This study describes the creation of an intelligent surveillance system based on deep learning that aims to improve real-time security monitoring by automatically identifying suspicious activity. By using cutting-edge computer vision techniques, the suggested system overcomes the drawbacks of conventional surveillance that depends on human observation to spot irregularities in public spaces. The system successfully completes motion detection, trajectory analysis, and emotion recognition by using the YOLOv8 model for object detection and DeepFace for facial emotion analysis. Roboflow is used for dataset annotation, model training with optimized parameters, and visualization of object trajectories and detection confidence. The findings show that abnormal behaviors can be accurately identified, with noteworthy observations made about the emotional expressions and movement patterns of those deemed to be threats. Even though the system performs well in real time, issues like misclassification, model explainability, and a lack of diversity in the dataset still exist. Future research will concentrate on integrating multimodal data fusion, deeper models, and temporal sequence analysis to further enhance detection robustness and system intelligence.

The growing demand for innovative home security solutions has accelerated the integration of advanced technologies to enhance safety, convenience, and operational efficiency. Artificial intelligence (AI) has become a pivotal element in revolutionizing home security systems by enabling real-time threat detection, automated surveillance, and intelligent decision-making. This study employs a systematic literature review (SLR) to explore recent advancements in AI-driven technologies, such as machine learning, computer vision, natural language processing, and the Internet of Things (IoT). These innovations enhance security by providing features like facial recognition, anomaly detection, voice-activated controls, and predictive analysis, delivering more accurate and responsive security solutions. Furthermore, this study addresses challenges related to data privacy, cybersecurity threats, and cost considerations while emphasizing AI’s potential to deliver scalable, efficient, and user-friendly systems. The findings demonstrate AI’s vital role in the evolution of home security technologies, paving the way for smarter and safer living environments.

The global transition from fossil fuels to renewable energy has become a critical priority in addressing environmental and sustainability challenges. However, the intermittent nature of renewable energy sources, such as solar and wind, poses significant challenges in ensuring a stable and reliable energy supply. This article explores the role of Internet of Things (IoT) technology in optimizing renewable energy storage systems to address these challenges. Using a systematic literature review (SLR) approach, the study identifies recent innovations in IoT technologies, including the integration of artificial intelligence (AI), big data analytics, and smart energy management systems, which enhance efficiency, sustainability, and reduce operational costs of energy storage systems. IoT enables real-time data monitoring, energy demand prediction, and decentralized energy management, reducing energy losses, extending the lifespan of storage devices, and supporting grid stability. The findings highlight that IoT integration offers innovative solutions to mitigate the intermittency of renewable energy, supporting the global energy transition toward more reliable, cost-effective, and environmentally friendly energy systems. This study provides valuable insights into the potential of IoT in advancing renewable energy storage technologies and contributes to the development of sustainable energy solutions for the future.