Parameter setting for LSTM. 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. …”

LITNET-2020 data splitting approach. 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. …”

Transformation of symbolic features in NSL-KDD. 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. …”

Parameters of the experiments. by Enrico Zardini (17382523)

“…Instead, for the classical counterpart, a performance enhancement with respect to the base models has already been proven. In this paper, we propose and evaluate the idea of exploiting a quantum locality technique to reduce the size and improve the performance of QML models. …”

Quantum pipeline workflow overview. by Enrico Zardini (17382523)

“…Instead, for the classical counterpart, a performance enhancement with respect to the base models has already been proven. In this paper, we propose and evaluate the idea of exploiting a quantum locality technique to reduce the size and improve the performance of QML models. …”

DataSheet_1_Patient-Level Effectiveness Prediction Modeling for Glioblastoma Using Classification Trees.docx by Tine Geldof (8380125)

“…Secondly, a classification tree algorithm was trained and validated for dividing individual patients into treatment response and non-response groups. …”

DataSheet_1_Histopathology image classification: highlighting the gap between manual analysis and AI automation.pdf by Refika Sultan Doğan (17799677)

“…Our findings show that artificial intelligence-based systems can achieve 0.91 and 0.97 accuracy in binary and multi-class classifications. …”

Table_1_Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning.docx by Hyo-jae Lee (11780051)

“…Objective<p>This study was conducted in order to investigate the feasibility of using radiomics analysis (RA) with machine learning algorithms based on breast magnetic resonance (MR) images for discriminating malignant from benign MR-detected additional lesions in patients with primary breast cancer.…”

Table_1_Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning.docx by Hyo-jae Lee (11780051)

“…Objective<p>This study was conducted in order to investigate the feasibility of using radiomics analysis (RA) with machine learning algorithms based on breast magnetic resonance (MR) images for discriminating malignant from benign MR-detected additional lesions in patients with primary breast cancer.…”

Super.Complex identifies likely protein complexes within a PPI network using a distributed supervised AutoML method. by Meghana Venkata Palukuri (11890672)

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

“…<br> <br>Conclusion<br><br>The study concludes that the habitat variable, used in isolation, is insufficient to create a safe and reliable mushroom toxicity classification model. The consistent accuracy of 70.28% does not represent a flaw in the SVM. algorithm, but rather the predictive performance ceiling of the feature itself, whose simplicity and class overlap limit the model's discriminatory ability. …”

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

“…This work presents an efficient pipeline for binary and subtype classification of acute lymphoblastic leukemia. …”

GSE96058 information. by Sepideh Zununi Vahed (9861298)

“…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …”

The performance of classifiers. by Sepideh Zununi Vahed (9861298)

“…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …”

Image1_Benchmark of Data Processing Methods and Machine Learning Models for Gut Microbiome-Based Diagnosis of Inflammatory Bowel Disease.eps by Ryszard Kubinski (12105983)

“…In order to reduce time until diagnosis and improve patient wellbeing, machine learning algorithms capable of diagnosing IBD from the gut microbiome’s composition are currently being explored. …”

DataSheet_1_Deep Learning-Based Mapping of Tumor Infiltrating Lymphocytes in Whole Slide Images of 23 Types of Cancer.pdf by Shahira Abousamra (9417853)

“…Our new TIL workflow also incorporates automated thresholding to convert model predictions into binary classifications to generate TIL maps. The new TIL models all achieve better performance with improvements of up to 13% in accuracy and 15% in F-score. …”

fraud_oracle.csv by Mohamed Ibrahim (17783625)

“…Results show that not all resampling techniques improve algorithm performance, but all feature selection methods do. …”

Processed dataset to train and test the WGAN-GP_IMOA_DA_Ensemble model by Ramya Chinnasamy (21633527)

“…A dynamic attention-based ensemble (DA_Ensemble) comprising Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Feedforward Neural Network (FNN) models is employed to boost classification performance. …”

Supplementary Material 8 by Nishitha R Kumar (19750617)

“…</li><li><b>Radial basis function kernel-support vector machine (RBF-SVM): </b>A more flexible version of SVM that uses a non-linear kernel to capture complex relationships in genomic data, improving classification accuracy.</li><li><b>Extra trees classifier: </b>This tree-based ensemble method enhances classification by randomly selecting features and thresholds, improving robustness in <i>E. coli</i> strain differentiation.…”

30-Meter Resolution Dataset of Abandoned and Reclaimed Croplands in Inner Mongolia, China (2000-2022) by Deji Wuyun (18440981)

“…This method enables precise classification of cultivation status and adopts a binary classification strategy with adaptive optimization, improving the efficiency of sample generation for the Random Forest algorithm. …”