High Levels of Contamination by Antibiotic Residues in Raw and Fermented “lben” Cow’s Milk Collected in Guelma, Algeria| Chapter 1 | Current Perspectives in Agriculture and Food Science Vol. 2

 In an work to improve the character and quantity of cuisine production (specifically milk) and to prevent or treat animal diseases, the use of medicines in Algeria is on the rise. The raised use clearly donates to the emergence of larger levels of antibiotic slag contamination. Two arrangements were used in this place study to detect the attendance of antibiotic residues in raw and simmered cow's milk composed from Guelma's farms (in Algeria). The comparison of two together methods disclosed that Delvotest SP-NT is less reliable on account of the high number of wrong negative results. This was confirmed by LC-MS/MS, that revealed antibiotic traces in any of samples. Antibiotic residues were found in 65.46% of milk samples, signifying a lack of public health controls in addition to evidence of negligent medicine use in the livestock manufacturing, both of that pose a risk to public health.The study given an LC-MS/MS-based examining method that is connected to the internet in the Algerian National Residues Control Plan as a versatile examining tool for listening and determining.

Author(s) Details:

Samiha Layada,
Research Laboratory of Biology, Water and Environment, Biology Department, Faculty of Natural and Life Sciences, Earth and Universe Sciences, University 8 Mai 1945-Guelma, BP 401, Guelma 24000, Algeria.

Djemel-Eddine Benouareth,
Research Laboratory of Biology, Water and Environment, Biology Department, Faculty of Natural and Life Sciences, Earth and Universe Sciences, University 8 Mai 1945-Guelma, BP 401, Guelma 24000, Algeria.

Wim Coucke,
Scientific Institute of Public Health, Juliette Wytsman Street 14, 1050 Brussels, Belgium.

Mirjana Andjelkovic,
Scientific Institute of Public Health, Juliette Wytsman Street 14, 1050 Brussels, Belgium.

Please see the link here: https://stm.bookpi.org/CPAFS-V2/article/view/9403

Rule Learner and Multithreading Technique with Genetic Algorithm for Inline Intrusion Detection System for High Speed Network | Chapter 11 | Theory and Practice of Mathematics and Computer Science Vol. 10

 The importance of an intrusion detection system in detecting unauthorised users, anomalous packets, and malicious code in a network is critical. Many methodologies and strategies for intrusion detection systems have been proposed by investigators. Finding a suitable approach with a low false positive rate and high classification accuracy is a difficult issue in intrusion detection systems. For intrusion detection systems, rule-based classifiers or learners are the best option. These are both complicated and simple to use. The rules generated by the rule learner determine the performance of a rule-based intrusion detection system. Due to the large number of packets in networks, the rule creation process is slow and time demanding. The intrusion detection system uses an ensemble of rule learners to deliver excellent accuracy.

In this chapter, an unique intrusion detection system architecture based on a single rule learner is introduced. The rule learner with multi-threading methodology was used to create the system. The Ripple Down Rule learner is utilised as a classifier in this implementation, while the Genetic Algorithm is employed as a feature selection method with Multithreading. The advantages of multi-parallel threading's processing capabilities enable to handle massive traffic in high-speed networks. The system's cache management module is utilised to lower the memory access rate. The classification accuracy and false positive rate of the proposed intrusion detection system are assessed. The suggested intrusion detection system outperforms the existing standard classifier, according to the findings of the performance evaluation. The suggested system's logging method can be used to reprocess and analyse logged packets in the future for investigative and forensic purposes. It was also discovered that the time necessary to produce rules from the training data set is less than the time required to develop models in existing rule-based intrusion detection systems.

Author(s) Details

D. P. Gaikwad
Department of Computer Engineering, AISSMS College of Engineering, Pune, Maharashtra, India.

View Book :- https://stm.bookpi.org/TPMCS-V10/article/view/1081