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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: 706
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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A survey on risk assessments of heart attack using data mining approaches
Статья научная
This document presents the required layout of articles to Medical data mining has become one of the prominent issues in the field of data mining due to the delicate lifestyle opted by the people which are leading them towards various chronicle health diseases. Heart disease is one of the conspicuous public health concern worldwide issues. Since clinical data is growing rapidly owing to deficient health awareness, various techniques and scientific methods are opted for analyzing this huge data. Several data mining techniques such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision tree, Naïve Bayes and Artificial Neural Network (ANN) are introduced for the prediction of health disease. These techniques help to mine the relevant and useful amount of data, form the medical dataset which helps to provide beneficial information to the medical institutions. This study presents various issues related to healthcare and various machines learning algorithms which have to withstand to provide the best possible output. A comprehensive review of the literature has been summarized to put lights on the previous work done in this field.
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ADPCM Image Compression Techniques for Remote Sensing Applications
Статья научная
ISRO's remote sensing continuity mission Resourcesat-II provided better radiometric performance as compared to Resourcesat-I. However, this improvement required implementation of onboard image compression techniques to maintain same transmission interface. In LISS-4 payload, prediction based DPCM technique with 10:7 compression ratio was implemented. Based on received data from this payload, some ringing artifacts were reported at high contrast targets, which degrade image quality significantly. However occurrences of such instances were very few. For future missions, efforts are made to develop an improved low complex image compression technique with better radiometry and lesser artifacts. Adaptive DPCM (ADPCM) technique provides better radiometric performance. This technique has been implemented onboard by other space agencies with their own proprietary algorithm. To maintain existing 10:7 compression ratio, novel ADPCM techniques with adaptive quantizers are developed. Developed ADPCM techniques are unique w.r.t. predictor and encoding. Developed techniques improve RMSE from 1.3 to 10 times depending on image contrast. Ringing artifacts are reduced to 1% from 38% with previous technique. Developed techniques are of low complexity and can be implemented in low end FPGA.
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Статья научная
This research explores the integration of artificial intelligence (AI), the internet of things (IoT), and smart technologies in sustainable development. The study identifies the applications of AI in waste management, smart cities, energy optimization, the green internet of things (GIoT), environmental resilience, pollution mitigation, and sustainable agriculture practices. The research emphasizes the need for a comprehensive approach to harness the potential of AI and IoT for sustainable development. The study also highlights the economic, social, and environmental dimensions of sustainable development and the implications of AI in these areas. The findings suggest that AI can contribute to inclusive and responsible economic growth, social equity and well-being, environmental conservation, and efficient resource utilization. The research provides valuable insights for researchers, practitioners, and policymakers working on sustainable development.
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Статья научная
Scheduling is an NP-hard problem, and heuristic algorithms are unable to find approximate solutions within a feasible time frame. In Cloud Computing (CC) environments, efficient Task Scheduling (TS) plays a critical role in minimizing operational expenses and enhancing system reliability. This paper presents a novel task scheduling approach that uses the Coati Optimization Algorithm (COA) to address two pivotal challenges: reducing the total cost (sum of computational cost and communication cost) and minimizing Virtual Machine (VM) failure rates. Inspired by the cooperative foraging and adaptive behavior of coatis in dynamic environments, the proposed algorithm leverages intelligent exploration and exploitation strategies to identify optimal task-to-VM mappings under fluctuating workloads. The COA incorporates cost-awareness and failure probability metrics into its fitness function to ensure robust scheduling decisions that align with budgetary constraints and fault tolerance requirements. To assess the performance of the proposed model, comprehensive simulations were conducted using the CEA-Curie real-world workload. The results were compared against three state-of-the-art approaches, MoHHOTS, RTATSA2C, and TS-GWO. Experimental evaluations demonstrate that COA significantly outperforms these existing methods by achieving a 19.8% reduction in overall cost and a 22.5% decrease in VM failure rate. These findings demonstrate that COA offer a promising pathway toward sustainable, cost-effective, and resilient task execution in large-scale cloud infrastructures, particularly under diverse and realistic workload scenarios.
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AI-driven Psychographic and Behavioral Segmentation of Prospective University Students in Vietnam
Статья научная
The digital transformation of higher education marketing demands more sophisticated approaches to understanding prospective students beyond traditional demographic segmentation. This study develops a machine learning-based psychographic and behavioral segmentation framework for prospective university students in Vietnam, integrating constructs from consumer choice theory and technology adoption literature. We employ established unsupervised and supervised machine learning techniques (k-means clustering, Gaussian Mixture Models, and XGBoost classification) rather than claiming novel artificial intelligence architectures. Analyzing survey data from 1,486 Grade-12 students, our hybrid methodological approach identified three distinct segments: Intrinsically-Motivated Digital Explorers (27.7%), Prestige-Driven Traditionalists (38.9%), and Undecided Ambivalents (33.4%). Supervised learning (XGBoost) achieved 87.2% accuracy in predicting segment membership, with feature importance analysis revealing intrinsic motivation, technology readiness, and risk aversion as the primary discriminators. The findings extend higher education consumer choice theory by integrating technology readiness as an independent discriminative factor and demonstrate the methodological value of combining unsupervised and supervised machine learning for market segmentation.
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ASR for Tajweed Rules: Integrated with Self-Learning Environments
Статья научная
Due to the recent progress in technology, the traditional learning setting in several fields has been renewed by different environments of learning, most of which involve the use of computers and networking to achieve a type of e-learning. With great interest surrounding the Holy Quran related research, only a few scientific research has been conducted on the rules of Tajweed (intonation) based on automatic speech recognition (ASR). In this research, the use of ASR and MVC design is proposed. This system enhances the learners’ basic knowledge of Tajweed and facilitates self-learning. The learning process that is based on ASR ensures that the students have the proper pronunciation of the verses of the Holy Quran. However, the traditional method requires that both students and teacher meet face-to-face. This requirement is a limitation to enhancing individuals’ learning. The purpose of this research is to use speech recognition techniques to correct students’ recitation automatically, bearing in mind the rules of Tajweed. In the final steps, the system is integrated with self-learning environments which depend on MVC architectures.
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Статья научная
In this age the emergence of information and communication technologies (ICTs) has been identified as a major step toward solve the problems challenged the nation development. Problems such as corruption, delays in service delivery, lack of public sector accountability can be overcome with ICT. Furthermore, ICT are the key factors in improving government business and human sustainable development in all life aspects. Whilst the ICT considered the key to these problems but owning these technologies was facing many obstacles staring from bought them to continuous use, and create a gap between countries and within a country from the perspective of who does have computer and networks communication and who doesn't, and this refers to the digital divide. Some aspects of the digital divide exist everywhere and not only related to developing countries but also the size of the gap, which is different in countries and within a single community. This study focuses on the digital divide problem by exploring the current state of the access digital divide in Kingdom of Saudi Arabia (KSA) based on three main research questions. And to achieve that, Data collected from International Telecommunication Union (ITU), Communications and Information Technology Commission (CITC), and World Bank were used. The study found that Saudi Arabia is suffering from the access digital divide, and there is a strong link between household income and the access digital divide resulting from unaffordable prices in both ICT and broadband services and this gap tends to be larger in the regions where the inhabitants have the lowest income level. The study recommends that the government should give improving household income the highest priorities and at the same time offering affordable prices for broadband services. Also, the study finds that mobile penetration represents a valuable resource for the Saudi Arabia government to be investing in delivering government services through mobile platforms. Finally, the study recommends that public-private partnerships with promoting and encouraging the private sectors to invest in ICT is one of the most important measurements in bridging the access digital divide.
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Aesthetic QR: Approaches for Beautified, Fast Decoding, and Secured QR Codes
Статья научная
A QR code is a two-dimensional code that encodes data but it is unattractive and not ideal. QR codes have been applied in item identifications, publicity campaigns, advertisements, product promotions, etc. so they need to be visually good in appearance. Visually good and decorated QR codes degrade the decoding rate as compared to the standard QR code decoding rate. As they are used for mobile payments and logins some security must be there. For this many researchers have contributed using various approaches to beautify QR codes with high decoding accuracy and to make them secure. This paper aims towards the study of works carried out in the direction of beautification of QR codes using blended type techniques and artificial intelligence based techniques by different authors. The present state of prior strategies, methods, and major features used are described in this survey.
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Agile Method: Challenges and Adaptations for Complex Project Environments
Статья научная
This paper conducts a comparative analysis of three widely adopted Agile methodologies: Scrum, Kanban, and Extreme Programming (XP). By examining their application across diverse software development environments, the study highlights each methodology's inherent strengths and explores their practical implications for managing complex, large-scale projects. Central to this investigation are the scalability challenges that become particularly pronounced in settings with extensive stakeholder groups and complex coordination needs. The research draws upon a robust literature review and case studies to identify these challenges, setting the stage for a discussion of innovative solutions aimed at refining Agile practices. While specific solutions are reserved for detailed treatment in the proposed solutions section, the abstract is written to underscore the critical need for scalable strategies that can adapt to the dynamic landscapes of modern project management. This comparative inquiry not only enriches the academic discourse on Agile methodologies but also serves as a vital resource for practitioners seeking to optimize their project management strategies in complex scenarios.
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Статья научная
Research devoted to the categorization and creation of semantic annotations for scientific articles stands out as an essential direction of development in the context of the growing volume of scientific literature. The application of machine learning and natural language processing in this field allows you to effectively organize and provide access to scientific information. The article discusses methods of automatic annotation of texts. Based on the review, the use of the constraint propagation model is proposed to improve the technique of text relationship maps. The developed software system is aimed at automating the process of analysis and categorization of scientific materials, which opens the way to improving the speed and accuracy of searching for the necessary information for researchers. The use of advanced machine learning models, such as roBERTa and RAG, ensures the highest quality of data processing and creation of semantic annotations. The accuracy of predicting article categories after improving the model reached 88%. The novelty of the approach is the combination of categorization and semantic annotation to increase the convenience and speed of searching for scientific information. The software system opens up opportunities for future expansion and improvement through the use of advanced technologies and machine learning models. This study is noted for its relevance, originality of approach and potential for practical application in the field of scientific research and development of science as a whole. The proposed approach contributes to the development of the Information Engineering and Electronic Business industry through the following key aspects: automation of categorization and annotation of scientific articles, improving the accuracy of information search, increasing the efficiency of scientific research, and the flexibility and scalability of the solution.
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Статья научная
This paper presents the development of an intelligent information system for analysing the happiness index and life satisfaction based on sociological survey data from various countries. The research addresses the need to improve the accuracy and efficiency of social research by integrating data mining and machine learning methods – specifically K-means clustering and multiple regression analysis – into the system design. The proposed module enables automated classification of countries and cities by life satisfaction levels, allowing stakeholders to make informed decisions on urban planning and social policy. The system also facilitates the identification of favourable living environments, providing valuable insights into the social, economic, and environmental factors affecting well-being. The experimental results on real-world datasets confirm the module’s effectiveness and predictive capabilities.
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Amplification-based Attack Models for Discontinuance of Conventional Network Transmissions
Статья научная
Amplification attacks take advantage of insecurity of different OSI layers. By targeting broadcast address of the victim networks and sending a few packets by the attackers, they force the legitimate user in the victim networks to response to these packets and attack their own trusted networks unknowingly. Despite importance of amplification attacks, there is not any work to implement these attacks to identify their procedure and quantify and compare their impacts on the networks. In this work, we use NS2 to achieve these goals. A variety range of scenarios are designed to implement DDoS amplification attacks and collect the results in terms of different network performance measures. The quantitative results prove devastating impact of the attacks which are easily capable of rendering the target wireless networks disable for their legitimate users.
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Статья научная
Feature size reduction in microelectronic circuits has been an important contributing factor to the dramatic increase in the processing power of computer arithmetic circuits. However, it is generally accepted that MOS based circuits cannot be reduced further in feature size due to fundamental physical restrictions. Therefore, several emerging technologies are currently being investigated. Nano devices offer greater scaling potential than MOS as well as ultra low power consumption. Nano devices display a switching behaviour that differs from traditional MOS devices. This provides new possibilities and challenges for implementing digital circuits using different techniques like CNTFET,SET, FinFET etc. In this work the design of Inverter and Nand gate using CNT, SET and FinFET has been analyzed elaborately with its own advantageous of the mentioned techniques.
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An Analysis of Fuzzy and Spatial Methods for Edge Detection
Статья научная
An image segmentation is an area in which image is subdivided into sub-regions for extracting characteristics of images which will help to analysis in various applications. For getting accuracy sharp changes of intensity is an important issue which is known as edge detection. In this paper various spatial edge detection methods and fuzzy based edge detection method has described and spatial edge detection methods and fuzzy if-then-else are compared to know which method will be more suitable to find edges for the enhancement of images.
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An Analysis of RDF Storage Models and Query Optimization Techniques
Статья научная
The Web provides access to substantial amount of information. Metadata that means data about data enables the discovery of such information. When the metadata is effectively used, it increases the usefulness of the original data/resource and facilitates the resource discovery. Resource Description Framework (RDF) is a basis for handling these metadata and is a graph-based, self-describing data format that represents information about web-based resources. It is necessary to store the data persistently for many Semantic Web applications that were developed on RDF to perform effective queries. Because of the difficulty of storing and querying RDF data, several storage techniques have been proposed for these tasks. In this paper, we present the motivations for using the RDF data model. Several storage techniques are discussed along with the methods for optimizing the queries for RDF datasets. We present the differences between the Relational Database and the XML technology. Additionally, we specify some of the use cases for RDF. Our findings will shed light on the current achievements in RDF research by comparing the different methodologies for storage and optimization proposed so far, thus identifying further research areas.
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An Android Based Automated Tool for Performance Evaluation of a Course Teacher (CTE)
Статья научная
For the betterment of teaching methodology student’s evaluation is an integral part of any educational organization. To achieve this process the authority needs to know how the teachers are teaching and therefore the interaction between the learners and therefore educators. This paper develops an android based automated tool for performance evaluation of a course teacher (CTE) which is able to create an educator’s performance report from the student’s evaluation based on some predefined questionnaire by using an android mobile device with internet connectivity from anywhere and anytime. The performance report is auto generated together with a graph and it also creates a file to send the teacher if the authority wants to inform the educator. With the assistance of this technique, course teachers can easily understand their current situation of their corresponding courses where they should focus on.
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