Khalid Alfalqi

Umm Al-Qura University, Makkah, Saudi Arabia, Computer Science, Faculty Member

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Papers by Khalid Alfalqi

Emergency events detection based on integration of federated learning and active learning

International Journal of Information Technology

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An Emergency Event Detection Ensemble Model Based on Big Data

Big Data and Cognitive Computing

Emergency events arise when a serious, unexpected, and often dangerous threat affects normal life... more Emergency events arise when a serious, unexpected, and often dangerous threat affects normal life. Hence, knowing what is occurring during and after emergency events is critical to mitigate the effect of the incident on humans’ life, on the environment and our infrastructures, as well as the inherent financial consequences. Social network utilization in emergency event detection models can play an important role as information is shared and users’ status is updated once an emergency event occurs. Besides, big data proved its significance as a tool to assist and alleviate emergency events by processing an enormous amount of data over a short time interval. This paper shows that it is necessary to have an appropriate emergency event detection ensemble model (EEDEM) to respond quickly once such unfortunate events occur. Furthermore, it integrates Snapchat maps to propose a novel method to pinpoint the exact location of an emergency event. Moreover, merging social networks and big data ...

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A Survey of Topic Modeling in Text Mining

International Journal of Advanced Computer Science and Applications, 2015

Topic models provide a convenient way to analyze large of unclassified text. A topic contains a c... more Topic models provide a convenient way to analyze large of unclassified text. A topic contains a cluster of words that frequently occur together. A topic modeling can connect words with similar meanings and distinguish between uses of words with multiple meanings. This paper provides two categories that can be under the field of topic modeling. First one discusses the area of methods of topic modeling, which has four methods that can be considerable under this category. These methods are Latent semantic analysis (LSA), Probabilistic latent semantic analysis (PLSA), Latent Dirichlet allocation (LDA), and Correlated topic model (CTM). The second category is called topic evolution models, which model topics by considering an important factor time. In the second category, different models are discussed, such as topic over time (TOT), dynamic topic models (DTM), multiscale topic tomography, dynamic topic correlation detection, detecting topic evolution in scientific literature, etc.

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IoT-Based Disaster Detection Model Using Social Networks and Machine Learning

2021 4th International Conference on Artificial Intelligence and Big Data (ICAIBD), 2021

The internet of things is the revolutionary concept of the traditional internet so that all the p... more The internet of things is the revolutionary concept of the traditional internet so that all the physical objects or devices can connect to the internet or each other for sharing information or perform specific functions through the network. Social network usage in disaster detection models can play an essential role by sharing information and update the user's status when a disaster occurs. Besides, big data has demonstrated its value as a tool to aid and mitigate any disaster by processing a massive amount of data in a short period. This paper discusses that it is essential to have a proper disaster detection system to respond quickly once disasters occur. Besides, it proposes a novel method to detect the exact location of a disaster by utilizing a Snapchat map. Moreover, IoT, social networks and big data can accelerate the disaster detection system if they use together; by using data from the social networks and data from IoT devices, we can manage, monitor, analyze and detect disaster. The main objective of this paper is to propose a new and efficient IoT-based disaster Detection model (DDM) to find the exact location of a disaster for the collected data from social networks (SN) and other IoT devices using big data (BD) and machine learning (ML).

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Time Series Usage in Security Applications

—Time series modeling has a significant impact on different fields of study. In fact, researchers... more —Time series modeling has a significant impact on different fields of study. In fact, researchers have developed different models that can improve our understanding of many fields that rely on time series. As a result, a number of models have developed to improve the efficiency of time series modeling and forecasting.In general, this paper sheds light on some time series definitions,as well asobjectives. In addition, the paper describes time series components and models. Moreover, this paper examines time series in real world applications such as security. Keywords— Time series , Autoregressive Process , Autoregressive moving average (ARMA) , Fraud Detection and Malware Detection .

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