Towards Tumor Stage Classification and Treatment quality

Exact Stage tumor classification and treatment quality is a necessary feature of computer aided tumor diagnosis system for breast and lung cancer. This could achieve after accurate tumor identification because if the system is unable to detect accurate tumor then it is impossible to find exact stage of tumor and vice versa. For accurate identification a CAD is demonstrated in (Waqas Haider 2011). In this article the exact tumor stage classification and treatment quality phase is demonstrated. The proposed phase requires an accurate detected tumor area, the biological tumor stage information and treatment plan according to the stage of tumor and Neural Network based decision making ability. The proposed phase for achieving treatment quality make use of neural network utility of artificial intelligence and data mining , for automatic decision making upon detected area of tumor and stored information at CAD e.g tumor stage biological information and treatment plan. The demonstration shows that it helps in efficiency of computer aided tumor diagnosis system as it comprises on accurate early stage tumor detection, exact stage classification and automated treatment plan generation. So far with the help of image processing applications and artificial neural networks different CAD system are proposed which detect and classify lung and breast cancer, but still required a lot of improvements for exact tumor stage classification and treatment quality. The term treatment quality is highly dependent on accuracy and efficiency of CAD. Keywords: Computer Aided Tumor Diagnosis, Tumor stage classification, Neural network, Data minin

A review on Cloud Computing Architectures and Applications

In current time the IT based services demands , services deployment cost , scalability issues and many more constraints have paved the way for focusing on cloud computing. Cloud computing is the structure of a central server resources distributed on the platform scalable environment to provide "on demand" computing resources. In this research in detail the various structures of cloud computing are reviewed. The applications of these architectures are discussed for different areas of life. Also the different working domains of cloud computing architectures are summarized . The purpose of this research is to provide understanding to the students , professionals, developers and researchers about cloud computing

Towards Modeling Equal Humanity with Philanthropy and IT Constraints using Mathematical Utilities

In this research we present and analyzed the mathematical model for achieving equal humanity factor. The model revolves around human class, Information technology class and Philanthropy. The relative analysis of proposed mathematical model for humanity leads to expose several stable and unstable conditions of equal humanity. The presented model not only relies on Information technology constraint but also it is scalable enough to address equal humanity using any other constraint. During modeling and analysis we use basic set theories and logical operators

Towards Modeling Equal Humanity with Philanthropy and IT Constraints using Mathematical Utilities

In this research we present and analyzed the mathematical model for achieving equal humanity factor. The model revolves around human class, Information technology class and Philanthropy. The relative analysis of proposed mathematical model for humanity leads to expose several stable and unstable conditions of equal humanity. The presented model not only relies on Information technology constraint but also it is scalable enough to address equal humanity using any other constraint. During modeling and analysis we use basic set theories and logical operators

Exploring diet, exercise, chronic illnesses, occupational stressors and mental well-being of healthcare professionals in Punjab, Pakistan

OBJECTIVE: This data set was compiled to assess mental well-being, dietary pattern and physical health parameters of health care professionals in Pakistan. DATA DESCRIPTION: The Warwick-Edinburgh mental well-being scale was first evaluated for the Pakistani population then used, along with other measures like body mass index, exercise and dietary habits to assess health and wellbeing of health care providers. The importance of the data lies in the fact that no previous records or data exists in our knowledge that used a subjective index to assess wellbeing in Pakistani population. Furthermore, this data may be used as part of a global analysis to find differences in well-being and health habits of health care providers in developing countries as opposed to developed countrie

Developing reliable anomaly detection system for critical hosts: a proactive defense paradigm

Current host-based anomaly detection systems have limited accuracy and incur high processing costs. This is due to the need for processing massive audit data of the critical host(s) while detecting complex zero-day attacks which can leave minor, stealthy and dispersed artefacts. In this research study, this observation is validated using existing datasets and state-of-the-art algorithms related to the construction of the features of a host's audit data, such as the popular semantic-based extraction and decision engines, including Support Vector Machines, Extreme Learning Machines and Hidden Markov Models. There is a challenging trade-off between achieving accuracy with a minimum processing cost and processing massive amounts of audit data that can include complex attacks. Also, there is a lack of a realistic experimental dataset that reflects the normal and abnormal activities of current real-world computers. This thesis investigates the development of new methodologies for host-based anomaly detection systems with the specific aims of improving accuracy at a minimum processing cost while considering challenges such as complex attacks which, in some cases, can only be visible via a quantified computing resource, for example, the execution times of programs, the processing of massive amounts of audit data, the unavailability of a realistic experimental dataset and the automatic minimization of the false positive rate while dealing with the dynamics of normal activities. This study provides three original and significant contributions to this field of research which represent a marked advance in its body of knowledge. The first major contribution is the generation and release of a realistic intrusion detection systems dataset as well as the development of a metric based on fuzzy qualitative modeling for embedding the possible quality of realism in a dataset's design process and assessing this quality in existing or future datasets. The second key contribution is constructing and evaluating the hidden host features to identify the trivial differences between the normal and abnormal artefacts of hosts' activities at a minimum processing cost. Linux-centric features include the frequencies and ranges, frequency-domain representations and Gaussian interpretations of system call identifiers with execution times while, for Windows, a count of the distinct core Dynamic Linked Library calls is identified as a hidden host feature. The final key contribution is the development of two new anomaly-based statistical decision engines for capitalizing on the potential of some of the suggested hidden features and reliably detecting anomalies. The first engine, which has a forensic module, is based on stochastic theories including Hierarchical hidden Markov models and the second is modeled using Gaussian Mixture Modeling and Correntropy. The results demonstrate that the proposed host features and engines are competent for meeting the identified challenges

Achieving Accuracy in Early Stage Tumor Identification Systems based on Image Segmentation and 3D Structure Analysis

Cancer is a disease which can be removed if early stage tumor identification systems efficiently and accurately work at cancer hospitals. As the accuracy in detection of tumor means to detect exact size of the tumor. Because the best way to beat cancer is early stage tumor diagnosis and quality treatment. In this research article an accuracy module is proposed for computer aided tumor diagnosis system. The ultimate proposed CAD gets image of tumor infected lung and breast images from different state of the art early stage tumor detection methodologies as micrographic and mammographic based imaging systems. For accuracy in detection of early stage tumor, image enhancement and segmentation techniques are applied according to the imaging problems at input image. Also for accurate estimation of tumor the 3D image construction and 3D structure analysis are tried to realized. The realization of the proposed CAD proves that the accuracy module can assist well the computer aided tumor diagnosis systems with almost near to 100% accuracy in early stage tumor detection and size estimation for breast and lung cancer. Keywords: Computer Aided Tumor Detection, Accurate identificatio