A. Alice Blessie^1,*, P. Ramesh²

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1467-1484, 2023, DOI:10.32604/csse.2023.025399

Abstract The present progress of visual-based detection of the diseased area of a malady plays an essential part in the medical field. In that case, the image processing is performed to improve the image data, wherein it inhibits unintended distortion of image features or it enhances further processing in various applications and fields. This helps to show better results especially for diagnosing diseases. Of late the early prediction of cancer is necessary to prevent disease-causing problems. This work is proposed to identify lung cancer using lung computed tomography (CT) scan images. It helps to identify cancer cells’ affected areas. In the… More >

Sathit Prasomphan^*

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1295-1307, 2023, DOI:10.32604/csse.2023.025293

Abstract Fine-grained image classification is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a specific category caused by different poses and scales. A cultural heritage image is one of the fine-grained images because each image has the same similarity in most cases. Using the classification technique, distinguishing cultural heritage architecture may be difficult. This study proposes a cultural heritage content retrieval method using adaptive deep learning for fine-grained image retrieval. The key contribution of this research was the creation of a retrieval model that could handle incremental streams of… More >

R. Gopi¹, R. Sheeba², K. Anguraj³, T. Chelladurai⁴, Haya Mesfer Alshahrani⁵, Nadhem Nemri^6,*, Tarek Lamoudan⁷

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1567-1582, 2023, DOI:10.32604/csse.2023.025216

Abstract Rapid increase in the large quantity of industrial data, Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation, data sensing and collection, real-time data processing, and high request arrival rates. The classical intrusion detection system (IDS) is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity. To resolve these issues, this paper designs a new Chaotic Cuckoo Search Optimization Algorithm (CCSOA) with optimal wavelet kernel extreme learning machine (OWKELM) named CCSOA-OWKELM technique for IDS on the Industry 4.0 platform. The CCSOA-OWKELM technique focuses on the design of feature selection with classification… More >

Ahmed S. Almasoud^*

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1485-1498, 2023, DOI:10.32604/csse.2023.025190

Abstract The latest advancements in computer vision and deep learning (DL) techniques pave the way to design novel tools for the detection and monitoring of forest fires. In this view, this paper presents an intelligent wild forest fire detection and alarming system using deep learning (IWFFDA-DL) model. The proposed IWFFDA-DL technique aims to identify forest fires at earlier stages through integrated sensors. The proposed IWFFDA-DL system includes an Integrated sensor system (ISS) combining an array of sensors that acts as the major input source that helps to forecast the fire. Then, the attention based convolution neural network with bidirectional long short… More >

Narendran Sobanapuram Muruganandam, Umamakeswari Arumugam^*

Computer Systems Science and Engineering, Vol.44, No.2, pp. 979-989, 2023, DOI:10.32604/csse.2023.024943

Abstract In forecasting real time environmental factors, large data is needed to analyse the pattern behind the data values. Air pollution is a major threat towards developing countries and it is proliferating every year. Many methods in time series prediction and deep learning models to estimate the severity of air pollution. Each independent variable contributing towards pollution is necessary to analyse the trend behind the air pollution in that particular locality. This approach selects multivariate time series and coalesce a real time updatable autoregressive model to forecast Particulate matter (PM) PM2.5. To perform experimental analysis the data from the Central Pollution… More >

Darpan Majumder^1,*, S. Mohan Kumar²

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1201-1217, 2023, DOI:10.32604/csse.2023.024868

Abstract Edge computing is a cloud computing extension where physical computers are installed closer to the device to minimize latency. The task of edge data centers is to include a growing abundance of applications with a small capability in comparison to conventional data centers. Under this framework, Federated Learning was suggested to offer distributed data training strategies by the coordination of many mobile devices for the training of a popular Artificial Intelligence (AI) model without actually revealing the underlying data, which is significantly enhanced in terms of privacy. Federated learning (FL) is a recently developed decentralized profound learning methodology, where customers… More >

B. Dhiyanesh^1,*, K. Karthick², R. Radha³, Anita Venaik⁴

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1099-1107, 2023, DOI:10.32604/csse.2023.024691

Abstract Cloud computing (CC) is an advanced technology that provides access to predictive resources and data sharing. The cloud environment represents the right type regarding cloud usage model ownership, size, and rights to access. It introduces the scope and nature of cloud computing. In recent times, all processes are fed into the system for which consumer data and cache size are required. One of the most security issues in the cloud environment is Distributed Denial of Service (DDoS) attacks, responsible for cloud server overloading. This proposed system ID3 (Iterative Dichotomiser 3) Maximum Multifactor Dimensionality Posteriori Method (ID3-MMDP) is used to overcome… More >

G. Ignisha Rajathi¹, R. Ramesh Kumar², D. Ravikumar³, T. Joel⁴, Seifedine Kadry^4,5, Chang-Won Jeong⁶, Yunyoung Nam^7,*

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1793-1806, 2023, DOI:10.32604/csse.2023.024674

Abstract Recently, Internet of Medical Things (IoMT) has gained considerable attention to provide improved healthcare services to patients. Since earlier diagnosis of brain tumor (BT) using medical imaging becomes an essential task, automated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning models. With this motivation, this paper introduces a novel IoMT and cloud enabled BT diagnosis model, named IoMTC-HDBT. The IoMTC-HDBT model comprises the data acquisition process by the use of IoMT devices which captures the magnetic resonance imaging (MRI) brain images and transmit them to the cloud server. Besides, adaptive window filtering (AWF) based… More >

E. Dhiravidachelvi¹, M.Suresh Kumar², L. D. Vijay Anand³, D. Pritima⁴, Seifedine Kadry⁵, Byeong-Gwon Kang⁶, Yunyoung Nam^7,*

Computer Systems Science and Engineering, Vol.44, No.2, pp. 961-977, 2023, DOI:10.32604/csse.2023.024612

Abstract Human Activity Recognition (HAR) has been made simple in recent years, thanks to recent advancements made in Artificial Intelligence (AI) techniques. These techniques are applied in several areas like security, surveillance, healthcare, human-robot interaction, and entertainment. Since wearable sensor-based HAR system includes in-built sensors, human activities can be categorized based on sensor values. Further, it can also be employed in other applications such as gait diagnosis, observation of children/adult’s cognitive nature, stroke-patient hospital direction, Epilepsy and Parkinson’s disease examination, etc. Recently-developed Artificial Intelligence (AI) techniques, especially Deep Learning (DL) models can be deployed to accomplish effective outcomes on HAR process.… More >

K. Vignesh^1,*, A. Askarunisa², A. M. Abirami³

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1051-1067, 2023, DOI:10.32604/csse.2023.024475

Abstract Crop yield has been predicted using environmental, land, water, and crop characteristics in a prospective research design. When it comes to predicting crop production, there are a number of factors to consider, including weather conditions, soil qualities, water levels and the location of the farm. A broad variety of algorithms based on deep learning are used to extract useful crops for forecasting. The combination of data mining and deep learning creates a whole crop yield prediction system that is able to connect raw data to predicted crop yields. The suggested study uses a Discrete Deep belief network with Visual Geometry… More >