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International Journal of Information Engineering and Electronic Business @ijieeb
Статьи журнала - International Journal of Information Engineering and Electronic Business
Все статьи: 658
Feature Engineering based Approach for Prediction of Movie Ratings
Статья научная
The buying behavior of the consumer is grown nowadays through recommender systems. Though it recommends, still there are limitations to give a recommendation to the users. In order to address data sparsity and scalability, a hybrid approach is developed for the effective recommendation in this paper. It combines the feature engineering attributes and collaborative filtering for prediction. The proposed system implemented using supervised learning algorithms. The results empirically proved that the mean absolute error of prediction was reduced. This approach shows very promising results.
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Статья научная
Dimensionality reduction is generally performed when high dimensional data like text are classified. This can be done either by using feature extraction techniques or by using feature selection techniques. This paper analyses which dimension reduction technique is better for classifying text data like emails. Email classification is difficult due to its high dimensional sparse features that affect the generalization performance of classifiers. In phishing email detection, dimensionality reduction techniques are used to keep the most instructive and discriminative features from a collection of emails, consists of both phishing and legitimate, for better detection. Two feature selection techniques - Chi-Square and Information Gain Ratio and two feature extraction techniques – Principal Component Analysis and Latent Semantic Analysis are used for the analysis. It is found that feature extraction techniques offer better performance for the classification, give stable classification results with the different number of features chosen, and robustly keep the performance over time.
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Financial Forecasting with Deep Learning Models Based Ensemble Technique in Stock Market Analysis
Статья научная
In recent years, deep learning techniques have emerged as powerful tools for analyzing and predict- ing complex patterns in sequential data across various fields. This study employs an ensemble of advanced deep learning models: Long Short-Term Memory (LSTM), Bi-Directional LSTM, Gated Recurrent Unit (GRU), LSTM Convolutional Neural Network (CNN), and LSTM with Self-Attention, to enhance prediction accuracy in time series forecasting. These models are applied to three distinct financial datasets: Tata Motors, HDFC Bank, and INFY.NS, we conduct a thorough comparative analysis to assess their performance. Utilizing K-fold cross-validation, we convert loss (MSE) into RMSE and MAPE, which help estimate accuracy .we achieved train accuracies of 97.46% for Tata Motors, 75.93% for INFY.NS, and 56.60% for HDFC Bank. Our empirical results highlight the strengths and limitations of each model within the ensemble framework and pro- vide valuable insights into their effectiveness in capturing complex patterns in financial time series data. This research underscores the potential of deep learning-based ensemble techniques for improving stock price forecasting and offers significant implications for investors and the development of sophisticated trading and risk management systems.
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Finite State Machine Model in Jungle Adventure Game an Introduction to Survival Skills
Статья научная
Game is one of the big industries today and can be an alternative entertainment and fun for children and adults. Game can be used as an interactive and interesting learning media. One of the platform game development methods is the Finite State Machine (FSM). This method is used to adjust the behavior of the NPC (Non-Player Character) to guide the player through the game. This study developed an Android-based "Jungle Adventure" game with interesting gameplay to make the learning process very enjoyable so that the player is expected to be able to play while learning. Combination NPC with FSM will make that responses under certain conditions, and NPC will accompany player like a friend survival.
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Folding Bicycle Prospective Buyer Prediction Model
Статья научная
The trend of bicycle exercise during the pandemic has resulted in increased sales and even scarcity of bicycle stock in some shops. The phenomenon has raised attention from both the bicycle industry and government to provide necessary responses toward the trends. Even though it is a trend, many prospective buyers are still confused about their choices. The types of bicycles that sell the most on the market are folding bikes, mountain bikes, and racing bikes. The research data were collected from 242 bicycle users who came from various bicycle communities in major cities of Java Island, Indonesia. Some of the predictors used were age, gender, height, weight, and cycling speed. The target variable is the type of bicycle whose data is categorical. Predictor variables consist of nominal and ordinal variables, so preprocessing needs to be done using Python's Sklearn library. To test the accuracy of the model, the data was broken down into training data and test data with a test size of 20%. Several methods are used to form a classification model, including K-NN, Naive Bayes, Support Vector Machine, Decision Tree, and Random Forest. The results of the classification model evaluation show that the Support Vector Machine and Decision Tree have the highest accuracy of 90%, while Naive Bayes has the lowest accuracy of 73%. The model formed can be a predictive tool for potential bicycle buyers in order to be able to choose the right type of bicycle.
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Forecasting of Indonesian Digital Economy based on Available New Start-up
Статья научная
Since the last 5 years, digital economy is growing steadily in Indonesia. Right now, the digital economy faces some potential problems and Covid-19 pandemic. This paper presents current data of the national Gross Domestic Product (GDP) and other GDPs (billion IDR) and the number of start-up, and predicts near some categories of future GDP and numbers of available new start-up for the next few years. The forecast will use Markov chain analysis. The results indicate that, while there are problems faced by the digital economy industry, the GDP and numbers of start-up are significantly increasing.
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Formation of Innovativeness for the Business Processes of Enterprise Using Data Processing
Статья научная
The article discusses the issues of development and analysis of diagnostic procedures for business processes during enterprise management. The digitalization has become a priority at the state level of every country, influencing the daily lives of citizens and the enterprises activity. As a result, the ability to gather, analyze, process, and use the data has taken center place to support effective decision-making and sustain competitive market positions. The article considers the factors influencing the choice of data processing tools, analyses the difficulties faced during the data processing methods implementation, and outlines the essential features of such systems for effective management of enterprise activity. The main attention was paid to the development of a data processing method during the state diagnosis of business processes in case of assessing their compliance. The method involves calculating the probability density function for the costs of restoring the normal functioning of business processes and statistical characteristics of the probability of correct decision-making. Additionally, the article includes numerical examples demonstrating the use of this method to the business processes of an aviation enterprise engaged in providing and performing technological procedures for the operation of aircraft. The proposed data processing model can be used to analyze the efficiency of enterprises’ business processes and make decisions on organizational structure optimization to minimize the costs spent by enterprise.
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Framework for Evaluating Business Processes Modeling Techniques under Neutrosophic Environment
Статья научная
In today's, dynamic business environment and complexity of its operations make the need for modeling Business Processes (BP) is a very critical. Modeling BP is a very important task for improving BP and achieving business needs and goals. BP modeling techniques are necessary for making BP more understandable and easily maintainable which lead to successful Business Process Management (BPM). There many types of modeling techniques for expressing and modeling business process. However, each one has its own characteristics and not all modeling techniques are suitable to all parts of the process. Therefore, it is critical to determine the right and suitable modeling technique. The problem of evaluating BP modeling techniques has been addressed by many researches. However, there is need to handle uncertainty and take into account costs and benefits of BP modeling techniques during the evaluation process. This work aims to introduce different types of BP modeling techniques and present different views of characteristics, features and quality criteria of BPmodeling techniques that can help the modeler during the evaluation process. Also, this work aims to adapt and introduce neutrosophic framework to handle uncertainty and remove confusion during evaluating and determining the BP modeling suitable technique. Moreover, the proposed framework utilizes the neutrosophic benefits and costs method with simple way to improve its use and to balance between benefits and costs during the evaluation process. The proposed framework is applied to a real world case study and the results concluded that the proposed framework can be adopted by business organizations and institutes that need for determining the suitable BP modeling technique to improve their business processes. Also, the results concluded that the utilization of proposed framework can be helpful for handling uncertainty during the evaluation process.
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Framework for an E-Voting System Applicable in Developing Economies
Статья научная
Information technology has pervaded virtually every facet of human life. Even in the delivery of governance, information technology has gradually found a place. One of its applications is the use of electronic voting, also known as e-voting, as opposed to the traditional manual method of voting. This form of voting, however, is not immune to challenges generally associated with voting. Two of these include guaranteeing voting access to all eligible voters, and providing necessary voting security. The challenge of accessibility is especially peculiar to developing countries where IT adoption is still relatively low. This paper proposes a framework for an e-voting system that would most benefit developing economies. It ensures availability of the system to only eligible voters and integrity of the voting process through its capacity to identify and prevent ineligible voters and multiple voting. To guarantee accessibility to all eligible voters, it supports both online and offline voting capabilities. Adopting electronic form of voting would provide a more robust, easier to use, and reliable system of voting, which, consequently, would contribute towards enhancing the delivery of democratic dividends.
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Fuzzy Stability and Synchronization of New 3D Chaotic Systems
Статья научная
This paper presents fuzzy model-based designs for control and synchronization of new chaotic system. The T–S fuzzy models for new chaotic systems are exactly derived. Then the asymptotic stability and synchronization are achieved by generalized backstepping method. On the other hand, this paper presents fuzzy model-based designs for synchronization of another chaotic system. Based on the T–S fuzzy new chaotic models, the fuzzy controllers for two different chaotic synchronization are designed via the active control technique. Numerical simulation results are presented to show the effectiveness of the proposed method.
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Fuzzy entropy based MOORA model for selecting material for mushroom in viet nam
Статья научная
The role of materials in the proper design and operation of products has been acknowledged. An incorrectly selected material for a certain product may cause premature failure of the final product. The right choice of available materials is very important to the success and competitiveness of manufacturing organizations. In Vietnam, tropical monsoon climate conditions greatly affect mushroom cultivation. The raw materials, additives and the ratio between them will also affect the quality and yield of mushrooms. Therefore, selecting the options for growing mushrooms or choosing good materials to grow mushrooms effectively is also a matter of concern. This is a problem of many decision-making problems. In this paper we multi-objective optimization on the basis of ratio analysis (MOORA) method to evaluate mushroom cultivation options in Vietnam.
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Статья научная
Congetive method is used in this research to create portfilo of movement robot manipulator. Gradient descent (GD) artificial intelligence based switching feedback linearization controller was used and robot's postures and trajectory were expected in MATLAB/SIMULINK environment. Feedback linearization controller (CTC) is an influential nonlinear controller to certain systems which it is based on feedback linearization and computes the required torques using the nonlinear feedback control law in certain systems. Practically a large amount of systems have uncertainties accordingly this method has a challenge. Switching feedback linearization controller is a significant combination nonlinear stable-robust controller under condition of partly uncertain dynamic parameters of system. This technique is used to control of highly nonlinear systems especially in nonlinear time varient nonlinear dynamic system. To increase the stability and robustness with regards to improve the robustness switching methodology is applied to feedback linearization controller. Lyapunov stability is proved in proposed controller based on switching function. To compensate for the dependence on switching parameters baseline methodology is used.The nonlinear model dynamic formulation problem in uncertain system can be solved by using artificial intelligence theorem. Fuzzy logic theory is used to estimate the system dynamic. Forward kinematics implemented the manipulator's movements. Results validated the robot's range of possible postures and trajectories
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Статья научная
Game-theoretic resource allocation algorithms are essential to managing the interference that Device-to-Device (D2D) and cellular transmissions could generate to each other in cellular networks since game-theoretic solutions are naturally autonomous and robust. In this paper, we present a survey on D2D communication in cellular networks with respect to the performance of the existing and accessible game-theoretic resource allocation algorithms published in 2013-2019. Each of the game-theoretic resource allocation algorithms with its properties such as utility, complexity, fairness, overhead cost, and convergence rate are reviewed and compared. The survey proved that game-theoretic solutions could be a viable strategy for practical implementation in 5G networks as each of the reviewed scheme attempts to optimize one or various essential performance metrics in the system. Finally, the paper recommends that serious efforts should be made by standardization bodies in incorporating game-theoretic strategy in D2D-enabled 5G networks while considering it as a road map for reliable and resource-efficient solutions in future cellular networks.
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Generation Analysis of Blockchain Technology: Bitcoin and Ethereum
Статья научная
In this paper, the importance of blockchain technology have been discussed and the generations of blockchain (Bitcoin and Ethereum) have been compared provided different aspects. The blockchain is a technology which allows direct transaction without involving third party. Also, it offers many facilities like high translucency, high safety and security, improved trace-ability, greater proficient and transactions’ speed, and reduced costs. Moreover, the cryptocurrencies provide advance security level. The basic purpose of this study is to highlight different aspects of Blockchain, Bitcoin and Ethereum and to show which cryptocurrency is better approach. The research contributes to show the impact of this technology in different fields and a comparison of bitcoin and ethereum is presented to analyze and furnish a decision regarding the best among them. The use of blochchain technology in government applications can bring a drastic change in the world because it is safer and faster. Also, the comparison shows that ethereum is better than bitcoin as it is efficient and has more applications as compared to bitcoin. It offers more advanced services such as smart contracts. All in all, the analysis has concluded with Ethereum as faster and securer approach.
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Статья научная
In the past decades, many types of nature inspired optimization algorithms have been proposed to solve unconstrained global optimization problems. In this paper, a new hybrid algorithm is presented for solving the nonlinear unconstrained global optimization problems by combining the genetic algorithm (GA) and local search algorithm, which increase the capability of the algorithm to perform wide exploration and deep exploitation. The proposed algorithm is called a Genetic Local Search Algorithm with Self-Adaptive Population Resizing (GLSASAPR). GLSASAPR employs a self-adaptive population resizing mechanism in order to change the population size NP during the evolutionary process. Moreover, a new termination criterion has been applied in GLSASAPR, which is called population vector (PV ) in order to terminate the search instead of running the algorithm without any enhancement of the objective function values. GLSASAPR has been compared with eight relevant genetic algorithms on fifteen benchmark functions. The numerical results show that the proposed algorithm achieves good performance and it is less expensive and cheaper than the other algorithms.
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Grey wolf optimization for solving economic dispatch with multiple fuels and valve point loading
Статья научная
This paper bestows the newly developed Grey Wolf Optimization (GWO) method to solve the Economic Dispatch (ED) problem with multiple fuels. The GWO method imitates the superiority ranking and feeding mechanism of grey wolves in nature. For simulating the superiority ranking follows as alpha, beta, omega and delta. For feeding the prey grey wolves follows three steps, in the order of searching, encircling and attacking, are carry out to perform optimization. While searching for a better solution, GWO does not obligate any statistics about the gradient of the fitness function. The intention of ED is to curtail the fuel cost for any viable load demand and at the same time to determine the optimal power generation. The ED is modeled as a complex problem by considering multiple fuels, valve-point loading and transmission losses. The potency of the GWO method has been examined on ten units system with four different load demands by considering four different case studies. The result of the test systems shows, for practical power systems, that the GWO is a better option to solve the ED problems. Both the optimality of the solution to test system and the convergence speed of the GWO algorithm are promising.
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Статья научная
This paper presents a new approach to off-line handwritten numeral recognition based on structural and statistical features. Five different types of skeleton features: (horizontal, vertical crossings, end, branch, and cross points), number of contours in the image, Width-to-Height ratio, and distribution features are used for the recognition of numerals. We create two vectors Sample Feature Vector (SFV) is a vector which contains Structural and Statistical features of MNIST sample data base of handwritten numerals and Test Feature Vector (TFV) is a vector which contains Structural and Statistical features of MNIST test database of handwritten numerals. The performance of digit recognition system depends mainly on what kind of features are being used. The objective of this paper is to provide efficient and reliable techniques for recognition of handwritten numerals. A Euclidian minimum distance criterion is used to find minimum distances and k-nearest neighbor classifier is used to classify the numerals. MNIST database is used for both training and testing the system. A total 5000 numeral images are tested, and the overall accuracy is found to be 98.42%.
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Harnessing Social Networks Resources to Bring Social Interactions into Web-based IDEs
Статья научная
In modern Web, where users log to social networks has become a daily activity, the success of these applications to attract more users is often justified by offering social presence to the user on these virtual spaces. Developing applications in software engineering domain is a collaborative task that requires implementing more sophisticated tools to support all suitable collaboration aspects. Web-based IDEs have experienced a series of changes compared to their desktop versions, tending therefore to become more able to implement new collaboration approaches and mechanisms. Within the lack of social aspect in web-based IDEs, collaboration between developers does not seem to be optimal. This article presents a study of social networks resources that can be operated through the use of Application Programming Interfaces (APIs) thus providing a social aspect to web-based IDEs. It points how we can use these resources to create new collaborative experience in software engineering field.
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Health monitoring system for post-stroke management
Статья научная
This paper presents an intelligent health monitoring system for post management of stroke. Fitbit sensor was used to take the reading of four stroke patients in Federal Teaching Hospital, Gombe State, and the vital readings recorded were heartbeat rate, sleeping rate, the number of steps taken, for a period of four weeks. The developed AppFabric, Web service and AppFeedBack synchronized the operation of the sensor, the user mobile device and the medical diagnostic platform. The readings taken by the sensor were made available to the medical experts and the monitoring team using web service. The evaluation of the system in terms of efficiency and reliability using t-Test were (82.3, 85.9) and (1.729133, 2.093024) respectively. The results show that the developed system performed better than the existing manual method for monitoring stroke patients.
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Heart Disease Prediction Using Frequent Item Set Mining and Classification Technique
Статья научная
The heart is the most important part of the human body. Any abnormality in heart results heart related illness in which it obstructs blood vessels which causes heart attack, chest pain or stroke. Care and improvement of the health by the help of identification, prevention, and care of any kind of diseases is the main goal. So for this various prediction analysis methods are used which job is to identify the illness at prelim phase so that prevention and care of heart disease is done. This paper emphasizes on the care of heart diseases at a primitive phase so that it will lead to a successful cure. In this paper, diverse data mining classification method like Decision tree classification, Naive Bayes classification, Support Vector Machine classification, and k-NN classification are used for determination and safeguard of the diseases.
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