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

Proposed Algorithm. by Hend Bayoumi (22693738)

“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”

Comparisons between ADAM and NADAM optimizers. by Hend Bayoumi (22693738)

“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”

The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

An example of a type of texture of binary preferred/non-preferred ranges of raw vegetation index values type feature. by Elise Varaela Voltura (17592996)

IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

Sorting: Data Structures using C++ by Chandra Ram (13505545)

“…Among the most prominent are Bubble Sort, which repeatedly compares adjacent elements and swaps them if they are out of order, resulting in a gradual "bubbling" of the largest elements to the end; Selection Sort, which systematically selects the smallest (or largest) element from the unsorted portion and places it in its correct position; Insertion Sort which builds the sorted array one element at a time by inserting each new element into its appropriate position within the already sorted portion; Merge Sort, a divide-and-conquer algorithm that recursively splits the array into smaller subarrays, sorts them, and then merges them to produce a sorted array; Quick Sort, which partitions the array around a pivot and recursively sorts the partitions to achieve high efficiency on average; and Heap Sort, which utilizes a binary heap data structure to create a heap and then repeatedly extracts the maximum element to build a sorted array. …”

Concept of the Supporting Substructure in Intermetallics: V, Nb, and Ta Binary Compounds and Alloys by Olga A. Blatova (5081573)

“…A novel approach to the modeling and analysis of crystal structures of binary intermetallic compounds is proposed, which is based on the concept of a supporting substructure (supporting net). …”

Concept of the Supporting Substructure in Intermetallics: V, Nb, and Ta Binary Compounds and Alloys by Olga A. Blatova (5081573)

“…A novel approach to the modeling and analysis of crystal structures of binary intermetallic compounds is proposed, which is based on the concept of a supporting substructure (supporting net). …”

Concept of the Supporting Substructure in Intermetallics: V, Nb, and Ta Binary Compounds and Alloys by Olga A. Blatova (5081573)

“…A novel approach to the modeling and analysis of crystal structures of binary intermetallic compounds is proposed, which is based on the concept of a supporting substructure (supporting net). …”

Variable Selection and Estimation for Misclassified Binary Responses and Multivariate Error-Prone Predictors by Li-Pang Chen (9747423)

“…Typically, a main goal is to adopt predictors to characterize the primarily interested binary random variables. To model a binary response and predictors, parametric structures, such as logistic regression models or probit models, are perhaps commonly used approaches. …”

An illustrated example of Algorithm 1 (Fig 1). by Kathleen M. Jagodnik (10170617)

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)

An example binary tree for H-SuperVec(X): A hierarchical structure obtained by partitioning the class labels of each parent node into equal size subsets. by Dhananjay Kimothi (8575089)

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)

Data_Sheet_1_Crystal Structure Prediction of Binary Alloys via Deep Potential.PDF by Haidi Wang (9694004)

An Example of a WPT-MEC Network. by Hend Bayoumi (22693738)

“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”