark datasets Obtained results have shown that SVDD+RIPPER hybrid outperformed SVDD and RIPPER alone in terms of classification performance and explanation ability

A hybrid rule extraction method for One-Class Support Vector Machines

With the rapid development of the world population, large area of land is utilized to develop housing and the ability of producing food is reduced. Farming has become crucial in present trend and keeps food on the tables. Farming with IoT helps in mitigating the shortage of food by demanding the existing land for stronger utilization at minimum cost. Smart farming is a notion that quickly snaps on the agricultural field. This offers an automated farming techniques, useful data collection and high-rigor crop control. Sophisticated sensor-based architecture is proposed to monitor the conditions of the farms by using sensors, and the information extracted from these sensors is stored on the Internet. This stored information is obtained and evaluated to forecast the condition of farms, according to a period of time. Based on this evaluation, the necessary improvements can be made with better farming conditions in future.

Smart Farming Using IoT

The enormous issue of drop-out students or resigned is regarding scholarly accomplishment. The academic organization wants to catch up the prompting framework while counselors be supposed to direct the arranging educational programs to their advisors. The data mining methods are very much helpful to provide the qualitative education in the academic institutions and to analyze the student performance quickly. Different classification models which can be applied in academic data mining are focused in this paper. The student problems can be identified by applying different classification models. The goal of this paper is to enhance the performance of student and at that point anticipating the appropriated scholarly accomplishment in each major. To look at the investigation, we utilized 1200 students’ data. Two measures like accuracy and error rate are assessed the framework. This method produces the 95.5% accuracy and 4.5% error rate.

P. Jhansi Lakshmi

Papers

A hybrid rule extraction method for One-Class Support Vector Machines

Smart Farming Using IoT

Student Performance Monitoring System Using Decision Tree Classifier

Hybrid wavelet-gene expression programming and wavelet-support vector machine models for rainfall-runoff modeling.