International Journal of Education and Management Engineering @ijeme
Статьи журнала - International Journal of Education and Management Engineering
Все статьи: 625
Identifying Patterns and Trends in Campus Placement Data Using Machine Learning
Статья научная
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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Статья научная
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
Статья научная
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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Статья научная
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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Статья научная
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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Статья научная
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
Статья научная
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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Статья научная
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
Статья научная
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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Статья научная
The modernization of education requires secondary school teachers to improve their information-based teaching ability, and applying MOOCs to secondary school geography classroom teaching is also a new attempt. Applying MOOCs to secondary school geography classrooms can change the traditional teaching mode and cultivate students' independent learning ability. This paper mainly explores the feasibility of applying MOOC to secondary school geography teachers in the classroom and proposes strategies for teachers to effectively improve their informational teaching ability and level in applying MOOC to teaching. At the same time, the teaching method of combining MOOC with the flipped classroom is proposed for the inherent shortcomings of MOOC itself. The article also discusses practical ways to improve secondary school geography teachers' information ability, including changing teachers' teaching concepts, strengthening teachers' training, increasing hardware investment, and building digital campuses. The study can provide a reference for current secondary school geography teachers to improve their ability and use of information technology and innovate classroom teaching modes and methods.
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In what ways smart cities will get assistance from internet of things (IOT)
Статья научная
The concept of smart cities has become very popular in recent times and with much more clear understanding. The evaluation of Internet of things and recent progress in the technology has given smart city project a real lift. IOT models can easily be integrated in different fields and sections of a city to attain a working smart city. Modern Smart city should not only be technologically advanced but must also provide better quality of life and more opportunities improved lifestyle and development for its citizen. This Paper provides us a survey of how Internet of things can help us in the development of a smart city and also identifies the main components and elements characterizing a smart city. Furthermore we will also discuss the benefits a society will get from the working smart city project.
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Incorporating SAP® ERP Training into Industrial College Education: A Usability Evaluation
Статья научная
The main vision of Jubail Industrial College (JIC) is to "To excel in providing technology education and training, applied research, and innovation to support sustainable economic and industrial development of the Kingdom of Saudi Arabia". To ensure the readiness of JIC students for successful careers, they need to be exposed to the latest software tools and technologies currently used in the surrounding industry. The Enterprise Resource Planning (ERP) course is part of many degree programs at JIC where the academic staff is utilizing the SAP® University Alliance Program (SAP® UA) to inject a practical component into the theoretical ERP course. This paper presents insights into the integration of SAP training in an ERP course by assessing the usability of the SAP® UA system. In particular, our goal is to evaluate the usability aspect of the SAP® UA system among future industrial and petroleum employees. Since, the SAP® UA system has been used in the college for ten years, and the available relevant literature in this region and under the same circumstances is very limited, we undertook this research to determine the acceptance of the SAP® UA system among JIC students. The System Usability Scale (SUS) was the tool of choice in our research and the final results obtained generated a SUS value of 67 which falls within the acceptance range.
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Integration and Utilization of Network Course Resources for University Teachers
Статья научная
Network course resources are a mew development formation under the information technology condition. University teachers as the positive factor of network resources integration, how to make full use of modern information technology and thinking mode of pedagogy to turn shared resources into curriculum resources and make full use of it has become an worth discussing important problem under the background of the current university. Based on the university teachers how to integrate and use network course resources, some strategies has been put forward.
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Intelligent Detection Technique for Malicious Websites Based on Deep Neural Network Classifier
Статья научная
A major risk associated with internet usage is the access of websites that contain malicious content, since they serve as entry points for cyber attackers or as avenues for the download of files that could harm users. Recent reports on cyber-attacks have been registered via websites, drawing the attention of security researchers to develop robust methods that will proactively detect malicious websites and make the internet safer. This study proposes a deep learning method using radial basis function neural network (RBFN), to classify abnormal URLs which are the main sources of malicious websites. We train our neural network to learn benign web characteristics and patterns based on application layer and network features and apply binary cross entropy function to classify websites. We used publicly available datasets to evaluate our model. We then trained and assessed the results of our model against conventional machine learning classifiers. The experimental results show a very successful classification method, that achieved an accuracy of 89.72% on our datasets.
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Статья научная
Smartphones have been owned and used ubiquitously in all facets of society utilized for a wide number of tasks such as calling and messaging, social media, surfing as well as for entertainment. Spending a large amount of time on smartphone might lead to a dependence on it for a variety of purposes. This study uses objective measures of real time smartphone usage features to assess smartphone addiction. A purpose built android application to collect real time smartphone usage has been developed and linear classification models namely Support Vector Machine and Logistic Regression are used to predict smartphone addiction among university students. Furthermore, correlation and information gain measures are used to identify most vital features of smartphone usage which contribute maximum in assessment of smartphone addiction. It has been observed that both the linear models give worthy performance with more than 80% of accuracy. Also, the most important technical features impacting smartphone addiction are longest session spent for entertainment, total time used for communication, longest session spent for communication, longest session spent for work, total time used for entertainment, longest session for news and surfing, and data usage in other activities.
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Intelligent tour planning system using crowd sourced data
Статья научная
To observe the beauty of nature and to visit various places around the world, a vast number of tourists visit different countries and many tourist attraction sites now-a-days. But Most of the tourist places have failed to introduce itself as a tourist destination to the visitor due to lack of proper information and proper guideline to visit there. This paper tries to focus on some problems in the tourism industry and try to solve those problems using crowd sourced data with some customized algorithms. Some of the main problems are the lack of information about a destination tourist spot, combination on budget to visit the spot, time of travels etc. We proposed a customize algorithm which will provide maximum suggestion to visit a place with nearest all sub place based on user destination within their given budget and time. Using our method, user can choose the most suitable plan for them to visit those places.
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Interactive Web-interface for Competency-based Classroom Assessment
Статья научная
In this study, four phases of competency-based learning model, namely, i) unconscious incompetence, ii) conscious incompetence, iii) conscious competence and iv) unconscious competence is deployed in classroom teaching methodology. Competency-based learning model helps to understand a student's competence level on a particular topic that is already delivered in the classroom. The current work introduces a web-based competency-based learning model which is focused towards meeting learning objectives. Using the model, post lecture classroom online quiz will help to categorize the weaker student and also will also help to know the emotional state of the learners.
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Introducing Arabic-SQuADv2.0 for Effective Arabic Machine Reading Comprehension
Статья научная
Machine Reading Comprehension (MRC), known as the ability of computers to read and understand unstructured text and then answer questions, is still an open research field. MRC is considered one of the most research-demanding sub-tasks in Natural Language Processing (NLP) and Natural Language Understanding (NLU). MRC introduces multiple research challenges. One of these challenges is that the models should be trained to answer all questions and abstain from answering when the answer is not covered in the given context. Another challenge lies in dataset availability. These challenges are amplified for non-Latin-based languages; Arabic as an example. Currently, available Arabic MCR datasets are either small-sized high-quality collections or large-sized low-quality datasets. Additionally, they do not include unanswerable questions. This lack of resources depicts the model as incapable of real-world deployments. To tackle these challenges, this paper proposes a novel large-size high-quality Arabic MRC dataset that includes unanswerable questions, named “Arabic-SQuAD v2.0'”. The dataset consists of 96051 triplets {question, context, answer} in an attempt to help enrich the field of Arabic-MRC. Furthermore, a Machine Learning (ML)-based model is introduced that is capable of effectively solving Arabic MRC-with-unanswerable questions. The results of the proposed model are satisfactory and comparable with Latin-based language models. Furthermore, the results show a significant improvement of the current state-of-the-art Arabic MRC. To be exact, the model scores 71.49 F1-score and 65.12 Exact Match (EM). This proposed dataset and implementation pave the way to further Arabic MRC; aiming to reach a state when MRC models could mimic human text reasoning.
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Investigation and Study on the Status of the College Students with Left-Behind Experience in China
Статья научная
College students with left-behind experience are a special group and have different characteristics compared to students without left-behind experience. Based on investigation, college students with left-behind experience and interview some students around at them, this paper analyzed the basic situations of the college students with left-behind experience, as well as the key factors leading to these situations. Finally, the corresponding suggestions and expectations to improve the situations are put forward.
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Investigation of Student Dropout Problem by Using Data Mining Technique
Статья научная
Throughout the past twenty years, we've seen a huge increase in the number of school universities. Given the intense competition among major universities and schools, this attracts students to apply for admission to these institutions. Early school dropout prediction is a critical problem for learners, and it is hard to tackle. And a wide number of factors can impact student retention. In order to attain the best accuracy, the conclusion of the program, the standard classification approach that was used to solve this problem frequently needs to be applied the majority of organizations and courses launched by universities operate on either an auto model, therefore they always prefer course enrollment over student caliber. As a result, many students stop taking the course after the first year. In order to manage student dropout rates, this research provides a data mining application. The predictive model may provide an effective predictive list of students who typically require the greatest help from the student dropout program given updated data on new students. The results indicate that the object classification algorithm Random Forest data mining technique can create a reliable prediction model using existing student academic data. Future research on student dropout rates will continue to be vital for informing policy decisions, identifying at-risk populations, evaluating interventions, enhancing support services, predicting trends, understanding long-term consequences, and promoting global learning and collaboration in education.
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