Optimization-Driven Steganographic System Based on Fused Maps and Blowfish Encryption by Ahmed Aljughaiman (21793526)

Pseudo-code for encrypting high-dimensional medical data attributes. by Yonggang Huang (223155)

Access control pseudo-code. by Yonggang Huang (223155)

Number of features selected. by Islam Zada (21755819)

“…In this paper, a Lean-based hybrid Intrusion Detection framework using Particle Swarm Optimization and Genetic Algorithm (PSO-GA) to select the features and Extreme Learning Machine and Bootstrap Aggregation (ELM-BA) to classify the features is introduced. …”

Data Encryption and Compression by Chandra Shekhar Ram (20325126)

“…Advanced compression techniques, including <b>Huffman coding</b> and <b>Lempel-Ziv algorithms</b>, are covered to illustrate their practical applications in modern data systems. …”

Correlation heatmap of selected features. by Islam Zada (21755819)

“…In this paper, a Lean-based hybrid Intrusion Detection framework using Particle Swarm Optimization and Genetic Algorithm (PSO-GA) to select the features and Extreme Learning Machine and Bootstrap Aggregation (ELM-BA) to classify the features is introduced. …”

Convergence curve of the DBO algorithm. by Ma Haohao (22177538)

“…The improved Dung Beetle Optimization algorithm, Back Propagation Neural Network, Finite Element Analysis, and Response Surface Methodology provide a strong guarantee for the selection of robot polishing process parameters. …”

Image 2_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

“…Classical exchange-type algorithms from optimal design theory can be employed for this purpose. …”

Image 8_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

“…Classical exchange-type algorithms from optimal design theory can be employed for this purpose. …”

Image 7_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

“…Classical exchange-type algorithms from optimal design theory can be employed for this purpose. …”

Image 3_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

“…Classical exchange-type algorithms from optimal design theory can be employed for this purpose. …”

Image 10_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

“…Classical exchange-type algorithms from optimal design theory can be employed for this purpose. …”

Image 4_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

“…Classical exchange-type algorithms from optimal design theory can be employed for this purpose. …”

Image 6_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

“…Classical exchange-type algorithms from optimal design theory can be employed for this purpose. …”

Image 5_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

“…Classical exchange-type algorithms from optimal design theory can be employed for this purpose. …”

Image 1_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

“…Classical exchange-type algorithms from optimal design theory can be employed for this purpose. …”

Image 9_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

“…Classical exchange-type algorithms from optimal design theory can be employed for this purpose. …”

Selection of Optimum Clusters for the Sample Microscopic Image using Partition Entropy. by Claudia Barile (20678698)

FORCE training spiking LIF and LIF-matched rate networks. by Thomas Robert Newton (21756023)

<b>Force-Position-Speed Planning and Roughness rediction for Robotic Polishing</b> by Ma Haohao (19780875)

“…The improved dung beetle optimization algorithm, back propagation neural network, finite element analysis and response surface method provide a strong guarantee for the selection of robotic polishing process parameters. …”