P. Saravanakumar¹, T. V. P. Sundararajan², Rajesh Kumar Dhanaraj³, Kashif Nisar^4,*, Fida Hussain Memon^5,6, Ag. Asri Bin Ag. Ibrahim⁴

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1835-1847, 2022, DOI:10.32604/iasc.2022.018953 - 24 March 2022

Abstract Confidentiality and data integrity are essential paradigms in data aggregation owing to the various cyberattacks in wireless sensor networks (WSNs). This study proposes a novel technique named Lamport certificateless signcryption-based shift-invariant connectionist artificial deep neural networks (LCS-SICADNN) by using artificial deep neural networks to develop the data aggregation security model. This model utilises the input layer with several sensor nodes, four hidden layers to overcome different attacks (data injection, compromised node, Sybil and black hole attacks) and the output layer to analyse the given input. The Lamport one-time certificateless signcryption technique involving three different processes… More >

Khudran M. Alzhrani^*

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 483-500, 2022, DOI:10.32604/iasc.2022.023914 - 05 January 2022

Abstract Individuals inadvertently allow emotions to drive their rational thoughts to predetermined conclusions regarding political partiality issues. Being well-informed about the subject in question mitigates emotions’ influence on humans’ cognitive reasoning, but it does not eliminate bias. By nature, humans tend to pick a side based on their beliefs, personal interests, and principles. Hence, journalists’ political leaning is defining factor in the rise of the polarity of political news coverage. Political bias studies usually align subjects or controversial topics of the news coverage to a particular ideology. However, politicians as private citizens or public officials are… More >

Ahmed Elaraby^1,*, Walid Hamdy², Madallah Alruwaili³

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 4019-4031, 2022, DOI:10.32604/cmc.2022.022161 - 07 December 2021

Abstract Plant diseases are a major impendence to food security, and due to a lack of key infrastructure in many regions of the world, quick identification is still challenging. Harvest losses owing to illnesses are a severe problem for both large farming structures and rural communities, motivating our mission. Because of the large range of diseases, identifying and classifying diseases with human eyes is not only time-consuming and labor intensive, but also prone to being mistaken with a high error rate. Deep learning-enabled breakthroughs in computer vision have cleared the road for smartphone-assisted plant disease and… More >

V. V. S. Vijaya Krishna^1,*, N. Pappa¹, S. P. Joy Vasantharani²

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 781-794, 2022, DOI:10.32604/iasc.2022.022181 - 17 November 2021

Abstract Industries use soft sensors for estimating output parameters that are difficult to measure on-line. These parameters can be determined by laboratory analysis which is an offline task. Now a days designing Soft sensors for complex nonlinear systems using deep learning training techniques has become popular, because of accuracy and robustness. There is a need to find pertinent hardware for realizing soft sensors to make it portable and can be used in the place of general purpose PC. This paper aims to propose a new strategy for realizing a soft sensor using deep neural networks (DNN)… More >

Erssa Arif^1,*, Syed Khuram Shahzad², Rehman Mustafa¹, Muhammad Arfan Jaffar³, Muhammad Waseem Iqbal⁴

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 909-922, 2022, DOI:10.32604/iasc.2022.021061 - 17 November 2021

Abstract The conventional surveillance and control system of Closed-Circuit Television (CCTV) cameras require human resource supervision. Almost all the criminal activities take place using weapons mostly handheld gun, revolver, or pistol. Automatic gun detection is a vital requirement now-a-days. The use of real-time object detection system for the improvement of surveillance is a promising application of Convolutional Neural Networks (CNN). We are concerned about the real-time detection of weapons for the surveillance cameras, so we focused on the implementation and comparison of faster approaches such as Region (R-CNN) and Region Fully Convolutional Networks (R-FCN) with feature… More >

P. N. Palanisamy^*, N. Malmurugan

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 791-808, 2022, DOI:10.32604/cmc.2022.019921 - 03 November 2021

Abstract In recent years, deep neural networks have become a fascinating and influential research subject, and they play a critical role in video processing and analytics. Since, video analytics are predominantly hardware centric, exploration of implementing the deep neural networks in the hardware needs its brighter light of research. However, the computational complexity and resource constraints of deep neural networks are increasing exponentially by time. Convolutional neural networks are one of the most popular deep learning architecture especially for image classification and video analytics. But these algorithms need an efficient implement strategy for incorporating more real… More >

Kuntha Pin¹, Jee Ho Chang², Yunyoung Nam^3,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5821-5834, 2022, DOI:10.32604/cmc.2022.021943 - 11 October 2021

Abstract While the usage of digital ocular fundus image has been widespread in ophthalmology practice, the interpretation of the image has been still on the hands of the ophthalmologists which are quite costly. We explored a robust deep learning system that detects three major ocular diseases: diabetic retinopathy (DR), glaucoma (GLC), and age-related macular degeneration (AMD). The proposed method is composed of two steps. First, an initial quality evaluation in the classification system is proposed to filter out poor-quality images to enhance its performance, a technique that has not been explored previously. Second, the transfer learning… More >

Javaria Tahir¹, Syed Rameez Naqvi^2,*, Khursheed Aurangzeb³, Musaed Alhussein³

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3235-3250, 2022, DOI:10.32604/cmc.2022.018949 - 27 September 2021

Abstract Melanoma, due to its higher mortality rate, is considered as one of the most pernicious types of skin cancers, mostly affecting the white populations. It has been reported a number of times and is now widely accepted, that early detection of melanoma increases the chances of the subject’s survival. Computer-aided diagnostic systems help the experts in diagnosing the skin lesion at earlier stages using machine learning techniques. In this work, we propose a framework that accurately segments, and later classifies, the lesion using improved image segmentation and fusion methods. The proposed technique takes an image More >

I. Muthumani^1,*, N. Malmurugan², L. Ganesan³

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 917-928, 2022, DOI:10.32604/iasc.2022.018030 - 22 September 2021

Abstract Text content in videos includes applications such as library video retrievals, live-streaming advertisements, opinion mining, and video synthesis. The key components of such systems include video text detection and acknowledgments. This paper provides a framework to detect and accept text video frames, aiming specifically at the cursive script of Tamil text. The model consists of a text detector, script identifier, and text recognizer. The identification in video frames of textual regions is performed using deep neural networks as object detectors. Textual script content is associated with convolutional neural networks (CNNs) and recognized by combining ResNet More >

Bin Lin¹, Jixin Chen², Zhihong Zhang³, Yanlin Lai², Xinlong Wu², Lulu Tian⁴, Wangchi Cheng^5,*

Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 641-649, 2021, DOI:10.32604/iasc.2021.016818 - 11 August 2021

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 More >