M. Rajakani^1,*, R. J. Kavitha², A. Ramachandran³

Computer Systems Science and Engineering, Vol.44, No.1, pp. 265-280, 2023, DOI:10.32604/csse.2023.024994

Abstract Feature extraction is the most critical step in classification of multispectral image. The classification accuracy is mainly influenced by the feature sets that are selected to classify the image. In the past, handcrafted feature sets are used which are not adaptive for different image domains. To overcome this, an evolutionary learning method is developed to automatically learn the spatial-spectral features for classification. A modified Firefly Algorithm (FA) which achieves maximum classification accuracy with reduced size of feature set is proposed to gain the interest of feature selection for this purpose. For extracting the most efficient features from the data set,… More >

R. Bhaskaran¹, P. S. Sujith Kumar², G. Shanthi³, L. Raja⁴, Gyanendra Prasad Joshi⁵, Woong Cho^6,*

Computer Systems Science and Engineering, Vol.44, No.1, pp. 1-17, 2023, DOI:10.32604/csse.2023.024973

Abstract In recent times, real time wireless networks have found their applicability in several practical applications such as smart city, healthcare, surveillance, environmental monitoring, etc. At the same time, proper localization of nodes in real time wireless networks helps to improve the overall functioning of networks. This study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization (IM-EECNL) approach for real-time wireless networks. The proposed IM-EECNL technique involves two major processes namely node localization and clustering. Firstly, Chaotic Water Strider Algorithm based Node Localization (CWSANL) technique to determine the unknown position of the nodes. Secondly, an Oppositional Archimedes Optimization… More >

N. Jagadeeswari^1,*, V. Mohan Raj²

Computer Systems Science and Engineering, Vol.44, No.1, pp. 929-943, 2023, DOI:10.32604/csse.2023.024945

Abstract Virtualization is the backbone of cloud computing, which is a developing and widely used paradigm. By finding and merging identical memory pages, memory deduplication improves memory efficiency in virtualized systems. Kernel Same Page Merging (KSM) is a Linux service for memory pages sharing in virtualized environments. Memory deduplication is vulnerable to a memory disclosure attack, which uses covert channel establishment to reveal the contents of other colocated virtual machines. To avoid a memory disclosure attack, sharing of identical pages within a single user’s virtual machine is permitted, but sharing of contents between different users is forbidden. In our proposed approach,… More >

R. Krishnaswamy¹, Kamalraj Subramaniam², V. Nandini³, K. Vijayalakshmi⁴, Seifedine Kadry⁵, Yunyoung Nam^6,*

Computer Systems Science and Engineering, Vol.44, No.1, pp. 391-406, 2023, DOI:10.32604/csse.2023.024901

Abstract Recently, a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two Exabytes. At the same time, clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in it. Density-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial dataset. Big data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map… More >

M. Hema^1,*, S. Kanaga Suba Raja²

Computer Systems Science and Engineering, Vol.44, No.1, pp. 143-156, 2023, DOI:10.32604/csse.2023.024895

Abstract In cloud data centers, the consolidation of workload is one of the phases during which the available hosts are allocated tasks. This phenomenon ensures that the least possible number of hosts is used without compromise in meeting the Service Level Agreement (SLA). To consolidate the workloads, the hosts are segregated into three categories: normal hosts, under-loaded hosts, and overloaded hosts based on their utilization. It is to be noted that the identification of an extensively used host or underloaded host is challenging to accomplish. Threshold values were proposed in the literature to detect this scenario. The current study aims to… More >

D. Palanikkumar¹, Adel Fahad Alrasheedi², P. Parthasarathi³, S. S. Askar², Mohamed Abouhawwash^4,5,*

Computer Systems Science and Engineering, Vol.44, No.1, pp. 457-469, 2023, DOI:10.32604/csse.2023.024884

Abstract Data management becomes essential component of patient healthcare. Internet of Medical Things (IoMT) performs a wireless communication between E-medical applications and human being. Instead of consulting a doctor in the hospital, patients get health related information remotely from the physician. The main issues in the E-Medical application are lack of safety, security and privacy preservation of patient’s health care data. To overcome these issues, this work proposes block chain based IoMT Processed with Hybrid consensus protocol for secured storage. Patients health data is collected from physician, smart devices etc. The main goal is to store this highly valuable health related… More >

Sara A. Alameen^*, Areej M. Alhothali

Computer Systems Science and Engineering, Vol.44, No.1, pp. 895-912, 2023, DOI:10.32604/csse.2023.024643

Abstract Today, fatalities, physical injuries, and significant economic losses occur due to car accidents. Among the leading causes of car accidents is drowsiness behind the wheel, which can affect any driver. Drowsiness and sleepiness often have associated indicators that researchers can use to identify and promptly warn drowsy drivers to avoid potential accidents. This paper proposes a spatiotemporal model for monitoring drowsiness visual indicators from videos. This model depends on integrating a 3D convolutional neural network (3D-CNN) and long short-term memory (LSTM). The 3DCNN-LSTM can analyze long sequences by applying the 3D-CNN to extract spatiotemporal features within adjacent frames. The learned… 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 >

V. R. Kavitha¹, K. Nimala², A. Beno³, K. C. Ramya⁴, Seifedine Kadry⁵, Byeong-Gwon Kang⁶, Yunyoung Nam^7,*

Computer Systems Science and Engineering, Vol.44, No.1, pp. 579-593, 2023, DOI:10.32604/csse.2023.024553

Abstract Image Captioning is an emergent topic of research in the domain of artificial intelligence (AI). It utilizes an integration of Computer Vision (CV) and Natural Language Processing (NLP) for generating the image descriptions. It finds use in several application areas namely recommendation in editing applications, utilization in virtual assistance, etc. The development of NLP and deep learning (DL) models find useful to derive a bridge among the visual details and textual semantics. In this view, this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning (OHHO-DLIC) technique. The OHHO-DLIC technique involves the design of distinct levels… More >

R. Rajadevi¹, K. Venkatachalam², Mehedi Masud³, Mohammed A. AlZain⁴, Mohamed Abouhawwash^5,6,*

Computer Systems Science and Engineering, Vol.44, No.1, pp. 339-350, 2023, DOI:10.32604/csse.2023.024537

Abstract The Internet of Medical Things (IoMT) is an online device that senses and transmits medical data from users to physicians within a time interval. In, recent years, IoMT has rapidly grown in the medical field to provide healthcare services without physical appearance. With the use of sensors, IoMT applications are used in healthcare management. In such applications, one of the most important factors is data security, given that its transmission over the network may cause obtrusion. For data security in IoMT systems, blockchain is used due to its numerous blocks for secure data storage. In this study, Blockchain-assisted secure data… More >