This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and area of focus for researchers. Additionally, the study presents ANN application challenges, contributions, compare performances and critiques methods. The study covers many applications of ANN techniques in various disciplines which include computing, science, engineering, medicine, environmental, agriculture, mining, technology, climate, business, arts, and nanotechnology, etc. The study assesses ANN contributions, compare performances and critiques methods. The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems. Therefore, we proposed feedforward and feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance. Moreover, we recommend that instead of applying a single method, future research can focus on combining ANN models into one network-wide application.

The rapid expansion of drones otherwise known as Unmanned Aerial Vehicles (UAVs), in critical sectors has increased their exposure to cyber threats such as GPS spoofing, command hijacking, and firmware tampering. Existing forensic tools often fail to address UAV-specific challenges like volatile memory and limited storage, hindering effective investigations. Hence, to address this gap, this study proposes the Enhanced UAV Forensic Framework (EUAVFF) a modular, forensic-by-design model integrating blockchain audit trails, secure logging, telemetry offloading, and UAV-friendly encryption. Validated through a literature review and a stakeholder survey (n = 100), results showed that over 70% of respondents lacked awareness of UAV cyber risks, and current drones were rated poorly in key forensic areas, including tamper-proof logging and legal evidence handling. Only 28% were familiar with drone-specific threats, reflecting critical gaps in preparedness.These findings emphasize the urgent need for proactive forensic integration. EUAVFF offers a structured path to secure, accountable, and resilient UAV operations in increasingly hostile cyber environments.