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Artificial Intelligence (AI) based machine learning models predict glucose variability and hypoglycaemia risk in patients with type 2 diabetes on a multiple drug regimen who fast d...
Published 2020“…<h3>Objective</h3><p dir="ltr">To develop a machine-based algorithm from clinical and demographic data, physical activity and glucose variability to predict hyperglycaemic and hypoglycaemic excursions in patients with type 2 diabetes on multiple glucose lowering therapies who fast during Ramadan.…”
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203
Environmental/economic power dispatch using multiobjective evolutionary algorithms: a comparative study
Published 2003“…A comparative study of newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) applied to a nonlinear power system multiobjective optimization problem is presented in this paper. …”
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204
Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm
Published 2020“…In this paper, we present an approach based on Simulated Evolution algorithm for the design of SEN topology. …”
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Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm
Published 2020“…In this paper, we present an approach based on Simulated Evolution algorithm for the design of SEN topology. …”
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Minimizing Deadline Misses of Mobile IoT Requests in a Hybrid Fog- Cloud Computing Environment
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doctoralThesis -
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Practical single node failure recovery using fractional repetition codes in data centers
Published 2016“…Hence, a practical solution for node failures is presented by using a self-designed genetic algorithm that searches within the feasible solution space. …”
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conferenceObject -
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Scatter search for homology modeling
Published 2016“…Results obtained by our SS algorithm are compared with other approaches. The 3D models predicted by our algorithm show improved root mean standard deviations with respect to the native structures.…”
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conferenceObject -
209
KNNOR: An oversampling technique for imbalanced datasets
Published 2021“…<p>Predictive performance of Machine Learning (ML) models rely on the quality of data used for training the models. …”
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TIDCS: A Dynamic Intrusion Detection and Classification System Based Feature Selection
Published 2020“…TIDCS reduces the number of features in the input data based on a new algorithm for feature selection. …”
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Parameter identification of PV solar cells and modules using bio dynamics grasshopper optimization algorithm
Published 2024“…The study evaluates the BDGOA by applying it to identify unknown parameters of five solar modules. The algorithm's effectiveness is demonstrated through the extraction of parameters for RTC France, PWP201, SM55, KC200GT, and SW255 models, validated against experimental data under diverse conditions. …”
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Sentiment visualization of correlation of loneliness mapped through social intelligence analysis
Published 2024“…Social media platforms have become a valuable source of data to study this phenomenon.</p><h3>Objectives</h3><p dir="ltr">This paper aims to visualize the frequency of loneliness-related themes and topics in Twitter data. …”
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213
Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta
Published 2023“…Several dataset analysis models are utilised to study the data diversity. Further, this study demonstrates the application of neural network-based models to effectively predict the permeability. …”
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Sentiment Analysis of Dialectal Speech: Unveiling Emotions through Deep Learning Models
Published 2024“…Additionally, the GRU-CNN hybrid model attained a notable accuracy of 90%. These findings establish the robustness and effectiveness of hybrid architectures in enhancing emotion recognition accuracy in Arabic speech data, presenting a novel approach for Arabic dialect sentiment analysis.…”
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217
Energy-Efficient VoI-Aware UAV-Assisted Data Collection in Wireless Sensor Networks
Published 2025“…To address these objectives, our proposed approach incorporates deep reinforcement learning (RL-DQN) techniques to optimize UAV deployment, minimizing the number of UAVs while maximizing the number of successfully collected SNs with non-redundant data. The model considers VoI and energy constraints of the SNs, enhancing both efficiency and sustainability. …”
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Monitoring Bone Density Using Microwave Tomography of Human Legs: A Numerical Feasibility Study
Published 2021“…This study was performed using an in-house finite-element method contrast source inversion algorithm (FEM-CSI). …”
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