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Dr. Kia Dashtipour
University of Glasgow

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0 Speech Enhancement
0 text analysis
0 Machine learing
0 Artificail intelligence
0 sentiment analysis

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sentiment analysis
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Journal article
Published: 18 June 2021 in Frontiers in Big Data
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With the advancement of social media networks, there are lots of unlabeled reviews available online, therefore it is necessarily to develop automatic tools to classify these types of reviews. To utilize these reviews for user perception, there is a need for automated tools that can process online user data. In this paper, a sentiment analysis framework has been proposed to identify people’s perception towards mobile networks. The proposed framework consists of three basic steps: preprocessing, feature selection, and applying different machine learning algorithms. The performance of the framework has taken into account different feature combinations. The simulation results show that the best performance is by integrating unigram, bigram, and trigram features.

ACS Style

Kia Dashtipour; William Taylor; Shuja Ansari; Mandar Gogate; Adnan Zahid; Yusuf Sambo; Amir Hussain; Qammer H. Abbasi; Muhammad Ali Imran. Public Perception of the Fifth Generation of Cellular Networks (5G) on Social Media. Frontiers in Big Data 2021, 4, 1 .

AMA Style

Kia Dashtipour, William Taylor, Shuja Ansari, Mandar Gogate, Adnan Zahid, Yusuf Sambo, Amir Hussain, Qammer H. Abbasi, Muhammad Ali Imran. Public Perception of the Fifth Generation of Cellular Networks (5G) on Social Media. Frontiers in Big Data. 2021; 4 ():1.

Chicago/Turabian Style

Kia Dashtipour; William Taylor; Shuja Ansari; Mandar Gogate; Adnan Zahid; Yusuf Sambo; Amir Hussain; Qammer H. Abbasi; Muhammad Ali Imran. 2021. "Public Perception of the Fifth Generation of Cellular Networks (5G) on Social Media." Frontiers in Big Data 4, no. : 1.

Journal article
Published: 04 June 2021 in Sensors
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The health status of an elderly person can be identified by examining the additive effects of aging along with disease linked to it and can lead to ‘unstable incapacity’. This health status is determined by the apparent decline of independence in activities of daily living (ADLs). Detecting ADLs provides possibilities of improving the home life of elderly people as it can be applied to fall detection systems. This paper presents fall detection in elderly people based on radar image classification by examining their daily routine activities, using radar data that were previously collected for 99 volunteers. Machine learning techniques are used classify six human activities, namely walking, sitting, standing, picking up objects, drinking water and fall events. Different machine learning algorithms, such as random forest, K-nearest neighbours, support vector machine, long short-term memory, bi-directional long short-term memory and convolutional neural networks, were used for data classification. To obtain optimum results, we applied data processing techniques, such as principal component analysis and data augmentation, to the available radar images. The aim of this paper is to improve upon the results achieved using a publicly available dataset to further improve upon research of fall detection systems. It was found out that the best results were obtained using the CNN algorithm with principal component analysis and data augmentation together to obtain a result of 95.30% accuracy. The results also demonstrated that principal component analysis was most beneficial when the training data were expanded by augmentation of the available data. The results of our proposed approach, in comparison to the state of the art, have shown the highest accuracy.

ACS Style

William Taylor; Kia Dashtipour; Syed Shah; Amir Hussain; Qammer Abbasi; Muhammad Imran. Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning. Sensors 2021, 21, 3881 .

AMA Style

William Taylor, Kia Dashtipour, Syed Shah, Amir Hussain, Qammer Abbasi, Muhammad Imran. Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning. Sensors. 2021; 21 (11):3881.

Chicago/Turabian Style

William Taylor; Kia Dashtipour; Syed Shah; Amir Hussain; Qammer Abbasi; Muhammad Imran. 2021. "Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning." Sensors 21, no. 11: 3881.

Journal article
Published: 12 May 2021 in Entropy
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Sentiment analysis aims to automatically classify the subject’s sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as a topic, product, movie, news, etc. Deep learning has recently emerged as a powerful machine learning technique to tackle the growing demand for accurate sentiment analysis. However, the majority of research efforts are devoted to English-language only, while information of great importance is also available in other languages. This paper presents a novel, context-aware, deep-learning-driven, Persian sentiment analysis approach. Specifically, the proposed deep-learning-driven automated feature-engineering approach classifies Persian movie reviews as having positive or negative sentiments. Two deep learning algorithms, convolutional neural networks (CNN) and long-short-term memory (LSTM), are applied and compared with our previously proposed manual-feature-engineering-driven, SVM-based approach. Simulation results demonstrate that LSTM obtained a better performance as compared to multilayer perceptron (MLP), autoencoder, support vector machine (SVM), logistic regression and CNN algorithms.

ACS Style

Kia Dashtipour; Mandar Gogate; Ahsan Adeel; Hadi Larijani; Amir Hussain. Sentiment Analysis of Persian Movie Reviews Using Deep Learning. Entropy 2021, 23, 596 .

AMA Style

Kia Dashtipour, Mandar Gogate, Ahsan Adeel, Hadi Larijani, Amir Hussain. Sentiment Analysis of Persian Movie Reviews Using Deep Learning. Entropy. 2021; 23 (5):596.

Chicago/Turabian Style

Kia Dashtipour; Mandar Gogate; Ahsan Adeel; Hadi Larijani; Amir Hussain. 2021. "Sentiment Analysis of Persian Movie Reviews Using Deep Learning." Entropy 23, no. 5: 596.

Journal article
Published: 05 April 2021 in Journal of Medical Internet Research
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Background Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general public. As such, it is crucial that governments and public health agencies understand public sentiments toward vaccines, which can help guide educational campaigns and other targeted policy interventions. Objective The aim of this study was to develop and apply an artificial intelligence–based approach to analyze public sentiments on social media in the United Kingdom and the United States toward COVID-19 vaccines to better understand the public attitude and concerns regarding COVID-19 vaccines. Methods Over 300,000 social media posts related to COVID-19 vaccines were extracted, including 23,571 Facebook posts from the United Kingdom and 144,864 from the United States, along with 40,268 tweets from the United Kingdom and 98,385 from the United States from March 1 to November 22, 2020. We used natural language processing and deep learning–based techniques to predict average sentiments, sentiment trends, and topics of discussion. These factors were analyzed longitudinally and geospatially, and manual reading of randomly selected posts on points of interest helped identify underlying themes and validated insights from the analysis. Results Overall averaged positive, negative, and neutral sentiments were at 58%, 22%, and 17% in the United Kingdom, compared to 56%, 24%, and 18% in the United States, respectively. Public optimism over vaccine development, effectiveness, and trials as well as concerns over their safety, economic viability, and corporation control were identified. We compared our findings to those of nationwide surveys in both countries and found them to correlate broadly. Conclusions Artificial intelligence–enabled social media analysis should be considered for adoption by institutions and governments alongside surveys and other conventional methods of assessing public attitude. Such analyses could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccines, help address the concerns of vaccine sceptics, and help develop more effective policies and communication strategies to maximize uptake.

ACS Style

Amir Hussain; Ahsen Tahir; Zain Hussain; Zakariya Sheikh; Mandar Gogate; Kia Dashtipour; Azhar Ali; Aziz Sheikh. Artificial Intelligence–Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study. Journal of Medical Internet Research 2021, 23, e26627 .

AMA Style

Amir Hussain, Ahsen Tahir, Zain Hussain, Zakariya Sheikh, Mandar Gogate, Kia Dashtipour, Azhar Ali, Aziz Sheikh. Artificial Intelligence–Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study. Journal of Medical Internet Research. 2021; 23 (4):e26627.

Chicago/Turabian Style

Amir Hussain; Ahsen Tahir; Zain Hussain; Zakariya Sheikh; Mandar Gogate; Kia Dashtipour; Azhar Ali; Aziz Sheikh. 2021. "Artificial Intelligence–Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study." Journal of Medical Internet Research 23, no. 4: e26627.

Journal article
Published: 13 March 2021 in Entropy
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Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application in several fields, such as office automation and document processing. However, OAHR continues to face several challenges, including high variability of the Arabic script and its intrinsic characteristics such as cursiveness, ligatures, and diacritics, the unlimited variation in human handwriting, and the lack of large public databases. In this paper, we introduce a novel context-aware model based on deep neural networks to address the challenges of recognizing offline handwritten Arabic text, including isolated digits, characters, and words. Specifically, we propose a supervised Convolutional Neural Network (CNN) model that contextually extracts optimal features and employs batch normalization and dropout regularization parameters. This aims to prevent overfitting and further enhance generalization performance when compared to conventional deep learning models. We employ a number of deep stacked-convolutional layers to design the proposed Deep CNN (DCNN) architecture. The model is extensively evaluated and shown to demonstrate excellent classification accuracy when compared to conventional OAHR approaches on a diverse set of six benchmark databases, including MADBase (Digits), CMATERDB (Digits), HACDB (Characters), SUST-ALT (Digits), SUST-ALT (Characters), and SUST-ALT (Names). A further experimental study is conducted on the benchmark Arabic databases by exploiting transfer learning (TL)-based feature extraction which demonstrates the superiority of our proposed model in relation to state-of-the-art VGGNet-19 and MobileNet pre-trained models. Finally, experiments are conducted to assess comparative generalization capabilities of the models using another language database , specifically the benchmark MNIST English isolated Digits database, which further confirm the superiority of our proposed DCNN model.

ACS Style

Rami Ahmed; Mandar Gogate; Ahsen Tahir; Kia Dashtipour; Bassam Al-Tamimi; Ahmad Hawalah; Mohammed El-Affendi; Amir Hussain. Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts. Entropy 2021, 23, 340 .

AMA Style

Rami Ahmed, Mandar Gogate, Ahsen Tahir, Kia Dashtipour, Bassam Al-Tamimi, Ahmad Hawalah, Mohammed El-Affendi, Amir Hussain. Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts. Entropy. 2021; 23 (3):340.

Chicago/Turabian Style

Rami Ahmed; Mandar Gogate; Ahsen Tahir; Kia Dashtipour; Bassam Al-Tamimi; Ahmad Hawalah; Mohammed El-Affendi; Amir Hussain. 2021. "Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts." Entropy 23, no. 3: 340.

Journal article
Published: 01 February 2021 in IEEE Sensors Journal
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The posture detection received lots of attention in the fields of human sensing and artificial intelligence. Posture detection can be used for the monitoring health status of elderly remotely by identifying their postures such as standing, sitting and walking. Most of the current studies used traditional machine learning classifiers to identify the posture. However, these methods do not perform well to detect the postures accurately. Therefore, in this study, we proposed a novel hybrid approach based on machine learning classifiers (i. e., support vector machine (SVM), logistic regression (KNN), decision tree, Naive Bayes, random forest, Linear discrete analysis and Quadratic discrete analysis) and deep learning classifiers (i. e., 1D-convolutional neural network (1D-CNN), 2D-convolutional neural network (2D-CNN), LSTM and bidirectional LSTM) to identify posture detection. The proposed hybrid approach uses prediction of machine learning (ML) and deep learning (DL) to improve the performance of ML and DL algorithms. The experimental results on widely benchmark dataset are shown and results achieved an accuracy of more than 98%.

ACS Style

Sidrah Liaqat; Kia Dashtipour; Kamran Arshad; Khaled Assaleh; Naeem Ramzan. A Hybrid Posture Detection Framework: Integrating Machine Learning and Deep Neural Networks. IEEE Sensors Journal 2021, 21, 9515 -9522.

AMA Style

Sidrah Liaqat, Kia Dashtipour, Kamran Arshad, Khaled Assaleh, Naeem Ramzan. A Hybrid Posture Detection Framework: Integrating Machine Learning and Deep Neural Networks. IEEE Sensors Journal. 2021; 21 (7):9515-9522.

Chicago/Turabian Style

Sidrah Liaqat; Kia Dashtipour; Kamran Arshad; Khaled Assaleh; Naeem Ramzan. 2021. "A Hybrid Posture Detection Framework: Integrating Machine Learning and Deep Neural Networks." IEEE Sensors Journal 21, no. 7: 9515-9522.

Preprint content
Published: 18 December 2020
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BACKGROUND Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general public. As such, it is crucial that governments and public health agencies understand public sentiments toward vaccines, which can help guide educational campaigns and other targeted policy interventions. OBJECTIVE The aim of this study was to develop and apply an artificial intelligence–based approach to analyze public sentiments on social media in the United Kingdom and the United States toward COVID-19 vaccines to better understand the public attitude and concerns regarding COVID-19 vaccines. METHODS Over 300,000 social media posts related to COVID-19 vaccines were extracted, including 23,571 Facebook posts from the United Kingdom and 144,864 from the United States, along with 40,268 tweets from the United Kingdom and 98,385 from the United States from March 1 to November 22, 2020. We used natural language processing and deep learning–based techniques to predict average sentiments, sentiment trends, and topics of discussion. These factors were analyzed longitudinally and geospatially, and manual reading of randomly selected posts on points of interest helped identify underlying themes and validated insights from the analysis. RESULTS Overall averaged positive, negative, and neutral sentiments were at 58%, 22%, and 17% in the United Kingdom, compared to 56%, 24%, and 18% in the United States, respectively. Public optimism over vaccine development, effectiveness, and trials as well as concerns over their safety, economic viability, and corporation control were identified. We compared our findings to those of nationwide surveys in both countries and found them to correlate broadly. CONCLUSIONS Artificial intelligence–enabled social media analysis should be considered for adoption by institutions and governments alongside surveys and other conventional methods of assessing public attitude. Such analyses could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccines, help address the concerns of vaccine sceptics, and help develop more effective policies and communication strategies to maximize uptake.

ACS Style

Amir Hussain; Ahsen Tahir; Zain Hussain; Zakariya Sheikh; Mandar Gogate; Kia Dashtipour; Azhar Ali; Aziz Sheikh. Artificial Intelligence–Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study (Preprint). 2020, 1 .

AMA Style

Amir Hussain, Ahsen Tahir, Zain Hussain, Zakariya Sheikh, Mandar Gogate, Kia Dashtipour, Azhar Ali, Aziz Sheikh. Artificial Intelligence–Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study (Preprint). . 2020; ():1.

Chicago/Turabian Style

Amir Hussain; Ahsen Tahir; Zain Hussain; Zakariya Sheikh; Mandar Gogate; Kia Dashtipour; Azhar Ali; Aziz Sheikh. 2020. "Artificial Intelligence–Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study (Preprint)." , no. : 1.

Preprint content
Published: 11 December 2020
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Background: Global efforts towards the development and deployment of a vaccine for SARS-CoV-2 are rapidly advancing. We developed and applied an artificial-intelligence (AI)-based approach to analyse social-media public sentiment in the UK and the US towards COVID-19 vaccinations, to understand public attitude and identify topics of concern. Methods: Over 300,000 social-media posts related to COVID-19 vaccinations were extracted, including 23,571 Facebook-posts from the UK and 144,864 from the US, along with 40,268 tweets from the UK and 98,385 from the US respectively, from 1st March - 22nd November 2020. We used natural language processing and deep learning based techniques to predict average sentiments, sentiment trends and topics of discussion. These were analysed longitudinally and geo-spatially, and a manual reading of randomly selected posts around points of interest helped identify underlying themes and validated insights from the analysis. Results: We found overall averaged positive, negative and neutral sentiment in the UK to be 58%, 22% and 17%, compared to 56%, 24% and 18% in the US, respectively. Public optimism over vaccine development, effectiveness and trials as well as concerns over safety, economic viability and corporation control were identified. We compared our findings to national surveys in both countries and found them to correlate broadly. Conclusions: AI-enabled social-media analysis should be considered for adoption by institutions and governments, alongside surveys and other conventional methods of assessing public attitude. This could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccinations, help address concerns of vaccine-sceptics and develop more effective policies and communication strategies to maximise uptake.

ACS Style

Amir Hussain; Ahsen Tahir; Zain Hussain; Zakariya Sheikh; Mandar Gogate; Kia Dashtipour; Azhar Ali; Aziz Sheikh. Artificial intelligence-enabled analysis of UK and US public attitudes on Facebook and Twitter towards COVID-19 vaccinations. 2020, 1 .

AMA Style

Amir Hussain, Ahsen Tahir, Zain Hussain, Zakariya Sheikh, Mandar Gogate, Kia Dashtipour, Azhar Ali, Aziz Sheikh. Artificial intelligence-enabled analysis of UK and US public attitudes on Facebook and Twitter towards COVID-19 vaccinations. . 2020; ():1.

Chicago/Turabian Style

Amir Hussain; Ahsen Tahir; Zain Hussain; Zakariya Sheikh; Mandar Gogate; Kia Dashtipour; Azhar Ali; Aziz Sheikh. 2020. "Artificial intelligence-enabled analysis of UK and US public attitudes on Facebook and Twitter towards COVID-19 vaccinations." , no. : 1.

Journal article
Published: 26 November 2020 in Information
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The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocardiogram (ECG) can improve the early detection of diagnosis. In this paper, we have further developed a framework for processing the ECG signal in order to determine the AF episodes. We have implemented machine learning and deep learning algorithms to detect AF. Moreover, the experimental results show that better performance can be achieved with long short-term memory (LSTM) as compared to other algorithms. The initial experimental results illustrate that the deep learning algorithms, such as LSTM and convolutional neural network (CNN), achieved better performance (10%) as compared to machine learning classifiers, such as support vectors, logistic regression, etc. This preliminary work can help clinicians in AF detection with high accuracy and less probability of errors, which can ultimately result in reduction in fatality rate.

ACS Style

Sidrah Liaqat; Kia Dashtipour; Adnan Zahid; Khaled Assaleh; Kamran Arshad; Naeem Ramzan. Detection of Atrial Fibrillation Using a Machine Learning Approach. Information 2020, 11, 549 .

AMA Style

Sidrah Liaqat, Kia Dashtipour, Adnan Zahid, Khaled Assaleh, Kamran Arshad, Naeem Ramzan. Detection of Atrial Fibrillation Using a Machine Learning Approach. Information. 2020; 11 (12):549.

Chicago/Turabian Style

Sidrah Liaqat; Kia Dashtipour; Adnan Zahid; Khaled Assaleh; Kamran Arshad; Naeem Ramzan. 2020. "Detection of Atrial Fibrillation Using a Machine Learning Approach." Information 11, no. 12: 549.

Journal article
Published: 18 November 2020 in Signals
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A paramount challenge of prohibiting increased CO2 emissions for network densification is to deliver the Fifth Generation (5G) cellular capacity and connectivity demands, while maintaining a greener, healthier and prosperous environment. Energy consumption is a demanding consideration in the 5G era to combat several challenges such as reactive mode of operation, high latency wake up times, incorrect user association with the cells, multiple cross-functional operation of Self-Organising Networks (SON), etc. To address this challenge, we propose a novel Mobility Management-Based Autonomous Energy-Aware Framework for analysing bus passengers ridership through statistical Machine Learning (ML) and proactive energy savings coupled with CO2 emissions in Heterogeneous Network (HetNet) architecture using Reinforcement Learning (RL). Furthermore, we compare and report various ML algorithms using bus passengers ridership obtained from London Overground (LO) dataset. Extensive spatiotemporal simulations show that our proposed framework can achieve up to 98.82% prediction accuracy and CO2 reduction gains of up to 31.83%.

ACS Style

Syed Muhammad Asad; Shuja Ansari; Metin Ozturk; Rao Naveed Bin Rais; Kia Dashtipour; Sajjad Hussain; Qammer H. Abbasi; Muhammad Ali Imran. Mobility Management-Based Autonomous Energy-Aware Framework Using Machine Learning Approach in Dense Mobile Networks. Signals 2020, 1, 170 -187.

AMA Style

Syed Muhammad Asad, Shuja Ansari, Metin Ozturk, Rao Naveed Bin Rais, Kia Dashtipour, Sajjad Hussain, Qammer H. Abbasi, Muhammad Ali Imran. Mobility Management-Based Autonomous Energy-Aware Framework Using Machine Learning Approach in Dense Mobile Networks. Signals. 2020; 1 (2):170-187.

Chicago/Turabian Style

Syed Muhammad Asad; Shuja Ansari; Metin Ozturk; Rao Naveed Bin Rais; Kia Dashtipour; Sajjad Hussain; Qammer H. Abbasi; Muhammad Ali Imran. 2020. "Mobility Management-Based Autonomous Energy-Aware Framework Using Machine Learning Approach in Dense Mobile Networks." Signals 1, no. 2: 170-187.

Journal article
Published: 02 November 2020 in Electronics
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This paper suggests an optimisation approach in heterogeneous computing systems to balance energy power consumption and efficiency. The work proposes a power measurement utility for a reinforcement learning (PMU-RL) algorithm to dynamically adjust the resource utilisation of heterogeneous platforms in order to minimise power consumption. A reinforcement learning (RL) technique is applied to analyse and optimise the resource utilisation of field programmable gate array (FPGA) control state capabilities, which is built for a simulation environment with a Xilinx ZYNQ multi-processor systems-on-chip (MPSoC) board. In this study, the balance operation mode for improving power consumption and performance is established to dynamically change the programmable logic (PL) end work state. It is based on an RL algorithm that can quickly discover the optimization effect of PL on different workloads to improve energy efficiency. The results demonstrate a substantial reduction of 18% in energy consumption without affecting the application’s performance. Thus, the proposed PMU-RL technique has the potential to be considered for other heterogeneous computing platforms.

ACS Style

Zheqi Yu; Pedro Machado; Adnan Zahid; Amir Abdulghani; Kia Dashtipour; Hadi Heidari; Muhammad Imran; Qammer Abbasi. Energy and Performance Trade-Off Optimization in Heterogeneous Computing via Reinforcement Learning. Electronics 2020, 9, 1812 .

AMA Style

Zheqi Yu, Pedro Machado, Adnan Zahid, Amir Abdulghani, Kia Dashtipour, Hadi Heidari, Muhammad Imran, Qammer Abbasi. Energy and Performance Trade-Off Optimization in Heterogeneous Computing via Reinforcement Learning. Electronics. 2020; 9 (11):1812.

Chicago/Turabian Style

Zheqi Yu; Pedro Machado; Adnan Zahid; Amir Abdulghani; Kia Dashtipour; Hadi Heidari; Muhammad Imran; Qammer Abbasi. 2020. "Energy and Performance Trade-Off Optimization in Heterogeneous Computing via Reinforcement Learning." Electronics 9, no. 11: 1812.

Review
Published: 03 October 2020 in Sensors
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COVID-19, caused by SARS-CoV-2, has resulted in a global pandemic recently. With no approved vaccination or treatment, governments around the world have issued guidance to their citizens to remain at home in efforts to control the spread of the disease. The goal of controlling the spread of the virus is to prevent strain on hospitals. In this paper, we focus on how non-invasive methods are being used to detect COVID-19 and assist healthcare workers in caring for COVID-19 patients. Early detection of COVID-19 can allow for early isolation to prevent further spread. This study outlines the advantages and disadvantages and a breakdown of the methods applied in the current state-of-the-art approaches. In addition, the paper highlights some future research directions, which need to be explored further to produce innovative technologies to control this pandemic.

ACS Style

William Taylor; Qammer H. Abbasi; Kia Dashtipour; Shuja Ansari; Syed Aziz Shah; Arslan Khalid; Muhammad Ali Imran. A Review of the State of the Art in Non-Contact Sensing for COVID-19. Sensors 2020, 20, 5665 .

AMA Style

William Taylor, Qammer H. Abbasi, Kia Dashtipour, Shuja Ansari, Syed Aziz Shah, Arslan Khalid, Muhammad Ali Imran. A Review of the State of the Art in Non-Contact Sensing for COVID-19. Sensors. 2020; 20 (19):5665.

Chicago/Turabian Style

William Taylor; Qammer H. Abbasi; Kia Dashtipour; Shuja Ansari; Syed Aziz Shah; Arslan Khalid; Muhammad Ali Imran. 2020. "A Review of the State of the Art in Non-Contact Sensing for COVID-19." Sensors 20, no. 19: 5665.

Chapter
Published: 10 July 2020 in Information and Communication Technology for Intelligent Systems
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In recent years, sentiment analysis received a great deal of attention due to the accelerated evolution of the Internet, by which people all around the world share their opinions and comments on different topics such as sport, politics, movies, music and so on. The result is a huge amount of available unstructured information. In order to detect positive or negative subject’s sentiment from this kind of data, sentiment analysis technique is widely used. In this context, here, we introduce an ensemble classifier for Persian sentiment analysis using shallow and deep learning algorithms to improve the performance of the state-of-art approaches. Specifically, experimental results show that the proposed ensemble classifier achieved accuracy rate up to 79.68%.

ACS Style

Kia Dashtipour; Cosimo Ieracitano; Francesco Carlo Morabito; Ali Raza; Amir Hussain. An Ensemble Based Classification Approach for Persian Sentiment Analysis. Information and Communication Technology for Intelligent Systems 2020, 207 -215.

AMA Style

Kia Dashtipour, Cosimo Ieracitano, Francesco Carlo Morabito, Ali Raza, Amir Hussain. An Ensemble Based Classification Approach for Persian Sentiment Analysis. Information and Communication Technology for Intelligent Systems. 2020; ():207-215.

Chicago/Turabian Style

Kia Dashtipour; Cosimo Ieracitano; Francesco Carlo Morabito; Ali Raza; Amir Hussain. 2020. "An Ensemble Based Classification Approach for Persian Sentiment Analysis." Information and Communication Technology for Intelligent Systems , no. : 207-215.

Journal article
Published: 03 July 2020 in Electronics
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Dehydration and overhydration can help to improve medical implications on health. Therefore, it is vital to track the hydration level (HL) specifically in children, the elderly and patients with underlying medical conditions such as diabetes. Most of the current approaches to estimate the hydration level are not sufficient and require more in-depth research. Therefore, in this paper, we used the non-invasive wearable sensor for collecting the skin conductance data and employed different machine learning algorithms based on feature engineering to predict the hydration level of the human body in different body postures. The comparative experimental results demonstrated that the random forest with an accuracy of 91.3% achieved better performance as compared to other machine learning algorithms to predict the hydration state of human body. This study paves a way for further investigation in non-invasive proactive skin hydration detection which can help in the diagnosis of serious health conditions.

ACS Style

Sidrah Liaqat; Kia Dashtipour; Kamran Arshad; Naeem Ramzan. Non Invasive Skin Hydration Level Detection Using Machine Learning. Electronics 2020, 9, 1086 .

AMA Style

Sidrah Liaqat, Kia Dashtipour, Kamran Arshad, Naeem Ramzan. Non Invasive Skin Hydration Level Detection Using Machine Learning. Electronics. 2020; 9 (7):1086.

Chicago/Turabian Style

Sidrah Liaqat; Kia Dashtipour; Kamran Arshad; Naeem Ramzan. 2020. "Non Invasive Skin Hydration Level Detection Using Machine Learning." Electronics 9, no. 7: 1086.

Journal article
Published: 06 May 2020 in Sensors
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Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed. At present wearable devices can provide real-time monitoring by deploying equipment on a person’s body. However, putting devices on a person’s body all the time makes it uncomfortable and the elderly tend to forget to wear them, in addition to the insecurity of being tracked all the time. This paper demonstrates how human motions can be detected in a quasi-real-time scenario using a non-invasive method. Patterns in the wireless signals present particular human body motions as each movement induces a unique change in the wireless medium. These changes can be used to identify particular body motions. This work produces a dataset that contains patterns of radio wave signals obtained using software-defined radios (SDRs) to establish if a subject is standing up or sitting down as a test case. The dataset was used to create a machine learning model, which was used in a developed application to provide a quasi-real-time classification of standing or sitting state. The machine-learning model was able to achieve 96.70% accuracy using the Random Forest algorithm using 10 fold cross-validation. A benchmark dataset of wearable devices was compared to the proposed dataset and results showed the proposed dataset to have similar accuracy of nearly 90%. The machine-learning models developed in this paper are tested for two activities but the developed system is designed and applicable for detecting and differentiating x number of activities.

ACS Style

William Taylor; Syed Aziz Shah; Kia Dashtipour; Adnan Zahid; Qammer H. Abbasi; Muhammad Ali Imran. An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare. Sensors 2020, 20, 2653 .

AMA Style

William Taylor, Syed Aziz Shah, Kia Dashtipour, Adnan Zahid, Qammer H. Abbasi, Muhammad Ali Imran. An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare. Sensors. 2020; 20 (9):2653.

Chicago/Turabian Style

William Taylor; Syed Aziz Shah; Kia Dashtipour; Adnan Zahid; Qammer H. Abbasi; Muhammad Ali Imran. 2020. "An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare." Sensors 20, no. 9: 2653.

Conference paper
Published: 01 February 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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Sentiment analysis is probably the most actively growing area of natural language processing nowadays, which leverages huge amount of user-contributed data on Internet to improve income of businesses and quality of life of consumer. The majority of existent sentiment-analysis systems is focused on English, due to lack of resources and tools for other languages. To fill this gap for Persian language, in our previous work we have compiled the first version of PerSent Persian sentiment lexicon, which was small and included only words and phrases from general domain. In this paper, we present its extension with words from three different domains and evaluate its performance on polarity classification task using various machine learning-based classifiers. We use a multi-domain dataset to evaluate the performance of our new lexicon on various domains. Our results demonstrate usefulness of the new lexicon for analysis of product and movie reviews and especially of political news in Persian language.

ACS Style

Kia Dashtipour; Ali Raza; Alexander Gelbukh; Rui Zhang; Erik Cambria; Amir Hussain. PerSent 2.0: Persian Sentiment Lexicon Enriched with Domain-Specific Words. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 497 -509.

AMA Style

Kia Dashtipour, Ali Raza, Alexander Gelbukh, Rui Zhang, Erik Cambria, Amir Hussain. PerSent 2.0: Persian Sentiment Lexicon Enriched with Domain-Specific Words. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():497-509.

Chicago/Turabian Style

Kia Dashtipour; Ali Raza; Alexander Gelbukh; Rui Zhang; Erik Cambria; Amir Hussain. 2020. "PerSent 2.0: Persian Sentiment Lexicon Enriched with Domain-Specific Words." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 497-509.

Conference paper
Published: 01 February 2020 in Algorithms and Data Structures
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In pattern recognition, automatic handwriting recognition (AHWR) is an area of research that has developed rapidly in the last few years. It can play a significant role in broad-spectrum of applications rending from, bank cheque processing, application forms processing, postal address processing, to text-to-speech conversion. However, most research efforts are devoted to English-language only. This work focuses on developing Offline Arabic Handwriting Recognition (OAHR). The OAHR is a very challenging task due to some unique characteristics of the Arabic script such as cursive nature, ligatures, overlapping, and diacritical marks. In the recent literature, several effective Deep Learning (DL) approaches have been proposed to develop efficient AHWR systems. In this paper, we commission a survey on emerging AHWR technologies with some insight on OAHR background, challenges, opportunities, and future research trends.

ACS Style

Rami Ahmed; Kia Dashtipour; Mandar Gogate; Ali Raza; Rui Zhang; Kaizhu Huang; Ahmad Hawalah; Ahsan Adeel; Amir Hussain. Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances. Algorithms and Data Structures 2020, 457 -468.

AMA Style

Rami Ahmed, Kia Dashtipour, Mandar Gogate, Ali Raza, Rui Zhang, Kaizhu Huang, Ahmad Hawalah, Ahsan Adeel, Amir Hussain. Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances. Algorithms and Data Structures. 2020; ():457-468.

Chicago/Turabian Style

Rami Ahmed; Kia Dashtipour; Mandar Gogate; Ali Raza; Rui Zhang; Kaizhu Huang; Ahmad Hawalah; Ahsan Adeel; Amir Hussain. 2020. "Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances." Algorithms and Data Structures , no. : 457-468.

Conference paper
Published: 10 October 2018 in Transactions on Petri Nets and Other Models of Concurrency XV
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In the recent years, people all around the world share their opinions about different fields with each other over Internet. Sentiment analysis techniques have been introduced to classify these rich data based on the polarity of the opinion. Sentiment analysis research has been growing rapidly; however, most of the research papers are focused on English. In this paper, we review English-based sentiment analysis approaches and discuss what adaption these approaches require to become applicable to the Persian language. The results show that approaches initially suggested for English language are competitive with those developed specifically for Persian sentiment analysis.

ACS Style

Kia Dashtipour; Amir Hussain; Alexander Gelbukh. Adaptation of Sentiment Analysis Techniques to Persian Language. Transactions on Petri Nets and Other Models of Concurrency XV 2018, 129 -140.

AMA Style

Kia Dashtipour, Amir Hussain, Alexander Gelbukh. Adaptation of Sentiment Analysis Techniques to Persian Language. Transactions on Petri Nets and Other Models of Concurrency XV. 2018; ():129-140.

Chicago/Turabian Style

Kia Dashtipour; Amir Hussain; Alexander Gelbukh. 2018. "Adaptation of Sentiment Analysis Techniques to Persian Language." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 129-140.

Conference paper
Published: 06 October 2018 in Spatial Data and Intelligence
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Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology (ICT) systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catastrophic scenario can be envisaged where a nation-state intercepting encrypted financial data gets hacked. Thus, intelligent cybersecurity systems have become inevitably important for improved protection against malicious threats. However, as malware attacks continue to dramatically increase in volume and complexity, it has become ever more challenging for traditional analytic tools to detect and mitigate threat. Furthermore, a huge amount of data produced by large networks have made the recognition task even more complicated and challenging. In this work, we propose an innovative statistical analysis driven optimized deep learning system for intrusion detection. The proposed intrusion detection system (IDS) extracts optimized and more correlated features using big data visualization and statistical analysis methods, followed by a deep autoencoder (AE) for potential threat detection. Specifically, a preprocessing module eliminates the outliers and converts categorical variables into one-hot-encoded vectors. The feature extraction module discards features with null values grater than 80% and selects the most significant features as input to the deep autoencoder model trained in a greedy-wise manner. The NSL-KDD dataset (an improved version of the original KDD dataset) from the Canadian Institute for Cybersecurity is used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed IDS system for improving intrusion detection as compared to existing state-of-the-art methods.

ACS Style

Cosimo Ieracitano; Ahsan Adeel; Mandar Gogate; Kia Dashtipour; Francesco Carlo Morabito; Hadi Larijani; Ali Raza; Amir Hussain. Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection. Spatial Data and Intelligence 2018, 759 -769.

AMA Style

Cosimo Ieracitano, Ahsan Adeel, Mandar Gogate, Kia Dashtipour, Francesco Carlo Morabito, Hadi Larijani, Ali Raza, Amir Hussain. Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection. Spatial Data and Intelligence. 2018; ():759-769.

Chicago/Turabian Style

Cosimo Ieracitano; Ahsan Adeel; Mandar Gogate; Kia Dashtipour; Francesco Carlo Morabito; Hadi Larijani; Ali Raza; Amir Hussain. 2018. "Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection." Spatial Data and Intelligence , no. : 759-769.

Conference paper
Published: 06 October 2018 in Transactions on Petri Nets and Other Models of Concurrency XV
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Sentiment analysis mainly focused on the automatic recognition of opinions’ polarity, as positive or negative. Nowadays, sentiment analysis is replacing the web-based and traditional survey methods commonly conducted by companies for finding the public opinion about their products and services to improve their marketing strategy and product advertisement and help to improve customer service. The online availability of large text makes it important to be analyzed. The automatic analysis of this information involves a deep understanding of natural languages. Sentiments and emotions play a pivotal role in our daily lives. They assist decision-making, learning, communication, and situation awareness in human environments. The importance of processing and understanding dialect text is increasing due to the growth of socially generated dialectal content in social media. In addition to existing materials such as local proverbs, advice and folklore that are found spread on the web. This paper focused on text sentiment analysis as dialect text, as quick review to identify relevant contributions that address languages aspect for a specific dialect.

ACS Style

Intisar O. Hussien; Kia Dashtipour; Amir Hussain. Comparison of Sentiment Analysis Approaches Using Modern Arabic and Sudanese Dialect. Transactions on Petri Nets and Other Models of Concurrency XV 2018, 615 -624.

AMA Style

Intisar O. Hussien, Kia Dashtipour, Amir Hussain. Comparison of Sentiment Analysis Approaches Using Modern Arabic and Sudanese Dialect. Transactions on Petri Nets and Other Models of Concurrency XV. 2018; ():615-624.

Chicago/Turabian Style

Intisar O. Hussien; Kia Dashtipour; Amir Hussain. 2018. "Comparison of Sentiment Analysis Approaches Using Modern Arabic and Sudanese Dialect." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 615-624.