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  • Open Access

    ARTICLE

    Improving Stock Price Forecasting Using a Large Volume of News Headline Text

    Daxing Zhang1,*, Erguan Cai2

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3931-3943, 2021, DOI:10.32604/cmc.2021.012302

    Abstract Previous research in the area of using deep learning algorithms to forecast stock prices was focused on news headlines, company reports, and a mix of daily stock fundamentals, but few studies achieved excellent results. This study uses a convolutional neural network (CNN) to predict stock prices by considering a great amount of data, consisting of financial news headlines. We call our model N-CNN to distinguish it from a CNN. The main concept is to narrow the diversity of specific stock prices as they are impacted by news headlines, then horizontally expand the news headline data to a higher level for… More >

  • Open Access

    ARTICLE

    The Data Acquisition and Control System Based on IoT-CAN Bus

    He Gong1,2,3,4, Ji Li1, RuiWen Ni1, Pei Xiao1, Hang Ouyang1, Ye Mu1,*, Thobela Louis Tyasi5

    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 1049-1062, 2021, DOI:10.32604/iasc.2021.019730

    Abstract Presently, the adoption of Internet of things(IOT)-related technologies in the Smart Farming domain is rapidly emerging. The increasing number of sensors used in diverse applications has provided a massive number of continuous, unbounded, rapid data and requires the management of distinct protocols, interfaces and intermittent connections. A low-cost, low-power, and low data-rate solution is proposed to fulfill the requirements of information monitoring for actual large-scale agricultural farms, which we will need pressingly in the future. This paper designs a heterogeneous data acquisition and control system for differentiated agricultural information monitoring terminal. Based on the IoT-CAN bus architecture, the system can… More >

  • Open Access

    ARTICLE

    Adversarial Examples Generation Algorithm through DCGAN

    Biying Deng1, Ziyong Ran1, Jixin Chen1, Desheng Zheng1,*, Qiao Yang2, Lulu Tian3

    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 889-898, 2021, DOI:10.32604/iasc.2021.019727

    Abstract In recent years, due to the popularization of deep learning technology, more and more attention has been paid to the security of deep neural networks. A wide variety of machine learning algorithms can attack neural networks and make its classification and judgement of target samples wrong. However, the previous attack algorithms are based on the calculation of the corresponding model to generate unique adversarial examples, and cannot extract attack features and generate corresponding samples in batches. In this paper, Generative Adversarial Networks (GAN) is used to learn the distribution of adversarial examples generated by FGSM and establish a generation model,… More >

  • Open Access

    ARTICLE

    Cryptanalysis of an Online/Offline Certificateless Signature Scheme for Internet of Health Things

    Saddam Hussain1, Syed Sajid Ullah2,*, Mohammad Shorfuzzaman3, Mueen Uddin4, Mohammed Kaosar5

    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 983-993, 2021, DOI:10.32604/iasc.2021.019486

    Abstract Recently, Khan et al. [An online-offline certificateless signature scheme for internet of health things,” Journal of Healthcare Engineering, vol. 2020] presented a new certificateless offline/online signature scheme for Internet of Health Things (IoHT) to fulfill the authenticity requirements of the resource-constrained environment of (IoHT) devices. The authors claimed that the newly proposed scheme is formally secured against Type-I adversary under the Random Oracle Model (ROM). Unfortunately, their scheme is insecure against adaptive chosen message attacks. It is demonstrated that an adversary can forge a valid signature on a message by replacing the public key. Furthermore, we performed a comparative analysis… More >

  • Open Access

    ARTICLE

    Radio Labeling Associated with a Class of Commutative Rings Using Zero-Divisor Graph

    Azeem Haider1,*, Ali N.A. Koam1, Ali Ahmad2

    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 787-794, 2021, DOI:10.32604/iasc.2021.019391

    Abstract Graph labeling is useful in networks because each transmitter has a different transmission capacity to send or receive wired or wireless links. An interference of signals can occur when transmitters that are close together receive close frequencies. This problem has been modeled mathematically in the radio labeling problem on graphs, where vertices represent transmitters and edges indicate closeness of the transmitters. For this purpose, each vertex is labeled with a unique positive integer, and to minimize the interference, the difference between maximum and minimum used labels has to be minimized. A radio labeling for a graph is a function from… More >

  • Open Access

    ARTICLE

    Lowest-Opportunities User First-Based Subcarrier Allocation Algorithm for Downlink NOMA Systems

    Mohammed Abd-Elnaby*, Sameer Alsharif, Hesham Alhumyani, Fahad Alraddady

    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 1033-1048, 2021, DOI:10.32604/iasc.2021.019341

    Abstract Non-orthogonal multiple access (NOMA) is one of the promising 5G technologies to improve spectral efficiency massive connectivity and cell-edge throughput. The performance of NOMA systems mainly depends on the efficiency of the subcarrier allocation algorithm. This paper aims to jointly optimize spectral efficiency (SE), outage probability, and fairness among users with respect to the subcarrier allocation for downlink NOMA systems. We propose a low-complexity greedy-based subcarrier allocation algorithm based on the lowest-opportunities user’s first precept. This precept is based on computing the number of opportunities for each user to select a subcarrier with good channel gain by counting the number… More >

  • Open Access

    ARTICLE

    Performance Comparison of PoseNet Models on an AIoT Edge Device

    Min-Jun Kim1, Seng-Phil Hong2, Mingoo Kang1, Jeongwook Seo1,*

    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 743-753, 2021, DOI:10.32604/iasc.2021.019329

    Abstract In this paper, we present an oneM2M-compliant system including an artificial intelligence of things (AIoT) edge device whose principal function is to estimate human poses by using two PoseNet models built on MobileNet v1 and ResNet-50 backbone architectures. Although MobileNet v1 is generally known to be much faster but less accurate than ResNet50, it is necessary to analyze the performances of whole PoseNet models carefully and select one of them suitable for the AIoT edge device. For this reason, we first investigate the computational complexity of the models about their neural network layers and parameters and then compare their performances… More >

  • Open Access

    ARTICLE

    Compression of Grayscale Images in DRPE-based Encrypted Domain

    Osama S. Faragallah1,*, Ensherah A. Naeem2, Hala S. Elsayed3, Fathi E. Abd El-Samie4

    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 1019-1031, 2021, DOI:10.32604/iasc.2021.019185

    Abstract Compressing the encrypted images is considered an important issue in many applications such as cloud computing. From this perspective, this paper introduces an efficient approach for compression processing of images in the encrypted domain. The images are optically encrypted using the Double Random Phase Encoding (DRPE). The Joint Photographic Experts Group (JPEG) and the Set Partitioning in Hierarchical Trees (SPIHT) compression schemes have been used to compress the encrypted images. The process starts by converting the original image into an optical signal by an optical emitter like an optical source and encrypting it with DRPE. The DRPE applies two-phase modulations… More >

  • Open Access

    ARTICLE

    Computational Methods for Non-Linear Equations with Some Real-World Applications and Their Graphical Analysis

    Amir Naseem1, M.A. Rehman1, Thabet Abdeljawad2,3,4,*

    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 805-819, 2021, DOI:10.32604/iasc.2021.019164

    Abstract In this article, we propose some novel computational methods in the form of iteration schemes for computing the roots of non-linear scalar equations in a new way. The construction of these iteration schemes is purely based on exponential series expansion. The convergence criterion of the suggested schemes is also given and certified that the newly developed iteration schemes possess quartic convergence order. To analyze the suggested schemes numerically, several test examples have been given and then solved. These examples also include some real-world problems such as van der Wall’s equation, Plank’s radiation law and kinetic problem equation whose numerical results… More >

  • Open Access

    ARTICLE

    A Two-Step Approach for Improving Sentiment Classification Accuracy

    Muhammad Azam1, Tanvir Ahmed1, Rehan Ahmad2, Ateeq Ur Rehman3, Fahad Sabah1, Rao Muhammad Asif4,*

    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 853-867, 2021, DOI:10.32604/iasc.2021.019101

    Abstract Sentiment analysis is a method for assessing an individual’s thought, opinion, feeling, mentality, and conviction about a specific subject on indicated theme, idea, or product. The point could be a business association, a news article, a research paper, or an online item, etc. Opinions are generally divided into three groups of positive, negative, and unbiased. The way toward investigating different opinions and gathering them in every one of these categories is known as Sentiment Analysis. The enormously growing sentiment data on the web especially social media can be a big source of information. The processing of this unstructured data is… More >

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