- All articles 732
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International Journal of Information Engineering and Electronic Business @ijieeb
Journal articles - International Journal of Information Engineering and Electronic Business
All articles: 732
Analysis of the Time Trends of Precipitation over Mediterranean Region
Scientific article
Time trends of precipitation in the north of Algeria from meteorological radar are analysed. A probabilistic approach presented here proposes to study the evolution of the rainfall phenomenon in two distinct study areas, one located in sea and other located in ground. A decision criterion is established and based on radar reflectivity in order to classify the precipitation events located in both areas. At each radar observation, a state of precipitation is classified, either convective (heavy precipitation) or stratiform (average precipitation) both for the "sea" and for the "ground". In all, a time series of precipitation composed of three states; no raining, stratiform precipitation and convective precipitation, is obtained for each of the two areas. Thereby, we studied and characterized the behavior of precipitation in time by a Markov chain of order one with three states. Transition probabilities are calculated. The results show that rainfall is well described by a Markov chain of order one with three states. Indeed, the stationary probabilities, which are calculated by using the Markovian model, and the actual probabilities are almost identical.
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Analysis on Shape Image Retrieval Using DNN and ELM Classifiers for MRI Brain Tumor Images
Scientific article
The problem of searching a digital image in a very huge database is called Content Based Image Retrieval (CBIR). Shape is a significant cue for describing objects. In this paper, we have developed a shape feature extraction of MRI brain tumor image retrieval. We used T1 weighted image of MRI brain tumor images. There are two modules: feature extraction process and classification. First, the shape features are extracted using techniques like Scale invariant feature transform (SIFT), Harris corner detection and Zernike Moments. Second, the supervised learning algorithms like Deep neural network (DNN) and Extreme learning machine (ELM) are used to classify the brain tumor images. Experiments are performed using 1000 brain tumor images. In the performance evaluation, sensitivity, specificity, accuracy, error rate and f-measure are five measures are used. The Experiment result shows that highest average accuracy has got at Zernike Moments– 99%. So, Zernike Moments are better than SIFT and Harris corner detection techniques. The average time taken for DNN- 0.0901 sec, ELM- 0.0218 sec. So, ELM classifier is better than DNN. It increases the retrieval time and improves the retrieval accuracy significantly.
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Analyzing the Energy Efficiency and Sustainability Implications of IoT Tools in Smart Homes
Scientific article
This research endeavors to provide a thorough and insightful analysis of Internet of Things (IoT) tools within the context of smart homes. As the IoT continues to revolutionize the domestic landscape, understanding the integration, functionality, and user experience of these tools becomes paramount. The study surveys and categorizes prevalent IoT tools, encompassing sensors, processors, actuators, and databases. Integration capabilities are scrutinized, emphasizing interoperability and compatibility to ascertain the seamless incorporation of diverse IoT tools. Functional roles and contributions of each tool are dissected to illuminate their impact on automation, inter-connectivity, and overall control mechanisms in smart homes. The research extends its gaze to the user experience, exploring factors such as ease of use, reliability, and customization options, shaping the holistic perspective of IoT tools’ impact on residents. Realworld implementations and case studies provide tangible insights into practical applications, while surveys and interviews capture user perspectives, forming a comprehensive view of the challenges and limitations associated with these tools. This study contributes valuable insights for informed decision-making, empowering both users and developers to navigate the evolving landscape of IoT tools within the realm of smart homes.
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Scientific article
The surge in scholarly articles on e-Commerce mirrors its rapid ascent in the market's legitimacy. According to customer product recommendation theory, e-Commerce research may exhibit a bias toward specific customer product recommendations due to its evolving nature. To address this concern, this study examines five of the leading e-Commerce journals. The findings reveal a predominant focus on two main groups: customers and the integration of artificial intelligence (AI) in e-commerce recommendation systems. However, there is a notable lack of attention toward other critical groups, such as suppliers, indirect stakeholders, investors, and regulators. With e-Commerce continuing to mature, it is crucial to explore these neglected themes, sectors, and entities. This paper identifies gaps in current research through targeted keyword searches by aiming to bring these overlooked areas to the forefront. By highlighting persisting challenges in e-Commerce research, this study seeks to raise discourse and innovation in the field by ensuring that emerging topics are not overlooked. The role of AI in e-Commerce, particularly in the development of advanced recommendation systems, is identified as a key area shaping consumer experiences and market dynamics.
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Android Based Forest Fire Monitoring System
Scientific article
Forest fires are burning areas of forest or land in large or small areas. Forest fires are often uncontrollable and when this happens, the fire will burn anything nearby for two reasons, one of which is burning naturally or burning caused by humans. One of the man-made fires deliberate by humans is the burning used by the community around the forest to open or clear agricultural or plantation land. The community feels that clearing land with fire does not require a long time and is more economical, if the use of fire is not used properly it can cause the forest to burn. Forest fires in Riau area are still classified as minimal or fires rarely occur. However, the community is at least aware of the forest fires in Riau due to a lack of media information about where the fire hotspots occurred. With this application, it can help the public to better know where the hotspots have been burned. This application consists of 2 levels of access, namely: admin and user. For admins and users, the manufacturing process uses the Android Studio application with Java as the programming language, this application uses Firebase as its database to find out the location of fire hotspots using the Google Maps API. Testing on the application is created to test whether the application has run as desired. Test results using white box testing method.
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Anxiety Classification Using an Information Geometry–Driven Fuzzy Neural Network
Scientific article
Information geometry is a contemporary geometric exploration of the universe of information. Presently, informational theories have primarily been researched using algebraic, logical, analytical, and probabilistic approaches. Geometry, which analyses reciprocal relationships between components such as distance and curvature, could give major advantages to the science of information. In this study, we proposed a novel model for EEG signals classification based on a Riemannian space fuzzy neural network. The sample covariance matrix is utilized as an EEG signal characteristic in this suggested approach, and Riemannian mean and distance are used to categorize the matrix, therefore achieving the usage of manifold learning. The experimental findings indicate that this procedure is more successful and precise than earlier methods for recognizing messages from brain waves.
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Scientific article
In an increasingly competitive world, retail companies are implementing marketing strategies aimed at gaining customer preference by building lasting emotional relationships. This facilitates the conversion of so-called followers into loyal customers and brand ambassadors, thereby increasing return on investment (ROI), facilitating direct feedback to improve the product mix, and reducing customer acquisition costs over time, generating competitive advantages. The purpose of this systematic review is to analyze recent scientific production (2020-2025) on the application of Artificial Intelligence (AI) for the personalization of engagement in B2C business models, in order to analyze the predominant techniques, evaluate their impact on the consumer and determine the critical factors that condition long-term customer loyalty; the methodology used consisted of a systematic review in the main scientific databases in publications of high-impact journals. The main findings suggest that personalization tends to be dominated by machine learning models focused on algorithmic efficiency; however, a critical asymmetry was observed: the literature shows a hyper-focus on quantitative studies and functional efficiency, while studies on trust and ethics are the least relevant. Additionally, knowledge is biased by Asian dominance (53%) and the "novelty effect," limiting the applicability of strategies in markets with high demands for transparency, such as the American market. Furthermore, an emerging trend toward an Ethical-Relational Model has been observed in the literature. It is concluded that moving beyond the functional efficiency-centric approach and integrating Ethical AI as a strategic consideration may be key to fostering sustainable engagement, as evidenced by the reviewed literature.
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Application of Biometric Security in Agent based Hotel Booking System - Android Environment
Scientific article
The process of finding the finest hotel in central location is time consuming, information overload and overwhelming and in some cases poses a security risk to the client. Some of the more advanced web sites allow for a search of the destination via a map for example hotelguidge.com and jamaica.hotels.hu. Booking of hotels is secured by the standard Secure Sockets Layer (SSL) to confirm the identity of a website or server, encrypt data during transmission, and ensure the integrity of transmitted data. Even with this in place, hackers have broken the Secure Sockets Layer (SSL) by targeting weaknesses in the MD5 algorithm. Recently good amount of work been carried in the use of Intelligent agents towards hotel search on J2ME based mobile handset which still has some weakness. So taking the weakness in the current agent based system, smart software agents been developed that overcomes the weakness in the previous system In addition to smart agent based system been developed and published elsewhere, we here propose to extend the system with the booking capability that allows the user to book a hotel of choice where the authenticity of the client may be determined securely using biometric security and information transmitted using Secure Sockets Layer (SSL) (Server-Gated Cryptography (SGC)) on the internet which is novel and unique. This will be facilitated on Android 2.2-enabled mobile phone using JADE-LEAP Agent development kit.
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Application of Data Mining Techniques in Weather Prediction and Climate Change Studies
Scientific article
Weather forecasting is a vital application in meteorology and has been one of the most scientifically and technologically challenging problems around the world in the last century. In this paper, we investigate the use of data mining techniques in forecasting maximum temperature, rainfall, evaporation and wind speed. This was carried out using Artificial Neural Network and Decision Tree algorithms and meteorological data collected between 2000 and 2009 from the city of Ibadan, Nigeria. A data model for the meteorological data was developed and this was used to train the classifier algorithms. The performances of these algorithms were compared using standard performance metrics, and the algorithm which gave the best results used to generate classification rules for the mean weather variables. A predictive Neural Network model was also developed for the weather prediction program and the results compared with actual weather data for the predicted periods. The results show that given enough case data, Data Mining techniques can be used for weather forecasting and climate change studies.
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Application of Krill Herd and Water Cycle Algorithms on Dynamic Economic Load Dispatch Problem
Scientific article
Dynamic economic dispatch (DED) is a complicated nonlinear constrained optimization problem and one of the most important problems in operation of power systems. In this paper two novel optimization algorithms have been proposed to be applied on DED problem. The first method, Krill herd (KHA) is a novel meta heuristic algorithm for solving optimization problems which is based on the simulation of the herding of the krill swarms as a biological and environmental inspired method and is applied on DED problem with two configurations named KHA1 and KHA2. The second algorithm is based on how the streams and rivers flow downhill toward the sea and change back in nature, named Water Cycle (WCA) method. Two common case studies considering various constraints have been used to show the effectiveness of these methods. The results and convergence characteristics show that the proposed methods are capable of giving high quality results which are better than many other previously applied algorithms.
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Application of an integrated support vector regression method in prediction of financial returns
Scientific article
Nowadays there are lots of novel forecasting approaches to improve the forecasting accuracy in the financial markets. Support Vector Machine (SVM) as a modern statistical tool has been successfully used to solve nonlinear regression and time series problem. Unlike most conventional neural network models which are based on the empirical risk minimization principle, SVM applies the structural risk minimization principle to minimize an upper bound of the generalization error rather than minimizing the training error. To build an effective SVM model, SVM parameters must be set carefully. This study proposes a novel approach, support vector machine method combined with genetic algorithm (GA) for feature selection and chaotic particle swarm optimization(CPSO) for parameter optimization support vector Regression(SVR),to predict financial returns. The advantage of the GA-CPSO-SVR (Support Vector Regression) is that it can deal with feature selection and SVM parameter optimization simultaneously A numerical example is employed to compare the performance of the proposed model. Experiment results show that the proposed model outperforms the other approaches in forecasting financial returns.
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Architecture model of integrated Web-based e-trading business process management system
Scientific article
This paper describes development of architecture for integrated informational Web-based e-trading business process management system. An approach is justified for formation of embedded informational system architecture based on typical IT solutions. A Service Oriented Architecture (SOA) using Enterprise Service Bus (ESB) concept is offered for architectural implementation of the Web-based system. Typical informational systems that belong to the Web-system are used as services. The web-services implement SOAP protocol based on WSDL (Web Services Description Language) and UDDI (Universal Description, Discovery and Integration) specifications. Application of ESB and SOA provides scalability of Web-based e-trading management system, possibility for their enhancement and upgrading. The principle of basic architecture of Internet has been implemented in the architecture of integrated trading informational system taking into account transition to informational level.
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Area & power optimization of asynchronous processor using Xilinx ISE & Vivado
Scientific article
As the technology era has been changing, the designing pattern of an IC is also changing. An IC de-signing is now divided into two definite fields i.e. Front-End design and Back-End design. The Front-End design is using HDLs (Hardware Description Languages i.e. VHDL or Verilog) and the verification of those ICs, whereas the Back-End Design is related to the Physical Design techniques. But both of the IC design techniques required some extra efforts in terms of their Speed, Shape, and Size, which needs the Optimization efforts. This pa-per deals with the area and power optimization efforts in terms of the logic utilization by using XST & Vivado Tools. After applying area optimization techniques i.e. Logic Optimization, LUT mapping and Resource Sharing etc. on already designed asynchronous microprocessor to be used as model for proposed optimization, reasonable results in terms of power and area utilization have been achieved.
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Scientific article
In this research, an artificial chattering free adaptive fuzzy modified sliding mode control design and application to continuum robotic manipulator has proposed in order to design high performance nonlinear controller in the presence of uncertainties. Regarding to the positive points in sliding mode controller, fuzzy logic controller and online tuning method, the output improves. Each method by adding to the previous controller has covered negative points. The main target in this research is design of model free estimator on-line sliding mode fuzzy algorithm for continuum robot manipulator to reach an acceptable performance. Continuum robot manipulators are highly nonlinear, and a number of parameters are uncertain, therefore design model free controller by both analytical and empirical paradigms are the main goal. Although classical sliding mode methodology has acceptable performance with known dynamic parameters such as stability and robustness but there are two important disadvantages as below: chattering phenomenon and mathematical nonlinear dynamic equivalent controller part. To solve the chattering fuzzy logic inference applied instead of dead zone function. To solve the equivalent problems in classical sliding mode controller this paper focuses on applied on-line tuning method in classical controller. This algorithm works very well in certain and uncertain environment. The system performance in sliding mode controller is sensitive to the sliding function. Therefore, compute the optimum value of sliding function for a system is the next challenge. This problem has solved by adjusting sliding function of the on-line method continuously in real-time. In this way, the overall system performance has improved with respect to the classical sliding mode controller. This controller solved chattering phenomenon as well as mathematical nonlinear equivalent part by applied modified PID supervisory method in modified fuzzy sliding mode controller and tuning the sliding function.
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Assessing Similarity between Software Requirements: A Semantic Approach
Scientific article
The majority of projects fail to achieve their intended objectives, according to research. This could arise for a number of reasons, such as ensuring requirements are managed, excessive documentation of the code, or the difficulty in delivering software that includes all the requested features on time. An effort could be made to overcome such failure rates by establishing a proper management of requirements and concept of reusability. The correct requirements can be identified by checking similarity between the requirements received from the various stakeholders. A reusable software component can result in substantial savings in both time and money. It can be challenging to make a choice regarding the reuse of certain software components. A comparison of the requirements of a new project with those of previous projects prior to starting a new project or even at a later stage during development is useful for identifying reusable components. This paper proposes a framework (ReSim) for identifying software requirements' similarities, in an attempt to improve reusability and identify the correct requirements. A crucial component of ReSim is to measure similarity between software requirements. Different well-known similarity measurement techniques used by the researchers to evaluate the similarity between the software requirements. Some of the methods used to measure this include dice, jaccard, and cosine coefficients, but in this paper, we have used recently developed hybrid method which considers not only semantic information including lexical databases, word embeddings, and corpus statistics, but also implied word order information and produced significant improvements in the results related to the measurement of semantic similarity between words and sentences. As part of the experiments, the study used PURE dataset - in order to demonstrate the efficacy of the proposed framework. As a result, recently developed hybrid method of measuring the requirements similarity is more accurate than Dice, Jaccard, and Cosine, while Cosine is a better choice than Dice, and Jaccard is more accurate than Dice. Thus, ReSim outperforms existing approaches when tested on the PURE dataset, providing the most accurate results for both functional and non-functional requirements.
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Assessing the Behaviour of Web Services using Finite States
Scientific article
Web service are the technology of a choice when developing business applications that needs to be loosely coupled, platform independent and capable to cross enterprise boundaries. The interactions that occur between web services need to be captured because such interactions would be very useful if captured using appropriate structures and analyzed for various purposes such as assessing the responsiveness of a web service to complete peer's requests. Since the invocations of web services (WS) are dynamic, the behaviour of the WS will be dynamic depending on how the invocations discover, and get serviced by WSs. For this reason if the states of the behaviour of a WS can be captured and assessed, then tuning the WS and its performance improvement can be engineered at any stage. This work is presented in this paper.
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Assessing the Importance of Attributes for Diagnosis of Diabetes Disease
Scientific article
Diabetes is a chronic, metabolic disease related to the rise of levels of blood glucose. According to the current data from the World Health Organization, 422 million adults have diabetes in the world and prevalence of diabetes is 13.2%. Disregarding the diagnosis and treatment of the disease leads to some major problems on kidneys, heart and blood vessels, eyes, nerves, pregnancy and wound healing. The most common type of diabetes and usually in adults, Type 2 diabetes occurs when the body becomes resistant to insulin or does not make enough insulin. The main objective of this study is to make more successful this disease by investigating the important attributes based on assessing the importance of attributes using the Stability Selection method. The proposed method might be a powerful tool for the importance of attributes, and effective diagnosis of this disease with the classification accuracy is 78.57% and ROC value is 0.75.
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Scientific article
Software development always faces unexpected events such as technology changes, environmental changes, changing user needs. These changes will increase the risk in software projects. We need to risk management to deal with software risks. Risk assessment is one of the most important factors in risk and project management of software projects. In this paper, we use Wallace’s work and five factors to present an efficient method to measure software risk using fuzzy logic. Team, Planning, Complexity, Requirements and User are factors that we use in this paper. Results of experiments shows that our framework is more efficient than other frameworks and approaches for risk assessment in software projects.
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Scientific article
Path-loss propagation models are useful in radio communications for the prediction of signal’s coverage area, link’s design and power budget. They are equally used for radio channel characterization to accurately predict television coverage, interference analysis and ensure coexistence between the primary service providers and secondary users (through frequency re-use). One of the challenges with the application of a predictive path loss model for any environment other than the one it was developed for is the issue of high prediction errors. This is due to their high dependence on environmental complexity and terrain. In this paper, we investigated the error bounds of eight empirical path loss models to evaluate their reliability of predicting path losses on the UHF band in Ekiti State, South West Nigeria. Measurement of the signal strength for the UHF channel 41 (631.25MHz), Television Broadcasting Station at Ado-Ekiti, was carried out via major routes spanning through the Urban and Suburban areas of the State, using the station as reference. The signal strength values were converted to path losses and compared with predictions of eight selected models. The prediction error, relative error, root mean square error (RMSE), spread corrected mean square error (SC-RMSE), skewness and the normalized error probability density function metrics was calculated to determine the error bound which was used to validate the best predictive model for the routes under consideration. The results of this investigation show that no single model gives an accurate prediction consistently based on the evaluating metrics. However, the Electronic Communication Committee (ECC) 33 model provides better values for the overall metrics considered with RMSE values of 8.48 dB and 9.62 dB (between it and measured values) for Ekiti Suburban and Urban routes respectively. Therefore, optimizing ECC 33 model will bring the RMSE values to the standard acceptable range for both sub - urban and urban routes. The significance of this finding is that ECC 33 model has the least prediction error compared to other selected models and by extension the closest value to the measured values. This validates it to be suitable for the prediction of path losses on the UHF band over the study area.
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Scientific article
Knowledge management is one of the contemporary developments that ensure today’s institutions generate, distribute and apply knowledge to help in administrative decision making, encourage creativity, increasing competitiveness and improving their performance. The study aimed to identify the degree of application of knowledge management concept in Sudanese banks, "Case study: Commercial Farmer's Bank". The study focuses on knowledge management and the statistical differences related to gender, age, academic qualification and period of service at the bank. To achieve the aim of the study, the descriptive analytical approach was used. The research relied on the questionnaire as the main tool for data collection. The study population consisted of bank employees in their various positions. A total of 60 questionnaires were distributed, of which 58 were returned for statistical analysis. The results of the study followed standard methodology based on the state of the arts researches. The study found that the degree of application of knowledge management was medium in the field of knowledge creation and acquisition, reaching 68% and high in the field of knowledge storage 74%, knowledge sharing 76% and knowledge application 77%. The results also showed that there were no statistical differences related to gender, age, academic qualification and period of service.
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