Indonesian Journal of Electrical Engineering and Computer Science
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An efficient DVHOP localization algorithm based on simulated annealing for wireless sensor network
Get PDFIn the last decade, the research community has devoted significant attention to wireless sensor networks (WSNs) because they contribute positively to some critical issues encountered in nature and even in industry. On the other hand, localization is one of the most important parts of WSN. Hence, the conception of an efficient method of localization has become a hot research topic. Lastly, it has been invented, a set of optimal positioning methods that make locate a node with low cost and give precise results. In our contribution, we investigate the source of imprecision in the distance vectorhop (DVHOP) localization algorithm. However, we found the last step of DVHOP caused an imprecision in the calculation. Consequently, our work was to replace this step, aiming to reach satisfactory precision. For that purpose, we created three improved versions of this algorithm by adopting two meta-heuristic (simulated annealing, particle swarm optimization) and Fmincon solver dedicated to optimization in the field of WSN node localization. The experimental results obtained in this work prove the efficiency of simulated annealing (SA)-DVHOP in terms of accuracy. Furthermore, the enhanced algorithm outperforms its opponents by varying the percentage of anchors and the number of nodes
Alzheimer’s disease stage prediction using a novel transfer learning-Alzheimer’s network architecture
Get PDFThe root cause of Alzheimer’s disease (AD) is unknown except for a very tiny number of family instances caused by a genetic mutation. A thorough examination of particular brain disorders’ tissues is necessary to correctly identify the circumstances using scans of magnetic resonance imaging (MRI), and specific non-brain tissues, like the neck, skin, muscle, and fat, make further investigation challenging and can be seen in MRI scans. This work aims to use the FSL-BET skull stripping tool to remove non-brain tissues and extract the significant region of the brain- deep learning (DL) techniques rather than machine learning (ML) models helpful in classification and predictions. The most frequent issue with DL models is which needs a lot of training data, causes to problems with class imbalance. To avoid imbalance issues, we used data augmentation to ensure that the samples were distributed equally among the classes. A novel transfer learning Alzheimer’s disease network (TL-AzNet) based visual geometry group-19 (VGG19) technique was developed in this study. Conducted a comparison study using the base and suggested models, comparing over data with oversampling versus non-oversampling. The novel model predicted AD with a 95% accuracy rate
Systematic literature review of learning model using augmented reality for generation Z in higher education
Get PDFHigher education is evolving with innovations aimed at enhancing the quality of learning, and one prominent innovation is the integration of augmented reality (AR) technology into the learning process. AR merges real-world and virtual elements in real-time, creating interactive and immersive educational experiences. This technology supports the display and interaction with virtual objects, enhancing engagement and comprehension among students. However, effective integration of AR in higher education faces challenges such as limited technological infrastructure, the need for skilled lecturers, and the adaptation of teaching methods to suit generation Z's learning preferences. Despite their technological proficiency, many educational institutions struggle to optimally implement innovations like AR. This systematic literature review aims to explore and identify an AR-based learning model suitable for generation Z in higher education. Findings suggest that AR technology can significantly enhance learning by offering engaging visualizations and interactive experiences, aligning well with generation Z's characteristics and learning styles. Effective AR implementation requires suitable platforms, such as mobile, desktop, wearable, and projection platforms, each offering unique benefits. By designing AR learning models that cater to generation Z, educational institutions can improve learning outcomes and experiences
Enhancing points of interest recommendation by integrating users’ proximity into the calculation of their similarities
Get PDFIn recent years, tourists have increasingly used location-based social networks (LBSNs) to share their travel experiences with friends. Within the context of smart tourism, collaborative filtering (CF) is widely recognized as one of the most commonly used methods for point-of-interest (POI) recommendation systems. This approach analyzes user similarities using measures such Jaccard, or cosine similarity to predict the probabilities of choosing POIs to visit. However, traditional similarity measures fail to account for the physical distances between users and the locations of POIs. To address these limitations, we propose a novel similarity measure called IPUMC (integrating proximity of users in modified cosine similarity). This measure builds on the cosine similarity approach while incorporating geographic proximity between users into the calculation. Experimental results conducted on the Foursquare dataset reveal that IPUMC improves precision by 8.14%, mean average precision (MAP) by 18.01%, and normalized discounted cumulative gain (NDCG) by 16.99% compared to traditional similarity measures, specifically Pearson correlation, Spearman correlation, Euclidean distance, cosine similarity, adjusted cosine and Jaccard
Advancing SSVEP-based brain-computer interfaces: a novel approach using cross-subject multi-modal fusion technique
Get PDFBrain-computer interfaces (BCIs) represent an innovative paradigm for device control and communication, relying solely on the analysis of brain activity. Steady-state visually evoked potentials (SSVEPs), characterized by neurophysiological responses synchronized with periodic visual stimuli, have gained prominence in BCI research due to their high information transfer rates (ITRs) and minimal user training requirements. However, the translation of SSVEP-based BCIs into practical applications faces challenges stemming from variations in user responses and stimuli. To address these issues, this study introduces a groundbreaking methodology known as the cross-subject multi-modal fusion technique (CMFT). CMFT revolutionizes template design by creating invariant templates resilient to user and stimulus differences, thereby enhancing SSVEP detection across diverse subjects and stimuli. The implications of this research extend to various fields, including assistive technologies, human-computer interaction, and cognitive neuroscience. CMFT presents a promising solution to make SSVEP-based BCIs more practical and widely applicable. The methodology involves intricate steps, including spatial filters, data pre-processing, and template generation, ensuring precise SSVEP detection. Through CMFT, this study contributes to advancing the effectiveness and versatility of SSVEP-based BCIs, fostering improved accessibility and interaction in a range of domains
Ethics in human-robot interaction research
Get PDFThis paper explores the basic ethical and bioethical considerations necessary to mediate interaction with various everyday robots, analyzing several stateof-the-art reports and own research, considering advances in human-robot interaction (HRI) and artificial intelligence (AI). It is important to indicate that the adoption of robotic assistance systems is limited by users' nervousness about the enforcement of ethics, security and privacy of their information, in addition to the regular threats of Internet use, considering that HRI must reason its social and ethical impacts by including specific issues associated with HRI such as autonomy, transparency, deception and policies. In this way, it is relevant both to evaluate how robotic architectures influence people's daily lives and to study how to avoid possible negative impacts. Finally, it is significant to establish the ethical considerations required to enable the development of AI algorithms that help HRI in a natural way
Plagiarism detection using text-representing centroids techniques
Get PDFThis study addresses the limitations of traditional plagiarism detection methods by introducing the text-representing centroid (TRC) technique. TRC is designed to improve the accuracy of detecting semantic similarities and sophisticated forms of plagiarism. It utilizes a co-occurrence graph to identify centroid terms that represent the core meaning of text documents, effectively capturing the contextual associations between terms. Extensive experiments were conducted on a dataset of academic papers to assess TRC’s performance against traditional techniques across various categories of plagiarism, including near-copy, modified-copy, and paraphrasing. The results demonstrate the effectiveness of the TRC technique, achieving an average precision of 0.96 and a recall of 0.71. This performance surpasses methods such as Jaccard and Cosine similarity in accurately detecting more, complex forms of plagiarism. These findings highlight TRC’s potential as a robust tool for both academic and industry applications, helping to ensure integrity in textual content through precise and comprehensive plagiarism detection
Forecasting of nuclear energy trends in Romania using XGBoost
Get PDFThe energy demand continues to rise due to the exponential growth of the world's population. In today's world, every aspect of life, including industry, education, household, transport, and healthcare, relies on energy. Generating power in an environmentally friendly manner is a major concern. Predicting nuclear energy production depends on various factors. Researchers used the extreme gradient boost (XGBoost) machine learning algorithm for prediction. The study revealed that the RMSE validation value is 25.10810, while the training value is 15.01759 after 2000 iterations. According to the study, Romania has the potential to produce 1,300 MW of electricity in a single day through nuclear energy. Nuclear energy production can be a viable solution for decarburization and meeting energy needs. The prompt of nuclear energy in the present world is harnessing to the utmost level so that energy crisis can be mitigated for a long run. This paper tries to show the potentiality of nuclear energy in Romania predicting the future trends with the help of time series analysis
Prediction of broiler shear force using near infrared spectroscopy with second derivative linear modeling
Get PDFThis study explores the use of linear predictive models, specifically principal component regression (PCR) and partial least squares (PLS), in combination with a cost-effective near infrared spectroscopy (NIRS) system to noninvasively assess the texture of raw broiler meat. The findings demonstrate that appropriate pre-processing techniques, such as excluding the visible spectrum and applying the second-order Savitzky-Golay (SG) derivative with an optimal filter length (FL), enhance model performance. Notably, the PLS model outperformed PCR, requiring fewer latent variables (LVs) to achieve accurate predictions. This suggests that PLS more effectively captures key spectral features associated with meat texture, making it a promising approach for assessing raw broiler meat quality in a practical, cost-efficient, and non-invasive manner. These results highlight the potential of integrating linear predictive models with NIRS technology for reliable texture analysis in the poultry industry
Impact of multipath delay and co-channel interference on MIMO and STBC-MIMO
Get PDFThis paper presents a comparative analysis of conventional multiple-input multiple-output (MIMO) systems and space-time block coding (STBC) enhanced MIMO systems across three distinct wireless channel scenarios: flat Rayleigh fading, multipath delay, and multipath delay with co-channel interference. The ability of STBC to exploit multiple independent signal paths between the transmitter and receiver reduces the likelihood of signal fading, which is tried to represent through this work. Using MATLAB simulations, we evaluate system performance under realistic channel conditions, focusing on the key metric of bit error rate (BER). Results show that STBC significantly improves reliability and reduces BER compared to conventional MIMO, particularly under multipath and interference-laden environments