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  • Open Access

    ARTICLE

    An Adversarial Network-based Multi-model Black-box Attack

    Bin Lin1, Jixin Chen2, Zhihong Zhang3, Yanlin Lai2, Xinlong Wu2, Lulu Tian4, Wangchi Cheng5,*

    Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 641-649, 2021, DOI:10.32604/iasc.2021.016818

    Abstract Researches have shown that Deep neural networks (DNNs) are vulnerable to adversarial examples. In this paper, we propose a generative model to explore how to produce adversarial examples that can deceive multiple deep learning models simultaneously. Unlike most of popular adversarial attack algorithms, the one proposed in this paper is based on the Generative Adversarial Networks (GAN). It can quickly produce adversarial examples and perform black-box attacks on multi-model. To enhance the transferability of the samples generated by our approach, we use multiple neural networks in the training process. Experimental results on MNIST showed that our method can efficiently generate… More >

  • Open Access

    ARTICLE

    Fault Detection Algorithms for Achieving Service Continuity in Photovoltaic Farms

    Sherif S. M. Ghoneim1,*, Amr E. Rashed2, Nagy I. Elkalashy1

    Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 467-479, 2021, DOI:10.32604/iasc.2021.016681

    Abstract This study uses several artificial intelligence approaches to detect and estimate electrical faults in photovoltaic (PV) farms. The fault detection approaches of random forest, logistic regression, naive Bayes, AdaBoost, and CN2 rule induction were selected from a total of 12 techniques because they produced better decisions for fault detection. The proposed techniques were designed using distributed PV current measurements, plant current, plant voltage, and power. Temperature, radiation, and fault resistance were treated randomly. The proposed classification model was created using the Orange platform. A classification tree was visualized, consisting of seven nodes and four leaves, with a depth of four… More >

  • Open Access

    ARTICLE

    AttEF: Convolutional LSTM Encoder-Forecaster with Attention Module for Precipitation Nowcasting

    Wei Fang1,2,*, Lin Pang1, Weinan Yi1, Victor S. Sheng3

    Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 453-466, 2021, DOI:10.32604/iasc.2021.016589

    Abstract Precipitation nowcasting has become an essential technology underlying various public services ranging from weather advisories to citywide rainfall alerts. The main challenge facing many algorithms is the high non-linearity and temporal-spatial complexity of the radar image. Convolutional Long Short-Term Memory (ConvLSTM) is appropriate for modeling spatiotemporal variations as it integrates the convolution operator into recurrent state transition functions. However, the technical characteristic of encoding the input sequence into a fixed-size vector cannot guarantee that ConvLSTM maintains adequate sequence representations in the information flow, which affects the performance of the task. In this paper, we propose Attention ConvLSTM Encoder-Forecaster(AttEF) which allows… More >

  • Open Access

    ARTICLE

    Improved Algorithm Based on Decision Tree for Semantic Information Retrieval

    Zhe Wang1,2, Yingying Zhao1, Hai Dong3, Yulong Xu1,*, Yali Lv1

    Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 419-429, 2021, DOI:10.32604/iasc.2021.016434

    Abstract The quick retrieval of target information from a massive amount of information has become a core research area in the field of information retrieval. Semantic information retrieval provides effective methods based on semantic comprehension, whose traditional models focus on multiple rounds of detection to differentiate information. Since a large amount of information must be excluded, retrieval efficiency is low. One of the most common methods used in classification, the decision tree algorithm, first selects attributes with higher information entropy to construct a decision tree. However, the tree only matches words on the grammatical level and does not consider the semantic… More >

  • Open Access

    ARTICLE

    Liver Lesions and Acute Intracerebral Hemorrhage Detection Using Multimodal Fusion

    Osama S. Faragallah1,*, Abdullah N. Muhammed2, Taha S. Taha3, Gamal G. N. Geweid4,5

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 215-225, 2021, DOI:10.32604/iasc.2021.019058

    Abstract Medical image fusion is designed to help physicians in their decisions by providing them with a preclinical image with enough information. Accurate assessment and effective treatment of the disease reduce the time it takes to relieve the symptoms of the disease. This article utilizes an effective data fusion approach to work on two different imaging modalities; computed tomography (CT) and magnetic resonance imaging (MRI). The data fusion approach is based on the combination of singular value decomposition (SVD) and the Fast Discrete Curvelet Transform (FDCT) techniques to reduce processing time during the fusion process. The SVD-FDCT data fusion approach is… More >

  • Open Access

    ARTICLE

    Semantic Analysis of Urdu English Tweets Empowered by Machine Learning

    Nadia Tabassum1, Tahir Alyas2, Muhammad Hamid3,*, Muhammad Saleem4, Saadia Malik5, Zain Ali2, Umer Farooq2

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 175-186, 2021, DOI:10.32604/iasc.2021.018998

    Abstract Development in the field of opinion mining and sentiment analysis has been rapid and aims to explore views or texts on various social media sites through machine-learning techniques with the sentiment, subjectivity analysis and calculations of polarity. Sentiment analysis is a natural language processing strategy used to decide if the information is positive, negative, or neutral and it is frequently performed on literature information to help organizations screen brand, item sentiment in client input, and comprehend client needs. In this paper, two strategies for sentiment analysis is proposed for word embedding and a bag of words on Urdu and English… More >

  • Open Access

    ARTICLE

    Modelling Supply Chain Information Collaboration Empowered with Machine Learning Technique

    Naeem Ali1,*, Alia Ahmed1, Leena Anum2, Taher M. Ghazal3,4, Sagheer Abbas5, Muhammad Adnan Khan6,7, Haitham M. Alzoubi8, Munir Ahmad5

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 243-257, 2021, DOI:10.32604/iasc.2021.018983

    Abstract Information Collaboration of the supply chain is the domination and control of product flow information from the producer to the customer. The data information flow is correlated with demand fill-up, a role delivering service, and feedback. The collaboration of supply chain information is a complex contrivance that impeccably manages the efficiency flow and focuses on its vulnerable area. As there is always room for growth in the current century, major companies have shown a growing tendency to improve their supply chain’s productivity and sustainability to increase customer consumption in complying with environmental regulations. Therefore, in supply chain collaboration, it is… More >

  • Open Access

    ARTICLE

    An Intelligent Business Model for Product Price Prediction Using Machine Learning Approach

    Naeem Ahmed Mahoto1, Rabia Iftikhar1, Asadullah Shaikh2,*, Yousef Asiri2, Abdullah Alghamdi2, Khairan Rajab2,3

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 147-159, 2021, DOI:10.32604/iasc.2021.018944

    Abstract The price of a product plays a vital role in its market share. Customers usually buy a product when it fits their needs and budget. Therefore, it is an essential area in the business to make decisions about prices for each product. The major portion of the business profit is directly connected with the percentage of the sale, which relies on certain factors of customers including customers’ behavior and market competitors. It has been observed in the past that machine learning algorithms have made the decision-making process more effective and profitable in businesses. The fusion of machine learning with business… More >

  • Open Access

    ARTICLE

    Research on Detection Method of Interest Flooding Attack in Named Data Networking

    Yabin Xu1,2,*, Peiyuan Gu2, Xiaowei Xu3

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 113-127, 2021, DOI:10.32604/iasc.2021.018895

    Abstract In order to effectively detect interest flooding attack (IFA) in Named Data Networking (NDN), this paper proposes a detection method of interest flooding attack based on chi-square test and similarity test. Firstly, it determines the detection window size based on the distribution of information name prefixes (that is information entropy) in the current network traffic. The attackers may append arbitrary random suffix to a certain prefix in the network traffic, and then send a large number of interest packets that cannot get the response. Targeted at this problem, the sensitivity of chi-square test is used to detect the change of… More >

  • Open Access

    ARTICLE

    Intelligent Nutrition Diet Recommender System for Diabetic’s Patients

    Nadia Tabassum1, Abdul Rehman2, Muhammad Hamid3,*, Muhammad Saleem4, Saadia Malik5, Tahir Alyas2

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 319-335, 2021, DOI:10.32604/iasc.2021.018870

    Abstract Diabetes is one of the ever-increasing menace crippling millions of people worldwide. It is an independent risk factor for many cardiovascular diseases including medium and small vessels and results in heart attack, stroke, kidney failure, blindness, and lower-limb amputations. According to a World Health Organization (WHO) report estimated 1.6 million deaths were the direct result of diabetes. Nutrition plays a vital role in diabetes management alongside physical activity, drugs, and insulin. Weight management can help to avert or delay at pre-diabetic stages. This research work explains the features of the Nutrition Diet Expert System (NDES), which will preferably be used… More >

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