Radioactive Target Detection Using Statistical Method

Radioactive materials are extremely dangerous as it threatens the earth survival. They contaminate the environment with a lot of damages. Nowadays revealing radioactive material become an important topic for public health, national security, and environmental services. This paper discusses a statistical method to detect and identify an unobserved radioactive source, where the counts of radiation particles received by those detectors are spatially distributed of Poisson distribution. Also, each count composed of a signal and a background. This paper provides a numerical method to estimate the strength and location of unobserved radioactive source based on the physical law of signal degradation with distance. Based on these estimates, a likelihood ratio statistic is used to test the existence of the source. Simulation results show that the proposed statistical method is efficient for revealing the unobserved radioactive source

Prediction of Mortality Rate of COVID-19 Patients using Machine Learning Techniques in Nigeria

Corona virus disease pandemic have highly destructive effects around the world and this virus has affected both developed and developing nations. In this paper, predictive model for the mortality rate of patients infected with corona virus in Nigeria using data mining techniques is developed. Oral interview was conducted with virologist at health institution (The Federal medical centre, Owo, Ondo state, Nigeria) to ask for some basic factors that causes mortality in infected corona virus patients. Online survey was done based on these ten basic factors and three hundred and two responses were collected and preprocessed. A ten fold cross validation technique was used to partition the datasets into training and testing data in which predictive models were developed using data mining algorithms (Multilayer Perception, Naïve Bayes, Decision Tree and Decision Rule) . Waikato Environment for Knowledge Analysis (WEKA) was used to simulate the models and the result shows that the four models developed have the capability to forecast mortality rate of corona virus adequately. Conclusively, multilayer perception has the highest level of performance with 85% accuracy. Multilayer Perception model is effective, reliable and is recommended to forecast the rate of mortality of patients infected with corona virus. Moreover, this prediction is important because the death of any patients is emotional and physically challenging to the morning families

A Graph Based Approach to Prioritization of Software Functional Requirements

Requirements prioritization plays important role for a successful requirements implementation. Functional requirements, in specific, represents the specification of behavior between the inputs and outputs. They are prioritized based on the high-level requirements of the system and subsystems functionalities, as well as the type of software, expected users and the type of system where the software is used. Nonetheless, prioritization of functional requirements is very challenging considering a project where the requirements are huge. In practice, prioritization of functional requirements highly depends on stakeholders preference for giving priorities in features selection instead of based on its internal structure and characteristics. This is because the key information in functional requirements concern on business processes, security, performance, data migration and conversion. This paper proposes a graph-based approach for prioritizing functional requirements using directed acyclic graphs for relating requirements with one another on the basis of its importance to overall project and how much it is required for other requirements.The proposed approach is then evaluated in terms of total time estimation to project completion. The experimental results showed that the graph-based approach is able to effectively prioritize functional requirements with lower estimated project completion time as compared to non-prioritized requirements. The approach will help software vendors to deliver projects well within the total project duration

Mapping Character Position Based Cryptographic algorithm with Numerical Conversions

Security of data is the challenging aspects of modern information technology. An improved cryptology algorithm is introduced in this paper to offer comparatively higher security. We divide our message into several blocks as 8bits per block then convert each character into its corresponding positional number, where uppercase letters, lowercase letters, digits and special characters are mapped into some range of numbers. Then replace each decimal number into their binary equivalent consisting of 7-bits. Then combine 8 blocks of binary numbers into a single string. After performing some operation on the data, we get the final encrypted message. For decryption, we use same method in reverse way. Taking the decrypted message we perform some basic operation as replacing by binary or equivalent decimal and position then we get the original message back. Though the length of the encrypted message is larger than original message in this proposed algorithm, it offers higher security for the real-time communications

An Impact of CS Undergraduate Programming Skill on Career Development

This study serves as a pilot study for a larger future study. The purpose of this pilot study was to find a relationship between programming skill and CGPA of CS graduates. Ten ICT managers of ICT firms were interviewed and 802 CS graduates’ school records, from Rangsit University in Thailand were collected and analyzed. This pilot study reports the finding from both interviews and analysis of school records, detailing the critical skill-programming skill- of CS graduates, desirable in employment ICT workers. The results revealed that programming skill of CS graduates is today less than their academic performance. Consequently, ICT firms fail to fulfill the hopes of them. Finally, CS academic setting was discussed and proposed the roles of 3-levels-CS department chair, lecturers, and students-, playing to improve CS graduates’ programming skill. Therefore, knowledge, ability and skill of CS graduates would meet ICT firms’ requirements for future CS career development

Need of Computational Intelligence for Post Graduate Students as an Academic Learning

The proper definition of intelligence is not widely known and describable within some selected words till now. There is a great controversy on its definition because generally people have not enough knowledge about it. Computational Intelligence is a subset of Artificial Intelligence and based on particular six approaches. These are Fuzzy Logics, Probabilistic Mechanisms, Natural Swarm Intelligence, Neural Networks and Evolutionary Computing. Traditional artificial intelligence use to develop intelligent systems that require proper and comprehensive information about some task to perform. But numerous real-world systems cannot provide exact and complete information about real-world phenomena. On the other hand, the main concern of Computational intelligence is to design intelligent systems that can be able to make decisions on uncertain or ambiguous information and now this becomes basic future system’s need. Both subjects AI and CI have their own importance, but we can analyze that as future needs more intelligent systems, so it required more work, research, understandings and knowledge for computational intelligence. We conduct a survey and meet results that even students of master’s degrees not even know about the term “computational intelligence”. Therefore, this paper proposed that computational intelligence should be an integral subject of courses as enhancement of artificial intelligence related to at least engineering and computer related fields. It will provide knowledge to students and rise their interest for computational intelligence and encourage them to do work to build more intelligent systems that will be able to deal real word problems in future

Methods for Determining Nitrogen, Phosphorus, and Potassium (NPK) Nutrient Content Using Features from Accelerated Segment Test (FAST)

Nutrient Content NPK is macro nutrient content that important for the growth of a plant. The measurement of NPK conducted periodically, but the measurement using laboratories test need relatively long time. This Research is conducted to determine the nutrient content of the soil, consisted of nitrogen, phosphor, and calcium (NPK) using digital image processing based on Features from Accelerated Segment Test (FAST) and backpropagation artificial neural network. The data sample in this research taken from the rice field soil in Daerah Istimewa Yogyakarta province where the soil taken at the length of 30 cm to 110 cm with l20 cm interval, and -30° to 30° degree with interval 10°. The model from this measurement system based on texture’s characteristic that extracted using Scale Invariant Feature Transform from soil’s image that already passed pre-processing process. The characteristic result will be the input from the artificial neural network with a variation on parameter’s model. The model tested for the purpose of knowing the influence the distance and degree where the image taken and the influence of parameter’s artificial neural network. The result from the research, is a accurate value of the measurement for each nutrient in the soil, nitrogen (94.86%), phosphor (58.93%) and calcium (63.57%), with the mean 72,46%. The corresponding result obtained from image taken with optimal height of 70 cm and degree 0o