Engineering and Technology Journal

Intrusion detection system is responsible for monitoring the systems and detect attacks, whether on (host or on a network) and identifying attacks that could come to the system and cause damage to them, that’s mean an IDS prevents unauthorized access to systems by giving an alert to the administrator before causing any serious harm. As a reasonable supplement of the firewall, intrusion detection technology can assist systems to deal with offensive, the Intrusions Detection Systems (IDSs) suffers from high false positive which leads to highly bad accuracy rate. So this work is suggested to implement (IDS) by using a Recursive Feature Elimination to select features and use Deep Neural Network (DNN) and Recurrent Neural Network (RNN) for classification, the suggested model gives good results with high accuracy rate reaching 94%, DNN was used in the binary classification to classify either attack or Normal, while RNN was used in the classifications for the five classes (Normal, Dos, Probe, R2L, U2R). The system was implemented by using (NSL-KDD) dataset, which was very efficient for offline analyses systems for IDS.   

Today in the business world, significant loss can happen when the borrowers ignore paying their loans. Convenient credit-risk management represents a necessity for lending institutions. In most times, some persons prefer to late their monthly payments, otherwise, they may face difficulties in the loan payment process to the financial institution. Mainly, most fiscal organizations are considered managed and refined client classification systems, scanning a valid client from invalid ones. This paper produces the data mining idea, specifically the classification technique of data mining and builds a system of data mining process structure. The credit scoring problem will be applied using the Taiwan bank dataset. Besides that, three classification methods are adopted, Naïve Bayesian, Decision Tree (C5.0), and Artificial Neural Network. These classifiers are implemented in the WEKA machine learning application. The results show that the C5.0 algorithm is the best among them, it achieves 0.93 accuracy rates, 0.94 detection rates, 0.96 precision rates, and 0.95 F-Measure which is higher than Naïve Bayesian and Artificial Neural Network; also, the False Positive Rate in C5.0 algorithm achieves 0.1 which is less than Artificial Neural Network and Naïve Bayesian

The lungs are portion of a complex unit, enlarging and relaxing numerus times every day to supply oxygen and exude CO2. Lung disease might occur from troubles in any part of it. Carcinoma often called Cancer is the generally rising and it is the most harmful disease happened in humankind. Carcinoma occurs because of uncontrolled growth of malignant cells inside the tissues of the lungs. Earlier diagnosis of cancer can help save large numbers of lives, while any delay or fail in detection may cause additional serious problems leading to sudden fatal death. The objective of this study is to design an automated system with an ability to improve the detection process in order to perform advanced recognition of the disease. The diagnosis techniques include: X-rays, MRI, CT images etc. X-ray is the common and low-cost technique that is widely used and it is relatively available for everyone. Rather than new techniques like CT and MRI, X-ray is human dependable, meaning it needs a Doctor and X-ray specialist in order to determine lung cases, so developing a system which can enhance and aid in diagnosis, can help specialist to determine cases in easily.

Recently, automatic lips reading ALR acquired a significant interest among many researchers due to its adoption in many applications. One such application is in speech recognition system in noisy environment, where visual cue that contain some integral information added to the audio signal, as well as the way that person merges audio-visual stimulus to identify utterance. The unsolved part of this problem is the utterance classification using only the visual cues without the availability of acoustic signal of the talker's speech. By taking into considerations a set of frames from recorded video for a person uttering a word; a robust image processing technique is used to isolate the lips region, then suitable features are extracted that represent the mouth shape variation during speech. These features are used by the classification stage to identify the uttered word. This paper is solve this problem by introducing a new segmentation technique to isolate the lips region together with a set of visual features base on the extracted lips boundary which able to perform lips reading with significant result. A special laboratory is designed to collect the utterance of twenty six English letters from a multiple speakers which are adopted in this paper (UOTEletters corpus). Moreover; two type of classifier (using Numeral Virtual generalization (NVG) RAM and K nearest neighborhood KNN) where adopted to identify the talker’s utterance. The recognition performance for the input visual utterance when using NVG RAM is 94.679%, which is utilized for the first time in this work. While; 92.628% when KNN is utilize.

Character recognition has been very popular and interested area for researches, and it continues to be a challenging and impressive research topic due to its diverse applicable environment. The optical character recognition has been introduced as a fast and accurate method to convert both existing text images as well as large archives of existing paper documents to editable digital text format.
However, existing optical character recognition algorithms suffer from flawed tradeoffs between accuracy and speed, making them less effective and impractical for large and complex documents. This paper describes a suggested method for Assyrian optical character recognition using modified back propagation artificial neural network based on moments. The experimental results show that the proposed method achieves higher recognition accuracy rate in compared with the standard algorithm.

In fractal compression technique, an image is partitioned into sub blocks called range blocks, each of which is encoded by matching it (after an appropriate affine transformation) with a block chosen from a large pool of domain blocks, which is constructed from the image itself. The problem is that the encoding is very time consuming because of the need to search in a very large domain pool.
Our proposed approach presents a speed algorithm to reduce the encoding time called Classification Range Blocks. This technique will be reducing the size of the domain pool. The proposed method yields superior performance over conventional fractal encoding.
In our proposed speeding technique, we partitioned the image by using fixed block size partitioning and computing the mean and variance for each blocks. The blocks have the variance ranging from (250, 500, 750, 1000, and 1250) only used in matching process between pair range-domain blocks.

The classification of textures images has attracted the attention of many
researchers. The multiscale techniques for gray level texture analysis have been
intensively studied. In this paper, we aim on extending texture classification of color
images by using the multiwavelets transform, a new notion addition to wavelet. The
recognition of textures deals with both feature extraction and classification phases. In
the classification phase the evolutionary computation techniques (genetic
programming) was used for more speed recognition result evaluation. In our
experiment results the proposed method has achieved 99.6% test accuracy on an
average. In addition, the experimental results also show that classification rules
generated by this approach are robust to some noises on textures

Different soft-computing based methods have been proposed in recent years
for the development of intrusion detection systems. The purpose of this work is to
development, implement and evaluate an anomaly off-line based intrusion
detection system using three techniques; data mining association rules, decision
trees, and artificial neural network, then comparing among them to decide which
technique is better in its performance for intrusion detection system. Several
methods have been proposed to modify these techniques to improve the
classification process. For association rules, the majority vote classifier was
modified to build a new classifier that can recognize anomalies. With decision
trees, ID3 algorithm was modified to deal not only with discreet values, but also
to deal with numerical values. For neural networks, a back-propagation algorithm
has been used as the learning algorithm with different number of input patterns
(118, 51, and 41) to introduce the important knowledge about the intruder to the
neural networks. Different types of normalization methods were applied on the
input patterns to speed up the learning process. The full 10% KDD Cup 99 train
dataset and the full correct test dataset are used in this work. The results of the
proposed techniques show that there is an improvement in the performance
comparing to the standard techniques, furthermore the Percentage of Successful
Prediction (PSP) and Cost Per Test (CPT) of neural networks and decision trees
are better than association rules. On the other hand, the training time for neural
network takes longer time than the decision trees.