Detecting and Predicting Archaeological Sites Using Remote Sensing and Machine Learning—Application to the Saruq Al-Hadid Site, Dubai, UAE by Ben Romdhane, Haifa

“…In fact, SAR is the only satellite-based technology able to detect buried artefacts from space, and it is expected that fine-resolution images of ALOS/PALSAR-2 (L-band SAR) would be able to detect large features (>1 m) that might be buried in the subsurface (<2 m) under optimum conditions, i.e., dry and bare soil. …”
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Detecting and Predicting Archaeological Sites Using Remote Sensing and Machine Learning—Application to the Saruq Al-Hadid Site, Dubai, UAE by Ben-Romdhane, Haïfa

“…In fact, SAR is the only satellite-based technology able to detect buried artefacts from space, and it is expected that fine-resolution images of ALOS/PALSAR-2 (L-band SAR) would be able to detect large features (>1 m) that might be buried in the subsurface (<2 m) under optimum conditions, i.e., dry and bare soil. …”
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A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models by Osama Bassam J. Rabie (21323741)

“…First, the data preprocessing and normalization operations are performed to generate the balanced IoT dataset for improving the detection accuracy of classification. Then, the DRF optimization algorithm is applied to optimally tune the features required for accurate intrusion detection and classification. …”

Exposure of Botnets in Cloud Environment by Expending Trust Model with CANFES Classification Approach by Nagendra Prabhu Selvaraj (17542041)

“…The performance of the proposed bot detection system in the internet environment is analyzed latency, detection rate, packet delivery ration, energy availability and precision.…”

Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects by Majdi Mansouri (16869885)

“…Thus, a key factor to be taken into consideration in high-efficiency grid-connected PV systems is the fault detection and diagnosis (FDD). The performance of the FDD method depends mainly on the quality of the extracted features including real-time changes, phase changes, trend changes, and faulty modes. …”

Type 2 Diabetes Mellitus Automated Risk Detection Based on UAE National Health Survey Data: A Framework for the Construction and Optimization of Binary Classification Machine Learn... by Mohamed, AlShuweihi

“…LR with the reduced feature set using the intersection between CS and RFE proved to be the best model among the tested algorithms. …”
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Diabetic Sensorimotor Polyneuropathy Severity Classification Using Adaptive Neuro Fuzzy Inference System by Fahmida Haque (16896489)

“…The model accuracy was validated with the results from different machine learning algorithms. The Accuracy, sensitivity, and specificity of the ANFIS model are 91.17±1.18%, 92±2.26%, 96.72±0.93%, respectively. …”

Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring by Muhammad E.H. Chowdhury (17151154)

“…Twenty-seven t-domain, f-domain, and Mel frequency cepstral coefficients (MFCC) features were used to train a public database to identify the best-performing algorithm for classifying abnormal and normal heart sound (HS). …”

PAST-AI: Physical-Layer Authentication of Satellite Transmitters via Deep Learning by Gabriele Oligeri (14151426)

“…<p dir="ltr">Physical-layer security is regaining traction in the research community, due to the performance boost introduced by deep learning classification algorithms. This is particularly true for sender authentication in wireless communications via radio fingerprinting. …”

Automatic and Intelligent Stressor Identification Based on Photoplethysmography Analysis by Sami Elzeiny (16891521)

“…In particular, this study proposes a novel algorithm that first detects instances of stress and then classifies the stressor type using photoplethysmography (PPG) data from wearable smartwatches. …”

Enhanced DC Microgrid Protection: a Neural Network and Wavelet Transform Approach by El Gohary, Youssef Hesham

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A novel hybrid methodology for fault diagnosis of wind energy conversion systems by Khaled Dhibi (16891524)

“…The proposed technique involved two major steps: feature selection and fault classification. Feature selection pre-processing is an important step to increase the accuracy of the classification algorithm and decrease the dimensionality of a dataset. …”

A 3-D vision-based man-machine interface for hand-controlled telerobot by Al-Mouhamed, M.A.

“…Two digital cameras are used to monitor a four-ball-based feature frame that is held by the operator hand. To determine the three-dimensional (3-D) position a tracking algorithm based on uncalibrated cameras with weak perspective projection model is used. …”
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A Digital DNA Sequencing Engine for Ransomware Analysis using a Machine Learning Network by KHAN, FIROZ

“…The proposed work contains three significant phases: Preprocessing and Feature Selection, DNA Sequence Generation and Ransomware Detection. …”
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An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm Optimization by Majdi Mansouri (16869885)

“…First, an efficient feature selection algorithm based on particle swarm optimization (PSO) is proposed. …”

A conjugate self-organizing migration (CSOM) and reconciliate multi-agent Markov learning (RMML) based cyborg intelligence mechanism for smart city security by S. Shitharth (12017480)

“…Then, the Conjugate Self-Organizing Migration (CSOM) optimization algorithm is deployed to select the most relevant features to train the classifier, which also supports increased detection accuracy. …”

Optimizing ADWIN for Steady Streams by Moharram, Hassan

“…Over time, several drift detection approaches have been proposed. A prominent approach is adaptive windowing (ADWIN) which can detect changes in features data distribution without explicit feedback on the correctness of the prediction. …”
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Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers by Yousef, Hibba

“…In particular, Isolation Forest (iForest) was applied as an anomaly detection algorithm to address class imbalance. iForest was trained on the control group data to detect cases of high risk for T2DM development as outliers. …”
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ADD. (c2002) by Varjabedian, Ralph A.

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The Green Sleepers of Tehran Park Bench Semiotics by Hosseinnia, Maryam

“…Therefore, eight-dimensional feature space has been developed. Finally, K-mean clustering algorithm has been implemented based on the extracted features of PD pulse shape and PD current waveform to define their clusters in the feature space. …”
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