M. Muthalakshmi^1,*, T. Merlin Inbamalar², C. Chandravathi³, K. Saravanan⁴

Computer Systems Science and Engineering, Vol.46, No.1, pp. 735-747, 2023, DOI:10.32604/csse.2023.032449

Abstract This research work develops new and better prognostic markers for predicting Childhood MedulloBlastoma (CMB) using a well-defined deep learning architecture. A deep learning architecture could be designed using ideas from image processing and neural networks to predict CMB using histopathological images. First, a convolution process transforms the histopathological image into deep features that uniquely describe it using different two-dimensional filters of various sizes. A 10-layer deep learning architecture is designed to extract deep features. The introduction of pooling layers in the architecture reduces the feature dimension. The extracted and dimension-reduced deep features from the arrangement of convolution layers and pooling… More >

G. Bhargavi^*, J. Arunnehru

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1257-1272, 2023, DOI:10.32604/iasc.2023.034335

Abstract Landslides are a natural hazard that is unpredictable, but we can prevent them. The Landslide Susceptibility Index reduces the uncertainty of living with landslides significantly. Planning and managing landslide-prone areas is critical. Using the most optimistic deep neural network techniques, the proposed work classifies and analyses the severity of the landslide. The selected experimental study area is Kerala’s Idukki district. A total of 3363 points were considered for this experiment using historic landslide points, field surveys, and literature searches. The primary triggering factors slope degree, slope aspect, elevation (altitude), normalized difference vegetation index (NDVI), and distance from road, lithology, and… More >

Priyesh Kumar^*, Ponniyin Selvan

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2041-2054, 2023, DOI:10.32604/iasc.2023.030291

Abstract The exponential growth of Internet of Things (IoT) and 5G networks has resulted in maximum users, and the role of cognitive radio has become pivotal in handling the crowded users. In this scenario, cognitive radio techniques such as spectrum sensing, spectrum sharing and dynamic spectrum access will become essential components in Wireless IoT communication. IoT devices must learn adaptively to the environment and extract the spectrum knowledge and inferred spectrum knowledge by appropriately changing communication parameters such as modulation index, frequency bands, coding rate etc., to accommodate the above characteristics. Implementing the above learning methods on the embedded chip leads… More >

Muhammad Shahid Amin¹, Jamal Hussain Shah¹, Mussarat Yasmin¹, Ghulam Jillani Ansari², Muhamamd Attique Khan³, Usman Tariq⁴, Ye Jin Kim⁵, Byoungchol Chang^6,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4423-4439, 2022, DOI:10.32604/cmc.2022.030432

Abstract Due to rapid development in Artificial Intelligence (AI) and Deep Learning (DL), it is difficult to maintain the security and robustness of these techniques and algorithms due to emergence of novel term adversary sampling. Such technique is sensitive to these models. Thus, fake samples cause AI and DL model to produce diverse results. Adversarial attacks that successfully implemented in real world scenarios highlight their applicability even further. In this regard, minor modifications of input images cause “Adversarial Attacks” that altered the performance of competing attacks dramatically. Recently, such attacks and defensive strategies are gaining lot of attention by the machine… 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 >

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 >

Mirza Adnan Baig^*, Ghulam Ali Mallah, Noor Ahmed Shaikh

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 4201-4213, 2022, DOI:10.32604/cmc.2022.026225

Abstract Precipitation prediction (PP) have become one of the significant research areas of deep learning (DL) and machine vision (MV) techniques are frequently used to predict the weather variables (WV). Since the climate change has left significant impact upon weather variables (WV) and continuously changes are observed in temperature, humidity, cloud patterns and other factors. Although cloud images contain sufficient information to predict the precipitation pattern but due to changes in climate, the complex cloud patterns and rapid shape changing behavior of clouds are difficult to consider for rainfall prediction. Prediction of rainfall would provide more meticulous assistance to the farmers… More >

Nighat Parveen Shaikh^*, Mumtaz Hussain Mahar

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3145-3158, 2022, DOI:10.32604/cmc.2022.024843

Abstract Recent patterns of human sentiments are highly influenced by emoji based sentiments (EBS). Social media users are widely using emoji based sentiments (EBS) in between text messages, tweets and posts. Although tiny pictures of emoji contains sufficient information to be considered for construction of classification model; but due to the wide range of dissimilar, heterogynous and complex patterns of emoji with similar meanings (SM) have become one of the significant research areas of machine vision. This paper proposes an approach to provide meticulous assistance to social media application (SMA) users to classify the EBS sentiments. Proposed methodology consists upon three… More >

A. Arivazhagi^1,*, S. Raja Kumar²

Computer Systems Science and Engineering, Vol.43, No.1, pp. 141-157, 2022, DOI:10.32604/csse.2022.021851

Abstract Intelligent Intrusion Detection System (IIDS) for networks provide a resourceful solution to network security than conventional intrusion defence mechanisms like a firewall. The efficiency of IIDS highly relies on the algorithm performance. The enhancements towards these methods are utilized to enhance the classification accuracy and diminish the testing and training time of these algorithms. Here, a novel and intelligent learning approach are known as the stabbing of intrusion with learning framework (SILF), is proposed to learn the attack features and reduce the dimensionality. It also reduces the testing and training time effectively and enhances Linear Support Vector Machine (l-SVM). It… More >

Venkateswari Pichaimani¹, K. R. Manjula^2,*

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 383-397, 2022, DOI:10.32604/iasc.2022.023105

Abstract The indoor positioning system comprises portable wireless devices that aid in finding the location of people or objects within the buildings. Identification of the items is through the capacity level of the signal received from various access points (i.e., Wi-Fi routers). The positioning of the devices utilizing some algorithms has drawn more attention from the researchers. Yet, the designed algorithm still has problems for accurate floor planning. So, the accuracy of position estimation with minimum error is made possible by introducing Gaussian Distributive Feature Embedding based Deep Recurrent Perceptive Neural Learning (GDFE-DRPNL), a novel framework. Novel features from the dataset… More >