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Total Articles : 10
Machine Learning Based Optical Fiber Fault Detection and Localization Using OTDR Signals
Abstract: Optical fiber networks provide high bandwidth, low transmission loss, and immunity to electromagnetic interference, making them fundamental to modern communication infrastructure. However, fiber faults caused by cable cuts, excessive bending, …
[This section belongs to Trends in Opto-electro & Optical Communication ]
Machine Learning Based Optimization of Optical Fiber Parameters for Minimizing Dispersion and Attenuation
Abstract: Optical fibers provide high-bandwidth and low-loss transmission for modern communication networks; however, attenuation and chromatic dispersion remain two important physical limitations affecting transmission distance, pulse integrity, and system …
[This section belongs to Trends in Opto-electro & Optical Communication ]
Thermal-Drift-Aware Adaptive Wavelength Stabilization Model for Silicon-Photonic Optical Communication Systems
Abstract: The increasing integration density and operating speed of silicon-photonic communication devices have intensified the influence of thermo-optic effects on wavelength stability, resonance alignment, optical transmission, and communication reliability. …
[This section belongs to Trends in Opto-electro & Optical Communication ]
Mirror-Symmetric NoC Topologies for 64-Core Arrays: Investigating Latency and Energy Trade-offs versus Mesh and Torus Interconnects for Block-Level Matrix Multiplication Workloads
Abstract: Modern chip multiprocessors increasingly rely on many-core arrays to accelerate dense linear algebra kernels such as block-level general matrix multiplication (GEMM), where inter-tile communication often rivals computation cost. Network-on-Chip (NoC) …
[This section belongs to Journal of Operating Systems Development & Trends ]
AI-PASS: An AI-Based Predictive, Adaptive and Energy Aware Task Scheduling Framework for Heterogeneous Parallel Computing
Abstract: Heterogeneous parallel computing systems are made up of a combination of multicore CPUs, GPUs, and specialized devices to boost computational power and utilize resources. But changes in workload intensity and processor capability, energy consumption …
[This section belongs to Journal of Operating Systems Development & Trends ]
Next-Generation OS Architectures for Low-Power Smart Hardware: A Comprehensive Review
Abstract: The ubiquity of smart devices has dramatically transformed how humans engage with technology. Devices with high characteristics do use standard operating systems like Linux, but those with low characteristics cannot do so because of extreme re-source …
[This section belongs to Journal of Operating Systems Development & Trends ]
AI-Driven Malware Detection in Linux: A Survey of Techniques, Challenges, and Future Directions
Abstract: With the rapid growth of Linux-based systems across cloud computing, Internet of Things (IoT) devices, servers, and embedded platforms, Linux environments have increasingly become attractive targets for sophisticated malware attacks. This survey …
[This section belongs to Journal of Operating Systems Development & Trends ]
Comparative Analysis of Classical, Metaheuristic, and Reinforcement Learning Approaches to Load Balancing in Cloud Computing
Abstract: Efficient load balancing remains one of the most critical challenges in cloud computing, particularly as modern cloud environments rely on heterogeneous virtual machines (VMs) with varying computational capabilities. The effective distribution of …
[This section belongs to Journal of Operating Systems Development & Trends ]
Sentiment Analysis: A Comprehensive Review of NLP Techniques on Social Media Data
Abstract: The growth of social media platforms has created a large volume of written content generated by users via interactions. The data consists of individuals’ opinions and feelings expressed about products and services and political and social issues. …
[This section belongs to International Journal of Algorithms Design and Analysis Review ]
Misinformation Detection and Analysis of Social Media Platforms Using Machine Learning and Deep Learning
Abstract: Social media platforms have become deeply embedded in everyday life, reshaping how people produce, distribute, and engage with information. Although these platforms facilitate quick communication, they have also developed into important avenues for …
[This section belongs to International Journal of Algorithms Design and Analysis Review ]
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