Continual learning on audio scene classification using representative data and memory replay GANs

This paper proposes a methodology aimed at resolving catastropic forgetting problem by choosing a limited portion of the historical dataset to act as a representative memory. This method harness the capabilities of generative adversarial networks (GANs) to create samples that expand upon the representative memory. The main advantage of this method is that it not only prevents catastrophic forgetting but also improves backward transfer and has a relatively stable and small size. The experimental results show that combining real representative data with artificially generated data from GANs, yielded better outcomes and helped counteract the negative effects of catastrophic forgetting more effectively than solely relying on GAN-generated data. This mixed approach creates a richer training environment, aiding in the retention of previous knowledge. Additionally, when comparing different methods for selecting data as the proportion of GAN-generated data increases, the low probability and mean cluster methods performed the best. These methods exhibit resilience and consistency by selecting more informative samples, thus improving overall performance

Doppler radar-based pothole sensing using spectral features in k-nearest neighbors

Potholes, resulting from wear, weather, and traffic, pose a substantial road safety concern, driving up maintenance costs and government liabilities. Numerous studies have explored pothole detection systems, however, there is a limited focus on radar-based approaches. This study investigates the use of Doppler radar mounted on moving vehicles to collect asphalt road surface data, with the aim to leverage this unique perspective point. Spectral features from power spectral density (PSD) are extracted and explored by incorporating Doppler signal PSD features into a k-nearest neighbors (KNN) within a machine learning framework for road condition classification. Six KNN algorithms are applied, and results indicate that potholes exhibit distinct spectral differences characterized by higher variability, with fine KNN performing the best, achieving an accuracy rate of 95.38% on the test dataset. In summary, this research underscores the effectiveness of Doppler radar-based pothole sensing and emphasizes the significance of algorithm and feature selection for achieving accurate results, proposing the viability of radar systems and machine learning

Analog artificial intelligence hardware for neural networks: design trends and considerations

The increasing deployment of artificial intelligence (AI) in real-time and edge applications intensified the demand for energy-efficient hardware capable of high-throughput processing. Conventional digital processors were constrained by sequential data processing, memory bandwidth limitations, and high-power consumption, making them suboptimal for edge-based AI. This review presented a comprehensive analysis of analog very-large-scale integration (VLSI) design approaches for neural network (NN) implementation focusing on circuit-level architectures including in-memory analog computing, current-mode circuits, switched-capacitor (SC) techniques, and operational transconductance amplifier (OTA)-based designs. Significant hardware design considerations such as process variation, crossbar scalability, precision–linearity trade-offs, and mixed-signal interface challenges were critically examined. Furthermore, training methodologies—spanning offline learning, circuit calibration, and programmability were discussed in the context of analog AI hardware. The review incorporated case studies, recent developments in edge deployment, and a comparative analysis of advanced analog VLSI chips. Key performance evaluation metrics such as accuracy, calibration overhead, noise robustness, and energy per inference, were also addressed. Circuit-level design aspects that impacted the performance, precision, and reliability of analog computing blocks were discussed. The paper concluded by identifying research gaps and future directions for the development of analog AI hardware suitable for real-world edge applications

An efficient clustering approach in electrical energy consumption patterns

A comprehensive understanding of electrical energy consumption patterns is essential for strategizing and monitoring the use of energy resources. Industry and business customers of electrical have energy consumption patterns that vary widely depending on the type of industry, business size, and operating hours. This research uses clustering analysis to obtain electrical energy consumption patterns in industrial and business electricity customer groups by grouping data into similar groups. The variables used in this research are daytime, active power (kW), apparent (kVa), and power factor (PF). The objective of this research is to determine the efficacy and benefits of each clustering technique employed in load profile analysis. The clustering algorithm approach used in this research is k-means and fuzzy subtractive clustering (FSC). The trials carried out on these two approaches provide valuable knowledge regarding the effectiveness and superiority of each algorithm in producing significant clusters from the data used in this research. The evaluation conducted using the Davies-Bouldin index (DBI) indicates that the quality value for FSC is 0.25 for business customers and 0.31 for industrial customers. On the other hand, the quality value for k-means is 0.55 for business customers and 0.56 for industrial customers

Design and implementation of a solar-powered IoT-based real-time air quality monitoring system

Air pollution has become a global issue due to rapid urbanization and industrialization. Air quality monitoring is essential for mitigating the adverse effects of air pollution on public health and the environment. This study presents a solar-powered internet of thing (IoT)-based air quality monitoring system designed for autonomous operation in outdoor settings. The prototype integrates an ESP32 microcontroller with low-cost sensors for PM2.5, PM10, temperature, humidity, and heat index. Powered by a solar panel and battery, the system ensures off-grid functionality, while Wi-Fi transmission to the Blynk platform, enables real-time visualization, historical record storage, and instant user access through mobile dashboards. The system was calibrated against reference instruments and deployed for 14 consecutive days. Results confirmed stable data transmission and reliable performance that suitability for outdoor use without reliance on grid power under real-world conditions. Furthermore, correlation analysis showed a strong relationship between PM2.5 and PM10, and moderate associations with humidity. Regression analysis further identified humidity and heat index as the most significant predictors, while temperature exhibited only minor influence. These findings demonstrate the feasibility of a low-cost, portable, and energy-autonomous IoT monitoring system, providing accurate real-time insights to support evidence-based air quality management

Chili leaf segmentation using meta-learning for improved model accuracy

Recognizing chili plant varieties through chili leaf image samples automatically at low costs represents an intriguing area of study. While maintaining and protecting the quality of chili plants is a priority, classifying leaf images captured randomly requires considerable effort. The quality of the captured leaf images significantly impacts the development of the model. This study applies a meta-learning approach to chili leaf image data, creating a dataset and classifying leaf images captured using mobile devices with varying camera specifications. The images were organized into 14 experimental groups to assess accuracy. The approach included 2-way and 3-way classification tasks, with 3-shot, 5-shot, and 10-shot learning scenarios, to analyze the influence of various chili leaf image factors and optimize the classification and segmentation model's accuracy. The findings demonstrate that a minimum of 10 shots from the meta-test dataset is sufficient to achieve an accuracy of 84.87% using 2-way classification meta-learning combined with the mix-up augmentation technique

Optimizing turbine location in upgraded wind farm using grasshopper optimization algorithm

This research explores the use of the grasshopper optimization algorithm (GOA) for optimizing the placement of additional turbines in an established wind farm. The primary objective is to increase the annual energy production (AEP) of the wind farm while minimizing the wake effects caused by both existing and new turbines. The research evaluates three different turbine types (1.5 MW, 2.0 MW, and 2.5 MW) to identify the most appropriate choice for increasing the wind farm's capacity. The GOA’s performance is compared with the commercial software windPRO and validated using WAsP software for energy calculations. Numerical results indicate that the GOA effectively improves wind farm layout, with the 1.5 MW turbines identified as the optimal choice for maximizing AEP and minimizing wake interactions. This study provides practical insights for wind farm operators and contributes to the development of advanced optimization techniques in wind energy

Revving up insights: machine learning-based classification of OBD II data and driving behavior analysis using g-force metrics

This research work uses machine learning (ML) approaches to classify on-board diagnostics II (OBD II) data and g-force measures to provide a thorough analysis of driving behavior. The research paper effectively demonstrates the classification of driving behaviours using OBD II and g-force data. Driving behaviours are analyzed by using ML algorithms such as random forest (RF), AdaBoost, and K-nearest neighbors (KNN). The analysis goes beyond a summary by discussing how OBD II data, g-force metrics, and the algorithms interrelate to classify ten distinct driving behaviors (e.g., weaving, swerving, and sideslipping). The RF classifier achieved the highest accuracy, which reinforces the strength of the chosen models. The inclusion of comparisons with other techniques supports arguments about the model's performance. The related works section connects the references to the central topic by highlighting prior approaches and research studies related to OBD II and driver behaviour analysis. The goals of this study are improving the accuracy of driving behaviour classification, with implications for traffic safety, driver education, and insurance sectors

On-edge 2D-to-3D generative pipeline for seamless instance transformation

Despite ongoing challenges with fragmented workflows, latency in device imports, and the main issue of limitations in object reconstruction functionality, relying on imperfect extraction networks remains an impractical solution for scalable object generation. To deal with these constraints, we proposed an end-to-end pipeline that leverages a re-designed self-consistency mechanism—aimed at reducing discrimination, along with the beneficial enhancement from level-set projection and gradient-surface orthogonality. In addition, our approach designs dynamic 3D object creation with minimal manual effort by unifying surface topology and optimizing data loading, enabling a streamlined reconstruction process and more flexible object projection. Our method supports rapid, resource-efficient mesh reconstruction and consistently demonstrates performance improvements across multiple instance benchmarks, covering virtual projection tasks. Improvements in mesh topology reconstruction, as measured by the L1 Chamfer distance (CD) metric, are consistently higher, while the system also achieves significant transmission speedups—up to 56.5×—near-instant importing—along with lowering latency in practical rendering on virtual reality (VR) devices. This result highlights that refining mesh binding improves re-creation fidelity. Our approach to scalability leads to faster user engagement and allows automated deployment without requiring human intervention during importing

Hyperparameter tuning for deep learning model used in multimodal emotion recognition data

This study attempts to address overfitting, a frequent problem with multimodal emotion identification models. This study proposes model optimization using various hyperparameter approaches, such as dropout layer, l2 kernel regularization, batch normalization, and learning rate schedule, and discovers which approach yields the most impact for optimizing the model from overfitting. For the emotion dataset, this research utilizes the interactive emotional dyadic motion capture (IEMOCAP) dataset and uses the motion capture and speech audio data modality. The models used in this experiment are convolutional neural network (CNN) for the motion capture data and CNN-bidirectional long short-term memory (CNN-BiLSTM) for the audio data. This study also applied a smaller model batch size in the experiment to accommodate the limited computing resources. The result of the experiment is that the optimization using hyperparameter tuning raises the validation accuracy to 73.67% and the f1-score to 73% on audio and motion capture data, respectively, from the base model of this research and can competitively compete with another research model result. It is hoped that the optimization experiment results in this study can be useful for future emotion recognition research, especially for those who have encountered overfitting problems