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  • Open Access

    ARTICLE

    Detecting and Analysing Fake Opinions Using Artificial Intelligence Algorithms

    Mosleh Hmoud Al-Adhaileh1, Fawaz Waselallah Alsaade2,*

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 643-655, 2022, DOI:10.32604/iasc.2022.021225

    Abstract In e-commerce and on social media, identifying fake opinions has become a tremendous challenge. Such opinions are widely generated on the internet by fake viewers, also called fraudsters. They write deceptive reviews that purport to reflect actual user experience either to promote some products or to defame others. They also target the reputations of e-businesses. Their aim is to mislead customers to make a wrong purchase decision by selecting undesired products. Such reviewers are often paid by rival e-business companies to compose positive reviews of their products and/or negative reviews of other companies’ products. The main objective of this paper… More >

  • Open Access

    ARTICLE

    Prediction of Suitable Candidates for COVID-19 Vaccination

    R. Sujatha1, B. Venkata Siva Krishna1, Jyotir Moy Chatterjee2, P. Rahul Naidu1, NZ Jhanjhi3,*, Challa Charita1, Eza Nerin Mariya1, Mohammed Baz4

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 525-541, 2022, DOI:10.32604/iasc.2022.021216

    Abstract In the current times, COVID-19 has taken a handful of people’s lives. So, vaccination is crucial for everyone to avoid the spread of the disease. However, not every vaccine will be perfect or will get success for everyone. In the present work, we have analyzed the data from the Vaccine Adverse Event Reporting System and understood that the vaccines given to the people might or might not work considering certain demographic factors like age, gender, and multiple other variables like the state of living, etc. This variable is considered because it explains the unmentioned variables like their food habits and… More >

  • Open Access

    ARTICLE

    Optimized U-Net Segmentation and Hybrid Res-Net for Brain Tumor MRI Images Classification

    R. Rajaragavi1,*, S. Palanivel Rajan2

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 1-14, 2022, DOI:10.32604/iasc.2022.021206

    Abstract A brain tumor is a portion of uneven cells, need to be detected earlier for treatment. Magnetic Resonance Imaging (MRI) is a routinely utilized procedure to take brain tumor images. Manual segmentation of tumor is a crucial task and laborious. There is a need for an automated system for segmentation and classification for tumor surgery and medical treatments. This work suggests an efficient brain tumor segmentation and classification based on deep learning techniques. Initially, Squirrel search optimized bidirectional ConvLSTM U-net with attention gate proposed for brain tumour segmentation. Then, the Hybrid Deep ResNet and Inception Model used for classification. Squirrel… More >

  • Open Access

    ARTICLE

    DAMFO-Based Optimal Path Selection and Data Aggregation in WSN

    S. Sudha Mercy1,*, J. M. Mathana2, J. S. Leena Jasmine3

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 589-604, 2022, DOI:10.32604/iasc.2022.021068

    Abstract Wireless Sensor Network (WSN) encompasses several tiny devices termed as Sensor Nodes (SN) that have restriction in resources with lower energy, memory, together with computation. Data Aggregation (DA) is required to optimize WSN for secured data transmission at Cluster Head (CH) together with Base Station (BS). With regard to the Energy Efficiency (EE) along with the privacy conservation requirements of WSN in big-data processing and aggregation, this paper proposed Diversity centered Adaptive Moth-Flame Optimization (DAMFO) for Optimal Path Selection (OPS) and DA in WSN. In the proposed work, initially, the Trust Evaluation (TE) process is performed. The Pompeiu Distance-centered Fuzzy… More >

  • Open Access

    ARTICLE

    Massive IoT Malware Classification Method Using Binary Lifting

    Hae-Seon Jeong1, Jin Kwak2,*

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 467-481, 2022, DOI:10.32604/iasc.2022.021038

    Abstract Owing to the development of next-generation network and data processing technologies, massive Internet of Things (IoT) devices are becoming hyperconnected. As a result, Linux malware is being created to attack such hyperconnected networks by exploiting security threats in IoT devices. To determine the potential threats of such Linux malware and respond effectively, malware classification through an analysis of the executed code is required; however, a limitation exists in that each heterogeneous architecture must be analyzed separately. However, the binary codes of a heterogeneous architecture can be translated to a high-level intermediate representation (IR) of the same format using binary lifting… More >

  • Open Access

    ARTICLE

    Deep Learning-Based Decoding and AP Selection for Radio Stripe Network

    Aman Kumar Mishra, Vijayakumar Ponnusamy*

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 131-148, 2022, DOI:10.32604/iasc.2022.021017

    Abstract

    Cell-Free massive MIMO (mMIMO) offers promising features such as higher spectral efficiency, higher energy efficiency and superior spatial diversity, which makes it suitable to be adopted in beyond 5G (B5G) networks. However, the original form of Cell-Free massive MIMO requires each AP to be connected to CPU via front haul (front-haul constraints) resulting in huge economic costs and network synchronization issues. Radio Stripe architecture of cell-free mMIMO is one such architecture of cell-free mMIMO which is suitable for practical deployment. In this paper, we propose DNN Based Parallel Decoding in Radio Stripe (DNNBPDRS) to decode the symbols of User Equipments… More >

  • Open Access

    ARTICLE

    Intelligent Chimp Metaheuristics Optimization with Data Encryption Protocol for WSN

    P. Manjula1,*, Dr. S. Baghavathi Priya2

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 573-587, 2022, DOI:10.32604/iasc.2022.020969

    Abstract Recent developments in low power electronic devices integrated into wireless communication technologies resulted in the domain of wireless sensor networks (WSN), which finds in applications in diverse data gathering and tracking applications. Since WSN is mostly deployed in harsh and inaccessible environments, it is necessary to design energy efficient and security solutions. The clustering technique is an effective way to lengthen the lifetime of WSN. But most of the clustering techniques elect cluster heads (CHs) irrespective of clusters. To resolve this issue, this paper presents a new intelligent metaheuristics based energy aware clustering with data encryption protocol (IMEAC-DEP) for WSN.… More >

  • Open Access

    ARTICLE

    Applying t-SNE to Estimate Image Sharpness of Low-cost Nailfold Capillaroscopy

    Hung-Hsiang Wang1, Chih-Ping Chen2,*

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 237-254, 2022, DOI:10.32604/iasc.2022.020665

    Abstract Machine learning can classify the image clarity of low-cost nailfold capillaroscopy (NC) and can be applied to the design verification for other medical devices. The method can be beneficial for systems that require a large number of image datasets. This investigation covers the design, integration, image sharpness estimation, and deconvolution sharpening of the NC. The study applies this device to record two videos and extract 600 photos, including blurry and sharp images. It then uses the Laplace operator method for blur detection of the pictures. Statistics are recorded for each image’s Laplace score and the distribution of clear photos in… More >

  • Open Access

    ARTICLE

    COVID-19 Cases Prediction in Saudi Arabia Using Tree-based Ensemble Models

    Abdulwahab Ali Almazroi1, Raja Sher Afgun Usmani2,*

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 389-400, 2022, DOI:10.32604/iasc.2022.020588

    Abstract COVID-19 pandemic has affected more than 144 million people and spread to over 200 countries. The prediction of COVID-19 behaviour and trend is crucial to prevent its spreading. Kingdom of Saudi Arabia (KSA) is Asia’s fifth largest country, and it hosts the two holiest cities of the Islamic world. KSA hosts millions of pilgrims every year, and it is of great importance to predict the COVID-19 spread to organize these religious activities and bring life to normality in KSA. This study proposes four tree-based ensemble methods to predict the COVID-19 daily new cases in KSA. Tree-based ensemble methods are suggested… More >

  • Open Access

    ARTICLE

    Performance Analysis of Photovoltaic Systems and Energy Return on the Environment Economy

    Murad A. A. Almekhlafi1, Fahd N. Al-Wesabi2,3, Anwer Mustafa Hilal4, Manar Ahmed Hamza4,*, Abdelzahir Abdelmaboud5, Mohammed Rizwanullah4

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 557-571, 2022, DOI:10.32604/iasc.2022.020576

    Abstract Using fossil fuels and non-renewable forms of energy has many adverse effects on the global ecosystem, and global demand exceeds the limited available resources. Renewable energy is one of the essential elements of social and economic development in any civilized country. This study comprises a feasibility study of the implementation of PV systems in a hybrid diesel network and analyzes the relationship between the effective uses of photovoltaic systems, the return of energy to the environment, and that country’s national economy. As a potential solution for the public and private utilities, the sunny web design application was used to calculate… More >

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