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Found 27 papers, 0 papers with code
Optimization Algorithms in Smart Grids: A Systematic Literature Review
We also provide a brief overview of objective functions and parameters used in the solutions for energy and cost effectiveness as well as discuss different open research challenges for future research works.
Arabic Fake News Detection Based on Deep Contextualized Embedding Models
Social media is becoming a source of news for many people due to its ease and freedom of use.
Transformation Invariant Cancerous Tissue Classification Using Spatially Transformed DenseNet
In this work, we introduce a spatially transformed DenseNet architecture for transformation invariant classification of cancer tissue.
Breast cancer detection using artificial intelligence techniques: A systematic literature review
In this research work, we systematically reviewed previous work done on detection and treatment of breast cancer using genetic sequencing or histopathological imaging with the help of deep learning and machine learning.
Artificial Intelligence and Statistical Techniques in Short-Term Load Forecasting: A Review
The most successful duration for short-term forecasting has been identified as prediction for a duration of one day at an hourly interval.
Novel Hybrid DNN Approaches for Speaker Verification in Emotional and Stressful Talking Environments
The test results of the aforementioned hybrid models demonstrated that the proposed HMM-DNN leveraged the verification performance in emotional and stressful environments.
Novel Dual-Channel Long Short-Term Memory Compressed Capsule Networks for Emotion Recognition
Using the Arabic Emirati-accented corpus, our results demonstrate that the proposed work yields average emotion recognition accuracy of 89. 3% compared to 84. 7%, 82. 2%, 69. 8%, 69. 2%, 53. 8%, 42. 6%, and 31. 9% based on CapsNet, CNN, support vector machine, multi-layer perceptron, k-nearest neighbor, radial basis function, and naive Bayes, respectively.
The exploitation of Multiple Feature Extraction Techniques for Speaker Identification in Emotional States under Disguised Voices
Due to improvements in artificial intelligence, speaker identification (SI) technologies have brought a great direction and are now widely used in a variety of sectors.
COVID-19 Electrocardiograms Classification using CNN Models
In this study, a novel approach is proposed to automatically diagnose the COVID-19 by the utilization of Electrocardiogram (ECG) data with the integration of deep learning algorithms, specifically the Convolutional Neural Network (CNN) models.
Empirical evaluation of shallow and deep learning classifiers for Arabic sentiment analysis
Additionally, the comparison includes state-of-the-art models such as the transformer architecture and the araBERT pre-trained model.
Empirical Analysis on Productivity Prediction and Locality for Use Case Points Method
This paper examines the impact of data locality approaches on productivity and effort prediction from multiple UCP variables.
Software Engineering
CASA-Based Speaker Identification Using Cascaded GMM-CNN Classifier in Noisy and Emotional Talking Conditions
This work aims at intensifying text-independent speaker identification performance in real application situations such as noisy and emotional talking conditions.
Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities
Machine learning (ML) provides a mechanism for humans to process large amounts of data, gain insights about the behavior of the data, and make more informed decision based on the resulting analysis.
Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection
Cyber-security garnered significant attention due to the increased dependency of individuals and organizations on the Internet and their concern about the security and privacy of their online activities.
Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection
Network attacks have been very prevalent as their rate is growing tremendously.
Multi-split Optimized Bagging Ensemble Model Selection for Multi-class Educational Data Mining
Furthermore, this work aims to predict the students' performance at two stages of course delivery (20% and 50% respectively).
Data Mining with Big Data in Intrusion Detection Systems: A Systematic Literature Review
Cloud computing has become a powerful and indispensable technology for complex, high performance and scalable computation.
Systematic Ensemble Model Selection Approach for Educational Data Mining
A plethora of research has been done in the past focusing on predicting student's performance in order to support their development.
Emirati-Accented Speaker Identification in Stressful Talking Conditions
This research is dedicated to improving text-independent Emirati-accented speaker identification performance in stressful talking conditions using three distinct classifiers: First-Order Hidden Markov Models (HMM1s), Second-Order Hidden Markov Models (HMM2s), and Third-Order Hidden Markov Models (HMM3s).
Ensemble of Learning Project Productivity in Software Effort Based on Use Case Points
It is well recognized that the project productivity is a key driver in estimating software project effort from Use Case Point size metric at early software development stages.
v-SVR Polynomial Kernel for Predicting the Defect Density in New Software Projects
Statistical significance test showed that v-SVR with polynomial kernel was better than that of SLR when new software projects were developed on mainframes and coded in programming languages of third generation
Three-Stage Speaker Verification Architecture in Emotional Talking Environments
In this work, a three-stage speaker verification architecture has been proposed to enhance speaker verification performance in emotional environments.
Fuzzy Model Tree For Early Effort Estimation
Use Case Points (UCP) is a well-known method to estimate the project size, based on Use Case diagram, at early phases of software development.
Enhancing Use Case Points Estimation Method Using Soft Computing Techniques
Use case points method relies on the use case diagram to estimate the size and effort of software projects.
Pareto Efficient Multi Objective Optimization for Local Tuning of Analogy Based Estimation
Therefore, the main theme of this research is how to come up with best decision variables that improve adaptation strategy and thus, the overall evaluation measures without degrading the others.
A Hybrid Intelligent Model for Software Cost Estimation
Although many techniques and algorithmic models have been developed and implemented by practitioners, accurate software development effort prediction is still a challenging endeavor in the field of software engineering, especially in handling uncertain and imprecise inputs and collinear characteristics.
A Comparison Between Decision Trees and Decision Tree Forest Models for Software Development Effort Estimation
Accurate software effort estimation has been a challenge for many software practitioners and project managers.
