Article
Construction & Building Technology
Mohammadsepehr Karimiziarani, Keighobad Jafarzadegan, Peyman Abbaszadeh, Wanyun Shao, Hamid Moradkhani
Summary: The study conducted a comprehensive spatiotemporal analysis of millions of tweets on Twitter during Hurricane Harvey, introducing the Hazard Risk Awareness (HRA) Index and subdividing tweet content to inform crisis management. This research provides valuable information for disaster preparedness and response at the county level.
SUSTAINABLE CITIES AND SOCIETY
(2022)
Article
Computer Science, Software Engineering
S. S. Arumugam
Summary: This article presents an argument mining model with sentimental data analysis to extract specific arguments from Twitter content, using natural language processing techniques for decision-making and classification.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Automation & Control Systems
Jing Fan, Shaowen Gao, Yong Liu, Xinqiang Ma, Jiandang Yang, Changjie Fan
Summary: This article proposes a parallel semisupervised framework for categorizing specific game players with the help of bait players. The approach is based on a feature representation model at the player level granularity and a semisupervised clustering method to extract specified players.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Review
Chemistry, Multidisciplinary
Yili Wang, Jiaxuan Guo, Chengsheng Yuan, Baozhu Li
Summary: Twitter Sentiment Analysis is an active subfield of text mining, which has attracted considerable interest among researchers. This research provides a comprehensive review of the latest developments in this area, including newly proposed algorithms and applications. The survey classifies each publication based on its significance to specific TSA methods and depicts the current research direction in the field of TSA.
APPLIED SCIENCES-BASEL
(2022)
Article
Mathematics, Interdisciplinary Applications
Linyu Li, Xiaoyu Luo
Summary: The creation of language models is crucial in natural language processing as it provides possible distributions in word sequences and unique representations for each word occurrence. An innovative neural network model with attention mechanism and coverage optimization is proposed to better align the output with the input sequence and offer helpful information.
FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY
(2022)
Review
Computer Science, Information Systems
Saja Aldera, Ahmad Emam, Muhammad Al-Qurishi, Majed Alrubaian, Abdulrahman Alothaim
Summary: Social media networks are increasingly being utilized by extremist groups to spread radical ideologies and recruit supporters, making it a top priority for counter-terrorist agencies, technology companies, and governments to identify extremist content and accounts. A systematic literature review of studies published between 2015 and 2020 revealed challenges, technical pitfalls, and opportunities for improving understanding and direction of online extremism. This critical analysis contributes to the growing field of research on online extremism.
Article
Business
Sabri Boubaker, Zhenya Liu, Ling Zhai
Summary: This study explores the relationship between news diversity and financial market crashes using modern textual analysis methods and change-point detection approach. The empirical analysis reveals that big data is a relatively new and useful tool for evaluating financial market movements and that there is a relationship between news diversity and financial market movements.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2021)
Article
Computer Science, Cybernetics
P. Kaladevi, K. Thyagarajah
Summary: The paper introduces a sentiment analysis method based on ICNN-LSTM-DNN, which is capable of extracting user facts and opinions in real-time from large-scale social data, particularly suitable for analyzing opinions and facts in tweets.
BEHAVIOUR & INFORMATION TECHNOLOGY
(2021)
Article
Computer Science, Hardware & Architecture
Mohammadreza Sheikhattar, Alireza Mansouri
Summary: This study focuses on analyzing unstructured risk data and extracting knowledge, and proposes a Topic Map-based knowledge discovery system using the word embedding approach. The experiment shows that the proposed system can effectively analyze unstructured data and performs well in supply chain analysis.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Engineering, Environmental
Edoardo Ramalli, Timoteo Dinelli, Andrea Nobili, Alessandro Stagni, Barbara Pernici, Tiziano Faravelli
Summary: Validation and analysis of experiments and models are crucial in various engineering fields. This study proposes a systematic and automated methodology that utilizes the concept of a 'data ecosystem' to provide comprehensive insights about experiments and predictive models. The methodology focuses on data assessment, model performance measurement, and behavior insight extraction through data science techniques. It can be applied to different domains where predictive models are validated against big data in chemical engineering.
CHEMICAL ENGINEERING JOURNAL
(2023)
Article
Computer Science, Information Systems
Zia Ul Rehman, Sagheer Abbas, Muhammad Adnan Khan, Ghulam Mustafa, Hira Fayyaz, Muhammad Hanif, Muhammad Anwar Saeed
Summary: The research aims to identify radical text in social media with contributions such as creating a new dataset, analyzing variations in different datasets, training classifiers to detect radicalization, and examining the differences in the use of violent and bad words between extremist and random users.
CMC-COMPUTERS MATERIALS & CONTINUA
(2021)
Article
Management
Philipp Borchert, Kristof Coussement, Arno De Caigny, Jochen De Weerdt
Summary: This study investigates the added value of incorporating textual website content into traditional business failure prediction (BFP) models. The results confirm that including textual website data improves BFP predictive performance, and that textual features extracted by transformers add the most value to the BFP models in this benchmark setting.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Information Systems
Fang Qiao, Jago Williams
Summary: This study examines the topics and sentiments of global warming discussion on Twitter using big data analytics techniques. It identifies seven main topics frequently debated, including factors causing global warming, consequences of global warming, actions necessary to stop global warming, and other related issues. The sentiment analysis reveals that most people express positive emotions about global warming, with fear being the most evoked emotion.
JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING
(2022)
Article
Computer Science, Information Systems
Kairat Imanbayev, Bakhtgerey Sinchev, Saulet Sibanbayeva, Axulu Mukhanova, Assel Nurgulzhanova, Nurgali Zaurbekov, Nurbike Zaurbekova, Natalya Korolyova, Lyazzat Baibolova
Summary: Big data processing, stemming from intensive development of information technology, presents an urgent challenge. This article proposes a two-stage problem decomposition approach involving semantic analysis and association rule mining to tackle large data volumes. By processing news events using a semantic model and classification-based association rules, the likelihood of specific events in a market segment can be assessed effectively.
PEER-TO-PEER NETWORKING AND APPLICATIONS
(2021)
Article
Business
Maria Assunta Barchiesi, Andrea Fronzetti Colladon
Summary: The study proposed a new methodological approach combining text mining, social network, and big data analytics to assess stakeholders' attitudes towards company core values in Italy. The research identified three predominant core values orientations and three latent ones in the Italian scenario. The contribution of this study lies in extending research on text mining and online big data analytics applied in complex business contexts.
JOURNAL OF BUSINESS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Amit Kumar Jaiswal, Prayag Tiwari, M. Shamim Hossain
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Heena Rathore, Amr Mohamed, Mohsen Guizani, Shailendra Rathore
Summary: This paper introduces a machine learning approach called NueroFATH for the physical assessment of athletes. It uses neural networks and fuzzy c-means techniques to predict the potential of athletes winning medals. The study also identifies important physical characteristics related to the assessment results.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Theory & Methods
Honnesh Rohmetra, Navaneeth Raghunath, Pratik Narang, Vinay Chamola, Mohsen Guizani, Naga Rajiv Lakkaniga
Summary: The COVID-19 pandemic has overwhelmed healthcare systems and posed risks to healthcare professionals. Remote monitoring of patient symptoms using machine learning and deep learning techniques offers a promising solution, utilizing common devices like smartphones.
Article
Business
Atika Qazi, Chiranjib Bhowmik, Fayaz Hussain, Shuiqing Yang, Usman Naseem, Abayomi-Alli Adebayo, Abdu Gumaei, Mabrook Al-Rakhami
Summary: This article investigates the mediating role of awareness in the intention to accept renewable-energy sources (RES) and selects the optimal RES alternative using an integrated MCDM method. The results show that awareness plays a significant role in shaping public opinions and performance expectancy for the acceptance of RES.
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
(2023)
Article
Engineering, Civil
Ghulam Muhammad, M. Shamim Hossain
Summary: This paper proposes light convolutional neural network (CNN) models for cognitive networking in an intelligent transportation system (ITS). The models include a 1D CNN for processing 1D temporal data and a deep tree CNN for processing image data from car camera sensors. By processing data independently on edge devices, the load and time of model execution are reduced. The proposed method achieves an accuracy of approximately 94-96% and an information density of 4.4 when tested on a publicly available facial emotion database.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Ghulam Muhammad, M. Shamim Hossain, Sahil Garg
Summary: With the rapid growth of the Internet of Things (IoT) and the increase in data volume and network traffic, it has become easier for intruders to launch network attacks. This article proposes an intrusion detection system (IDS) based on stacked autoencoders (AE) and deep neural networks (DNN) to address this issue. The system achieved high accuracy rates of 94.2%, 99.7%, and 99.9% for multiclass classification on different datasets.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Chemistry, Analytical
Esraa Hassan, Samir Elmougy, Mai R. Ibraheem, M. Shamim Hossain, Khalid AlMutib, Ahmed Ghoneim, Salman A. AlQahtani, Fatma M. Talaat
Summary: Retinal optical coherence tomography (OCT) imaging is a valuable tool for assessing the condition of the back part of the eye. In this paper, an enhanced OCT model based on modified ResNet (50) and random forest algorithms is proposed to classify retinal OCT. The experimentation results demonstrate high performance in sensitivity, specificity, and precision.
Article
Chemistry, Analytical
Tallat Jabeen, Ishrat Jabeen, Humaira Ashraf, N. Z. Jhanjhi, Abdulsalam Yassine, M. Shamim Hossain
Summary: The Internet of Things (IoT) employs wireless networks to deploy numerous wireless sensors for tracking system, physical, and environmental factors. This study proposes an intelligent healthcare system using nano sensors to collect real-time health status and transfer it to the doctor's server. A genetic-based encryption method is advocated for protecting data transmission over wireless channels, and an authentication procedure is proposed for user access to the data channel.
Article
Chemistry, Multidisciplinary
Mohammed Zakariah, Salman A. A. AlQahtani, Mabrook S. S. Al-Rakhami
Summary: Traditional firewalls and data encryption techniques are insufficient for IoT network security due to increasing network threats. Intrusion detection solutions, such as the proposed deep learning model, combining attention mechanism with LSTM network, can effectively detect traffic anomalies. The proposed models demonstrated good performance in binary-class classification, with acceptable precision and recall for each class.
APPLIED SCIENCES-BASEL
(2023)
Article
Chemistry, Multidisciplinary
Salman Khan, Muhammad Abbas Khan, Mukhtaj Khan, Nadeem Iqbal, Salman A. AlQahtani, Mabrook S. Al-Rakhami, Dost Muhammad Khan
Summary: With the advancement of computational biology, high throughput Next-Generation Sequencing (NGS) has become the standard technology for gene expression studies. However, the exponential growth of raw sequencing datasets has posed a big data challenge. Accurate recognition and classification of Anti-Inflammatory Peptides (AIPs) are time-consuming and challenging for traditional technology and conventional machine learning algorithms. This study proposes an efficient high-throughput anti-inflammatory peptide predictor based on a parallel deep neural network model, demonstrating high speedup and scalability compared to traditional classification algorithms.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Hardware & Architecture
Himanshi Babbar, Shalli Rani, Sahil Garg, Georges Kaddoum, Md. Jalil Piran, M. Shamim Hossain
Summary: This article proposes a secure multilayer SDN architecture that separates the paradigm into terrestrial, aerial, and ground domains and facilitates security solutions. The specifics of the architecture's development and implementation are explored, revealing some problems and unanswered questions. Descriptive results demonstrate that the proposed architecture will significantly improve the multilayer efficiency gains of configuration upgrading and decision-making.
IEEE CONSUMER ELECTRONICS MAGAZINE
(2023)
Article
Mathematics
Saad I. Nafisah, Ghulam Muhammad, M. Shamim Hossain, Salman A. AlQahtani
Summary: This study demonstrates the capabilities of computer-aided design (CAD) systems in identifying respiratory system disorders using chest X-ray (CXR) medical imaging. The proposed system based on explainable artificial intelligence is capable of detecting COVID-19 and visualizing the infected areas in CXR images, providing doctors with a second option for decision support.
Article
Engineering, Civil
Jiawei Dong, Abdulsalam Yassine, Andy Armitage, M. Shamim Hossain
Summary: This paper proposes a Multi-agent Reinforcement Learning (MARL) mechanism that optimizes the peak shaving performance of the electric grid by scheduling the day-ahead discharging process of EV batteries. The model overcomes energy prediction inaccuracy by allowing autonomous decision-making of EVs, ensuring the integrity of the model and protecting EVs' private information.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Health Care Sciences & Services
Md. Harun-Ar-Rashid, Oindrila Chowdhury, Muhammad Minoar Hossain, Mohammad Motiur Rahman, Ghulam Muhammad, Salman A. AlQahtani, Mubarak Alrashoud, Abdulsalam Yassine, M. Shamim Hossain
Summary: In recent years, the healthcare system and its surrounding technology have become an important area for development. Significant research has been conducted on biomedical and telemedicine studies, leading to improvements in various areas. However, transferring large amounts of data, such as images, to IoT servers using certain protocols can be difficult and time-consuming. To address this issue, a proposed model involves displaying images and patient data on an IoT dashboard, with a Raspberry Pi processing and transferring the image data to an FTP server. With an implemented simulation environment, the real-time ultrasound image data monitoring on the IoT server has shown impressive system performance, which will enhance telemedicine facilities for both patients and physicians.
Article
Computer Science, Hardware & Architecture
Mhd Saria Allahham, Amr Mohamed, Aiman Erbad, Mohsen Guizani
Summary: Mobile edge learning (MEL) is a learning paradigm that enables distributed training of machine learning models over heterogeneous edge devices. This study proposes an incentive mechanism to motivate the participation of edge devices in the training process and evaluates its performance through numerical experiments.
IEEE CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING
(2023)
