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)

“…In this paper, a novel, and improved version of the Long Short-Term Memory (ILSTM) algorithm was proposed. The ILSTM is based on the novel integration of the chaotic butterfly optimization algorithm (CBOA) and particle swarm optimization (PSO) to improve the accuracy of the LSTM algorithm. …”

Binary encoding scheme. by Binh Thanh Dang (12368831)

Optimal image settings for object detection of Iba1 immunolabelled cells in DAB. by Danish M. Anwer (15383441)

Table_1_A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple... by Yangyang Liu (807797)

Data_Sheet_3_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.docx by Vishnu Ramasubramanian (4951354)

Data_Sheet_7_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.docx by Vishnu Ramasubramanian (4951354)

Data_Sheet_2_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.ZIP by Vishnu Ramasubramanian (4951354)

Data_Sheet_4_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.xlsx by Vishnu Ramasubramanian (4951354)

Data_Sheet_6_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.docx by Vishnu Ramasubramanian (4951354)

Data_Sheet_1_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.PDF by Vishnu Ramasubramanian (4951354)

Data_Sheet_5_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.xlsx by Vishnu Ramasubramanian (4951354)

GeneScan profiles of human peripheral blood samples. by Nika Abdollahi (4966120)

Illustration of the BRIDES algorithm and the selection process. by Cindy Bouchard (6844979)

Subjects:

IGHV/J gene usage distribution of the AMR1 repertoire compared to the polyclonal background. by Nika Abdollahi (4966120)

IGHV/J gene usage distribution of the AMR2 repertoire compared to the polyclonal background. by Nika Abdollahi (4966120)

IGHV/J gene usage distribution of the AMR3 repertoire compared to the polyclonal background. by Nika Abdollahi (4966120)

Distributions of model parameters mirror differentially expressed ion channel genes. by Daniele Linaro (227790)

<i>OptRAM</i>: <i>In-silico</i> strain design via integrative regulatory-metabolic network modeling by Fangzhou Shen (5140994)

“…To address challenges in metabolic engineering, computational strain optimization algorithms based on genome-scale metabolic models have increasingly been used to aid in overproducing products of interest. …”