K. Nithya^1,*, V. Rajamani²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2655-2666, 2023, DOI:10.32604/csse.2023.027275

Abstract Recent days, Image retrieval has become a tedious process as the image database has grown very larger. The introduction of Machine Learning (ML) and Deep Learning (DL) made this process more comfortable. In these, the pair-wise label similarity is used to find the matching images from the database. But this method lacks of limited propose code and weak execution of misclassified images. In order to get-rid of the above problem, a novel triplet based label that incorporates context-spatial similarity measure is proposed. A Point Attention Based Triplet Network (PABTN) is introduced to study propose code that gives maximum discriminative ability.… More >

T. Jagadesh^1,2, B. Sheela Rani^3,*

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2569-2583, 2023, DOI:10.32604/csse.2023.027239

Abstract RSs (Radar Systems) identify and trace targets and are commonly employed in applications like air traffic control and remote sensing. They are necessary for monitoring precise target trajectories. Estimations of RSs are non-linear as the parameters TDEs (time delay Estimations) and Doppler shifts are computed on receipt of echoes where EKFs (Extended Kalman Filters) and UKFs (Unscented Kalman Filters) have not been examined for computations. RSs, certain times result in poor accuracies and SNRs (low signal to noise ratios) especially, while encountering complicated environments. This work proposes IUKFs (Iterated UKFs) to track online filter performances while using optimization techniques to… More >

R. Vijay Anandh^1,*, S. Rukmani Devi²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 1897-1910, 2023, DOI:10.32604/csse.2023.027187

Abstract The demand for the exploration of ocean resources is increasing exponentially. Underwater image data plays a significant role in many research areas. Despite this, the visual quality of underwater images is degraded because of two main factors namely, backscattering and attenuation. Therefore, visual enhancement has become an essential process to recover the required data from the images. Many algorithms had been proposed in a decade for improving the quality of images. This paper aims to propose a single image enhancement technique without the use of any external datasets. For that, the degraded images are subjected to two main processes namely,… More >

Mayada Tarek^1,2,*

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2049-2063, 2023, DOI:10.32604/csse.2023.027139

Abstract Cancellable biometrics is the solution for the trade-off between two concepts: Biometrics for Security and Security for Biometrics. The cancelable template is stored in the authentication system’s database rather than the original biometric data. In case of the database is compromised, it is easy for the template to be canceled and regenerated from the same biometric data. Recoverability of the cancelable template comes from the diversity of the cancelable transformation parameters (cancelable key). Therefore, the cancelable key must be secret to be used in the system authentication process as a second authentication factor in conjunction with the biometric data. The… More >

B. Kalpana¹, S. Dhanasekaran², T. Abirami³, Ashit Kumar Dutta⁴, Marwa Obayya⁵, Jaber S. Alzahrani⁶, Manar Ahmed Hamza^7,*

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2243-2257, 2023, DOI:10.32604/csse.2023.027129

Abstract Biomedical data classification has become a hot research topic in recent years, thanks to the latest technological advancements made in healthcare. Biomedical data is usually examined by physicians for decision making process in patient treatment. Since manual diagnosis is a tedious and time consuming task, numerous automated models, using Artificial Intelligence (AI) techniques, have been presented so far. With this motivation, the current research work presents a novel Biomedical Data Classification using Cat and Mouse Based Optimizer with AI (BDC-CMBOAI) technique. The aim of the proposed BDC-CMBOAI technique is to determine the occurrence of diseases using biomedical data. Besides, the… More >

Navleen S Rekhi^1,2,*, Jagroop S Sidhu², Amit Arora²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2791-2807, 2023, DOI:10.32604/csse.2023.026976

Abstract Due to improper acquisition settings and other noise artifacts, the image degraded to yield poor mean preservation in brightness. The simplest way to improve the preservation is the implementation of histogram equalization. Because of over-enhancement, it failed to preserve the mean brightness and produce the poor quality of the image. This paper proposes a multi-scale decomposition for brightness preservation using gamma correction. After transformation to hue, saturation and intensity (HSI) channel, the 2D- discrete wavelet transform decomposed the intensity component into low and high-pass coefficients. At the next phase, gamma correction is used by auto-tuning the scale value. The scale… More >

Mahmoud Ragab^1,2,3,*, Jaber Alyami^4,5

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2309-2322, 2023, DOI:10.32604/csse.2023.026877

Abstract Liver cancer is one of the major diseases with increased mortality in recent years, across the globe. Manual detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis (CAD) models have been developed to detect the presence of liver cancer accurately and classify its stages. Besides, liver cancer segmentation outcome, using medical images, is employed in the assessment of tumor volume, further treatment plans, and response monitoring. Hence, there is a need exists to develop automated tools for liver cancer detection in a precise manner. With this motivation, the current study introduces an Intelligent… More >

Zhipeng Li^1,2, Shanshan Feng^3,*, Jun Shi², Yang Zhou², Yong Liao^1,2, Yangzhao Yang², Yangyang Li⁴, Nenghai Yu¹, Xun Shao⁵

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2411-2423, 2023, DOI:10.32604/csse.2023.026823

Abstract Accurate prediction of future events brings great benefits and reduces losses for society in many domains, such as civil unrest, pandemics, and crimes. Knowledge graph is a general language for describing and modeling complex systems. Different types of events continually occur, which are often related to historical and concurrent events. In this paper, we formalize the future event prediction as a temporal knowledge graph reasoning problem. Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process. As a result, they cannot effectively reason over temporal knowledge graphs… More >

A. Durgadevi^*, N. Shanmugavadivoo

Computer Systems Science and Engineering, Vol.44, No.3, pp. 1951-1971, 2023, DOI:10.32604/csse.2023.026810

Abstract

Contemporarily, the development of distributed generations (DGs) technologies is fetching more, and their deployment in power systems is becoming broad and diverse. Consequently, several glitches are found in the recent studies due to the inappropriate/inadequate penetrations. This work aims to improve the reliable operation of the power system employing reliability indices using a metaheuristic-based algorithm before and after DGs penetration with feeder system. The assessment procedure is carried out using MATLAB software and Modified Salp Swarm Algorithm (MSSA) that helps assess the Reliability indices of the proposed integrated IEEE RTS79 system for seven different configurations. This algorithm modifies two control… More >

Rehab Alanazi^*, Ahamed Aljuhani

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2361-2378, 2023, DOI:10.32604/csse.2023.026712

Abstract The evolution of the Internet of Things (IoT) has empowered modern industries with the capability to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational and financial harm to organizations. To preserve the confidentiality, integrity, and availability of IIoT networks, an anomaly-based intrusion detection system (IDS) can be used to provide secure, reliable, and efficient IIoT ecosystems. In this paper, we propose an anomaly-based IDS for IIoT networks as an effective security solution to efficiently and effectively… More >