Asian Journal of Research in Computer Science
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Centric Data Analytics Framework for Solar Energy Efficiency in the Rural Settings
No full textClimate change periodically, and one of its natural causes is solar variation. Solar energy generation is now gaining more attention in developed nations, and its usefulness is becoming acceptable in rural settings. This research focused on Centric Data Analytics Framework for Solar Energy Efficiency called DAFSEE to solve power supply deficiency in resource-limited settings in Kwara State. Statistically, resource-limited regions feature a low population with a shortfall of social amenities. Significantly, households in urban areas have more electricity consumption than rural ones, making solar energy more relevant. The researcher then determined the efficiency of solar energy in the selected regions over time using an experimental study using cloud computing to create a dataset in six geo-locations in Kwara State. A predictive model was adopted and yielded 98.99% from the recurring analytics of the installed solar cells. The outcome of this study suggested best practices to sustain renewable energy in the area across all seasons. The study provides a starting point to distil policy implications for a centric analytics framework of improving rural electrification relevant for collective resource-limited settings across Nigeria
Evaluation of Auto Pilot Situational Awareness System Using Gunshot Detection Algorithm in a Localized Environment: Case Study Federal Polytechnic Offa, Mini Campus
No full textGunshot detection technologies are more applicable in many industries for the security enhancement of public places like the Federal Republic Territory FCT Abuja in Nigeria. Many factors affect the accuracy of the gun detection algorithm. This paper describes an audio-based video surveillance system in an auto pilot situational awareness to detect gunshots in Federal Polytechnic Offa. The Time Difference of Arrival (TDOA) of Shock Wave and Muzzle Blast is integrated to estimate the shooter location in the study are. The proposed design and algorithm was validated and shooter origin was resolute that was very close to theoretical values. The video camera is steering regarding the initial position to localize the acoustic source\u27s position. Implementing an auto pilot situational awareness system is an experimental procedure with a gunshot detection algorithm in a localized environment. In the direction of the weapon, the distance between firearms, types of ammunition, types of study environment, and diffraction of audio, the standard feature for gunshot recognition are Mel frequency cepstral coefficients in terms of uniform gamma-tone filters linearly spaced over the whole frequency range from 0KHZ to 16KHZ. Experiments show that our system can detect gunshots with a precision of 93% at a false rejection rate of 5% when the SNR is 10db while proving the estimate of the source direction of the gunshot with an accuracy of one degree. The outcomes reveal that the data generated by the system can be leveraged by the firefighting department to quickly locate the whereabouts of the indoor fires, and the VR gamification scenarios can expedite the development of situational awareness for the trainees. The research recommends a real-time system implementation for protecting the Federal Polytechnic Offa against any form of treats. 
Explicit Risk Management in Agile Software Projects: Its Relevance and Benefits
No full textRisk management is the systematic process of controlling risks and it is critical to the success of all software development projects. Agile software development methodologies by the inbuilt features utilized, control risks. However, this does not work for all cases of software development projects and supplementary means may need to be applied. This can be addressed by introducing an intentional and formal way of managing risks in the agile environment. Explicit risk management promises numerous benefits if properly implemented.
This study intends to review and deduce based on the risks identified, the relevance and benefits of formally managing risks in agile software development projects. To achieve the aim of this study, the researcher reviewed risk management procedures in a typical agile setting as well as research that exposed the insufficiency of the inherent risk management process in agile projects and the identified risks. Related research papers from peer-reviewed journals and other reliable sources were reviewed to extract risks that occurred using agile without explicit risks management. The study inferred that some risks do exist that occurred with the introduction and use of the agile method itself. Also, there could be risks that surface when the project size exceeds a limit. Thus, managing risks explicitly will go a long way to address such risks. Consequently, the researcher was able to deduce the relevance and benefits of implementing explicit risk management in an agile software development project.
This study showed that it is beneficial to incorporate formal risk management procedures in agile software development when mega software projects are being developed. However, to maintain the agility of the agile methods which happens to be a major benefit of the utilization of agile methods, more research is needed to further explore explicit risk management in the agile environment without violating the swiftness in the agile settings
Can the Physical Parameters with the Support Vector Machine (SVM) Method Able to Classify Benign and Malignant Breast Cancer?
No full textObjective: Evaluating the diagnostic performance of SVM to classify benign and malignant by performing a meta-analysis.
Methods: The data used for this study were secondary data. It consisted of 221 mammogram images (mean age 57.5 years) with 164 malignant and 57 benign, taken from a radiological database that has been examined by a radiologist with more than 20 years of experience. Also, histopathological record data that had been examined by an oncologist with more than 20 years of experience. Mammograms were taken from January 2022 to June 2022. In all, 221 mammograms consisting of 164 malignant and 57 benign were used as SVM method training, and 20 mammograms consisting of 10 malignant and 10 benign were used to test the performance of the SVM method. It was then evaluated using pathology results as the gold standard.
Results: Benign had a significantly lower deviation (an average of 29.2661230 ± 10.14916673) than malignant (an average of 33.1841234 ± 11.70238757). The SVM method performance value obtained the values of TP, FP, TN, FN, accuracy, sensitivity, Specificity, and Precision, respectively 7,7, 3, 3, 50%, 70%, 30%, and 50%.
Conclusion: A proper performance to distinguish benign and malignant can be obtained using the physical deviation parameters with the SVM classification approach. However, these findings should be proven in larger datasets with different mammographic scanners. Our meta-analysis shows that the physical parameters and SVM have high sensitivity but low specificity. Of the nine physical parameters in the mammogram, only the parameter deviation was significant to distinguish between benign and malignant. The SVM method proved to be able to differentiate between benign and malignant
Predicting Malnutrition Status of Under-Five Children in Dhamar Governorate, Yemen Using Data Mining Techniques
No full textMalnutrition is characterised by the insufficient intake of certain nutrients and the inability of the body to absorb or use these nutrients. This health problem keep going to be a real challenge among children under five years of age in developing countries, including Yemen, despite good aids provided. So, malnutrition is a health problem that significantly participates to child mortality rate in Yemen. The overall prevalence of malnutrition among children in Dhamar Governorate has significantly higher rates compared to other Yemeni governorates.
In this paper, an intelligent predictive system using data mining classification techniques such as J48 decision tree, Bagging and Multi-Layer Perceptron Neural Network (MLPNN) for predicting malnutrition status of under-five children in Dhamar Governorate is proposed.
The main objective of the present paper is to study these classification techniques to predict the 2018-2019 Dhamar Governorate, Yemen Demographic and Health Survey (DGYDHS) dataset and find an efficient technique for prediction. This dataset is imbalanced, so Synthetic Minority Over-sampling TEchnique (SMOTE) is utilised to balance the dataset.
The obtained results were evaluated by the famous performance metrics like Accuracy, TP (True Positive)-rate, FP (False Positive)-rate, Precision, F-Measure, Receiver Operating Characteristics (ROC) graph and execution time. The obtained results revealed that the three classifiers with all attributes have higher predictive accuracy and are generally comparable in predicting malnutrition cases
Estimation of New Student Admissions Using the Moving Average Method
No full textAcceptance of new students at several tertiary institutions, both public and private, will affect the learning and teaching process at these tertiary institutions. Because knowing the number of new students accepted is one way that can be used in determining the teaching and learning process.
This study aims to predict the number of new students for the coming year with the Moving Average method and calculate the absolute mean error rate for this type of data analysis. This method is applied to one of the private universities in Indonesia. To know the prediction of the number of new students every year, or to find out the estimated number of new students for the coming year.
The data used is the previous 7 years at one of the Indonesian private universities.
The results of this study are expected to be able to assist these tertiary institutions in planning a good teaching and learning process, because the number of new students will affect the number of existing lecturer ratios
The Impact, Comparison and Usefulness of Digital Marketing Communications Tools on Organizational Profit Maximization Using Facebook
No full textThe importance of Chatbots in marketing, particularly in employment or training progressions and maintenance for definite promotional goals, tools as well as approaches was examined in this study. The study seeks to ascertain the opportunities associated with the usage of chatbots in marketing, with specific emphasis on its influence in the process of Human-to-Machine communications, and find out the extent to which Chatbots could be effectively use to examine competitive companies or brands. The researcher explores how chatbot can interact with users using Facebook messenger and investigate the impact and usefulness of digital marketing communications tools on organizational profit maximization, using a real estate business. The study focuses on three sub-processes in the chatbot design, which includes writing handling, language acceptance, as well as reaction generation. Additionally, the survey was piloted with arrangement of chatbot assessment methods and their examination in relation to chatbot categories as well as three central appraisal schemes, which include content estimation, user gratification, and chat function. Findings of the study established that Chatbots could be effectively used to enable companies or brands intensify organizational profit maximization and proved that the limitations of the human agents have been taken over by this automatic Bot, which have been trained to act like human, give responses to customers’ requests and even suggest responses to users. It recommends that every organization marketing teams should acquire innovative communication approaches about how to preserve and advance enduring relations with standing consumers as well as how to get the attention of potential consumers
Multivocal Literature Review on the Security of DevSecOp
No full textDevOps revolutionize the software development lifecycle by providing agile and fast-paced solutions. DevOps ignores security prospects since it focuses only on increasing development and speed. DevSecOp is a notion of implanting Security into DevOps operation without distressing its agile nature by discovering contemporary security practices. This research aims to reveal a comprehensive overview of DevSecOp. Here we presented a brief overview of the research methodology. Afterward, it presented the method of gathering the required information. This research paper is distributed in the following section. Section II presented the research methodology, while section III provided results from this study. In the end, we conclude our research
In this study, we discover essential DevSecOp concepts, leverages of DevSecOp, and potential research challenges in implementing it. We used a Multivocal literature review to explore the aforementioned subjects. For this Multivocal literature review, we searched grey data and, after processed that data, found answers to our research questions. This review concluded that DevSecOp, although challenging to implement, can be a constructive addition to the DevOps paradigm.
DevSecOp is a relatively new concept that is not even fully concise in its name and definition. The key idea of DevSecOp is to implant security into DevOps procedures to make them more secure. We presented MLR on DevSecOp, keeping in mind pre-designed research questions. Since DevSecOp is not as popular and does not contain enough academic literature, we had to include grey data for our literature review. This MLR concluded that DevSecOp is mainly defined as integrating Security into DevOp
Digital Framework Strategy for Patient Medication Adherence and Improvement in Medical Healthcare Centre Offa, Kwara State
No full textThe prospect behind digital transformation strategies makes the healthcare systems more safe, affordable, and accessible with remarkable opportunities. The reasons why patients decided to have negative medication adherence became a critical challenge in the healthcare system. This study presents a Digital Framework Strategy DSF for patient medication adherence to improve patient health during the treatment regime. A case study of the Federal Polytechnic Medical centre and clinical activities of Offa General Hospital examines the existing treatment of chronic diseases. The cloud-based server revolves around Convolution Neural Network (CNN) feature to perform a real-time collection of data and analytics of patient information. When thoroughly combined, the CNN of the neural network has a model of the application, which will form part of the desired output. This output presents a level of patient medication adherence within the parameters—the data around the enclosed sources. The approach data was acquired with the patient wearing a sensor and smartphone devices. The model throughputs presented detailed analytics of individual patient adherence behaviors. The result of CNN performance revealed 96.99% accuracy of medication adherence level on a tested dataset collation, and the essence of digital framework analytics helped the healthcare workers (HCW) and healthcare providers to make prompt decisions on patients’ medical conditions
Digital Laboratory Framework (DLF): A Tool for Effective Practical Delivery in Federal Polytechnic Offa, Nigeria
No full textThe research provides vital insights into technical and engineering education and practice as a key to effective teaching of practical for Computer Engineering Students in an efficient and applied manner which is an indispensable factor. Since the introduction of Computer Engineering in 1997 at Federal Polytechnic Offa, applying the theories has allowed the students to develop their commercial literacy and ambition in the Polytechnics. Using laboratories for routine practical has been traditional, and the technology trend has gone beyond conventional methods. Digital Laboratory Framework (DLF) is a tool for technical education, where practical teaching will take the form of virtual learning. The team intends to develop a digital laboratory to complement the traditional method for National Diploma (ND) and Higher National Diploma (HND) in Computer Engineering to cover core digital and analogue electronics pre-requisite courses. The teaching and practice of Engineering serve as the essential entrepreneurship skills with a keynote to address the fundamental requirements needed to establish a successful career using student cognitive learning, student performance and evaluation concerning technical skills acquisition. The result prepares undergraduates ahead for industrial skills. It enables them to specialize in any computer engineering options and help produce competent Nigerian graduates as engineers with a total capacity to develop the nation through innovative technologies