The folder containing the code is provided as supportive documentation associated with this paper. by Quxi Kuang (22493270)

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Research data for paper: Efficient Event-based Delay Learning in Spiking Neural Networks by Balázs Mészáros (16890225)

“…Our method supports multiple spikes per neuron and introduces a delay learning algorithm that can, in contrast to previous methods, also be applied to recurrent Spiking Neural Networks. …”

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

Corporate network anomaly detection methodology utilizing machine learning algorithms by Nazerke Baisholan (22048322)

Research on Vehicle Detection Algorithms Based on Deep Learning in Complex Environments by Xiaodan Li (22309438)

The proposed algorithm of the outlier detection method. by Sunil Kumar (102321)

Comparison of target detection algorithms. by Diansheng Zhang (22640973)

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Comparison of detection performance of the algorithms. by Lin Jun (2187955)

Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm by Hussein Ali Bardan (21976208)

“…The goal of this </p><p dir="ltr">research is to combine state-of-the-art deep learning techniques with optimization algorithms to develop a precise </p><p dir="ltr">and efficient predictive system for melanoma detection. …”

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

QRS detection in ECG signal using pan-tompkins algorithm with R-peak detection. by Rida Nayyab (21519512)

Confusion Matrix for Subspace algorithm (3000 Learning Cycles) providing insight into the performance evaluation of Subspace algorithm. by Sadia Nazim (21440115)

Performance Metrics Comparison for Various Detection Algorithms. by Persiya J. (21743388)

Description of the dataset used for the machine learning algorithm. by Tri Ngo Quang (20390189)

Understanding the Behaviors and Detecting the Mental Health Symptoms Among Students Using Machine Learning Algorithms by Sangita Pokhrel (21020093)

Research progress on anomaly target detection algorithms for hyperspectral imagery by Baozhi Cheng (22754994)

“…Through systematic sorting and analysis, this paper summarizes the existing anomaly target detection algorithms in detail, and evaluates the key issues involved in HSI-ATD, future technological development directions (such as deep learning, multimodal, and real-time processing, etc), as well as the problems existing in the algorithms. …”

Hyperparameters of machine learning algorithms used grid search. by Mahade Hasan (20536430)

Results of different algorithms for ship detection and tracking. by Guoqing Zhang (151441)

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Machine learning algorithm validation performance comparison plot. by Hilman Nordin (20615178)

Performance comparison of mainstream algorithms. by Junjie Lu (160350)

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