Open Access

ARTICLE

R. Bharathi^1,*, T. Abirami²
Computer Systems Science and Engineering, Vol.44, No.1, pp. 797-811, 2023, DOI:10.32604/csse.2023.025336
Abstract With the exponential developments of wireless networking and inexpensive Internet of Things (IoT), a wide range of applications has been designed to attain enhanced services. Due to the limited energy capacity of IoT devices, energy-aware clustering techniques can be highly preferable. At the same time, artificial intelligence (AI) techniques can be applied to perform appropriate disease diagnostic processes. With this motivation, this study designs a novel squirrel search algorithm-based energy-aware clustering with a medical data classification (SSAC-MDC) model in an IoT environment. The goal of the SSAC-MDC technique is to attain maximum energy efficiency and disease diagnosis in the IoT… More >

View

1046

Download

615

Open Access

ARTICLE

M. Geetha^1,*, R. Vijayabhasker², Suresh Seetharaman¹
Computer Systems Science and Engineering, Vol.44, No.1, pp. 813-832, 2023, DOI:10.32604/csse.2023.025575
Abstract A basic 7-level MLI topology is developed and the same is extended to the 9-level then further increased to 17-levels. The developed structure minimizes the component’s count and size to draw out the system economy. Despite the various advantages of MLIs, efficiency and reliability play a major role since the usage of components is higher for getting a low Total Harmonics Distortion (THD) value. This becomes a major challenge incorporated in boosting the efficiency without affecting the THD value. Various parametric observations are done and realized for the designed 9-level and 17-level MLI, being the Total Standing Voltage (TSV), efficiency,… More >

View

1129

Download

576

Open Access

ARTICLE

S. Mohan Kumar^*, T. Kumanan
Computer Systems Science and Engineering, Vol.44, No.1, pp. 833-844, 2023, DOI:10.32604/csse.2023.022385
Abstract The main cause of skin cancer is the ultraviolet radiation of the sun. It spreads quickly to other body parts. Thus, early diagnosis is required to decrease the mortality rate due to skin cancer. In this study, an automatic system for Skin Lesion Classification (SLC) using Non-Subsampled Shearlet Transform (NSST) based energy features and Support Vector Machine (SVM) classifier is proposed. At first, the NSST is used for the decomposition of input skin lesion images with different directions like 2, 4, 8 and 16. From the NSST’s sub-bands, energy features are extracted and stored in the feature database for training.… More >

View

994

Download

442

Open Access

ARTICLE

Muhammad Shahzeb Ali¹, Ali Alqahtani^2,*, Ansar Munir Shah¹, Adel Rajab², Mahmood Ul Hassan³, Asadullah Shaikh², Khairan Rajab², Basit Shahzad⁴
Computer Systems Science and Engineering, Vol.44, No.1, pp. 845-858, 2023, DOI:10.32604/csse.2023.025449
Abstract Throughout the use of the small battery-operated sensor nodes encourage us to develop an energy-efficient routing protocol for wireless sensor networks (WSNs). The development of an energy-efficient routing protocol is a mainly adopted technique to enhance the lifetime of WSN. Many routing protocols are available, but the issue is still alive. Clustering is one of the most important techniques in the existing routing protocols. In the clustering-based model, the important thing is the selection of the cluster heads. In this paper, we have proposed a scheme that uses the bubble sort algorithm for cluster head selection by considering the remaining… More >

View

1459

Download

662

Open Access

ARTICLE

Fawaz Alassery¹, Ahmed Alzahrani², Asif Irshad Khan², Kanika Sharma³, Masood Ahmad⁴, Raees Ahmad Khan^4,*
Computer Systems Science and Engineering, Vol.44, No.1, pp. 859-872, 2023, DOI:10.32604/csse.2023.025796
Abstract In recent years, it has been observed that the disclosure of information increases the risk of terrorism. Without restricting the accessibility of information, providing security is difficult. So, there is a demand for time to fill the gap between security and accessibility of information. In fact, security tools should be usable for improving the security as well as the accessibility of information. Though security and accessibility are not directly influenced, some of their factors are indirectly influenced by each other. Attributes play an important role in bridging the gap between security and accessibility. In this paper, we identify the key… More >

View

1408

Download

630

Open Access

ARTICLE

D. Suvitha^*, M. Vijayalakshmi
Computer Systems Science and Engineering, Vol.44, No.1, pp. 873-894, 2023, DOI:10.32604/csse.2023.025189
Abstract Recent advancement in low-cost cameras has facilitated surveillance in various developing towns in India. The video obtained from such surveillance are of low quality. Still counting vehicles from such videos are necessity to avoid traffic congestion and allows drivers to plan their routes more precisely. On the other hand, detecting vehicles from such low quality videos are highly challenging with vision based methodologies. In this research a meticulous attempt is made to access low-quality videos to describe traffic in Salem town in India, which is mostly an un-attempted entity by most available sources. In this work profound Detection Transformer (DETR)… More >

View

1325

Download

528

Open Access

ARTICLE

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 >

View

1673

Download

1039

Like

1

Open Access

ARTICLE

M. Manikandakumar^1,*, P. Karthikeyan²
Computer Systems Science and Engineering, Vol.44, No.1, pp. 913-927, 2023, DOI:10.32604/csse.2023.025434
Abstract Weed is a plant that grows along with nearly all field crops, including rice, wheat, cotton, millets and sugar cane, affecting crop yield and quality. Classification and accurate identification of all types of weeds is a challenging task for farmers in earlier stage of crop growth because of similarity. To address this issue, an efficient weed classification model is proposed with the Deep Convolutional Neural Network (CNN) that implements automatic feature extraction and performs complex feature learning for image classification. Throughout this work, weed images were trained using the proposed CNN model with evolutionary computing approach to classify the weeds… More >

View

1242

Download

777

Open Access

ARTICLE

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 >

View

1123

Download

508

Open Access

ARTICLE

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 >

View

1173

Download

538