Zobrazeno 1 - 10
of 284
pro vyhledávání: '"A. Rahouti"'
As the boundaries of human computer interaction expand, Generative AI emerges as a key driver in reshaping user interfaces, introducing new possibilities for personalized, multimodal and cross-platform interactions. This integration reflects a growin
Externí odkaz:
http://arxiv.org/abs/2411.10234
Autism Spectrum Disorder (ASD) is often underdiagnosed in females due to gender-specific symptom differences overlooked by conventional diagnostics. This study evaluates machine learning models, particularly Random Forest and convolutional neural net
Externí odkaz:
http://arxiv.org/abs/2411.05880
Autor:
Yakubu, Paul Badu, Owusu, Evans, Santana, Lesther, Rahouti, Mohamed, Chehri, Abdellah, Xiong, Kaiqi
Denial of Service (DoS) attacks pose a significant threat in the realm of AI systems security, causing substantial financial losses and downtime. However, AI systems' high computational demands, dynamic behavior, and data variability make monitoring
Externí odkaz:
http://arxiv.org/abs/2411.03355
Thanks to the high potential for profit, trading has become increasingly attractive to investors as the cryptocurrency and stock markets rapidly expand. However, because financial markets are intricate and dynamic, accurately predicting prices remain
Externí odkaz:
http://arxiv.org/abs/2410.06935
Adjusting the control actions of a wheeled robot to eliminate oscillations and ensure smoother motion is critical in applications requiring accurate and soft movements. Fuzzy controllers enable a robot to operate smoothly while accounting for uncerta
Externí odkaz:
http://arxiv.org/abs/2409.17161
Autor:
Jagatheesaperumal, Senthil Kumar, Rahouti, Mohamed, Alfatemi, Ali, Ghani, Nasir, Quy, Vu Khanh, Chehri, Abdellah
Publikováno v:
IEEE Internet of Things Magazine, Year: 2024, Volume: 7, Issue: 5
Federated Learning (FL) represents a paradigm shift in machine learning, allowing collaborative model training while keeping data localized. This approach is particularly pertinent in the Industrial Internet of Things (IIoT) context, where data priva
Externí odkaz:
http://arxiv.org/abs/2409.02127
Autor:
Aledhari, Mohammed, Rahouti, Mohamed
Gene and RNA editing methods, technologies, and applications are emerging as innovative forms of therapy and medicine, offering more efficient implementation compared to traditional pharmaceutical treatments. Current trends emphasize the urgent need
Externí odkaz:
http://arxiv.org/abs/2409.09057
The rapid growth of the stock market has attracted many investors due to its potential for significant profits. However, predicting stock prices accurately is difficult because financial markets are complex and constantly changing. This is especially
Externí odkaz:
http://arxiv.org/abs/2407.11786
Automated scraping stands out as a common method for collecting data in deep learning models without the authorization of data owners. Recent studies have begun to tackle the privacy concerns associated with this data collection method. Notable appro
Externí odkaz:
http://arxiv.org/abs/2406.02883
Autor:
Martinez, Fernando, Mapkar, Mariyam, Alfatemi, Ali, Rahouti, Mohamed, Xin, Yufeng, Xiong, Kaiqi, Ghani, Nasir
Distributed Denial of Service (DDoS) attacks pose an increasingly substantial cybersecurity threat to organizations across the globe. In this paper, we introduce a new deep learning-based technique for detecting DDoS attacks, a paramount cybersecurit
Externí odkaz:
http://arxiv.org/abs/2406.02632