The graph-theoretic based studies employing bipartite network approach mostly focus on surveying the statistical properties of the structure and behavior of the network systems under the domain of complex network analysis. They aim to provide the big-picture-view insights of a networked system by looking into the dynamic interaction and relationship among the vertices. Nonetheless, incorporating the features of individual vertex and capturing the dynamic interaction of the heterogeneous local rules governing each of them in the studies is lacking. The methodology in achieving this could hardly be found. Consequently, this study intends to propose a methodology framework that considers the influence of heterogeneous features of each node to the overall network behavior in modeling real-world bipartite network system. The proposed framework consists of three main stages with principal processes detailed in each stage, and three libraries of techniques to guide the modeling activities. It is iterative and process-oriented in nature and allows future network expansion. Two case studies from the domain of communicable disease in epidemiology and habitat suitability in ecology employing this framework are also presented. The results obtained suggest that the methodology could serve as a generic framework in advancing the current state of the art of bipartite network approach.

The automated diagnosis of schizophrenia utilizing Magnetic Resonance Imaging (MRI) has been the subject of numerous investigations, the majority of which have primarily directed their focus towards disorder detection.This study, however, aims to transcend detection, endeavoring to estimate the severity of schizophrenia symptoms by leveraging structural MRI data.Such capabilities are anticipated to enhance the monitoring of treatment efficacy, guide clinical decision-making, and ultimately contribute to improved schizophrenia management.MRI datasets for schizophrenia patients ( 23) and control subjects (20) were sourced from the OpenNeuro database.Each structural MRI was processed to extract a grayscale image, which was then segmented into White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF).Statistical attributes-such as standard deviation, moment, and skewness-were derived from each segment to form feature representations of the grayscale images.An SVM with a linear kernel was trained, distinguishing schizophrenia subjects from healthy controls.Furthermore, for the schizophrenia subjects, the sums of their respective Scale for the Assessment of Positive Symptoms (SAPS) and Scale for the Assessment of Negative Symptoms (SANS) scores were computed.A twelve-layer artificial neural network (ANN) was then trained to estimate these symptom severity scores.The SVM model achieved optimal classification accuracy at 81.8%, while the ANN demonstrated a correlation coefficient of 0.811 and a mean absolute error of 1.44 on the validation dataset.This performance surpasses that of a comparable study estimating schizophrenia symptom severity from electroencephalogram (EEG) data, which yielded correlation coefficients ranging from -0.6 to -0.702.The paper concludes with a proposed software architecture for practical application of these findings.

The impact of timely response to road emergencies cannot be overemphasized. Responding quickly to road emergencies makes it possible for emergency responders to save lives and properties. It is believed that one-minute improvement in response time can result in six percent (6%) increase in the lives saved. Emergency responders depend on the road networks that other road users also depend upon for their day-to-day activities. All stakeholders (emergency responders inclusive) must therefore, cooperatively and collaboratively use the road networks to, particularly, allow responders have priority access on the roads. This article thus, suggests the use of a mobile application to include the various stakeholders in emergency response operation cycle, ranging from reporting and notification to awareness creation and information dissemination and, all the other related activities.

Abstract Millions of software are lunched yearly and this software depend on data to produce required output. Personal data privacy and security has been a source of public concern for some time, and is usually interpreted in terms of data obtained from interaction with software. It is difficult to know whether a software system's decisions are fair and what considerations were put in place in the system's internal decision-making process if the system's decisions are opaque. This has the potential to cause injustice and bias. In addition, a lack of openness may lead to a decrease in user acceptance and happiness. Algorithmic data-driven decision-making systems are becoming more automated, and they've had a lot of success in a lot of different applications. The General Data Protection Regulation of the European Union and other regulations limits algorithmic use of personal data and has fueled the dispute over the right to disclosure. This research adapted a crowd requirements elicitation model to develop a framework for the proper elicitation non-functional requirement. The developed model uses natural language processing integrated into a chatbot and a document extraction strategy since non-functional requirement exist also as government regulations and industrial standards. Proper and comprehensive elicitation of non-functional requirements will give accurate information on how the system performs its required task and such documents are best in terms of openness to the use of data by algorithms to avoid algorithm decision making concerns.

The applications of information technology (IT) tools and techniques have, over the years, simplified complex problem solving procedures. But the power of automation is inhibited by the technicality in manning advanced equipment. To this end, tools deliberately combating this inhibition and advancing technological growth are the Unmanned Aerial Vehicles (UAVs). UAVs are rapidly taking over major industries such as logistics, security, and cinematography. Among others, this is a very efficient way of carrying out missions unconventional to humans. An application area of this technology is the local film industry which is not producing quality movies primarily due to the lack of technical know-how in utilizing these systems. This study therefore aim to devise an autonomous object tracking UAV system that would eliminate the complex procedure involved in stabilizing an aerial camera (aerial bot) midair and promote the creation of quality aerial video shooting. The study adopted Unified Modeling Language (UML) tools in modeling the system’s functionality. The traditional Server-Client model architecture was adopted. The OpenCV library employed proved highly efficient in aiding the tracking procedure. The system provided a usable web controller which provides easy interaction between the pilot and the drone. Conclusively, investments in UAVs would enhance creation of quality graphic contents.

Knowledge propagation is a necessity, both in academics and in the industry. The focus of this work is on how to achieve rapid knowledge propagation using collaborative study groups. The practice of knowledge sharing in study groups finds relevance in conferences, workshops, and class rooms. Unfortunately, there appears to be only few researches on empirical best practices and techniques on study groups formation, especially for achieving rapid knowledge propagation. This work bridges this gap by presenting a workflow driven computational algorithm for autonomous and unbiased formation of study groups. The system workflow consists of a chronology of stages, each made of distinct steps. Two of the most important steps, subsumed within the algorithmic stage, are the algorithms that resolve the decisional problem of number of study groups to be formed, as well as the most effective permutation of the study group participants to form collaborative pairs. This work contributes a number of new algorithmic concepts, such as autonomous and unbiased matching, exhaustive multiplication technique, twisted round-robin transversal, equilibrium summation, among others. The concept of autonomous and unbiased matching is centered on the constitution of study groups and pairs purely based on the participants' performances in an examination, rather than through any external process. As part of practical demonstration of this work, study group formation as well as unbiased pairing were fully demonstrated for a collaborative learning size of forty (40) participants, and partially for study groups of 50, 60 and 80 participants. The quantitative proof of this work was done through the technique called equilibrium summation, as well as the calculation of inter-study group Pearson Correlation Coefficients, which resulted in values higher than 0.9 in all cases. Real life experimentation was carried out while teaching Object-Oriented Programming to forty (40) undergraduates between February and May 2021. Empirical result showed that the performance of the learners was improved appreciably. This work will therefore be of immense benefit to the industry, academics and research community involved in collaborative learning.

The ability of a Database Management System (DBMS) to detect fraudulent activities at the earliest possible time cannot be overemphasized.The concept of critical data surveillance is the ability of system to automatically watch over some selected database objects, and take cognizance of ensuing events.This work achieves this solution through the implementation of database triggers, which keeps track of the major database events.The focus of this work is on surveillance and protection against updates to critical database tables.The work implements real life audit-based surveillance of automated product sales enterprises database tables.The trigger was created from the scratch, and programmed to monitor the critical database object -the product pricing table, which keeps record of the price tagged on each the products in the supermarket.Thus, this work ensures that the trigger is fired accordingly, and that changes to the contents are reported to an audit table for further review by only authorized persons.This research was implemented using PostgreSQL.

The importance of a well implemented academic project cannot be overemphasized, especially in the area of Computer Science and Engineering. This work is aimed at making Computer Science and Engineering final year undergraduate projects easier for both the students involved and the supervisor. This key objective is achieved through the evolution of algorithmic narrations. The four major areas covered are Research Abstracts, Literature Review, Introductory Chapter, and Reference Section. The corresponding algorithms developed to achieve these are: Research Abstracts Check Algorithm (RACA), Literature Bank Generation Algorithm (LBGA), Introductory Chapter Check Algorithm (ICCA) and Partial Reference Review Algorithm (PRRA). This work is based on practical experiences of the researchers on academic project supervision at both undergraduate and graduate levels, in Computer Science and Engineering. By way of evaluation, the algorithmic strategies presented in this work were applied in real life supervision of six undergraduate projects between August 2020 and April 2021. The overall project completion time is estimated to have been reduced by about 2 months, compared to the outcome of previous supervisions in the year 2019 to 2020. Thus, this work is expected to be very useful to both students and supervisors involved in undergraduate Computer Science and Engineering projects.

Scientific Visualization remains an integral and inevitable part of every meaningful scientific, industrial and academic research. The focus of this study is to demystify the evolution, design and programmatic construction of scientific visualizations. Real life demonstrations have been achieved in this work using Python Programming Language. This work begins by exploring the programming environment based on a Python Integrated Development Environment (IDE) – the Anaconda. The IDE usage was shown in a chronological sequence with accompanying visual outcomes. The use of plot libraries was discussed, and implemented in real life. One of the demonstration projects is the Zig-Zag plot. Studies were also done on sub-plotting, and how it is used in scientific visualizations, especially where there is necessity to generate variations of outputs originating from a singular dataset. The work also explores the use of pie charts for presentations. A real-life case of how this could be used to visualize the halls of a residential university was demonstrated, with each of the data components labelled in distinct colours. It is hoped that this work will serve as a foothold and useful guide to researchers and other practitioners involved in real life scientific visualization.

Software Engineering is a branch of Computer Science that evolved as a result of urgent need to deal with decades of software crisis, characterized by low theoretical knowledge and practice of the construction of error-free and efficient software. The introduction of well-organized scientific, engineering and management strategies in the process of software development no doubt led to major breakthroughs, and solutions to software failures. One of the obvious game-changer in this regard is the evolution of Software Development Life Cycle, also known as Software Process Model for driving the different phases of software construction. A sound understanding of the process model is therefore inevitable, not just for software developers, but also to users and researchers. Such a theoretical cum practical understanding will enhance decisions on which process model is best for a particular job or perspective. This invariably, contributes immensely to the probability of success or failure of the project in question. Thus, the necessity for this research. This work presents an unambiguous expository of selected software development model variants. A total of four process model variants were studied, in a theoretical, visual and analytical manner. The variants were analyzed using strength versus weakness (SVW) tabular scenario. This work was concluded by presenting guides towards choice of these models. This research is expected to be a useful reference to software practitioners and researchers.

This work demonstrates in practical terms the evolutionary concepts and computational applications of Parametric Curves. Specific cases were drawn from higher order parametric Bezier curves of degrees 2 and above. Bezier curves find real life applications in diverse areas of Engineering and Computer Science, such as computer graphics, robotics, animations, virtual reality, among others. Some of the evolutionary issues explored in this work are in the areas of parametric equations derivations, proof of related theorems, first and second order calculus related computations, among others. A Practical case is demonstrated using a graphical design, physical hand sketching, and programmatic implementation of two opposite-faced handless cups, all evolved using quadratic Bezier curves. The actual drawing was realized using web graphics canvas programming based on HTML 5 and JavaScript. This work will no doubt find relevance in computational researches in the areas of graphics, web programming, automated theorem proofs, robotic motions, among others.

This work is a combination of conceptual and hands on based study aimed at laying a foundation for practical Object-Oriented software construction. First it presents a conceptual study of a number of backbone concepts of modern Object-Oriented Programming (OOP) languages. Secondly, it attempts to demonstrate real-life implementations of these concepts using Python Programming Language. This work touches on practical issues on Class and Object Creation, especially on the syntax and creation, and demystifies the subject matter using a simple table of rules. The OOP concept of Inheritance was studied, with focus on the three major types of inheritance. The self-argument, and constructors were studied, with focus on the three constructors - default, parameterized, and non-parameterized constructors. A brief discussion, and pictorial illustration was also made on the disparity between normal mathematical functions and OOP method calls. Further areas of studies are the concept of overriding between the parent and child class, as well as the OOP puzzle commonly known as Diamond Problem, including code segment and diagrammatic illustration of Python-based solutions. There are a number of other back-bone concepts in OOP not covered in this study, such as Encapsulation, Abstraction, Meta-Programming, among others, which will form areas of focus in future studies. Effort was made to enhance the overall presentation through practical illustrations using source codes, annotated diagrams, and discussions. It is hoped that this work will be very useful to researchers and other practitioners in Object Oriented implementations.

A number of critical questions remain unanswered in Health-care organizations, which are key to tactical planning, remaining competitive, among other issues that seek for answers from the information technologies spheres. Numerous establishments are evolving to fuse clinical computer unit, which dispense a solitary point of entry for permission to administrative, patient-related and research information. The medical record in a new embodiment lies at the center of developing clinical computer unit: accessible, protected, confidential, acceptable to patients and clinicians; fused with other, non-patient information that are specific. This study presented a review of Electronic Medical Record (EMR), security concerns and security frameworks proposed to improve the security concerns of EMRs. This study was able to provide an insight into the security concerns of EMRs, as well as, the barriers in use and adoption of EMR by health institutions which will bring about improved health services, especially in developing countries where manual record system are still prominent.

The advancement of technology has heralded novel computing devices and gadgets like self-driving cars, IoT devices, and autonomous systems. These advancements required high computational demand in achieving its goals. In matching the high computational demand of these new technologies, machine learning, parallelism, multicore processing and scaling are some of the approaches and techniques put in place. However, there is a pressure on the architectural development of recent computing devices as the traditional transistors seem to be fast outgrown. This article examines the reliability of autonomous systems using the PRISMA approach. Autonomous systems are systems that can fully operate and perform operations (computational or otherwise) with minimal human intervention. They are also capable of evaluating their performance. Thus, there is a need for a high degree of reliability. Several existing autonomous systems were reviewed and reliability issues of these systems were discussed. It was discovered that the reliability of a complex system is dependent on the reliability of underlying individual components and compromise of any of the underlying components of the autonomous system can affect the overall reliability of the entire system. The effort to enhance the reliability of these components will, in turn, improve the reliability of the entire system.

The necessity for adequate security of lives and properties cannot be overemphasized. Tackling insecurity in an effective manner calls for active computer-based researches and solutions. Essentially a security system is designed to help detect intrusion and prevent unauthorized access. These security threats are tracked by installing various security access doors and alarm systems. Findings have shown that many access doors use either password or biometric form of authentication separately which are easily compromised. This paper presents a two level automated access control for door security with mobile alert system, powered by a 5V, 6000mAH battery. In addition a multi factor authentication using biometrics (fingerprint) and password was also used to enhance access control for this system. A mobile phone call alert and alarm system was uniquely included to this design to notify owners of the presence of an intruder when multiple attempts of incorrect login credentials are presented to the system over a short period of time. The program for the microcontroller was written in C language. This paper concluded that the use of multi factor authentication with mobile phone call alert will help improve the security for access doors used in banks, residential buildings, hospitals, military properties and other institutions and where confidentiality and restrictions are needed.

The necessity for contact tracing in the fight against infectious diseases including pandemics like COVID 19 cannot be overemphasized.One of the obvious challenges is how to device practical strategies for computational evolution and construction of a homogenous network for contact tracing.A second challenge is how to evolve practical tools and algorithms for the visualization of contact network that models transmission.This research evolves a new algorithm which first, builds a new specialized data structure known as PiVector, and then a corresponding contact network visualization system.The resulting network could be used for contact tracing of the transmission of infectious diseases.This work practically demonstrates a programmatic and data-driven approach to network evolution, construction and visualization.This work was implemented using Python Programing Language and other related data mining technologies.

This paper, through critical literature review, has investigated the application of smart transportation system in handling traffic congestion with the aim of enhancing traffic flow in overcrowded cities.It has been discovered that the movement of people to urban areas in search of infrastructures and livelihood has drastically increased the population of people in such areas.This has resulted in traffic congestion, and created mobility challenges as people move from one place to another to carry out their daily activities.Many people have tried a number of traffic congestion avoidance techniques in order to meet-up with their daily appointments, one of which is moving at odd hours such as very early in the morning or late at night.This is far from being a reliable solution to the problem of traffic congestion.This research therefore, proposes an IT-based solution for traffic congestion through the concept of smart transportation systems also known as Intelligent Transport System (ITS).Its usage has given rise to the proliferation of smart systems in different urban sectors and it is projected to cover more areas of human endeavour because of its ability to proffer solutions through its adoption.

An exploration of riskit analysis graph (RAG) as a major technique of Riskit method is presented in this work with the aim of enhancing its capability for better risk identification (and management) and subsequently, contribute to software delivery time.The study begin with a brief background of the riskit as a major tool in risk analysis; pointing to the need for an enhancement of the tool and associated benefits plus disadvantages.After this, a review of closely related works in the field of study is presented leading to identification of some perceived limitations and challenges in the generic Riskit methods (RAG inclusive).Next, an analysis of a typical riskit analysis graph process visà-vis its main components is presented.Using the stepwise approach to risk profiling, a prototype of the intended model called the "enhanced risk analysis model-ERAM"is presented based on risk ontology and prognosis states.The ERAM was developed in phases through leaning on the basic approach of risk models which comprises of a generic four steps -establishing the likelihood of occurrence of risks in the task pool, identification of major variables for measuring the impact (should it occur); a computer simulation is performed leading to a well defined risk profile and finally, a conclusion was drawn on the fact that Riskit can actually be extended through a deep analysis of it process components.