Gold serves as a key inflation hedge and portfolio stabilizer, making accurate price forecasting essential for investors. Hourly gold prices exhibit pronounced non-linearity and microstructure noise that limit traditional econometric models, motivating a shift toward deep learning. Existing CNN-LSTM architectures cascade convolutional and recurrent layers sequentially, without a mechanism to reconcile their complementary representations. We propose a Dual-Branch CNN-LSTM architecture with Gated Fusion, combining a convolutional-recurrent deep branch with a parallel raw-input skip branch, adaptively merged by a learned gate inspired by the Gated Multimodal Unit. Input sequences are restructured via sliding windows across three forecast horizons (24→1, 48→2, and 72→3 hours). The model is validated on 37,140 hourly XAUUSDm observations (Exness, 2020-2026) against 1D-CNN, LSTM, and Sequential CNN-LSTM baselines, achieving the best or tied-best accuracy across all configurations. For the 24-hour horizon, MAE = 9.00 USD, R² = 0.9994, and DA = 52.6%, on the original USD scale. Diebold-Mariano tests confirm significant gains over the 1D-CNN and Sequential CNN-LSTM baselines (p < 0.001), while McNemar tests confirm directional-accuracy gains in 8 of 9 comparisons (p < 0.05). These results show a modest but robust improvement over prior CNN-LSTM designs, offering a reliable foundation for risk management pending economic backtesting.
Accurate geolocation for low-power wide-area (LPWAN) devices is desirable when GNSS is unavailable or too energy-expensive, yet RSSI-/TDoA-based approaches are often fragile under channel variability, collisions and cross-device heterogeneity. We address this gap with a reproducible, tabular pipeline that maps LoRa RSSI/SNR/ToA and PHY metadata to 2D positions, compares strong tabular baselines (k-NN, Random Forest, LightGBM, XGBoost), and crucially evaluates them under group-aware (device-wise) splits to avoid identity leakage. On an ns-3-generated LoRa dataset of about 3.3 × 104 labeled receptions, Random Forest attains the tightest distribution with p50 ≈ 0 m and p95 < 1 m, whereas k-NN, despite a low median, exhibits a much heavier tail (p95 ≈ 187 m), underscoring the need to report both central and tail metrics. These results indicate that simple, edge-feasible models can perform gateway-side inference with robust accuracy when fed cleaned features and evaluated with realistic splits, making the approach attractive for practical LPWAN/IoT deployments.
INTRODUCTION: Elderly and disabled populations worldwide are growing faster than care systems can absorb them, creating strong demand for affordable home-automation tools that let residents remain safe at home without continuous caregiver presence. Most low-cost IoT prototypes reported in the literature cover only one or two functional subsystems and send alerts to a single recipient, leaving a practical gap for multi-subsystem, multi-recipient solutions. OBJECTIVES: To design, build, and test a modular home-automation system that brings together four specialised sensor nodes—radio-frequency access control, ultrasonic proximity detection, combustible-gas monitoring, and rain-triggered physical protection—plus a fifth node combining multi-recipient alert coordination with remote lighting control across five areas of the dwelling, all over a coordinated wireless network with real-time alerts sent to multiple users simultaneously. METHODS: A waterfall lifecycle combined with test-driven development guided component selection and firmware coding. Five ESP32 microcontrollers exchange data through a lightweight publish-subscribe protocol; each node handles its own control logic and actuator independently, while a companion mobile application allows remote supervision and manual override. RESULTS: All four subsystems were functionally validated through virtual simulation followed by physical demonstration on a scale dwelling model: the RFID module granted or denied access correctly for authorised and unauthorised cards, the ultrasonic module opened and closed the back door in response to detected presence, the gas sensor triggered visual, audible, and remote alerts above the configured threshold, and the rain sensor activated the laundry-protection servo on detected precipitation. The Telegram notification channel delivered alerts to the user with an observed average response time of under two seconds. Total component cost came to approximately 73 United States dollars, far below comparable commercial products. CONCLUSION: The five-node distributed architecture demonstrates functional feasibility and meets the cost target needed for practical use in urban homes in developing countries, and offers a reproducible testbed for future assistive-technology research that includes controlled reliability and latency testing.
The increasing prevalence of mosquito-borne diseases demands scalable, accurate, and energy-efficient surveillance systems capable of real-time operation in resource-constrained environments. This extended study presents an FPGA-optimized, drone-based mosquito breeding site detection framework leveraging quantized deep learning models for edge deployment. Building upon prior work, this paper introduces an enhanced implementation of YOLOv8-Tiny and YOLOv9-Small architectures, optimized through INT8 quantization, batch normalization folding, and FPGA-aware architectural refinements for execution on the PYNQ-Z2 platform. High-resolution aerial imagery acquired from unmanned aerial vehicles (UAVs) is processed in real time using the proposed system to identify and classify potential mosquito breeding sites such as stagnant water bodies and container habitats. Experimental evaluations carried out on six state-of-the-art object detection architectures showed that the proposed quantized YOLOv8-Tiny variant offers the optimal compromise among accuracy, speed, and energy efficiency by achieving 90.2% accuracy in the field, 20 FPS performance, and consuming 7.8 W of power. It is also proved that the proposed system reduces DSP and BRAM resources by more than 20% over the floating-point processor. The results obtained in the real-world deployment scenario proved the robustness of the system under different environmental situations. It is also found that the system can cover 10 km² of area per hour while providing a 44% reduction in operational costs over traditional methods. The proposed framework using FPGA acceleration and quantization awareness is useful for developing efficient vector surveillance systems.
Classical computing is approaching its physical limits, so quantum computing as an alternative model requires careful evaluation. This study compares various aspects of two systems by looking at formal algorithmic complexity, simulation-based comparisons with realistic noise models, quantitative meta-analysis, figuring out the difference between physical and logical error rates, and clearly defining contributions. A thorough study examined 24,100 papers published between 2015 and 2024 from leading scientific databases. A study of algorithmic complexity using Big-O notation confirmed that Shor's algorithm is faster than standard O(exp((64/9)²/³ (log N)²/³ (log log N)²/³)) by a super polynomial factor, Grover's algorithm is faster by a quadratic factor (O(√N) versus O(N)), and quantum simulation is faster by an exponential factor. There were Five quantum machines exhibited physical error rates ranging from 10⁻² to 10⁻⁵. Simulation-based testing revealed that the quantum advantage emerges above certain complexity thresholds: at extremely high complexity, quantum processing reached 10.3 μs versus 40.2 μs for classical computing, with better error resistance (2.3% for quantum vs. 5.9% for classical). Quantum computing has real benefits in terms of speed, mistake tolerance, and energy economy for problems that are too complicated to solve with traditional methods. However, significant challenges remain in scalability and workforce development. Hybrid quantum-classical models offer the most promising path to near-term practical benefits. On the other hand, the shift to postquantum security needs instant attention, regardless of when quantum hardware will be available.
