Amr Abozeid^1,*, Ahmed I. Taloba^1,2, Rasha M. Abd El-Aziz^1,3, Alhanoof Faiz Alwaghid¹, Mostafa Salem³, Ahmed Elhadad^1,4

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2637-2650, 2023, DOI:10.32604/csse.2023.037761

Abstract Indoor localization methods can help many sectors, such as healthcare centers, smart homes, museums, warehouses, and retail malls, improve their service areas. As a result, it is crucial to look for low-cost methods that can provide exact localization in indoor locations. In this context, image-based localization methods can play an important role in estimating both the position and the orientation of cameras regarding an object. Image-based localization faces many issues, such as image scale and rotation variance. Also, image-based localization’s accuracy and speed (latency) are two critical factors. This paper proposes an efficient 6-DoF deep-learning model for image-based localization. This… More >

Tariq Ahamed Ahanger^1,*, Fadl Dahan^2,3, Usman Tariq¹

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2429-2445, 2023, DOI:10.32604/csse.2023.037692

Abstract In the Internet of Things (IoT), the users have complex needs, and the Web Service Composition (WSC) was introduced to address these needs. The WSC’s main objective is to search for the optimal combination of web services in response to the user needs and the level of Quality of Services (QoS) constraints. The challenge of this problem is the huge number of web services that achieve similar functionality with different levels of QoS constraints. In this paper, we introduce an extension of our previous works on the Artificial Bee Colony (ABC) and Bat Algorithm (BA). A new hybrid algorithm was… More >

Mingguang Yu^1,2, Xia Zhang^1,2,*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2447-2469, 2023, DOI:10.32604/csse.2023.037506

Abstract Modern cloud services are monitored by numerous multidomain and multivendor monitoring tools, which generate massive numbers of alerts and events that are not actionable. These alerts usually carry isolated messages that are missing service contexts. Administrators become inundated with tickets caused by such alert events when they are routed directly to incident management systems. Noisy alerts increase the risk of crucial warnings going undetected and leading to service outages. One of the feasible ways to cope with the above problems involves revealing the correlations behind a large number of alerts and then aggregating the related alerts according to their correlations.… More >

Lingyun Xiang^1,*, Rong Wang¹, Yuhang Liu¹, Yangfan Liu¹, Lina Tan^2,3

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2383-2399, 2023, DOI:10.32604/csse.2023.037067

Abstract Prevailing linguistic steganalysis approaches focus on learning sensitive features to distinguish a particular category of steganographic texts from non-steganographic texts, by performing binary classification. While it remains an unsolved problem and poses a significant threat to the security of cyberspace when various categories of non-steganographic or steganographic texts coexist. In this paper, we propose a general linguistic steganalysis framework named LS-MTL, which introduces the idea of multi-task learning to deal with the classification of various categories of steganographic and non-steganographic texts. LS-MTL captures sensitive linguistic features from multiple related linguistic steganalysis tasks and can concurrently handle diverse tasks with a… More >

Sungjin Lee¹, Ahyoung Lee², Min Hong^3,*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2543-2554, 2023, DOI:10.32604/csse.2023.037055

Abstract Specific medical data has limitations in that there are not many numbers and it is not standardized. to solve these limitations, it is necessary to study how to efficiently process these limited amounts of data. In this paper, deep learning methods for automatically determining cardiovascular diseases are described, and an effective preprocessing method for CT images that can be applied to improve the performance of deep learning was conducted. The cardiac CT images include several parts of the body such as the heart, lungs, spine, and ribs. The preprocessing step proposed in this paper divided CT image data into regions… More >

Neenavath Veeraiah^1,*, Youseef Alotaibi², Ahmad F. Subahi³

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2039-2058, 2023, DOI:10.32604/csse.2023.036985

Abstract Leukemia, often called blood cancer, is a disease that primarily affects white blood cells (WBCs), which harms a person’s tissues and plasma. This condition may be fatal when if it is not diagnosed and recognized at an early stage. The physical technique and lab procedures for Leukaemia identification are considered time-consuming. It is crucial to use a quick and unexpected way to identify different forms of Leukaemia. Timely screening of the morphologies of immature cells is essential for reducing the severity of the disease and reducing the number of people who require treatment. Various deep-learning (DL) model-based segmentation and categorization… More >

Savas Takan^1,*, Gokmen Katipoglu²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2075-2089, 2023, DOI:10.32604/csse.2023.036791

Abstract A measure of the “goodness” or efficiency of the test suite is used to determine the proficiency of a test suite. The appropriateness of the test suite is determined through mutation analysis. Several Finite State Machine (FSM) mutants are produced in mutation analysis by injecting errors against hypotheses. These mutants serve as test subjects for the test suite (TS). The effectiveness of the test suite is proportional to the number of eliminated mutants. The most effective test suite is the one that removes the most significant number of mutants at the optimal time. It is difficult to determine the fault… More >

Ramya Sridhar^1,*, Vijayalakshimi Patteeswaran²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2109-2122, 2023, DOI:10.32604/csse.2023.036781

Abstract In this paper, the investigation of a novel compact 2 × 2, 2 × 1, and 1 × 1 Ultra-Wide Band (UWB) based Multiple-Input Multiple-Output (MIMO) antenna with Defected Ground Structure (DGS) is employed. The proposed Electromagnetic Radiation Structures (ERS) is composed of multiple radiating elements. These MIMO antennas are designed and analyzed with and without DGS. The feeding is introduced by a microstrip-fed line to significantly moderate the radiating structure’s overall size, which is 60 × 40 × 1 mm. The high directivity and divergence characteristics are attained by introducing the microstrip-fed lines perpendicular to each other. And the… More >

Saud Yonbawi¹, Sultan Alahmari², B. R. S. S. Raju³, Chukka Hari Govinda Rao⁴, Mohamad Khairi Ishak⁵, Hend Khalid Alkahtani⁶, José Varela-Aldás^7,*, Samih M. Mostafa⁸

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2319-2335, 2023, DOI:10.32604/csse.2023.036721

Abstract Artificial intelligence (AI) technologies and sensors have recently received significant interest in intellectual agriculture. Accelerating the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart agriculture. Automatic irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation techniques. Automatic learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an agronomist. With this motivation, this study develops a modified black widow optimization with a… More >

C. Vairavel^1,*, N. S. Nithya²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2159-2175, 2023, DOI:10.32604/csse.2023.036598

Abstract The immune system goes through a profound transformation during pregnancy, and certain unexpected maternal complications have been correlated to this transition. The ability to correctly examine, diagnoses, and predict pregnancy-hastened diseases via the available big data is a delicate problem since the range of information continuously increases and is scalable. Many approaches for disease diagnosis/classification have been established with the use of data mining concepts. However, such methods do not provide an appropriate classification/diagnosis model. Furthermore, single learning approaches are used to create the bulk of these systems. Classification issues may be made more accurate by combining predictions from many… More >