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

الموضوعات:

Efficient Topology-Optimized Couplers for On-Chip Single-Photon Sources حسب Omer Yesilyurt (11513040)

"…Here we develop an adjoint topology optimization scheme to design high-efficiency couplers between a photonic waveguide and an SPS in hexagonal boron nitride (hBN). …"

Event-driven data flow processing. حسب Yixian Wen (12201388)

"…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …"

UK Biobank data wrangling statistics and results. حسب Simeone Marino (81051)

Flow diagram of the proposed model. حسب 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. …"

Experimental observation of gastruloids and shape quantification using LOCO-EFA. حسب Martijn A. de Jong (17896732)

Data_Sheet_1_A Data-Driven Framework for Identifying Intensive Care Unit Admissions Colonized With Multidrug-Resistant Organisms.docx حسب Çaǧlar Çaǧlayan (12253934)

"…</p>Materials and Methods<p>Leveraging data from electronic healthcare records and a unique MDRO universal screening program, we developed a data-driven modeling framework to predict MRSA, VRE, and CRE colonization upon intensive care unit (ICU) admission, and identified the associated socio-demographic and clinical factors using logistic regression (LR), random forest (RF), and XGBoost algorithms. …"

Confusion matrix. حسب Yixian Wen (12201388)

"…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …"

Parameter settings. حسب Yixian Wen (12201388)

"…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …"

Dynamic resource allocation process. حسب Yixian Wen (12201388)

"…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …"

Thesis-RAMIS-Figs_Slides حسب Felipe Santibañez-Leal (10967991)

"…In this direction, the option of estimating the statistics of the model directly from the training image (performing a refined pattern search instead of simulating data) is a very promising.<br><br>Finally, although the developed concepts, ideas and algorithms have been developed for inverse problems in geostatistics, the results are applicable to a wide range of disciplines where similar sampling problems need to be faced, included but not limited to design of communication networks, optimal integration and communication of swarms of robots and drones, remote sensing.…"

ECE6327_SmartGrid حسب Xingpeng Li (11825663)

Harnessing Natural Modularity of Metabolism with Goal Attainment Optimization to Design a Modular Chassis Cell for Production of Diverse Chemicals حسب Sergio Garcia (3423011)

"…In this study, we developed the goal attainment formulation compatible with optimization algorithms that guarantee solution optimality. …"

Harnessing Natural Modularity of Metabolism with Goal Attainment Optimization to Design a Modular Chassis Cell for Production of Diverse Chemicals حسب Sergio Garcia (3423011)

"…In this study, we developed the goal attainment formulation compatible with optimization algorithms that guarantee solution optimality. …"

Image 1_A multimodal AI-driven framework for cardiovascular screening and risk assessment in diverse athletic populations: innovations in sports cardiology.png حسب Minjin Guo (22751300)

"…RSEE projects heterogeneous input data into an exertion-conditioned latent space, aligning model predictions with observed physiological variance and mitigating false positives by explicitly modeling the overlap between athletic remodeling and subclinical pathology.…"

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows حسب Pierre-Alexis DELAROCHE (22092572)

"…Reproducibility Framework Container Orchestration # Kubernetes deployment manifest for reproducible environment apiVersion: apps/v1 kind: Deployment metadata: name: energy-benchmark-pod spec: replicas: 1 selector: matchLabels: app: benchmark-runner template: metadata: labels: app: benchmark-runner spec: nodeSelector: hardware.profile: "high-performance" containers: - name: benchmark-container image: albumforge/energy-benchmark:v2.1.3 resources: requests: cpu: "8000m" memory: "16Gi" nvidia.com/gpu: 1 limits: cpu: "16000m" memory: "32Gi" env: - name: MEASUREMENT_PRECISION value: "high" - name: POWER_SAMPLING_RATE value: "1000" # 1kHz sampling Dependency Management FROM ubuntu:22.04-cuda11.8-devel RUN apt-get update && apt-get install -y \ perf-tools \ powertop \ intel-gpu-tools \ nvidia-smi \ cpupower \ msr-tools \ && rm -rf /var/lib/apt/lists/* COPY requirements.txt /opt/ RUN pip install -r /opt/requirements.txt Usage Examples and API Documentation Python Data Analysis Interface import pandas as pd import numpy as np from scipy import stats import matplotlib.pyplot as plt import seaborn as sns # Load dataset with optimized dtypes for memory efficiency df = pd.read_csv('ecological_benchmark_dataset.csv', dtype={'hardware_config': 'category', 'test_type': 'category'}) # Compute energy efficiency metrics df['energy_per_photo'] = df['energy_consumption_kwh'] / df['photo_count'] df['co2_per_gigabyte'] = df['co2_equivalent_g'] / df['total_volume_gb'] # Statistical analysis with confidence intervals local_energy = df[df['test_type'] == 'local_processing']['energy_consumption_kwh'] cloud_energy = df[df['test_type'] == 'cloud_processing']['energy_consumption_kwh'] t_stat, p_value = stats.ttest_ind(local_energy, cloud_energy) effect_size = (cloud_energy.mean() - local_energy.mean()) / np.sqrt((cloud_energy.var() + local_energy.var()) / 2) print(f"Statistical significance: p = {p_value:.2e}") print(f"Cohen's d effect size: {effect_size:.3f}") R Statistical Computing Environment library(tidyverse) library(lme4) # Linear mixed-effects models library(ggplot2) library(corrplot) # Load and preprocess data df <- read_csv("ecological_benchmark_dataset.csv") %>% mutate( test_type = factor(test_type), hardware_config = factor(hardware_config), log_energy = log(energy_consumption_kwh), efficiency_ratio = energy_consumption_kwh / processing_time_sec ) # Mixed-effects regression model accounting for hardware heterogeneity model <- lmer(log_energy ~ test_type + log(photo_count) + (1|hardware_config), data = df) # Extract model coefficients with confidence intervals summary(model) confint(model, method = "Wald") Advanced Analytics and Machine Learning Integration Predictive Modeling Framework from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor from sklearn.model_selection import cross_val_score, GridSearchCV from sklearn.preprocessing import StandardScaler, LabelEncoder from sklearn.metrics import mean_absolute_error, r2_score # Feature engineering pipeline def create_feature_matrix(df): features = df[['photo_count', 'avg_file_size_mb', 'total_volume_gb']].copy() # Polynomial features for capturing non-linear relationships features['photo_count_squared'] = features['photo_count'] ** 2 features['size_volume_interaction'] = features['avg_file_size_mb'] * features['total_volume_gb'] # Hardware configuration encoding le = LabelEncoder() features['hardware_encoded'] = le.fit_transform(df['hardware_config']) return features # Energy consumption prediction model X = create_feature_matrix(df) y = df['energy_consumption_kwh'] # Hyperparameter optimization param_grid = { 'n_estimators': [100, 200, 500], 'max_depth': [10, 20, None], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4] } rf_model = RandomForestRegressor(random_state=42) grid_search = GridSearchCV(rf_model, param_grid, cv=5, scoring='neg_mean_absolute_error') grid_search.fit(X, y) print(f"Best cross-validation score: {-grid_search.best_score_:.6f}") print(f"Optimal hyperparameters: {grid_search.best_params_}") Carbon Footprint Calculation Methodology Emission Factor Coefficients Carbon intensity calculations employ region-specific emission factors from the International Energy Agency (IEA) database: EMISSION_FACTORS = { 'EU_AVERAGE': 0.276, # kg CO₂/kWh (European Union average 2024) 'FRANCE': 0.057, # kg CO₂/kWh (Nuclear-dominant grid) 'GERMANY': 0.485, # kg CO₂/kWh (Coal transition period) 'NORWAY': 0.013, # kg CO₂/kWh (Hydroelectric dominant) 'GLOBAL_AVERAGE': 0.475 # kg CO₂/kWh (Global weighted average) } def calculate_carbon_footprint(energy_kwh: float, region: str = 'EU_AVERAGE') -> float: """ Calculate CO₂ equivalent emissions using lifecycle assessment methodology Args: energy_kwh: Energy consumption in kilowatt-hours region: Geographic region for emission factor selection Returns: CO₂ equivalent emissions in grams """ emission_factor = EMISSION_FACTORS.get(region, EMISSION_FACTORS['GLOBAL_AVERAGE']) co2_kg = energy_kwh * emission_factor return co2_kg * 1000 # Convert to grams Citation and Attribution This dataset is released under Creative Commons Attribution 4.0 International (CC BY 4.0) license. …"