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

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

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

Data for "Machine Learning-based Prediction of Passive Gears from Vessel Tracking Data in Small-Scale Multi-gear Fisheries" by Pamela Lattanzi (16454871)

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Flowchart of DQN algorithm. by Jiandong Qiu (20389944)

<b>Discrimination of gold deposit types based on big data analysis and machine learning techniques</b> by Lihao Feng (20837036)

Algorithmic experimental parameter design. by Chuanxi Xing (20141665)

“…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”

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

“…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”

Comparison of clustering algorithms (K-Means, MeanShift, etc.) based on the average Silhouette Score. by Manju M. S. (22793350)

Part of data. by Taochang Li (20642935)

Training data is a mix of straightforward and challenging cases. by Weheliye H. Weheliye (22022140)

Skeleton accuracy for test data subgroups and types of errors. by Weheliye H. Weheliye (22022140)

Visual representation of how Cell-TRACTR uses track queries to track cells and detect divisions. by Owen M. O’Connor (11965460)

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Example measurement and analysis units for landscape elements. by Jaewon Han (8660295)

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Risk element category diagram. by Yao Hu (3479972)

“…This article used these data to establish an LSTM model, which trained LSTM to identify potential risks and provide early warning by learning patterns and trends in historical data. …”

Supporting data for “Developing algorithms to optimize recommender system for self-regulated learning promotion in online contexts: a design-based study” by Long Zhang (11129139)

Comparison of tracking results with other mainstream methods. by Guoqing Zhang (151441)

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

Cell-TRACTR detects cells in the first frame at time t 0. Object queries predicted to be a “cell” are converted to track queries. For Track Group Denoising (TGD), random noise is a... by Owen M. O’Connor (11965460)

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Cells are initially detected in frame t₀ and then tracked to frame t₁. Object queries are processed in the first layer of the decoder for object detection. In... by Owen M. O’Connor (11965460)

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