University of Kairouan

About: University of Kairouan is a education organization based out in Kairouan, Tunisia. It is known for research contribution in the topics: Magnetic refrigeration & Dielectric. The organization has 240 authors who have published 566 publications receiving 3899 citations. The organization is also known as: UnivK.

Fault Detection in Wireless Sensor Networks Through SVM Classifier

Abstract: Wireless sensor networks (WSNs) are prone to many failures such as hardware failures, software failures, and communication failures. The fault detection in WSNs is a challenging problem due to sensor resources limitation and the variety of deployment field. Furthermore, the detection has to be precise to avoid negative alerts, and rapid to limit loss. The use of machine learning seems to be one of the most convenient solutions for detecting failure in WSNs. In this paper, support vector machines (SVMs) classification method is used for this purpose. Based on statistical learning theory, SVM is used in our context to define a decision function. As a light process in term of required resources, this decision function can be easily executed at cluster heads to detect anomalous sensor. The effectiveness of SVM for fault detection in WSNs is shown through an experimental study, comparing it to latest for the same application.

Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms

Abstract: There have been several empirical studies addressing breast cancer using machine learning and soft computing techniques. Many claim that their algorithms are faster, easier, or more accurate than others are. This study is based on genetic programming and machine learning algorithms that aim to construct a system to accurately differentiate between benign and malignant breast tumors. The aim of this study was to optimize the learning algorithm. In this context, we applied the genetic programming technique to select the best features and perfect parameter values of the machine learning classifiers. The performance of the proposed method was based on sensitivity, specificity, precision, accuracy, and the roc curves. The present study proves that genetic programming can automatically find the best model by combining feature preprocessing methods and classifier algorithms.

Solvent extraction effects on phytochemical constituents profiles, antioxidant and antimicrobial activities and functional group analysis of Ecballium elaterium seeds and peels fruits

Abstract: Ecballium elaterium is a perennial herb with multiple medicinal properties. It was widely used in folk medicine as cathartic, anti-inflammatory and analgesic agents. The present study was devoted to investigate the effect of diethyl ether, acetone, and methanol solvent on the extraction, phytochemicals profiles, antioxidant and antimicrobial activities of Ecballium elaterium seeds and peels fruits. The total phenolic, flavonoid, flavonol, condensed tannins and carotenoids contents were estimated. Maximum phenolic (107 ± 4 mg GAE/g) and flavonoid (18 ± 0 mg QE/g) contents were also found in the methanol peels fruits extract. Results showed that methanol peels fruits extract have the highest antioxidant activity with IC50 values of 1.2 ± 0.1 and 1 ± 0 mg/mL for DPPH and ABTS, respectively, and EC50 value of 1040 ± 5 mg/mL for reducing power assays. Acetone and diethyl ether peels fruits extracts showed the best antibacterial agents especially against Micrococcus luteus, however no antifungal activity was observed. Spectral data of FT-IR analysis of Ecballium elaterium seeds and peels fruits extracts revealed the presence of functional groups such as ─OH, C─H, C─O and C=O. Due to their high antioxidant and antibacterial activities, E. elaterium seeds and peels fruits extracts have promising potential as future natural antioxidant and antibacterial agents in food industry.