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81
Assessment of static pile design methods and non-linear analysis of pile driving
Published 2006“…The pile/soil interaction system is described by a mass/spring/dashpot system where the properties of each component are derived from rigorous analytical solutions or finite element analysis. The outcome of this research is an algorithm that can be used to predict pile displacement and driving stresses. …”
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masterThesis -
82
XBeGene: Scalable XML Documents Generator by Example Based on Real Data
Published 2012“…Clustering experiments demonstrate high correlation levels between the specified user requirements and the characteristics of the generated XML data, while timing results confirm our approach's scalability to large scale document collections.…”
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conferenceObject -
83
A New Hamiltonian Semi-Analytical Approach to Vibration Analysis of Piezoelectric Multi-Layered Plates
Published 2024“…The whole piezoelectric multilayered plate’s dynamic stiffness is then built, from which its circular frequencies are computed with the help of the Wittrick-Williams algorithm. A detailed discussion is provided on the implementation aspects, followed by some numerical examples to assess the robustness, accuracy and effectiveness of the proposed method. …”
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84
UAV-Aided Projection-Based Compressive Data Gathering in Wireless Sensor Networks
Published 2018“…Our problem definition aims at clustering the sensors, constructing an optimized forwarding tree per cluster, and gathering the data from selected cluster head nodes based on projection-based CDG with minimized UAV trajectory distance. …”
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85
LaScaDa: A Novel Scalable Topology for Data Center Network
Published 2020“…<p dir="ltr">The growth of cloud-based services is mainly supported by the core networking infrastructures of large-scale data centers, while the scalability of these services is influenced by the performance and dependability characteristics of data centers. …”
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86
BioNetApp: An interactive visual data analysis platform for molecular expressions
Published 2019“…BioNetApp also provides data clustering based on molecular concentrations using Self Organizing Maps (SOM), K-Means, K-Medoids, and Farthest First algorithms.…”
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87
Joint energy-distortion aware algorithms for cooperative video streaming over LTE networks
Published 2013“…In the proposed approach, mobiles are grouped into collaborative clusters using a low-complexity clustering algorithm. …”
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88
A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performance
Published 2024“…Hence, in this study, deep learning algorithms, such as VGG16, EfficientNetB4, and ResNet, are utilized to diagnose various diseases, such as Alzheimer's, brain tumors, skin diseases, and lung diseases. …”
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89
Modeling and Control of a Thermally Driven MEMS Actuator for RF Applications
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doctoralThesis -
90
Damage assessment and recovery from malicious transactions using data dependency for defensive information warfare
Published 2007“…To make the process of damage assessment and recovery fast and efficient and in order not to scan the whole log, researchers have proposed different methods for segmenting the log, and accordingly presented different damage assessment and recovery algorithms. Since even segmenting the log into clusters may not solve the problem, as clusters/segments may grow to be humongous in size, this is in case of high data/transaction dependency, we suggest a method for segmenting the log into clusters and its sub-clusters; i.e, segmenting the cluster; based on exact data dependency [12], into sub-clusters; based on two different criteria: number of data items or space occupied. …”
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91
Energy-Efficient VoI-Aware UAV-Assisted Data Collection in Wireless Sensor Networks
Published 2025“…In the first problem, the proposed approach clusters SNs and prioritizes non-redundant data by assigning VoI, while neglecting redundant data. …”
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masterThesis -
92
Hybrid Deep Learning-based Models for Crop Yield Prediction
Published 2022“…The algorithms evaluated in our study are the XGBoost machine learning (ML) algorithm, Convolutional Neural Networks (CNN)-Deep Neural Networks (DNN), CNN-XGBoost, CNN-Recurrent Neural Networks (RNN), and CNN-Long Short Term Memory (LSTM). …”
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93
An efficient approach for textual data classification using deep learning
Published 2022“…Next, we employ machine learning algorithms: logistic regression, random forest, K-nearest neighbors (KNN), and deep learning algorithms: long short-term memory (LSTM), artificial neural network (ANN), and gated recurrent unit (GRU) for classification. …”
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Arabic Text Classification Using Modified Artificial Bee Colony Algorithm for Sentiment Analysis: The Case of Jordanian Dialect
Published 2022“…The second phase, modified the Artificial Bee Colony (ABC) Algorithm, with Upper Confidence Bound (UCB) Algorithm, to promote the exploitation ability for the minimum dimension, to get the minimum number of the optimal feature, then using forward feature selection strategy by four classifiers of machine learning algorithms: (K-Nearest Neighbors (KNN), Support vector machines (SVM), Naïve-Bayes (NB), and Polynomial Neural Networks (PNN). …”
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96
Empowering IoT Resilience: Hybrid Deep Learning Techniques for Enhanced Security
Published 2024“…We developed two novel hybrid deep learning mechanisms, CNN-GRU (Convolutional Gated Recurrent Neural Networks) and CNN-LSTM (Convolutional Long Short-Term Memory Neural Networks), and extensively evaluated their performance on the state-of-the-art Kitsune and TON-IoT publicly available datasets. …”
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97
Distributed Tree-Based Machine Learning for Short-Term Load Forecasting With Apache Spark
Published 2021“…The paper proposes a concurrent job scheduling algorithm in a multi-energy data source environment using Apache Spark. …”
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98
Multi-Objective Optimisation of Injection Moulding Process for Dashboard Using Genetic Algorithm and Type-2 Fuzzy Neural Network
Published 2024“…Computational techniques, like the finite element method, are used to analyse behaviours based on varied input parameters. …”
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Sentiment Analysis of Dialectal Speech: Unveiling Emotions through Deep Learning Models
Published 2024“…Dialect Speech Sentiment Analysis is an evolutional field where machine learning algorithms are utilized to detect emotions in spoken language. …”
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Data-Driven Electricity Demand Modeling for Electric Vehicles Using Machine Learning
Published 2024Get full text
doctoralThesis