Comparison between ILSTM and machine learning-based algorithms in binary classification. by Asmaa Ahmed Awad (16726315)

Comparison between ILSTM and deep learning-based algorithms in binary classification. by Asmaa Ahmed Awad (16726315)

MSE for ILSTM algorithm in binary classification. by Asmaa Ahmed Awad (16726315)

“…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”

Several loop patterns found in binary code. by Alba Pedro-Zapater (8600382)

Texas Rio Grande Valley Red–crowned Parrot predicted general use habitat distribution as determined via a consensus-based feature ensemble of 12 calibrated (binary presence/absence... by Elise Varaela Voltura (17592996)

Texas Rio Grande Valley Red–crowned Parrot predicted roost site habitat distribution as determined via a consensus-based feature ensemble of 12 calibrated (binary presence/absence)... by Elise Varaela Voltura (17592996)

Texas Rio Grande Valley Red–crowned Parrot predicted nest site habitat distribution as determined via a consensus-based feature ensemble of 12 calibrated (binary presence/absence)... by Elise Varaela Voltura (17592996)

Structural diagram of model. by Ji Seung Ryu (16187857)

The overlap of Texas Rio Grande Valley Red–crowned Parrot predicted general use, roost site, and nest site habitat distributions as determined using consensus-based feature ensembl... by Elise Varaela Voltura (17592996)

<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

Table_1_On the Origin of Biomolecular Networks.pdf by Heeralal Janwa (6574409)

Data_Sheet_1_On the Origin of Biomolecular Networks.PDF by Heeralal Janwa (6574409)

Table_2_On the Origin of Biomolecular Networks.pdf by Heeralal Janwa (6574409)

Image4_Automatic recognition of complementary strands: lessons regarding machine learning abilities in RNA folding.png by Simon Chasles (16927875)

Image1_Automatic recognition of complementary strands: lessons regarding machine learning abilities in RNA folding.png by Simon Chasles (16927875)

Image2_Automatic recognition of complementary strands: lessons regarding machine learning abilities in RNA folding.png by Simon Chasles (16927875)

Image3_Automatic recognition of complementary strands: lessons regarding machine learning abilities in RNA folding.png by Simon Chasles (16927875)

Fig 1 - by Dillion M. Fox (4043159)

Fig 2 - by Dillion M. Fox (4043159)

Random forest model performs better than support vector machine algorithms and when it primarily uses spontaneous photopic ERG of 60-s duration in humans. by Ramsés Noguez Imm (6658637)