Abstract : The Wireless Sensor Networks (WSNs) are vulnerable to numerous security hazards that could affect the entire network performance, which could lead to catastrophic problems such as a denial of service attacks (DoS). The WSNs cannot protect these types of attacks by key management protocols, authentication protocols, and protected routing. A solution to this issue is the intrusion detection system (IDS). It evaluates the network with adequate data obtained and detects the sensor node(s) abnormal behavior. For this work, it is proposed to use the intrusion detection system (IDS), which recognizes automated attacks by WSNs. This IDS uses an improved LEACH protocol cluster-based architecture designed to reduce the energy consumption of the sensor nodes. In combination with the Multilayer Perceptron Neural Network, which includes the Feed Forward Neutral Network (FFNN) and the Backpropagation Neural Network (BPNN), IDS is based on fuzzy rule-set anomaly and abuse detection based learning methods based on the fugitive logic sensor to monitor hello, wormhole and SYBIL attacks.

Keywords: Magnetic Resonance Imaging, Brain tumor, and Deep Belief Network.

Abstract : Determining the size of the tumor is a significant obstacle in brain tumour preparation and objective assessment. Magnetic Resonance Imaging (MRI) is one of the non-invasive methods that has emanated without ionizing radiation as a front-line diagnostic method for brain tumour. Several approaches have been applied in modern years to segment MRI brain tumours automatically. These methods can be divided into two groups based on conventional learning, such as support vector machine (SVM) and random forest, respectively hand-crafted features and classifier method. However, after deciding hand-crafted features, it uses manually separated features and is given to classifiers as input. These are the time consuming activity, and their output is heavily dependent upon the experience of the operator. This research proposes fully automated detection of brain tumor using Convolutional Neural Network (CNN) to avoid this problem. It also uses brain image of high grade gilomas from the BRATS 2015 database. The suggested research performs brain tumor segmentation using clustering of k-means and patient survival rates are increased with this proposed early diagnosis of brain tumour using CNN.

Keywords: Brain tumor, Computer-Aided Diagnosis, Deep Learning, Fusion Feature, Recurrent Extreme Learning Machine, Artificial Bee Colony.

Abstract : Breast cancer is a prevalent cause of death, and is the only form of cancer that is common among women worldwide and mammograms-based computer-aided diagnosis (CAD) program that allows early detection, diagnosis and treatment of breast cancer. But the performance of the current CAD systems is still unsatisfactory. Early recognition of lumps will reduce overall breast cancer mortality. This study investigates a method of breast CAD, focused on feature fusion with deep features of the Convolutional Neural Network (CNN). First, present a scheme of mass detection based on CNN deep features and modified clustering of the Extreme Learning Machine (MRELM). It forecasts load through Recurrent Extreme Learning Machine (RELM) and utilizes Artificial Bee Colony (ABC) to optimize weights and biases. Second, a collection of features is constructed that relays deep features, morphological features, texture features, and density features. Third, MRELM classifier is developed to distinguish benign and malignant breast masses using the fused feature set. Extensive studies show the precision and efficacy of the proposed method of mass diagnosis and classification of breast cancer.

Keywords: Breast cancer, Computer-Aided Diagnosis, Deep Learning, Fusion Feature, Recurrent Extreme Learning Machine, Artificial Bee Colony.

Abstract : For real-world applications, such as video monitoring, interaction between human machines and safety systems, face recognition is very critical. Deep learning approaches have demonstrated better results in terms of precision and processing speed in image recognition compared to conventional methods. In comparison to traditional methods. While facial detection problems with different commercial applications have been extensively studied for several decades, they still face problems with many specific scenarios, due to various problems such as severe facial occlusions, very low resolutions, intense lighting and exceptional changes in image or video compression artifacts, etc. The aim of this work is to robustly solve the issues listed above with a facial detection approach called Convolution Neural Network with Long short-term Model (CNN-mLSTM). This method first flattened the original frame, calculating the gradient image with Gaussian filter. The edge detection algorithm Canny-Kirsch Method will then be used to identify edge of the human face. The experimental findings suggest that the technique proposed exceeds the current modern methods of face detection.

Keywords: Brain tumor, Computer-Aided Diagnosis, Deep Learning, Fusion Feature, Recurrent Extreme Learning Machine, Artificial Bee Colony.

Abstract : The brain tumors are by far the most severe and violent disease, contributing to the highest degree of a very low life expectancy. Therefore, recovery preparation is a crucial step in improving patient quality of life. In general , different imaging techniques such as computed tomography ( CT), magnetic resonance imaging ( MRI) and ultrasound imaging have been used to examine the tumor in the brain, lung , liver, breast , prostate ... etc. MRI images are especially used in this research to diagnose tumor within the brain with classification results. The massive amount of data produced by the MRI scan, therefore, destroys the manual classification of tumor vs. non-tumor in a given period. However for a limited number of images, it is presented with some constraint that is precise quantitative measurements. Consequently, a trustworthy and automated classification scheme is important for preventing human death rates. The automatic classification of brain tumors is a very challenging task in broad spatial and structural heterogeneity of the surrounding brain tumor area. Automatic brain tumor identification is suggested in this research by the use of the classification with Deep Belief Network (DBN). Experimental results show that the DBN archive rate with low complexity seems to be 97 % accurate compared to all other state of the art methods.

Keywords: Magnetic Resonance Imaging, Brain tumor and Deep Belief Network .