An Integrated Reinforcement DQNN Algorithm to Detect Crime Anomaly Objects in Smart Cities
- Title
- An Integrated Reinforcement DQNN Algorithm to Detect Crime Anomaly Objects in Smart Cities
- Creator
- Mandala D.J.; Akhila P.; Reddy V.S.
- Description
- In olden days it is difficult to identify the unsusceptible forces happening in the society but with the advancement of smart devices, government has started constructing smart cities with the help of IoT devices, to capture the susceptible events happening in and around the surroundings to reduce the crime rate. But, unfortunately hackers or criminals are accessing these devices to protect themselves by remotely stopping these devices. So, the society need strong security environment, this can be achieved with the usage of reinforcement algorithms, which can detect the anomaly activities. The main reason for choosing the reinforcement algorithms is it efficiently handles a sequence of decisions based on the input captured from the videos. In the proposed system, the major objective is defined as minimum identification time from each frame by defining if then decision rules. It is a sort of autonomous system, where the system tries to learn from the penalties posed on it during the training phase. The proposed system has obtained an accuracy of 98.34% and the time to encrypt the attributes is also less. 2021. All Rights Reserved.
- Source
- International Journal of Advanced Computer Science and Applications, Vol-12, No. 12, pp. 348-352.
- Date
- 2021-01-01
- Publisher
- Science and Information Organization
- Subject
- Advanced Encryption Standard (AES); anomaly detection; crime rate prediction; HybridFly; RCNN; reinforcement; security attacks
- Coverage
- Mandala D.J., Department of CSE, School of Engineering & Technology, CHRIST (Deemed to be University), Bengaluru, India; Akhila P., Department of CSE, Gayatri Vidya Parishad College of Engineering (A), Andhra Pradesh, Visakhapatnam, India; Reddy V.S., Department of IT, VBIT Telangana, India
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 2158107X
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Mandala D.J.; Akhila P.; Reddy V.S., “An Integrated Reinforcement DQNN Algorithm to Detect Crime Anomaly Objects in Smart Cities,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/15945.