Transformation Capabilities Research Articles

Assessing the impact of digital transformation capability for international market growth: the role of leadership support

PurposeThis study aims to examine the impact of an organization’s digital transformation capability (DTC) on its dynamic capabilities and strategic depth, which, in turn, could impact the organization’s international market growth (IMG). This study also investigates the moderating role of leadership support (LES) in using the DTC.Design/methodology/approachThe study reviews various kinds of literature related to international marketing, dynamic capabilities, digital transformation, strategic marketing and LES. Following this, a dynamic capability view is adopted to develop a research model. This research model is later tested using partial least squares structure equation modeling with data from 385 respondents from different types of organizations.FindingsThe study demonstrates that an organization’s DTC significantly enhances its dynamic capabilities and strategic depth. LES is vital for effectively applying and leveraging DTCs since it influences the association between DTCs, competitive advantage and global marketing intelligence. The study also recognizes that strategic depth and dynamic capabilities contribute to IMG.Research limitations/implicationsThe study uncovers the impacts of DTCs, influencing IMG. The study also provides valuable insights regarding the role of leadership in effectively leveraging the organization’s DTCs. It suggests the organization’s management should invest in improving DTC.Originality/valueThis study contributes to the prevailing literature on DTC, leadership and IMG. The study highlights the importance of DTC and how it impacts more established capabilities and leads to IMG of the organization. Further, this study is also unique as it establishes the importance of LES in leveraging the maximum benefits of the DTC.

Digital transformation capability and employee turnover intentions: the role of knowledge management capability and digital leadership

PurposeThe primary objective of this research is to examine the effects of digital transformation capability (DTC) on employee turnover intention (ETI) in Jordanian universities. Based on the social cognitive theory (SCT). The study also investigates the mediated moderation role of knowledge management capability (KMC) and digital leadership (DL) in the relationship between DTC and ETI.Design/methodology/approachA survey questionnaire was used to obtain data from 384 faculty members at Jordanian universities in Jordan, and the SmartPLS4 and SPSS software were used to test the hypotheses.FindingsThe results reveal that DTC significantly mitigates ETI through providing a favourable environment for knowledge sharing and innovation. Using DL as a moderator, the results show that the relationship between DTC and ETI becomes stronger in the presence of high DL.Originality/valueThis research uniquely integrates SCT to explore the combined impact of DTC, KMC and DL on faculty turnover intentions at Jordanian universities. It offers novel viewpoints on retaining academic staff by implementing smart digital attempts.

Size and Charge Dual‐Switchable Nanoparticles for Achieving Chemosensitization and Immune Infiltration against Pancreatic Ductal Adenocarcinoma

AbstractPancreatic ductal adenocarcinoma (PDAC), one of the most challenging cancers, is uniquely characterized by a biological barrier composed of multiple components in the extracellular matrix, preventing penetration of chemotherapeutic agents and hindering clinical drug treatment. Collagen I arrangement is critical. In this study, gemcitabine is chosen as the chemotherapeutic agent and dasatinib as the collagen‐I‐disrupting agent. When collagen I arrangement is disrupted, not only the chemotherapeutic drug enters the tumor area better, but the infiltration of CD8+ T cells into the tumor also increases. Therefore, a hypoxia‐responsive linker‐crosslinked polymer, DGD/L@GBI, is designed, which can be simultaneously loaded with two therapeutic agents, gemcitabine and dasatinib. The polymer is encapsulated by nucleic acid aptamers that target the PDAC stromal microenvironment and shield the surface. The aptamer is removed upon arrival at the PDAC hypoxic microenvironment, exposing the positively charged core to achieve charge reversal. Particle size transformation is achieved using linkers that respond to a hypoxic microenvironment. Increased intratumoral penetration is achieved using the dual transformation capabilities of the formulation. Pairing these two agents can result in a powerful efficacy in chemotherapy sensitization and immune infiltration.

Influence of Thermomechanical Treatments and Chemical Composition on the Phase Transformation of Cu-Al-Mn Shape Memory Alloy Thin Sheets

This paper investigates the interrelated effects of thermomechanical treatments and chemical composition on the phase transformation capabilities of thin sheets made from Cu-Al-Mn shape memory alloys. The transformation capacity and transition temperatures were determined using DSC and DMA testing, while composition measurements were performed using SEM/EDX analysis. The results demonstrate that applying hot-rolling treatments to alloys of reduced thickness leads to manganese oxidation and modifications in chemical composition, adversely impacting the phase transformation performance. This effect can be mitigated by the use of cold rolling. Additionally, the presence of phosphorus impurities can create inclusions that bind manganese, preventing it from remaining in the solid solution and further affecting phase transformation capabilities.

Enhancing Lung and Breast Cancer Screening with Advanced AI and Image Processing Techniques

This research investigates the application of CNNs for diagnostics improvements in lung and breast cancers based on AI image classification approaches. Using the datasets with 15,000 images describing lung cancer and 10,000 images describing cases of breast cancer, the models showed high performance: 90% for lung cancer and 99% for breast cancer classification. The descriptive analysis pointed out different features in imaging, such as dense tissue structure and irregular cell patterns; the models successfully identified these. The findings underlined the vital role that AI could play in assisting radiologists by delivering preliminary analysis, triaging high-risk cases, and leading to early cancer detection. Essential challenges were highlighted: ethical considerations concerning patients' privacy and AI algorithms' transparency. The limitation of the dataset diversity resulted in the conclusion that only broader data can ensure good generalization in various clinical settings. They recommended integrating the AI tool with clinical workflow and also called for training radiologists for effectiveness. Future research directions include real-time imaging and patient data integration for comprehensive diagnostic support and multi-modal approaches that combine imaging with genomics for more precise predictions. This leads to a more personalized cancer diagnosis and treatment plan, thus ultimately improving the results of the patients. This research, therefore, underlines the transformative capabilities of AI and image processing in modernizing cancer screening and diagnostics toward more accurate and efficient healthcare practices.

Cloud-Native ETL: Integrating Databricks and Azure Data Factory for Scalable Data Processing in Enterprise Environments

This article examines the implementation of cloud-native ETL solutions leveraging Databricks and Azure Data Factory (ADF) for scalable data processing in enterprise environments. The article presents a comprehensive analysis of the architectural design, integration strategies, and performance optimization techniques for combining Databricks' powerful data transformation capabilities with ADF's robust workflow orchestration. Through a series of case studies and empirical evaluations, we demonstrate how this integrated approach addresses the challenges of big data processing, including scalability, flexibility, and cost-effectiveness. Our findings reveal significant improvements in processing efficiency and resource utilization, with observed reductions in ETL pipeline execution times by up to 40% and overall cloud infrastructure costs by 25%. The article also highlights best practices for data governance, security, and quality management within this framework. These insights provide valuable guidance for data engineers and IT professionals seeking to modernize their data processing infrastructure and harness the full potential of cloud-native ETL solutions.

Bending and sensing performances of electro-ionic soft actuators based on carboxylated cellulose nanofibers reinforced with graphene nanoplatelets

Abstract Soft robots not only possess greater degrees of freedom and the capability for continuous transformation, but they also offer exceptionally high safety in human-robot interactions, avoiding harm to the human body. Soft actuators are essential for developing high-performance soft robots, offering significant bending deformation, rapid response times, and prolonged operational capabilities. Herein, we present an ionic electroactive soft actuator based on functional cellulose nanofibers, graphene nanoplatelets, and ionic liquid. The proposed actuator achieved a large displacement about ±8 mm under 2.0 V at 0.1 Hz, with long working stability (98% of initial peak displacement maintained after 1260 cycles of cycling). The human-robot interaction applications of this actuator were explored by simulating human fingers. More importantly, the static and dynamic sensing performances of the actuator were investigated, finding that it generated a sensing voltage of 0.37 V at a vibration displacement of only 1.75 mm. The designed actuator provides a promising approach for developing high-performance soft robots, soft actuators, flexible sensors, and flexible active devices.

Evaluation for digital transformation capability of construction enterprises: a case study in China

Evaluation for digital transformation capability of construction enterprises: a case study in China

Accountability management system of superior elementary school for digital transformation

The purpose of this research is to find out i) the quality of the accountability management system of superior schools, ii) elementary school digital transformation capabilities, and iii) the relationship between the quality of the accountability management system for superior schools and the digital transformation capabilities of elementary schools in Indonesia. The research approach used is quantitative. The research location is in public elementary schools in Malang City. The population of this research is all 195 public elementary schools. The research sample is 22 public elementary schools. The research subjects were school principals, class teachers, and subject teachers totaling 51 people. Data collection is done by using a questionnaire. Elementary schools with superior school accountability management systems have the ability to drive their digital transformation capabilities. Excellent school accountability management systems and school digital transformation capabilities guarantee equal access to education for all students. Implications for improving teaching and learning are also discussed in this paper. The variables studied can be used to determine the priority scale for planning, implementing and evaluating superior school accountability management systems in digital transformation, in increasing teacher competency.

Decoupled Edge Physics Algorithms for Collaborative XR Simulations

ABSTRACTThis work proposes a novel approach to transform any modern game engine pipeline, for optimized performance and enhanced user experiences in extended reality (XR) environments decoupling the physics engine from the game engine pipeline and using a client‐server architecture creates a scalable solution, efficiently serving multiple graphics clients on head‐mounted displays (HMDs) with a single physics engine on edge‐cloud infrastructure. This approach ensures better synchronization in multiplayer scenarios without introducing overhead in single‐player experiences, maintaining session continuity despite changes in user participation. Relocating the Physics Engine to an edge or cloud node reduces strain on local hardware, dedicating more resources to high‐quality rendering and unlocking the full potential of untethered HMDs. We present four algorithms that decouple the physics engine, increasing frame rates and Quality of Experience (QoE) in VR simulations, supporting advanced interactions, numerous physics objects, and multiuser sessions with over 100 concurrent users. Incorporating a Geometric Algebra interpolator reduces inter‐calls between dissected parts, maintaining QoE and easing network stress. Experimental validation, with more than 100 concurrent users, 10,000 physics objects, and softbody simulations, confirms the technical viability of the proposed architecture, showcasing transformative capabilities for more immersive and collaborative XR applications without compromising performance.

예비유아교사의 현장실습을 통한 변혁적 역량 탐구

Objectives The purposes of this study were seek to explore the transformative capabilities of prospective early childhood teachers through early childhood education field practice experiences. Methods The study was conducted using qualitative research methods, and the participants were 15 pre-service early childhood teachers from K University in Gyeonggi-do. Data were collected through play observation records and reflection sheets in 3-, 4-, and 5-year-old classes, and the collected data was divided into high and low categories based on the extracted key keywords and qualitatively analyzed to derive results. Results The transformative competencies of prospective early childhood teachers through field practice were, first, respecting children with a loving heart and attitude as a basic competency, discovering personality and aptitude as an early childhood teacher, and establishing a teaching perspective with expectations of becoming an early childhood teachers. Second, the ability to understand the field was found to be overcoming fear and tension by understanding the field and learners, understanding the teacher's role by integrating knowledge and technology, and acquiring practical knowledge through decision-making in dilemma situations. Third, future response capabilities included practicing adaptability and flexibility according to change, establishing sensitivity to various perspectives through a sense of community, and utilizing information technology by incorporating ideas. Conclusions In order to improve the transformative capabilities of prospective early childhood teachers, the need for a practical program that allows students to experience learning through educational service experience and subject matter connection linked to daycare centers and kindergartens was discussed.

Guide wire Insurance suite: Integration and Deployment Strategies

This article explores two critical aspects of Guidewire InsuranceSuite that are pivotal for insurance companies navigating digital transformation: the Integration Gateway and Blue/Green deployment. The Integration Gateway serves as a powerful middleware solution, facilitating seamless communication between Guidewire's core applications and external systems, thereby enabling insurers to create a more connected and responsive technology ecosystem. Blue/Green deployment, on the other hand, is presented as an advanced release management technique that minimizes downtime and reduces risks associated with software updates. The article discusses the key features of the Integration Gateway, including standardized interfaces, data transformation capabilities, and enhanced security measures. It also delves into the Blue/Green deployment process, highlighting its benefits such as minimal downtime, reduced risk, improved testing, and easy rollback. By examining these technologies, the article aims to provide insurance professionals and technology leaders with insights into how Guidewire InsuranceSuite can be leveraged to create more agile, efficient, and resilient insurance operations in an increasingly digital marketplace.

Improving Systematic Generalization of Linear Transformer Using Normalization Layers and Orthogonality Loss Function

A Linear Transformer linearizes the attention mechanism of the vanilla Transformer architecture, significantly improving efficiency and achieving linear theoretical complexity with respect to sequence length. However, few studies have explored the capabilities of the Linear Transformer beyond its efficiency. In this work, we investigate the systematic generalization capability of the Linear Transformer, a crucial property for strong generalization to unseen data. Through preliminary experiments, we identify two major issues contributing to its unstable systematic generalization performance: (i) unconstrained norms of Queries and Keys, and (ii) high correlation among Values across the sequence. To address these issues, we propose two simple yet effective methods: normalization layers for Queries and Keys, and an orthogonality loss function applied to Values during training. In experiments, we demonstrate that applying these methods to the Linear Transformer significantly improves its stability and systematic generalization performance across several well-known tasks. Furthermore, our proposed methods outperform the vanilla Transformer on specific systematic generalization tasks, such as the sort-of-CLEVR and SCAN tasks.

Biochemical characterization of a recombinant laccase from Halalkalibacterium halodurans C-125 and its application in the biotransformation of organic compounds

ObjectivesThis study aimed to produce an engineered recombinant laccase from extremophilic Halalkalibacterium halodurans C-125 (Lac-HhC-125) with higher protein yield, into a more active conformation and with properties that meet the fundamental needs of biotechnological application.ResultsThe rLac-HhC125 was partially purified by size exclusion chromatography and concentrated by ultrafiltration (10 kDa) with a yield of 57.6%. Oxidation reactions showed that adding 2 mM CuSO4 to the assay solution led to activating the laccase. To increase its initial activity, the rLac-HhC125 was treated at 50 °C for 20 min before the assays, improving its performance by fourfold using the syringaldazine as a substrate. When treated with EDTA, methanol, ethanol, and DMSO, the rLac-HhC125 maintained more than 80% of its original activity. Interestingly, the acetonitrile induced a twofold activity of the rLac-HhC125. The putative rLac-HhC125 demonstrated a capability of efficient transformation of different organic compounds at pH 6, known as dye precursors, into coloured molecules.ConclusionThe rLac-HhC125 was active at high temperatures and alkaline pH, exhibited tolerance to organic solvents, and efficiently transformed different hydroxy derivatives into coloured compounds, which indicates that it can be used in various biotechnological processes.

Inequalities in access to healthcare for older adults during COVID-19 pandemics in Lithuania

Abstract Background During COVID-19 pandemic, healthcare systems experienced unprecedented shock. Older adults were the most vulnerable group of Lithuanian society, facing distinctive health challenges and highest mortality rates. The aim of the study was to examine perceived inequalities and challenges in access to healthcare of Lithuanian population over 65 years old during COVID-19 pandemic. Methods Representative sample of Lithuanian population over 65 years old (1503 individuals) was involved in the anonymous questionnaire survey, performed in January, 2024. Results 22 % of respondents reported not using healthcare services during COVID-19 pandemics. Among them, 76% did not have any need for healthcare services, while 13.6% did not approach healthcare due to fear of getting infected by Covid-19, and the other 10.4% had various accessibility challenges. 15.8% reported frequent problems in registering with family doctors, and 20.0% - with the specialist appointment. There was a clear social gradient observed among those underserved: it was associated with educational level and place of residence. Those with lower education and living in smaller towns and rural areas reported more common healthcare accessibility problems. Higher education, income and better self-perceived health correlated with higher satisfaction with travel time reaching health care services and registering for the appointment (p < 0.05). Conclusions Health system resilience reflects the ability to continue service delivery in the face of extraordinary shocks. Limited health care accessibility and clear social gradient could increase vulnerability of older adults, which resulted in exceptionally high mortality rates during COVID-19 pandemics. Results of the survey will be used for further development of transformative capabilities of healthcare system, responding to public health disasters and protection of the vulnerable groups of the society. Key messages • Limited health care accessibility and clear social gradient could increase vulnerability of older adults, which resulted in exceptionally high mortality rates during COVID-19 pandemics. • Results of the survey will be used for further development of transformative capabilities of healthcare system, responding to public health disasters.

YOLOv8-Based Estimation of Estrus in Sows Through Reproductive Organ Swelling Analysis Using a Single Camera

Accurate and efficient estrus detection in sows is crucial in modern agricultural practices to ensure optimal reproductive health and successful breeding outcomes. A non-contact method using computer vision to detect a change in a sow’s vulva size holds great promise for automating and enhancing this critical process. However, achieving precise and reliable results depends heavily on maintaining a consistent camera distance during image capture. Variations in camera distance can lead to erroneous estrus estimations, potentially resulting in missed breeding opportunities or false positives. To address this challenge, we propose a robust six-step methodology, accompanied by three stages of evaluation. First, we carefully annotated masks around the vulva to ensure an accurate pixel perimeter calculation of its shape. Next, we meticulously identified keypoints on the sow’s vulva, which enabled precise tracking and analysis of its features. We then harnessed the power of machine learning to train our model using annotated images, which facilitated keypoint detection and segmentation with the state-of-the-art YOLOv8 algorithm. By identifying the keypoints, we performed precise calculations of the Euclidean distances: first, between each labium (horizontal distance), and second, between the clitoris and the perineum (vertical distance). Additionally, by segmenting the vulva’s size, we gained valuable insights into its shape, which helped with performing precise perimeter measurements. Equally important was our effort to calibrate the camera using monocular depth estimation. This calibration helped establish a functional relationship between the measurements on the image (such as the distances between the labia and from the clitoris to the perineum, and the vulva perimeter) and the depth distance to the camera, which enabled accurate adjustments and calibration for our analysis. Lastly, we present a classification method for distinguishing between estrus and non-estrus states in subjects based on the pixel width, pixel length, and perimeter measurements. The method calculated the Euclidean distances between a new data point and reference points from two datasets: “estrus data” and “not estrus data”. Using custom distance functions, we computed the distances for each measurement dimension and aggregated them to determine the overall similarity. The classification process involved identifying the three nearest neighbors of the datasets and employing a majority voting mechanism to assign a label. A new data point was classified as “estrus” if the majority of the nearest neighbors were labeled as estrus; otherwise, it was classified as “non-estrus”. This method provided a robust approach for automated classification, which aided in more accurate and efficient detection of the estrus states. To validate our approach, we propose three evaluation stages. In the first stage, we calculated the Mean Squared Error (MSE) between the ground truth keypoints of the labia distance and the distance between the predicted keypoints, and we performed the same calculation for the distance between the clitoris and perineum. Then, we provided a quantitative analysis and performance comparison, including a comparison between our previous U-Net model and our new YOLOv8 segmentation model. This comparison focused on each model’s performance in terms of accuracy and speed, which highlighted the advantages of our new approach. Lastly, we evaluated the estrus–not-estrus classification model by defining the confusion matrix. By using this comprehensive approach, we significantly enhanced the accuracy of estrus detection in sows while effectively mitigating human errors and resource wastage. The automation and optimization of this critical process hold the potential to revolutionize estrus detection in agriculture, which will contribute to improved reproductive health management and elevate breeding outcomes to new heights. Through extensive evaluation and experimentation, our research aimed to demonstrate the transformative capabilities of computer vision techniques, paving the way for more advanced and efficient practices in the agricultural domain.

Influence of Knowledge Capabilities on Performance of Kenyan Fintech Companies

Purpose: The purpose of the study was to examine the influence of knowledge capability on performance in Kenyan fintech companies. The research questions that guided the study were to establish the influence of knowledge acquisition, knowledge assimilation, knowledge transformation and knowledge exploitation on performance of Kenyan fintech companies. The theoretical review of the study used the dynamic capabilities theory. Methodology: The study adopted a descriptive research design with a target population of 850 employees at management level working at 85 Kenyan fintech companies. Stratified random sampling technique was used to select a sample size of 272 management level employees. Primary data was collected using a questionnaire and analysed using descriptive and inferential statistics. The statistical package for the Social Science was used as a data analysis tool and thereafter the data was presented in figures and tables. Findings: Findings on the first research question, indicated that there was a negative correlation between fintech performance and knowledge acquisition capability. The multiple linear regression results revealed that knowledge acquisition had no statistically significant influence on the performance of fintech. The results from the regression coefficients indicated that knowledge acquisition capability did not significantly predict the performance of fintech firms in Kenya. For the second research question, the correlation analysis findings indicated that knowledge assimilation capability had a negative relationship with performance of the fintech companies. The regression coefficient results indicated that knowledge assimilation capability did not significantly predict the performance of fintech organizations in Kenya. Regarding the third research question, correlation results noted that knowledge transformation capability had a negative relationship with performance of fintech firms. Findings of the regression coefficients indicated that knowledge transformation capability significantly did not predict performance of fintech organizations in Kenya. On the last research question, correlation results revealed that knowledge exploitation capability had a negative relationship with performance of fintech firms. Regression coefficients result indicated that knowledge exploitation capabilities did not significantly predict the performance of fintech firms in Kenya. Unique Contribution to Theory, Policy and Practice: The study concluded that knowledge capabilities did not significantly influence the performance of Kenyan fintech companies. This is mainly attributed to the other elements and factors that influence performance more. The study recommends that fintech institutions should increase their focus on knowledge capabilities to compete sustainably in turbulent business environments. Fintech organizations can fully exploit the knowledge they receive to effectively achieve strategic objectives. Additionally, due to other factors being more influential in predicting performance, further research is required with these additional variables considered.

Student entrepreneurs with triple bottom line objectives: capabilities in creating viable business models

PurposeThe objective of this instrumental case study is to investigate issues impacting capability development/transformation with respect to student entrepreneurs’ aspirations to create a viable, evolving business model for their start-ups, namely, that are underpinned by goals featuring the “triple bottom line”. That is, instead of simply profits as an economic performance metric, there is also a consideration of wider social issues within the notion of “responsible leadership”, involving people and the planet.Design/methodology/approachUnderpinned by a “capabilities lens”, the research setting focuses on the perceptions of student entrepreneurs together with other stakeholders in a Canadian university. The country context is pertinent, since to gain scalability, owners of start-up firms may need to internationalise to overcome limited domestic demand (despite the large geographic area); also, stakeholders’ support is sometimes needed to facilitate growth.FindingsNew insights demonstrate the need for appropriate stakeholders to facilitate effective transformative capability development amongst student entrepreneurs with triple bottom line objectives. Student entrepreneurs’ capability of validating facets of a viable evolving business model that address “all” and not “some” aspects of the triple bottom line is especially important. This is alongside the capability of being able to pivot product-market strategies where necessary, and this may feature the need to internationalise in the event there is limited domestic demand. A capability to develop soft skills is also likely to help student entrepreneurs communicate with stakeholders. In turn, such capabilities are likely to help move ideation, passed validation and through to commercialisation.Originality/valueThe utility of institutions and associated stakeholders offering education and training support to develop capabilities amongst students as aspiring entrepreneurs has featured in earlier studies. Likewise, issues associated with the notion of sustainability have also been previously considered. The originality of this instrumental case study is to offer a more nuanced investigation into salient issues associated with capability development amongst student entrepreneurs exhibiting triple bottom line objectives in their start-ups. That is, focusing on considerations related to validating their evolving business models and especially when facing limited domestic market demand.

DMCCT: Dual-Branch Multi-Granularity Convolutional Cross-Substitution Transformer for Hyperspectral Image Classification

In the field of hyperspectral image classification, deep learning technology, especially convolutional neural networks, has achieved remarkable progress. However, convolutional neural network models encounter challenges in hyperspectral image classification due to limitations in their receptive fields. Conversely, the global modeling capability of Transformers has garnered attention in hyperspectral image classification. Nevertheless, the high computational cost and inadequate local feature extraction hinder its widespread application. In this study, we propose a novel fusion model of convolutional neural networks and Transformers to enhance performance in hyperspectral image classification, namely the dual-branch multi-granularity convolutional cross-substitution Transformer (DMCCT). The proposed model adopts a dual-branch structure to separately extract spatial and spectral features, thereby mitigating mutual interference and information loss between spectral and spatial data during feature extraction. Moreover, a multi-granularity embedding module is introduced to facilitate multi-scale and multi-level local feature extraction for spatial and spectral information. In particular, the improved convolutional cross-substitution Transformer module effectively integrates convolution and Transformer, reducing the complexity of attention operations and enhancing the accuracy of hyperspectral image classification tasks. Subsequently, the proposed method is evaluated against existing approaches using three classical datasets, namely Pavia University, Kennedy Space Center, and Indian Pines. Experimental results demonstrate the efficacy of the proposed method, achieving significant classification results on these datasets with overall classification accuracies of 98.57%, 97.96%, and 96.59%, respectively. These results establish the superiority of the proposed method in the context of hyperspectral image classification under similar experimental conditions.

The survey on the dual nature of xAI challenges in intrusion detection and their potential for AI innovation

In the rapidly evolving domain of cybersecurity, the imperative for intrusion detection systems is undeniable; yet, it is increasingly clear that to meet the ever-growing challenges posed by sophisticated threats, intrusion detection itself stands in need of the transformative capabilities offered by the explainable artificial intelligence (xAI). As this concept is still developing, it poses an array of challenges that need addressing. This paper discusses 25 of such challenges of varying research interest, encountered in the domain of xAI, identified in the course of a targeted study. While these challenges may appear as obstacles, they concurrently present as significant research opportunities. These analysed challenges encompass a wide spectrum of concerns spanning the intersection of xAI and cybersecurity. The paper underscores the critical role of xAI in addressing opacity issues within machine learning algorithms and sets the stage for further research and innovation in the quest for transparent and interpretable artificial intelligence that humans are able to trust. In addition to this, by reframing these challenges as opportunities, this study seeks to inspire and guide researchers towards realizing the full potential of xAI in cybersecurity.