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One of the chronic diseases is Diabetes, Diabetes is a metabolic disorder category caused by continued high levels of blood sugar. It is regarded as one of the most deadly diseases in the world. If accurate early prediction is possible, Diabetes severity and risk factor can be significantly lowered. Machine learning has become more popular in the medical community as a result of its ascent, and in the field of diseases in particular. In this paper, we propose a model that can predict where the patient has or hasn't have diabetes. Our model is based on the prediction precision of certain powerful machine learning (ML) algorithms based on different measures such as precision, recall, and F1-measure. The Pima Indian Diabetes (PIDD) dataset has been used, that can predict diabetic onset based on diagnostics manner. The results we obtained using Logistic Regression (LR), Naive Bayes (NB), and K-nearest Neighbor (KNN) algorithms were 94%, 79%, and 69% respectively. The results show that LR is more efficient at predicting diabetes Compared to other algorithms.

Face recognition systems played a vital role in the last decades, artificial intelligence techniques are exploited to perform such systems. Time and Accuracy of real-time systems are very important evaluation criteria. This paper proposes a real-time face recognition system using software agent, three world datasets are experimented as static images, 98% recognition rate is achieved, and 90% for real-time face recognition, with one frame per second, which is a significant percentage in comparison with related works.

In this Paper an Approach For brain tumour types detection and classification is proposed. This study was developed using agency system classification brain tumors. It includes sensor to get the input image. The agency program comprises preprocessing to reduce the salt and pepper noise. The detection of tumor is materialized by using multi-level threshold segmentation, opening and closing morphological operation to enhance the multi-level threshold, watershed transform to segment the tumor, extract important feature using binary object feature, and classification the types of brain tumor. The actuator is used to output the classification and decision. The agency system achieves an accuracy (100%) to classify the images into normal tissues, meningioma tumor, meningioma benign, Gliomas and metastases.

Nowadays intelligent transportation system has become an active research field, automated vehicle identification is the primary aspect for achieving good intelligent traffic analysis, this paper illustrates a comparative study among three techniques that used for identifying vehicles: ANPR system, Barcode system, and RFID system. In addition, this paper describes similarities, differences, and challenges.

Video compression techniques play a vital role in storage & transmission through a limited bandwidth. As a resulted containing video files repeated sequence of still image, each second represented by 24 images or more which is represent the motion in video file. The repeated sequence generates a large amount from temporal redundancy through similarities of sequence image with little difference between them, in addition to the spatial redundancy contained in image data itself. In this work we use image with 24 color bit. VCDCUT used to split the image into the sequence of frames and then split its color (RGB) to (YUV) component. Skew algorithm used to apply motion only on the Y luminance component of sequence of frame, then apply wavelet transform on Components and apply multi multisampling process on (UV) components. The quantization stage has been done to transform image from two dimension to one dimension by run length encoding (RLE) algorithm. In the decompression process the inverse length encoding has been done first and de quantization process has been applied to transform the (RGB) to the (YUV). The inverse of wavelet transform apply on the Y component and de sampling process has been made on the (UV) component then return YUV component to RGB color . Enhancement process done on the result in order to remove noise. Finally PSNR has been computed to check the compression ratio in the resulted compressed image.

The World Wide Web (WWW) has become infinite repository of information over the world because of the vast amount of information that they can gain. Also the access to the web enables any user easily to get information anytime and anywhere. But with these advantages, there is also some disadvantages such as the some websites contains harmful material like terrorism, hate, violence, and pornographic. Therefore developing a system that can filter these contents is become necessary. In this paper we introduce a web content filtering using multi agents, in this system we used FCM algorithm and some webpage feature in classification process. Experimental results based on (2000) different websites are given us an efficient results. On the testing set, the system was able to achieve correct acceptance and reject of (97.5). English pornographic websites uses as a case study.