Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things by Ashok Kumar K (21441108)

“…Hence, Binary Black Widow Optimization Algorithm (BBWOA) is proposed in this manuscript to improve the BRBPNN classifier that detects intrusion precisely. …”

Homomorphic binary tree. by Jianhua He (341366)

Bayesian sequential design for sensitivity experiments with hybrid responses by Yuxia Liu (1779592)

“…To deal with the problem of complex computation involved in searching for optimal designs, fast algorithms are presented using two strategies to approximate the optimal criterion, denoted as SI-optimal design and Bayesian D-optimal design, respectively. …”

Flow diagram of the proposed model. by Uğur Ejder (22683228)

“…<div><p>Machine learning models are increasingly applied to assisted reproductive technologies (ART), yet most studies rely on conventional algorithms with limited optimization. This proof-of-concept study investigates whether a hybrid Logistic Regression–Artificial Bee Colony (LR–ABC) framework can enhance predictive performance in in vitro fertilization (IVF) outcomes while producing interpretable, hypothesis-driven associations with nutritional and pharmaceutical supplement use. …”

Data_Sheet_1_A Global Optimizer for Nanoclusters.PDF by Maya Khatun (7437011)

“…In this recursive algorithm, an n-sized cluster is built from the geometries of n−1 clusters. …”

Data_Sheet_1_Posiform planting: generating QUBO instances for benchmarking.pdf by Georg Hahn (12530469)

“…<p>We are interested in benchmarking both quantum annealing and classical algorithms for minimizing quadratic unconstrained binary optimization (QUBO) problems. …”

Table_1_An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.pdf by Muhammad Awais (263096)

“…Next, a hybrid feature extraction approach is presented leveraging transfer learning from selected deep neural network models, InceptionV3 and DenseNet201, to extract comprehensive feature sets. To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. …”

Sample image for illustration. by Indhumathi S. (19173013)

“…Additionally, we provide a solution to determine the optimal block size for describing nonlinear regions, thereby enhancing resolution. …”

Comparison analysis of computation time. by Indhumathi S. (19173013)

“…Additionally, we provide a solution to determine the optimal block size for describing nonlinear regions, thereby enhancing resolution. …”

Process flow diagram of CBFD. by Indhumathi S. (19173013)

“…Additionally, we provide a solution to determine the optimal block size for describing nonlinear regions, thereby enhancing resolution. …”

Precision recall curve. by Indhumathi S. (19173013)

“…Additionally, we provide a solution to determine the optimal block size for describing nonlinear regions, thereby enhancing resolution. …”

Quadratic polynomial in 2D image plane. by Indhumathi S. (19173013)

“…Additionally, we provide a solution to determine the optimal block size for describing nonlinear regions, thereby enhancing resolution. …”

Image1_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF by Sizhuo Yu (11429743)

“…Its current hardware implementation relies on D-Wave’s Quantum Processing Units, which are limited in terms of number of qubits and architecture while being restricted to solving quadratic unconstrained binary optimization (QUBO) problems. …”

Image3_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF by Sizhuo Yu (11429743)

“…Its current hardware implementation relies on D-Wave’s Quantum Processing Units, which are limited in terms of number of qubits and architecture while being restricted to solving quadratic unconstrained binary optimization (QUBO) problems. …”

Image2_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF by Sizhuo Yu (11429743)

“…Its current hardware implementation relies on D-Wave’s Quantum Processing Units, which are limited in terms of number of qubits and architecture while being restricted to solving quadratic unconstrained binary optimization (QUBO) problems. …”

DataSheet1_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.pdf by Sizhuo Yu (11429743)

“…Its current hardware implementation relies on D-Wave’s Quantum Processing Units, which are limited in terms of number of qubits and architecture while being restricted to solving quadratic unconstrained binary optimization (QUBO) problems. …”

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…No segmented cell(s) occupied a space larger than 80x80 pixels, including the three-overlapping RBCs. As a result, the algorithm centred/padded each cell(s) within an 80x80 pixel-sized image, generating mask, cropped, and segmented images, all following a standardized naming convention that begins with the slide/smear number, followed by the patch number, and concludes with the (XYWH) coordinates. …”