Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Artificial Intelligence and Robotics (1)
- Biomedical (1)
- Computer Engineering (1)
- Computer Sciences (1)
- Computer and Systems Architecture (1)
-
- Data Science (1)
- Data Storage Systems (1)
- Digital Communications and Networking (1)
- Electrical and Computer Engineering (1)
- Engineering (1)
- Operational Research (1)
- Operations Research, Systems Engineering and Industrial Engineering (1)
- Other Computer Engineering (1)
- Physical Sciences and Mathematics (1)
- Robotics (1)
- Signal Processing (1)
- Systems and Communications (1)
Articles 1 - 19 of 19
Full-Text Articles in Entire DC Network
Sustainable Waste Management Through The Lens Of Artificial Intelligence: An In-Depth Review, Noha Emad El-Sayad, Shereen Zakaria
Sustainable Waste Management Through The Lens Of Artificial Intelligence: An In-Depth Review, Noha Emad El-Sayad, Shereen Zakaria
Journal of Engineering Research
One of the major issues facing the world, particularly developing countries, is waste management. “Waste” is any material that is not needed or has no intended use. Neglecting this waste endangers the safety of the public and causes harm, as it emits dangerous gases that have negative effects on human health. Egypt has made distinguished efforts to achieve the goals of Egypt Vision 2030 and the Sustainable Development Goals. These efforts, which have been implemented through massive government projects throughout the past few years and are set to be followed by more in the future, still require a lot of …
Sustainable Waste Management Through The Lens Of Artificial Intelligence: An In-Depth Review, Noha Emad El-Sayad, Shereen Zakaria
Sustainable Waste Management Through The Lens Of Artificial Intelligence: An In-Depth Review, Noha Emad El-Sayad, Shereen Zakaria
Journal of Engineering Research
One of the major issues facing the world, particularly developing countries, is waste management. “Waste” is any material that is not needed or has no intended use. Neglecting this waste endangers the safety of the public and causes harm, as it emits dangerous gases that have negative effects on human health. Egypt has made distinguished efforts to achieve the goals of Egypt Vision 2030 and the Sustainable Development Goals. These efforts, which have been implemented through massive government projects throughout the past few years and are set to be followed by more in the future, still require a lot of …
Deep Learning-Based Polyp Detection In Wireless Capsule Endoscopy Images, Doaa Saeed, Mahmoud Sleem, Amira S. Ashour
Deep Learning-Based Polyp Detection In Wireless Capsule Endoscopy Images, Doaa Saeed, Mahmoud Sleem, Amira S. Ashour
Journal of Engineering Research
Gastrointestinal (GI) system diseases have increased significantly, where colon and rectum cancer is considered the second cause of death in 2020. Wireless Capsule Endoscopy (WCE) is a revolutionary procedure for detecting Colorectal lesions. It was automatically used to detect the polyps, multiple SB lesions, bleeding, and Ulcer. The acquired video by the WCE can be processed using a Computer-Aided Diagnosis (CAD) system. However, such videos suffer several problems, including burling, high illumination. and distortion. These effects obligate the development of image processing techniques of high accuracy in detection using deep learning-based segmentation. In this paper, a transfer learning-based U-Net was …
A Cnn-Lstm-Based Deep Learning Approach For Driver Drowsiness Prediction, Mohamed Gomaa
A Cnn-Lstm-Based Deep Learning Approach For Driver Drowsiness Prediction, Mohamed Gomaa
Journal of Engineering Research
Abstract: The development of neural networks and machine learning techniques has recently been the cornerstone for many applications of artificial intelligence. These applications are now found in practically all aspects of our daily life. Predicting drowsiness is one of the most particularly valuable of artificial intelligence for reducing the rate of traffic accidents. According to earlier studies, drowsy driving is at responsible for 25 to 50% of all traffic accidents, which account for 1,200 deaths and 76,000 injuries annually. The goal of this research is to diminish car accidents caused by drowsy drivers. This research tests a number of popular …
A Cnn-Lstm-Based Deep Learning Approach For Driver Drowsiness Prediction, Mohamed Gomaa
A Cnn-Lstm-Based Deep Learning Approach For Driver Drowsiness Prediction, Mohamed Gomaa
Journal of Engineering Research
Abstract: The development of neural networks and machine learning techniques has recently been the cornerstone for many applications of artificial intelligence. These applications are now found in practically all aspects of our daily life. Predicting drowsiness is one of the most particularly valuable of artificial intelligence for reducing the rate of traffic accidents. According to earlier studies, drowsy driving is at responsible for 25 to 50% of all traffic accidents, which account for 1,200 deaths and 76,000 injuries annually. The goal of this research is to diminish car accidents caused by drowsy drivers. This research tests a number of popular …
An Intelligent Hybrid Optimization With Deep Learning Model-Based Schizophrenia Identification From Structural Mri, Raed N. Alabdali
An Intelligent Hybrid Optimization With Deep Learning Model-Based Schizophrenia Identification From Structural Mri, Raed N. Alabdali
Information Sciences Letters
One of the fatal diseases that claim women while they are pregnant or nursing is schizophrenia. Despite several developments and symptoms, it can be challenging to discern between benign and malignant conditions. The main and most popular imaging method to predict Schizophrenia is MR Images. Furthermore, a few earlier models had a definite accuracy when diagnosing the condition. Stable MRI criteria must also be implemented immediately. Compared to other imaging technologies, the MRI imaging method is the simplest, safest, and most common for predicting Schizophrenia. The following factors are mostly involved in the subprocess for the initial MRI image. Before …
Using Artificial Intelligence And Cybersecurity In Medical And Healthcare Applications, Ahmad A. Alzahrani
Using Artificial Intelligence And Cybersecurity In Medical And Healthcare Applications, Ahmad A. Alzahrani
Information Sciences Letters
Healthcare fields have made substantial use of cybersecurity systems to provide excellent patient safety in many healthcare situations. As dangers increase and hackers work tirelessly to elude law enforcement, cybersecurity has been a rapidly expanding field in the news over the past ten years. Although the initial motivations for conducting cyberattacks have generally remained the same over time, hackers have improved their methods. It is getting harder to identify and stop evolving threats using conventional cybersecurity tools. The development of AI methodologies offers hope for equipping cybersecurity professionals to fend against the ever-evolving threat posed by attackers. Therefore, an artificial …
Fast Facial Expression Recognition System: Selection Of Models, L. Atymtayeva, M. Kanatov, A. M. A Musleh, G. Tulemissova
Fast Facial Expression Recognition System: Selection Of Models, L. Atymtayeva, M. Kanatov, A. M. A Musleh, G. Tulemissova
Applied Mathematics & Information Sciences
Facial Expression Recognition (FER) is rapidly developing field of Computer Vision and Pattern Recognition directions. FER can be helpful for various purposes: in security systems for aggression recognition, in education for students interests recognition, in marketing for customer satisfaction and in the many other fields. Usually we can distinguish seven common facial expressions for all persons. However, it is often important to know: whether a person is positive or negative. This paper describes the recognition system for facial expression in real time, which defines relatively fast and accurate the positive or negative emotion of the faces in the camera view …
Deep Learning Model Based On Resnet-50 For Beef Quality Classification, S. E. Abdallah, Wael M. Elmessery, M. Y. Shams, N. S. A. Al-Sattary
Deep Learning Model Based On Resnet-50 For Beef Quality Classification, S. E. Abdallah, Wael M. Elmessery, M. Y. Shams, N. S. A. Al-Sattary
Information Sciences Letters
Food quality measurement is one of the most essential topics in agriculture and industrial fields. To classify healthy food using computer visual inspection, a new architecture was proposed to classify beef images to specify the rancid and healthy ones. In traditional measurements, the specialists are not able to classify such images, due to the huge number of beef images required to build a deep learning model. In the present study, different images of beef including healthy and rancid cases were collected according to the analysis done by the Laboratory of Food Technology, Faculty of Agriculture, Kafrelsheikh University in January of …
Detecting Covid-19 In X-Ray Images Using Transfer Learning, Jamal Alsakran, Loai Alnemer, Nouh Alhindawi, Omayya Muard
Detecting Covid-19 In X-Ray Images Using Transfer Learning, Jamal Alsakran, Loai Alnemer, Nouh Alhindawi, Omayya Muard
Information Sciences Letters
Accurate and speedy detection of COVID-19 is essential to curb the spread of the disease and avoid overwhelming the health care system. COVID-19 detection using X-ray images is commonly practiced at medical centers; however, it requires the intervention of medical professionals trained in diagnosing and interpreting medical imagining. In this paper, we employ deep transfer learning models to detect COVID-19 on a dataset of over 20,000 X-ray images. Our results on 5 pretrained models (VGG19, InceptionV3, MobileNetV2, DenseNet121, and ResNet101V2) show high performance of 99% without image augmentation, and 93\% when image augmentation is used.
Mouth Image Based Person Authentication Using Dwlstm And Gru, Showkat A. Dar, S. Palanivel, M. Kalaiselvi Geetha, M. Balasubramanian
Mouth Image Based Person Authentication Using Dwlstm And Gru, Showkat A. Dar, S. Palanivel, M. Kalaiselvi Geetha, M. Balasubramanian
Information Sciences Letters
Recently several classification methods were introduced to solve mouth based biometric authentication systems. The results of previous investigations into mouth prints are insufficient and produce lesser authentication results. This is mainly due to the difficulties that accompany any analysis of the mouths: mouths are very flexible and pliable, and successive mouth print impressions even those obtained from the same person may significantly differ from one other. The existing machine learning methods, may not achieve higher performance and only few methods are available using deep learning for mouth biometric authentication. The use of deep learning based mouth biometrics authentication gives higher …
Univariate And Multivariate Regression Models For Short-Term Wind Energy Forecasting, Che Siti Amira Md Azmi, Ammar Ahmed Alkahtani, Chong Kok Hen, Fuad Noman
Univariate And Multivariate Regression Models For Short-Term Wind Energy Forecasting, Che Siti Amira Md Azmi, Ammar Ahmed Alkahtani, Chong Kok Hen, Fuad Noman
Information Sciences Letters
Wind energy resource is a never-ending resource that is categorized under renewable energy. Electricity generated from the wind when the wind blows across the wind turbine system produces high kinetic energy once it goes through the wind blades, rotating and turning it into useful mechanical energy. That motion of the generator produces electricity. However, in Malaysia, the inconsistency in terms of wind speed required for wind turbines to operate efficiently and generate a suitable amount of electrical power is a major problem. Different locations have different weather parameters that affect wind speed and wind energy production. Wind energy forecasting is …
Univariate And Multivariate Regression Models For Short-Term Wind Energy Forecasting, Che Siti Amira Md Azmi, Ammar Ahmed Alkahtani, Chong Kok Hen, Fuad Noman
Univariate And Multivariate Regression Models For Short-Term Wind Energy Forecasting, Che Siti Amira Md Azmi, Ammar Ahmed Alkahtani, Chong Kok Hen, Fuad Noman
Information Sciences Letters
Wind energy resource is a never-ending resource that is categorized under renewable energy. Electricity generated from the wind when the wind blows across the wind turbine system produces high kinetic energy once it goes through the wind blades, rotating and turning it into useful mechanical energy. That motion of the generator produces electricity. However, in Malaysia, the inconsistency in terms of wind speed required for wind turbines to operate efficiently and generate a suitable amount of electrical power is a major problem. Different locations have different weather parameters that affect wind speed and wind energy production. Wind energy forecasting is …
Customer Churn Prediction In Telecommunication Industry Using Deep Learning, Samah Wael Fujo, Suresh Subramanian, Moaiad Ahmad Khder
Customer Churn Prediction In Telecommunication Industry Using Deep Learning, Samah Wael Fujo, Suresh Subramanian, Moaiad Ahmad Khder
Information Sciences Letters
Without proper analysis and forecasting, industries will find themselves repeatedly churning customers, which the telecom industry in particular cannot afford. A predictable model for customers will allow companies to retain current customers and to obtain new ones. Deep-BP-ANN implemented in this study using two feature selection methods, Variance Thresholding and Lasso Regression, in addition, our model strengthened by early stopping technique to stop training at right time and prevent overfitting. We compared the efficiency of minimizing overfitting between dropout and activity regularization strategies for two real datasets: IBM Telco and Cell2cell. Different evaluation approaches used: Holdout, and 10-fold cross-validation to …
A Deep Learning Approach For Forecasting Global Commodities Prices, Ahmed Saied Elberawi, Mohamed Belal Prof.
A Deep Learning Approach For Forecasting Global Commodities Prices, Ahmed Saied Elberawi, Mohamed Belal Prof.
Future Computing and Informatics Journal
Forecasting future values of time-series data is a critical task in many disciplines including financial planning and decision-making. Researchers and practitioners in statistics apply traditional statistical methods (such as ARMA, ARIMA, ES, and GARCH) for a long time with varying accuracies. Deep learning provides more sophisticated and non-linear approximation that supersede traditional statistical methods in most cases. Deep learning methods require minimal features engineering compared to other methods; it adopts an end-to-end learning methodology. In addition, it can handle a huge amount of data and variables. Financial time series forecasting poses a challenge due to its high volatility and non-stationarity …
Deep Learning Methods For Solar Fault Detection And Classification: A Review, Rawad Al-Mashhadani, Gamal Alkawsi, Yahia Baashar, Ammar Ahmed Alkahtani, Farah Hani Nordin, Wahidah Hashim
Deep Learning Methods For Solar Fault Detection And Classification: A Review, Rawad Al-Mashhadani, Gamal Alkawsi, Yahia Baashar, Ammar Ahmed Alkahtani, Farah Hani Nordin, Wahidah Hashim
Information Sciences Letters
In light of the continuous and rapid increase in reliance on solar energy as a suitable alternative to the conventional energy produced by fuel, maintenance becomes an inevitable matter for both producers and consumers alike. Electroluminescence technology is a useful technique in detecting solar panels’ faults and determining their life span using artificial intelligence tools such as neural networks and others. In recent years, deep learning technology has emerged to open new horizons in the accuracy of learning and extract meaningful information from many applications, particularly those that depend mainly on images, such as the technique of electroluminescence. From the …
Multilayer Perceptron With Auto Encoder Enabled Deep Learning Model For Recommender Systems, Subhashini Narayan
Multilayer Perceptron With Auto Encoder Enabled Deep Learning Model For Recommender Systems, Subhashini Narayan
Future Computing and Informatics Journal
In this modern world of ever-increasing one-click purchases, movie bookings, music, health- care, fashion, the need for recommendations have increased the more. Google, Netflix, Spotify, Amazon and other tech giants use recommendations to customize and tailor their search engines to suit the user’s interests. Many of the existing systems are based on older algorithms which although have decent accuracies, require large training and testing datasets and with the emergence of deep learning, the accuracy of algorithms has further improved, and error rates have reduced due to the use of multiple layers. The need for large datasets has declined as well. …
Co-Extraction Of Feature Sentiment And Context Terms For Context-Sensitive Feature-Based Sentiment Classification Using Attentive-Lstm, S. K .Lavanya, B. Parvathavarthini
Co-Extraction Of Feature Sentiment And Context Terms For Context-Sensitive Feature-Based Sentiment Classification Using Attentive-Lstm, S. K .Lavanya, B. Parvathavarthini
Applied Mathematics & Information Sciences
In the field of business intelligence, the context in which customers see, hear and think about a product plays an important role in their call of buying the product. Context-sensitive sentiment classification methods determine the polarity of the sentiment terms by considering the contexts of the target word. Most of the present techniques consider only product-level, user-level contexts for sentiment classification. These contexts are more general and depend on additional features to achieve good performance. Feature-level contexts e.g., car’s features include mileage and its context comprises city, highway, short-trips, long-trips and hill station providing fine-grained information needed for sentiment classification. …
Improved Facial Expression Recognition With Xception Deep Net And Preprocessed Images, Maksat Kanatov, Lyazzat Atymtayeva, Mateus Mendes
Improved Facial Expression Recognition With Xception Deep Net And Preprocessed Images, Maksat Kanatov, Lyazzat Atymtayeva, Mateus Mendes
Applied Mathematics & Information Sciences
Automated Facial Expression Recognition (FER) is an important part of computer-human interaction. For decades, researchers and scientists have been trying to create a model of artificial intelligence that could think, learn, make decisions and act in a way similar to a real person. Among other skills, such model needs to recognise human facial expression to understand non-verbal language. The present paper describes a method to fine tune the FER process in images, using deep learning CNN model Xception, with preprocessing the images. The method has shown improved results when applied to different datasets.