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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: 626
A Smart System for Monthly Electrical Energy Consumption Prediction Using Machine Learning
Статья научная
Forecasting electrical energy consumption is becoming increasingly important for a country's citizens as it addresses rising energy demand and energy waste issues. A useful electrical energy consumption prediction scheme could help users estimate their monthly electricity bills and the use of new electrical appliances in their homes. Traditional energy consumption prediction methods are time-consuming and necessitate expert assistance to analyze and calculate energy use over time. The limitations of the existing works are that the existing literature does not accurately predict monthly energy consumption and costs using machine learning. They concentrated on electrical energy consumption over a short period of time in a single building, using seasonal data rather than automating the system for repeated use. To address these issues, this paper proposes machine learning-based automation systems that predict monthly energy consumption, estimate costs, and identify relevant features using data from electrical home appliances in Bangladesh. Several regression models, including Random Forest, Decision Tree, XGBoost, Boosting, and LightGBM Regressor, are tested to find the best prediction model. We have performed dataset collection, dataset cleaning, feature extraction, scaling, normalization, hyper parameter tuning, training, testing, and model selection activities. The simulation results clearly indicated that the Random Forest regressor model performed better than the other models, with higher R squared values and lower error values. The comparison results revealed that the proposed random forest regression model outperforms previous works by at least 4% in accuracy and 7% in mean absolute error. The proposed mobile application helps users make informed decisions by calculating energy consumption for new home appliances, making recommendations, delivering updated news from the power board, and providing required guidelines. The mobile application feature evaluation results revealed that our proposed application received an excellent rating from more than 70% of customers.
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A Stochastic Model for Document Processing Systems
Статья научная
This work is focused on the stationary behavior of a document processing system. This problem can be handled using workflow models; knowing that the techniques used in workflow modeling heavily rely on constrained Petri nets. When using a document processing system, one wishes to know how the system behaves when a new document enters in order to give precise support to the manager's decision. This requires a good analysis of the system's performances. But according to many authors, stochastic models, specifically waiting lines should be used instead of Petri nets at a strategic level in order to lead such analysis. The need to study a new model comes from the fact that we wish to provide tools for a decision maker to lead accurate performance analysis in a document processing system. In this paper, amodel for document management systems in an organization is studied. The model has a static and a dynamic component. The static one is a graph which represents transitions between processing units. The dynamic component is composed of a Markov processes and a network of queues which model the set of waiting-lines at each processing unit. Key performance indicators are defined and studied point-wise and on the average. Formulas are given for some example models.
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A Study on Analysis of SMS Classification Using Document Frequency Thresold
Статья научная
Recent years, feature selection is chief concern in text classification. A major characteristic in text classification is the high dimensionality of the feature space. Therefore, feature selection is strongly considered as one of the crucial part in text document categorization. Selecting the best features to represent documents can reduce the dimensionality of feature space hence increase the performance. Feature selection is performed here using Document Frequency Threshold. This paper focus on SVM based text message classification using document frequency threshold. The experiment is performed with NUS SMS text messages data set. An experimental result shows that the results of proposed method are more efficient.
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Статья научная
Monitoring and Evaluation (M&E) system are used across the world by organizations or governments to track progress, measure and evaluate outcomes of projects. Organizations can improve their performance, effectiveness and achieving results in project success by strengthening their monitoring and evaluation systems. Moreover, various studies reveal the need for information and communication technology systems in monitoring and evaluation activities because most of the government organizations do not employ computerized monitoring and evaluation systems and those having these systems lack a systematic early informing mechanism of the projects' progress. Currently, the Ministry of Health in Tanzania monitors and evaluates its projects manually, due to this, they face the risks and challenges during the implementation of projects because of a lack of having timely adoption of remedial action. Monitoring and evaluation staffs spent a lot of time in manual work, manual compilation of data, due to data being in separate systems, delay in submission of data, data is lost between primary registries to monthly summaries, from monthly to quarterly summaries, system does not contain all details about projects/program as well as budget information, no early alert information about the status of the project, poor information sharing among stakeholder. In this study, we collect representative data from three monitoring and evaluation staff, four ICT staff and five project members by using interviews, focus group discussion and document review. The result showed that the electronic monitoring and evaluation system will solve a presented challenge. Development of a web-based monitoring and evaluation system for the ministry of health projects will provides timely, accurate information, that for tracking the implementation progress of projects improved monitoring and evaluation.
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A Survey on Descendants of LEACH Protocol
Статья научная
A wireless sensor network (WSN) is an emerging field comprising of sensor nodes as basic units. These sensor nodes have limited resources like power, memory etc. WSNs can be used to monitor the remote areas where recharging or replacing the battery power of sensor nods is not possible. This limitation of WSNs makes energy consumption as a most challenging issue. Low-Energy Adaptive Clustering Hierarchy (LEACH) is an easiest and first significant protocol which consumes less amount of energy while routing the data to the base station. A lot of work has been done to improve energy efficiency of routing protocol by taking LEACH as a base protocol. In this review paper section I has introduction to Wireless Sensor Networks, section II has introduction of LEACH Protocol and all descendant protocols of LEACH with comparison table have been discussed in section III.
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A Survey on Effective Defect Prevention - 3T Approach
Статья научная
Defects are most detrimental entities which deter the smooth operation and deployment of the software system and can arise in any part of the life cycle, they are most feared, but still Defect Prevention is mostly discounted field of software quality. Unattended defects cause a lot of rework and waste of effort. Hence only finding the defects is not important, finding the root cause of the defect is also important which is quite difficult due to levels of abstraction in terms of people, process, complexity, environment and other factors. Through this study various techniques of Defect classification, prevention and root cause analysis are analysed. The intent of this paper is to demonstrate the structured process showing defect prevention flow and inferring three T's (Tracking, Technique and Training) after analysis.
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A Survey on Hybrid Recommendation Engine for Businesses and Users
Статья научная
Various techniques have been used over the years to implement recommendation systems. In this research, we have analyzed several papers and majority of them have used collaborative and content-based filtering techniques to implement recommender system. To build a recommender system, we require abundant amount of data which comprises of a spectrum of reviews and sentiments from all user domains. Websites like Yelp and TripAdvisor, allow users to post reviews for various businesses, products and services. In this work we have two objectives 1) Recommend restaurants to user based on user reviews in Yelp dataset and 2) Suggest improvements to business based on user reviews. In the first scenario, we will use the comments and ratings available in the Yelp dataset to generate restaurant recommendations and personalize them with user profile data. In the second scenario, we intend to suggest improvements to businesses based on various user reviews and provide them with a ranked list of predefined parameters to help them understand where they stand with respect to their competitors and where they should improve to do better. For both scenarios, we will perform two major steps to achieve our objective 1) Sentiment Analysis and 2) Content Based Recommendation. The first step gives us the - sentiment, quality, subject of discussion relevant to product and in the second step we use the outcomes of first step for personalizing and ranking our results. We came across Gensim and Latent Dirichlet Allocation which seemed to be interesting and was tailored to our requirements. In the yelp dataset, user comments are a mixture of various topics which are extracted by the algorithm (LDA) to provide accurate recommendation for all the users. A prototype of this method provided us with 93% accuracy.
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A Survey on the Generation of Recommender Systems
Статья научная
In the era of Internet, web is a giant source of information. The constantly growing rate of information in the web makes people confused to decide which product is relevant to them. To find relevant product in today's era is very time consuming and tedious task. Everyday a lot of information is uploaded and retrieved from the web. The web is overloaded with information and it is very essential to cop up with this overloaded and overlooked information. Recommender systems are the solution which can help a user to get relevant information from the bulk of information. Recommender systems provide customized or personalized and non personalized recommendations to interested users. Recommender systems are in its evolution stage. Recommender systems have been evolved from first generation to third generation through second generation. First generation or Web 1.0 recommender systems deal with E-commerce, Second generation or web 2.0 recommender systems use social network and social contextual information for accurate and diverse recommendations, and Third generation recommender systems use location based information or internet of things for generating recommendations. In this paper, three generation of recommender systems and are discussed. Similarity measures and evaluation metrics are used in these generations are also discussed.
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A Top-Down Partitional Method for Mutual Subspace Clusters Using K-Medoids Clustering
Статья научная
In most of the applications, data in multiple data sources describes the same set of objects. The analysis of the data has to be carried with respect to all the data sources. To form clusters in subspaces of the data sources the data mining task has to find interesting groups of objects jointly supported by the multiple data sources. This paper addresses the problem of mining mutual subspace clusters in multiple sources. The authors propose a partitional model using k-medoids algorithm to determine k-exclusive subspace clusters and signature subspaces corresponding to multiple data sources, where k is the number of subspace clusters to be specified by the user. The proposed algorithm generates mutual subspace clusters in multiple data sources in less time without the loss of cluster quality when compared to the existing algorithm.
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A VMI Model in Supplier-Driven Supply Chain and Its Performance Simulation
Статья научная
This research builds a VMI inventory decision model of supplier-driver supply chain to analyze the effects of VMI on a supply chain performance and announce the important function of VMI in promoting supply chain coordination, reducing costs and increasing profits. According to this model, VMI is found always to reduce the system’s(buyer and supplier together) and the buyer’s inventory related costs in the short-term, but the supplier's inventory related costs varies. Under certain cost conditions between buyer and supplier, VMI can increase the system’s and the buyer’s profits. And the supplier's profits can be increased under other conditions. Using the corresponding MATLAB program, obtains some calculation results. And through computer simulation, analyzes the effects of VMI on supply chain performance, and announces the important function of VMI in promoting supply chain coordination, reducing costs and increasing profits.
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A Virtualized Network Architecture for Improving QoS
Статья научная
Recently, researches have shown that today'sbest-effort Internet with "one fits all" principle comes to an impasse. Addressed to the different QoS requirements oftoday's network services, we propose a new network architecture based on network virtualization technology for improving Internet QoS. It divides a “thick” network into multiple “thin” virtual networks deploying on the same substrate, each is customized with a special designing goal and runs a customized protocol. Then the traffic with different QoS requirements is classified at the ingress router,and distributed to different virtual networks, which are the most suitable for carrying the special traffic. Also, we can deploy a service on multiple virtual networks, and making them working collaboratively to achieve a better QoS. To verify this idea, a prototype is implemented in LAN-scale network. By some simply designed experiments for comparison, we observe that, by use of our network architecture, service provider with specific QoS requirement can take its choice to choose appropriate virtual networks to achieve better QoS performance.
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A Web 2.0 Supported Business Process Management Environment for Collaborative Research
Статья научная
Collaborative research includes research activities conducted by a group of people working at different locations and has become a hot issue due to the effects of globalization and advances in information technology (IT). The aim of this study is to design, develop, implement and evaluate an IT environment to better manage the standard processes of a collaborative research by providing more efficient use of the resources. Inspired by the studies in the literature, the basic steps and requirements of a typical collaborative research are identified and the related process flow diagram is generated. Next a Web 2.0 supported business process management (BPM) environment is developed in the direction of the process flow diagram to support collaborative researches. A commercial BPM system is used to automate and monitor the processes, whereas Web 2.0 platform is used for communications management, workspace sharing and data collection. The proposed environment is experimented by a case study conducted with a group of researchers; its performance is evaluated and directions for improvements are identified. It is concluded that in general the Web 2.0 supported BPM environment is functional, reliable and useful for collaborative research. The environment is found to be more suitable for research support processes compared to basic research processes.
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Статья научная
Selecting proper crops for farmland involves a sequence of activities. These activities and the entire process of farming require a help of expert knowledge. However, there is a shortage of skilled experts who provide advice for farmers at district level in developing countries. This study proposed designing knowledge based solution through the collaboration of experts’ knowledge with the machine learning knowledge base to recommending suitable agricultural crops for a farm land. To design the collaborative approach the knowledge was acquired from document analysis, domain experts’ interview and hidden knowledge were extracted from Ethiopia national meteorology agency weather dataset and from central statistics agency crop production dataset by using machine learning algorithms. The study follows the design science research methodology, with CommonKADS and HYBRID models; and WEKA, SWI-Prolog 7.32 and Java NetBeans tools for the whole process of extracting knowledge, develop the knowledge base and for developing graphical user interface respectively. Based on the objective measurement PART rule induction have the highest classifier algorithm which classified correctly 82.6087% among 9867 instances. The designed collaborative approach of experts’ knowledge with the knowledge discovery for agricultural crop selections based on the domain expert, farmers and agriculture extension evaluation 95.23%, 82.2 % and 88.5 % overall performance respectively.
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A cost measurement system of logistics process
Статья научная
Because logistic is a process-oriented business, we propose in this paper a measurement system of decision support for assessing the costs associated with each logistics process. This system allows calculating economic, environmental and social costs of logistics process to ensure a sustainable logistics. We have formulated the problem and we present some simulation for testing our system. This proposition allows the decision-maker to have knowledge of economic, ecological and social cost before making a decision.
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A mobile based intelligent question answering system for education domain
Статья научная
The domain of intelligent question answering systems is leading the major role in fulfill the user requirement with specific answers stimulate QA research to the next level with machine learning techniques. In this paper, we present mobile based question answering system acts as a personal assistant in learning and for providing the user with information on computers, software and hardware, book reviews by using natural language for the communications. The proposed Mobile based QA models will accept the natural language query, analysis and match them with information stored in the knowledge base and display the optimized result. The knowledge base created from the benchmark data set such as Amazon book reviews, 20newsgroup and Yahoo! Answer data set clustered with content specific clustering and displays the outcome in the form of snippets as output. Sentiment analysis used to decrease the vocabulary gap among the user query and retrieved candidate answer solutions. The results of the proposed interface evaluated with standard metrics such as Precision, Recall, F1-Score, Inverse precision and Inverse recall for the appropriate return of relevant answer.
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A novel algorithm for association rule hiding
Статья научная
Current days privacy concern about an individual, an organization and social media etc. plays a vital role. Online business deals with millions of transactions daily, these transactions may leads to privacy issues. Association rule hiding is a solution to these privacy issue, which focuses on hiding the sensitive information produces from online departmental stores ,face book datasets etc..These techniques are used to identify the sensitive rules and provide the privacy to the sensitive rules, so that results the lost rules and ghost rules. Algorithms developed so far are lack in achieving the better outcomes. This paper propose two novel algorithm that uses the properties from genetic algorithm and water marking algorithm for better way of hiding the sensitive association rules.
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Статья научная
A Cyber-Physical System strongly depends on the sensor data to understand the current condition of the environment and act on that. Due to network faults, insufficient power supply, and rough environment, sensor data become noisy and the system may perform unwanted operations causing severe damage. In this paper, a technique has been proposed to analyze the trustworthiness of a sensor reading before performing operation based on the record. The technique employs regression analysis to select nearby sensors and develops a linear model for a target sensor. Using the linear model, target sensor reading is predicted in a particular time stamp with respect to each nearby sensor’s reading. If the difference between the predicted and actual value is within a given limit, the reading is considered as trustworthy for the corresponding nearby sensor. At last, majority consensus is taken to consider the reading as trustworthy. To evaluate the proposed technique, a data set containing temperature reading of 8 sensors for 24 hours was used where first 90% data was used for nearby sensor selection and linear model construction, and rest 10% for testing. The result analysis shows that the proposed technique detects 19, 69, and 73 trustworthy data from 73 records with respect to 3%, 4% and 5% deviation from actual reading.
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A review on data analytics for supply chain management: a case study
Статья научная
The present study bridges the gap between the two intersecting domains, data science and supply chain management. The data can be analyzed for inventory management, forecasting and prediction, which is in the form of reports, queries and forecasts. Because of the price, weather patterns, economic volatility and complex nature of business, the forecasts may not be accurate. This has resulted in the growth of Supply chain analytics. It is the application of qualitative and quantitative methods to solve relevant problems and to predict the outcomes by considering quality of data. The issues like increased collaboration between companies, customers, retailers and governmental organizations, companies are adopting Big Data solutions. Big Data applications can be linked for Supply Chain Management across the fields like procurement, transportation, warehouse operations, marketing and also for smart logistics. As supply chain networks becoming vast, more complex and driven by demands for more exacting service levels, the type of data that is managed and analyzed also becomes more complex. The present work aims at providing an overview of adoption of capabilities of Data Analytics as part of a “next generation” architecture by developing a linear regression model on a sales-data. The paper also covers the survey of how big data techniques can be used for storage, processing, managing, interpretation and visualization of data in the field of Supply chain.
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A smart and cost-effective fire detection system for developing country: an IoT based approach
Статья научная
Disaster caused by sudden uncertain fire is one of the main reasons for a great loss of properties and human lives. In our paper, we have developed a smart and cost-effective fire detection system based on the IoT that can detect the sudden uncertain fire in a quick succession to reduce the significant loss. The device houses a sensor-based smoke detection system and a camera which could be accessed by the user from anywhere through the use of internet for taking necessary preventive actions based on the reliable assessment. The notification system takes advantage of an online short message service which is connected to the Raspberry Pi module that gets triggered when the smoke sensors detect the smoke and informs the users about the predicament. The device also has a buzzer connected to central module to notify the nearby users.
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A study on test variable selection and balanced data for cervical cancer disease
Статья научная
Cancer is a pestilent disease. One of the most important cancer kinds, cervical cancer is a malignant tumor which threats women's life. In this study, the importance of test variables for cervical cancer disease is investigated by utilizing Stability Selection method. Also, Random Under-Sampling and Random Over-Sampling methods are implemented on the dataset. In this context, the learning model is designed by using Random Forest algorithm. The experimental results show that Stability Selection, Random Over-Sampling and Random Forest based model are more successful, approximately 98% accuracy.
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