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Full-Text Articles in Physical Sciences and Mathematics
Sustainable Energysense: A Predictive Machine Learning Framework For Optimizing Residential Electricity Consumption, Murad Al-Rajab, Samia Loucif
Sustainable Energysense: A Predictive Machine Learning Framework For Optimizing Residential Electricity Consumption, Murad Al-Rajab, Samia Loucif
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In a world where electricity is often taken for granted, the surge in consumption poses significant challenges, including elevated CO2 emissions and rising prices. These issues not only impact consumers but also have broader implications for the global environment. This paper endeavors to propose a smart application dedicated to optimizing the electricity consumption of household appliances. It employs Augmented Reality (AR) technology along with YOLO to detect electrical appliances and provide detailed electricity consumption insights, such as displaying the appliance consumption rate and computing the total electricity consumption based on the number of hours the appliance was used. The application …
Dataset Of Arabic Spam And Ham Tweets, Sanaa Kaddoura, Safaa Henno
Dataset Of Arabic Spam And Ham Tweets, Sanaa Kaddoura, Safaa Henno
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This data article provides a dataset of 132421 posts and their corresponding information collected from Twitter social media. The data has two classes, ham or spam, where ham indicates non-spam clean tweets. The main target of this dataset is to study a way to classify whether a post is a spam or not automatically. The data is in Arabic language only, which makes the data essential to the researchers in Arabic natural language processing (NLP) due to the lack of resources in this language. The data is made publicly available to allow researchers to use it as a benchmark for …
Advancing The Understanding Of Clinical Sepsis Using Gene Expression–Driven Machine Learning To Improve Patient Outcomes, Asrar Rashid, Feras Al-Obeidat, Wael Hafez, Govind Benakatti, Rayaz A. Malik, Christos Koutentis, Javed Sharief, Joe Brierley, Nasir Quraishi, Zainab A. Malik, Arif Anwary, Hoda Alkhzaimi, Syed Ahmed Zaki, Praveen Khilnani, Raziya Kadwa, Rajesh Phatak, Maike Schumacher, M. Guftar Shaikh, Ahmed Al-Dubai, Amir Hussain
Advancing The Understanding Of Clinical Sepsis Using Gene Expression–Driven Machine Learning To Improve Patient Outcomes, Asrar Rashid, Feras Al-Obeidat, Wael Hafez, Govind Benakatti, Rayaz A. Malik, Christos Koutentis, Javed Sharief, Joe Brierley, Nasir Quraishi, Zainab A. Malik, Arif Anwary, Hoda Alkhzaimi, Syed Ahmed Zaki, Praveen Khilnani, Raziya Kadwa, Rajesh Phatak, Maike Schumacher, M. Guftar Shaikh, Ahmed Al-Dubai, Amir Hussain
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Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of machine learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information …
Enhancedbert: A Feature-Rich Ensemble Model For Arabic Word Sense Disambiguation With Statistical Analysis And Optimized Data Collection, Sanaa Kaddoura, Reem Nassar
Enhancedbert: A Feature-Rich Ensemble Model For Arabic Word Sense Disambiguation With Statistical Analysis And Optimized Data Collection, Sanaa Kaddoura, Reem Nassar
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Accurate assignment of meaning to a word based on its context, known as Word Sense Disambiguation (WSD), remains challenging across languages. Extensive research aims to develop automated methods for determining word senses in different contexts. However, the literature lacks the presence of datasets generated for the Arabic language WSD. This paper presents a dataset comprising a hundred polysemous Arabic words. Each word in the dataset encompasses 3–8 distinct senses, with ten example sentences per sense. Some statistical operations are conducted to gain insights into the dataset, enlightening its characteristics and properties. Subsequently, a novel WSD approach is proposed to utilize …
Algorithm Selection Using Edge Ml And Case-Based Reasoning, Rahman Ali, Muhammad Sadiq Hassan Zada, Asad Masood Khatak, Jamil Hussain
Algorithm Selection Using Edge Ml And Case-Based Reasoning, Rahman Ali, Muhammad Sadiq Hassan Zada, Asad Masood Khatak, Jamil Hussain
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In practical data mining, a wide range of classification algorithms is employed for prediction tasks. However, selecting the best algorithm poses a challenging task for machine learning practitioners and experts, primarily due to the inherent variability in the characteristics of classification problems, referred to as datasets, and the unpredictable performance of these algorithms. Dataset characteristics are quantified in terms of meta-features, while classifier performance is evaluated using various performance metrics. The assessment of classifiers through empirical methods across multiple classification datasets, while considering multiple performance metrics, presents a computationally expensive and time-consuming obstacle in the pursuit of selecting the optimal …
Predicting New Crescent Moon Visibility Applying Machine Learning Algorithms, Murad Al-Rajab, Samia Loucif, Yazan Al Risheh
Predicting New Crescent Moon Visibility Applying Machine Learning Algorithms, Murad Al-Rajab, Samia Loucif, Yazan Al Risheh
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The world's population is projected to grow 32% in the coming years, and the number of Muslims is expected to grow by 70%—from 1.8 billion in 2015 to about 3 billion in 2060. Hijri is the Islamic calendar, also known as the lunar Hijri calendar, which consists of 12 lunar months, and it is tied to the Moon phases where a new crescent Moon marks the beginning of each month. Muslims use the Hijri calendar to determine important dates and religious events such as Ramadan, Haj, Muharram, etc. Till today, there is no consensus on deciding on the beginning of …
Migrating 120,000 Legacy Publications From Several Systems Into A Current Research Information System Using Advanced Data Wrangling Techniques, Yrjö Lappalainen, Matti Lassila, Tanja Heikkilä, Jani Nieminen, Tapani Lehtilä
Migrating 120,000 Legacy Publications From Several Systems Into A Current Research Information System Using Advanced Data Wrangling Techniques, Yrjö Lappalainen, Matti Lassila, Tanja Heikkilä, Jani Nieminen, Tapani Lehtilä
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This article describes a complex CRIS (current research information system) implementation project involving the migration of around 120,000 legacy publication records from three different systems. The project, undertaken by Tampere University, encountered several challenges in data diversity, data quality, and resource allocation. To handle the extensive and heterogenous dataset, innovative approaches such as machine learning techniques and various data wrangling tools were used to process data, correct errors, and merge information from different sources. Despite significant delays and unforeseen obstacles, the project was ultimately successful in achieving its goals. The project served as a valuable learning experience, highlighting the importance …
Malfe—Malware Feature Engineering Generation Platform, Avinash Singh, Richard Adeyemi Ikuesan, Hein Venter
Malfe—Malware Feature Engineering Generation Platform, Avinash Singh, Richard Adeyemi Ikuesan, Hein Venter
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The growing sophistication of malware has resulted in diverse challenges, especially among security researchers who are expected to develop mechanisms to thwart these malicious attacks. While security researchers have turned to machine learning to combat this surge in malware attacks and enhance detection and prevention methods, they often encounter limitations when it comes to sourcing malware binaries. This limitation places the burden on malware researchers to create context-specific datasets and detection mechanisms, a time-consuming and intricate process that involves a series of experiments. The lack of accessible analysis reports and a centralized platform for sharing and verifying findings has resulted …
A Survey Of Eeg And Machine Learning-Based Methods For Neural Rehabilitation, Jaiteg Singh, Farman Ali, Rupali Gill, Babar Shah, Daehan Kwak
A Survey Of Eeg And Machine Learning-Based Methods For Neural Rehabilitation, Jaiteg Singh, Farman Ali, Rupali Gill, Babar Shah, Daehan Kwak
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One approach to therapy and training for the restoration of damaged muscles and motor systems is rehabilitation. EEG-assisted Brain-Computer Interface (BCI) may assist in restoring or enhancing ‘lost motor abilities in the brain. Assisted by brain activity, BCI offers simple-to-use technology aids and robotic prosthetics. This systematic literature review aims to explore the latest developments in BCI and motor control for rehabilitation. Additionally, we have explored typical EEG apparatuses that are available for BCI-driven rehabilitative purposes. Furthermore, a comparison of significant studies in rehabilitation assessment using machine learning techniques has been summarized. The results of this study may influence policymakers’ …
Machine Learning Techniques For The Identification Of Risk Factors Associated With Food Insecurity Among Adults In Arab Countries During The Covid-19 Pandemic, Radwan Qasrawi, Maha Hoteit, Reema Tayyem, Khlood Bookari, Haleama Al Sabbah, Iman Kamel, Somaia Dashti, Sabika Allehdan, Hiba Bawadi, Mostafa Waly, Mohammed O. Ibrahim, Stephanny Vicuna Polo, Diala Abu Al-Halawa
Machine Learning Techniques For The Identification Of Risk Factors Associated With Food Insecurity Among Adults In Arab Countries During The Covid-19 Pandemic, Radwan Qasrawi, Maha Hoteit, Reema Tayyem, Khlood Bookari, Haleama Al Sabbah, Iman Kamel, Somaia Dashti, Sabika Allehdan, Hiba Bawadi, Mostafa Waly, Mohammed O. Ibrahim, Stephanny Vicuna Polo, Diala Abu Al-Halawa
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BACKGROUND: A direct consequence of global warming, and strongly correlated with poor physical and mental health, food insecurity is a rising global concern associated with low dietary intake. The Coronavirus pandemic has further aggravated food insecurity among vulnerable communities, and thus has sparked the global conversation of equal food access, food distribution, and improvement of food support programs. This research was designed to identify the key features associated with food insecurity during the COVID-19 pandemic using Machine learning techniques. Seven machine learning algorithms were used in the model, which used a dataset of 32 features. The model was designed to …
Sentence Embedding Approach Using Lstm Auto-Encoder For Discussion Threads Summarization, Abdul Wali Khan, Feras Al-Obeidat, Afsheen Khalid, Adnan Amin, Fernando Moreira
Sentence Embedding Approach Using Lstm Auto-Encoder For Discussion Threads Summarization, Abdul Wali Khan, Feras Al-Obeidat, Afsheen Khalid, Adnan Amin, Fernando Moreira
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Online discussion forums are repositories of valuable information where users interact and articulate their ideas and opinions, and share experiences about numerous topics. These online discussion forums are internet-based online communities where users can ask for help and find the solution to a problem. A new user of online discussion forums becomes exhausted from reading the significant number of irrelevant replies in a discussion. An automated discussion thread summarizing system (DTS) is necessary to create a candid view of the entire discussion of a query. Most of the previous approaches for automated DTS use the continuous bag of words (CBOW) …
An Optimized Bagging Ensemble Learning Approach Using Bestrees For Predicting Students’ Performance, Edmund Evangelista
An Optimized Bagging Ensemble Learning Approach Using Bestrees For Predicting Students’ Performance, Edmund Evangelista
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Every academic institution's goal is to identify students who require additional assistance and take appropriate actions to improve their performance. As such, various research studies have focused on developing prediction models that can detect correlated patterns influencing students' performance, dropout, collaboration, and engagement. Among the influential predictive models available, the bagging ensemble has captured the interest of researchers seeking to improve prediction accuracy over single classifiers. However, prior work in this area has focused mainly on selecting single classifiers as the base classifier of the bagging ensemble, with little to no further optimization of the proposed framework. This study aims …
An Advanced Deep Learning Models-Based Plant Disease Detection: A Review Of Recent Research, Muhammad Shoaib, Babar Shah, Shaker Ei-Sappagh, Akhtar Ali, Asad Ullah, Fayadh Alenezi, Tsanko Gechev, Tariq Hussain, Farman Ali
An Advanced Deep Learning Models-Based Plant Disease Detection: A Review Of Recent Research, Muhammad Shoaib, Babar Shah, Shaker Ei-Sappagh, Akhtar Ali, Asad Ullah, Fayadh Alenezi, Tsanko Gechev, Tariq Hussain, Farman Ali
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Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on …
Towards Machine Learning-Based Fpga Backend Flow: Challenges And Opportunities, Imran Taj, Umer Farooq
Towards Machine Learning-Based Fpga Backend Flow: Challenges And Opportunities, Imran Taj, Umer Farooq
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Field-Programmable Gate Array (FPGA) is at the core of System on Chip (SoC) design across various Industry 5.0 digital systems—healthcare devices, farming equipment, autonomous vehicles and aerospace gear to name a few. Given that pre-silicon verification using Computer Aided Design (CAD) accounts for about 70% of the time and money spent on the design of modern digital systems, this paper summarizes the machine learning (ML)-oriented efforts in different FPGA CAD design steps. With the recent breakthrough of machine learning, FPGA CAD tasks—high-level synthesis (HLS), logic synthesis, placement and routing—are seeing a renewed interest in their respective decision-making steps. We focus …
Prediction Of Wilms’ Tumor Susceptibility To Preoperative Chemotherapy Using A Novel Computer-Aided Prediction System, Israa Sharaby, Ahmed Alksas, Ahmed Nashat, Hossam Magdy Balaha, Mohamed Shehata, Mallorie Gayhart, Ali Mahmoud, Mohammed Ghazal, Ashraf Khalil, Rasha T. Abouelkheir, Ahmed Elmahdy, Ahmed Abdelhalim, Ahmed Mosbah, Ayman El-Baz
Prediction Of Wilms’ Tumor Susceptibility To Preoperative Chemotherapy Using A Novel Computer-Aided Prediction System, Israa Sharaby, Ahmed Alksas, Ahmed Nashat, Hossam Magdy Balaha, Mohamed Shehata, Mallorie Gayhart, Ali Mahmoud, Mohammed Ghazal, Ashraf Khalil, Rasha T. Abouelkheir, Ahmed Elmahdy, Ahmed Abdelhalim, Ahmed Mosbah, Ayman El-Baz
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Wilms’ tumor, the most prevalent renal tumor in children, is known for its aggressive prognosis and recurrence. Treatment of Wilms’ tumor is multimodal, including surgery, chemotherapy, and occasionally, radiation therapy. Preoperative chemotherapy is used routinely in European studies and in select indications in North American trials. The objective of this study was to build a novel computer-aided prediction system for preoperative chemotherapy response in Wilms’ tumors. A total of 63 patients (age range: 6 months–14 years) were included in this study, after receiving their guardians’ informed consent. We incorporated contrast-enhanced computed tomography imaging to extract the texture, shape, and functionality-based …
Application Of A Gene Modular Approach For Clinical Phenotype Genotype Association And Sepsis Prediction Using Machine Learning In Meningococcal Sepsis, Asrar Rashid, Arif R. Anwary, Feras Al-Obeidat, Joe Brierley, Mohammed Uddin, Hoda Alkhzaimi, Amrita Sarpal, Mohammed Toufiq, Zainab A. Malik, Raziya Kadwa, Praveen Khilnani, M. Guftar Shaikh, Govind Benakatti, Javed Sharief, Syed Ahmed Zaki, Abdulrahman Zeyada, Ahmed Al-Dubai, Wael Hafez, Amir Hussain
Application Of A Gene Modular Approach For Clinical Phenotype Genotype Association And Sepsis Prediction Using Machine Learning In Meningococcal Sepsis, Asrar Rashid, Arif R. Anwary, Feras Al-Obeidat, Joe Brierley, Mohammed Uddin, Hoda Alkhzaimi, Amrita Sarpal, Mohammed Toufiq, Zainab A. Malik, Raziya Kadwa, Praveen Khilnani, M. Guftar Shaikh, Govind Benakatti, Javed Sharief, Syed Ahmed Zaki, Abdulrahman Zeyada, Ahmed Al-Dubai, Wael Hafez, Amir Hussain
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Sepsis is a major global health concern causing high morbidity and mortality rates. Our study utilized a Meningococcal Septic Shock (MSS) temporal dataset to investigate the correlation between gene expression (GE) changes and clinical features. The research used Weighted Gene Co-expression Network Analysis (WGCNA) to establish links between gene expression and clinical parameters in infants admitted to the Pediatric Critical Care Unit with MSS. Additionally, various machine learning (ML) algorithms, including Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Artificial Neural Network (ANN) were implemented to predict sepsis survival. The findings revealed a transition …
Facial Expression Recognition Using Lightweight Deep Learning Modeling, Mubashir Ahmad, Saira, Omar Alfandi, Asad Masood Khattak, Syed Furqan Qadri, Iftikhar Ahmed Saeed, Salabat Khan, Bashir Hayat, Arshad Ahmad
Facial Expression Recognition Using Lightweight Deep Learning Modeling, Mubashir Ahmad, Saira, Omar Alfandi, Asad Masood Khattak, Syed Furqan Qadri, Iftikhar Ahmed Saeed, Salabat Khan, Bashir Hayat, Arshad Ahmad
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Facial expression is a type of communication and is useful in many areas of computer vision, including intelligent visual surveillance, human-robot interaction and human behavior analysis. A deep learning approach is presented to classify happy, sad, angry, fearful, contemptuous, surprised and disgusted expressions. Accurate detection and classification of human facial expression is a critical task in image processing due to the inconsistencies amid the complexity, including change in illumination, occlusion, noise and the over-fitting problem. A stacked sparse auto-encoder for facial expression recognition (SSAE-FER) is used for unsupervised pre-training and supervised fine-tuning. SSAE-FER automatically extracts features from input images, and …
Of Stances, Themes, And Anomalies In Covid-19 Mask-Wearing Tweets, Jwen Fai Low, Benjamin C.M. Fung, Farkhund Iqbal, Ebrahim Bagheri
Of Stances, Themes, And Anomalies In Covid-19 Mask-Wearing Tweets, Jwen Fai Low, Benjamin C.M. Fung, Farkhund Iqbal, Ebrahim Bagheri
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COVID-19 is an opportunity to study public acceptance of a ‘‘new’’ healthcare intervention, universal masking, which unlike vaccination, is mostly alien to the Anglosphere public despite being practiced in ages past. Using a collection of over two million tweets, we studied the ways in which proponents and opponents of masking vied for influence as well as the themes driving the discourse. Pro-mask tweets encouraging others to mask up dominated Twitter early in the pandemic though its continued dominance has been eroded by anti-mask tweets criticizing others for their masking behavior. Engagement, represented by the counts of likes, retweets, and replies, …
Social Media Bot Detection With Deep Learning Methods: A Systematic Review, Kadhim Hayawi, Susmita Saha, Mohammad Mehedy Masud, Sujith Samuel Mathew, Mohammed Kaosar
Social Media Bot Detection With Deep Learning Methods: A Systematic Review, Kadhim Hayawi, Susmita Saha, Mohammad Mehedy Masud, Sujith Samuel Mathew, Mohammed Kaosar
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Social bots are automated social media accounts governed by software and controlled by humans at the backend. Some bots have good purposes, such as automatically posting information about news and even to provide help during emergencies. Nevertheless, bots have also been used for malicious purposes, such as for posting fake news or rumour spreading or manipulating political campaigns. There are existing mechanisms that allow for detection and removal of malicious bots automatically. However, the bot landscape changes as the bot creators use more sophisticated methods to avoid being detected. Therefore, new mechanisms for discerning between legitimate and bot accounts are …
The Role Of Radiomics And Ai Technologies In The Segmentation, Detection, And Management Of Hepatocellular Carcinoma, Dalia Fahmy, Ahmed Alksas, Ahmed Elnakib, Ali Mahmoud, Heba Kandil, Ashraf Khalil, Mohammed Ghazal, Eric Van Bogaert, Sohail Contractor, Ayman El-Baz
The Role Of Radiomics And Ai Technologies In The Segmentation, Detection, And Management Of Hepatocellular Carcinoma, Dalia Fahmy, Ahmed Alksas, Ahmed Elnakib, Ali Mahmoud, Heba Kandil, Ashraf Khalil, Mohammed Ghazal, Eric Van Bogaert, Sohail Contractor, Ayman El-Baz
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Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.
Emotion Quantification Using Variational Quantum State Fidelity Estimation, Jaiteg Singh, Farman Ali, Babar Shah, Kamalpreet Singh Bhangu, Daehan Kwak
Emotion Quantification Using Variational Quantum State Fidelity Estimation, Jaiteg Singh, Farman Ali, Babar Shah, Kamalpreet Singh Bhangu, Daehan Kwak
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Sentiment analysis has been instrumental in developing artificial intelligence when applied to various domains. However, most sentiments and emotions are temporal and often exist in a complex manner. Several emotions can be experienced at the same time. Instead of recognizing only categorical information about emotions, there is a need to understand and quantify the intensity of emotions. The proposed research intends to investigate a quantum-inspired approach for quantifying emotional intensities in runtime. The inspiration comes from manifesting human cognition and decision-making capabilities, which may adopt a brief explanation through quantum theory. Quantum state fidelity was used to characterize states and …
An Approach For Improved Students’ Performance Prediction Using Homogeneous And Heterogeneous Ensemble Methods, Edmund Evangelista, Benedict Sy
An Approach For Improved Students’ Performance Prediction Using Homogeneous And Heterogeneous Ensemble Methods, Edmund Evangelista, Benedict Sy
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Web-based learning technologies of educational institutions store a massive amount of interaction data which can be helpful to predict students’ performance through the aid of machine learning algorithms. With this, various researchers focused on studying ensemble learning methods as it is known to improve the predictive accuracy of traditional classification algorithms. This study proposed an approach for enhancing the performance prediction of different single classification algorithms by using them as base classifiers of homogeneous ensembles (bagging and boosting) and heterogeneous ensembles (voting and stacking). The model utilized various single classifiers such as multilayer perceptron or neural networks (NN), random forest …
Predicting The Level Of Respiratory Support In Covid-19 Patients Using Machine Learning, Hisham Abdeltawab, Fahmi Khalifa, Yaser Elnakieb, Ahmed Elnakib, Fatma Taher, Norah Saleh Alghamdi, Harpal Singh Sandhu, Ayman El-Baz
Predicting The Level Of Respiratory Support In Covid-19 Patients Using Machine Learning, Hisham Abdeltawab, Fahmi Khalifa, Yaser Elnakieb, Ahmed Elnakib, Fatma Taher, Norah Saleh Alghamdi, Harpal Singh Sandhu, Ayman El-Baz
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In this paper, a machine learning-based system for the prediction of the required level of respiratory support in COVID-19 patients is proposed. The level of respiratory support is divided into three classes: class 0 which refers to minimal support, class 1 which refers to non-invasive support, and class 2 which refers to invasive support. A two-stage classification system is built. First, the classification between class 0 and others is performed. Then, the classification between class 1 and class 2 is performed. The system is built using a dataset collected retrospectively from 3491 patients admitted to tertiary care hospitals at the …
Evaluation Of Machine Learning Algorithm On Drinking Water Quality For Better Sustainability, Sanaa Kaddoura
Evaluation Of Machine Learning Algorithm On Drinking Water Quality For Better Sustainability, Sanaa Kaddoura
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Water has become intricately linked to the United Nations' sixteen sustainable development goals. Access to clean drinking water is crucial for health, a fundamental human right, and a component of successful health protection policies. Clean water is a significant health and development issue on a national, regional, and local level. Investments in water supply and sanitation have been shown to produce a net economic advantage in some areas because they reduce adverse health effects and medical expenses more than they cost to implement. However, numerous pollutants are affecting the quality of drinking water. This study evaluates the efficiency of using …
Using Deep Learning To Detect Social Media ‘Trolls’, Áine Macdermott, Michal Motylinski, Farkhund Iqbal, Kellyann Stamp, Mohammed Hussain, Andrew Marrington
Using Deep Learning To Detect Social Media ‘Trolls’, Áine Macdermott, Michal Motylinski, Farkhund Iqbal, Kellyann Stamp, Mohammed Hussain, Andrew Marrington
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Detecting criminal activity online is not a new concept but how it can occur is changing. Technology and the influx of social media applications and platforms has a vital part to play in this changing landscape. As such, we observe an increasing problem with cyber abuse and ‘trolling’/toxicity amongst social media platforms sharing stories, posts, memes sharing content. In this paper we present our work into the application of deep learning techniques for the detection of ‘trolls’ and toxic content shared on social media platforms. We propose a machine learning solution for the detection of toxic images based on embedded …
An Overview Of Technologies Deployed In Gcc Countries To Combat Covid-19, Samia Loucif, Murad Al-Rajab, Reem Salem, Nadine Akkila
An Overview Of Technologies Deployed In Gcc Countries To Combat Covid-19, Samia Loucif, Murad Al-Rajab, Reem Salem, Nadine Akkila
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Since December 2019, COVID-19 and all of its variants continue to ravage the planet with consequent negative impact that has completely changed our lives within a short period of time after the outbreak of the Virus. On March 11, 2020, COVID-19 was declared a global pandemic by the World Health Organization. Since then, a group of new COVID-19 variants has emerged posing a greater danger to humanity. By the start of August 2021, the reported COVID-19 related death toll across the globe has rocketed to 4,233,139. To deal with the COVID-19 pandemic, countries across the world have rushed to develop …
A Systematic Review On Machine Learning Models For Online Learning And Examination Systems, Sanaa Kaddoura, Daniela Elena Popescu, Jude D. Hemanth
A Systematic Review On Machine Learning Models For Online Learning And Examination Systems, Sanaa Kaddoura, Daniela Elena Popescu, Jude D. Hemanth
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Examinations or assessments play a vital role in every student’s life; they determine their future and career paths. The COVID pandemic has left adverse impacts in all areas, including the academic field. The regularized classroom learning and face-to-face real-time examinations were not feasible to avoid widespread infection and ensure safety. During these desperate times, technological advancements stepped in to aid students in continuing their education without any academic breaks. Machine learning is a key to this digital transformation of schools or colleges from real-time to online mode. Online learning and examination during lockdown were made possible by Machine learning methods. …
The Role Of 3d Ct Imaging In The Accurate Diagnosis Of Lung Function In Coronavirus Patients, Ibrahim Shawky Farahat, Ahmed Sharafeldeen, Mohamed Elsharkawy, Ahmed Soliman, Ali Mahmoud, Mohammed Ghazal, Fatma Taher, Maha Bilal, Ahmed Abdel Khalek Abdel Razek, Waleed Aladrousy, Samir Elmougy, Ahmed Elsaid Tolba, Moumen El-Melegy, Ayman El-Baz
The Role Of 3d Ct Imaging In The Accurate Diagnosis Of Lung Function In Coronavirus Patients, Ibrahim Shawky Farahat, Ahmed Sharafeldeen, Mohamed Elsharkawy, Ahmed Soliman, Ali Mahmoud, Mohammed Ghazal, Fatma Taher, Maha Bilal, Ahmed Abdel Khalek Abdel Razek, Waleed Aladrousy, Samir Elmougy, Ahmed Elsaid Tolba, Moumen El-Melegy, Ayman El-Baz
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Early grading of coronavirus disease 2019 (COVID-19), as well as ventilator support machines, are prime ways to help the world fight this virus and reduce the mortality rate. To reduce the burden on physicians, we developed an automatic Computer-Aided Diagnostic (CAD) system to grade COVID-19 from Computed Tomography (CT) images. This system segments the lung region from chest CT scans using an unsupervised approach based on an appearance model, followed by 3D rotation invariant Markov–Gibbs Random Field (MGRF)-based morphological constraints. This system analyzes the segmented lung and generates precise, analytical imaging markers by estimating the MGRF-based analytical potentials. Three Gibbs …
A Novel Approach To Face Pattern Analysis, Shashi Bhushan, Mohammed Alshehri, Neha Agarwal, Ismail Keshta, Jitendra Rajpurohit, Ahed Abugabah
A Novel Approach To Face Pattern Analysis, Shashi Bhushan, Mohammed Alshehri, Neha Agarwal, Ismail Keshta, Jitendra Rajpurohit, Ahed Abugabah
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Recognizing facial expressions is a major challenge and will be required in the latest fields of research such as the industrial Internet of Things. Currently, the available methods are useful for detecting singular facial images, but they are very hard to extract. The main aim of face detection is to capture an image in real‐time and search for the image in the available dataset. So, by using this biometric feature, one can recognize and verify the person’s image by their facial features. Many researchers have used Principal Component Analysis (PCA), Support Vector Machine (SVM), a combination of PCA and SVM, …
Early Fire Detection: A New Indoor Laboratory Dataset And Data Distribution Analysis, Amril Nazir, Husam Mosleh, Maen Takruri, Abdul Halim Jallad, Hamad Alhebsi
Early Fire Detection: A New Indoor Laboratory Dataset And Data Distribution Analysis, Amril Nazir, Husam Mosleh, Maen Takruri, Abdul Halim Jallad, Hamad Alhebsi
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Fire alarm systems are typically equipped with various sensors such as heat, smoke, and gas detectors. These provide fire alerts and notifications of emergency exits when a fire has been detected. However, such systems do not give early warning in order to allow appropriate action to be taken when an alarm is first triggered, as the fire may have already caused severe damage. This paper analyzes a new dataset gathered from controlled realistic fire experiments conducted in an indoor laboratory environment. The experiments were conducted in a controlled manner by triggering the source of fire using electrical devices and charcoal …