Indonesian Journal of Electrical Engineering and Computer Science
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Improving recommendations with implicit trust propagation from ratings and check-ins
Get PDFThis paper investigates how the propagation of implicit trust between users affects the quality of point-of-interest (POI) recommendations in location-based social networks (LBSNs). Through the analysis of user interactions via ratings and check-ins, this work proposes a recommendation model known as propagation of rating/check-in for implicit trust (PRCT). This model relies on two primary approaches: Similarity trust rating (STR), which utilizes user ratings, and similarity trust check-in (STC), which focuses on check-ins data. Both approaches employ trust propagation to enhance their similarity matrices between users. An evaluation of the PRCT model using the Yelp dataset shows that the STR approach surpasses other variants in terms of PRECISION and RECALL, while the STC approach demonstrates superior performance in terms of RMSE. Furthermore, while trust propagation in the PRCT model increases the density of its similarity matrices, it does not consistently enhance its PRECISION parameter. Only the similarity Jaccard check-in (SJC) and similarity cosine check-in (SCC) approaches show a significant improvement of this parameter.
Federated learning in edge AI: a systematic review of applications, privacy challenges, and preservation techniques
Get PDFEdge artificial intelligence (Edge AI) involves the implementation of AI algorithms and models directly on local edge devices, such as sensors or internet of things (IoT) devices. This allows for immediate processing and analysis of data without the need for continuous dependence on cloud infrastructure. Concerns about privacy have grown importance in recent years for businesses looking to uphold end-user expectations and safeguard business models. Federated learning (FL) has emerged as a novel approach to enhance privacy. To improve generalization qualities, FL trains local models on local data. These models then collaborate to update a global model. Each edge device (like smartphones, IoT sensors, or autonomous vehicles) trains a local model on its own data. This local training helps in capturing data patterns specific to each device or node. Poisoning, backdoors, and generative adversarial network (GAN)-based attacks are currently the main security risk. Nevertheless, the biggest threat to FL’s privacy is from inference-based assaults such as model inversion attacks, differential privacy shortcomings and FL utilizes blockchain and cryptography technologies to improve privacy on edge devices. This paper presents a thorough examination of the current literature on this subject. In more detail, we study the background of FL and its different existing applications, types, privacy threats and its techniques for privacy preservation
Augmented reality in the context of universal design for hearing impaired student
Get PDFAdvancing equal rights and prohibiting discrimination based on disability are essential to achieving social equity. Education serves as a vital mechanism in this effort, particularly through inclusive practices that support diverse learners. Sakon Nakhon Rajabhat University advances these values by admitting students with disabilities, including those with hearing impairments, and by fostering accessible learning environments. This study presents the development of an augmented reality (AR) application, designed according to universal design (UD) principles, to enhance learning for students with hearing impairments. The AR technology integrates real and virtual elements to create an engaging and interactive educational experience. Evaluation results indicate a high level of effectiveness, with the assessment dimension receiving the highest mean score ( = 4.87, = 0.35), and overall effectiveness rated similarly ( = 4.78, = 0.42). User satisfaction was also rated at a very high level across all aspects ( = 4.67, = 0.54). These findings highlight the potential of AR technology, when guided by inclusive design principles, to improve learning outcomes for students with hearing impairments
Design and implementation of heterogeneous IoT wearables for multi-disease monitoring with OFDM-based spectrum allocation
Get PDFThis research proposes a comprehensive and scalable architecture for intelligent healthcare monitoring, integrating heterogeneous wearable biosensors, edge computing, and bio-inspired optimization techniques employing an orthogonal frequency division multiplexing (OFDM)-based spectrum allocation strategy. The system continuously monitors key physiological parameters, including heart rate, electrocardiogram (ECG), blood glucose levels, body temperature, blood pressure, and respiratory rate, using low-power, biocompatible sensors with wireless communication capabilities. An edge computing layer performs real-time signal preprocessing (noise filtering, normalization, compression), significantly reducing latency and bandwidth demands. To optimize system performance, the walrus optimization algorithm (WOA), a novel metaheuristic inspired by walrus social and hunting behaviors, is employed. WOA is utilized to dynamically adjust critical parameters, including transmission power, modulation index, bandwidth allocation, and routing efficiency. Experimental results demonstrate notable improvements: signal-to-noise ratio (SNR) increased from 5 dB to over 31 dB, latency reduced from 10 ms to under 4 ms, and bit error rate (BER) was minimized to 8×10⁻⁶. Hybrid models incorporating WOA with machine learning (WOA-ANN, WOA-SVM) achieved spectral efficiencies up to 3.7 bits/s/Hz and energy efficiencies up to 22 bits/Joule. The proposed system supports reliable, real-time health data acquisition and transmission in both urban and remote healthcare environments. Its modular, power-efficient, and adaptive architecture demonstrates high potential for deployment in telemedicine, chronic disease management, and emergency response systems, establishing a robust foundation for next-generation smart healthcare infrastructure
Comparing machine learning and binary regression approach for motor insurance prediction
Get PDFThis study compares the performance of binary regression with the power cauchit (PC) link function and random forest in predicting motor insurance policyholder behavior using an imbalanced dataset. The dataset comprises 4,000 policyholders, with the response variable indicating whether a client purchased a full coverage plan (1) or not (0). Predictors include characteristics such as men, urban, private, age, and seniority. Binary regression was implemented using PyStan, while random forest was created with scikit-learn without additional hyperparameter tuning. Results demonstrate that random forest outperformed binary regression in a range of performance metrics, as well as specialized metrics suitable for imbalanced data. Findings point to the effectiveness of machine learning (ML) algorithms, exemplified by random forest, offer more robust performance in handling complex, imbalanced datasets compared to traditional statistical models. This highlights the potential of random forest to improve predictive accuracy in applications such as motor insurance policyholder behavior analysis
Application of Naïve Bayes Algorithm in Expert System for Diagnosing Chilli Plant Diseases Based on Growth Phase on Peatland
Get PDFAgricultural development on peatlands has its own challenges, especially in the cultivation of chili plants that are susceptible to various diseases. Therefore, an expert system is needed that can help farmers diagnose chili plant diseases quickly and accurately based on the plant growth phase. This research aims to apply the Naïve Bayes algorithm to the expert system for diagnosing Capsicum annum L (Chilli) plant diseases. The results of the expert system research offer an innovative and adaptive solution for the management of plant diseases in peatlands, with great potential to increase agricultural productivity and plant resistance to disease. The expert system is able to diagnose several types of diseases on chili plants in peatlands, such as anthracnose, fusarium wilt, and leaf curl disease. Each diagnosis is based on symptoms observed in each phase of plant growth, from the vegetative phase to the generative phase. Expert system testing results. This system is expected to increase the productivity and quality of chili crops on peatlands, as well as reduce losses due to disease attacks. In addition, this research also shows that the Naive Bayes algorithm has great potential to be applied in expert systems in other agricultural fields
Enhancing hypertension prediction: a hybrid machine learning optimization approach
Get PDFEarly identification of hypertension is crucial to prevent its serious complications, which can lead to devastating health effects by threatening lifestyle quality and significantly increasing premature mortality. This study aims to evaluate the effectiveness of machine learning techniques in predicting the presence of hypertension from an unbalanced dataset consisting of 4,363 records and 35 features. To balance the dataset, we employed the synthetic minority over-sampling technique (SMOTE) algorithm. In addition, to select the most relevant features, we used ant colony optimization. Next, we applied various algorithms, including logistic regression (LR), K-nearest neighbors (KNNs), support vector machine (SVM), extra trees (ETs), and AdaBoost (AB). We also evaluated the optimization of hyperparameters using two methods: Bayesian optimization (BO) and particle swarm optimization (PSO). The results reveal that the combination of AB with BO demonstrated superior performance, with an accuracy of 97.60%, a recall of 98.93%, and a precision of 98.59%. This research emphasizes the potential of machine learning techniques for anticipating hypertension and highlights the importance of optimization techniques in improving predictive models’ performance
Archimedes assisted LSTM model for blockchain based privacy preserving IoT with smart cities
Get PDFPresently, the emergence of internet of things (IoT) has significantly improved the processing, analysis, and management of the substantial volume of big data generated by smart cities. Among the various applications of smart cities, notable ones include location-based services, urban design and transportation management. These applications, however, come with several challenges, including privacy concerns, mining complexities, visualization issues and data security. The integration of blockchain (BC) technology into IoT (BIoT) introduces a novel approach to secure smart cities. This work presents an Archimedes assisted long short-term memory (LSTM) model intrusion detection for BC based privacy preserving (PP) IoT with smart cities. After the stage of pre-processing, the LSTM is utilized for automated feature extraction and classification. At last, the Archimedes optimizer (AO) is utilized to optimize the LSTM’s hyper-parameters. In addition, the BC technology is utilized for securing the data transmission
Characterization of A2G UAV communication channels under rician fading conditions
Get PDFThe variation in the k-factor value significantly influences the performance of unmanned aerial vehicle (UAV) air-to-ground point-to-point line of sight (A2G PTP LOS) communications over a Rician channel at 1,800 MHz using quadrature phase shift keying (QPSK) modulation and orthogonal frequency division multiplexing (OFDM) techniques. The research emphasizes the impact of the k-factor, which quantifies the dominance of the line-of-sight component over multipath scattering. The variation in the k-factor significantly influences UAV A2G PTP LOS communication performance for the empirical model (EM), as it involves precise measurements of the received power level in dBm from UAV to ground control station (GCS) across varying distances and altitudes. We introduce a method to compute the k-factor by assessing the ratio of the line-of-sight signal power to the multipath signal power, thereby enhancing channel modeling accuracy. Empirical analysis shows a strong correlation between bit error rate (BER) and signal-to-noise ratio (SNR) with differing k-factor values; a higher k-factor of 16.3 markedly improves performance, virtually eliminating errors at a 10 dB SNR, while a lower k-factor of 2.39 still shows significant errors at a 30 dB SNR. These results highlight the necessity of optimizing the k-factor in UAV A2G PTP LOS systems to ensure stable and reliable communication under diverse operational conditions
An enhanced least recently used page replacement algorithm
Get PDFPage replacement algorithms play a crucial role in enhancing the performance issue brought on by variations in processor speeds and memory by effectively removing pages from computer memory to improve overall efficiency. The majority of these algorithms can address the page replacement problems, but their implementation is challenging. This paper introduces a new efficient page replacement algorithm, i.e., enhanced least-replacement (E-LRU) based on two introduced features used to select the victim page. By incorporating elements of traditional algorithms such as first in first out (FIFO) and least recently used (LRU), E-LRU presents itself as a new approach with potential benefits for memory management. This study evaluates the effectiveness of E-LRU in reducing power consumption by reducing cache faults and compares its performance to existing algorithms in various settings. The results provide insight into the advantages and disadvantages of E-LRU and essential perspectives on its potential benefits for contemporary memory management algorithms. Furthermore, the study puts E-LRU into the perspective of evolving algorithms and provides directions for future investigation and improvement in the ever-changing field of memory management. The study proved that E-LRU works better than FIFO and LRU algorithms