The taxonomy of data augmentation. by Rodrigo Gutiérrez Benítez (19746443)

Subjects:

(HSCRC) encoding scheme. by R. Sakthivel (2589547)

“…<div><p>Loss-less data compression becomes the need of the hour for effective data compression and computation in VLSI test vector generation and testing in addition to hardware AI/ML computations. …”

Reverse engineering information flow using stimulus-encoding and stimulus-transfer estimation algorithms. by Gabriel Matías Lorenz (21094672)

Minimal data set definition. by Hui Yang (91136)

Subjects:

Performance of machine learning algorithms. by Wenxue Liu (6877754)

Algorithmic experimental parameter design. by Chuanxi Xing (20141665)

“…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”

Schematic diagram of the pillar encoding. by Xinpeng Yao (18882573)

Spatial spectrum of the 100th frame under array distortion conditions (20 elements). by Gaoxiang Xing (21755689)

ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

LPCRC-Scheme- 2 encoding. by R. Sakthivel (2589547)

“…<div><p>Loss-less data compression becomes the need of the hour for effective data compression and computation in VLSI test vector generation and testing in addition to hardware AI/ML computations. …”

(EBRGC)-Scheme- 3 encoding. by R. Sakthivel (2589547)

“…<div><p>Loss-less data compression becomes the need of the hour for effective data compression and computation in VLSI test vector generation and testing in addition to hardware AI/ML computations. …”

(HSCRC) Scheme-1 encoding. by R. Sakthivel (2589547)

“…<div><p>Loss-less data compression becomes the need of the hour for effective data compression and computation in VLSI test vector generation and testing in addition to hardware AI/ML computations. …”

Simulated solution and the original spectral data of the actual water sample. by Wang hongmei (22598690)

Comparison of PSNR values of different algorithms. by Xiaojun Wang (50332)

Subjects:

Changes in FID values of different algorithms. by Xiaojun Wang (50332)

Subjects:

Comparison of SSIM values of different algorithms. by Xiaojun Wang (50332)

Subjects:

Changes in loss values for different algorithms. by Xiaojun Wang (50332)

Subjects:

Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things by Ashok Kumar K (21441108)

“…This research presents Data-Driven Intrusion Detection System in Internet of Things utilizing Optimized Bayesian Regularization-Back Propagation Neural Network (DIDS-BRBPNN-BBWOA-IoT) to overcome these issues. …”

Overview of the C&CG method. by Tianhe Sun (21145341)

“…The model is solved iteratively using the column generation algorithm and strong duality theory. Case studies on a Northeast China power grid demonstrate that, by optimally configuring generation and storage capacity guided by flexibility and other indicators, the proposed method reduces curtailment/load shedding costs and system flexibility insufficiency probability by 45% and 4.3% respectively. …”

Spatial spectrum estimation for three algorithms. by Chuanxi Xing (20141665)

“…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”