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Full-Text Articles in Computer Sciences

Efficientnet-Lite And Hybrid Cnn-Knn Implementation For Facial Expression Recognition On Raspberry Pi, Mohd Nadhir Ab Wahab, Anthony Tan Zhen Ren, Amril Nazir, Mohd Halim Mohd Noor, Muhammad Firdaus Akbar, Ahmad Sufril Azlan Mohamed Jan 2021

Efficientnet-Lite And Hybrid Cnn-Knn Implementation For Facial Expression Recognition On Raspberry Pi, Mohd Nadhir Ab Wahab, Anthony Tan Zhen Ren, Amril Nazir, Mohd Halim Mohd Noor, Muhammad Firdaus Akbar, Ahmad Sufril Azlan Mohamed

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Facial expression recognition (FER) is the task of determining a person’s current emotion. It plays an important role in healthcare, marketing, and counselling. With the advancement in deep learning algorithms like Convolutional Neural Network (CNN), the system’s accuracy is improving. A hybrid CNN and k-Nearest Neighbour (KNN) model can improve FER’s accuracy. This paper presents a hybrid CNN-KNN model for FER on the Raspberry Pi 4, where we use CNN for feature extraction. Subsequently, the KNN performs expression recognition. We use the transfer learning technique to build our system with an EfficientNet-Lite model. The hybrid model we propose replaces the …

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Enhanced Concept-Level Sentiment Analysis System With Expanded Ontological Relations For Efficient Classification Of User Reviews, Asad Khattak, Muhammad Zubair Asghar, Zain Ishaq, Waqas Haider Bangyal, Ibrahim A. Hameed Jan 2021

Enhanced Concept-Level Sentiment Analysis System With Expanded Ontological Relations For Efficient Classification Of User Reviews, Asad Khattak, Muhammad Zubair Asghar, Zain Ishaq, Waqas Haider Bangyal, Ibrahim A. Hameed

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Background/introduction: Concept-level sentiment analysis deals with the extraction and classification of concepts and features from user reviews expressed online about products and other entities like political leaders, government policies, and others. The prior studies on concept-level sentiment analysis have used a limited set of linguistic rules for extracting concepts and their associated features. Furthermore, the ontological relations used in the early works for performing concept-level sentiment analysis need enhancement in terms of the extended set of features concepts and ontological relations. Methods: This work aims at addressing the aforementioned issues and tries to bridge the literature gap by proposing an …

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Characterizing Visual Programming Approaches For End-User Developers: A Systematic Review, Mohammad Amin Kuhail, Shahbano Farooq, Rawad Hammad, Mohammed Bahja Jan 2021

Characterizing Visual Programming Approaches For End-User Developers: A Systematic Review, Mohammad Amin Kuhail, Shahbano Farooq, Rawad Hammad, Mohammed Bahja

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Recently many researches have explored the potential of visual programming in robotics, the Internet of Things (IoT), and education. However, there is a lack of studies that analyze the recent evidence-based visual programming approaches that are applied in several domains. This study presents a systematic review to understand, compare, and reflect on recent visual programming approaches using twelve dimensions: visual programming classification, interaction style, target users, domain, platform, empirical evaluation type, test participants' type, number of test participants, test participants' programming skills, evaluation methods, evaluation measures, and accessibility of visual programming tools. The results show that most of the selected …

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Files Cryptography Based On One-Time Pad Algorithm, Ahmad Mohamad Al-Smadi, Ahmad Al-Smadi, Roba Mahmoud Ali Aloglah, Nisrein Abu-Darwish, Ahed Abugabah Jan 2021

Files Cryptography Based On One-Time Pad Algorithm, Ahmad Mohamad Al-Smadi, Ahmad Al-Smadi, Roba Mahmoud Ali Aloglah, Nisrein Abu-Darwish, Ahed Abugabah

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The Vernam-cipher is known as a one-time pad of algorithm that is an unbreakable algorithm because it uses a typically random key equal to the length of data to be coded, and a component of the text is encrypted with an element of the encryption key. In this paper, we propose a novel technique to overcome the obstacles that hinder the use of the Vernam algorithm. First, the Vernam and advance encryption standard AES algorithms are used to encrypt the data as well as to hide the encryption key; Second, a password is placed on the file because of the …

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Convolutional Neural Network Based Vehicle Classification In Adverse Illuminous Conditions For Intelligent Transportation Systems, Muhammad Atif Butt, Asad Masood Khattak, Sarmad Shafique, Bashir Hayat, Saima Abid, Ki Il Kim, Muhammad Waqas Ayub, Ahthasham Sajid, Awais Adnan Jan 2021

Convolutional Neural Network Based Vehicle Classification In Adverse Illuminous Conditions For Intelligent Transportation Systems, Muhammad Atif Butt, Asad Masood Khattak, Sarmad Shafique, Bashir Hayat, Saima Abid, Ki Il Kim, Muhammad Waqas Ayub, Ahthasham Sajid, Awais Adnan

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© 2021 Muhammad Atif Butt et al. In step with rapid advancements in computer vision, vehicle classification demonstrates a considerable potential to reshape intelligent transportation systems. In the last couple of decades, image processing and pattern recognition-based vehicle classification systems have been used to improve the effectiveness of automated highway toll collection and traffic monitoring systems. However, these methods are trained on limited handcrafted features extracted from small datasets, which do not cater the real-time road traffic conditions. Deep learning-based classification systems have been proposed to incorporate the above-mentioned issues in traditional methods. However, convolutional neural networks require piles of …

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Adversarial Reconstruction Loss For Domain Generalization, Bekkouch Imad Eddine Ibrahim, Dragos Constantin Nicolae, Adil Khan, S. M. Ahsan Kazmi, Asad Masood Khattak, Bulat Ibragimov Jan 2021

Adversarial Reconstruction Loss For Domain Generalization, Bekkouch Imad Eddine Ibrahim, Dragos Constantin Nicolae, Adil Khan, S. M. Ahsan Kazmi, Asad Masood Khattak, Bulat Ibragimov

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The biggest fear when deploying machine learning models to the real world is their ability to handle the new data. This problem is significant especially in medicine, where models trained on rich high-quality data extracted from large hospitals do not scale to small regional hospitals. One of the clinical challenges addressed in this work is magnetic resonance image generalization for improved visualization and diagnosis of hip abnormalities such as femoroacetabular impingement and dysplasia. Domain Generalization (DG) is a field in machine learning that tries to solve the model’s dependency on the training data by leveraging many related but different data …

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A Parallelized Database Damage Assessment Approach After Cyberattack For Healthcare Systems, Sanaa Kaddoura, Ramzi A. Haraty, Karam Al Kontar, Omar Alfandi Jan 2021

A Parallelized Database Damage Assessment Approach After Cyberattack For Healthcare Systems, Sanaa Kaddoura, Ramzi A. Haraty, Karam Al Kontar, Omar Alfandi

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In the current Internet of things era, all companies shifted from paper-based data to the electronic format. Although this shift increased the efficiency of data processing, it has security drawbacks. Healthcare databases are a precious target for attackers because they facilitate identity theft and cybercrime. This paper presents an approach for database damage assessment for healthcare systems. Inspired by the current behavior of COVID-19 infections, our approach views the damage assessment problem the same way. The malicious transactions will be viewed as if they are COVID-19 viruses, taken from infection onward. The challenge of this research is to discover the …

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Data-Fusion For Epidemiological Analysis Of Covid-19 Variants In Uae, Anoud Bani-Hani, Anaïs Lavorel, Newel Bessadet Jan 2021

Data-Fusion For Epidemiological Analysis Of Covid-19 Variants In Uae, Anoud Bani-Hani, Anaïs Lavorel, Newel Bessadet

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Since December 2019, a new pandemic has appeared causing a considerable negative global impact. The SARS-CoV-2 first emerged from China and transformed to a global pandemic within a short time. The virus was further observed to be spreading rapidly and mutating at a fast pace, with over 5,775 distinct variations of the virus observed globally (at the time of submitting this paper). Extensive research has been ongoing worldwide in order to get a better understanding of its behaviour, influence and more importantly, ways for reducing its impact. Data analytics has been playing a pivotal role in this research to obtain …

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Q-Learning Based Routing Protocol For Congestion Avoidance, Daniel Godfrey, Beom Su Kim, Haoran Miao, Babar Shah, Bashir Hayat, Imran Khan, Tae Eung Sung, Ki Il Kim Jan 2021

Q-Learning Based Routing Protocol For Congestion Avoidance, Daniel Godfrey, Beom Su Kim, Haoran Miao, Babar Shah, Bashir Hayat, Imran Khan, Tae Eung Sung, Ki Il Kim

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The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes. Among lots of feasible approaches to avoid congestion efficiently, congestion-aware routing protocols tend to search for an uncongested path toward the destination through rule-based approaches in reactive/incident-driven and distributed methods. However, these previous approaches have a problem accommodating the changing network environments in autonomous and self-adaptive operations dynamically. To overcome this drawback, we present a new congestion-aware routing protocol based on a Q-learning algorithm in software-defined networks where logically centralized network operation enables intelligent control and management of network resources. In a proposed routing protocol, …

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A Smart Dynamic Crowd Evacuation System For Exhibition Centers, Faouzi Kamoun, May El Barachi, Fatna Belqasmi, Abderrazak Hachani Jan 2021

A Smart Dynamic Crowd Evacuation System For Exhibition Centers, Faouzi Kamoun, May El Barachi, Fatna Belqasmi, Abderrazak Hachani

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In this paper, we consider the problem of finding the safest evacuation route in a multi-exit exhibition center while the fire hazard spreads. We first propose a system composed of sensor nodes to collect pertinent safety data. We present a real-time dynamic evacuation system that considers the changing conditions in the risks associated with each hallway segment in terms of walking distance, heat, two major asphyxiant fire gases and congestion. Our system activates smart panels placed at major junctions of the hallways to guide evacuees towards the appropriate exit by displaying the proper escape direction. This work can pave the …

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Early Detection Of Lung Cancer - A Challenge, Fatma Taher, Neema Prakash, Ashraf Alzaabi Jan 2021

Early Detection Of Lung Cancer - A Challenge, Fatma Taher, Neema Prakash, Ashraf Alzaabi

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Lung cancer or lung carcinoma, is a common and serious type of cancer caused by rapid cell growth in tissues of the lung. Lung cancer detection at its earlier stage is very difficult because of the structure of the cell alignment which makes it very challenging. Computed tomography (CT) scan is used to detect the presence of cancer and its spread. Visual analysis of CT scan can lead to late treatment of cancer; therefore, different steps of image processing can be used to solve this issue. A comprehensive framework is used for the classification of pulmonary nodules by combining appearance …

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Variational Autoencoders And Wasserstein Generative Adversarial Networks For Improving The Anti-Money Laundering Process, Zhiyuan Chen, Waleed Soliman, Amril Nazir, Mohammad Shorfuzzaman Jan 2021

Variational Autoencoders And Wasserstein Generative Adversarial Networks For Improving The Anti-Money Laundering Process, Zhiyuan Chen, Waleed Soliman, Amril Nazir, Mohammad Shorfuzzaman

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There has been much recent work on fraud and Anti Money Laundering (AML) detection using machine learning techniques. However, most algorithms are based on supervised techniques. Studies show that supervised techniques often have the limitation of not adapting well to new irregular fraud patterns when the dataset is highly imbalanced. Instead, unsupervised learning can have a better capability to find anomalous and irregular patterns in new transaction. Despite this, unsupervised techniques also have the disadvantage of not being able to give state-of-the-art detection results. We propose a suite of unsupervised and deep learning techniques to implement an anti-money laundering and …

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Deceptive Opinions Detection Using New Proposed Arabic Semantic Features, Amel Ziani, Nabiha Azizi, Didier Schwab, Djamel Zenakhra, Monther Aldwairi, Nassira Chekkai, Nawel Zemmal, Marwa Hadj Salah Jan 2021

Deceptive Opinions Detection Using New Proposed Arabic Semantic Features, Amel Ziani, Nabiha Azizi, Didier Schwab, Djamel Zenakhra, Monther Aldwairi, Nassira Chekkai, Nawel Zemmal, Marwa Hadj Salah

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Some users try to post false reviews to promote or to devalue other’s products and services. This action is known as deceptive opinions spam, where spammers try to gain or to profit from posting untruthful reviews. Therefore, we conducted this work to develop and to implement new semantic features to improve the Arabic deception detection. These features were inspired from the study of discourse parse and the rhetoric relations in Arabic. Looking to the importance of the phrase unit in the Arabic language and the grammatical studies, we have analyzed and selected the most used unit markers and relations to …

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Opposition-Based Quantum Bat Algorithm To Eliminate Lower-Order Harmonics Of Multilevel Inverters, Jahedul Islam, Sheikh Tanzim Meraj, Ammar Masaoud, Md Apel Mahmud, Amril Nazir, Muhammad Ashad Kabir, Md Moinul Hossain, Farhan Mumtaz Jan 2021

Opposition-Based Quantum Bat Algorithm To Eliminate Lower-Order Harmonics Of Multilevel Inverters, Jahedul Islam, Sheikh Tanzim Meraj, Ammar Masaoud, Md Apel Mahmud, Amril Nazir, Muhammad Ashad Kabir, Md Moinul Hossain, Farhan Mumtaz

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Selective harmonic elimination (SHE) technique is used in power inverters to eliminate specific lower-order harmonics by determining optimum switching angles that are used to generate Pulse Width Modulation (PWM) signals for multilevel inverter (MLI) switches. Various optimization algorithms have been developed to determine the optimum switching angles. However, these techniques are still trapped in local optima. This study proposes an opposition-based quantum bat algorithm (OQBA) to determine these optimum switching angles. This algorithm is formulated by utilizing habitual characteristics of bats. It has advanced learning ability that can effectively remove lower-order harmonics from the output voltage of MLI. It can …

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Real-Time Privacy Preserving Framework For Covid-19 Contact Tracing, Akashdeep Bhardwaj, Ahmed A. Mohamed, Manoj Kumar, Mohammed Alshehri, Ahed Abugabah Jan 2021

Real-Time Privacy Preserving Framework For Covid-19 Contact Tracing, Akashdeep Bhardwaj, Ahmed A. Mohamed, Manoj Kumar, Mohammed Alshehri, Ahed Abugabah

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The recent unprecedented threat from COVID-19 and past epidemics, such as SARS, AIDS, and Ebola, has affected millions of people in multiple countries. Countries have shut their borders, and their nationals have been advised to self-quarantine. The variety of responses to the pandemic has given rise to data privacy concerns. Infection prevention and control strategies as well as disease control measures, especially real-time contact tracing for COVID-19, require the identification of people exposed to COVID-19. Such tracing frameworks use mobile apps and geolocations to trace individuals. However, while the motive may be well intended, the limitations and security issues associated …

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Synergygrids: Blockchain-Supported Distributed Microgrid Energy Trading, Moayad Aloqaily, Ouns Bouachir, Öznur Özkasap, Faizan Safdar Ali Jan 2021

Synergygrids: Blockchain-Supported Distributed Microgrid Energy Trading, Moayad Aloqaily, Ouns Bouachir, Öznur Özkasap, Faizan Safdar Ali

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Growing intelligent cities is witnessing an increasing amount of local energy generation through renewable energy resources. Energy trade among the local energy generators (aka prosumers) and consumers can reduce the energy consumption cost and also reduce the dependency on conventional energy resources, not to mention the environmental, economic, and societal benefits. However, these local energy sources might not be enough to fulfill energy consumption demands. A hybrid approach, where consumers can buy energy from both prosumers (that generate energy) and also from prosumer of other locations, is essential. A centralized system can be used to manage this energy trading that …

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Active Learning Strategy For Covid-19 Annotated Dataset, Amril Nazir, Ricky Maulana Fajri Jan 2021

Active Learning Strategy For Covid-19 Annotated Dataset, Amril Nazir, Ricky Maulana Fajri

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The efficient diagnosis of COVID-19 plays a key role in preventing its spread. Recently, many artificial intelligence techniques, such as the deep neural network approach, have been implemented to help efficient diagnosis of COVID-19. However, the accurate performance of deep learning depends on the tuning of many hyperparameters and a large amount of labeled data. This COVID-19 data bottleneck also leads to insufficient human resources for data labeling, which presents a challenging obstacle. In this paper, a novel discriminative batch-mode active learning (DS3) is proposed to allow faster and more effective COVID-19 data annotation. The framework specifically designed to suit …

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Automatic Fall Risk Detection Based On Imbalanced Data, Yen-Hung Liu, Patrick C. K. Hung, Farkhund Iqbal, Benjamin C. M. Fung Jan 2021

Automatic Fall Risk Detection Based On Imbalanced Data, Yen-Hung Liu, Patrick C. K. Hung, Farkhund Iqbal, Benjamin C. M. Fung

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In recent years, the declining birthrate and aging population have gradually brought countries into an ageing society. Regarding accidents that occur amongst the elderly, falls are an essential problem that quickly causes indirect physical loss. In this paper, we propose a pose estimation-based fall detection algorithm to detect fall risks. We use body ratio, acceleration and deflection as key features instead of using the body keypoints coordinates. Since fall data is rare in real-world situations, we train and evaluate our approach in a highly imbalanced data setting. We assess not only different imbalanced data handling methods but also different machine …

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Multi-Level Resource Sharing Framework Using Collaborative Fog Environment For Smart Cities, Tariq Qayyum, Zouheir Trabelsi, Asad Waqar Malik, Kadhim Hayawi Jan 2021

Multi-Level Resource Sharing Framework Using Collaborative Fog Environment For Smart Cities, Tariq Qayyum, Zouheir Trabelsi, Asad Waqar Malik, Kadhim Hayawi

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No abstract provided.

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Rfid Adaption In Healthcare Organizations: An Integrative Framework, Ahed Abugabah, Louis Sanzogni, Luke Houghton, Ahmad Ali Alzubi, Alaa Abuqabbeh Jan 2021

Rfid Adaption In Healthcare Organizations: An Integrative Framework, Ahed Abugabah, Louis Sanzogni, Luke Houghton, Ahmad Ali Alzubi, Alaa Abuqabbeh

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Radio frequency identification (RFID), also known as electronic label technology, is a non-contact automated identification technology that recognizes the target object and extracts relevant data and critical characteristics using radio frequency signals. Medical equipment information management is an important part of the construction of a modern hospital, as it is linked to the degree of diagnosis and care, as well as the hospital's benefits and growth. The aim of this study is to create an integrated view of a theoretical framework to identify factors that influence RFID adoption in healthcare, as well as to conduct an empirical review of the …

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Multi-Branch Gabor Wavelet Layers For Pedestrian Attribute Recognition, Imran N. Junejo Jan 2021

Multi-Branch Gabor Wavelet Layers For Pedestrian Attribute Recognition, Imran N. Junejo

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CCBYNCND Surveillance cameras are everywhere, keeping an eye on pedestrians as they navigate through a scene. With this context, our paper addresses the problem of pedestrian attribute recognition (PAR). This problem entails recognizing attributes such as age-group, clothing style, accessories, footwear style etc. This is a multi-label problem and challenging even for human observers. The problem has rightly attracted attention recently from the computer vision community. In this paper, we adopt trainable Gabor wavelets (TGW) layers and use it with a convolution neural network (CNN). Whereas other researchers are using fixed Gabor filters with the CNN, the proposed layers are …

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Autobiographical Meaning Making Protects The Sense Of Self-Continuity Past Forced Migration, Christin Camia, Rida Zafar Jan 2021

Autobiographical Meaning Making Protects The Sense Of Self-Continuity Past Forced Migration, Christin Camia, Rida Zafar

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Forced migration changes people’s lives and their sense of self-continuity fundamentally. One memory-based mechanism to protect the sense of self-continuity and psychological well-being is autobiographical meaning making, enabling individuals to explain change in personality and life by connecting personal experiences and other distant parts of life to the self and its development. Aiming to replicate and extend prior research, the current study investigated whether autobiographical meaning making has the potential to support the sense of self-continuity in refugees. We therefore collected life narratives from 31 refugees that were coded for autobiographical reasoning, selfevent connections, and global narrative coherence. In line …

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An Empirical Investigation Of U.K. Environmental Targets Disclosure: The Role Of Environmental Governance And Performance, Tantawy Moussa, Amr Kotb, Akrum Helfaya Jan 2021

An Empirical Investigation Of U.K. Environmental Targets Disclosure: The Role Of Environmental Governance And Performance, Tantawy Moussa, Amr Kotb, Akrum Helfaya

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Although an increasing number of companies have publicly declared environmental targets (ETs), scant research has been conducted in this area. This study, therefore, investigates the extent of corporate environmental targets disclosure (ETD) and empirically examines whether environmental governance and performance influence the ETD of companies in the U.K. during the 2005–2013 period. We find that firms show a large degree of variability and inconsistency in their reporting of ETs. The results indicate that U.K. firms, particularly those with high environmental sensitivity, tend to disclose symbolic soft or semi-hard ETs to manage stakeholder perceptions and legitimize their existence. Moreover, Global Reporting …

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A Comprehensive Review On Medical Diagnosis Using Machine Learning, Kaustubh Arun Bhavsar, Ahed Abugabah, Jimmy Singla, Ahmad Ali Alzubi, Ali Kashif Bashir, Nikita Jan 2021

A Comprehensive Review On Medical Diagnosis Using Machine Learning, Kaustubh Arun Bhavsar, Ahed Abugabah, Jimmy Singla, Ahmad Ali Alzubi, Ali Kashif Bashir, Nikita

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The unavailability of sufficient information for proper diagnosis, incomplete or miscommunication between patient and the clinician, or among the healthcare professionals, delay or incorrect diagnosis, the fatigue of clinician, or even the high diagnostic complexity in limited time can lead to diagnostic errors. Diagnostic errors have adverse effects on the treatment of a patient. Unnecessary treatments increase the medical bills and deteriorate the health of a patient. Such diagnostic errors that harm the patient in various ways could be minimized using machine learning. Machine learning algorithms could be used to diagnose various diseases with high accuracy. The use of machine …

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An Adaptive Protection Of Flooding Attacks Model For Complex Network Environments, Bashar Ahmad Khalaf, Salama A. Mostafa, Aida Mustapha, Mazin Abed Mohammed, Moamin A. Mahmoud, Bander Ali Saleh Al-Rimy, Shukor Abd Razak, Mohamed Elhoseny, Adam Marks Jan 2021

An Adaptive Protection Of Flooding Attacks Model For Complex Network Environments, Bashar Ahmad Khalaf, Salama A. Mostafa, Aida Mustapha, Mazin Abed Mohammed, Moamin A. Mahmoud, Bander Ali Saleh Al-Rimy, Shukor Abd Razak, Mohamed Elhoseny, Adam Marks

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Currently, online organizational resources and assets are potential targets of several types of attack, the most common being flooding attacks. We consider the Distributed Denial of Service (DDoS) as the most dangerous type of flooding attack that could target those resources. The DDoS attack consumes network available resources such as bandwidth, processing power, and memory, thereby limiting or withholding accessibility to users. The Flash Crowd (FC) is quite similar to the DDoS attack whereby many legitimate users concurrently access a particular service, the number of which results in the denial of service. Researchers have proposed many different models to eliminate …

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A Deep Learning Approach For Real-Time Analysis Of Attendees’ Engagement In Public Events, Sujith Samuel Mathew, Manar Alkhatib, May El Barachi Jan 2021

A Deep Learning Approach For Real-Time Analysis Of Attendees’ Engagement In Public Events, Sujith Samuel Mathew, Manar Alkhatib, May El Barachi

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Smart city analytics requires the harnessing and analysis of emotions and sentiments conveyed by images and video footage. In recent years, facial sentiment analysis attracted significant attention for different application areas, including marketing, gaming, political analytics, healthcare, and human computer interaction. Aiming at contributing to this area, we propose a deep learning model enabling the accurate emotion analysis of crowded scenes containing complete and partially occluded faces, with different angles, various distances from the camera, and varying resolutions. Our model consists of a sophisticated convolutional neural network (CNN) that is combined with pooling, densifying, flattening, and Softmax layers to achieve …

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Smart Pansharpening Approach Using Kernel-Based Image Filtering, Ahmad A.L. Smadi, Shuyuan Yang, Atif Mehmood, Ahed Abugabah, Min Wang, Muzaffar Bashir Jan 2021

Smart Pansharpening Approach Using Kernel-Based Image Filtering, Ahmad A.L. Smadi, Shuyuan Yang, Atif Mehmood, Ahed Abugabah, Min Wang, Muzaffar Bashir

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Remote sensing image fusion plays important roles in numerous applications, including monitoring, metrology, and agriculture. Image fusion gathers essential information from several image sources and consolidates them into a single image called a fused image. The fused image involves relevant data, and it is more informative than any other images extracted from one source. This study proposed a pansharpening technique based on image filtering utilising a bilateral filter to generate high-frequency details from panchromatic image. The various types of side window guided filters are employed to enhance the multispectral band from panchromatic image and then used these filters to adjust …

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The Impact Of Arabic Part Of Speech Tagging On Sentiment Analysis: A New Corpus And Deep Learning Approach, Abdul Munem Nerabie, Manar Alkhatib, Sujith Samuel Mathew, May El Barachi, Farhad Oroumchian Jan 2021

The Impact Of Arabic Part Of Speech Tagging On Sentiment Analysis: A New Corpus And Deep Learning Approach, Abdul Munem Nerabie, Manar Alkhatib, Sujith Samuel Mathew, May El Barachi, Farhad Oroumchian

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Sentiment Analysis is achieved by using Natural Language Processing (NLP) techniques and finds wide applications in analyzing social media content to determine people’s opinions, attitudes, and emotions toward entities, individuals, issues, events, or topics. The accuracy of sentiment analysis depends on automatic Part-of-Speech (PoS) tagging which is required to label words according to grammatical categories. The challenge of analyzing the Arabic language has found considerable research interest, but now the challenge is amplified with the addition of social media dialects. While numerous morphological analyzers and PoS taggers were proposed for Modern Standard Arabic (MSA), we are now witnessing an increased …

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A Novel Efficient Quantum Random Access Memory, Mohammed Zidan, Abdel-Haleem Abdel-Aty, Ashraf Khalil, Mahmoud Abdel-Aty, Hichem Eleuch Jan 2021

A Novel Efficient Quantum Random Access Memory, Mohammed Zidan, Abdel-Haleem Abdel-Aty, Ashraf Khalil, Mahmoud Abdel-Aty, Hichem Eleuch

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Owing to the significant progress in manufacturing desktop quantum computers, the quest to achieve efficient quantum random access memory (QRAM) became inevitable. In this paper, we propose a novel efficient random access memory for quantum computers. The proposed QRAM has a fixed structure and can be used efficiently to store both known and unknown classical/quantum data. The storage capacity of the proposed QRAM is more efficient than that of the classical RAMs and can be used to store both classical and quantum information. Furthermore, the proposed model can access an arbitrary location in O(1) compared with other state-of-the-art models.

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A Data-Based Guiding Framework For Digital Transformation, Zakaria Maamar, Saoussen Cheikhrouhou, Said Elnaffar Jan 2021

A Data-Based Guiding Framework For Digital Transformation, Zakaria Maamar, Saoussen Cheikhrouhou, Said Elnaffar

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This paper presents a framework for guiding organizations initiate and sustain digital transformation initiatives. Digital transformation is a long-term journey that an organization embarks on when it decides to question its practices in light of management, operation, and technology challenges. The guiding framework stresses out the importance of data in any digital transformation initiative by suggesting 4 stages referred to as collection, processing, storage, and dissemination. Because digital transformation could impact different areas of an organization for instance, business processes and business models, each stage suggests techniques to expose data. 2 case studies are adopted in the paper to illustrate …

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