A FeedForward–Convolutional Neural Network to Detect Low-Rate DoS in IoT by Harun Surej Ilango (17545728)

“…The performance of FFCNN is compared to the machine learning algorithms-J48, Random Forest, Random Tree, REP Tree, SVM, and Multi-Layer Perceptron (MLP). …”

Machine learning comparative study for human posture classification using wearable sensors by Rababaah, Aaron

“…The study considered two categories of models, supervised and unsupervised learning algorithms. After intensive training and testing of all algorithms, multi-layer perceptron and K-Means outperformed other algorithms with an impressive classification accuracy of 99.88%.…”
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Reconstruction and simulation of neocortical microcircuitry by Khazen, Georges

“…The reconstruction uses cellular and synaptic organizing principles to algorithmically reconstruct detailed anatomy and physiology from sparse experimental data. …”
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Energy-Efficient Computation Offloading in Vehicular Edge Cloud Computing by Xin Li (51274)

“…Finally, we propose a low-complexity heuristic resource allocation algorithm based on this novel theoretical discovery. …”

Supervised term-category feature weighting for improved text classification by Attieh, Joseph

“…Training the ANN using the gradient descent algorithm allows updating the term-category matrix until reaching convergence. …”
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Propagation-aware Knowledge Extraction for Fault Detection in Wireless Sensor Networks via RF Link Quality, Text, and Data Mining by Bostani, Ali

“…In this article, the authors describe a state-of-the-art propagation-aware knowledge extraction framework to address robust fault detection through the combination of RF link-quality characterization, text mining in network logs, and scalable data-mining algorithms. The given approach integrates multi-source information, including physical-layer measurements, semantic event logs, and real-time data streams into an adaptive decision engine. …”
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Kolmogorov–Arnold Networks for predicting carotid intima-media thickness in cardiovascular risk assessment by Al Bataineh, Ali

“…The KAN, implemented with ELU-activated hidden layers and a Softmax output was benchmarked against six conventional algorithms like Support Vector Machine, Decision Tree, Logistic Regression, Stochastic Gradient Descent, Deep Neural Network, Random Forest and Multi-Layer Perceptron. …”
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Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks by Javed Butt , Usman

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NEURAL NETWORK MODEL FOR PLANNED REPLACEMENT OF BOEING 737 BRAKES by Al-Garni, Ahmed Z.

“…., Boeing 737, is analyzed using the Artificial Neural Network and Weibull regression models. One-layered feed-forward back-propagation algorithm for artificial neural network whereas three parameters model for Weibull are used for the analysis. …”
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Data visualization and pattern discovery in IoT by Bektemirov, Abdukhamid

“…The given method is a combination of nonlinear optimization of features with the help of metaheuristic algorithms and sophisticated dimensionality reduction techniques to preserve the important information with reducing redundancy. …”
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Failure-Rate Prediction for De Havilland Dash-8 Tires Employing Neural-Network Technique by Al-Garni, Ahmed Z.

“…An artificial neural-network model for predicting the failure rate of De Havilland Dash-8 airplane tires utilizing the two-layered feedforward back-propagation algorithm as a learning rule is developed. …”
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FAILURE RATE ANALYSIS OF BOEING 737 BRAKES EMPLOYING NEURAL NETWORK by Al-Garni, Ahmed Z.

“…., Boeing 737, is analyzed using the artificial neural network and Weibull regression models. One-layered feed-forward back-propagation algorithm for artificial neural network whereas three parameters model for Weibull are used for the analysis. …”
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Theoretical Estimation of the Dispersion Curves in an Orthotropic Composite Plate by Abdellah Nouala

“…In this work, root-finding using the bisection algorithm is proposed to solve the transcendental dispersion relations and evaluate the dispersion curves of ultrasonic-guided waves propagating in an orthotropic composite plate. …”
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Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta by Vaisali Chandrasekar (16904526)

“…<p dir="ltr">Owing to limited drug testing possibilities in pregnant population, the development of computational algorithms is crucial to predict the fate of drugs in the placental barrier; it could serve as an alternative to animal testing. …”

Distinguishing Between Fake and Real Smiles Using EEG Signals and Deep Learning by Moussa, Mostafa Mohamed

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Electric Vehicles Charging Station Load Forecasting Integration With Renewable Energy Using Novel Deep EfficientBiLSTMNet by Vineet Dhanawat (22361395)

“…The EfficientBiLSTMNet model, which integrates the EfficientNet and BiLSTM layers, is trained on the preprocessed datasets. The model’s hyperparameters are optimized using an Enhanced Firefly Algorithm (EFA). …”

Predicting business cycle turning points with neural networks in an information-poor economy by Nasr, George E.

“…The NN has as inputs seven indicators of economic activity and as output the probability of a recession. The three-layered network is estimated using the back propagation algorithm. …”
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YOLO-SAIL: Attention-Enhanced YOLOv5 With Optimized Bi-FPN for Ship Target Detection in SAR Images by Prabu Selvam (22330264)

“…It has recently become increasingly popular to apply deep learning algorithms to the identification of ships in SAR images. …”

Artificial intelligence models for predicting the mode of delivery in maternal care by Rawan AlSaad (14159019)

“…Five machine learning algorithms were evaluated: XGBoost, AdaBoost, random forest, decision tree, and multi-layer perceptron (MLP) classifier. …”

Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks by Mohammed Almehdhar (22046597)

“…Our systematic review covers a range of AI algorithms, including traditional ML, and advanced neural network models, such as Transformers, illustrating their effectiveness in IDS applications within IVNs. …”