Artificial neural network (ANN) plays an important role in many medical imaging applications. The detection of cervical cancer cells uses an ANN for classifying the normal and abnormal cells in the cervix region of the uterus. Cervical cancer detection is very challenging because this cancer occurs without any symptoms. The classification between the normal, abnormal and cancerous cells is identified by using an artificial neural network which produces accurate results than the manual screening methods like Pap smear and Liquid cytology based (LCB) test. The ANN uses several architectures for easy and accurate detection of cervical cells. In this paper, a survey and analysis on the different types of architecture in the ANN with its accuracy results and performance are discussed. A brief description about the working and detection of cervical cancer is presented which is useful for the classification of normal and abnormal cervical cells

The accurate and reliable derivation of the pap smear cell, which contains cytoplasm and nucleus regions, depends on the segmentation process employed in the cervical cancer detection mechanism. In this paper, an Improved Boykov’s Graph Cut-based Conditional Random Fields and Superpixel imposed Semantic Segmentation Technique (IBGC-CRF-SPSST) is proposed for efficient cervical cancer detection. This proposed IBGC-CRF-SPSST embeds the complete benefits of constraint association among pixels and superpixel edge data for accurate determination of the nuclei and cytoplasmic boundaries so as to ensure efficient differentiation of the healthy and unhealthy cancer cells. Finally, the pixel-level forecasting potential of Conditional Random Fields is included for enhancing the degree of semantic-based segmentation accuracy to a predominant level. The experimental evaluated results of the proposed IBGC-CRF-SPSST aim to produce an accuracy of 99.78%, a mean processing time of 2.18sec, a precision of 96%, a sensitivity of 98.92%, and a specificity of 99.32% value which is determined to be excellent and on par with the existing detection techniques used for investigating cervical cancer.

Brain computer interface is a direct technological interface between a brain and the computer. It is a fast growing emergent technology in which researches try to build a direct channel between a brain and a computer. In this paper we use a Hybrid BCI in order to provide large number of commands with increased accuracy to the BCI user. BCI can serve as powerful aids for severely disabled people in their daily life. This technology is especially used to help the disabled people to move voluntarily. In BCI wheelchair the movement of the wheel chair is going to be controlled by adding voice recognition sensors in order to give an effective result with less effort. The voice and the brain signals when synchronises with each other then it is converted as a movement in the wheelchair by moving it towards left, right, accelerating, decelerating etc,. In this paper we propose a hybrid brain computer ionterface in wheelchair using voice recognition sensors which can be used by physically challenged persons by reducing the effort for high concentration power.

A temporal network is a directed graph whose vertices are events and edges are labeled by subsets of Allen's basic temporal relations. Temporal networks play an important role in many fields of AI applications like planning, scheduling, natural language processing etc. The temporal network when applied to planning and scheduling can perform efficiently if the Reference Event based Temporal Relations (RETR) are introduced in the network. Path consistency algorithm is used to determine the consistency of the network with Allen's relations given as temporal constraint. In this paper the consistency of a temporal network is determined using path consistency algorithm where an extension of Allen's relations, RETR is introduced as temporal constraint in the network. This has been applied to the AI applications such as planning theoretically and found that the planning is being carried out more efficiently and accurately.

Breast cancer is one of the most commonly occurring cancers among women globally. The accurate detection and classification of the abnormalities such as masses and microcalcifications in mammograms is a challenging task for the radiologist without which the survival rate of the breast cancer patients may increase worldwide. This paper presents a novel Computer Aided Diagnosis (CAD) system which uses Cellular Neural Network (CNN) technique, which is optimized using Particle Swarm Optimization (PSO) for detection and Particle Swarm Optimised Probabilistic Neural Network (PSOPNN) for the classification of breast masses as benign or malignant. The breast mass texture feature extraction is carried out using Gray Level Co-occurrence Matrix (GLCM) and the optimal texture features are selected using a particle swarm optimized feature selection. The performance of the proposed system can be evaluated using the True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN) values.

Intelligent tutoring system is becoming more popular in the recent times since its beneficiaries include a wide range of people of all categories. In this paper, we propose architecture for an Intelligent Tutoring System (ITS) using agents. Self adaption mechanism is promoted through control loop. The key concept of this paper revolves around the integration of Multi-agent framework with facial feedback mechanism and issuing of Digital Certificate. And the proposed system is implemented in Net beans platform. The features addressed are effective course management, enhanced teaching process and increased level of student knowledge.