In recent years, the world witnessed a rapid growth in attacks on the internet which resulted in deficiencies in networks performances. The growth was in both quantity and versatility of the attacks. To cope with this, new detection techniques are required especially the ones that use Artificial Intelligence techniques such as machine learning based intrusion detection and prevention systems. Many machine learning models are used to deal with intrusion detection and each has its own pros and cons and this is where this paper falls in, performance analysis of different Machine Learning Models for Intrusion Detection Systems based on supervised machine learning algorithms. Using Python Scikit-Learn library KNN, Support Ve

The traditional centralized network management approach presents severe efficiency and scalability limitations in large scale networks. The process of data collection and analysis typically involves huge transfers of management data to the manager which cause considerable network throughput and bottlenecks at the manager side. All these problems processed using the Agent technology as a solution to distribute the management functionality over the network elements. The proposed system consists of the server agent that is working together with clients agents to monitor the logging (off, on) of the clients computers and which user is working on it. file system watcher mechanism is used to indicate any change in files. The results were presente

 The aim of this paper, is to discuss several high performance training algorithms fall into two main categories. The first category uses heuristic techniques, which were developed from an analysis of the performance of the standard gradient descent algorithm. The second category of fast algorithms uses standard numerical optimization techniques such as: quasi-Newton . Other aim is to solve the drawbacks related with these training algorithms and propose an efficient training algorithm for FFNN

we conclude that Alaotaby in his Designation, with respect to defects in speech or speech pathology, he cited a number of terms function on speech defects in voice and accent like , Aphasia, Alokla, Alaay, Alramz, Alhasr, Alfadm,and Alaghop, and pointed to the sound stop as a result of an accident or a problem or the speech organ deny the will of the speech, which refers to the refrain defect in sound organic.
He also marked the disorders individual sound caused by the bug of sample and disability among individual like Alokla, node and aphasia - which hinders communication as well as other factors such as irregular sound product and not reporting to be into the future toward the Aljamjamah and whispering, and it can be said that he po

An Auto Crop method is used for detection and extraction signature, logo and stamp from the document image. This method improves the performance of security system based on signature, logo and stamp images as well as it is extracted images from the original document image and keeping the content information of cropped images. An Auto Crop method reduces the time cost associated with document contents recognition. This method consists of preprocessing, feature extraction and classification. The HSL color space is used to extract color features from cropped image. The k-Nearest Neighbors (KNN) classifier is used for classification.