Comparison of optical and silicon-based neural network implementations. by Reyhane Ahmadi (14857363)

Subjects:

Comparison of power consumption in optical and silicon-based neural network implementations. by Reyhane Ahmadi (14857363)

Subjects:

Accuracy of different electrical and optical neural networks for various input noise levels. by Reyhane Ahmadi (14857363)

Subjects:

Accuracy of different electrical and optical neural network on Caltech and ETH-80 data sets. by Reyhane Ahmadi (14857363)

Subjects:

Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code by Goutami Sooda (20714729)

The flowchart of QLDE algorithm. by Guanqun Wang (705958)

“…This paper proposes a customer segmentation framework within the realm of digital marketing, which integrates a reinforcement learning-based differential evolution algorithm with <i>K</i>-means clustering using dimensionality reduction techniques to address challenges in the customer segmentation process. …”

The simulation of OSCNN involves loading data from a laser and SLM and simulating different optical free space components. by Reyhane Ahmadi (14857363)

Subjects:

Schematic diagram of <i>K</i>-means algorithm. by Guanqun Wang (705958)

“…This paper proposes a customer segmentation framework within the realm of digital marketing, which integrates a reinforcement learning-based differential evolution algorithm with <i>K</i>-means clustering using dimensionality reduction techniques to address challenges in the customer segmentation process. …”

Data and code resources. by Sam Hall-McMaster (10343795)

Component connection topology of LSTM. by Riaz Ud Din (21101290)

Description of the two custom bacteria odor datasets. by Jia Liu (41593)

Specifications of the involved sensors of the custom e-nose. by Jia Liu (41593)

Different implementations on MNIST object detection accuracy (%) with input image size. by Reyhane Ahmadi (14857363)

Subjects:

Outline of OSCNN. by Reyhane Ahmadi (14857363)

Subjects:

Gabor filters are created as sine wave filters in four different directions, namely 0, <i>π</i>/2, <i>π</i>, and 3<i>π</i>/4 degrees. by Reyhane Ahmadi (14857363)

Subjects:

OSCNN accuracy with different feature extractors. by Reyhane Ahmadi (14857363)

Subjects:

The parameter setting of the evolutionary neural network. by Wenbing Shi (5806160)

Subjects:

The genome coding scheme. by Wenbing Shi (5806160)

Subjects:

Depicts the schematic of the algorithm. by Dong Hyeok Choi (12562048)

Subjects: “…chemical constituent elements…”

The proportion of different customer categories. by Guanqun Wang (705958)

“…This paper proposes a customer segmentation framework within the realm of digital marketing, which integrates a reinforcement learning-based differential evolution algorithm with <i>K</i>-means clustering using dimensionality reduction techniques to address challenges in the customer segmentation process. …”