The rapid expansion of the Internet of Things (IoT) has introduced new vulnerabilities that traditional security mechanisms often fail to address effectively. Signature-based intrusion detection systems cannot adapt to zero-day attacks, while rule-based solutions lack scalability for the diverse and high-volume traffic in IoT environments. To strengthen the security framework for IoT, this paper proposes a deep learning-based anomaly detection approach that integrates Convolutional Neural Networks (CNNs) and Bidirectional Gated Recurrent Units (BiGRUs). The model is further optimized using the Moth–Flame Optimization (MFO) algorithm for automated hyperparameter tuning. To mitigate class imbalance in benchmark datasets, we employ Generative Adversarial Networks (GANs) for synthetic sample generation alongside Z-score normalization. The proposed CNN–BiGRU + MFO framework is evaluated on two widely used datasets, UNSW-NB15 and UCI SECOM. Experimental results demonstrate superior performance compared to several baseline deep learning models, achieving improvements across accuracy, precision, recall, F1-score, and ROC–AUC. These findings highlight the potential of combining hybrid deep learning architectures with evolutionary optimization for effective and generalizable intrusion detection in IoT systems.

This study presents a Generative AI-Enhanced Cybersecurity Framework designed to strengthen enterprise data privacy management while improving threat detection accuracy and scalability. By leveraging Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and traditional anomaly detection methods, the framework generates synthetic datasets that mimic real-world data, ensuring privacy and regulatory compliance. At its core, the anomaly detection engine integrates machine learning models, such as Random Forest and Support Vector Machines (SVMs), alongside deep learning techniques like Long Short-Term Memory (LSTM) networks, delivering robust performance across diverse domains. Experimental results demonstrate the framework’s adaptability and high performance in the financial sector (accuracy: 94%, recall: 95%), healthcare (accuracy: 96%, precision: 93%), and smart city infrastructures (accuracy: 91%, F1 score: 90%). The framework achieves a balanced trade-off between accuracy (0.96) and computational efficiency (processing time: 1.5 s per transaction), making it ideal for real-time enterprise deployments. Unlike analog systems that achieve > 0.99 accuracy at the cost of higher resource consumption and limited scalability, this framework emphasizes practical applications in diverse sectors. Additionally, it employs differential privacy, encryption, and data masking to ensure data security while addressing modern cybersecurity challenges. Future work aims to enhance real-time scalability further and explore reinforcement learning to advance proactive threat mitigation measures. This research provides a scalable, adaptive, and practical solution for enterprise-level cybersecurity and data privacy management.

This study proposes a novel approach to improve the reliability of trading signals for gold market prediction by integrating technical analysis indicators, Moving Averages (MAs), MACD, and Ichimoku Cloud, with a Particle Swarm-Optimized Extreme Learning Machine (PSO-ELM). Traditional time-series models often fail to capture the complex, non-linear dynamics of financial markets, whereas technical indicators combined with machine learning enhance predictive accuracy. Using daily gold prices from January–October 2020, the PSO-ELM model demonstrated superior performance in filtering false signals, achieving high precision, recall, and overall accuracy. The results highlight the effectiveness of combining technical analysis with machine learning for robust signal validation, providing a practical framework for traders and investors. While focused on gold, this methodology can be extended to other financial assets and market conditions. The integration of machine learning and blockchain enhances both predictive reliability and operational trust, offering traders, investors, and institutions a robust framework for decision support in dynamic financial environments.

The retail sector in the USA is currently facing persistent supply chain waste problems, which encompass inefficiencies, excessive inventory, and logistical obstacles. To tackle these problems, specific tactics are needed to decrease escalating expenditures, minimise ecological consequences, and restore diminished financial viability. This study used a qualitative research methodology, utilising secondary data sources, to investigate strategies for reducing supply chain waste in the US retail business. The paper classifies various forms of waste within the supply chain, examines the associated difficulties and reasons that contribute to waste production, and presents a streamlined framework tailored specifically for the retail industry in the United States. Current study emphasis on enhancing the operational efficiency and sustainability aspects of organisations by reducing the supply chain waste. Furthermore, the study will highlight the need of taking proactive actions to mitigate the supply chain waste in USA retail industry by encouraging environmental stewardship. This analysis provides a comprehensive analysis, sensible recommendations, and a reverse logistics model to help US retailers expand and compete with each other and with global players as well.

The Pharmaceutical Industry (PI) is deemed as one amongst the most substantial industrial sectors. Therefore, for the healthcare system, effective management of the Pharmaceutical Supply Chain (PSC) is crucial. Thus, an efficient medicine demand prediction system is proposed in this paper by using the Logistic Tanh activation adapted Single Exponential Smoothing (LTH-SES)-based Machine Learning (ML) technique with PSC. The previous medication sales data from the PSC are collected from publicly available sources to predict future medication needs. Later, to reduce the vast amount of information present in the dataset, the MapReduce model is performed. Later, the features are extracted. After that, by using the Renyi Entropy Principal Component Analysis (REPCA) technique, the dimensionality is reduced. Later, by using Min-Max Distance Centroid Fuzzy C-Means (M2DCFCM) clustering, the medications are grouped together. Lastly, for forecasting the future demand for medications, the LTH-SES technique is used. The proposed system’s performance is further validated and the outcomes exhibited that the proposed methodology forecasts the demand more effectively than other prevailing techniques.