Enhancing Facial Emotion Recognition with a Modified Deep Convolutional Neural Network DOI Creative Commons
Samah A. Gamel, Hatem A. Khater

Journal of Engineering Research - Egypt/Journal of Engineering Research, Год журнала: 2023, Номер 7(5), С. 118 - 125

Опубликована: Ноя. 1, 2023

Understanding and predicting human character traits play a crucial role in various domains ranging from psychology to resources.With the advent of artificial intelligence (AI) deep learning algorithms, researchers have explored potential analyzing facial images predict accurately.In this paper, we present comprehensive study application AI techniques for recognition.We review existing literature on image analysis, personality prediction.Furthermore, propose methodology that leverages convolutional neural networks (CNNs) extract meaningful features images.Our experiments demonstrate effectiveness our approach accurately showcasing promising results using small-scale datasets.We discuss implications findings psychology, resources, personalized user experiences.Additionally, ethical considerations, such as privacy bias, are addressed.This research contributes growing field AI-driven recognition, providing insights further advancements practical applications understanding behavior.

Язык: Английский

Enhancing crop recommendation systems with explainable artificial intelligence: a study on agricultural decision-making DOI Creative Commons
Mahmoud Y. Shams, Samah A. Gamel, Fatma M. Talaat

и другие.

Neural Computing and Applications, Год журнала: 2024, Номер 36(11), С. 5695 - 5714

Опубликована: Янв. 11, 2024

Abstract Crop Recommendation Systems are invaluable tools for farmers, assisting them in making informed decisions about crop selection to optimize yields. These systems leverage a wealth of data, including soil characteristics, historical performance, and prevailing weather patterns, provide personalized recommendations. In response the growing demand transparency interpretability agricultural decision-making, this study introduces XAI-CROP an innovative algorithm that harnesses eXplainable artificial intelligence (XAI) principles. The fundamental objective is empower farmers with comprehensible insights into recommendation process, surpassing opaque nature conventional machine learning models. rigorously compares prominent models, Gradient Boosting (GB), Decision Tree (DT), Random Forest (RF), Gaussian Naïve Bayes (GNB), Multimodal (MNB). Performance evaluation employs three essential metrics: Mean Squared Error (MSE), Absolute (MAE), R-squared (R2). empirical results unequivocally establish superior performance XAI-CROP. It achieves impressively low MSE 0.9412, indicating highly accurate yield predictions. Moreover, MAE 0.9874, consistently maintains errors below critical threshold 1, reinforcing its reliability. robust R 2 value 0.94152 underscores XAI-CROP's ability explain 94.15% data's variability, highlighting explanatory power.

Язык: Английский

Процитировано

35
Improving the accuracy of diagnostic predictions for power transformers by employing a hybrid approach combining SMOTE and DNN DOI
Samah A. Gamel, Sherif S. M. Ghoneim,

Yara A. Sultan

и другие.

Computers & Electrical Engineering, Год журнала: 2024, Номер 117, С. 109232 - 109232

Опубликована: Апрель 12, 2024

Язык: Английский

Процитировано

6
SleepSmart: an IoT-enabled continual learning algorithm for intelligent sleep enhancement DOI Creative Commons
Samah A. Gamel, Fatma M. Talaat

Neural Computing and Applications, Год журнала: 2023, Номер 36(8), С. 4293 - 4309

Опубликована: Дек. 11, 2023

Abstract Sleep is an essential physiological process that crucial for human health and well-being. However, with the rise of technology increasing work demands, people are experiencing more disrupted sleep patterns. Poor quality quantity can lead to a wide range negative outcomes, including obesity, diabetes, cardiovascular disease. This research paper proposes smart sleeping enhancement system, named SleepSmart, based on Internet Things (IoT) continual learning using bio-signals. The proposed system utilizes wearable biosensors collect data during sleep, which then processed analyzed by IoT platform provide personalized recommendations optimization. Continual techniques employed improve accuracy system's over time. A pilot study subjects was conducted evaluate performance, results show SleepSmart significantly reduce disturbance. has potential practical solution sleep-related issues enhance overall With prevalence problems, be effective tool individuals monitor their quality.

Язык: Английский

Процитировано

6
Winds of Power: Data Analysis for the Relationship between Wind Speed, Gust, and Power Output DOI Creative Commons
Samah A. Gamel,

Yara A. Sultan

Journal of Engineering Research - Egypt/Journal of Engineering Research, Год журнала: 2023, Номер 7(5), С. 189 - 194

Опубликована: Ноя. 1, 2023

Wind turbines are the most cost-effective and quickly evolving renewable energy technology. Benefits of this technology include no carbon emissions, resource conservation, job creation, flexible applications, modularity, fast installation, rural power grid improvement, potential for agricultural or industrial use.

Язык: Английский

Процитировано

2
Advancing Cardiac Image Processing: An Innovative Model Utilizing Canny Edge Detection For Enhanced Diagnostics DOI

Sally Mohamed,

Mohamed B. Elboshy,

Hatem A. Khater

и другие.

Опубликована: Апрель 16, 2024

Cardiovascular disease is a leading cause of mortality worldwide, necessitating the development advanced diagnostic techniques. This research paper introduces an innovative model utilizing edge detection algorithms to enhance cardiac image processing and diagnostics. The proposed aims improve accuracy by accurately identifying delineating boundaries structures abnormalities. A comprehensive images, including both healthy individuals patients with known abnormalities, was utilized for evaluation. outcomes demonstrated effectiveness in enhancing processing, paving way improved patient care field cardiology.

Язык: Английский

Процитировано

0
Transforming Ophthalmic Care: The Role of AI in Accurate Eye Disease Classification EDC DOI
Samah A. Gamel, Iman Alansari, Safa’a S. Saleh

и другие.

Опубликована: Апрель 16, 2024

This research describes a unique strategy to classifying eye illnesses utilizing Convolutional Neural Network (CNN) modification. The objective is develop an automated system that accurately diagnoses and classifies diseases, leading improved patient care outcomes. A comprehensive dataset of images was collected from various sources preprocessed enhance quality quantity. proposed Eye Disease Classification (EDC) model trained optimized using well-known algorithms. experimental findings illustrate the superiority suggested approach, achieving high precision ($95.63 \%$), recall (98.20%), F1-score (94.30%), accuracy (94.50%), SVM, Decision Tree, KNN, Random Forest are among most often used classifiers, results demonstrate potential technology transform disease detection therapy.

Язык: Английский

Процитировано

0
Intelligent Bayesian Inference for Multiclass Lung Infection Diagnosis: Network Analysis of Ranked Gray Level Co-occurrence (GLCM) Features DOI

Raja Nadir Mahmood Khan,

Abdul Majid, Seong‐O Shim

и другие.

New Generation Computing, Год журнала: 2024, Номер 42(5), С. 997 - 1048

Опубликована: Авг. 28, 2024

Язык: Английский

Процитировано

0
Enhancing Facial Emotion Recognition with a Modified Deep Convolutional Neural Network DOI Creative Commons
Samah A. Gamel, Hatem A. Khater

Journal of Engineering Research - Egypt/Journal of Engineering Research, Год журнала: 2023, Номер 7(5), С. 118 - 125

Опубликована: Ноя. 1, 2023

Understanding and predicting human character traits play a crucial role in various domains ranging from psychology to resources.With the advent of artificial intelligence (AI) deep learning algorithms, researchers have explored potential analyzing facial images predict accurately.In this paper, we present comprehensive study application AI techniques for recognition.We review existing literature on image analysis, personality prediction.Furthermore, propose methodology that leverages convolutional neural networks (CNNs) extract meaningful features images.Our experiments demonstrate effectiveness our approach accurately showcasing promising results using small-scale datasets.We discuss implications findings psychology, resources, personalized user experiences.Additionally, ethical considerations, such as privacy bias, are addressed.This research contributes growing field AI-driven recognition, providing insights further advancements practical applications understanding behavior.

Язык: Английский

Процитировано

0