T. Manojpraphakar^*, A. Soundarrajan

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 699-713, 2022, DOI:10.32604/iasc.2022.025090

Abstract Event recommendation allows people to identify various recent upcoming social events. Based on the Profile or User recommendation people will identify the group of users to subscribe the event and to participate, despite it faces cold-start issues intrinsically. The existing models exploit multiple contextual factors to mitigate the cold-start issues in essential applications on profile recommendations to the event. However, those existing solution does not incorporate the correlation and covariance measures among various contextual factors. Moreover, recommending similar profiles to various groups of the events also has not been well analyzed in the existing literature. The proposed prototype model Correlation… More >

Yurananatul Mangalmurti, Naruemon Wattanapongsakorn^*

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 733-752, 2022, DOI:10.32604/iasc.2022.025073

Abstract This paper presents effective techniques for automatic detection/classification of COVID-19 and other lung diseases using machine learning, including deep learning with convolutional neural networks (CNN) and classical machine learning techniques. We had access to a large number of chest X-ray images to use as input data. The data contains various categories including COVID-19, Pneumonia, Pneumothorax, Atelectasis, and Normal (without disease). In addition, chest X-ray images with many findings (abnormalities and diseases) from the National Institutes of Health (NIH) was also considered. Our deep learning approach used a CNN architecture with VGG16 and VGG19 models which were pre-trained with ImageNet. We… More >

R. Vidhya^1,*, T. T. Mirnalinee²

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 911-925, 2022, DOI:10.32604/iasc.2022.025060

Abstract Computer tomography (CT) scan images can provide more helpful diagnosis information regarding the lung cancers. Many machine learning and deep learning algorithms are formulated using CT input scan images for the improvisation in diagnosis and treatment process. But, designing an accurate and intelligent system still remains in darker side of the research side. This paper proposes the novel classification model which works on the principle of fused features and optimized learning network. The proposed framework incorporates the principle of saliency maps as a first tier segmentation, which is then fused with deep convolutional neural networks to improve the classification maps… More >

Nabila Mansouri^1,2,*, Khalid Sultan³, Aakash Ahmad⁴, Ibrahim Alseadoon⁴, Adal Alkhalil⁴

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1247-1264, 2022, DOI:10.32604/iasc.2022.025046

Abstract The outbreak of novel Coronavirus COVID-19, an infectious disease caused by the SARS-CoV-2 virus, has caused an unprecedented medical, economic, and social emergency that requires data-driven intelligence and decision support systems to counter the subsequent pandemic. Data-driven models and intelligent systems can assist medical researchers and practitioners to identify symptoms of COVID-19 infection. Several solutions based on medical image processing have been proposed for this purpose. However, the most shortcoming of hand craft image processing systems is the lower provided performances. Hence, for the first time, the proposed solution uses a deep learning model that is applied to Computed Tomography… More >

Subaselvi Sundarraj^*, Gunaseelan Konganathan

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1197-1213, 2022, DOI:10.32604/iasc.2022.025026

Abstract Software Defined Wireless Sensor Networks (SDWSN) provides a centralized scheduling algorithm to decrease energy consumption compared to WSN. The sensor nodes have a finite battery capacity in the SDWSN that reduces the lifetime of the nodes. To harvest energy for energy depleted nodes without interfering with the eventful data transfer in the clustered SDWSN, an energy efficient mobile harvesting scheme with the Multiple Input Single Output (MISO) beamforming technique is proposed. The mobile harvesting scheme transfer the energy to the energy starving node and the beamforming algorithm which transmits the energy in the desired direction increases the lifetime of the… More >

Ahtsham Alam¹, Syed Ahmed Abdullah¹, Israr Akhter¹, Suliman A. Alsuhibany^2,*, Yazeed Yasin Ghadi³, Tamara al Shloul⁴, Ahmad Jalal¹

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 941-955, 2022, DOI:10.32604/iasc.2022.024840

Abstract Robotics is a part of today's communication that makes human life simpler in the day-to-day aspect. Therefore, we are supporting this cause by making a smart city project that is based on Artificial Intelligence, image processing, and some touch of hardware such as robotics. In particular, we advocate a self automation device (i.e., autonomous car) that performs actions and takes choices on its very own intelligence with the assist of sensors. Sensors are key additives for developing and upgrading all forms of self-sustaining cars considering they could offer the information required to understand the encircling surroundings and consequently resource the… More >

S. Jeevitha^1,*, K. Valarmathi²

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1123-1139, 2022, DOI:10.32604/iasc.2022.024746

Abstract Pulmonary Embolism is a blood clot in the lung which restricts the blood flow and reduces blood oxygen level resulting in mortality if it is untreated. Further, pulmonary embolism is evidenced prominently in the segmental and sub-segmental regions of the computed tomography angiography images in COVID-19 patients. Pulmonary embolism detection from these images is a significant research problem in the challenging COVID-19 pandemic in the venture of early disease detection, treatment, and prognosis. Inspired by several investigations based on deep learning in this context, a two-stage framework has been proposed for pulmonary embolism detection which is realized as a segmentation… More >

Samirah Alzahrani^1,*, A.I. El-Maghrabi², M.S. Badr³

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 769-783, 2022, DOI:10.32604/iasc.2022.024682

Abstract The generalized structure of deoxyribonucleic acid (DNA) is based on the rules of topological spaces. DNA recombination is one of the most important processes within DNA, as it is essential in the pharmaceutical industry as well as in gene therapy. In this paper, we are discussing the relationship between rough sets, nano topological spaces (N), nano Z open (N) sets, and DNA recombination. We also created a new recombination mapping using the properties of the DNA recombination process. Further, by using the process of cutting and sticking of a sequence of genes, new topological structures are constructed and some of… More >

M. E. Shajini Sheeba^1,*, P. Jagatheeswari²

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1141-1156, 2022, DOI:10.32604/iasc.2022.024549

Abstract With abundant and non-polluting benefits in nature, sources of renewable energy have reached vast concentrations. This paper first discusses the number of MPPT (Maximum Power Point Tracking) techniques utilized by wind and photovoltaic (PV) to create hybrid systems for generating wind-PV energy. This hybrid system complements each other day and night to enable continuous power output. Then, a new MPPT technique was proposed to extract maximum power using a newly developed hybrid optimization algorithm, namely the Cukoo Fire Fly method (CFF). The CFF algorithm is derived from the integration of the cuckoo search (CS) algorithm and the Firefly (FF) optimization… More >

M. Nisha^1,*, M. Germin Nisha²

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1399-1413, 2022, DOI:10.32604/iasc.2022.024482

Abstract A new methodology is used in this paper, for the optimal tuning of Photovoltaic (PV) by integrating the hybrid Maximum Power Point Tracking (MPPT) algorithms is proposed. The suggested hybrid MPPT algorithms can raise the performance of PV systems under partial shade conditions. It attempts to address the primary research issues in partial shading conditions in PV systems caused by clouds, trees, dirt, and dust. The proposed system computes MPPT utilizing an innovative adaptive model-based approach. In order to manage the input voltage at the Maximum PowerPoint, the MPPT algorithm changes the duty cycle of the switch in the DC-DC… More >