Статьи журнала - International Journal of Education and Management Engineering
Все статьи: 590
Learning Preferences to Physiology of Undergraduate Students in a Chinese Medical School
Статья научная
Students learning may be classified according to the sensory modalities using VARK instrument, which categorizes learning modes as visual (V), auditory (A), reading-writing (R), or kinesthetic (K). We administered the VARK questionnaire to our second-year medical students, and 98 of 133 students (74%) returned the completed questionnaire. Only 14.3% of the students preferred a single mode of information presentation. In contrast, most students (85.7%) preferred multiple modes of information presentation. Knowing the students preferred modes and using web-based learning system may help the instructors to tailor to the student's individual preference in the teaching of medical science.
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Статья научная
This research was conducted to explain that long before the Covid 19 outbreak hit. There has been a deadly plague that has long affected human life at large. Anticipatory actions of the Dutch Colonial government were carried out intensively to inhibit the rate of spread of plagues at that time such as cholera, malaria, and smallpox. The influenza and bubonic plague epidemics of 1918 and 1911 threatened successively and smallpox emerged at the same time, hampering the pace of life in all fields. Moving on from this time, the Covid 19 outbreak has affected all fields including education. So far, when face-to-face began to be done again after a long time online, here a polemic emerged. Vaccination for children aged 6-11 years at primary school age. In field research, there are several obstacles experienced. Based on the results of interviews conducted in this study, most respondents were afraid of vaccines when administered to their elementary school-age children. Fear of risks such as hoaxes circulating on social media. Based on the results of the research conducted, in general, the people of Padang City are not entirely aware of the Covid-19 vaccination policy for elementary school-age children (6-11 years) because for them this is very risky because not all children in their bodies can receive vaccines. The research implementation procedure. This research uses the historical method (heuristics, criticism, interpretation, historiography). The purpose of the historical method is used starting with the collection of sources: first, literature and document studies, and field studies through in-depth interviews with several parents of students, teachers, such as elementary school residents in Padang City. Second, criticizing the sources obtained, Third, analyzing the relationship between the facts found, and finally the fourth is writing the findings. The purpose of this research to be achieved is to be able to explain the implications that the covid 19 outbreak has a lot of impact when it is required to vaccinate as a condition for face-to-face learning to be carried out again.
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Literature Survey on Educational Dropout Prediction
Статья научная
Educational Data Mining (EDM) is one of the crucial application areas of data mining which helps in predicting educational dropout and hence provides timely help to students. In Indian context, predicting educational dropouts is a major problem. By implementing EDM, we can predict the learning habits of the student. At present EDM has not been introduced at higher education level. Due to this we cannot recognize the genuine problems of students during their education. The objective of this analysis is to find the existing gaps in predicting educational dropout and find the missing attributes if any, which my further contribute for better prediction. After that we try to find the best attributes and DM techniques which are frequently used for dropout prediction. Based on the combination of missing attribute and best attribute of student data thus far, a new algorithm can be tested which may overcome the shortcomings of previous work done.
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Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques
Статья научная
One of the most challenging tasks in the education sector in India is to predict student's academic performance due to a huge volume of student data. In the Indian context, we don't have any existing system by which analyzing and monitoring can be done to check the progress and performance of the student mostly in Higher education system. Every institution has their own criteria for analyzing the performance of the students. The reason for this happing is due to the lack of study on existing prediction techniques and hence to find the best prediction methodology for predicting the student academics progress and performance. Another important reason is the lack in investigating the suitable factors which affect the academic performance and achievement of the student in particular course. So to deeply understand the problem, a detail literature survey on predicting student’s performance using data mining techniques is proposed. The main objective of this article is to provide a great knowledge and understanding of different data mining techniques which have been used to predict the student progress and performance and hence how these prediction techniques help to find the most important student attribute for prediction. Actually, we want to improve the performance of the student in academic by using best data mining techniques. At last, it could also provide some benefits for faculties, students, educators and management of the institution.
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Machine Learning Algorithms for Iron Deficiency Anemia Detection in Children Using Palm Images
Статья научная
Anemia is a common condition among adults, particularly in children and pregnant women. Anemia is defined as a lack of healthy red blood cells or hemoglobin. Early identification of anemia is critical for excellent health and well-being, which contributes to the sustainable development goals (SDGs), notably SDG 3. The intrusive way to detecting anemia has several hurdles, including anxiety and cost, which impedes health development. With the advent of technology, it is critical to create non-invasive techniques to diagnose anemia that can minimize costs while also improving detection efficacy. A distinct non-invasive technique is developed in this study employing machine learning (ML) models. This study's dataset contains 4260 observations of non-anemic (0) and anemic (1) children. To train the dataset, six (6) different ML models were employed: k-Nearest Neighbor (KNN), decision tree (DT), logistic regression (LR), nave bayes (NB), random forest (RF), and kernel-support vector machine (KSVM). The DT and RF models obtained the highest accuracy of 99.92%, followed by the KNN at 98.98%. The ML models used in this study produced substantial results. The models also received high marks on performance evaluation metrics such as accuracy, recall, F1-score, and Area Under the Curve-Receiver Operating Characteristics (AUC-ROC). When compared to the other ML models, the DT and RF had the best precision (1.000), recall (0.9987), F1-score (0.9994), and AUC-ROC (0.9994) ratings. According to the findings, ML models are crucial in the detection of anemia using a non-invasive technique, which is critical for health facilities to boost efficiency and quality healthcare. Various machine learning models were used in this study to detect anemia in children using palm images. Finally, the findings confirm earlier studies on the effectiveness of ML algorithms as a non-invasive means of detecting iron deficiency anemia.
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Machine Learning Applications in Algorithmic Trading: A Comprehensive Systematic Review
Статья научная
This paper reviews recent advancements in machine learning (ML) driven automated trading systems (ATS). ATS has progressed from simple rule-based systems to sophisticated ML models like deep reinforcement learning, deep learning, and Q-learning that can adapt to evolving markets. These techniques have been successfully applied across various financial instruments to optimize trading strategies, forecast prices, and enhance profits. The literature indicates that ML improves ATS performance over conventional methods by identifying intricate patterns and relationships in data. However, risks like overfitting, instability, and low interpretability exist. Techniques to mitigate these limitations include cross-validation, careful model management, and utilizing more transparent algorithms. Although challenges remain, ML creates valuable opportunities for ATS via alternative data sources, advanced feature engineering, optimized adaptive strategies, and holistic market modelling. While research shows ML improves market quality through increased liquidity and efficiency, heightened volatility needs further analysis. Promising future research directions include leveraging innovations in deep learning, reinforcement learning, sentiment analysis, and hybrid systems. More work is also needed on evaluating different techniques systematically. Overall, the progress in ML-driven ATS contributes significantly to the field, but judicious application and balanced regulations are required to address risks. Further advancements in ML will enable more capable, nuanced, and profitable algorithmic trading.
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Статья научная
The disease burden of Gastroesophageal Reflux Disease (GERD) varies across the globe and have a significant impact on the overall health of the communities. A number of complications and diseases stem from chronic GERD. In order to provide improved healthcare measures and to effectively monitor and control GERD, it is important to identify rate of incidence of the disease and the associated risk factors along with symptoms. Therefore, this study was conducted by retrieving the relevant data through machine learning. Principles of Artificial Neural Networks were applied to sort the data and the results were obtained in the form of a network by using VOSviewer software. These artificial intelligence and machine learning based results reveal that the Asian population is increasingly becoming prone to GERD and sporadic reports from Pakistan have surmounted to disclose that GERD is constantly present across different districts and cities of Pakistan. The major risk factors identified among the Pakistani population in different research articles include consumption of oily foods, the habit of having late dinners, sedentary lifestyles and a lack of understanding about disease diagnosis, and GERD management and treatment. Our results suggest that acid reflux and inflammation of esophageal cavity are some of the main symptoms of the disease. On the basis of the results obtained, it is speculated that this study will provide a ground to improve the symptomatic diagnosis of GERD by closely observing and analyzing the risk factors and the rate of incidence with symptoms. It would enable the healthcare facilities to effectively monitor the GERD cases so that the disease burden due to GERD and related illnesses could be reduced. Moreover, the identification of regional differences and a comparative data would help us in identifying the disease hotspots where more efforts would be needed to manage and control the disease.
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Malay language mobile learning system (MLMLS) using NFC technology
Статья научная
This paper proposes a portable learning framework that uses cell phones and Near Field Communication (NFC) innovation in which this application permits understudies to connect with genuine questions and get data from the labels that are put on the item by filtering the tag put on the article. These gimmicks empower the learning procedure at all over the place (pervasive learning) and enhance the viability of the learning methodology. In this paper, Mobile Application Development Lifecycle (MADLC) model was utilized to safeguard effective M-Lang framework conveyance. M-lang framework clients are required to utilize cell phones to advance the involvement in Malay Language learning.
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Management of Changes in Software Requirements during Development Phases
Статья научная
Change, in software requirements during its development phases, is considered as a major risk which may affect a software project to fail. Therefore, software engineering processes, methods, and tools are used in order to manage these risks whereas changes in requirements have many negative influences such as effects on system development life cycle (SDLC) phases, project progress, and outcome of a software project. Consequently, project managers must use risk management strategies, activities, and estimation techniques in order to manage and mitigate these risks which are caused due to changes in requirements. A novel model is proposed in this paper to manage and mitigate risks related to changing requirements. The proposed model is validated through qualitative research design. The results are in favor of the proposed model to show its effectiveness. It is anticipated that the proposed model will solve the problems of software companies in major to deal with risks about changing requirements.
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Material Physics & Chemistry Quality Network Curriculum Construction and Teaching Practice
Статья научная
Based on the construction and teaching practices of school's quality network curriculum of material physics and chemistry in Chengdu University of Technology, a few related hot issues are discussed in this paper. The orientation and characterization of the quality network curriculum are analyzed. And a series of the concrete optimization countermeasures and measures are put forward to improve the curriculum construction and teaching quality, such as teaching aims and demands, curriculum system and structure, teaching methods and means, curriculum management and evaluation, and websites construction and service quality and so on. We point out that the long-time construction, maintenance and service of the excellent quality website play a key role in the quality network curriculum construction and teaching. This has important realistic significance to increase continue the teaching effect and promotes professional cultivation of materials science and engineering.
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Статья научная
Many teachers experience difficulties in starting an instruction and in how to make the students learn appropriately. Meanwhile, the teacher has to make the students learn in order the teaching conforms to the four pillars of education according to UNESCO, i.e.: learning to know, learning to do, learning to be, and learning to live together in peace and harmony. In relation to this, a study was conducted to the students of primary schools in Singaraja to see the effectiveness of BLCS model (Bruner, Local Culture, and Scaffolding) in the teaching of mathematics through a measurement using Expert System - based CSE-UCLA. The effectiveness of the teaching model was seen from learning activities, learning achievement, and the students' response to the teaching. Data were collected through observation, test and questionnaire and were then analyzed descriptively. The results show that BLCS model is an effective model to be used in the teaching of mathematics.
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Medicine Management System: Its Design and Development
Статья научная
The researchers conducted this study with the main purpose of helping the residents of the municipality to expedite the process of obtaining free medicine. In the current setup, an individual who needs to avail of free medicine from the barangay or municipal health center personally visits the place to request maintenance medicine. This motivated the researchers to make a research study focusing on converting the manual requisition system to something that people can access quickly and comfortably without necessarily going out of their households, especially during these challenging times – the pandemic. The researchers called it a “Medicine Management System”. The researchers aimed to speed up the requisition of medicine using this online system. The patients or qualified recipients need not consume time lining up to request medicine from the municipal health center. This system can be accessed over the internet anytime and anywhere. Users must register and upload a legit doctor’s prescription. Researchers have created this system using HTML for the system interface, XAMPP for maintaining database records, and PHP for other system functionalities.
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Melamine Analysis in Liquid Milk by Simple and Robust Neural Network Based Method
Статья научная
Melamine (2,4,6-triamino-1,3,5-triazine) is a nitrogen-rich chemical implicated in the pet and human food recalls and in the global food safety scares involving milk products. Due to the serious health concerns associated with melamine consumption and the extensive scope of affected products, rapid and sensitive methods to detect melamine’s presence are essential. We propose the use of spectroscopy data – produced by near-infrared (near-IR/NIR) and mid-infrared (mid-IR/MIR) spectroscopies, in particular – for melamine detection in complex dairy matrixes. It was found that infrared spectroscopy is an effective tool to detect melamine in liquid milk. The limit of detection (LOD) below 1 ppm (0.76±0.11 ppm) can be reached if a correct spectrum pre-processing (pre-treatment) technique and a correct multivariate (MDA) algorithm: partial least squares regression (PLS), polynomial PLS (Poly-PLS), or artificial neural network (ANN) – is used for spectrum analysis. The relationship between MIR/NIR spectrum of milk product and melamine content is non-linear. So, non-linear regression methods are needed to correctly predict the triazine-derivative content. It can be concluded that mid- and near-infrared spectroscopy can be regarded as a quick, sensitive, robust, and low-cost method for liquid milk analysis. The technique can be applied for the automation of milk analysis.
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Method of Uninitialized Variable Detecting for C++ Program
Статья научная
We present a testing approach which is a partially automated, partially manual inspection process that reports defects in C++ source code. In this paper we considered one of the faults type- uninitialized Variable- which the approach can detect. Uninitialized Variable is a common kind of error in programs written in C++, it often causes error result or system collapse. This paper analyses the classical C++ uninitialized variable errors, and describes a detecting method of uninitialized variable errors combining the advantage of ASI technology which based on static analysis.
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Mobile Device-based Cargo Gridlocks Management Framework for Urban Areas in Nigeria
Статья научная
A number of recommendations for the adoption of ICTs in tackling traffic congestion problems in developing countries have been made in studies. Such studies have rather focused on assessing and evaluating the causes and effects of gridlocks than proffering solutions. The absence of implementable ICT models that can be effectively deployed to salvage the gridlocks, especially those generated by cargo transporters has added to the movement difficulty in these countries. This paper formulates a mobile device-based model supported by web technologies, called MobileCGM that can help avoid incidences of gridlocks emanating from Tin Can Island and Apapa sea ports in Lagos, Nigeria. This novel approach will allow timely pick-ups and deliveries of freights in the area by utilizing the deep penetration of GSM and mobile network services in Nigeria to solve the local problem. The model design and specification of the framework was achieved using the Unified Modelling Language (UML). The implementation of this model will render it needless for trucks and transporters to hang around the vicinity of where their cargos will be dropped off or picked up or cluster on the roads, as both cargo owners and transporters will know in advance when to pick up or deliver their cargo and get there just in time.
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Multilevel Authentication based Data Security and Verification over Cloud Computing Environment
Статья научная
There are various algorithm proposed in the area of cloud computing environment. Now a days the cloud computing is the very interesting area for research purpose. Cloud computing environment provides the security of user data and integrity verification with the users data, also cloud provide on demand network access to a shared pool of configurable computing resources like network, servers, storage, applications and services that can be rapidly provisioned and provide with minimum user effort and service provider interaction. Uses of cloud computing services is very easy and also in very low cost. The cloud computing services are on demand over the internet, so it provides the facility to clients that access these services remotely from anywhere, anytime with the help of internet and any devices like pc, laptop mobiles etc. The data or information of cloud user is save in cloud service provider so in the cloud computing environment the security of data and privacy of data is primary issue[1]. In this paper a security model is proposed here we provide a mechanism to cloud user to encrypt their data or sensitive information and generate a integrity verification key then save the data on cloud in encrypted form. The cloud user have many choices like private phase, public phase and hybrid phase and the hybrid phase tier1 and tier 2. In all three sections there are various encryption techniques are implemented like AES (Advanced Encryption Scheme)[9], IDEA, Blowfish[12], And SAES based on the security factors namely authentication, confidentiality, security, privacy, non-repudiation and integrity. In Private phase a Unique token generation mechanism with the help of SHA-2 implemented it helps to ensure the authenticity of the user, The Hybrid section of the model provides On Demand Security Choices Tier one or Tier two and Public section provides faster execution with the help of Blowfish algorithm. Overall the user data Is wrapped in two folds of encryption and integrity verification in all sections.
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Multimedia Pedagogy among Literature Lecturers in Two Universities in Uganda post COVID-19
Статья научная
Multimedia pedagogy embodies the use of text materials, photography and other still images, audio files, video presentations and forms of animation, which are all excellent vehicles for Literature teaching. Prior to COVID-19, most lecturers in the selected universities had used multimedia to watch literature films but not to conduct entire lectures. After that pandemic, when schools reopened, the ODEL (Online Distance Education Learning) department's pleas to lecturers to continue with some online courses fell on deaf ears. This study undertook ethnographic research to establish the reasons for the apparent reluctance by both lecturers and students to interface using multimedia during creative writing classes. The observations and interview findings revealed that subject specifications and psychological factors influenced the lecturer and student more than the economic factors against the use of multimedia during lectures. The study recommended retooling teacher trainers in multimedia pedagogy because a person cannot effectively conduct online lectures unless taught how to. The study concluded that human beings are social beings drawn to learning using methods that involve close interaction between the teacher and the taught, and the elaborateness of literature with its genres consists of the teaching of intangible interactions between words and listener, demand for physical interface between teacher and teacher trainee to chisel and polish the genre specifications.
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NGO Support Software Solution: for effective reachability
Статья научная
In India there are currently a lot of NGOs working for noble causes. Citizens are also eager to contribute. Unfortunately for a lot of these NGOs there is a shortage or absence of IT infrastructure, hindering their reach and effectiveness. We aim to aid such NGOs and provide them with the necessary IT infrastructure to optimize use of resources and increase their reach for food and money donations. The project includes a cross platform mobile application which will help them manage their volunteers, get orders by spreading awareness through a social media module to connect to people who wish to support them in this noble cause. There are many freelancing developers and existing apps, the goal is to extract all the best features, figure out best platforms, harness latest trends and develop the app at a cost that the NGO can afford. Our literature survey of existing app gave insights about designing the necessary modules that we must have. To have the analysis of people’s attitude, habits and trends, studying papers related to social media trends inspired us to harness its power. The study of other kinds of systems using mobile applications, encouraged us to consider options of 100% cost free background, enabling us to generate an economical solution.
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Need for anaphoric resolution towards sentiment analysis-a case study with scarlet pimpernel (Novel)
Статья научная
The problem of resolving references to earlier or later items in the discourse is commonly called as anaphora resolution or pronoun resolution. These items are usually noun phrases representing objects in the real world called referents but can also be verb phrases, whole sentences or paragraphs. Nowadays, anaphora resolution is addressed in numerous NLP (Natural Language Processing) applications. Proper treatment of anaphoric relations improves the performance of applications. Machine translation, information extraction, text summarization, or dialogue systems are some of the common applications of NLP. In early days, the machine translation systems processed on the basis of a sentence-by-sentence level. It did not consider the ties between sentences and resulted in an incoherent text as output. When the researcher forgets to handle the anaphora issue, it results in the striking problem of incorrect facts. It is very much needed to concentrate on the usage of pronoun, as it should match with their antecedents both in number and gender. Assigning inappropriate morphological features to the anaphor often may also lead to an undesirable change in the meaning of the sentence.
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Статья научная
Stock market trend can be predicted with the help of machine learning techniques. However, the stock market changes is uncertain. So it is very difficult and challenging to forecast stock price trend. The main goal of this paper is to implement a model for stock value trend prediction using share market news by machine learning techniques. Although this kind of work is implemented for the stock markets of various developed countries, it is not so common to observe such kind of analysis for the stock markets of underdeveloped countries. The model for this work is built on published stock data obtained from DSE (Dhaka Stock Exchange, Bangladesh), a representative stock market of an underdeveloped country. The empirical result reveals the effectiveness of Convolutional Neural Networks with LSTM model.
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