Real-Time Building Management System Visual Anomaly Detection Using Heat Points Motion Analysis Machine Learning Algorithm

The multiplicity of design, construction, and use of IoT devices in homes has made it crucial to provide secure and manageable building management systems and platforms. Increasing security requires increasing the complexity of the user interface and the access verification steps in the system. Today, multi-step verification methods are used via SMS, call, or e-mail to do this. Another topic mentioned here is physical home security and energy management. Artificial intelligence and machine learning-based tools and algorithms are used to analyze images and data from sensors and security cameras. However, these tools are not always available due to the increase in data volume over time and the need for large processing resources. In this study, a new method is proposed to reduce the usage of process resources and the percentage of system error in anomaly detection by reducing visual data to critical points by using thermal cameras. This method can also be used in energy management using home and ambient temperature and user activity measurements. The statistical results of the visual comparison between the proposed method and the legacy CCTV-based visual and sensory surveillance shown in the results section demonstrate its reliability and accuracy

LTC-SE: Expanding the Potential of Liquid Time-Constant Neural Networks for Scalable AI and Embedded Systems

We present LTC-SE, an improved version of the Liquid Time-Constant (LTC) neural network algorithm originally proposed by Hasani et al. in 2021. This algorithm unifies the Leaky-Integrate-and-Fire (LIF) spiking neural network model with Continuous-Time Recurrent Neural Networks (CTRNNs), Neural Ordinary Differential Equations (NODEs), and bespoke Gated Recurrent Units (GRUs). The enhancements in LTC-SE focus on augmenting flexibility, compatibility, and code organization, targeting the unique constraints of embedded systems with limited computational resources and strict performance requirements. The updated code serves as a consolidated class library compatible with TensorFlow 2.x, offering comprehensive configuration options for LTCCell, CTRNN, NODE, and CTGRU classes. We evaluate LTC-SE against its predecessors, showcasing the advantages of our optimizations in user experience, Keras function compatibility, and code clarity. These refinements expand the applicability of liquid neural networks in diverse machine learning tasks, such as robotics, causality analysis, and time-series prediction, and build on the foundational work of Hasani et al.Comment: 11 pages, 5 figures, 5 tables, This research work is partially drawn from the MSc thesis of Michael B. Khani. arXiv admin note: text overlap with arXiv:2006.04439 by other author

GENIE-NF-AI: Identifying Neurofibromatosis Tumors using Liquid Neural Network (LTC) trained on AACR GENIE Datasets

In recent years, the field of medicine has been increasingly adopting artificial intelligence (AI) technologies to provide faster and more accurate disease detection, prediction, and assessment. In this study, we propose an interpretable AI approach to diagnose patients with neurofibromatosis using blood tests and pathogenic variables. We evaluated the proposed method using a dataset from the AACR GENIE project and compared its performance with modern approaches. Our proposed approach outperformed existing models with 99.86% accuracy. We also conducted NF1 and interpretable AI tests to validate our approach. Our work provides an explainable approach model using logistic regression and explanatory stimulus as well as a black-box model. The explainable models help to explain the predictions of black-box models while the glass-box models provide information about the best-fit features. Overall, our study presents an interpretable AI approach for diagnosing patients with neurofibromatosis and demonstrates the potential of AI in the medical field.Comment: The authors would like to acknowledge the American Association for Cancer Research and its financial and material support in the development of the AACR Project GENIE registry, as well as members of the consortium for their commitment to data sharing. Interpretations are the responsibility of study author

Real-Time Building Management System Visual Anomaly Detection Using Heat Points Motion Analysis Machine Learning Algorithm

The multiplicity of design, construction, and use of IoT devices in homes has made it crucial to provide secure and manageable building management systems and platforms. Increasing security requires increasing the complexity of the user interface and the access verification steps in the system. Today, multi-step verification methods are used via SMS, call, or e-mail to do this. Another topic mentioned here is physical home security and energy management. Artificial intelligence and machine learning-based tools and algorithms are used to analyze images and data from sensors and security cameras. However, these tools are not always available due to the increase in data volume over time and the need for large processing resources. In this study, a new method is proposed to reduce the usage of process resources and the percentage of system error in anomaly detection by reducing visual data to critical points by using thermal cameras. This method can also be used in energy management using home and ambient temperature and user activity measurements. The statistical results of the visual comparison between the proposed method and the legacy CCTV-based visual and sensory surveillance shown in the results section demonstrate its reliability and accuracy

LoRaline: A Critical Message Passing Line of Communication for Anomaly Mapping in IoV Systems

The importance of road safety is felt nowadays more than ever, where various technologies, including self-driving cars, have become abundant. Nowadays, it has more demand to build autonomous and electrical vehicles with information retrieval systems within the received sensory data not only from the local sensors but also the online and live streaming data over networks. To increase road safety dissemination of critical information, including the possibility of an obstacle or danger being in the middle of the road, automotive navigation and control systems are required. A novel method is proposed to make this critical communication possible over a specially designed vehicular ad-hoc network, where natural or urban barriers can prevent signal propagation. The network is implemented using the LoRaWAN interface and SX127x LoRa Radio module. The SX1272MB2xAS is fitted with the SX1272 transceiver, which added to a high-performance FSK/OOK RF transceiver modem. Additionally, LoRa long-range modem provides highly power-efficient communication. For this aim, two new mechanisms have been proposed. The first mechanism enables the nodes to receive data from a suggested communication link. While the second mechanism is designed to extract vital information such as establishing the connection, closing the connection, successful data transmission, errors, etc. The findings demonstrate that the proposed mechanisms have successfully enabled LoRaWAN to operate in IoV environment. The evaluation reveals that metrics such as battery consumption and covering range outperform similar technologies. Finally, this paper proposes a message-passing strategy based on Belief Propagation (BP) which provides more accurate marginal probabilities to overcome the low data rate as a foundation for our future work