Computation time as a function of the number of variables on the random graph dataset. by Tomokaze Shiratori (9635271)

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F1 score of a given number of selected edges on the random graph dataset. by Tomokaze Shiratori (9635271)

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F1 score of edges selected through cross-validation on the random graph dataset. by Tomokaze Shiratori (9635271)

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Hyperparameter and model configurations. by Hongtao Wang (570131)

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Minimal data set definition. by Hui Yang (91136)

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3D CNN encoder. by Xueliang Guo (4797057)

“…The development of the 3D CNN model utilizes the ADAM optimization algorithm to facilitate the training process. …”

Data Sheet 1_A novel lossless encoding algorithm for data compression–genomics data as an exemplar.pdf by Anas Al-okaily (20601458)

“…In this study, a novel encoding algorithm is proposed, motivated by the compression of DNA data and associated characteristics. …”

Mitochondrial toxic prediction of marine alga toxins using a predictive model based on feature coupling and ensemble learning algorithms by Guangyin Jia (21075537)

“…By comparing 8 machine learning algorithms and using a weighted soft voting method to integrate the two optimal algorithms, we established 108 prediction models and identified the best ensemble learning model MACCS_LK for screening and defining its application domain. …”

Table 2_A novel lossless encoding algorithm for data compression–genomics data as an exemplar.xlsx by Anas Al-okaily (20601458)

“…In this study, a novel encoding algorithm is proposed, motivated by the compression of DNA data and associated characteristics. …”

Table 4_A novel lossless encoding algorithm for data compression–genomics data as an exemplar.xlsx by Anas Al-okaily (20601458)

“…In this study, a novel encoding algorithm is proposed, motivated by the compression of DNA data and associated characteristics. …”

Table 3_A novel lossless encoding algorithm for data compression–genomics data as an exemplar.xlsx by Anas Al-okaily (20601458)

“…In this study, a novel encoding algorithm is proposed, motivated by the compression of DNA data and associated characteristics. …”

Table 1_A novel lossless encoding algorithm for data compression–genomics data as an exemplar.xlsx by Anas Al-okaily (20601458)

“…In this study, a novel encoding algorithm is proposed, motivated by the compression of DNA data and associated characteristics. …”

Table 5_A novel lossless encoding algorithm for data compression–genomics data as an exemplar.xlsx by Anas Al-okaily (20601458)

“…In this study, a novel encoding algorithm is proposed, motivated by the compression of DNA data and associated characteristics. …”

Table 6_A novel lossless encoding algorithm for data compression–genomics data as an exemplar.xlsx by Anas Al-okaily (20601458)

“…In this study, a novel encoding algorithm is proposed, motivated by the compression of DNA data and associated characteristics. …”

CNOT encoded features and CZ encoded features. by Danish ul Khairi (19660534)

“…The experimental results align with the theoretical analysis: the CZ-based NNQEv2 model, when compared to the CNOT-based NNQEv1, consistently showed more stable training dynamics and yielded tighter confidence intervals in cross-validation. …”

Figure 1 from DeePathNet: A Transformer-Based Deep Learning Model Integrating Multiomic Data with Cancer Pathways by Zhaoxiang Cai (20445441)

Calculation algorithm implementing the proposed model. by Landry Djopkop Kouanang (17131748)

Calculation algorithm implementing the proposed model. by Landry Djopkop Kouanang (17131748)

Operational mechanism diagram of deep reinforcement learning retrieval optimization module, based on the user interaction retrieval strategy optimization process, encodes the user... by Yali Chen (24494)

Comparison of Forecasting Models in Terms of Accuracy and Efficiency. by Nasir Nauman (22272299)

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