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TIDCS: A Dynamic Intrusion Detection and Classification System Based Feature Selection
Published 2020“…The final classification decision for both models is estimated by incorporating the node's past behavior with the machine learning algorithm. Any detected attack reduces the trustworthiness of the nodes involved, leading to a dynamic system cleansing. …”
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A machine learning model for early detection of diabetic foot using thermogram images
Published 2021“…A comparison of the inference time for the best-performing networks confirmed that the proposed algorithm can be deployed as a smartphone application to allow the user to monitor the progression of the DFU in a home setting.…”
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Autonomous 3D Deployment of Aerial Base Stations in Wireless Networks with User Mobility
Published 2019“…We present performance results for the algorithm as a function of various system parameters assuming a random walk mobility model. …”
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Spectrum Sensing Algorithms for Cooperative Cognitive Radio Networks
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Malicious URL and Intrusion Detection using Machine Learning
Published 2024“…Experimental results demonstrated that the ML algorithms were able to achieve high accuracy in predicting website maliciousness and intrusion detection. …”
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Properties of simulated annealing and genetic algorithms for mapping data to multicomputers
Published 1997“…Some user parameters are included in the objective function and are architecture- or problem-dependent parameters. …”
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A utility-based algorithm for joint uplink/downlink scheduling in wireless cellular networks
Published 2012“…While most existing literature focuses on downlink-only or uplink-only scheduling algorithms, the proposed algorithm aims at ensuring a utility function that jointly captures the quality of service in terms of delay and channel quality on both links. …”
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Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives
Published 2021“…If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. …”
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A Novel Genetic Algorithm Optimized Adversarial Attack in Federated Learning for Android-Based Mobile Systems
Published 2025“…However, the increasing adoption of FL in these devices exposes them to adversarial attacks that can compromise user data and device security. Given that Android applications are frequent targets for malware, ensuring the integrity of FL-based malware detection systems is critical. …”
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A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks
Published 2022“…The latter is used to draw out load characteristics using daily intent-driven moments of user consumption actions. Besides micro-moment features extraction, we also experiment with a deep neural network architecture for efficient abnormality detection and classification. …”
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Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive survey
Published 2024“…As new <u>malware</u> gets introduced frequently by <u>malware developers</u>, it is very challenging to come up with comprehensive algorithms to detect this malware. There are many machine-learning and deep-learning algorithms have been developed by researchers. …”
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Assessing Factors Influencing Customers’ Adoption of AI-Based Voice Assistants
Published 2024“…It also provides implications for tech-managers and algorithm designers to build effective voice technology for superior user experience.…”
