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  • Open Access


    Deep Learning Based Cyber Event Detection from Open-Source Re-Emerging Social Data

    Farah Mohammad1,*, Saad Al-Ahmadi2, Jalal Al-Muhtadi1,2

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1423-1438, 2023, DOI:10.32604/cmc.2023.035741

    Abstract Social media forums have emerged as the most popular form of communication in the modern technology era, allowing people to discuss and express their opinions. This increases the amount of material being shared on social media sites. There is a wealth of information about the threat that may be found in such open data sources. The security of already-deployed software and systems relies heavily on the timely detection of newly-emerging threats to their safety that can be gleaned from such information. Despite the fact that several models for detecting cybersecurity events have been presented, it… More >

  • Open Access


    Automated Disabled People Fall Detection Using Cuckoo Search with Mobile Networks

    Mesfer Al Duhayyim*

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2473-2489, 2023, DOI:10.32604/iasc.2023.033585

    Abstract Falls are the most common concern among older adults or disabled people who use scooters and wheelchairs. The early detection of disabled persons’ falls is required to increase the living rate of an individual or provide support to them whenever required. In recent times, the arrival of the Internet of Things (IoT), smartphones, Artificial Intelligence (AI), wearables and so on make it easy to design fall detection mechanisms for smart homecare. The current study develops an Automated Disabled People Fall Detection using Cuckoo Search Optimization with Mobile Networks (ADPFD-CSOMN) model. The proposed model’s major aim… More >

  • Open Access


    Combing Type-Aware Attention and Graph Convolutional Networks for Event Detection

    Kun Ding1, Lu Xu2, Ming Liu1, Xiaoxiong Zhang1, Liu Liu1, Daojian Zeng2,*, Yuting Liu1,3, Chen Jin4

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 641-654, 2023, DOI:10.32604/cmc.2023.031052

    Abstract Event detection (ED) is aimed at detecting event occurrences and categorizing them. This task has been previously solved via recognition and classification of event triggers (ETs), which are defined as the phrase or word most clearly expressing event occurrence. Thus, current approaches require both annotated triggers as well as event types in training data. Nevertheless, triggers are non-essential in ED, and it is time-wasting for annotators to identify the “most clearly” word from a sentence, particularly in longer sentences. To decrease manual effort, we evaluate event detection without triggers. We propose a novel framework that combines More >

  • Open Access


    Criss-Cross Attention Based Auto Encoder for Video Anomaly Event Detection

    Jiaqi Wang1, Jie Zhang2, Genlin Ji2,*, Bo Sheng3

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1629-1642, 2022, DOI:10.32604/iasc.2022.029535

    Abstract The surveillance applications generate enormous video data and present challenges to video analysis for huge human labor cost. Reconstruction-based convolutional autoencoders have achieved great success in video anomaly detection for their ability of automatically detecting abnormal event. The approaches learn normal patterns only with the normal data in an unsupervised way due to the difficulty of collecting anomaly samples and obtaining anomaly annotations. But convolutional autoencoders have limitations in global feature extraction for the local receptive field of convolutional kernels. What is more, 2-dimensional convolution lacks the capability of capturing temporal information while videos change… More >

  • Open Access


    Multiple Events Detection Using Context-Intelligence Features

    Yazeed Yasin Ghadi1, Israr Akhter2, Suliman A. Alsuhibany3, Tamara al Shloul4, Ahmad Jalal2, Kibum Kim5,*

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1455-1471, 2022, DOI:10.32604/iasc.2022.025013

    Abstract Event detection systems are mainly used to observe and monitor human behavior via red green blue (RGB) images and videos. Event detection using RGB images is one of the challenging tasks of the current era. Human detection, position and orientation of human body parts in RGB images is a critical phase for numerous systems models. In this research article, the detection of human body parts by extracting context-aware energy features for event recognition is described. For this, silhouette extraction, estimation of human body parts, and context-aware features are extracted. To optimize the context-intelligence vector, we… More >

  • Open Access


    An Adaptive Classifier Based Approach for Crowd Anomaly Detection

    Sofia Nishath, P. S. Nithya Darisini*

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 349-364, 2022, DOI:10.32604/cmc.2022.023935

    Abstract Crowd Anomaly Detection has become a challenge in intelligent video surveillance system and security. Intelligent video surveillance systems make extensive use of data mining, machine learning and deep learning methods. In this paper a novel approach is proposed to identify abnormal occurrences in crowded situations using deep learning. In this approach, Adaptive GoogleNet Neural Network Classifier with Multi-Objective Whale Optimization Algorithm are applied to predict the abnormal video frames in the crowded scenes. We use multiple instance learning (MIL) to dynamically develop a deep anomalous ranking framework. This technique predicts higher anomalous values for abnormal More >

  • Open Access


    Vision Based Real Time Monitoring System for Elderly Fall Event Detection Using Deep Learning

    G. Anitha1,*, S. Baghavathi Priya2

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 87-103, 2022, DOI:10.32604/csse.2022.020361

    Abstract Human fall detection plays a vital part in the design of sensor based alarming system, aid physical therapists not only to lessen after fall effect and also to save human life. Accurate and timely identification can offer quick medical services to the injured people and prevent from serious consequences. Several vision-based approaches have been developed by the placement of cameras in diverse everyday environments. At present times, deep learning (DL) models particularly convolutional neural networks (CNNs) have gained much importance in the fall detection tasks. With this motivation, this paper presents a new vision based… More >

  • Open Access


    Tracking Dengue on Twitter Using Hybrid Filtration-Polarity and Apache Flume

    Norjihan Binti Abdul Ghani1,*, Suraya Hamid1, Muneer Ahmad1, Younes Saadi1, N.Z. Jhanjhi2, Mohammed A. Alzain3, Mehedi Masud4

    Computer Systems Science and Engineering, Vol.40, No.3, pp. 913-926, 2022, DOI:10.32604/csse.2022.018467

    Abstract The world health organization (WHO) terms dengue as a serious illness that impacts almost half of the world’s population and carries no specific treatment. Early and accurate detection of spread in affected regions can save precious lives. Despite the severity of the disease, a few noticeable works can be found that involve sentiment analysis to mine accurate intuitions from the social media text streams. However, the massive data explosion in recent years has led to difficulties in terms of storing and processing large amounts of data, as reliable mechanisms to gather the data and suitable… More >

  • Open Access


    Development of Social Media Analytics System for Emergency Event Detection and Crisis Management

    Shaheen Khatoon1,*, Majed A. Alshamari1, Amna Asif1, Md Maruf Hasan1, Sherif Abdou2, Khaled Mostafa Elsayed3, Mohsen Rashwan4

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3079-3100, 2021, DOI:10.32604/cmc.2021.017371

    Abstract Social media platforms have proven to be effective for information gathering during emergency events caused by natural or human-made disasters. Emergency response authorities, law enforcement agencies, and the public can use this information to gain situational awareness and improve disaster response. In case of emergencies, rapid responses are needed to address victims’ requests for help. The research community has developed many social media platforms and used them effectively for emergency response and coordination in the past. However, most of the present deployments of platforms in crisis management are not automated, and their operational success largely… More >

  • Open Access


    Automated and Precise Event Detection Method for Big Data in Biomedical Imaging with Support Vector Machine

    Lufeng Yuan, Erlin Yao, Guangming Tan

    Computer Systems Science and Engineering, Vol.33, No.2, pp. 105-113, 2018, DOI:10.32604/csse.2018.33.105

    Abstract This paper proposes a machine learning based method which can detect certain events automatically and precisely in biomedical imaging. We detect one important and not well-defined event, which is called flash, in fluorescence images of Escherichia coli. Given a time series of images, first we propose a scheme to transform the event detection on region of interest (ROI) in images to a classification problem. Then with supervised human labeling data, we develop a feature selection technique to utilize support vector machine (SVM) to solve this classification problem. To reduce the time in training SVM model,… More >

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