Incorporating more sophisticated deep learning algorithms

The gap

Evidence profile

Sourced from the future-work section and inline gaps of the source papers, classified as general, spanning 3 journals.

Supporting evidence — 3 representative gaps

• Smart City Transportation Deep Learning Ensemble Approach forTraffic Accident Detection (2026) · International Journal of Engineering Technology and Management Sciences · doi

  Incorporating more sophisticated deep learning algorithms and hybrid models can improve detection accuracy. Adopting edge computing can reduce latency and enable faster real-time decision-making. Integrating emerging technologies, such as 5G communication and advanced IoT frameworks, can enhance data transmission speed and system scalability.

  generalfuture-work section
  Keywords: incorporating sophisticated deep learning algorithms hybrid models improve
• A Lightweight WDGP-1DCSP Model for High-Precision Intrusion Detection in Resource-Constrained IoT Edge Devices (2026) · American Journal of Innovation in Science and Engineering · doi

  Future research will examine the implementation of this model in a real-world environment for testing and introducing more lightweight techniques to further reduce the model’s computational cost and improve intrusion detection capabilities. Machine learning- enabled iot security: Open issues and challenges under advanced persistent threats.

  generalinline gapsevidence 5/5
  Keywords: model future examine implementation real world environment testing introducing lightweight techniques further reduce computational cost
• Hybrid Protocol-Based Network Anomaly Detection Using Machine Learning (2026) · International Journal of Drug Delivery Technology · doi

  Future work will investigate replacing or augmenting the Ran- dom Forest classifier with CNN–LSTM hybrid architectures in order to improve the detection of complex and multi-stage cyber attacks. Finally, future work will focus on extending the protocol- aware detection modules to support IoT-specific communica- tion protocols such as MQTT, CoAP, and Zigbee. Future work will focus on integrating deep learning models, supporting edge-based deployment, and exploring federated learning approaches to further enhance scalability, adaptability, and privacy in large-scale network environments.

  generalinline gapsevidence 5/5
  Keywords: future detection focus learning investigate replacing augmenting forest classifier lstm hybrid architectures order improve complex

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