Multimodal feature fusion and ensemble learning for non-intrusive occupancy monitoring using smart meters حسب Sakib Mahmud (15302404)

"…We combine features from all three modes of MMF-NIOM to achieve a state-of-the-art non-intrusive occupancy classification performance of 91.5 % accuracy and 91.5 % f1-score, approximately, by an ensemble of fine-tuned classifiers on the electricity consumption & occupancy (ECO) dataset. The proposed method is sustainable, robust, adaptable to various households, and can be mass-implemented within smart meters at a much lower cost and effort compared to the traditional internet of things (IoT)-based intrusive systems.…"

Con-Detect: Detecting adversarially perturbed natural language inputs to deep classifiers through holistic analysis حسب Hassan, Ali

"…We thus propose Con-Detect—a Contribution based Detection method—for detecting adversarial attacks against NLP classifiers. …"
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Con-Detect: Detecting Adversarially Perturbed Natural Language Inputs to Deep Classifiers Through Holistic Analysis حسب Hassan Ali (3348749)

"…We thus propose Con-Detect—a Contribution based Detection method—for detecting adversarial attacks against NLP classifiers. …"

ECG Signal Reconstruction on the IoT-Gateway and Efficacy of Compressive Sensing Under Real-Time Constraints حسب Mohammed Al Disi (16855407)

"…Compressive sensing (CS) has been explored as a method to extend the battery lifetime of medical wearable devices. …"