P. Malathi^1,*, G. Charlyn Pushpa Latha²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1741-1758, 2023, DOI:10.32604/csse.2023.035185

Abstract Breast cancer (BCa) is a leading cause of death in the female population across the globe. Approximately 2.3 million new BCa cases are recorded globally in females, overtaking lung cancer as the most prevalent form of cancer to be diagnosed. However, the mortality rates for cervical and BCa are significantly higher in developing nations than in developed countries. Early diagnosis is the only option to minimize the risks of BCa. Deep learning (DL)-based models have performed well in image processing in recent years, particularly convolutional neural network (CNN). Hence, this research proposes a DL-based CNN model to diagnose BCa from… More >

Ahmed M. Elshewey¹, Mahmoud Y. Shams^2,*, Zahraa Tarek³, Mohamed Megahed⁴, El-Sayed M. El-kenawy⁵, Mohamed A. El-dosuky^3,6

Computer Systems Science and Engineering, Vol.46, No.1, pp. 765-781, 2023, DOI:10.32604/csse.2023.034324

Abstract Food choice motives (i.e., mood, health, natural content, convenience, sensory appeal, price, familiarities, ethical concerns, and weight control) have an important role in transforming the current food system to ensure the healthiness of people and the sustainability of the world. Researchers from several domains have presented several models addressing issues influencing food choice over the years. However, a multidisciplinary approach is required to better understand how various aspects interact with one another during the decision-making procedure. In this paper, four Deep Learning (DL) models and one Machine Learning (ML) model are utilized to predict the weight in pounds based on… More >

Karthick Panneerselvam^1,2,*, K. Mahesh¹, V. L. Helen Josephine³, A. Ranjith Kumar²

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1047-1061, 2023, DOI:10.32604/csse.2023.030513

Abstract Deep learning has reached many successes in Video Processing. Video has become a growing important part of our daily digital interactions. The advancement of better resolution content and the large volume offers serious challenges to the goal of receiving, distributing, compressing and revealing high-quality video content. In this paper we propose a novel Effective and Efficient video compression by the Deep Learning framework based on the flask, which creatively combines the Deep Learning Techniques on Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN). The video compression method involves the layers are divided into different groups for data processing, using… More >

Muhammad Nadeem¹, Ali Arshad², Saman Riaz², SyedaWajiha Zahra¹, Muhammad Rashid², Shahab S. Band^3,*, Amir Mosavi^4,5,6

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2641-2659, 2023, DOI:10.32604/cmc.2023.028796

Abstract Cloud computing is one of the most attractive and cost-saving models, which provides online services to end-users. Cloud computing allows the user to access data directly from any node. But nowadays, cloud security is one of the biggest issues that arise. Different types of malware are wreaking havoc on the clouds. Attacks on the cloud server are happening from both internal and external sides. This paper has developed a tool to prevent the cloud server from spamming attacks. When an attacker attempts to use different spamming techniques on a cloud server, the attacker will be intercepted through two effective techniques:… More >

Yang Yang^1,2,*, Pengfei Zheng^3,4, Fanru Zeng⁵, Peng Xin⁶, Guoxi He¹, Kexi Liao¹

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 267-291, 2023, DOI:10.32604/cmes.2022.020220

Abstract Accurate prediction of the internal corrosion rates of oil and gas pipelines could be an effective way to prevent pipeline leaks. In this study, a proposed framework for predicting corrosion rates under a small sample of metal corrosion data in the laboratory was developed to provide a new perspective on how to solve the problem of pipeline corrosion under the condition of insufficient real samples. This approach employed the bagging algorithm to construct a strong learner by integrating several KNN learners. A total of 99 data were collected and split into training and test set with a 9:1 ratio. The… More >

R. Punithavathi^1,*, S. Thenmozhi², R. Jothilakshmi³, V. Ellappan⁴, Islam Md Tahzib Ul⁵

Computer Systems Science and Engineering, Vol.45, No.1, pp. 247-261, 2023, DOI:10.32604/csse.2023.025972

Abstract During Covid pandemic, many individuals are suffering from suicidal ideation in the world. Social distancing and quarantining, affects the patient emotionally. Affective computing is the study of recognizing human feelings and emotions. This technology can be used effectively during pandemic for facial expression recognition which automatically extracts the features from the human face. Monitoring system plays a very important role to detect the patient condition and to recognize the patterns of expression from the safest distance. In this paper, a new method is proposed for emotion recognition and suicide ideation detection in COVID patients. This helps to alert the nurse,… More >

D. Maincer^1,*, Y. Benmahamed², M. Mansour¹, Mosleh Alharthi³, Sherif S. M. Ghonein³

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1957-1969, 2023, DOI:10.32604/iasc.2023.029210

Abstract In this paper, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) based methods are to be applied on fault diagnosis in a robot manipulator. A comparative study between the two classifiers in terms of successfully detecting and isolating the seven classes of sensor faults is considered in this work. For both classifiers, the torque, the position and the speed of the manipulator have been employed as the input vector. However, it is to mention that a large database is needed and used for the training and testing phases. The SVM method used in this paper is based on the Gaussian… More >

Hussah Nasser AlEisa¹, El-Sayed M. El-kenawy^2,3, Amel Ali Alhussan^1,*, Mohamed Saber⁴, Abdelaziz A. Abdelhamid^5,6, Doaa Sami Khafaga¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2371-2387, 2022, DOI:10.32604/cmc.2022.030447

Abstract Most children and elderly people worldwide die from pneumonia, which is a contagious illness that causes lung ulcers. For diagnosing pneumonia from chest X-ray images, many deep learning models have been put forth. The goal of this research is to develop an effective and strong approach for detecting and categorizing pneumonia cases. By varying the deep learning approach, three pre-trained models, GoogLeNet, ResNet18, and DenseNet121, are employed in this research to extract the main features of pneumonia and normal cases. In addition, the binary dipper throated optimization (DTO) algorithm is utilized to select the most significant features, which are then… More >

Hadil Shaiba¹, Radwa Marzouk², Mohamed K Nour³, Noha Negm^4,5, Anwer Mustafa Hilal^6,*, Abdullah Mohamed⁷, Abdelwahed Motwakel⁶, Ishfaq Yaseen⁶, Abu Sarwar Zamani⁶, Mohammed Rizwanullah⁶

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3367-3382, 2022, DOI:10.32604/cmc.2022.030067

Abstract The agricultural sector’s day-to-day operations, such as irrigation and sowing, are impacted by the weather. Therefore, weather constitutes a key role in all regular human activities. Weather forecasting must be accurate and precise to plan our activities and safeguard ourselves as well as our property from disasters. Rainfall, wind speed, humidity, wind direction, cloud, temperature, and other weather forecasting variables are used in this work for weather prediction. Many research works have been conducted on weather forecasting. The drawbacks of existing approaches are that they are less effective, inaccurate, and time-consuming. To overcome these issues, this paper proposes an enhanced… More >

G. Ravikumar¹, K. Venkatachalam², Mohammed A. AlZain³, Mehedi Masud⁴, Mohamed Abouhawwash^5,6,*

Computer Systems Science and Engineering, Vol.44, No.1, pp. 945-959, 2023, DOI:10.32604/csse.2023.024605

Abstract Sleep apnea syndrome (SAS) is a breathing disorder while a person is asleep. The traditional method for examining SAS is Polysomnography (PSG). The standard procedure of PSG requires complete overnight observation in a laboratory. PSG typically provides accurate results, but it is expensive and time consuming. However, for people with Sleep apnea (SA), available beds and laboratories are limited. Resultantly, it may produce inaccurate diagnosis. Thus, this paper proposes the Internet of Medical Things (IoMT) framework with a machine learning concept of fully connected neural network (FCNN) with k-nearest neighbor (k-NN) classifier. This paper describes smart monitoring of a patient’s… More >