Prediction of EV Charging Behavior Using Machine Learning by Shahriar, Sakib

“…Using data-driven tools and machine learning algorithms to learn the EV charging behavior can improve scheduling algorithms. …”
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Design of acoustic absorbing metasurfaces using a data-driven approach by Hamza Baali (14603380)

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Delay Optimization in LoRaWAN by Employing Adaptive Scheduling Algorithm With Unsupervised Learning by Zulfiqar Ali (117651)

“…This paper aims to optimize the delay in LoRaWAN by using an Adaptive Scheduling Algorithm (ASA) with an unsupervised probabilistic approach called Gaussian Mixture Model (GMM). …”

Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition by Dabeeruddin Syed (16864260)

“…Whilst different models are proposed for STLF, they are based on small historical datasets and are not scalable to process large amounts of big data as energy consumption data grow exponentially in large electric distribution networks. …”

Ensemble-Based Spam Detection in Smart Home IoT Devices Time Series Data Using Machine Learning Techniques by Ameema Zainab (16864263)

“…Statistical analysis and machine learning can play a vital role in detecting the anomalies in the data, which enhances the security level of the smart home IoT system which is the goal of this paper. …”

Global smart cities classification using a machine learning approach to evaluating livability, technology, and sustainability performance across key urban indices by Aya Hasan Alkhereibi (17151070)

“…Drawing on data from the Smart Cities Index (SCI) and other economic and sustainability competitiveness metrics, the study uses various <u>ML algorithms</u> to categorize cities into <u>performance classes</u>, ranging from high-achieving Class 1 to emerging Class 3 cities. …”

Cryptocurrency Exchange Market Prediction and Analysis Using Data Mining and Artificial Intelligence by Al Rayhi, Nasser

“…One of the best algorithms in terms of the result is the Long Short Term Memory (LSTM) since it is based on recurrent neural networks which uses loop as a method to learn from heuristics data. …”
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Allocation and re-allocation of data in a grid using an adaptive genetic algorithm by Mansour, N.

“…Allocation and re-allocation of data in a grid using an adaptive genetic algorithm. …”
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An efficient and secure compression technique for data protection using burrows-wheeler transform algorithm by M Baritha Begum (17541483)

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

“…By encoding the proxy behavior using a trie data structure, we obtain a logarithmically reduced search space for alignment computation compared to a set-based representation. …”
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On the Optimization of Band Gaps in Periodic Waveguides by Jamil Renno (14070771)

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Multimodal feature fusion and ensemble learning for non-intrusive occupancy monitoring using smart meters by Sakib Mahmud (15302404)

“…In this study, we introduce the multimodal feature fusion for non-intrusive occupancy monitoring (MMF-NIOM) framework, which leverages both classical and deep machine learning algorithms to achieve state-of-the-art occupancy detection performance using smart meter data. …”

Multilayer Reversible Data Hiding Based on the Difference Expansion Method Using Multilevel Thresholding of Host Images Based on the Slime Mould Algorithm by Abu Zitar, Raed

“…Moreover, the image pixels in different and more similar areas of the image are located next to one another in a group and classified using the specified thresholds. As a result, the embedding capacity in each class can increase by reducing the value of the difference between two consecutive pixels, and the distortion of the marked image can decrease after inserting the personal data using the DE method. …”
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Joint Inversion of Direct Current Resistivity and Seismic Refraction Data, using Local and Global Optimization Algorithms with Synthetic and Real Data Examples by unknown

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Using Machine Learning Algorithms to Forecast Solar Energy Power Output by Ali Jassim Lari (22597940)

“…We focused on the first 30-min, 3-h, 6-h, 12-h, and 24-h windows to gain an appreciation of the impact of forecasting duration on the accuracy of prediction using the selected machine learning algorithms. The study results show that Random Forest outperformed all other tested algorithms. …”

Limiting the Collection of Ground Truth Data for Land Use and Land Cover Maps with Machine Learning Algorithms by Usman Ali (6586886)

“…This was accomplished by (1) extracting reliable LULC information from Sentinel-2 and Landsat-8 s images, (2) generating remote sensing indices used to train ML algorithms, and (3) comparing the results with ground truth data. …”

A genetic algorithm for testable data path synthesis by Harmanani, H.

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A neural networks algorithm for data path synthesis by Harmanani, Haidar M.

“…This paper presents a deterministic parallel algorithm to solve the data path allocation problem in high-level synthesis. …”
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Data reductions and combinatorial bounds for improved approximation algorithms by Abu-Khzam, Faisal N.

“…Kernelization algorithms in the context of Parameterized Complexity are often based on a combination of data reduction rules and combinatorial insights. …”
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Distributed dimension reduction algorithms for widely dispersed data by Abu-Khzam, F.N.

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