ConvLSTMConv network: a deep learning approach for sentiment analysis in cloud computing

Ghorbani, Mohsen; Bahaghighat, Mahdi; Xin, Qin; Özen, Figen

doi:10.1186/s13677-020-00162-1

Cited by 53 publications

(20 citation statements)

References 41 publications

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“…Nowadays, data scientists are more interested in deploying emerging and strong deep learning (DL) algorithms [26][27][28][29][30]. DL can perform big data analytics efficiently.…”

Section: Discussionmentioning

confidence: 99%

Developing a Parallel Classifier for Mining in Big Data Sets

Shamseen¹,

Zanjireh

Bahaghighat

et al. 2021

IIUMEJ

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Data mining is the extraction of information and its roles from a vast amount of data. This topic is one of the most important topics these days. Nowadays, massive amounts of data are generated and stored each day. This data has useful information in different fields that attract programmers’ and engineers’ attention. One of the primary data mining classifying algorithms is the decision tree. Decision tree techniques have several advantages but also present drawbacks. One of its main drawbacks is its need to reside its data in the main memory. SPRINT is one of the decision tree builder classifiers that has proposed a fix for this problem. In this paper, our research developed a new parallel decision tree classifier by working on SPRINT results. Our experimental results show considerable improvements in terms of the runtime and memory requirements compared to the SPRINT classifier. Our proposed classifier algorithm could be implemented in serial and parallel environments and can deal with big data. ABSTRAK: Perlombongan data adalah pengekstrakan maklumat dan peranannya dari sejumlah besar data. Topik ini adalah salah satu topik yang paling penting pada masa ini. Pada masa ini, data yang banyak dihasilkan dan disimpan setiap hari. Data ini mempunyai maklumat berguna dalam pelbagai bidang yang menarik perhatian pengaturcara dan jurutera. Salah satu algoritma pengkelasan perlombongan data utama adalah pokok keputusan. Teknik pokok keputusan mempunyai beberapa kelebihan tetapi kekurangan. Salah satu kelemahan utamanya adalah keperluan menyimpan datanya dalam memori utama. SPRINT adalah salah satu pengelasan pembangun pokok keputusan yang telah mengemukakan untuk masalah ini. Dalam makalah ini, penyelidikan kami sedang mengembangkan pengkelasan pokok keputusan selari baru dengan mengusahakan hasil SPRINT. Hasil percubaan kami menunjukkan peningkatan yang besar dari segi jangka masa dan keperluan memori berbanding dengan pengelasan SPRINT. Algoritma pengklasifikasi yang dicadangkan kami dapat dilaksanakan dalam persekitaran bersiri dan selari dan dapat menangani data besar.

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“…Nowadays, data scientists are more interested in deploying emerging and strong deep learning (DL) algorithms [26][27][28][29][30]. DL can perform big data analytics efficiently.…”

Section: Discussionmentioning

confidence: 99%

Developing a Parallel Classifier for Mining in Big Data Sets

Shamseen¹,

Zanjireh

Bahaghighat

et al. 2021

IIUMEJ

Self Cite

View full text Add to dashboard Cite

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“…When an ANN has two or more hidden layers, it is called deep neural network (DNN) (Géron 2017). DL, as a new generation of ANN (Ghorbani et al 2020), is in the intersections among the research areas of neural networks, artificial intelligence, graphical modeling, optimization, pattern recognition, and signal processing (Deng and Yu 2014). Some DL algorithms (i.e.…”

Section: Artificial Intelligence Machine Learning and Deep Learningmentioning

confidence: 99%

Estimation of the excavator actual productivity at the construction site using video analysis

Šopić

Vukomanović

Car-Pušić

et al. 2021

Organization, Technology and Management in Construction: An International Journal

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Current estimates of the actual productivity of heavy construction machinery at a construction site are not supported by an appropriate and widely used methodology. Recently, for the purpose of estimating the actual productivity of heavy construction machinery, vision-based technologies are used. This paper emphasizes the importance of estimating actual productivity and presents a way (i.e. a research framework) to achieve it. Therefore, the aim of this paper is to propose a simple research framework (SRF) for quick and practical estimates of excavator actual productivity and cycle time at a construction site. The excavator actual productivity refers to the maximum possible productivity in real construction site conditions. The SRF includes the use of a video camera and the analysis of recorded videos using an advanced computer program. In cases of continuous application of SRF, a clear and transparent base for monitoring and control of earthworks can be obtained at an observed construction site.

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“…Input name sequences are roughly extracted from assembly source files, and therefore further data processing is an essential and primary step before feature representation [33]. Some extracted sequences contain many consecutive duplicate opcode and API call names which supply no more information for modeling.…”

Section: Pre-processingmentioning

confidence: 99%

Categorizing Malware via A Word2Vec-based Temporal Convolutional Network Scheme

et al. 2020

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As edge computing paradigm achieves great popularity in recent years, there remain some technical challenges that must be addressed to guarantee smart device security in Internet of Things (IoT) environment. Generally, smart devices transmit individual data across the IoT for various purposes nowadays, and it will cause losses and impose a huge threat to users since malware may steal and damage these data. To improve malware detection performance on IoT smart devices, we conduct a malware categorization analysis based on the Kaggle competition of Microsoft Malware Classification Challenge (BIG 2015) dataset in this article. Practically speaking, motivated by temporal convolutional network (TCN) structure, we propose a malware categorization scheme mainly using Word2Vec pre-trained model. Considering that the popular one-hot encoding converts input names from malicious files to high-dimensional vectors since each name is represented as one dimension in one-hot vector space, more compact vectors with fewer dimensions are obtained through the use of Word2Vec pre-training strategy, and then it can lead to fewer parameters and stronger malware feature representation. Moreover, compared with long short-term memory (LSTM), TCN demonstrates better performance with longer effective memory and faster training speed in sequence modeling tasks. The experimental comparisons on this malware dataset reveal better categorization performance with less memory usage and training time. Especially, through the performance comparison between our scheme and the state-of-the-art Word2Vec-based LSTM approach, our scheme shows approximately 1.3% higher predicted accuracy than the latter on this malware categorization task. Additionally, it also demonstrates that our scheme reduces about 90 thousand parameters and more than 1 hour on the model training time in this comparison.

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ConvLSTMConv network: a deep learning approach for sentiment analysis in cloud computing

Cited by 53 publications

References 41 publications

Developing a Parallel Classifier for Mining in Big Data Sets

Developing a Parallel Classifier for Mining in Big Data Sets

Estimation of the excavator actual productivity at the construction site using video analysis

Categorizing Malware via A Word2Vec-based Temporal Convolutional Network Scheme

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