Indonesian Journal of Electrical Engineering and Computer Science
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Web GIS-based postcode alternative system for resolving “last mile” problem in Jordan’s home delivery
Get PDFAs more and more people shop online, the postal code system must be more dependable. Due to the absence of a comprehensive postcode system, online purchases and shipping in the developing country of Jordan are complicated. This research paper proposes an alternative delivery system for delivering online purchases to customers without postal codes. Smartphone and computer-based geographic information system (GIS) applications evaluated in Jordan. The scientists found that the users were eager to adopt the system based on its ease of use and adoption rate. A questionnaire survey was distributed to 167 retail stores, delivery logistics employees, university students, and academics. The data collected were then analyzed using SPSS techniques such as POST HOC and ANOVA. To find a home delivery solution, we tested the suggested system app on both desktop and Smartphone platforms. The findings show that it is easier to locate a residential neighborhood. Customer trust and satisfaction with online purchases should increase due to the additional benefits of the system installation. Improve the effectiveness of home delivery services in Jordan with the use of artificial intelligence (AI). Both customers and stores prefer this system for online shopping rather than using postcodes. According to these data, experts can enhance their items by implementing digital sales strategies
Design and optimization strategy of HAWT using a local pitch angle adaptation
Get PDFIn this paper, a smart design of a horizontal wind turbine (HAWT) has been developed. The developed design allows improving kinetic energy recuperation with high efficiency. The considered design of the wind turbine is characterized by a specific mechanical structure of blades. Each blade contains separated elements with an adaptable local pitch angle. To develop the smart wind turbine, a new algorithm for controlling the blade elements has been implemented. It allows estimating the distribution of the twist angle of each blade element. The achieved twist angle corresponds to the extracted optimal power provides by the wind turbine. The obtained results show a significant improvement relative to the wind turbine power coefficient under operating conditions. In fact, for some rotating velocity, the rate of this coefficient is increased by 21%. Moreover, notable kinetic energy recuperation is observed. Furthermore, smart orientation of elements proved optimal energy recuperation for a large scale of tip speed ratio and wind speed. In addition, the proposed structure of the wind turbine is more beneficial to minimize the axial thrust. Furthermore, the axial thrust of the wind turbine has been decreased by 21% for some operating velocity and specific conditions. As perspectives for the future works many ideas are suggested
Emerging approaches of artificial intelligence tools for distance learning: a review
Get PDFLearning management system (LMS) is the best way to deliver educational content in the context of higher education, by settings students worldwide with high-quality educational material. This paper principally seeks to examine the use of e-learning platforms in the last years from 2019 to 2023, which has coincided with the pandemic period, by elucidating the benefits and limitations of e-learning platforms, analyzing the real-world artificial intelligence (AI) algorithms used and their operating context. A comprehensive literature search was conducted on different electronic databases to identify relevant studies related to e-learning and AI tools used during this period by applying inclusion, exclusion criteria and preferred reporting items for systematic reviews and meta-analysis (PRISMA) process. Based on this review the tools were necessary social media and free communication platforms that offer the flexibility and build autonomy to students. On the other hand, many challenges are arisen due to the lack of experience in the term of using those tools or due to technical problems, for this reason, the use of AI tools to enhance learning experience still one of the approved solutions
Renewable energy conversion systems for global emission neutralization
Get PDFFossil fuel power plants still play an essential role in providing energy worldwide, but their environmental impact will contribute significantly to emissions and environmental pollution. To reduce these emissions, renewable energy offers a solution to reduce global emissions. This study proposes a renewable energy modeling system using hybrid optimization of multiple energy resources (HOMER) simulation on renewable energy systems for economic savings. This simulation can combine photovoltaic (PV), wind power (WP), and converter systems. The hybrid combination of PV and WP is the most appropriate and economical choice at the research location. The results showed that the modeling of the renewable energy hybrid system made a significant contribution, with an initial investment cost of IDR 107,474.43 million and an annual operating cost of IDR 22,540.23 million, 41% lower on condition now with an estimated return on investment of 11 years. The results of this study can be used as recommendations for similar conditions in other places. Policymakers can use this model to provide incentives and have a positive impact on hybrid power plants (HPS) in neutralizing global emissions
Deep learning-based secured resilient architecture for IoT-driven critical infrastructure
Get PDFWhile enabling remote management and efficiency improvements, the infrastructure of the smart city becomes able to advance due to the consequences of the internet of things (IoT). The development of IoT in the fields of agriculture, robotics, transportation, computerization, and manufacturing. Based on the serious infrastructure environments, smart revolutions and digital transformation play an important role. According to various perspectives on issues of privacy and security, the challenge is heterogeneous data handling from various devices of IoT. The critical IoT infrastructure with its regular operations is jeopardized by the sensor communication among both IoT devices depending upon the attacker targets. This research suggested a novel deep belief network (DBN) and a secured data dissemination structure based on blockchain to address the issues of privacy and security infrastructures. The non-local means filter performs pre-processing and the feature selection is achieved using the improved crystal structure (ICS) algorithm. The DBN model for the classification of attack and non-attack data. For the non-attacked data, the security is offered via a blockchain network incorporated with the interplanetary file system
Smartphone-based fingerprint authentication using siamese neural networks with ridge flow attention mechanism
Get PDFAuthenticating finger photo images captured using a smartphone camera provides a good alternative solution in place of the traditional method-based sensors. This paper introduces a novel approach to enhancing fingerprint authentication by leveraging images captured via a mobile camera. The method employs a siamese neural network (SNN) combined with a ridge flow attention mechanism and convolutional neural networks (CNN). Our approach begins with collecting a dataset consisting of finger images from two individuals then we apply multiple preprocessing techniques to extract fingerprint images, followed by generating augmented data to improve model robustness, scaling, and normalizing them to form images suitable for model training. Next, we generate positive and negative pairs for training a SNN. We used the SNN with CNN for feature extraction, combined with an attention mechanism that focuses on the ridge flow pattern of fingerprints to improve feature relevance which significantly contributed to the performance enhancement. As for the testing performance, our model has an accuracy of 90%, precision of 89%, recall of 83%, F1 score of 86%, area under the curve (AUC) 95 %, and 13% of equal error rate (EER) when using smartphone-captured images for fingerprint recognition
A multi-path routing protocol for IoT-based sensor networks
Get PDFInternet of things (IoT) based sensors are to link a big number of low-cost and power-integrated devices in a reliable manner. Numerous military and adventurous applications are regulated by communication among IoT sensors. The multi-path routing protocol (MRP) approach presented in this research to enhance secure routing in IoT sensors is significant. This technique makes use of data transfer routing and the relationships between network components. It finds the most efficient route between the nodes that minimizes communication overhead and is both reliable and economical in terms of shortest duration. The particle swarm optimization (PSO) technique is used to find the shortest path that is most cost-effective. To reach the target node, end-to-end data transmission must transit via intermediary nodes, which are provided by the routing path node history. The optimal path is chosen by MRP from PSO, and it traces the path to identify the intermediate nodes. In the unlikely event of a crisis, MRP offers the most affordable backup route for data transfer. When compared to earlier techniques, the outcomes of these current approaches enhance network efficiency, balance energy consumption among nodes, and routing attacks
Technical-economic analysis for ON/OFF GRID solar photovoltaic system design
Get PDFThis manuscript presents a detailed techno-economic analysis of a hybrid solar photovoltaic (PV) system designed to operate in both grid-connected (ON GRID) and stand-alone (OFF GRID) modes. The study focuses on the Leonardo Da Vinci academic building at Universidad de Los Llanos, located in Villavicencio, Colombia, in the tropical Orinoquía region. Using local solar irradiance, temperature data, and real load profiles from the facility, the system was modeled to assess performance under true operating conditions. A key part of the system design involved a detailed shadow analysis to identify potential obstructions and optimize solar access. This step significantly improved the accuracy of energy yield predictions and contributed to long-term system reliability. Additionally, regression-based methods were used to determine optimal panel tilt angles and refine system sizing based on peak sun hours. Both ON GRID and OFF GRID configurations were evaluated in terms of energy output, levelized cost of electricity (LCOE), net present value (NPV), and internal rate of return (IRR). Results show that ON GRID systems are financially advantageous in urban environments with net metering, while OFF GRID systems are critical for ensuring energy autonomy in remote or underserved areas. The findings provide practical insights for the deployment of hybrid PV systems in institutional settings across equatorial regions
Advancements in gas leakage detection and risk assessment: a comprehensive survey
Get PDFGas leakage is the main problem that harms the environment, infrastructure and public safety. Technology is increasing rapidly nowadays. So, there must be advancements in the methods used. Many methods have been come across to solve this problem. This survey paper explores various methods and technology used to solve the problem. Many methodologies have been suggested to reduce the risk of gas leaks and improve detection systems. It investigates cutting-edge models for estimating the effects of liquefied natured gas (LNG) leakage accidents, comprehensive wireless sensor network (WSN) is set up for detecting gas leaks in advance, and neural network and Kalman filter-based gas leakage early warning systems. Current developments include factors like point of interest (PoI), human data movement and gas pipelines. As technology increases, there would be major threat of authentication. So, it also looks on methods for user authentication based on different patterns to mobile applications. Especially in smart home environments, there is a need to improve security. This survey provides complete understanding of present and future directions for the researchers in gas leakage detection and risk management through various methods and their evaluation
Internet of things based smart agriculture using K-nearest neighbor for enhancing the crop yield
Get PDFAgriculture is one of the major occupations in India and is one of the significant contributors to the economy of India. The agriculture plays a vital role in country gross domestic product (GDP) and is also part of civilization. The production of crop influences the economies of countries. However, still the agriculture filed stands technologically backward. In addition, the lack of favourable weather conditions might result loss of crops yields. The farmers need awareness about their soils, timely weather updates and techniques to improve their soil for growing healthy crops. Hence it is essential to develop a system which can technologically support the farmers for suggesting the crop and improving crop yields. With the development of electronics, researchers have been developed many applications and micro controllerbased systems to do agricultural operations. The internet of things (IoT) has opened many opportunities to design and implements a smart agriculture system and machine learning (ML) algorithm can help to obtain accurate performance. Hence, in this analysis, IoT based smart agriculture using K-nearest neighbor (KNN) for enhancing the crop yields is presented. With the combination of IoT and ML algorithm this system is designed which integrates primary agriculture operations such as recommendation of crops, automated watering and fertilizers recommendation