International Journal of Modern Education and Computer Science @ijmecs
Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1096
Data Mining with Associated Methods to Predict Consumer Purchasing Patterns
Статья научная
Technology is developed and utilized as an honest computer in order that it can provide useful information. With the aim of developing and meeting business objectives, the utilization of sales transaction data in minimarket GP is processed into information or knowledge as a recommendation to ascertain the possible value of purchased simultaneously. This processing uses data mining. Database buildup in computerized systems is justified by getting added value from this data set. Data mining can predicts trends and therefore the nature of business behavior which is extremely useful to support important deciding. The algorithm wont to form the association rules during this study is CT-Pro. CT-Pro algorithm may be a development of FP-Growth. The difference is within the second step where FP-Growth creates the FP-Tree arrangement while CT-Pro makes the Compressed FP-Tree (CFP-Tree) arrangement. The CT-Pro algorithm process by analyzing employing a tree system where the things most frequently purchased become root and other items will follow the basis. The CFP-Tree process will provide levels for every transaction and facilitate mining results. CT-Pro algorithm implementation with CFP-Tree arrangement applied to data mining systems is in a position to research sales data for 3 months, namely January 2020 – March 2020 with a complete data record of 1.303 and 320 sales transactions at minimarket GP become information or knowledge. The results of this study are the relationship between the tendency of products that are bought together based on categories in a kind of percentage to be used as a recommendation in structuring the position of items that are mutually frequent in certain categories.
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Data Protection Techniques for Building Trust in Cloud Computing
Статья научная
Cloud computing has played much important role in comparison to other fields of IT, in providing Data storage, Data security, Quality of Services (QoS) etc. In the last few years, it had emerged and evolved so quickly due to its number of facilities and advantages to the organizations and end users. Many data security factors have also increased due to this fast evolution of cloud in the IT industry. Therefore several security models and trust establishing techniques have been deployed and are been in execution for providing more security to the data, especially the sensitive data. Despite of that much security, many of the models/techniques lacks in one or more security threat measures. In this paper a new model have been designed and proposed which introduces Security Aware Cloud. First the trust of the user or organization is established successfully on cloud than the security to the data is granted through privacy and encryption module. Level of quality of service and security are achieved under the Contract Trust layer while the Authentication and Key Management are covered under Internal Trust layer. For critical data privacy and encryption, Homomorphism mechanism is used. By the use of proposed trust and security model, we can enhance Return on Investment factor in the cloud for the data security and service provided by it.
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Data Security in Mobile Cloud Computing: A State of the Art Review
Статья научная
Due to tremendous use of smartphones the concern of cloud computing in mobile devices emerges, which is known as Mobile Cloud Computing (MCC). It involves the usage of mobile devices and cloud computing to perform resource intensive tasks using the internet with minimum impact on cellular resources. Nowadays, people are relying on mobile devices due to their small size and user friendly interface but due to its limited storage capacity, people can no more rely on internal RAM. Therefore, this promotes a drastic need for technology to make it possible for anyone to access their data anywhere anytime. As a result, Mobile Cloud Computing facilitates mobile users with its enticing technology by providing its on-demand and scalable services. But privacy and security are the main concern for a mobile user in the modern era. Thus, issues regarding security can be divided into cloud security and mobile network user’s security, respectively. However, the primary focus of this study is to analyze how to secure the user's data in a mobile cloud. Leading to objectives, the current study presents a comprehensive analysis of existing techniques that can be considered for securing data in MCC efficiently. Moreover, this work will contribute a state-of-the-art roadmap to research and development communities for the right selection of proposed approach.
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Data analysis and visualization of continental cancer situation by twitter scraping
Статья научная
With the advent of user-generated content, usability, and interoperability of web platforms, people are today more eager to express and share their opinions on the web regarding both daily activities and global issues. Cancer is often undetected, leading to serious issues which continue to affect a person's life and his surroundings. Recently Twitter has been very popular to be used to predict and monitor real-world outcomes as well as health-related concerns. Nowadays people are using social media in any situation. Even cancer patients, their friends, and family are increasingly sharing their experience in social media, which has increased the ability of patients to find others similar to their conditions to discuss treatment options, suggest lifestyle changes, and to offer support. Our work targets to link patients with a particular illness (cancer) together and to provide researchers with enriched patient data that might be very useful for future analysis of this disease. We wanted to create a meeting point for the healthcare sector and social media through our work. Our target was to collect Twitter data from different continents of the world and analyze them. We scraped tweets from over the last two years from all around the world. Then clean the data using a regular expression and then process it to prepare our own dataset. We used sentiment analysis and natural language processing to classify them into positive, negative and neutral tweets to determine which of the tweet means to have cancer and which don't. We then analyzed the prepared dataset and visualized and compared them with veritable cancer-related information to ascertain if people's tweets are allied with actual cancer situation.
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Статья научная
The problems that exist in the school decline in student achievement ahead of class III, especially before approaching the national exam. If the learning achievement of third-grade students can be known earlier then the school can perform the actions necessary for students to achieve good learning achievement. This research uses two methods of data mining, Neural Network Model Multilayer Perceptron, and Decision Tree. For comparison, this study also uses t-statistic test, t-test and to compare precision/recall using Roc Curve. Neural Network Model Multilayer Perceptron Positive performance vector accuracy: 88.64% and Negative: 14.07%, precision (positive guidance class) positive 88.00% and negative 16.88%, recall (class: Ordinary guidance) positive 84.50%, and negative 21.73%. Decision Tree Positive performance vector accuracy: 84.82% and Negative: 15.24%, precision (positive guidance class) positive 86.55% and negative 18.52%, recall (class: ordinary guidance) positive 84.00% and negative 23.85%. Experiments conducted in this study aims to prove that data mining can predict student achievement by finding the best data mining method between the multilayer perceptron neural network and Decision tree to be implemented into integrated information system between student motivation data, student learning interest, and intelligence emotional students.
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Decision Mathematics as Preparation for Undergraduate Computer Science
Статья научная
In England and Wales, students commonly take what are called Advanced ‘A’ level examinations in their final two years of secondary school. Good passes in these examinations are required to be accepted onto university degree courses. 123 Computer Science students from 24 different UK universities participated in an online survey about their perceptions and experiences of A-level Mathematics and A-level Further Mathematics as preparation for the mathematical component of their degree. The majority reported that decision mathematics units that they had studied as part of either qualification had been good preparation for their degree. Additionally, those who had taken the Further Mathematics qualification were positive about their experience and its utility as preparation for undergraduate Computer Science. A-levels in Mathematics and Further Mathematics in England and Wales are currently undergoing significant reform. One of the major changes will see the removal of Decision Mathematics as an optional topic in A-level Mathematics, meaning that students who wish to study it must take Further Mathematics – a much less commonly studied subject. Consequently, this research might encourage admissions tutors to reconsider their admissions requirements for undergraduate Computer Science.
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Статья научная
Assessment of education is the standardization imposed by the government to be followed by all universities in Indonesia. Fulfillment of national standards of education aims to ensure quality higher education practices and the quality of its graduates, One of the education practices assessment are assessment of the ratio of lecturers to students. This study aims to help the managerial prepare lecturers to facilitate the process of education and the accreditation process to be faced. Model used with expert system for its inferencing engine with the help of ARIMA forecasting algorithm to predict until the period of accreditation, Forecasting results show the model (1,1,0) which is eligible for prediction.
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Deep Learning CNN–LSTM Hybrid Approach for Arabic Sentiment Analysis Using Word Embedding Models
Статья научная
In recent years, the widespread use of social networks has empowered online users to freely share their opinions on diverse aspects of life. Sentiment Analysis (SA) has consequently emerged as a pivotal domain within Natural Language Processing (NLP), serving a crucial role in discerning sentiment orientations and extracting valuable insights from public viewpoints. Analyzing sentiment in Arabic poses distinctive challenges due to its varied dialects, as well as its intricate morphological and syntactic structures. Deep Learning (DL) models, particularly Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), have exhibited remarkable proficiency in Sentiment Analysis. LSTM networks excel in capturing sequential data patterns, while CNNs offer inherent advantages in feature selection, yielding superior performance compared to conventional machine learning (ML) algorithms. In our study, we propose an ensemble approach that integrates CNN and LSTM techniques to classify and forecast sentiment in tweets. We evaluate the effectiveness of this hybrid model against individual LSTM and CNN methodologies employing the FastText word embedding model. Experimental findings illustrate that our LSTM-CNN hybrid approach, leveraging the FastText word embedding model, significantly improves text classification accuracy.
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Статья научная
Emotion recognition is a significant research topic for interactive intelligence system with the wide range of applications in different tasks, like education, social media analysis, and customer service. It is the process of perceiving user's emotional response automatically to the multimedia information by means of implicit explanation. With initiation of speech recognition and the computer vision, research on emotion recognition with speech and facial expression modality has gained more popularity in recent decades. Due to non-linear polarity of signals, emotion recognition results a challenging task. To achieve facial emotion recognition using multimodal signals, an effective Bat Rider Optimization Algorithm (BROA)-based deep learning method is proposed in this research. However, the proposed optimization algorithm named BROA is derived by integrating Bat Algorithm (BA) with Rider Optimization Algorithm (ROA), respectively. Here, the multimodal signals include face image, EEG signals, and physiological signals such that the features extracted from these modalities are employed for the process of emotion recognition. The proposed method achieves better performance against exiting methods by acquiring maximum accuracy of 0.8794, and minimum FAR and minimum FRR of 0.1757 and 0.1806.
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Defect Management in Agile Software Development
Статья научная
Agile development reduces the risk of developing low quality software in the first place by minimizing defects. In agile software development formal defect management processes help to build quality software. The core purpose of defect management is to make the software more effective and efficient in order to increase its quality. There are several methods for handling defects like defect prevention, defect discovery and resolution which are used by software developers and testers. Refactoring keeps the system clean by identifying and removing quality defects. To gain the full confidence of the customer defect management should be involved at every stage of development. Agile methodologies focus on delivering the software in form of short iterations. Thus each iteration helps to overcome defects and leads better development and end user satisfaction. This study paints the picture of handling the software defects using agile based Software Development Process.
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Definition Synthesis of Agility in Software Development: Comprehensive Review of Theory to Practice
Статья научная
Software development agility has been regarded as a critical pillar of modern businesses. However, there is still a way to find whether there exists a consistent, complete, precise, agreed and uniformed definition of it. In this regard, this study firstly reviews the existing definitions of agility in the software development domain from the literature. As one of the main results of this phase, we have seen that although agility has a remarkable root in the software development domain, even its definition is still debatable and there are other concepts close to agility in terms of definition but used interchangeably. There is another confusion about how some researchers define agility over other different concepts, although there is no apparent unifying factor in their origins except their historical co-occurrence. In addition, there are particular practices embedded into the agility definitions mostly from the manifesto and Scrum. After uncovering the deficiencies of the existing definitions, we aimed to ratify the definition of the agility concept. Then, we intended to synthesize the underlying facets of the identified definitions and propose a new yet more comprehensive definition revealing the agility characteristics properly by considering the interpretations of the existing definitions. Our study stands out by using a customized synthesis method for analysis, providing inputs to this analysis with a comprehensive literature review, and the comprehensive evaluation of the facets with the support of the literature. We are aware that agreeing on a definition is a valuable exercise and a good starting point for a better understanding of the agility phenomenon that could enable and lead to more realistic implementations, less disappointment and disillusionment, and possibly greater success rates for both practitioners and researchers.
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Density-Based LLE Algorithm for Network Forensics Data
Статья научная
In a network forensic system, there are huge amounts of data that should be processed, and the data contains redundant and noisy features causing slow training and testing processes, high resource consumption as well as poor detection rate. In this paper, a schema is proposed to reduce the data of the forensics using manifold learning. Manifold learning is a popular recent approach to nonlinear dimensionality reduction. Algorithms for this task are based on the idea that the dimensionality of many data sets is only artificially high. In this paper, we reduce the forensic data with manifold learning, and test the result of the reduced data.
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Статья научная
Now-a-days hundreds of millions of people of almost all levels of education and attitudes from different country communicate with each other for different purposes and perform their jobs on internet or other communication medium using various languages. Not all people know all language; therefore it is very difficult to communicate or works on various languages. In this situation the computer scientist introduce various inter language translation program (Machine translation). UNL is such kind of inter language translation program. One of the major problem of UNL is identified a name from a sentence, which is relatively simple in English language, because such entities start with a capital letter. In Bangla we do not have concept of small or capital letters. Thus we find difficulties in understanding whether a word is a proper noun or not. Here we have proposed analysis rules to identify proper noun from a sentence and established post converter which translate the name entity from Bangla to UNL. The goal is to make possible Bangla sentence conversion to UNL and vice versa. UNL system prove that the theoretical analysis of our proposed system able to identify proper noun from Bangla sentence and produce relative Universal word for UNL.
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Design Intelligent Model base Online Tuning Methodology for Nonlinear System
Статья научная
In various dynamic parameters systems that need to be training on-line adaptive control methodology is used. In this paper fuzzy model-base adaptive methodology is used to tune the linear Proportional Integral Derivative (PID) controller. The main objectives in any systems are; stability, robust and reliability. However PID controller is used in many applications but it has many challenges to control of continuum robot. To solve these problems nonlinear adaptive methodology based on model base fuzzy logic is used. This research is used to reduce or eliminate the PID controller problems based on model reference fuzzy logic theory to control of flexible robot manipulator system and testing of the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator.
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Design Intelligent Robust Back stepping Controller
Статья научная
The increasing demand for multi-degree-of-freedom (DOF) continuum robot in presence of highly nonlinear dynamic parameters in a number of industries has motivated a flurry of research in the development of soft computing nonlinear methodology. The robust backstopping controller proposed in this research is used to further demonstrate the appealing features exhibited by the continuum robot. Robust feedback controller is used to position control of continuum robot in presence of uncertainties. Using Lyapunov type stability arguments, a robust backstopping controller is designed to achieve this objective. The controller developed in this research is designed into two steps. Firstly, a robust stabilizing torque is designed for the nominal continuum robot dynamics derived using the constrained Lagrangian formulation based on modified PD backstopping controller. Next, the fuzzy logic methodology applied to it to solution uncertainty problem. The fuzzy model free problem is formulated to estimate the nonlinear formulation of continuum robot. The eventual stability of the controller depends on the torque generating capabilities of the continuum robots.
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Design Intelligent Robust Model-base Sliding Guidance Controller for Spherical Motor
Статья научная
Stability, robust and reliability are the main objectives to design a controller for highly nonlinear spherical motor. Most of linear and nonlinear controllers are stable, model-base controllers are reliable but in this group sliding mode controller is a robust controller. Therefore in this research sliding mode controller is used to design stable, robust and reliable controller. For intelligence part, the minimum rule base Proportional Integral Derivative (PID) Fuzzy hybrid guidance Controller for three dimensions spherical motor is presented in this research. Guidance control in a three dimensions spherical motor is performed by the robust sliding mode controllers producing the control signals which are applied to systems torque. Sliding mode controller has an important problem, namely chattering. In this research, chattering-free sliding mode controller is design as a robust guidance controller to their robust performance in a wide range of operating conditions. In this research the PID-like fuzzy controller can be constructed as a parallel structure of a PD-like fuzzy controller and a PI-like controller to have the minimum rule base. Nonlinear type robust sliding mode controller is used to modify PID fuzzy logic theory to design robust and reliable hybrid guidance methodology. This research is used to reduce or eliminate the fuzzy and conventional sliding mode controller problem based on minimum rule base fuzzy logic theory and modified it by sliding mode method to control of spherical motor system.
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Design Modified Fuzzy Hybrid Technique: Tuning By GDO
Статья научная
The Proportional Integral Derivative (PID) Fuzzy hybrid (switching mode computed torque sliding mode) Controller is presented in this research. The popularity of PID FHC controllers can be attributed to their robust performance in a wide range of operating conditions and partly to their functional simplicity. The process of setting of PID FHC controller can be determined as an optimization task. Over the years, use of intelligent strategies for tuning of these controllers has been growing. Biologically inspired evolutionary strategies have gained importance over other strategies because of their consistent performance over wide range of process models and their flexibility. This paper analyses the manual tuning techniques and compares the same with Gradient Descent tuning methods for tuning PID FHC controllers for flexible robot manipulator system and testing of the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator.
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Design New Intelligent PID like Fuzzy Backstepping Controller
Статья научная
The minimum rule base Proportional Integral Derivative (PID) Fuzzy backstepping Controller is presented in this research. The popularity of PID Fuzzy backstepping controller can be attributed to their robust performance in a wide range of operating conditions and partly to their functional simplicity. The process of setting of PID Fuzzy backstepping controller can be determined as an optimization task. Over the years, use of intelligent strategies for tuning of these controllers has been growing. PID methodology has three inputs and if any input is described with seven linguistic values, and any rule has three conditions we will need 7 × 7 × 7 = 343 rules. It is too much work to write 343 rules. In this research the PID-like fuzzy controller can be constructed as a parallel structure of a PD-like fuzzy controller and a PI-like controller to have the minimum rule base. However backstepping controller is work based on cancelling decoupling and nonlinear terms of dynamic parameters of each link, this controller is work based on manipulator dynamic model and this technique is highly sensitive to the knowledge of all parameters of nonlinear robot manipulator’s dynamic equation. This research is used to reduce or eliminate the backstepping controller problem based on minimum rule base fuzzy logic theory to control of flexible robot manipulator system and testing of the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator.
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Design New PID like Fuzzy CTC Controller: Applied to Spherical Motor
Статья научная
The minimum rule base Proportional Integral Derivative (PID) Fuzzy Computed Torque Controller with application to spherical motor is presented in this research. The popularity of PID Fuzzy Computed Torque Controller can be attributed to their robust performance in a wide range of operating conditions and partly to their functional simplicity. PID methodology has three inputs and if any input is described with seven linguistic values, and any rule has three conditions we will need 343 rules. It is too much work to write 343 rules and have lots of problem to design embedded control system e.g., Field Programmable Gate Array (FPGA). In this research the PID-like fuzzy controller can be constructed as a parallel structure of a PD-like fuzzy controller and a conventional PI controller to have the minimum rule base and acceptable trajectory follow disturbance to control of spherical motor. However computed torque controller is work based on cancelling decoupling and nonlinear terms of dynamic parameters for each direction of three degree of freedom spherical motor, this controller is work based on motor dynamic model and this technique is highly sensitive to the knowledge of all parameters of nonlinear spherical motor’s dynamic equation. This research is used to reduce or eliminate the computed torque controller problem based on minimum rule base fuzzy logic theory to control of three degrees of freedom spherical motor system and testing of the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator.
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Design Sliding Mode Modified Fuzzy Linear Controller with Application to Flexible Robot Manipulator
Статья научная
This paper studies the use of Modified Proportional-Integral-Derivative Sliding Mode Controller (MPIDSMC) control used to control a flexible manipulator. The control gain in the MPIDSMC controller has been determined in an empirical way so far. It is a considerable time-consuming process because the control performance depends not only on the control gain but also on the other parameters such as the payload, references and PID joint servo gains. Hence, the control gain must be tuned considering the other parameters. In order to find the optimal control gain for the MPIDSMC controller, a fuzzy logic approach is proposed in this paper. The proposed fuzzy logic scheme finds an optimum control gain that minimizes the tip vibration for the end effector of the flexible manipulator. Tuned gain response results are compared to results for other types of gains. The effectiveness of using the fuzzy logic appears in the reduction of the computational time and the ability to tune the gain with different loading condition and input parameters.
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