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A MODEL OF AN ONLINE STUDENT ADMISSION SYSTEM

INTRODUCTION

1.0 Background of the Study

The pursuit of education and knowledge has gained significant pace, thanks to technological advancements. It has boosted education; from online admission application systems to online lecture systems, technology can be found in almost every aspect of education.

Education encompasses our life and has served as the foundation of our civilization. Education stimulates our minds and develops inquisitive minds into intellectuals. Higher learning elevates the intellect, enabling a more in-depth comprehension of our surroundings.

All prospective students must go through the admissions application procedure. Despite the fact that the student application procedure is digitalized, this process is still manned manually.

This project aims to automate the process of admitting students by automating and simulating the regular admission procedures of federal unity institutions.

Admission is the process of being accepted into a programme after meeting the minimum requirements. Microsoft® Encarta® 2009 © 1993-2008 Microsoft Corporation (Encarta dictionary) defines it as the “right, ability, or permission to enter a place, organisation, or institution.” The ‘right’ to student admission includes meeting the requisite educational standards, among other conditions.

The Federal Government of Nigeria owns and operates Federal Unity Colleges. Following the Nigerian civil war in 1967, Gen. Yakubu Gowon developed the three Rs – Reconciliation, Restoration, and Renovation

– to build unity in the belief that her oneness had been sacrificed on the altar of war. As a result, federal unification colleges were established in the nation’s 12 states at the time.

Initially, it was founded in Nigeria’s four cardinal points: Federal Government College Sokoto (North), Federal Government College Warri (South), Federal Government College Odogbolu (West), and Federal Government College Enugu (East).

According to the Nigerian Best Forum, as published by Fortune on June 22, 2009 and written by Andrew, Gen. Gowon now has 104 unity colleges. (http://www.nigerianbestforum.com/generaltopics/why-I-Established-Unity-Schools-Gowon/, accessed on November 27, 2015)

Pupils who successfully complete their primary school are only admitted to any of Nigeria’s unification schools if they pass the National Common Entrance Examination (NCEE) administered by the National Examination Council (NECO).

This examination is administered nationwide and in some foreign countries on a set date, and the best students are chosen from each state of the federation, including the Federal Capital Territory (FCT), based on the cut-off mark

which is not uniform this time due to intelligence differences across the nation’s states. “The national cut off mark to any of the unity schools is 68”, http://ncee.assure.com.ng/sample-page/, accessed November 27, 2015.

Digitalization is making life easier and more exciting. Computers are used in many aspects of human life. From sleeping to eating, exercising to working, technology has become a human companion, and education is no exception.

Education, as the best legacy left to individuals, stimulates our minds and transforms inquisitive minds into intellectuals. It allows us to learn more about our lives and the world.

Every education icon wants to make education more environmentally friendly by incorporating technology. As a result, many began with online applications

computer-based tests (CBT), and the publication of admitted students’ names on the internet, but few have implemented an online admission system that will automate admission procedures and standard norms.

The current admission process to federal unity colleges remains manual, but the application process has been digitalized by the agency that coordinates and administers the national common entrance examination, NECO.

Applicants purchase the form and receive a card that allows them to fill out the online form at http://www.mynecoexams.com/ncee/ when the card’s pin and serial number are authenticated.

Only final-year primary school students are entitled to obtain this card/form for admission to Junior Secondary School 1 at the federal unity colleges/schools. Other classes at the unity schools are completed through transfer.

A system, defined as a collection of hardware, software (principles, rules, and procedures), and people working together to achieve a common purpose, includes information technology.

In this context, an online student admission system is a model of a system that will automate the minimal required requirements for the body/authority to meet before admitting a prospective student.

This approach will reduce human delays caused by the enormous number of applications received while also limiting the amount of applications for each course, similar to a seat reservation system.

In this approach, decisions are made frequently in order to avoid disagreement. Before decisions are made, submitted papers are validated via an external link to the body’s result checker website, which queries their database to determine whether the submission is true or false.

That is, after the national common entrance examination, the scores of candidates applying to a specific unity school will be validated from NECO’s exam website (http://www.mynecoexams.com/ncee/) to confirm the accuracy of the score submitted by the applicant.

Decisions in this system, as in previous systems, are mostly based on human principles and experiences, but intelligence is embedded into the computer system.

Computers, due to their speed and efficiency, make an excellent companion for decision making and business planning. This is facilitated by the usage of management information systems and decision support systems.

The system uses methods (rules), processes/procedures, and machine-readable measures. These are used to convert data/information fields and enforce the rules that the system has been developed for. This is why the system can use a user’s input to obtain a result, such as authentication, verification, or decision making.

1.1 Statement of Problem

Any manual system has several faults and setbacks. The manual admission procedure has numerous flaws, ranging from delays to favouritism in accepting students.

In our digitalized age when people and technocrats advocate for a paperless society, a manual admittance system will only increase the amount of paperwork.

Prospective students are alerted on paper by putting their names on the school notice board, which does not promote privacy because everyone sees the notice board, as opposed to when it is submitted solely through your application page/portal.

In a nutshell, the issues with the manual admittance procedure are:

Ill confidentiality – There is no secrecy because admission panels may disclose crucial and private information.

Privacy – Admission application papers can be hurled by the wind or by humans. As a result, even though the system appears to be private, it is not.

Speed – Human efficiency in terms of speed is extremely poor. If they compared every application to the specifications, one might have to conclude that the snail is faster.

Error – Most of the time, there are errors in mapping the application number to the application name, resulting in two different people being assigned to the same slot, generating major issues. There are many other inaccuracies, such as misspelt names and incorrect application numbers.

Class Range – Due to human inefficiencies, a class of fifty students may be overcrowded at the end of the admissions procedure, making learning difficult for the pupils.

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COMPUTER AWARENESS AND ACADEMIC ATTAINMENT

Chapter one

Introduction

1.1 Background of the Study

Computer literacy refers to a teacher’s awareness and proficiency with computers and other related technology at various levels. It can also refer to how comfortable a person feels when using computer and other device software, such as Microsoft Word, database management, and Microsoft PowerPoint.

Adio (2003) defined computer literacy as a computer user’s ability to use the computer system and its add-ons to solve a given problem. According to Onasanya (2001), as the world enters the third millennium

it will be challenging to update people’s learning and teaching methods, keep up with technological breakthroughs, and apply computer literacy in Nigeria’s educational system.

Because computers have been proved to simplify all recognisable activities, their use is now prevalent in all areas of human activity. In many nations, employing computers in schools in some form has become rather fashionable (Haruun 2005).

The computerization of technical training operations in industrialised countries can be copied in developing countries, particularly Nigeria, although such an objective will be difficult to achieve without a large pool of native trainers who are computer literate.

According to John Iloanusi’s (2008) contribution, computers are useful for storing information on crimes, offenders, and suspects. He suggested that both crime prevention and detection may gain substantially from it.

Such a system is compatible with photo and fingerprint capture systems. According to Okorie (2008), utilising a computer compresses data to a very small storage space. Information retrieval and storage are accelerated, and computer records can be easily changed when needed.

Today, students in our country are denied much-needed possibilities for multi-media learning due to a lack of computer science education facilities and a mainly computer illiterate populace (Akudolu 2001).

She believed that including computer studies into the curriculum would provide pupils with the tools they needed for both academic success and personal fulfilment.

According to Lawal (2000), including computer education into teacher education programmes will better train instructors on how to employ computer-driven techniques and resources to improve classroom instruction while preparing them for real-world classroom scenarios.

When it comes to retraining employees to fit into jobs, providing entrepreneurial skill and development programmes, and empowering individuals with knowledge, skills, and attitudes that will allow them to engage in productive work for the rest of their lives, computer science education is critical.

It also makes quality work talents and skills available, which is critical for obtaining stable employment. Also comprehending the

The Federal Government has produced the National Policy on Computer Education, which includes the National Computer Education Curriculum, as part of its efforts to address the importance of computer literacy.

Computer use is still in its early stages in Nigeria. Nigerians are becoming increasingly computer-savvy and aware of the numerous benefits it offers.

According to Adekunle Adegbaje (2000), computers are the most recent additions to the nation. It has gained widespread acceptance throughout the economy. It is getting increasingly popular, and this trend will likely continue.

Some people have such severe cases of network neurosis, terminal panic, or computer shock that they avoid adopting computerised learning methods (Shineiderman, 2000).

Bertino (2005) claimed that the target audience for computers has historically mistrusted and been terrified of computer technology. He claimed that the interfaces were confused, unclear, and frightening, causing students to become angry, restless, and even terrified.

Statement of the Problem

The growth of computer applications has ushered in the “computer age” and changed the world into a global village. Today, computers and software have had a substantial impact on many parts of human activity, including education, health care, the military, politics, agriculture, religion, and business.

This implies that computer literacy is a necessary prerequisite for anyone (students or any cooperative body) to function effectively and efficiently in any field of human endeavour today, particularly for science teachers and students to properly deliver in various fields of specialisation in the current computer age.

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COMPUTER AIDED LEARNING OF BASIC SCIENCE SUBJECTS

1.1 Background of the Study

Science consists of courses that serve as the foundation for most scientific, engineering, and medical fields of study. Physics, chemistry, and mathematics subjects instill anxiety in kids.

This is commonly referred to as “Science Phobia”. Science and technology currently dominate the global economy, and as a result, students are urged to be technologically savvy.

The word science derives from the Latin word “Scentia,” which means knowledge. Knowledge obtained through study or practice, or knowledge covering general truths about the functioning of general laws, and tested using a scientific study approach concerned with the physical world.

With the difficulty pronounced, students must study using tools they will enjoy using in order to bring their attention to the basic concepts, just as children are designed to learn with toys.

This approach aims to minimise this anxiety by utilising multimedia resources available on the computer. It also aims to make science subjects easier, more approachable, and less stressful.

While studying, the user can also perform other system-related activities. This concept, its conception, and its stage of evolution are discussed in depth in senior high school.

1.2 Statement of the Problem

Mathematics, physics, and chemistry serve as the foundation for success in any scientific subject.

The need to develop and improve fundamental scientific education is critical for a variety of reasons.

Students have poor follow-up after tutoring, poor performance in fundamental science subject examinations, and a decrease in the percentage of students who want to study science due to their feelings and science phobia.

We also have kids that are unable to cope with science because of a lack of science background and information delivered to them without sufficient illustration by the tutor.

1.3 Objectives of the Study

This research project activity aims to achieve a variety of goals.

Providing access to a variety of information and materials that might not have otherwise been available or accessible. In this study, there is a great requirement for interaction through the tutor, which provides the pupils the impression that they are being helped or assisted by an instructor.

Creating an environment that encourages an active approach to learning, developing a systematic method to make science study more enjoyable in order to help students improve in science, and possibly requiring users to solve questions at various stages while answers to frequently asked questions are stored in the software to assist students.

1.4 Significance of the Study

This project’s relevance relates to what students will learn academically as a result of the study or exercise, as well as to assist and encourage students in collaborative learning and to urge students to take responsibility for their own education.

Complementing classroom instruction demonstrates the computer’s enormous multipurpose utility and engages students through the judicious use of interactive course materials.

1.5 Scope of Study

This study is about a tutorial on secondary school basic science subjects, specifically SS1 introductory themes in mathematics and chemistry.

1.6 Limitations of Study

Normally, every work has its limitations, and this study is no exception.

This study’s restrictions include a consistent electricity power supply or alternate source, time limits for completion, and cost restraints. The programme was unable to function and was completed on time due to an outage of energy or power.

Due to time constraints, the researcher is unable to conduct a thorough investigation and analysis of the subject matter. While financial constraints limit the frequency with which the company conducts investigations to obtain the essential information for the study.

1.7 Definition of Terms

Teacher: Someone who teaches, particularly as a vocation or an event, idea, or object from which anything might be learnt.

A tutor is a teacher who instructs an individual student or a small group of students, particularly those in need of remedial education.

Software refers to programmes and applications that can operate on a computer system, such as word processing or database packages.Aided: The act of guiding someone through something.

Tests take the form of examinations or evaluations. a set of questions, challenges, or practical exercises to assess someone’s knowledge, aptitude, or experience.

Science:

1. The study of the physical world involves methodical observation and experimentation, commonly before a noun.

2. A branch of science refers to a specific field of study or understanding of the physical universe.The life sciences

3. Systematic body of knowledge refers to an organised collection of knowledge about a specific subject.The Behavioural Sciences

4. Methodically studied or executed activity: one that follows a well-developed procedure.The Science of Dressing for Success

5. Science provides knowledge gained via studying the physical world.

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APPLYING DEEP LEARNING METHODS FOR SHORT TEXT ANALYSIS

Chapter One: Background to the Study

1.1 Introduction.

The current surge of data has ushered in a new era for human society. Social media platforms such as Facebook, LinkedIn, and Twitter allow people to share information in real time; in 2016, Africa had 120 million Facebook users per month, and statistics for other social media platforms show similar growth (Fuseware & World Wide Worx, 2014; Parke, n.d.).

The growing use of social media in Africa cannot be overstated, as can its potential in meaningful projects such as event monitoring, perception evaluation, information extraction and retrieval, and thus its recommendation in disease prevention measures.

Despite the success of social media approaches in politics and business, as well as their potential in epidemiology as timely, collaborative, and population-centric, extensive analysis is required, as information is prone to misinterpretation when machines process natural language. As a result, social media analytics need deep learning approaches.

1.2 Background of the Study

Prior to the 15th century, Africa was plagued by pandemic illness outbreaks, which slowed both human population growth and development aspirations (Spinage & House, 2012).

The study of these cases reveals a pattern of illness epidemics recurring in previously affected nations and migration to nearby countries, which may be attributable to regional ecological shifts (Kebede, Duales, Yokouide, & Alemu, 2010; Spinage & House, 2012).

12

The recurring frequency and the related mortality rate raises questions about the level of readiness, surveillance efficacy and control efforts in place; hence, over the years, numerous ways have been studied and fused with existing methods to minimise disease spread.
1.2.i A historical overview of infectious disease outbreaks in Africa

According to records, cholera, meningitis, influenza, yellow fever, rickettsia, smallpox, HIV/AIDS, Lassa fever, and Ebola are all common viral infections in Africa.

The total mortality rate is in the millions, with Ebola and HIV/AIDS accounting for more than 3 million documented deaths (Spinage and House, 2012).

Table 1 displays a sample number of disease outbreak cases in Africa, together with the predicted casualty scores.
Table 1: History of disease outbreaks in Africa (13).

1.2.ii Disease control.

Walter R. Dowdle defined disease control as ‘the decrease of disease incidence, prevalence, morbidity, or mortality to a locally acceptable level as a result of deliberate efforts; continuous interventions are required to maintain the reduction’ (Dowdle, 1998).

Disease control measures aim to minimise the contact rate of transmission, maintain the infectious population low, shorten the infection period of the common disease, and achieve a disease-free equilibrium (DFE) in the population (Brauer and Castillo-Chavez, 2014).

Disease control includes prevention activities for disease surveillance, readiness, and fast reaction.

• Eradication activities involving the isolation, treatment, and rehabilitation of contagious individuals.

A solid disease control strategy includes both prevention and eradication, albeit they often overlap and can be carried out in different sequences within the intervention cycle.

A number of organisations collaborate with the World Health Organisation (WHO), the Centre for Disease Control (CDC), and other countries’ health ministries to combat disease.

These organisations have been involved in epidemic preparedness and response (EPR) and integrated disease surveillance response (IDSR) efforts.

Adapting established methods for data gathering, illness identification, outbreak occurrences and projections, casualty estimation, and other disease outbreak metrics.

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APPROACH TO A SIMULATION VIRTUAL MACHINE

Chapter one

1.1 Introduction to Computer Simulation.
A simulation is a calculation that mimics the behaviour of a real or imagined system over time. Computer simulation refers to computations done on a computer.

Simulations are commonly used to analyse system behaviour, such as fire spread, weather forecasting, air traffic control, decoding the human genome, and designing new telecommunication networks, without the need for physical prototypes.

Modelling and predicting complex system activity is challenging due to increased data volumes, statistical requirements, and global user groups.

Distributing simulation across many processors can minimise execution time, enable real-time execution, and combine simulators. For additional information on parallel and distributed simulations, refer to Fujimoto’s book (2000).

“Creating a model is the first step in studying any physical system for simulation. Such a model can be classified into numerous types: There are six types of models: conceptual, declarative, functional, constraint-based, spatial, and multimodel. (Fishwick & Lin 1996).

The conceptual model defines qualitative terminology and class hierarchies within the system. The conceptual model defines the properties, procedures, and characteristics of system components without assigning dynamics to them.

The following four model types focus on system construction, such as Petri nets, Queuing models, and cellular automata. The Multi model type combines fundamental model types to build a model with component models, each representing a level of abstraction for the system (Fishwick and Lin 1996).
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A simulation model is created by retaining relevant characteristics of a real or imagined system and discarding extraneous ones. This model can then be executed on a computer.

DEVS abbreviating. Discrete Event System Specification is a hierarchical formalism for modelling and analysing general systems, including discrete event systems (state transition tables), continuous state systems (differential equations), and hybrids of both (Wikipedia 2007). The following chapter will cover DEVS in further detail.

1.2 Objective.

Our goal is to create a virtual machine that can simulate a DEVS model on any hardware host, such as a LAN, WAN, Grid, Cluster, or the Internet.

The virtual machine has three layers: the modelling layer receives the DEVS model, the simulation layer simulates the model using either the pessimistic or optimistic synchronisation algorithms, and the Middleware layer maps the simulation to any hardware host.

The virtual machine’s kernel will include both CDEVS and PDEVS simulator implementations, as well as distributed versions.

1.3 Work Done
We implemented a PDEVS for an existing CDEVS simulator utilising meta modelling. The CDEVS and PDEVS simulation packages are available, but the virtual machine’s whole package is not yet available due to its research prototype status.

The CDEVS and PDEVS simulators are Java-based and use JVM as the virtual machine. We tested the simulators using an example and compared simulation durations.

3 1.4 Organisation

Chapter 1 is the introduction chapter. Chapter 2 describes the CDEVS and PDEVS formalisms. Chapter 3 presents the modelling of the simulators using the Unified Modelling Language (UML), including both static and dynamic views.

In Chapter 4, we explain how we transform the Model into Java code, including the packages and classes involved. Chapter 5 provides an example of comparing simulation times between two simulators. Chapter 6 is the final chapter.

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ENHANCING PREDICTION ACCURACY OF A MULTI-CRITERIA RECOMMENDER SYSTEM

Chapter one

1.1 Introduction.
Intelligent systems require knowledge organisation to interpret, evaluate, and analyse acquired data. Intelligent systems are required in the majority of our daily activities, including e-commerce, online booking, social media, e-shopping, and other information-rich environments.

Recommender systems engage with users in a personalised manner, gathering information about a user’s tastes or preferences and using that knowledge to make suggestions and provide aid in instances when users must choose from a variety of possibilities.

In this chapter, we will discuss the recommender system and its approaches, as well as introduce multi-criteria recommender systems and a genetic algorithm. This chapter will also introduce the problem statement, aims and objectives, significance, and scope of the investigation.

1.2 Background of the Study

The recommender system was recognised as a potential study field in the mid-1990s, when researchers began focusing on recommendation bugs that were clearly dependent on rating structure (Adomavicius & Tuzhilin, 2005).

Recommender systems (RSs) are methodologies and software tools for engaging with huge and complicated information spaces in order to prioritise and give suggestions on products, offers, and objects that may be of interest to a specific user (Ricci, Rokach, & Shapira, 2015).

These ideas inform a variety of decision-making processes, such as which thing or object to purchase, which movie to watch, which news to read online, which music to listen to, which airline to fly with, or which hotel to book (Ricci et al., 2015).

Thus, the variety of homogeneous products or services, related information, and choices available in the market place or in diverse application domains such as e-commerce, e-learning, e-government, and e-tourism has made the recommender system widely used (Shambour, Hourani, & Fraihat, 2016).

The recommender system’s accuracy is an important component in determining how efficiently it can receive and analyse information. This has made evaluation of the recommender system an important and difficult undertaking.

According to Sohrabi, Toloo, Moeini, and Nalchigar (2015), accuracy is a key metric for evaluating recommender systems. This project aims to create an adaptive evolutionary algorithm to improve prediction accuracy and achieve a high correlation between anticipated and actual values for a multi-criteria recommender system.

1.2.1 Recommender System Techniques

The knowledge base, addressed domain, method, or technique employed during development can all influence the recommender system. Burke (2002) classifies the recommender into six approaches:

Content-based: In this strategy, the system learns from the user’s previous likes and interests and then offers appropriate goods to the user based on that knowledge.

The content-based approach relies on item features, hence a learning mechanism is used to establish the sort of user profile that will be generated by the content-based recommender. The similarities between items are determined by the attributes linked with them (Ricci et al., 2015).

Collaborative filtering is the most popular, established, and frequently used technology (Burke, 2002). It makes recommendations to the active user based on prior items enjoyed by other users who share similar likes.

Collaborative filtering is known as people-to-people correlation since the similarity in preferences between two users is determined by their rating histories (Ricci et al., 2015).

Demographic: The primary goal of this method is to categorise users based on personal characteristics and recommend things based on their demographic profile (Ricci et al., 2015). It is possible that no prior user ratings are required.

Knowledge-based: This method recommends items based on a certain field of knowledge, such as how beneficial an item is to the user and how certain item attributes suit the user’s requirements and preferences. The similarity metric can be viewed as the utility of the recommendation.

Community-based: This strategy recommends goods to the user based on the preferences of their peers. According to Ricci et al. (2015), consumers prioritise referrals from friends above anonymous persons with similar tastes.

Hybrid recommendation systems: This sort of recommender system combines two or more of the strategies discussed above (Adomavicius & Tuzhilin, 2005). A hybrid system that combines two methodologies attempts to leverage the benefits of one to mitigate the disadvantages of the other.

1.2.2 Multi-criteria recommendation system

Traditionally, most RSs get the user’s overall or general preference for a certain item. In other words, it recommends things based on a single criterion rating by users, which serves as input material for the RS algorithm to evaluate user preference opinions.

In most circumstances, a single criterion rating may result in suggestions that do not fulfil the user’s demands because people can voice their thoughts based on specific qualities of an item.

In contrast, multi-criteria RSs allow users to declare their preferences for an item based on a variety of features (Ricci et al., 2015). Multi-criteria ratings provide additional information about the user’s preferences for numerous significant elements or components of an item (Adomavicius and Kwon, 2007). The additional information about each user’s preferences will result in more accurate and higher-quality recommendations.

In recent years, numerous recommender systems have implemented multi-criteria ratings in place of traditional single-criteria ratings (Ricci et al., 2015).

The goal of multi-criteria recommender systems is to take a step forward in evaluating and interpreting users’ interests and choices in a more efficient and exquisite manner, as well as delivering ideal answers.

1.2.3 Genetic Algorithm

Several computer scientists explored evolving systems independently in the 1950s and 1960s, with the goal of using evolution as an optimisation technique for engineering challenges.

The goal was to generate a population of potential solutions to a given problem utilising operators inspired by natural genetic diversity and selection (Mitchell, 2004).

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DIGITAL MARKETING AS AN EFFECTIVE TOOL IN ACHIEVING BUSINESS EXPANSION

Chapter one

Introduction

1.1 Background of the Study

Digital marketing has helped businesses across industries to gain real-time customer information while also creating and communicating value to customers more efficiently. This has resulted in a large expansion in client base and corresponding revenue growth.

Digital marketing research defines the many types of media channels and underlines the objectives of digital marketing, which involves leveraging online media to impact audiences and customer segments.

Khin et al investigated the stakeholders involved in digital marketing, including the merchant, network, publisher, and customer. Digital marketing emphasises the client across several media platforms.

However, it lacks an integrated structure and a strategic direction for various stakeholders to collaborate and progress towards smarter and more sustainable digital marketing.

The practice of digitization is consistent with the smart city concept, which is promoted by scholars and governments to address the community’s current needs by utilising a broader range of digital technologies, including the internet, digital twins, and three-dimensional (3D) city modelling in geographic information systems.

Companies thus demand smart and sustainable digital marketing strategies and models to aid them in ensuring proper implementation of such creative and sustainable digital marketing tactics.

Coronavirus is a new disease produced by Covid-19 that is currently infecting people’s joints all over the world. The Covid-19 pandemic is one of the world’s most serious health catastrophes, and it was unexpected.

According to the Centres for Disease Control and Prevention, covid-19 is a highly infectious and lethal virus. The Covid 19 epidemic has impacted the joints of life.

In the business world, a novel coronavirus in terms of social distance circumstances drives enterprises worldwide to require millions of employees to work from home (Choudhury et al., 2020).

At least 316 million people in 42 US states have been requested to stay at home, leaving firms exposed to the shift to virtual workplaces or layoffs (Kim, 2020).

Many employees are quickly adapting to digital transformation; Zoom’s online video conferencing software reports a 78% profit increase, and a Google Meet report reports a 60% increase in user traffic, with people meeting online for 2 billion minutes per day (Kim, 2020).

Employees may not want to return to the actual office once the pandemic has passed. The management believes that some of the consequences of covid 19 on the workplace will be long-lasting, with flexible working hours and digital meetings projected to increasingly replace traditional corporate culture.

The organisation is experimenting with decentralised decision making and new software to ensure that the new digital work culture is as successful and productive as working in a traditional office.

Although firms must execute this transition as efficiently and promptly as feasible, several other repercussions that are critical to business continuity are sometimes underestimated, including the influence of covid 19 on consumer and market behaviour.

Recently, management practitioners and academics conducted research in response to Covid 19, with an emphasis on the digital transformation of the workplace (Kim 2020). The “Mere Urgency Effect” states that people devote the majority of their resources and time to problems (Zhu et al., 2018).

Statement of the Problem

According to a recent poll of 304 organisations, 42% are concerned about income; nevertheless, most managers prefer to wait and see rather than take action to address the situation (Kim 2020).

Managers may expect sales to revive only as consumption grows after the pandemic has passed. However, this pandemic has had an impact on the culture of the location, the culture of customers, and may have changed the structure of the market.

Pandemics also have an impact on digital transformation, including consumer behaviour and how organisations may respond to digital marketing.

As a result, the goal of this study is to determine how the business opportunity with digital marketing is affected by the pandemic, as well as the marketing perspective.

Consumer insight during the epidemic demonstrates that the industry is shifting towards digital marketing. The development of online transactions opens up new avenues for marketing success following Covid-19.

The objectives of the study

The study’s aims are:

To investigate the effectiveness of digital marketing during the Covid19 pandemic at Scib Insurance Brokers.

To determine the relationship between digital marketing and business expansion during the Covid19 era at Scib Insurance Brokers

To research whether digital marketing is a useful technique in attaining business expansion during the COVID-19 period in Scib Insurance Brokers.

Research Hypotheses

To ensure the study’s success, the researcher developed the following research hypotheses:

H0: there is no association between digital marketing and business expansion during the Covid19 era. Scib Insurance Brokers

H1: There is a relationship between digital marketing and business expansion during the Covid-19 era at Scib Insurance Brokers.

H02: Digital marketing is not an effective instrument in attaining business expansion during the COVID-19 era in Scib Insurance Brokers.

H2: Digital marketing is an important technique in accomplishing business expansion during the COVID-19 age in Scib Insurance Brokers.

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Computer Aided Learning Of Basic Science Subjects.

Abstract 
This project’s work on computer-aided learning of basic science courses produces an environment in which science learning and evaluation are enjoyable, and learning opportunities are equitable for rural and urban students.

The primary goal of Computer Aided Learning Basic Science Subjects is to improve learning quality by providing “Learning Play,” “Assessment Fun,” and “Equal knowledge for all.”

The goal of computer-aided learning in fundamental science subject programmes is to attract students, keep them in school, and improve education quality through text-based educational content.

Reducing illiteracy in science is also the concept driving this project’s work, and students have been scared away from science because they believe that calculations are difficult.

The goal of the Computer Assisted Learning Program is to do this by presenting the subject in a comprehensible way and using thought-provoking jokes.

The aided instruction method increases learners’ enthusiasm in school courses, resulting in more school attendance and better exam achievement. The teaching process with the integration of IT in the class and improvement in learners and teachers promotes learning and productivity, as well as making learners better equipped for getting into any institution and continuing with Computer institution at a higher level.

Chapter 1 

1.1 Background of the Study

Science consists of courses that serve as the foundation for most scientific, engineering, and medical fields of study. Physics, chemistry, and mathematics subjects instill anxiety in kids.

This is commonly referred to as “Science Phobia”. Science and technology currently dominate the global economy, and as a result, students are urged to be technologically savvy.

The word science derives from the Latin word “Scentia,” which means knowledge. Knowledge obtained through study or practice, or knowledge covering general truths about the functioning of general laws, and tested using a scientific study approach concerned with the physical world.

With the difficulty pronounced, students must study using tools they will enjoy using in order to bring their attention to the basic concepts, just as children are designed to learn with toys.

This approach aims to minimise this anxiety by utilising multimedia resources available on the computer. It also aims to make science subjects easier, more approachable, and less stressful.

While studying, the user can also perform other system-related activities. This concept, its conception, and its stage of evolution are discussed in depth in senior high school.

1.2 Statement of the Problem

Mathematics, physics, and chemistry serve as the foundation for success in any scientific subject.

The need to develop and improve fundamental scientific education is critical for a variety of reasons.

Students have poor follow-up after tutoring, poor performance in fundamental science subject examinations, and a decrease in the percentage of students who want to study science due to their feelings and science phobia.

We also have kids that are unable to cope with science because of a lack of science background and information delivered to them without sufficient illustration by the tutor.

1.3 Objectives of the Study

This research project activity aims to achieve a variety of goals.

Providing access to a variety of information and materials that might not have otherwise been available or accessible. In this study, there is a great requirement for interaction through the tutor, which provides the pupils the impression that they are being helped or assisted by an instructor.

Creating an environment that encourages an active approach to learning, developing a systematic method to make science study more enjoyable in order to help students improve in science, and possibly requiring users to solve questions at various stages while answers to frequently asked questions are stored in the software to assist students.

1.4 Significance of the Study

This project’s relevance relates to what students will learn academically as a result of the study or exercise, as well as to assist and encourage students in collaborative learning and to urge students to take responsibility for their own education.

Complementing classroom instruction demonstrates the computer’s enormous multipurpose utility and engages students through the judicious use of interactive course materials.

1.5 Scope of Study

This study is about a tutorial on secondary school basic science subjects, specifically SS1 introductory themes in mathematics and chemistry.

1.6 Limitations of Study

Normally, every work has its limitations, and this study is no exception.

This study’s restrictions include a consistent electricity power supply or alternate source, time limits for completion, and cost restraints. The program was unable to function and was completed on time due to an outage of electricity or power.

Due to time constraints, the researcher is unable to conduct a thorough investigation and analysis of the subject matter. While financial constraints limit the frequency with which the company conducts investigations to obtain the essential information for the study.

1.7 Definition of Terms

Teacher: Someone who teaches, particularly as a vocation or an event, idea, or object from which anything might be learnt.

A tutor is a teacher who instructs an individual student or a small group of students, particularly those in need of remedial education.

Software refers to programs and applications that can operate on a computer system, such as word processing or database packages.

Aided: The act of guiding someone through something.

Tests take the form of examinations or evaluations. a set of questions, problems, or practical tasks to assess someone’s knowledge, ability, or experience.

Science:

1. The study of the physical world involves systematic observation and experimentation, often preceding a noun.

2. A branch of science refers to a specific area of study or understanding of the physical world.The life sciences

3. Systematic body of knowledge refers to an organised collection of knowledge on a specific subject.The Behavioural Sciences

4. Methodically studied or performed activity: one that follows a well-developed method.The Science of Dressing for Success

5. Science provides knowledge gained via studying the physical world.

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Enhancing Prediction Accuracy Of A Multi-Criteria Recommender System Using Adaptive Genetic Algorithm

Chapter one

1.1 Introduction.

Intelligent systems require knowledge organisation to interpret, evaluate, and analyse acquired data. Intelligent systems are required in the majority of our daily activities, including e-commerce, online booking, social media, e-shopping, and other information-rich environments. Recommender systems engage with users in a personalised manner, gathering information about a user’s tastes or preferences and using that knowledge to make suggestions and provide aid in instances when users must choose from a variety of possibilities.

In this chapter, we will discuss the recommender system and its approaches, as well as introduce multi-criteria recommender systems and a genetic algorithm. This chapter will also introduce the problem statement, aims and objectives, significance, and scope of the investigation.

1.2 Background of the Study

The recommender system was recognised as a potential study field in the mid-1990s, when researchers began focussing on recommendation flaws that were clearly dependent on rating structure (Adomavicius & Tuzhilin, 2005).

Recommender systems (RSs) are methodologies and software tools for engaging with huge and complicated information spaces in order to prioritise and give suggestions on products, offers, and objects that may be of interest to a specific user (Ricci, Rokach, & Shapira, 2015).

These ideas inform a variety of decision-making processes, such as which thing or object to purchase, which movie to watch, which news to read online, which music to listen to, which airline to fly with, or which hotel to book (Ricci et al., 2015).

Thus, the variety of homogeneous products or services, related information, and choices available in the market place or in diverse application domains such as e-commerce, e-learning, e-government, and e-tourism has made the recommender system widely used (Shambour, Hourani, & Fraihat, 2016).

The recommender system’s accuracy is an important component in determining how efficiently it can receive and analyse information. This has made evaluation of the recommender system an important and difficult undertaking.

According to Sohrabi, Toloo, Moeini, and Nalchigar (2015), accuracy is a key metric for evaluating recommender systems. This project aims to create an adaptive evolutionary algorithm to improve prediction accuracy and achieve a high correlation between anticipated and actual values for a multi-criteria recommender system.

1.2.1 Recommender System Techniques

The knowledge base, addressed domain, method, or technique employed during development can all influence the recommender system. Burke (2002) classifies the recommender into six approaches:

Content-based: In this strategy, the system learns from the user’s previous likes and interests and then offers appropriate goods to the user based on that knowledge.

The content-based approach relies on item features, hence a learning mechanism is used to establish the sort of user profile that will be generated by the content-based recommender. The similarities between items are determined by the attributes linked with them (Ricci et al., 2015).

Collaborative filtering: This is the most popular, developed, and widely used technique (Burke, 2002). It makes recommendations to the active user based on prior items enjoyed by other users who share similar likes.

Collaborative filtering is known as people-to-people correlation since the similarity in preferences between two users is determined by their rating histories (Ricci et al., 2015).

Demographic: The primary goal of this method is to categorise users based on personal characteristics and recommend things based on their demographic profile (Ricci et al., 2015). It is possible that no prior user ratings are required.

Knowledge-based: This method recommends items based on a certain field of knowledge, such as how beneficial an item is to the user and how certain item attributes suit the user’s requirements and preferences. The similarity metric can be viewed as the utility of the recommendation.

Community-based: This strategy recommends goods to the user based on the preferences of their peers. According to Ricci et al. (2015), consumers prioritise referrals from friends above anonymous persons with similar tastes.

Hybrid recommendation systems: This sort of recommender system combines two or more of the strategies discussed above (Adomavicius & Tuzhilin, 2005). A hybrid system that combines two methodologies attempts to leverage the benefits of one to mitigate the disadvantages of the other.

1.2.2 Multi-criteria recommendation system

Traditionally, most RSs get the user’s overall or general preference for a certain item. In other words, it recommends things based on a single criterion rating by users, which serves as input material for the RS algorithm to evaluate user preference opinions.

In most circumstances, a single criterion rating may result in suggestions that do not fulfil the user’s demands because people can voice their thoughts based on specific qualities of an item.

In contrast, multi-criteria RSs allow users to declare their preferences for an item based on a variety of features (Ricci et al., 2015). Multi-criteria ratings provide additional information about the user’s preferences for numerous significant elements or components of an item (Adomavicius and Kwon, 2007). The additional information about each user’s preferences will result in more accurate and higher-quality recommendations.

In recent years, numerous recommender systems have implemented multi-criteria ratings in place of traditional single-criteria ratings (Ricci et al., 2015). The goal of multi-criteria recommender systems is to take a step forward in evaluating and interpreting users’ interests and choices in a more efficient and exquisite manner, as well as delivering ideal answers.

1.2.3 Genetic algorithm

Several computer scientists explored evolving systems independently in the 1950s and 1960s, with the goal of using evolution as an optimisation technique for engineering challenges. The goal was to generate a population of potential solutions to a given problem utilising operators inspired by natural genetic diversity and selection (Mitchell, 2004).

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A genetic algorithm is an evolutionary stochastic search approach used for optimisation and learning. It is also a search method based on the notion of shifting from one population of “chromosomes” to another utilising natural selection (survival of the fittest) and natural genetics to solve an optimisation problem.

Furthermore, a genetic algorithm is an evolutionary method for addressing optimisation issues such as sequencing, travel, salesman problems, and scheduling (Schmitt 2001). Some significant components to consider in a genetic algorithm include:

Individuals are represented in a variety of ways, including bit strings, binary codes, and actual numbers.

Fitness function: Is concerned with performance measures that can be minimised or maximised?

Population: This contains a representation of various solutions.

Parent selection mechanism: Assists in distinguishing people based on their quality, allowing the superior individual to become the parent of the following generation.

Variation operators: These operators generate new individuals from existing ones. They are classified as crossover (single or two points), which is performed on selected individuals to combine generic information to create new individuals or children, and mutation (flipping).

The selection mechanism, also known as replacement, is predicated on survival of the fittest.

Because evolutionary algorithms are stochastic and rarely guarantee an optimum solution, a suitable termination condition, such as when the fitness evaluation hits a certain limit, is required.

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1.3 Statement of the Problem

The bulk of current RSs use an overall estimation of a user rating of an item or single criterion rating methodologies to assess users’ thoughts on previously encountered objects.

Because the suitability of a recommended item for a specific user may be determined by several important aspects or attributes in the user’s decision-making process, the traditional single criterion rating can be considered limited and inaccurate because it cannot justify the various items’ attributes.

For this reason, a multi-criteria recommendation is developed that incorporates users’ ratings on many or diverse aspects of an item using an aggregate function-based method. The suggested technique uses an adaptive evolutionary algorithm to gain an appropriate learning relationship, resulting in a more accurate and efficient prediction.

1.4 Goals and objectives of the study

The goal of this project is to apply an adaptive evolutionary algorithm to model a multi-criteria recommendation problem with an aggregation function-based method in order to achieve more accurate and efficient predictions.

The aims included developing an adaptable evolutionary algorithm and applying it to multi-criteria recommendation situations.
 Create a system capable of recommending acceptable items to users.

This study compares the prediction performance of a multi-criteria recommender technique employing an adaptive evolutionary algorithm to a classic recommender approach.

1.5 Significance of the Study

This study benefits web users and application domains like e-commerce, e-learning, e-government, social networks, and e-tourism by making decision-making easier, faster, and more efficient. Additionally, user ratings of items with multiple attributes can improve prediction accuracy for recommendations to other users.

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1.6 Scope of the Study

The study focusses on RSs, specifically multi-criteria RSs that offer proficient, accurate predictions for consumers. This research involves the creation of a complex system capable of recommending the best idea or item to consumers depending on their preferences.

1.7 Expected Results

The project’s goal is to offer predictive performance data for the proposed technology and compare it to existing methodologies. These results include a reduction in prediction errors, an increase in ranking accuracy, and a strong connection between expected and actual values.

1.8 Thesis Structure The thesis is arranged as follows: Chapter 2 provides an overview of the RS and multi-criteria RS, delves into the adaptive genetic algorithm and its component, and reviews relevant research.

The third chapter explains the study’s methodology and architecture. Chapter 4 describes in detail how the system was implemented. It also discusses the results. Chapter 5 concludes by discussing the summary, conclusion, and recommendations.

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