Handwritten signatures are widely utilized as a form of personal recognition. However, they have the unfortunate shortcoming of being easily abused by those who would fake the identification or intent of an individual which might be very harmful. Therefore, the need for an automatic signature recognition system is crucial. In this paper, a signature recognition approach based on a probabilistic neural network (PNN) and wavelet transform average framing entropy (AFE) is proposed. The system was tested with a wavelet packet (WP) entropy denoted as a WP entropy neural network system (WPENN) and with a discrete wavelet transform (DWT) entropy denoted as a DWT entropy neural network system (DWENN). Our investigation was conducted over several wavelet families and different entropy types. Identification tasks, as well as verification tasks, were investigated for a comprehensive signature system study. Several other methods used in the literature were considered for comparison. Two databases were used for algorithm testing. The best recognition rate result was achieved by WPENN whereby the threshold entropy reached 92%.

Electricity demand forecasting remains a challenging issue for power system scheduling at varying stages of energy sectors. Short Term load forecasting (STLF) plays a vital part in regulated power systems and electricity markets, which is commonly employed to predict the outcomes power failures. This paper presents an intelligent machine learning with evolutionary algorithm based STLF model, called (IMLEA-STLF) for power systems which involves different stages of operations such as data decomposition, data preprocessing, feature selection, prediction, and parameter tuning. Wavelet transform (WT) is used for the decomposition of the time series and Oppositional Artificial Fish Swarm Optimization algorithm (OAFSA) based feature selection technique to elect an optimal set of features. In order to improvise the convergence rate of AFSA, oppositional based learning (OBL) concept is integrated into it. Then, the water wave optimization (WWO) with Elman neural networks (ENN) model is employed for the predictive process. Finally, inverse WT is applied and obtained the hourly load forecasting data. To validate the effective predictive outcome of the IMLEA-STLF model, an extensive set of simulations take place on benchmark dataset. The resultant values ensured the promising results of the IMLEA-STLF model over the other compared methods.

Enhancing energy efficiency, content distribution, latency, and transmission speeds are vital components of communication systems. Multiple access methods hold great promise for boosting these performance indicators. This manuscript evaluates the effectiveness of Non-Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA) systems within a single cell, where users are scattered randomly and rely on relays for dependability. This paper presents a model for improving energy efficiency, content distribution, latency, and transmission speeds in communication systems using NOMA and OMA systems within a single cell. Additionally, this paper also proposes a caching strategy using unmanned aerial vehicles (UAVs) as aerial base stations for ground users. These UAVs distribute cached content to minimize the overall latency of content demands from ground users while modifying their positions. We carried out simulations using various cache capacities and user counts linked to their respective UAVs. Furthermore, we evaluated OMA and NOMA in terms of the achievable rate and energy efficiency. The proposed model has achieved noteworthy enhancement across various scenarios including different sum rates, numbers of mobility users, diverse cache sizes, and amounts of power allocation.

The maritime industry is under increasing pressure to reduce greenhouse gas emissions, especially in countries such as Saudi Arabia that are actively working to transition to cleaner energy. In this paper, a new hybrid shipboard power system, which incorporates wind turbines, solar photovoltaic (PV) panels, proton-exchange membrane fuel cells (PEMFCs), and a battery energy storage system (BESS) together for propulsion and hotel load services, is proposed. A multi-loop Energy Management System (EMS) based on proportional–integral control (PI) is developed to coordinate the interconnections of the power sources in real time. In contrast to the widely reported model predictive or artificial intelligence optimization schemes, the PI-derived EMS achieves similar power stability and hydrogen utilization efficiency with significantly reduced computational overhead and full marine suitability. By taking advantage of the high solar irradiance and coastal wind resources in Saudi Arabia, the proposed configuration provides continuous near-zero-emission operation. Simulation results show that the PEMFC accounts for about 90% of the total energy demand, the BESS (±0.4 MW, 2 MWh) accounts for about 3%, and the stationary renewables account for about 7%, which reduces the demand for hydro-gas to about 160 kg. The DC-bus voltage is kept within ±5% of its nominal value of 750 V, and the battery state of charge (SOC) is kept within 20% to 80%. Sensitivity analyses show that by varying renewable input by ±20%, diesel consumption is ±5%. These results demonstrate the system’s ability to meet International Maritime Organization (IMO) emission targets by delivering stable near-zero-emission operation, while achieving high hydrogen efficiency and grid stability with minimal computational cost. Consequently, the proposed system presents a realistic, certifiable, and regionally optimized roadmap for next-generation hybrid PEMFC–battery–renewable marine power systems in Saudi Arabian coastal operations.

In this paper, we investigated the modeling of the pathological features of the influenza disease on the human speech. The presented work is novel research based on a real database and a new combination of previously used methods, discrete wavelet transform (DWT) and linear prediction coding (LPC). Three verification system experiments, Normal/Influenza, Smokers/Influenza, and Normal/Smokers, were studied. For testing the proposed pathological system, several classification scores were calculated for the recorded database, from which we can see that the proposed method achieved very high scores, particularly for the Normal with Influenza verification system. The performance of the proposed system was also compared with other published recognition systems. The experiments of these schemes show that the proposed method is superior.

This paper proposes an advanced metering infrastructure (AMI)-informed hierarchical energy management framework for coordinated operation of electric vehicles (EVs), photovoltaic (PV) systems, and battery energy storage systems (BESS) in campus microgrids. The proposed two-layer architecture integrates a soft actor–critic (SAC) deep reinforcement learning (DRL) agent in the upper layer with a receding horizon model predictive control (MPC) optimizer in the lower layer. The key novelty is an AMI-to-control pipeline that transforms historical 15 min smart-meter measurements into operational flexibility features and embeds them into a hierarchical SAC–MPC architecture, where the DRL layer provides adaptive coordination and the MPC layer enforces grid, storage, and EV-service constraints. The proposed framework using the real-world Pecan Street data (15 min resolution) of 73 homes across Austin, Texas and California (2014–2019) achieves a 53.1% cost reduction and a 25.7% peak demand reduction when compared with uncontrolled charging, and the proposed framework outperforms MPC-only (50.9%), DRL-only (−5.2%), and rule-based (5.1%) baselines. The statistically significant contributions of network-aware constraints, demand-response activation, and predictive look-ahead horizon are statistically significant (n = 10 independent runs) contributions (p = 0.001). The state representation informed by AMI offers directional cost improvement (+8.4%, p = 0.055) with 11% faster convergence of training. The zero network constraint violation is observed in all evaluation scenarios and the average MPC solve time is around 150 ms, which is much less than the 15 min sampling period. Sensitivity analyses show that the hierarchical DRL–MPC architecture remains computationally feasible across EV penetration, seasonal, and forecast-uncertainty scenarios. However, BESS provided no net economic benefit under the evaluated energy-only TOU tariff, increasing weekly cost by $15.25 and peak grid demand by 14.2 kW. Break-even analysis indicates that demand charges of approximately $9.9/kW per month are required for BESS to become cost-effective in the proxy system, highlighting that storage value depends strongly on tariff design and peak-demand objective formulation.