International Journal of Education and Management Engineering @ijeme
Journal articles - International Journal of Education and Management Engineering
All articles: 694
Scientific article
The use of GSM phones in Nigeria started in 2001 after two communication giants MTN Nigeria and Econet (now Airtel Nigeria) were granted licenses by the Federal Government through the Nigerian Communication Commission (NCC). The study examined the GSM use pattern among five rural villages in Uruan Local Government Area of Akwa Ibom State in South - South zone of Nigeria. The study used the descriptive survey design. Researchers constructed questionnaire titled GSM use pattern and evaluation of income level among rural dwellers questionnaire "GUPEILRDQ" was administered on 500 respondents selected through simple random sampling from the five villages. Results indicate that the use of GSM phone among the people of this community is high. The pattern of use was found to decrease with age while men used GSM more than the women. Study also revealed that the use of GSM has positive impacts on the income levels and socio-economic lives of the people. Majority of the people relied on their friends, children, relatives, political and business associates to recharge their phones. Also most of the people were of the opinion that the introduction of GSM in the area is a blessing. A rural telephony project was embarked by the Federal Government of Nigeria but had been abandoned. Two major telephone operators MTN Nigeria and Airtel Nigeria have been embarking on rural telephone projects in the rural areas. The introduction of GSM in Nigeria has had great impact on the lives of the people.
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Game Analysis on Technology Innovation Purchase under Complete Information
Scientific article
In the fierce market competition, technological innovation becomes a crucial element which effects the sustainable development of enterprises. That enterprises buy technology from the external could save limited resources, so more resources could be put into core business. The external purchase of innovative technology is essentially the results of the game between enterprise and innovative research institutions. The two players in the game analyze counterpart’s policy to choose the best strategy to achieve the greatest benefits.
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Scientific article
Garbage is a major problem, because it can harm human health, cause bad odors, and air pollution. With the existence of trash bins, it seems that it doesn't matter because most people prefer to litter, as well as cleaning workers to check the capacity of the trash can who often forget to cause garbage to accumulate so that it can pollute the environment. To solve the waste problem, especially at universities, a smart campus concept was created to solve the problem of waste management. By utilizing GPS technology, Internet of Things, Wi-fi technology that is already available, and other hardware devices such as Arduino microcontrollers, ultrasonic sensors, and others. With this concept, it is hoped that the cleaning staff will arrive on time to transport the garbage according to the information from the existing application, where the information has shown the coordinates of the full trash can so that cleanliness and comfort are maintained.
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GeoNaija: Enhancing the Teaching and Learning of Geography through Mobile Applications
Scientific article
Recently, researchers have identified several challenges attendant to the teaching and learning of Geography in Nigerian secondary schools. Consequently, in order to address these issues we propose GeoNaija – an educational mobile application platform that aid the teaching and learning of Geography. For the development of the application, we employed the cyclical ever-evolving analysis, design, development, implementation, and evaluation (ADDIE) instructional design method. This method was chosen due to the fact that it is popular, easy to apply, allows for rapid prototyping, saves times and provides continual feedback. The newly developed mobile app will make the teaching and learning of Geography concepts easier, captivating and delightful for both the students and the teachers. The significance and value of the research was made clearly evident through a survey that elicited students’ responses on questions relating to the app’s presentation, visual, navigation and accessibility design. 90 secondary students was used for the survey. The study affirmed that developed mobile application will positively change the perspectives of the students and help eliminate the gap between the conventional and other informal forms of education.
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Haralick Feature-Based Mammographic Breast Cancer Classification Using Cuckoo Search Optimization
Scientific article
Medical image analysis plays an important role in early breast cancer detection through mammographic image analysis. But, some information in the GLCM based Haralick texture features is redundant and irrelevant, which can impact performance in classification. In this study, a feature selection framework based on Cuckoo Search Optimization (CSO) is presented to select the optimum Haralick features for the breast cancer classification. The proposed approach consists of the preprocessing of mammograms, extraction of features from the GLCM, optimization of the GLCM features using the CSO technique, and classification using machine learning algorithms. Fourteen extracted GLCM attributes were used in the experiments performed on MIAS. The CSO-KNN model obtained the best classification rate of 90.00% with 6 selected features, which means that 8 attributes have been removed. The proposed framework significantly reduces feature dimensionality and improves the classification efficiency in computer-aided breast cancer diagnosis.
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Harnessing the Power of Artificial Intelligence for Adaptive Learning Systems: A Systematic Review
Scientific article
This research paper delves into the transformative potential of Adaptive Learning Systems (ALS) in revolutionizing education through the integration of Artificial Intelligence (AI). With traditional educational approaches often failing to accommodate individual learning needs, the answer to this problem is adaptive learning system which focuses on personalized content delivery, instructional methods, and assessments. Through case studies spanning various educational contexts, including various countries, higher education, and diverse cultures, we have evaluated the effectiveness of different ALS techniques in terms of different educational needs and requirements. By reviewing these techniques in terms of their features, capabilities and functionalities, we have tried to figure out, how does the use of AI in adaptive learning systems contribute to personalized learning experiences for students. The paper also highlights the key challenges and limitations associated with the integration of AI in ALS. It addresses issues like data protection, analyzes the ALS principles and investigates the ethical consideration which arises during implementation of AI in adaptive learning systems. Furthermore, it underscores the pivotal role educators’ play in collaborating with AI systems to create a balanced learning environment. By providing insights into future directions, such as advancements in personalization techniques and lifelong learning, this paper contributes to understanding the complex interplay between AI and personalized education. Ultimately, the research advocates for the widespread integration of ALS as a transformative approach that has the potential to redefine education and cater to the diverse needs of learners in the digital age.
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Scientific article
During the operation of heated oil transportation pipeline, the shutdown is caused by some pipeline accidents or repairing. In order to ensure the safety operation of the pipeline, determine the temperature drop and restart pressure needed at different shutdown lasting time is important. This paper builds the heated oil pipeline shutdown and restart mathematic model based on heat transfer and fluid mechanics theory. The VB and MATLAB hybrid programming method which is on the basis of COM technical is utilized to develop heated oil pipeline shutdown and restart simulation software. The software application shows using of VB and MATLAB hybrid programming method can reduce the work of algorithm developing and enhance the reliability of heated oil pipeline shutdown and restart simulation software.
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Human Opinion Analysis through Text Mining
Scientific article
With the technological advancements, global communication has largely shifted to text-based communication. As a result, the process of extracting meaningful insights from human behavior by machine learning techniques applied to textual data has now been significantly simplified. This research utilizes text mining methods to analyze customers feedback from food reviews, employing them as effective tools for opinion analysis and rating prediction from feedback. This research utilizes two neural network techniques (Normal Neural Network and LSTM) to analyze textual data and generate predicted scores ranging from 1 to 5 for each review from Amazon food review dataset. After implementing two neural network models, the system automatically generates a predicted score on a scale from 1 to 5. This study employs widely-used neural network techniques and provides a foundation for advancing text-based emotion detection in future research. The primary focus of this study is on unaltered customer feedback and it aims to solve the problem of accurately analyzing customer sentiments or opinion and extracting meaningful insights from their feedback. By comparing the performance of LSTM and standard neural networks, we achieve a 62.12% accuracy, showcasing superior results in emotion prediction from unstructured textual reviews. These insights pave the way for more scalable and efficient solutions in text mining for emotion detection.
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Scientific article
With advent of technology online education has become the core of educational settings worldwide. This paper aims to identify the factors that contribute in enhancing the online learning usage model in context of India, an emerging leader in Educational settings across the globe. In this study, data mining techniques were applied on the data collected from the log files of online courses. The initial investigations supported the use of custom build framework for teaching online courses. Data Structure course was taught using online platform and the data was collected using the log files. The data collected was further analysed using data mining techniques using Rapid miner tool. Although the results from three different data mining techniques showed some variations but the inferences from the results identified few common factors that have influence on enhancing the online learning usage model. Clustering techniques revealed that factors related to timely checking of online contents and posting have positive impact on online learning however decision trees supported that timely completing the online assignments along with checking of online contents and posting of messages played an important role in terms of enhancing the academic performance. This paper identifies three factors for teachers teaching online courses to improve overall performance of the students by learning from Indian University Success.
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Scientific article
The dark web is an overwhelming and mysterious place that comprises hidden services. Dark web hidden services contain illegal or offensive content. Hidden services are not accessible through regular search engines or browsers and can only be accessed via specific software. The proposed work aims to identify these hidden services by analyzing their associated images and text data. Doing so, one can better understand the types of activities on the dark web and what kind of content is available. First, a dark web crawler is developed to collect dark web services. Images are then manually classified into four categories: Cards, Devices, Hackers, and Money. Next, preprocessing the collected dataset removed irrelevant images, and a Convolutional Neural Network (CNN) was trained to identify new dark web image classes. Finally, quantum Transfer Learning (QTL) improved the model’s performance. The proposed work goes beyond conventional methods of categorizing datasets by including new categories of image classes of dark web hidden services that have not been considered before. Also, the work examines image data and related text to establish a strong correlation between them. The proposed approach will provide insights into the dark web hidden service by confirming the relationship between the image and text data of the respective hidden-services.
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Identifying Patterns and Trends in Campus Placement Data Using Machine Learning
Scientific article
This research delves into the utilization of machine learning algorithms to address the urgent challenge of assisting students in navigating a highly competitive job market. Recognizing the limitations of conventional methods in delivering effective guidance for securing job opportunities, there is a growing imperative to integrate advanced technology. Our model using Machine Learning (ML) algorithms offers customized solutions and emphasizes the algorithms that exhibit the highest effectiveness within this context. In the contemporary employment, achieving success extends beyond mere academic credentials, necessitating a holistic grasp of industry trends and in-demand skills. Through the application of machine learning, a fresh approach is presented, encompassing the gathering, and preprocessing of diverse data that encompasses skill proficiencies. This data forms the bedrock upon which ML algorithms operate, predicting and enhancing students’ likelihood of securing favorable job placements. The proposed work focuses on the careful selection of suitable machine learning algorithms, with special attention given to classification techniques such as Linear Regression, Random Forest, Decision Tree Classifier, K-nearest neighbors Classifier, and ensembled models. By meticulous evaluation and Ensemble Technique, these algorithms unearth intricate patterns within the data, deciphering the multifaceted factors influencing job placement outcomes. By deconstructing the performance of each algorithm, the report provides valuable insights into their strengths and potential synergies.
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Scientific article
In this paper, we present a novel computational framework for assisting and assessing memorization tasks. Such a framework can be used in any cases where certain level of memorization is needed, like in memorizing words/sentences, learning (programming) language structures, etc. We aim to identify the common memorization steps followed in various disciplines and then automate some of these steps to enhance memorization process. Particularly, we focus on annotation of texts (used for memorization) based on state of the art image processing techniques. Once texts are annotated and optionally commented, personalized tests can be automatically generated, focusing on the weakness of a particular student. These tests can further enhance the memorization process. As a case study, we have implemented the framework for a classical example of memorization: memorizing the Qur’an, the sacred book in Islam. Qur’an memorization is a well-known process since the early days of Islam and represents an ideal case for implementing the proposed framework.
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Impact of COVID-19 Pandemic on the Human Behavior
Scientific article
The world has witnessed a sudden change of horizon in the legacy, lifestyle of the human being due to the COVID-19 (Corona virus). The set protocols made by the different states of the world to harness the available resources on earth for human development came under a halt due to COVID-19. We have conducted a study on the immediate effects and the unprecedented change in the world we live in due to the ongoing pandemic. The paper aims to discuss and analyze the impact of this on the people and suggesting the appropriate remedies. The data collected has been done through online modes and the behavior of the people is observed, analyzed, and finally the results are represented with suitable assessments. During the study, few important parameters taken under consideration are the impact of COVID-19 on health, relationships, lifestyle, online education, screen time and income, etc. The paper aims to highlight the immediate impact of the COVID-19 on the behavioral change of the people and assessment of awareness in the general population about COVID-19.
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Scientific article
The digital transformation of the healthcare sector has revolutionized operational efficiency and patient care, yet concurrently exposed healthcare organizations to unprecedented cybersecurity risks, jeopardizing patient confidentiality and organizational integrity. This study undertakes a comprehensive investigation into contemporary cybersecurity strategies and emerging trends within the healthcare industry. Through a meticulous examination of published literature from reputable databases, including PubMed/MEDLINE, CINAHL, and Web of Science, critical patterns and vulnerabilities are discerned, underlining the escalating frequency and severity of cyber threats such as ransomware and phishing attacks. Emphasizing the pivotal role of organizational cyber resilience governance and policies, the study identifies a notable gap in standardized cybersecurity risk assessment methodologies, signaling the urgent need for innovative approaches. In response to identified challenges, the research proposes the development of novel methodologies to fortify cybersecurity defenses and protect patient data. Leveraging cutting-edge technologies such as blockchain and artificial intelligence, the study advocates for proactive measures to mitigate emerging threats and ensure data security and patient privacy in healthcare environments. Moreover, the integration of end-to-end security measures and the adoption of DevOps methodologies are highlighted as promising avenues for enhancing cybersecurity resilience. Results from a systematic literature review underscore the imperative for ongoing research and collaboration to address cybersecurity challenges in healthcare effectively. By offering insights into key cybersecurity features, technologies, and responsibilities within the healthcare sector, this study aims to inform stakeholders and policymakers, facilitating the implementation of robust cybersecurity measures. Furthermore, the study presents key findings regarding the current state of cybersecurity in healthcare, including challenges faced and potential solutions identified through the research process. Ultimately, through concerted efforts and the utilization of innovative strategies, healthcare organizations can navigate the evolving cybersecurity landscape, safeguarding patient information and upholding the integrity of healthcare systems.
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Impact of ICT Literacy on Technology Adoption in Nigerian Higher Education: An Empirical Study
Scientific article
Information and Communication Technology (ICT) has become central to teaching and learning in higher education, yet effective integration depends on users’ ICT competence and institutional implementation strategies. This study examines the role of ICT literacy in supporting teaching and learning and evaluates whether cultural perceptions significantly influence ICT adoption among undergraduate students at Kaduna State University, Nigeria. A quantitative survey design was employed, with data collected from 150 students and academic staff and analysed using descriptive statistics and the Mann–Whitney U test. The findings suggest that ICT literacy is perceived as positively supporting access to learning resources, communication, and classroom engagement. However, statistical tests did not reveal significant differences between respondent groups, indicating broadly similar perceptions among students and staff. While respondents acknowledged cultural considerations, these factors did not exert a substantial influence on ICT acceptance or use within the institutional context. The results suggest that ICT literacy and institutional support structures play a more immediate role in shaping technology adoption than general cultural perceptions. From a management engineering perspective, the study highlights the importance of ICT infrastructure planning, structured digital skills training, and policy-driven ICT integration in higher education. The findings provide practical guidance for university administrators and education managers seeking to improve the effectiveness and sustainability of ICT-enabled teaching and learning systems.
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Scientific article
Adequate information about climate change helps farmers to prepare and helps boost crop yield. Over the years, crops prediction was performed by manually considering farmer's experience on the particular crop in relation to the weather. This method was Inadequate, depends on the farmer's unreliable memory and grossly inaccurate. There is a need to introduce computational means to study and predict optimal climatic factors for improved crop growth and yield. The aim of this research work is to study the impact of climatic changes on the yield production of roots and tubers crops. K-means classification algorithm, Multiple Linear Regression, Python programming language, Flask Framework, Python machine learning packages numpy, matplotlib, Scikit-learn are the methodology used. While the obtained results show that CO2 Emission and Temperature does not really play a key role on how climate impact yield of root and tubers, rainfall plays more role; therefore, the study concludes that the three variables (temperature, rainfall, and CO2 Emission) are not enough to predict agricultural yield. It is therefore recommended that further research should be carried out to determine how other climatic factors such as soil type; humidity, sunlight etc. affect the yield of crops. The objective of this research is to study climatic change using data mining techniques, to design a predictive model using multiple linear regression to find the most optimal temperature and rainfall for effective crop yield and to simulate the multiple linear regression model design that achieve a high accuracy and a high generality in terms of climate change to crop yield.
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Scientific article
In the present situation, COVID-19 is a very common and dangerous issue in the whole world. Ensuring our healthy mental state is very essential at the period of COVID-19. But as a result of being in the home quarantine for a long time, people are going to notice a mental change such as stress, depression, mood swing. We proposed an RHMCD model which helps us to reach our required goal. This model contains machine learning algorithms. We examined our work with Naive Bayes classifiers, Support Vector Machine, and logistic regression. For gaining the report of mental conditions we used the sentiment analysis technique. For measuring the level of depression we also used a decision tree approach.
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Implementation of Computer-assisted Learning in High School: Teachers and Students’ Perspective
Scientific article
This study intends to identify school readiness in implementing information and communication technology (ICT) assisted learning, especially the use of computers. This research applied descriptive quantitative. The research samples used were subject teachers and second grade of high school students. The data collection technique employed a survey method, carried out with a random questionnaire distribution to the research sample. The results of the research sample responses were analyzed quantitatively by interpreting the percentage. Information was obtained that schools were basically "ready" to implement ICT-assisted learning. Student responses’ results showed that 55% of students "agreed" that the school had a computer laboratory, meanwhile 41.7% of students stated that they had sufficient ability at operating computers. The teachers' responses showed that the school already has supported the computer-assisted learning process and they are interested in integrating ICT in classroom activities. This research can be a basis for educators in identifying the extent to which students, teachers and school facilities are prepared to support computer-based learning. Given that computers are one of the technologies that can be used for learning activities and have been empirically proven to be able to make it easier for students to understand learning material.
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Scientific article
With the onset of Digital age, digital videos have been highly prevalent in every sphere of our lives and have replaced other sources of entertainment, information sharing & social interaction. With the increasing use of Mobile devices, Internet & its application it has been quite evident that digital videos are generated and distributed with ease. Quite often such videos are used as evidence depending on the kind of information they provide. Since the video has been distributed at a large level it becomes very difficult to identify the generator device of the digital video especially if the case is of objectionable video contents etc. This paper aims at proposing a framework which will embed the generator device information in the video & will make sure the user identification information can't be changed during the distribution process using internet or other networking services (i.e. Bluetooth).
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Improved Particle Swarm Optimization for Constrained Optimization
Scientific article
In this paper, we present an improved particle swarm optimization (PSO) algorithm to solve constrained optimization problems. The proposed approach, called MPSO, employs a novel mutation operator to enhance the global search ability of PSO. In order to deal with constrains, MPSO uses mean violations mechanism and boundaries search. Simulation results on five famous benchmark problems show that MPSO achieves better results than standard PSO and another variant of PSO.
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