Zhangjie Fu^1,2,3,*, Yongjie Ding¹, Musaazi Godfrey¹

Journal of Cyber Security, Vol.3, No.1, pp. 11-28, 2021, DOI:10.32604/jcs.2021.016632

Abstract Mobile malware occupies a considerable proportion of cyberattacks. With the update of mobile device operating systems and the development of software technology, more and more new malware keep appearing. The emergence of new malware makes the identification accuracy of existing methods lower and lower. There is an urgent need for more effective malware detection models. In this paper, we propose a new approach to mobile malware detection that is able to detect newly-emerged malware instances. Firstly, we build and train the LSTM-based model on original benign and malware samples investigated by both static and dynamic analysis techniques. Then, we build… More >

Neeraj Kumar Sirohi, Mamta Bansal, S. N. Rajan

Journal on Big Data, Vol.3, No.2, pp. 97-97, 2021, DOI:10.32604/jbd.2021.041299

Abstract This article has no abstract. More >

Javaria Amin¹, Muhammad Almas Anjum², Muhammad Sharif³, Seifedine Kadry⁴, Yunyoung Nam^5,*, ShuiHua Wang⁶

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2693-2709, 2021, DOI:10.32604/cmc.2021.016871

Abstract Recognition of human gait is a difficult assignment, particularly for unobtrusive surveillance in a video and human identification from a large distance. Therefore, a method is proposed for the classification and recognition of different types of human gait. The proposed approach is consisting of two phases. In phase I, the new model is proposed named convolutional bidirectional long short-term memory (Conv-BiLSTM) to classify the video frames of human gait. In this model, features are derived through convolutional neural network (CNN) named ResNet-18 and supplied as an input to the LSTM model that provided more distinguishable temporal information. In phase II,… More >

Huiping Jiang^1,*, Rui Jiao¹, Demeng Wu¹, Wenbo Wu²

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2315-2327, 2021, DOI:10.32604/cmc.2021.016832

Abstract With the rapid development of deep learning and artificial intelligence, affective computing, as a branch field, has attracted increasing research attention. Human emotions are diverse and are directly expressed via non-physiological indicators, such as electroencephalogram (EEG) signals. However, whether emotion-based or EEG-based, these remain single-modes of emotion recognition. Multi-mode fusion emotion recognition can improve accuracy by utilizing feature diversity and correlation. Therefore, three different models have been established: the single-mode-based EEG-long and short-term memory (LSTM) model, the Facial-LSTM model based on facial expressions processing EEG data, and the multi-mode LSTM-convolutional neural network (CNN) model that combines expressions and EEG. Their… More >

Mei Sun¹, Qingtao Li², Peiguang Lin^2,*

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 369-378, 2021, DOI:10.32604/iasc.2021.014962

Abstract Stocks are the key components of most investment portfolios. The accurate forecasting of stock prices can help investors and investment brokerage firms make profits or reduce losses. However, stock forecasting is complex because of the intrinsic features of stock data, such as nonlinearity, long-term dependency, and volatility. Moreover, stock prices are affected by multiple factors. Various studies in this field have proposed ways to improve prediction accuracy. However, not all of the proposed features are valid, and there is often noise in the features—such as political, economic, and legal factors—which can lead to poor prediction results. To overcome such limitations,… More >

Muhammad Ashfaq Khan^1,2, Yangwoo Kim^1,*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 671-687, 2021, DOI:10.32604/cmc.2021.015647

Abstract Machine learning (ML) algorithms are often used to design effective intrusion detection (ID) systems for appropriate mitigation and effective detection of malicious cyber threats at the host and network levels. However, cybersecurity attacks are still increasing. An ID system can play a vital role in detecting such threats. Existing ID systems are unable to detect malicious threats, primarily because they adopt approaches that are based on traditional ML techniques, which are less concerned with the accurate classification and feature selection. Thus, developing an accurate and intelligent ID system is a priority. The main objective of this study was to develop… More >

Zhaoqing Xie^1,*, Qing Liu², Yulian Cao³

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 153-166, 2021, DOI:10.32604/iasc.2021.016246

Abstract Accurate prediction of water level in inland waterway has been an important issue for helping flood control and vessel navigation in a proactive manner. In this research, a deep learning approach called long short-term memory network combined with discrete wavelet transform (WA-LSTM) is proposed for daily water level prediction. The wavelet transform is applied to decompose time series into details and approximation components for a better understanding of temporal properties, and a novel LSTM network is used to learn generic water level features through layer-by-layer feature granulation with a greedy layer wise unsupervised learning algorithm. Six representative reaches in Yangtze… More >

Xiaoli Li¹, Shuailing Zhou¹, Zixu An^2,*, Zhenlong Du¹

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3765-3780, 2021, DOI:10.32604/cmc.2021.012422

Abstract Rough set theory has been widely researched for time series prediction problems such as rainfall runoff. Accurate forecasting of rainfall runoff is a long standing but still mostly significant problem for water resource planning and management, reservoir and river regulation. Most research is focused on constructing the better model for improving prediction accuracy. In this paper, a rainfall runoff forecast model based on the variable-precision fuzzy neighborhood rough set (VPFNRS) is constructed to predict Watershed runoff value. Fuzzy neighborhood rough set define the fuzzy decision of a sample by using the concept of fuzzy neighborhood. The fuzzy neighborhood rough set… More >

Neeraj Kumar Sirohi^1,*, Mamta Bansal¹, S. N. Rajan²

Journal on Big Data, Vol.3, No.1, pp. 35-47, 2021, DOI:10.32604/jbd.2021.015954

Abstract Nowadays, data is very rapidly increasing in every domain such as social media, news, education, banking, etc. Most of the data and information is in the form of text. Most of the text contains little invaluable information and knowledge with lots of unwanted contents. To fetch this valuable information out of the huge text document, we need summarizer which is capable to extract data automatically and at the same time capable to summarize the document, particularly textual text in novel document, without losing its any vital information. The summarization could be in the form of extractive and abstractive summarization. The… More >

Muhammad Ashfaq Khan, Yangwoo Kim^*

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 427-443, 2021, DOI:10.32604/cmc.2021.014682

Abstract Many approaches have been tried for the classification of arrhythmia. Due to the dynamic nature of electrocardiogram (ECG) signals, it is challenging to use traditional handcrafted techniques, making a machine learning (ML) implementation attractive. Competent monitoring of cardiac arrhythmia patients can save lives. Cardiac arrhythmia prediction and classification has improved significantly during the last few years. Arrhythmias are a group of conditions in which the electrical activity of the heart is abnormal, either faster or slower than normal. It is the most frequent cause of death for both men and women every year in the world. This paper presents a… More >