A. Selvi^*, S. Thilagamani

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2973-2987, 2023, DOI:10.32604/iasc.2022.029850

Abstract Mammography is considered a significant image for accurate breast cancer detection. Content-based image retrieval (CBIR) contributes to classifying the query mammography image and retrieves similar mammographic images from the database. This CBIR system helps a physician to give better treatment. Local features must be described with the input images to retrieve similar images. Existing methods are inefficient and inaccurate by failing in local features analysis. Hence, efficient digital mammography image retrieval needs to be implemented. This paper proposed reliable recovery of the mammographic image from the database, which requires the removal of noise using Kalman filter and scale-invariant feature transform… More >

Honglu He, Zhiying Zhu, Xinpeng Zhang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2617-2633, 2023, DOI:10.32604/cmes.2023.025923

Abstract In recent years, the number of parameters of deep neural networks (DNNs) has been increasing rapidly. The training of DNNs is typically computation-intensive. As a result, many users leverage cloud computing and outsource their training procedures. Outsourcing computation results in a potential risk called backdoor attack, in which a welltrained DNN would perform abnormally on inputs with a certain trigger. Backdoor attacks can also be classified as attacks that exploit fake images. However, most backdoor attacks design a uniform trigger for all images, which can be easily detected and removed. In this paper, we propose a novel adaptive backdoor attack.… More >

Mingyu Dong, Diqun Yan^*, Rangding Wang

Computer Systems Science and Engineering, Vol.46, No.1, pp. 1-12, 2023, DOI:10.32604/csse.2023.034568

Abstract Speech is easily leaked imperceptibly. When people use their phones, the personal voice assistant is constantly listening and waiting to be activated. Private content in speech may be maliciously extracted through automatic speech recognition (ASR) technology by some applications on phone devices. To guarantee that the recognized speech content is accurate, speech enhancement technology is used to denoise the input speech. Speech enhancement technology has developed rapidly along with deep neural networks (DNNs), but adversarial examples can cause DNNs to fail. Considering that the vulnerability of DNN can be used to protect the privacy in speech. In this work, we… More >

Bejoy Abraham¹, Jesna Mohan², Linu Shine³, Sivakumar Ramachandran^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 87-106, 2023, DOI:10.32604/cmes.2023.023384

Abstract Tear film, the outermost layer of the eye, is a complex and dynamic structure responsible for tear production. The tear film lipid layer is a vital component of the tear film that provides a smooth optical surface for the cornea and wetting the ocular surface. Dry eye syndrome (DES) is a symptomatic disease caused by reduced tear production, poor tear quality, or excessive evaporation. Its diagnosis is a difficult task due to its multifactorial etiology. Out of several clinical tests available, the evaluation of the interference patterns of the tear film lipid layer forms a potential tool for DES diagnosis.… 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 >

Umamaheswari Ramalingam^1,*, Senthil Kumar Murugesan², Karthikeyan Lakshmanan², Chidhambararajan Balasubramaniyan³

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1733-1744, 2023, DOI:10.32604/iasc.2023.032108

Abstract A Deep Neural Sentiment Classification Network (DNSCN) is developed in this work to classify the Twitter data unambiguously. It attempts to extract the negative and positive sentiments in the Twitter database. The main goal of the system is to find the sentiment behavior of tweets with minimum ambiguity. A well-defined neural network extracts deep features from the tweets automatically. Before extracting features deeper and deeper, the text in each tweet is represented by Bag-of-Words (BoW) and Word Embeddings (WE) models. The effectiveness of DNSCN architecture is analyzed using Twitter-Sanders-Apple2 (TSA2), Twitter-Sanders-Apple3 (TSA3), and Twitter-DataSet (TDS). TSA2 and TDS consist of… More >

Amira Tarek Mahmoud^1,*, Wael A. Awad², Gamal Behery², Mohamed Abouhawwash^3,4, Mehedi Masud⁵, Hanan Aljuaid⁶, Ahmed Ismail Ebada⁷

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2007-2023, 2023, DOI:10.32604/iasc.2023.031692

Abstract The accuracy of fingerprint recognition model is extremely important due to its usage in forensic and security fields. Any fingerprint recognition system has particular network architecture whereas many other networks achieve higher accuracy. To solve this problem in a unified model, this paper proposes a model that can automatically specify itself. So, it is called an automatic deep neural network (ADNN). Our algorithm can specify the appropriate architecture of the neural network used and some significant parameters of this network. These parameters are the number of filters, epochs, and iterations. It guarantees the highest accuracy by updating itself until achieving… More >

Mutiullah Jamil¹, Hafeez ul Rehman¹, SaleemUllah¹, Imran Ashraf^2,*, Saqib Ubaid¹

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5545-5557, 2023, DOI:10.32604/cmc.2023.033449

Abstract Wheat species play important role in the price of products and wheat production estimation. There are several mathematical models used for the estimation of the wheat crop but these models are implemented without considering the wheat species which is an important independent variable. The task of wheat species identification is challenging both for human experts as well as for computer vision-based solutions. With the use of satellite remote sensing, it is possible to identify and monitor wheat species on a large scale at any stage of the crop life cycle. In this work, nine popular wheat species are identified by… More >

S. Amutha^*, J. Raja Sekar

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1863-1880, 2023, DOI:10.32604/iasc.2023.028903

Abstract In this digital era, Cardio Vascular Disease (CVD) has become the leading cause of death which has led to the mortality of 17.9 million lives each year. Earlier Diagnosis of the people who are at higher risk of CVDs helps them to receive proper treatment and helps prevent deaths. It becomes inevitable to propose a solution to predict the CVD with high accuracy. A system for predicting Cardio Vascular Disease using Deep Neural Network with Binarized Butterfly Optimization Algorithm (DNN–BBoA) is proposed. The BBoA is incorporated to select the best features. The optimal features are fed to the deep neural… More >

Mingyong Li¹, Lirong Tang¹, Longfei Ma¹, Honggang Zhao¹, Jinyu Hu¹, Yan Wei^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2349-2371, 2023, DOI:10.32604/cmes.2023.022604

Abstract The learning status of learners directly affects the quality of learning. Compared with offline teachers, it is difficult for online teachers to capture the learning status of students in the whole class, and it is even more difficult to continue to pay attention to students while teaching. Therefore, this paper proposes an online learning state analysis model based on a convolutional neural network and multi-dimensional information fusion. Specifically, a facial expression recognition model and an eye state recognition model are constructed to detect students’ emotions and fatigue, respectively. By integrating the detected data with the homework test score data after… More >