A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks حسب Umesh Kumar Lilhore (17727684)

الموضوعات:

Efficient Approximate Conformance Checking Using Trie Data Structures حسب Awad, Ahmed

الموضوعات: "…Estimation error,Runtime,Computational modeling,Data structures,Approximation algorithms,Encoding,Computational efficiency…"
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An efficient and secure compression technique for data protection using burrows-wheeler transform algorithm حسب M Baritha Begum (17541483)

The unified effect of data encoding, ansatz expressibility and entanglement on the trainability of HQNNs حسب Muhammad Kashif (3923483)

"…We focus on the combined influence of data encoding, qubit entanglement, and ansatz expressibility in hybrid quantum neural networks (HQNNs) for multi-class classification tasks. …"

Intelligent Fusion of Deep Features for Improved Waste Classification حسب Kashif Ahmad (12592762)

الموضوعات:

Artificial intelligence-based methods for fusion of electronic health records and imaging data حسب Farida Mohsen (16994682)

"…In our analysis, a typical workflow was observed: feeding raw data, fusing different data modalities by applying conventional machine learning (ML) or deep learning (DL) algorithms, and finally, evaluating the multimodal fusion through clinical outcome predictions. …"

On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network حسب Nasru Minallah (9427338)

A Parallel Neural Networks Algorithm for the Clique Partitioning Problem حسب Harmanani, Haidar M.

"…The proposed algorithm has a time complexity of O(1) for a neural network with n vertices and c cliques. …"
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A FUZZY EVOLUTIONARY ALGORITHM FOR TOPOLOGY DESIGN OF CAMPUS NETWORKS حسب Youssef, H.

"…In this paper, we present a Simulated Evolution algorithm for the design of campus network topology. …"
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Inertial Navigation system of In-pipe Inspection Robot حسب Al-Masri, Wasim

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Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm حسب Youssef, H.

"…The problem consists of deciding the number, types, and locations of the network active elements (hubs, switches, and routers), as well as the links and their capacities. …"
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Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm حسب Youssef, H.

"…The problem consists of deciding the number, types, and locations of the network active elements (hubs, switches, and routers), as well as the links and their capacities. …"
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Decision-level Gait Fusion for Human Identification at a Distance حسب Al-Tayyan, Amer

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Optimum Track to Track Fusion Using CMA-ES and LSTM Techniques حسب Fares, Samar

"…The first method uses an offline technique based on a global optimizer called the CMA-ES algorithm and the second one uses LSTM in its different forms to learn the online adjustment of the fusion weights between the two tracks. …"
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Multi-Objective Optimisation of Injection Moulding Process for Dashboard Using Genetic Algorithm and Type-2 Fuzzy Neural Network حسب Mohammad Reza Chalak Qazani (13893261)

"…Computational techniques, like the finite element method, are used to analyse behaviours based on varied input parameters. …"

Real-Time Path-Planning using Depth/INS Sensor Fusion for Localization حسب Alkhawja, Fares Amin E

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Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information حسب M. Ghoniem, Rania

"…For classifying unimodal data of either speech or EEG, a hybrid fuzzy c-means-genetic algorithm-neural network model is proposed, where its fitness function finds the optimal fuzzy cluster number reducing the classification error. …"
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Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms حسب Arafat Rahman (8065562)

Predicting the Heats of Fusion of Ionic Liquids via Group Contribution Modeling and Machine Learning حسب Zeinab, Samira Jihad

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CNN feature and classifier fusion on novel transformed image dataset for dysgraphia diagnosis in children حسب Jayakanth, Kunhoth

"…The feature fusion approach substantially improved the classification accuracy, with the SVM trained on fused features from the task specific-data achieving an accuracy of 97.3%. …"
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