Papers by Nehad M Ibrahim
Big Data and Cognitive Computing
Plant taxonomy is the scientific study of the classification and naming of various plant species.... more Plant taxonomy is the scientific study of the classification and naming of various plant species. It is a branch of biology that aims to categorize and organize the diverse variety of plant life on earth. Traditionally, plant taxonomy has been performed using morphological and anatomical characteristics, such as leaf shape, flower structure, and seed and fruit characters. Artificial intelligence (AI), machine learning, and especially deep learning can also play an instrumental role in plant taxonomy by automating the process of categorizing plant species based on the available features. This study investigated transfer learning techniques to analyze images of plants and extract features that can be used to cluster the species hierarchically using the k-means clustering algorithm. Several pretrained deep learning models were employed and evaluated. In this regard, two separate datasets were used in the study comprising of seed images of wild plants collected from Egypt. Extensive exp...
Computers, Materials & Continua
proposed scheme exhibits 99.214% accuracy. The proposed prediction model is a potential contribut... more proposed scheme exhibits 99.214% accuracy. The proposed prediction model is a potential contribution towards smart cities environment.
Applied Sciences
Breast cancer is a primary cause of human deaths among gynecological cancers around the globe. Th... more Breast cancer is a primary cause of human deaths among gynecological cancers around the globe. Though it can occur in both genders, it is far more common in women. It is a disease in which the patient’s body cells in the breast start growing abnormally. It has various kinds (e.g., invasive ductal carcinoma, invasive lobular carcinoma, medullary, and mucinous), which depend on which cells in the breast turn into cancer. Traditional manual methods used to detect breast cancer are not only time consuming but may also be expensive due to the shortage of experts, especially in developing countries. To contribute to this concern, this study proposed a cost-effective and efficient scheme called AMAN. It is based on deep learning techniques to diagnose breast cancer in its initial stages using X-ray mammograms. This system classifies breast cancer into two stages. In the first stage, it uses a well-trained deep learning model (Xception) while extracting the most crucial features from the pa...
Smart Innovation, Systems and Technologies, 2022
2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)
International Journal of Advanced Computer Science and Applications
Lecture notes on data engineering and communications technologies, 2022
Sensors
In the oil and gas industries, predicting and classifying oil and gas production for hydrocarbon ... more In the oil and gas industries, predicting and classifying oil and gas production for hydrocarbon wells is difficult. Most oil and gas companies use reservoir simulation software to predict future oil and gas production and devise optimum field development plans. However, this process costs an immense number of resources and is time consuming. Each reservoir prediction experiment needs tens or hundreds of simulation runs, taking several hours or days to finish. In this paper, we attempt to overcome these issues by creating machine learning and deep learning models to expedite the process of forecasting oil and gas production. The dataset was provided by the leading oil producer, Saudi Aramco. Our approach reduced the time costs to a worst-case of a few minutes. Our study covered eight different ML and DL experiments and achieved its most outstanding R2 scores of 0.96 for XGBoost, 0.97 for ANN, and 0.98 for RNN over the other experiments.
Computers, Materials & Continua, 2021
COVID-19 turned out to be an infectious and life-threatening viral disease, and its swift and ove... more COVID-19 turned out to be an infectious and life-threatening viral disease, and its swift and overwhelming spread has become one of the greatest challenges for the world. As yet, no satisfactory vaccine or medication has been developed that could guarantee its mitigation, though several efforts and trials are underway. Countries around the globe are striving to overcome the COVID-19 spread and while they are finding out ways for early detection and timely treatment. In this regard, healthcare experts, researchers and scientists have delved into the investigation of existing as well as new technologies. The situation demands development of a clinical decision support system to equip the medical staff ways to timely detect this disease. The state-of-the-art research inArtificial intelligence (AI), Machine learning (ML) and cloud computing have encouraged healthcare experts to find effective detection schemes. This study aims to provide a comprehensive review of the role of AI & ML in investigating prediction techniques for the COVID-19. A mathematical model has been formulated to analyze and detect its potential threat. The proposed model is a cloud-based smart detection algorithm using support vector machine (CSDC-SVM) with cross-fold validation testing. The experimental results have achieved an accuracy of 98.4% with 15-fold cross-validation strategy. The comparisonwith similar state-of-the-artmethods reveals that the proposed CSDC-SVM model possesses better accuracy and efficiency. © 2021 Tech Science Press. All rights reserved.
Multimedia Tools and Applications, Mar 29, 2022
Deep learning techniques have been playing an important role in the identification and classifica... more Deep learning techniques have been playing an important role in the identification and classification of problems such as diseases in medical science, marketing in the industry, manufacturing in engineering, and identification in plant taxonomy science. Fruit identification and its family classification is among one of the areas that need more emphasis for the sake of automation. With this inspiration, fruit images for 52 species belonging to four different families (Apiaceae, Brassicaceae, Asteraceae, and Apocynaceae) have been used in this study to build a deep learning analysis dataset. Further, the dataset has been augmented to 3800 images, divided to 2660 images for training and 1440 for testing, and different 14 fruit images belonging to the same families have been used for prediction of the testing module. A novel Convolution Neural Network (CNN) model architecture has been proposed to extract the fruit features, classify each image with its family, and use the trained model to predict that the new fruits belong to the same four families. The maximum accuracy obtained for the training and testing module was 99.82%. The prediction for this module succeeded by 93% since all fruits’ success predicted was attained except one from the family number 2 (Brassicaceae). The same dataset was applied to two different models to evaluate our proposed model, the Deep learning model, aka Residual Neural Network, 20 layers (ResNet-20), and Support Vector Machine (SVM). The proposed CNN model achieved higher accuracy and efficiency than the ResNet-20 and SVM.
Saudi Journal of Engineering and Technology, 2019
A major challenge in article clustering is high dimensionality, because this will affect directly... more A major challenge in article clustering is high dimensionality, because this will affect directly to the accuracy. However, it is becoming more important due to the huge textual information available online. In this paper, we proposed an Arabic word net dictionary to extract, select and reduce the features. Additionally, we use the embedding Word2Vector model as feature weighting technique. Finally, for the clustering uses the hierarchy clustering. Our methods are using the Arabic word net dictionary with word embedding, additionally by using the discretization. This method are effective and can enhance improve the accuracy of clustering, which shown in our experimental results.
IEEE Access
Coughing is a common symptom of several respiratory diseases. The sound and type of cough are use... more Coughing is a common symptom of several respiratory diseases. The sound and type of cough are useful features to consider when diagnosing a disease. Respiratory infections pose a significant risk to human lives worldwide as well as a significant economic downturn, particularly in countries with limited therapeutic resources. In this study we reviewed the latest proposed technologies that were used to control the impact of respiratory diseases. Artificial Intelligence (AI) is a promising technology that aids in data analysis and prediction of results, thereby ensuring people's well-being. We conveyed that the cough symptom can be reliably used by AI algorithms to detect and diagnose different types of known diseases including pneumonia, pulmonary edema, asthma, tuberculosis (TB), COVID19, pertussis, and other respiratory diseases. We also identified different techniques that produced the best results for diagnosing respiratory disease using cough samples. This study presents the most recent challenges, solutions, and opportunities in respiratory disease detection and diagnosis, allowing practitioners and researchers to develop better techniques. INDEX TERMS Artificial intelligence (AI), cough detection, 2019 novel coronavirus disease (Covid-19), respiratory illness diagnosis, cough-based diagnosis.
Sensors
The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social... more The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social, societal, and health systems around the world. Controlling such epidemic requires understanding its characteristics and behavior, which can be identified by collecting and analyzing the related big data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures. However, due to the vast amount of data available on COVID-19 from various sources, there is a need to review the roles of big data analysis in controlling the spread of COVID-19, presenting the main challenges and directions of COVID-19 data analysis, as well as providing a framework on the related existing applications and studies to facilitate future research on COVID-19 analysis. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of COVID-19-based big data analysis. The study presents as a tax...
Sensors
With population growth and aging, the emergence of new diseases and immunodeficiency, the demand ... more With population growth and aging, the emergence of new diseases and immunodeficiency, the demand for emergency departments (EDs) increases, making overcrowding in these departments a global problem. Due to the disease severity and transmission rate of COVID-19, it is necessary to provide an accurate and automated triage system to classify and isolate the suspected cases. Different triage methods for COVID-19 patients have been proposed as disease symptoms vary by country. Still, several problems with triage systems remain unresolved, most notably overcrowding in EDs, lengthy waiting times and difficulty adjusting static triage systems when the nature and symptoms of a disease changes. In this paper, we conduct a comprehensive review of general ED triage systems as well as COVID-19 triage systems. We identified important parameters that we recommend considering when designing an e-Triage (electronic triage) system for EDs, namely waiting time, simplicity, reliability, validity, scala...
It is evident from day to day web usage experience that a huge number of PDF sources have been up... more It is evident from day to day web usage experience that a huge number of PDF sources have been uploaded on daily basis. For example, there are several scientific societies that publish volumes of articles and periodicals like IEEE, ACM, Elsevier, and Springer etc. Most of these resources are unstructured or semi-structured that makes it difficult to search and retrieve information. In this paper, an effective model for digital library creation is proposed which is originally motivated by an automated ontological information extraction framework (OFIE). The framework takes a PDF published paper, extracts its structural information like title, authors, abstract, funding information, table of contents, references etc. with the help of fuzzy rule-based system (FRBS) and word sense disambiguation (WSD) approach. Consequently, this extracted information is converted to RDF triples. The proposed scheme takes this extracted information and converts into a digital library stored in MS-SQL databased by Extract, Transform and Load (ETL) process. This digital library can be an institute's library or an individual scholar's library who is interested in synthesizing his downloaded PDF files for better search and retrieve purposes. Moreover, by using the SQL queries based front-end design, the information can be searched, retrieved, and exported in the form of reports.
Saudi J Eng Technol, 2019; 4(10): 401-406, 2019
A major challenge in article clustering is high dimensional, because this will affect directly to... more A major challenge in article clustering is high dimensional, because this will affect directly to the accuracy. However, it is becoming more important due to the huge textual information available online. In this paper, we proposed an Arabic word net dictionary to extract, select and reduce the features. Additionally, we use the embedding Word2Vector model as feature weighting technique. Finally, for the clustering uses the hierarchy clustering. Our methods are using the Arabic word net dictionary with word embedding, additionally by using the discretization. This method are effective and can enhance improve the accuracy of clustering, which shown in our experimental results.
Keywords: Machine Learning, Clustering, CBOW, SKIP-GRAM, Word Embedding, Arabic Word Net Dictionary.
an online blogs provide facility to its users to write and read text-based posts known as "articl... more an online blogs provide facility to its users to write and read text-based posts known as "articles". It became one of the most commonly used social networks. However, an important problem arises is that the returned articles, when searching for a topic phrase, are only sorted by recently not relevancy. This makes the user to manually read through the articles in order to understand what are primarily saying about the particular topic. Some strategies were developed for clustering English text but Arabic text clustering is still an active research area. A major challenge in article clustering is the extremely high dimensionality. In this paper we proposed the new method for features reduction using stemming, (Arabic WordNet) (Arabic Word Net) dictionary and Arabic diacritics, Also, new method in measuring similarity by using (Arabic WordNet) relations to enhance accuracy of clustering.
International Journal of Scientific & Engineering Research, 2018
Deep Learning has efficient and accurate methods of learning which come back to the research area... more Deep Learning has efficient and accurate methods of learning which come back to the research area again after rapidly developments in the hardware, Also the text learning either supervised or unsupervised open area for the research. This paper aims to provide the researcher in (deep learning for text learning supervised or unsupervised) domain by comprehensive knowledge in this domain, it represents an overview of important articles over the last five years and discus methods that used and the conclusion. This article conducted to address relevant researches about the deep learning use in text mining by using the Google Scholar to define the period (issued between 2013 and 2018).
IJCSIS, 2017
Abstract—an online blogs provide facility to its users to
write and read text-based posts known a... more Abstract—an online blogs provide facility to its users to
write and read text-based posts known as "articles". It
became one of the most commonly used social networks.
However, an important problem arises is that the returned
articles, when searching for a topic phrase, are only sorted
by recently not relevancy. This makes the user to manually
read through the articles in order to understand what are
primarily saying about the particular topic. Some
strategies were developed for clustering English text but
Arabic text clustering is still an active research area.
A major challenge in article clustering is the extremely high
dimensionality. In this paper we proposed the new method for
features reduction using stemming, (Arabic WordNet) (Arabic
Word Net) dictionary and Arabic diacritics, Also, new method in
measuring similarity by using (Arabic WordNet) relations to
enhance accuracy of clustering.
Uploads
Papers by Nehad M Ibrahim
Keywords: Machine Learning, Clustering, CBOW, SKIP-GRAM, Word Embedding, Arabic Word Net Dictionary.
write and read text-based posts known as "articles". It
became one of the most commonly used social networks.
However, an important problem arises is that the returned
articles, when searching for a topic phrase, are only sorted
by recently not relevancy. This makes the user to manually
read through the articles in order to understand what are
primarily saying about the particular topic. Some
strategies were developed for clustering English text but
Arabic text clustering is still an active research area.
A major challenge in article clustering is the extremely high
dimensionality. In this paper we proposed the new method for
features reduction using stemming, (Arabic WordNet) (Arabic
Word Net) dictionary and Arabic diacritics, Also, new method in
measuring similarity by using (Arabic WordNet) relations to
enhance accuracy of clustering.
Keywords: Machine Learning, Clustering, CBOW, SKIP-GRAM, Word Embedding, Arabic Word Net Dictionary.
write and read text-based posts known as "articles". It
became one of the most commonly used social networks.
However, an important problem arises is that the returned
articles, when searching for a topic phrase, are only sorted
by recently not relevancy. This makes the user to manually
read through the articles in order to understand what are
primarily saying about the particular topic. Some
strategies were developed for clustering English text but
Arabic text clustering is still an active research area.
A major challenge in article clustering is the extremely high
dimensionality. In this paper we proposed the new method for
features reduction using stemming, (Arabic WordNet) (Arabic
Word Net) dictionary and Arabic diacritics, Also, new method in
measuring similarity by using (Arabic WordNet) relations to
enhance accuracy of clustering.