Increase the in population and kindergarten number, especially in urban areas made it difficult to properly manage waste. Thus, this paper proposed a system dedicated to kindergartens to manage to dispose of waste, the system can be called smart garbage based on internet of things (SGI). To ensure a healthy environment and an intelligent waste in the kindergarten management system in an integrated manner and supported by the internet of things (IoT), we presented it in detail identification, the SGI system includes details like a display system, an automatic lid system, and a communication system. This system supplied capabilities to monitor the status of waste continuously and on IoT website can show the percentage of waste placed inside the bin. And by using a Wi-Fi communication system, between the system unit and the monitoring body, to collect waste when the trash is full. The smart system proposed in this paper is the most efficient system of traditional waste management systems because it reduces the use of manpower and significantly limits the spread of waste and fully controls it. Additionally, it can be linked via the IoT to the mobile, thus forming an integrated monitoring system.

Background/Objectives: The implementation of artificial intelligence-based systems for disease detection using biomedical signals is challenging due to the limited availability of training data. This paper deals with the generation of synthetic EEG signals using deep learning-based models, to be used in future research for training Parkinson’s disease detection systems. Methods: Linear models, such as AR, MA, and ARMA, are often inadequate due to the inherent non-linearity of time series. To overcome this drawback, long short-term memory (LSTM) networks are proposed to learn long-term dependencies in non-linear EEG time series and subsequently generate synthetic signals to enhance the training of detection systems. To learn the forward and backward time dependencies in the EEG signals, a Bidirectional LSTM model has been implemented. The LSTM model was trained on the UC San Diego Resting State EEG Dataset, which includes samples from two groups: individuals with Parkinson’s disease and a healthy control group. Results: To determine the optimal number of cells in the model, we evaluated the mean squared error (MSE) and cross-correlation between the original and synthetic signals. This method was also applied to select the length of the hidden state vector. The number of hidden cells was set to 14, and the length of the hidden state vector for each cell was fixed at 4. Increasing these values did not improve MSE or cross-correlation and unnecessarily increased computational complexity. The proposed model’s performance was evaluated using the mean-squared error (MSE), Pearson’s correlation coefficient, and the power spectra of the synthetic and original signals, demonstrating the suitability of the proposed method for this application. Conclusions: The proposed model was compared to Autoregressive Moving Average (ARMA) models, demonstrating superior performance. This confirms that deep learning-based models, such as LSTM, are strong alternatives to statistical models like ARMA for handling non-linear, multifrequency, and non-stationary signals.

This paper investigates the impact of internal factors, such as information technology readiness and owner/manager commitment, and external factors, such as regulatory support and competitive pressure, on the use of accounting information systems (AIS) levels among Iraqi small and medium-sized enterprises (SMEs). A quantitative approach via self-administered questionnaires; 190 self-administered questionnaires were distributed to the managers and owners of SMEs in Basra using a purposive sampling technique and analyzed using the partial least squares structural equation modeling (PLS-SEM) approach. The findings of this study demonstrated that internal factors, including information technology readiness and owner/manager commitment, and external factors, including regulatory support and competitive pressure, significantly and positively influenced SMEs’ AIS usage levels. The study provides meaningful contributions by expanding understanding of the use of AIS in controlling processes, operational efficiency, planning processes, and financial reporting, which assist SME managers and owners in sound decision-making and in creating short- and long-term strategies to survive in a competitive market, with a focus on both internal and external factors. The study recommended that the managers and owners could focus on multiple levels of AIS use when developing system processes.

The implementation of artificial intelligence-based systems for disease detection using biomedical signals is challenging due to the limited availability of training data. The ability to synthetically augment training datasets is therefore crucial. This paper proposes using Long Short-Term Memory (LSTM) networks to learn long-term dependencies in non-linear time series, and subsequently employing the trained model to generate synthetic signals for improved training of detection systems. Linear models, such as AR, MA, or ARMA statistical models, are often inadequate due to the inherent non-linearity of the time series. The original data consist of Electroencephalogram (EEG) recordings.

Flying Ad Hoc Networks (FANETs) composed of highly mobile Unmanned Aerial Vehicles (UAVs) are increasingly employed in mission-critical applications. However, the open wireless medium, high mobility, and resource constraints of UAVs expose FANETs to severe security and privacy threats, particularly at the Medium Access Control (MAC) layer. Existing authentication and key agreement schemes often suffer from high computational overhead and insufficient protection against advanced attacks. To address these challenges, this paper proposes a Lightweight Key Agreement Scheme (LWKAS) based on signcryption using Hyper Elliptic Curve Cryptography (HECC) and one-way cryptographic hash functions. The proposed scheme integrates MAC-layer security while simultaneously ensuring secure authentication, confidentiality, and location privacy through the Chinese Remainder Theorem (CRT). The security analysis demonstrates that LWKAS resists multiple attacks, including Man-In-The-Middle (MITM), replay, impersonation, Ephemeral Secret Leakage (ESL), cloning, desynchronization, and DoS attacks. The proposed scheme is implemented in NS-3 and evaluated against existing state-of-the-art methods in terms of authentication delay, communication overhead, and computational cost. The results demonstrate that LWKAS achieves significantly lower computational complexity and communication overhead while providing stronger security guarantees suitable for resource-constrained FANET environments.