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Data mining plays an important role in processing large volumes of data. It refers to the process of obtaining knowledge from raw data. Classification is the most widely used data mining techniques, which employs some set of preclassified samples to develop a model called a classifier. Many researches showed that C4.5 algorithm need to be improvised to maximize accuracy, handle large amounts of data, where C5.0 is the improved version. The major goal of the classification technique is to predict the target class accurately for each case in the data. The main objective of this research work is to predict diseases using classification algorithms such as Decision trees, C5.0 and Bayesian Networks. The performance of classification algorithms is compared using the datasets, Breast cancer and Heart disease. The experimental results are compared based on different performance parameters like dataset scalability, accuracy and error rate values. The research shows that in terms of scalability Bayesian networks algorithm was proved to have more accuracy rate and less error rate than the C5.0 algorithm.

The brain tumor is an abnormal cell growth in the human body. To know which type of brain tumor it is and where is the exact location of it. We are using the MR image is a tomographic imaging technique. MRI is based on Nuclear M a g n e t i c R e s o n a n c e signals. A brain tumor is of two types 1. Benignant 2. malignant. Benignant belongs to I and II grade; t h i s type of tumor is not active cells and have a low-grade tumor. It has a uniform structure. Malignant belongs to III and IV grades, this type of tumor are active cells and have a high grade. It has a non-uniformity structure. The initial phase I n p u t MR image is transformed into a binary image by the Otsu threshold technique. The second step k-means segmentation process is used on binary images. Third step D i s c r e t e Wavelet Transform is used on segmented image for extracting the image and it reduces the large dimensionality by using PCA. It identifies the tumor by using S u p p o r t Vector Machine classification it gives the final output of a brain tumor that normal or abnormal. The proposed paper experimented on the detection of brain tumors using classification algorithms dataset about B r a T S dataset and compared with existing methodologies, and it is then proved that superior to existed.

IJAIP fosters the exchange and dissemination of applications and case studies in the area of advanced intelligence paradigms among education and research professionals. The thrust of the journal is to publish papers dealing with the design, development, testing, implementation and management of advanced intelligent systems, and to provide guidelines in the development/management of these systems. IJAIP publishes archival articles and assessments of current trends, providing a medium for exchanging scientific research and technological achievements accomplished by the international community.

Pedestrians in the vehicle way are in peril of being hit, along these lines making extreme damage walkers and vehicle inhabitants. Hence, constant person on foot identification was done through a set of recorded videos and the system detects the persons/pedestrians in the given input videos. In this survey, a continuous plan was proposed dependent on Aggregated Channel Features (ACF) and CPU. The proposed technique doesn't have to resize the information picture neither the video quality. We also use SVM with HOG and SVM with HAAR to detect the pedestrians. In addition, the Convolutional Neural Networks (CNN) were trained with a set of pedestrian images datasets and later tested on some test-set of pedestrian images. The analyses demonstrated that the proposed technique could be utilized to distinguish people on foot in the video with satisfactory mistake rates and high prediction accuracy. In this manner, it tends to be applied progressively for any real-time streaming of videos and also for prediction of pedestrians in prerecorded videos.