Nigeria economy until recently depended on cash transactions. By Central Bank of Nigeria (CBN) Act 2007, cashless banking through Automated Teller Machine (ATM) was officially introduced into some segments of Nigeria Banking sector to improve banking operation and bring about fiscal growth of the economy. The cashless programmes launched included ATM, Debit Card and Credit Card transactions. The popularity of transactions through ATM can be ascribed to convenience of use. However, at present in Nigeria, ATM is not satisfying the set objectives in the area of transaction reliability, compliance of process, and transaction response time thereby resulting to ATM fraud and security flaws; delay operations; and erroneous reporting and feedback. To a large extent, these problems can be attributed to defective policy framework and implementation. Therefore, this paper focuses on improving the operation of ATM using the combination of Service Component Architecture (SCA) and Case Based Reasoning (CBR) as a framework for Service Oriented Computing. It describes a new service oriented modeling approach where the SCA is used to facilitate the composition of service within ATM operation while the CBR is used to execute the service functions selected by users. These models are relevant to Commercial Bank and CBN layers of Service Model under the regulation of CBN based on customers' preferences over customers' requirements via the ATM and internet banking interface. The model serves as a framework that could guarantee better operation in ATM banking. General Terms

Across the globe, kidney cancer and other cancerous diseases has been a threat to human lives. The incidence and mortality rate represent a significant and growing threat to both developed and developing countries especially in Africa, where most cancers are diagnosed at an advanced stage. This typically contributes to its complications and high rate of mortality, and has been attributed to limited awareness of early signs and symptoms of the disease, lack of detective mechanism and inaccessible cancer care in our health care centres. To preclude the harm and mortality caused by the disease, an intelligent mechanism for early prediction and prognosis of the syndrome is vital. However, early detection and prognosis requires an accurate information and analytic procedure that will assist and equip the health-care providers/public with the skills to identify early the indicators of the disease. Efforts in this work, produced a model for early prediction of kidney cancer using data analytic approach. Dataset and reports pertaining to the disease were acquired from selected private and public hospitals in fifty-two (52) selected LGA in Nigeria. A two-layered classifier system consisting of Artificial Neural Networks (ANN) and Decision Tree (DT) designed for the work was successfully employed in the model building. Waikato Environment for Knowledge Analysis (WEKA) platform was used for the experiment. The performance of the classifiers considered was compared using standard metrics of accuracy and time taken as benchmark. Experimental results show that the J48 decision tree algorithm outperform all other algorithms in the classifier family with correctly classified instances of 74.7%, F-Measure of 0.614, TP rate of 0.747, FP rate of 0.135, precision and recall of 0.687 and 0.714 respectively. It took the algorithm, 0.03 seconds to build the model. The performance of this algorithm proved its suitability as a valuable tool for the research purpose. The model will in no small measure support the efforts of the national health scheme in preventing the disease mortality rate.

A Model for Prediction of Kidney Cancer Using Data Analytics Technique

The environmental impact assessment for chemical substances on human health damage has been of significantinterest sometime in the EU, USA, and Japan. In Thailand, such an environmental impact is now receiving more attention.The present study focuses on developing the damage factors of chemical substances on human health based on the multimedia box type fate and exposure model via IMPACT 2002, with the model adapted to Thailand. Human health damagefactors are expressed in terms of disability–adjusted life year (DALY) per kg emission. The development method includesfour steps: fate analysis, exposure analysis, potency, and severity analysis. This study derived new damage factors of 144chemical substances that quantify the impact damage of an emission change on human health damage. It was found that thecharacterization factors for human health damage range from 7.34×10-9to 1.30×103 DALY per kg emitted. This workprovides new information for damage factors on human health in Thailand based on the IMPACT 2002 model, modified forThailand. Future research should include uncertainty analysis of the major relevant parameters, which could provideinformation on the reliability of the damage function.

In practice, things aren’t quite as simpleas this. You have to eat the right kind of fat, in theright quantity, and at the right times. It also makesall the difference what else you are putting into yourbody. The quickest way to become enormous, forexample, is to consume high quantities of both fatand carbohydrate, as found in the worst, hardest-to-resist kind of donut. But the basic messageremains true. It isn’t fat that’s responsible for theepidemic of obesity sweeping the planet. It’s sugar!Intuitively, it sounds crazy to suggest that the wayto lose body fat is to eat more fat than you arealready doing. But remember, biology is not simplemathematics or physics. Our bodies break downwhat we eat and turn it into other things, includingenergy