- All articles 719
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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: 719
Scientific article
Refer to this paper, an intelligent-fuzzy feed-forward computed torque estimator for Proportional-Integral-Derivative (PID) controller is proposed for highly nonlinear continuum robot manipulator. In the absence of robot knowledge, PID may be the best controller, because it is model-free, and its parameters can be adjusted easily and separately and it is the most used in robot manipulators. In order to remove steady-state error caused by uncertainties and noise, the integrator gain has to be increased. This leads to worse transient performance, even destroys the stability. The integrator in a PID controller also reduces the bandwidth of the closed-loop system. Model-based compensation for PD control is an alternative method to substitute PID control. Computed torque compensation is one of the nonlinear compensator. The main problem of the pure computed torque compensator (CTC) was highly nonlinear dynamic parameters which related to system’s dynamic parameters in certain and uncertain systems. The nonlinear equivalent dynamic problem in uncertain system is solved by using feed-forward fuzzy inference system. To eliminate the continuum robot manipulator system’s dynamic; Mamdani fuzzy inference system is design and applied to CTC. This methodology is based on design feed-forward fuzzy inference system and applied to CTC. The results demonstrate that the model base feed-forward fuzzy CTC estimator works well to compensate linear PID controller in presence of partly uncertainty system (e.g., continuum robot).
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Scientific article
During the implementation of the work on the creation of the system of tonality recognition and text categorization in the news, a study of the subject area was conducted, which allowed the understanding of the processes of text analysis in the mass media to be enriched. The necessary data for further processing was found. The work resulted from a program that consists of an information parser, a data analyser and cleaner, a Large Language Models model, a neural network, and a database with vectorized data. These components were integrated into the user interface and implemented as a program window. The program can analyse news texts, determining their tone and categories. At the same time, it provides the user with a convenient interface for entering text and receiving analysis results. Therefore, the created system is a powerful tool for automated analysis of textual data in mass media, which can be used for various purposes, including monitoring the news space, analysis of public opinion, and others. Also, the developed information technology successfully meets the set tasks aimed at tonality analysis and categorization of news. It effectively solves the task of collecting, analysing and classifying news materials, which allows users to receive operational and objective information. Its architecture and functionality allow for easy changes and additions in the future, making it a flexible and adaptable tool for news analytics and decision-making in various business sectors.
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Intergovernmental Allocation of Public Resources, Fiscal Decentralization and Economic Growth
Scientific article
Incorporating a two-level government structure into an endogenous growth model, we discussed the growth impacts of different intergovernmental allocation of public resources, i.e. intergovernmental transfer payments and the power of revenue autonomy of the lower-level government, along with fiscal decentralization. we showed that (1) there was an “Inverted U-shaped” relationship between fiscal decentralization and economic growth; (2) Different intergovernmental allocation of public resources does not affect the “Inverted U-shape” relationship between fiscal decentralization and economic growth.
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Internal Control Improvement for Creating Good Governance
Scientific article
Cooperative, which support the national economy of Indonesia, still has many problems. In 2015 the report by the Ministry of Cooperatives and SMEs shows many cooperatives are inactive because of having incompetence. Some issues such as the value of savings that do not fit between the cooperative and its customers, embezzlement of customer funds by parties who are not responsible, refunds troubled loans so that the cooperative suffered losses. These problems will be used as a critical point to do mapping with COBIT 5 framework. Based on the result of this mapping, it will be prepared internal control based on the framework of possible internal controls such as COBIT(Control Objective for Information & Related Technology), COSO(Committee of Sponsoring Organizations), ITIL(IT Infrastructure Library) and national regulations that are prevailing in Indonesia. Control is expected to discuss the internal problems that occurred in each business process that used in the cooperative.
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Interoperability in Nigeria Healthcare System: The Ways Forward
Scientific article
Interoperability of health related information is one of the agendas of many counties in the world, with no exception to Nigeria and other developing countries. This is because healthcare costs are rising exponentially. However, interoperability of health related information seem largely unattainable in Nigeria due to reluctance to change from the traditional paper based healthcare system to the use of e-health systems, inadequate ICT infrastructure, poor utilization of the available ICT resources, erratic power supply, increased burden of underdevelopment, poverty, political instability, shortage of educational capacity in Nigeria rural and urban healthcare centers, low level of ICT awareness, poor maintenance culture as well as corruption. Consequently, the healthcare system in Nigeria is saddled with high cost, high rate of disease outbreak driven by HIV/AIDs, malaria and other infectious diseases which results in a high rate of mortality. Nevertheless, the urgent need to meaningfully exchange safe and reliable health information is a key priority to the healthcare system in Nigeria as the qualities of patients’ care depend majorly on the timely acquisition, processing and retrieval of data related to the patient. Thus, this paper attempts to unravel the factors hindering interoperability in the Nigeria healthcare system and suggests ways of making total interoperability a reality in Nigeria healthcare system as well as other developing countries.
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Scientific article
Neutrosophic set is a power general formal framework, which generalizes the concept of the classic set, fuzzy set, interval valued fuzzy set, intuitionistic fuzzy set, and interval intuitionistic fuzzy set from philosophical point of view. In Geographical Information Systems (GIS) there is a need to model spatial regions with indeterminate boundary and under indeterminacy. In this paper, we first gives fundamental concepts and properties of a neutrosophic spatial region.
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Investigation of Machine Learning Algorithms for Network Intrusion Detection
Scientific article
Network intrusion is an increasing major concern as we are rapidly advancing in technology. To detect network intrusion, Intrusion Detection Systems are required. Among the wide range of intrusion detection technologies, machine learning methods are the most appropriate. In this paper we investigated different machine learning techniques using NSL-KDD dataset, with steps of building a model. We used Decision Tree, Support Vector Machine, Random Forest, Naïve Bayes, Neural network, adaBoost machine leaning algorithms. At step one, one-hot-encoding is applied to convert categorical to numeric features. At step two, different feature scaling techniques, including normalization and standardization, are applied on these six selected machine learning algorithms with the encoded dataset. Further in this step, for each of the six machine learning algorithms, the better scaling technique application outcome is selected for the comparison in the next step. We considered six pairs of better scaling technique with each machine learning algorithm. Among these six scaling-machine learning pairs, one pair (Naïve Bayes) is dropped for having inferior performance. Hence, the outcome of this step is five scaling-machine learning pairs. At step three, different feature reduction techniques, including low variance filter, high correlation filter, Random Forest, Incremental PCA, are applied to the five scaling-machine learning pairs from step two. Further in this step, for each of the five scaling-machine learning pairs, the better feature reduction technique application outcome is selected for the comparison in the next step. The outcome of this step is five feature reduced scaling-machine learning pairs. At step four, different sampling techniques, including SMOTE, Borderline-SMOTE, ADASYN are applied to the five feature reduced scaling-machine learning pairs. The outcome of this step is five over sampled, feature reduced scaling-machine learning pairs. This outcome is then finally compared to find the best pairs to be used for intrusion detection system.
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Investigations of Cellular Automata Game of Life Rules for Noise Filtering and Edge Detection
Scientific article
In digital image processing, edge detection of images is an important and difficult task. Also, if the images are corrupted by noise, it smears some details and thus resulting in inaccurate edge detection. Hence, a pre-processing step must be taken before the edge detection. In this paper a new approach for edge detection with noise filtering of digital images using Cellular Automata Game of Life is presented. This procedure can easily be generalized and used for any type of digital media. To illustrate the proposed method, some experiments have been performed on standard test images and compared with popular methods. The results reveal that the proposed method has relatively desirable performance.
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IoT Based Smart Energy Consumption Prediction for Home Appliances
Scientific article
Optimizing energy management for household appliances is essential for maximizing domestic energy utilization and enabling preventive maintenance. Recent studies indicate that traditional forecasting approaches frequently lack the necessary accuracy and real-time learning capabilities required for effective management of household energy. This study demonstrates the implementation of a comprehensive strategy that integrates Internet of Things (IoT) data, machine learning (ML), and explainable artificial intelligence (XAI) to improve the accuracy and interpretability of predicting energy usage in residential buildings. Our research focuses on the rising issues faced by IoT-based smart systems, partic- ularly the deficiencies in the performance of current solutions. Therefore, as compared to the other 17 models that were examined, polynomial regression demonstrated outstanding performance. Our solution utilizes a non-intrusive sensor to collect data without disrupting its operation. Real-time data collecting is achieved through a Flask-based web page with Ngrok for external access.The efficacy of the proposed system was assessed using many metrics, yielding highly satisfac- tory results: the root mean square error (RMSE) was 0.03, the mean absolute error (MAE) was 0.02, the mean absolute percentage error (MAPE) was 0.04, and the coefficient of determination (R²) was 0.9989. However, modern cutting-edge methods still face considerable hurdles when it comes to interpretability. In order to tackle these problems, we include XAI techniques such as SHAP and LIME. Explainable Artificial Intelligence (XAI) improves the interpretability of the model by elucidating the impact of various variables on energy consumption forecasts. Not only does this increase the effectiveness of the model, but it also promotes comprehension of the data and enables them to identify the elements that influence home energy usage.
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IoT for Industrial Automation: A Review on Industry 4.0 Applications
Scientific article
The beginning of the fourth industrial revolution or Industry 4.0 has changed the concept of automation in industries by adopting the Internet of Things (IoT) in manufacturing, logistics, and production processes. The IoT is the digital foundation of Industry 4.0 that allows real-time monitoring, predictive maintenance, data-based decision making, and autonomous processes with in-between devices and smart sensors. This review explores the uses of the IoT in industrial automation through analyzing the enabling technologies, communication protocols, integration with cloud computing, wireless sensor networks, edge computing, artificial intelligence (AI) and machine learning (ML), as well as presenting important applications, current challenges, and future trends in smart industrial systems. Instead of considering the technologies separately, this paper takes the system-level viewpoint by integrating the way IoT architecture, communication protocol, intelligent analytics, and security controls collectively facilitate Industry 4.0 automation.
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Scientific article
Precision agriculture is revolutionizing the agricultural sector by integrating advanced technologies to enhance productivity and sustainability. In aquaculture, precision agriculture can significantly improve fish farming practices through precise monitoring and data-driven decision-making, addressing challenges such as optimizing resource usage and improving fish health. This paper presents the development and implementation of an IoT-based Fish Recommendation System designed to optimize aquaculture practices through a mobile application. This system uses different sensors for extracting data continuously regarding temperature, PH and Turbidity etc. These parameters can be analysed in real-time to help fish farmers make decisions on when or how much the system should feed and aerate, and what approach of water treatment is best for their fishes. This information is stored to create individual datasets, offering researchers valuable insights into optimal conditions for each fish species. This can enhance their survival rates and promote growth. In this study, we evaluate a series of machine learning algorithms for their ability to predict the optimal fish species based on water quality parameters. Among these algorithms, Random Forest demonstrated superior performance, achieving an accuracy of 92.5%, precision of 93%, recall of 93%, and F1-score of 92%. These findings highlight the effectiveness of our approach in integrating machine learning with IoT for precise aquaculture management. Implemented through a user-friendly mobile application, our system enhances accessibility and usability for fish farmers.
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IoT-Based Smart Homes: Technologies, Security Risks and Countermeasures
Scientific article
The advent of (Internet of Things) IoT technologies has essentially transformed traditional houses into intelligent, equipped, and networked smart houses that serve to improve the quality in the lives of human beings with respect to security, energy efficiency, and comfort through massive automation, sensing, and remote control. However, with such a shift of paradigm, due to the diversity of devices, the limitation of resources, problems of interoperability, and a growing array of cyberthreats, opens up numerous avenues for security and privacy threats. This review attempts a holistic coverage of IoT-based smart home technologies and then provides a systematic classification of the security vulnerabilities from device, network, cloud, and application layers. The key threats include unauthorized access, data leakage, propagation of malware, denial of service, and exploits targeted against AI, with an analysis of their causes and occurrences in the real world. The paper undertakes a critical assessment of contemporary countermeasures, ranging from lightweight cryptographic protocols, AI-driven intrusion detection systems, blockchain-based authentication, privacy-preserving edge computing, and zero-trust frameworks. A comparative insight into each approach conversed with the views of the established literature draws out trade-offs between security efficacy, scalability, computational overheads, and user adoption. Based on a synthesis of the modern findings, continued gaps are identified, and future directions provided: including quantum-resistant encryption, interoperable standards, and user-centric security design, acting as the working platform or actionable directions for any researchers, developers, or policymakers in building of secure, resilient, and privacy preserving smart home ecosystem.
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IoT-Enabled Estrus Detection in Dairy Cattle Using Machine Learning Technique
Scientific article
In the dairy industry, optimizing reproductive management is crucial for sustainable operations and enhancing animal welfare. The traditional manual detection methods usually miss many of the estrus incidences and hence have resulted in a 20-30% decline in conception rates and further massive economic losses.This paper presents an advanced framework integrating machine learning and Internet of Things (IoT) technologies to improve estruses detection in dairy cattle, thereby supporting efficient herd management and productivity. The proposed solution leverages a stacking model of Random Forest and Gradient Boosting Machine (GBM) algorithms to accurately identify estruses events, providing a reliable method for reproductive monitoring. The experimental evaluation yields accuracies of 92.1 % using RF, 92.3 % using GBM, and an improved 93.19 % when the stacking model is applied, along with improvements in precision of 94 and an F1-score of 94 %, reflecting its strength in complex behavioral pattern recognition. Rigorous evaluation across key performance metrics confirms the model’s high accuracy, underscoring its suitability for practical deployment. The system employs IoT-enabled smart collars equipped with temperature sensors, accelerometers, GPS, and RFID to gather real-time data on cattle health and reproductive status. By analyzing this data, the system delivers precise and timely insights into estruses cycles, enabling targeted breeding interventions and enhanced reproductive management. Data collected through the smart collars is securely stored in Google Firebase, facilitating efficient data archiving and rapid access via a user-friendly web application. The proposed integration of IoT, machine learning, and cloud computing presents a holistic, scalable, and economically viable solution for enhancing reproductive efficiency, animal welfare, and sustainable dairy management.
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Scientific article
Precision agriculture transform the agricultural sector by integrating advanced technologies to enhance productivity and sustainability. In crop farming, precision agriculture can significantly improve practices through precise monitoring and data-driven decision-making, addressing challenges such as optimizing resource usage and improving crop health. This study presents the development and implementation of an IoT-based Crop Recommendation System designed to optimize farming practices through a mobile application. This system uses different sensors to continuously extract data regarding the temperature, pH, NPK value and other relevant parameters. These parameters can be analyzed in real-time to help farmers make informed decisions on irrigation, fertilization, and crop selection, tailored to specific field conditions. This information is stored to create individual datasets, offering researchers valuable insights into optimal conditions for various crops. This can improve yield and promote sustainable farming practices. In this study, we evaluated a series of machine learning algorithms for their ability to predict an optimal crop based on environmental parameters. Among these algorithms, Naive Bayes demonstrated superior performance, achieving an accuracy of 99.55%, precision of 99.58%, recall of 99.55%, and F1-score of 99.54%. These findings highlight the effectiveness of our approach in integrating machine learning with the IoT for precise crop management. Implemented through a user-friendly mobile application, the proposed system enhances accessibility and usability for farmers.
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Irregular Function Estimation with LR-MKR
Scientific article
Estimating the irregular function with multi-scale structure is a hard problem. The results achieved by the traditional kernel learning are often unsatisfactory, since underfitting and overfitting cannot be simultaneously avoided, and the performance relative to boundary is often unsatisfactory. In this paper, we investigate the data-based local reweighted regression model under kernel trick and propose an iterative method to solve the kernel regression problem, local reweighted multiple kernel regression (LR-MKR). The new framework of kernel learning approach includes two parts. First, an improved Nadaraya-Watson estimator based on blockwised approach is constructed to organize a data-driven localized reweighted criteria; Second, an iterative kernel learning method is introduced in a series decreased active set. Experiments on simulated and real data sets demonstrate the proposed method can avoid under fitting and over fitting simultaneously and improve the performance relative to the boundary effetely.
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Isochronous and Anisochronous Modulation Schemes in Wireless Optical Communication Systems
Scientific article
The choice of the modulation format is one of the principle factors in realizing a high performance wireless optical communication system at a reasonable cost and acceptable complexity. The purpose of this paper is to make a comparison between isochronous and anisochronous modulation scheme categories from Discrete (digital) pulse time modulations (PTM) through the simplest scheme in each family; PPM and DPIM respectively, in term of bandwidth requirement, power efficiency and transmission capacity. This is done to give a wider view on the performance of such schemas under wide range of design parameters. In this paper, the properties of PPM and DPIM have been analyzed, from this analysis it has been shown that DPIM or anisochronous modulation schemes in general are strong candidates when synchronization and transmission capacity are taken into account, and when it comes to power performance PPM or isochronous modulations are better.
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Iterative Shrinkage Operator for Direction of Arrival Estimation
Scientific article
In this correspondence we present the application of iterative shrinkage (IS) operator to the DOA estimation task. In particular we focus our attention to Stage wise Orthogonal Matching Pursuit (StOMP) algorithm. We compare StOMP against MUSIC, which is state of the art in DOA estimation. StOMP belongs to compressive sensing regime where as MUSIC is parametric technique based upon sub-space processing. To best of our knowledge IS operators have not been analyzed for DOA estimation. The comparison is performed using extensive numerical simulations.
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Scientific article
The growing need for accessible websites cannot be overemphasized as it has posed a major challenge in the world of Information and Communication Technology (ICT). Most businesses have gone online in order to improve their market value; the banking sector is not an exception. In an attempt to satisfying customers, websites developers have violated most of the websites standards. The banking sector is one area that carries out most of its activities online. Therefore, it is important that their websites be accessible to all especially people with visually impaired disability and more so, regardless of the browsing technology being used. This study evaluates the accessibility and usability of Nigeria banking websites using some automated tools and manual inspection method. This is done in order to know the conformance of the banking websites with standard as specified by Web Accessibility Initiate (WAI). Results from the study indicate that some of the websites do not conform to the expected standard. Hence, there is need for substantial improvements on most bank websites in Nigeria
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Knowledge Template Based Multi-perspective Car Recognition Algorithm
Scientific article
In order to solve the problem due to the vehicle-oriented society such as traffic jam or traffic accident, intelligent transportation system(ITS) is raised and become scientist’s research focus, with the purpose of giving people better and safer driving condition and assistance. The core of intelligent transport system is the vehicle recognition and detection, and it’s the prerequisites for other related problems. Many existing vehicle recognition algorithms are aiming at one specific direction perspective, mostly front/back and side view. To make the algorithm more robust, our paper raised a vehicle recognition algorithm for oblique vehicles while also do research on front/back and side ones. The algorithm is designed based on the common knowledge of the car, such as shape, structure and so on. The experimental results of many car images show that our method has fine accuracy in car recognition.
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Knowledge Transfer and Innovation through University - Industry Collaboration
Scientific article
University–industry collaboration (UIC) has become an essential mechanism for fostering innovation and transferring knowledge across institutional boundaries. It is a powerful driver for innovation and sustainable economic development. This study investigates the role of UIC in facilitating knowledge transfer and its impact on innovation outcomes within industries. The study also identifies barriers such as organizational misalignment, lack of trust, and limited funding. A conceptual model is proposed to demonstrate the dynamics of UIC. Recommendations include policy reforms, structured collaboration frameworks, and enhanced R&D investments. Drawing from both qualitative and quantitative methods, the study investigates the nature of UIC, the influencing factors, and its outcomes in terms of innovation capabilities. The findings underscore the importance of trust, absorptive capacity, and aligned goals in enhancing knowledge transfer. The study also identifies critical enablers and barriers, offering strategic insights for optimizing collaborative frameworks.
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