B. Gomathi^1,*, R. Manimegalai¹, Srivatsan Santhanam², Atreya Biswas³

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1749-1765, 2024, DOI:10.32604/csse.2024.056659 - 22 November 2024

Abstract In enterprise operations, maintaining manual rules for enterprise processes can be expensive, time-consuming, and dependent on specialized domain knowledge in that enterprise domain. Recently, rule-generation has been automated in enterprises, particularly through Machine Learning, to streamline routine tasks. Typically, these machine models are black boxes where the reasons for the decisions are not always transparent, and the end users need to verify the model proposals as a part of the user acceptance testing to trust it. In such scenarios, rules excel over Machine Learning models as the end-users can verify the rules and have more… More >

Alonso Alvarez, Borja Bordel Sánchez^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2935-2966, 2024, DOI:10.32604/cmc.2024.056990 - 18 November 2024

Abstract Agile Transformations are challenging processes for organizations that look to extend the benefits of Agile philosophy and methods beyond software engineering. Despite the impact of these transformations on organizations, they have not been extensively studied in academia. We conducted a study grounded in workshops and interviews with 99 participants from 30 organizations, including organizations undergoing transformations (“final organizations”) and companies supporting these processes (“consultants”). The study aims to understand the motivations, objectives, and factors driving and challenging these transformations. Over 700 responses were collected to the question and categorized into 32 objectives. The findings show More >

Sonia Goel^1,#, Meena Tushir¹, Jyoti Arora², Tripti Sharma², Deepali Gupta³, Ali Nauman^4,#, Ghulam Muhammad^5,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3125-3145, 2024, DOI:10.32604/cmc.2024.054476 - 18 November 2024

Abstract In numerous real-world healthcare applications, handling incomplete medical data poses significant challenges for missing value imputation and subsequent clustering or classification tasks. Traditional approaches often rely on statistical methods for imputation, which may yield suboptimal results and be computationally intensive. This paper aims to integrate imputation and clustering techniques to enhance the classification of incomplete medical data with improved accuracy. Conventional classification methods are ill-suited for incomplete medical data. To enhance efficiency without compromising accuracy, this paper introduces a novel approach that combines imputation and clustering for the classification of incomplete data. Initially, the linear More >

Priyanka¹, Damodarakurup Sajeev², Shaik Ahmed³, Shankar Pidishety³, Ram Soorat^3,*

Journal of Quantum Computing, Vol.6, pp. 53-66, 2024, DOI:10.32604/jqc.2024.054000 - 14 November 2024

Abstract The present study delves into the application of investigating quantum state behaviour, particularly focusing on coherent and superposition states. These states, characterized by their remarkable stability and precision, have found extensive utility in various domains of quantum mechanics and quantum information processing. Coherent states are valuable for manipulating quantum systems with accuracy. Superposition states allow quantum systems to exist in numerous configurations at the same time, which paves the way for quantum computing’s capacity for parallel processing. The research accentuates the crucial role of quantum error correction (QEC) in ensuring the stability and reliability of… More >

A. NOEL GRAVINA^1,2, NOELIA D´ELÍA^1,2, LUCIANO A. BENEDINI^2,3,*, PAULA MESSINA^1,2

BIOCELL, Vol.48, No.11, pp. 1517-1520, 2024, DOI:10.32604/biocell.2024.055513 - 07 November 2024

Abstract This article navigates the relationship between biomaterials and osteogenic cell adhesion, highlighting the importance of mimicking the physiological response for bone tissue regeneration. Within this spirit is an initial description of the interaction between osteoblasts and osteoprogenitor cells with the extracellular matrix, explaining the leading role of integrins and cadherins in cell adhesion, and the intracellular signaling pathways elicited. Additionally, there is a focus on the strategies of advanced biomaterials that foster osteogenesis by replicating the native environment, taking advantage of these known specific signaling pathways. The final remarks lay on the need for careful More >

Xin Lin^1,2,3, Kunpeng Zhu^1,2,3,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.012499

Abstract This paper reveals the interplay mechanism between melt pool, spattering and vapors, aiming to further improve the forming quality through in-situ monitoring with a CMOS camera. A Residual Network based on Convolutional Block Attention Module and Focal loss function is proposed to extract multi-scale features of single tracks and learn about their behavior changes. A t-SNE clustering analysis is utilized to analysis a large amount of time sequence data on the melt pool by collecting the schlieren photographs. It is found that patterns of unstable melt pool changing corelate to the defects in single tracks, More >

Brij B. Gupta^1,2,3,4,*, Akshat Gaurav⁵, Varsha Arya^6,7, Razaz Waheeb Attar⁸, Shavi Bansal⁹, Ahmed Alhomoud¹⁰, Kwok Tai Chui¹¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2165-2183, 2024, DOI:10.32604/cmes.2024.056473 - 31 October 2024

Abstract Phishing attacks present a serious threat to enterprise systems, requiring advanced detection techniques to protect sensitive data. This study introduces a phishing email detection framework that combines Bidirectional Encoder Representations from Transformers (BERT) for feature extraction and CNN for classification, specifically designed for enterprise information systems. BERT’s linguistic capabilities are used to extract key features from email content, which are then processed by a convolutional neural network (CNN) model optimized for phishing detection. Achieving an accuracy of 97.5%, our proposed model demonstrates strong proficiency in identifying phishing emails. This approach represents a significant advancement in More >

Md Mahfuzur Rahman^1,*, Md Solaiman Chowdhury², Mohammad Shorfuzzaman³, Lutful Karim⁴, Md Shafiullah⁵, Farag Azzedin¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2501-2525, 2024, DOI:10.32604/cmes.2024.055065 - 31 October 2024

Abstract This article presents an innovative approach that leverages interpretable machine learning models and cloud computing to accelerate the detection of septic shock by analyzing electronic health data. Unlike traditional methods, which often lack transparency in decision-making, our approach focuses on early detection, offering a proactive strategy to mitigate the risks of sepsis. By integrating advanced machine learning algorithms with interpretability techniques, our method not only provides accurate predictions but also offers clear insights into the factors influencing the model’s decisions. Moreover, we introduce a preference-based matching algorithm to evaluate disease severity, enabling timely interventions guided… More >

Lu Jia, Ye Zhang^*, Sijia Yu

International Journal of Mental Health Promotion, Vol.26, No.10, pp. 837-846, 2024, DOI:10.32604/ijmhp.2024.053586 - 31 October 2024

Abstract Background: Adolescents frequently engage in Non-Suicidal Self-Injury (NSSI), with recent trends indicating an increase in this behavior. At the same time, Chinese adolescents have a higher incidence of NSSI than Western adolescents. Therefore, it is necessary to explore the relationship between interparental conflict and NSSI among adolescents within the context of Chinese families. Methods: The research sample comprised 755 senior high school students (46.62% male; age M = 16.82, SD = 0.94 years) who completed the Interparental Conflict Child Perception Scale (CPIC), Adolescent Self-Injury Behavior Questionnaire (ASHS), Toronto Alexithymia Scale-20 (TAS-20), and Scale of Adolescent Resilience… More >

Sejin Choi^1,2, Ho-Seok Lee^1,2,*

Phyton-International Journal of Experimental Botany, Vol.93, No.10, pp. 2519-2534, 2024, DOI:10.32604/phyton.2024.058012 - 30 October 2024

Abstract Plants interact with a complex network of microorganisms, forming a dynamic holobiont that is crucial for their health, growth, and adaptation. This interconnected system is deeply influenced by environmental factors, which modulate the relationships within the plant microbiome. Key environmental drivers such as light, temperature, and moisture can alter the balance of these interactions, impacting plant immunity, resilience, and overall fitness. The traditional disease triangle model, which emphasizes plant-pathogen-environment interactions, is enhanced by incorporating the role of the microbiome, revealing how microbial communities contribute to disease outcomes. This review highlights the importance of shifting focus More >