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)

“…Therefore, the proposed work intends to implement a group of novel methodologies for developing an effective Cyborg intelligence security model to secure smart city systems. Here, the Quantized Identical Data Imputation (QIDI) mechanism is implemented at first for data preprocessing and normalization. …”

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

“…The validation of these results was performed using previous archaeological works as well as geological and geomorphological field surveys. The modelling and prediction accuracies are expected to improve with the insertion of a neural network and backpropagation algorithms based on the performed cluster groups following more recent field surveys. …”
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A modified coronavirus herd immunity optimizer for capacitated vehicle routing problem by Abu Zitar, Raed

“…To evaluate the modified CHIO, twosets of data sets are used: the first data set has ten Synthetic CVRP models while the second is an ABEFMPdata set which has 27 instances with different models. …”
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Cross entropy error function in neural networks by Nasr, G.E.

“…To forecast gasoline consumption (GC), the ANN uses previous GC data and its determinants in a training data set. …”
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Establishing Autonomous AUS-Quadrotor by Al Younes, Younes

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An Infrastructure-Assisted Workload Scheduling for Computational Resources Exploitation in the Fog-Enabled Vehicular Network by Sorkhoh, Ibrahim

“…A Dantzig–Wolfe decomposition algorithm is proposed which yields to a master program solvable by the Barrier algorithm and subproblems solve...…”
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A geographic information system method to generate long term regional solar radiation resource maps: enhancing decision-making by Sachin Jain (19161721)

“…Digital surface model data is used as an auxiliary variable to further scale down to 30 m resolution. …”

Scheduling and allocation in high-level synthesis using stochastic techniques by Sait, Sadiq M.

“…High-level synthesis is the process of automatically translating abstract behavioral models of digital systems to implementable hardware. …”
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Positive Unlabelled Learning to Recognize Dishes as Named Entity by TAREK, AIMAN

“…With the lack of labelled data, I try to overcome the cold start and avoid manual labelling by building a lookup table from a dictionary. …”
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Digital Image Watermarking Using Balanced Multiwavelets by Ghouti, L.

“…In this paper, a robust watermarking algorithm using balanced multiwavelet transform is proposed. …”
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A hybrid approach for XML similarity by Tekli, Joe

“…Various algorithms for comparing hierarchically structured data, e.g. …”
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Exploring Digital Competitiveness through Bayesian Belief Networks by Qazi, Abroon

“…The methodology involves constructing BBN models using data from the IMD Digital Competitiveness Ranking 2023 for 64 countries. …”
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EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach by Muhammad Adeel Asghar (6724982)

“…Features extracted from the proposed BoDF model have considerably smaller dimensions. The proposed model achieves better classification accuracy compared to the recently reported work when validated on SJTU SEED and DEAP data sets. …”

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

“…The validation of these results was performed using previous archaeological works as well as geological and geomorphological field surveys. The modelling and prediction accuracies are expected to improve with the insertion of a neural network and backpropagation algorithms based on the performed cluster groups following more recent field surveys. …”
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Predicting COVID-19 cases using bidirectional LSTM on multivariate time series by Ahmed Ben Said (14158926)

“…Unlike other forecasting techniques, our proposed approach first groups the countries having similar demographic and socioeconomic aspects and health sector indicators using K-means clustering algorithm. The cumulative case data of the clustered countries enriched with data related to the lockdown measures are fed to the bidirectional LSTM to train the forecasting model. …”

Process Mining over Unordered Event Streams by Awad, Ahmed

“…This requires online algorithms that, instead of keeping the whole history of event data, work incrementally and update analysis results upon the arrival of new events. …”
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Anomaly Detection Based Framework for Profile Monitoring by Al-Rahim, Nour Al-Huda Z. Al-Abed

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A novel few shot learning derived architecture for long-term HbA1c prediction by Marwa Qaraqe (10135172)

“…For the first time in the literature, this work proposes a novel FSL-derived algorithm for the long-term prediction of clinical HbA1c measures. …”

Development of a deep learning-based group contribution framework for targeted design of ionic liquids by Sadah Mohammed (18192859)

“…This computational framework can expedite and improve the process of finding desirable molecular structures of IL via accurate property predictions in a data-driven manner. Our proposed framework consists of two essential steps: establishing a correlation between IL viscosity and CO₂ solubility by merging two deep learning models (DNN-GC and ANN-GC) and utilizing this correlation to identify the optimal IL structure with maximal CO₂ absorption capacity. …”

Active distribution network type identification method of high proportion new energy power system based on source-load matching by Qinlin, Shi

“…Firstly, the typical daily output scenarios of DG are extracted by clustering method, and the generalized load curve model is solved by the optimization algorithm to obtain the source load operation data; Secondly, calculate the source-load matching indicators (including matching performance, matching degree, and matching rate) according to the source load data of each region, and identify the distribution network type according to the range of the index values; Finally, several indicators are introduced to quantify the characteristics of different types of distribution networks. …”
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