V. Ulagamuthalvi¹, G. Kulanthaivel^2,*, A. Balasundaram³, Arun Kumar Sivaraman⁴

Computer Systems Science and Engineering, Vol.43, No.1, pp. 275-289, 2022, DOI:10.32604/csse.2022.023737

Abstract One of the fast-growing disease affecting women’s health seriously is breast cancer. It is highly essential to identify and detect breast cancer in the earlier stage. This paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer accurately. Deep learning algorithms are fully automatic in learning, extracting, and classifying the features and are highly suitable for any image, from natural to medical images. Existing methods focused on using various conventional and machine learning methods for processing natural and medical images. It is inadequate for the image where the coarse structure matters… More >

R. Swathy^*, B. Vinayagasundaram

Computer Systems Science and Engineering, Vol.43, No.1, pp. 159-174, 2022, DOI:10.32604/csse.2022.023706

Abstract This paper proposes an algorithm for scheduling Virtual Machines (VM) with energy saving strategies in the physical servers of cloud data centers. Energy saving strategy along with a solution for productive resource utilization for VM deployment in cloud data centers is modeled by a combination of “Virtual Machine Scheduling using Bayes Theorem” algorithm (VMSBT) and Virtual Machine Migration (VMMIG) algorithm. It is shown that the overall data center’s consumption of energy is minimized with a combination of VMSBT algorithm and Virtual Machine Migration (VMMIG) algorithm. Virtual machine migration between the active physical servers in the data center is carried out… More >

V. Kumar^1,*, N. Jayapandian², P. Balasubramanie³

Computer Systems Science and Engineering, Vol.43, No.1, pp. 77-86, 2022, DOI:10.32604/csse.2022.023481

Abstract Through Wireless Sensor Networks (WSN) formation, industrial and academic communities have seen remarkable development in recent decades. One of the most common techniques to derive the best out of wireless sensor networks is to upgrade the operating group. The most important problem is the arrangement of optimal number of sensor nodes as clusters to discuss clustering method. In this method, new client nodes and dynamic methods are used to determine the optimal number of clusters and cluster heads which are to be better organized and proposed to classify each round. Parameters of effective energy use and the ability to decide… More >

C. Ram Kumar^1,*, K. Murali Krishna², Mohammad Shabbir Alam³, K. Vigneshwaran⁴, Sridharan Kannan⁵, C. Bharatiraja⁶

Computer Systems Science and Engineering, Vol.43, No.1, pp. 259-273, 2022, DOI:10.32604/csse.2022.023477

Abstract The Wireless Sensor Networks (WSN) is a self-organizing network with random deployment of wireless nodes that connects each other for effective monitoring and data transmission. The clustering technique employed to group the collection of nodes for data transmission and each node is assigned with a cluster head. The major concern with the identification of the cluster head is the consideration of energy consumption and hence this paper proposes an hybrid model which forms an energy efficient cluster head in the Wireless Sensor Network. The proposed model is a hybridization of Glowworm Swarm Optimization (GSO) and Artificial Bee Colony (ABC) algorithm… More >

M. Anuradha^1,*, G. Mani², T. Shanthi³, N. R. Nagarajan⁴, P. Suresh⁵, C. Bharatiraja⁶

Computer Systems Science and Engineering, Vol.43, No.1, pp. 381-396, 2022, DOI:10.32604/csse.2022.023321

Abstract In the digital area, Internet of Things (IoT) and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal traffic. Though IoT networks are popular and widely employed in real world applications, security in IoT networks remains a challenging problem. Conventional intrusion detection systems (IDS) cannot be employed in IoT networks owing to the limitations in resources and complexity. Therefore, this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning (IMFSDL) based classification model, called IMFSDL-IDS for IoT networks. The proposed IMFSDL-IDS… More >

M. Kavitha^*, M. Sujaritha

Computer Systems Science and Engineering, Vol.43, No.1, pp. 245-257, 2022, DOI:10.32604/csse.2022.023320

Abstract Sensing the content of macronutrients in the agricultural soil is an essential task in precision agriculture. It helps the farmers in the optimal use of fertilizers. It reduces the cost of food production and also the negative environmental impacts on atmosphere and water bodies due to indiscriminate dosage of fertilizers. The traditional chemical-based laboratory soil analysis methods do not serve the purpose as they are hardly suitable for site specific soil management. Moreover, the spectral range used in the chemical-based laboratory soil analysis may be of 350–2500 nm, which leads to redundancy and confusion. Developing sensors based on the discovery of… More >

T. S. Balaji^1,2, S. Srinivasan^3,*, S. Prasanna Bharathi⁴, B. Ramesh⁵

Computer Systems Science and Engineering, Vol.43, No.1, pp. 291-304, 2022, DOI:10.32604/csse.2022.023269

Abstract Due to the advanced developments in communication technologies, Internet of vehicles and vehicular adhoc networks (VANET) offers numerous opportunities for effectively managing transportation problems. On the other, the cloud environment needs to disseminate the emergency message to the vehicles which are consistently distributed on the roadway so that every vehicle gets the messages from closer vehicles in a straightforward way. To resolve this issue, clustering and routing techniques can be designed using computational intelligence approaches. With this motivation, this paper presents a new type-2 fuzzy sets based clustering with metaheuristic optimization based routing (T2FSC-MOR) technique for secure communication in VANET.… More >

Chia-Nan Wang¹, Ngoc-Ai-Thy Nguyen^1,*, Thanh-Tuan Dang^1,2, Jing-Wein Wang³

Computer Systems Science and Engineering, Vol.43, No.1, pp. 347-362, 2022, DOI:10.32604/csse.2022.022941

Abstract Along with vast non-fossil potential and significant expertise, there is a question of whether Asian nations are attaining efficient consumption and exploitation of renewable resources. From this perspective, the paper aims to evaluate the efficiency of 14 potential Asia countries in renewable energy consumption during the six-year periods (2014–2019). In analyzing the performance of the renewable energy sector, the data envelopment analysis (DEA) with an undesirable output model approach has been widely utilized to measure the efficiency of peer units compared with the best practice frontier. We consider four inputs and two outputs to a DEA-based efficiency model. Labor force,… More >

Safaa El-Gazar¹, Walid El-Shafai^2,3,*, Ghada El-Banby⁴, Hesham F. A. Hamed¹, Gerges M. Salama¹, Mohammed Abd-Elnaby⁵, Fathi E. Abd El-Samie^2,6

Computer Systems Science and Engineering, Vol.43, No.1, pp. 87-102, 2022, DOI:10.32604/csse.2022.022722

Abstract Biometric authentication is a rapidly growing trend that is gaining increasing attention in the last decades. It achieves safe access to systems using biometrics instead of the traditional passwords. The utilization of a biometric in its original format makes it usable only once. Therefore, a cancelable biometric template should be used, so that it can be replaced when it is attacked. Cancelable biometrics aims to enhance the security and privacy of biometric authentication. Digital encryption is an efficient technique to be used in order to generate cancelable biometric templates. In this paper, a highly-secure encryption algorithm is proposed to ensure… More >

Abdul Majid¹, Sumaira Kausar^1,*, Samabia Tehsin¹, Amina Jameel²

Computer Systems Science and Engineering, Vol.43, No.1, pp. 27-43, 2022, DOI:10.32604/csse.2022.022590

Abstract In recent years, a gain in popularity and significance of science understanding has been observed due to the high paced progress in computer vision techniques and technologies. The primary focus of computer vision based scene understanding is to label each and every pixel in an image as the category of the object it belongs to. So it is required to combine segmentation and detection in a single framework. Recently many successful computer vision methods has been developed to aid scene understanding for a variety of real world application. Scene understanding systems typically involves detection and segmentation of different natural and… More >