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Your search keyword '"Ullah, Zahid"' showing total 519 results
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519 results on '"Ullah, Zahid"'

7. Ensemble CNNs for Breast Tumor Classification

Author

Farooq, Muhammad Umar, Ullah, Zahid, and Gwak, Jeonghwan

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

To improve the recognition ability of computer-aided breast mass classification among mammographic images, in this work we explore the state-of-the-art classification networks to develop an ensemble mechanism. First, the regions of interest (ROIs) are obtained from the original dataset, and then three models, i.e., XceptionNet, DenseNet, and EfficientNet, are trained individually. After training, we ensemble the mechanism by summing the probabilities outputted from each network which enhances the performance up to 5%. The scheme has been validated on a public dataset and we achieved accuracy, precision, and recall 88%, 85%, and 76% respectively., Comment: SMA 2021: The 10th International Conference on Smart Media and Applications Gunsan Saemangeum Convention Center and Kunsan National University Gunsan-si, South Korea, September 9-11, 2021

Published
2023

10. Speed vs. efficiency: A framework for high-frequency trading algorithms on FPGA using Zynq SoC platform

Author

Abbas Ali, Abdullah Shah, Azaz Hassan Khan, Malik Umar Sharif, Zaka Ullah Zahid, Rabia Shahid, Tariqullah Jan, and Mohammad Haseeb Zafar

Subjects
System-on-Chip, High-frequency trading, Xilinx Zynq-7000, HW/SW co-design, Technical indicators, Cryptocurrencies, Engineering (General). Civil engineering (General), TA1-2040
Abstract

Software-based technical indicators have been widely used for the stock market forecasting, aiming to predict market direction. Even though many algorithms for the software based technical indicators are presented, there are almost no hardware implementations reported in the literature. In this paper, the hardware implementation is presented for three commonly used technical indicators: Moving Average Convergence/Divergence (MACD), Relative Strength Index (RSI), and Aroon. Latency evaluation is conducted for Bitcoin and Ethereum within a single-day timeframe, utilizing the Xilinx Zynq-7000 programmable SoC XC7Z020-CLG484-1 platform.Additionally, various hardware/software (HW/SW) partitioning strategies are explored to leverage the flexibility of software alongside the performance advantages of hardware via the Zynq SoC platform. The results show that the best performing technical indicator is MACD with a speedup of 30 times over its software only counterpart. Furthermore, a hybrid design integrating multiple technical indicators is proposed, pairing MACD with RSI due to their competitive throughput values, differing by only 0.38 microseconds. This hybrid approach capitalizes on the parallel processing capabilities of hardware, enabling multiple systems to operate simultaneously.

Published
2024
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20. Highly Accurate and Reliable Wireless Network Slicing in 5th Generation Networks: A Hybrid Deep Learning Approach

Author

Khan, Sulaiman, Khan, Suleman, Ali, Yasir, Khalid, Muhammad, Ullah, Zahid, and Mumtaz, Shahid

Subjects
Computer Science - Networking and Internet Architecture, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

In the current era, the next-generation networks like 5th generation (5G) and 6th generation (6G) networks require high security, low latency with a high reliable standards and capacity. In these networks, reconfigurable wireless network slicing is considered as one of the key elements for 5G and 6G networks. A reconfigurable slicing allows the operators to run various instances of the network using a single infrastructure for a better quality of services (QoS). The QoS can be achieved by reconfiguring and optimizing these networks using Artificial intelligence and machine learning algorithms. To develop a smart decision-making mechanism for network management and restricting network slice failures, machine learning-enabled reconfigurable wireless network solutions are required. In this paper, we propose a hybrid deep learning model that consists of a convolution neural network (CNN) and long short term memory (LSTM). The CNN performs resource allocation, network reconfiguration, and slice selection while the LSTM is used for statistical information (load balancing, error rate etc.) regarding network slices. The applicability of the proposed model is validated by using multiple unknown devices, slice failure, and overloading conditions. The overall accuracy of 95.17% is achieved by the proposed model that reflects its applicability.

Published
2021

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