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The knowledge gained from data mining is highly dependent on the experience of an expert for further analysis to increase effectiveness and wise decision-making. This mined knowledge requires actionability enhancement before it can be applied to real-world problems. The literature highlights the reasons that emerged the need to incorporate human wi...
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... In the process of promoting data utilization, the management of data will also receive increasing attention. Faced with massive data, indepth analysis promises to make service robots' self-checking more intelligent, enabling the transition from data mining to wisdom mining [72]. ...
The market for service robots is expanding as labor costs continue to rise. Faced with intricate working environments, fault detection and diagnosis are crucial to ensure the proper functioning of service robots. The objective of this review is to systematically investigate the realm of service robots’ fault diagnosis through the application of Structural Topic Modeling (STM). A total of 289 papers were included, culminating in ten topics, including advanced algorithm application, data learning-based evaluation, automated equipment maintenance, actuator diagnosis for manipulator, non-parametric method, distributed diagnosis in multi-agent systems, signal-based anomaly analysis, integrating complex control framework, event knowledge assistance, mobile robot particle filtering method. These topics spanned service robot hardware and software failures, diverse service robot systems, and a range of advanced algorithms for fault detection in service robots. Asia-Pacific, Europe, and the Americas, recognized as three pivotal regions propelling the advancement of service robots, were employed as covariates in this review to investigate regional disparities. The review found that current research tends to favor the use of artificial intelligence algorithms to address service robots’ complex system faults and vast volumes of data. The topics of algorithms, data learning, automated maintenance, and signal analysis are advancing with the support of artificial intelligence, gaining increasing popularity as a burgeoning trend. Additionally, variations in research focus across different regions were found. The Asia-Pacific region tends to prioritize algorithm-related studies, while Europe and the Americas show a greater emphasis on robot safety issues. The integration of diverse technologies holds the potential to bring forth new opportunities for future service robot fault diagnosis. Simultaneously, regional standards about data, communication, and other aspects can streamline the development of methods for service robots’ fault diagnosis.
... The influence of social media, big data, and data mining has grown more obvious, transforming the way in which organizations work, connect with one another, and adapt to the intricacies of their different industries. A comprehensive investigation of the myriad ways in which these technology components influence the organizational behavior of entities operating within the Jordanian telecommunications sector is the focus of this empirical study (Khan & Shaheen, 2023). Since businesses are working hard to keep their competitive edge and improve their overall performance, the implementation of cutting-edge technologies has become of primary importance. ...
... Data mining also has a major effect on strategic management . The challenge for firms in today's fast-paced and ever-changing business climate is to make well-informed decisions quickly (Khan & Shaheen, 2023). Data mining equips users with the means to examine both past and present data, yielding insights that improve long-term planning . ...
... Data mining for predictions allows businesses to take the initiative to improve their standing in the market (Tsui et al., 2023). One important part of strategic management is supply chain management, and data mining can help with that (Khan & Shaheen, 2023). Optimization of supply chain processes is possible through analysis of data pertaining to demand patterns, inventory levels, and supplier performance. ...
The aim of this study was to evaluate the impact of social media, big data, and data mining on the development of organizational behavior within the telecommunications industry in Jordan. The main objective of this study was to investigate the effects of technological components on the alteration of organizational behavior in the communications sector of Jordan. To accomplish this objective, a thorough empirical investigation was undertaken, encompassing the collecting of data from key stakeholders within the telecommunications sector in Jordan. A sample size of 412 participants , encompassing people from diverse roles within the communications sector, was chosen for the purpose of this study. The participants' replies and perspectives were gathered via the administration of surveys and conducting interviews, resulting in a comprehensive data set suitable for analysis. This study investigated the intricate relationship between the utilization of social media, the application of big data analytics, and the implementation of data mining techniques in influencing the dynamics of organizational behavior. The study's results underscored the substantial impact that social media platforms have on communication patterns and collaboration within telecommunication firms. Furthermore, the utilization of big data analysis has emerged as a significant catalyst for the enhancement of informed decision-making processes, exerting influence on diverse facets of organizational behavior, including strategic planning, employee engagement, and customer interactions. Data mining techniques have been identified as having a crucial function in extracting significant patterns and trends from extensive datasets, hence helping to the improvement of organizational learning and adaptation. The research findings indicated that the incorporation of social media, big data, and data mining technologies had a beneficial effect on the development of organizational behavior within the telecommunications industry in Jordan. The findings underscore the importance for enterprises to proactively utilize these technologies to cultivate a work environment that is characterized by increased agility, responsiveness, and collaboration. This study provides significant contributions to the subject of organizational behavior by examining the impact of social media, big data, and data mining within the specific context of the telecommunication sector in Jordan. The research sheds light on the transformative consequences of these technological advancements. The consequences of these findings have broad relevance for organizational leaders, politicians, and researchers, serving as a basis for further investigations in the dynamic realm of technology-driven organizational behavior.
... Extracting valuable information 8,9 and knowledge from large datasets [10][11][12] requires the use of various approaches such as statistics, machine learning, artificial intelligence, and database technology to find patterns, associations [13][14][15][16] , and rules in diverse areas [17][18][19] . Zadeh 20 utilized association rule mining methods to explore potential drugs or drug combinations associated with newly diagnosed diabetes. ...
With the rapid development of AI and big data mining technologies, computerized medical decision-making has become increasingly prominent. The aim of high-utility pattern mining (HUPM) is to discover meaningful patterns in medical databases that contribute to maximizing the utility from the perspective of diagnosis. However, HUPM pays less attention to the interpretability and explainability of these patterns in medical decision-making scenarios. This paper proposes a novel algorithm called the Improved fuzzy high-utility pattern mining (IF-HUPM) to address this problem. First, the paper applies a fuzzy preprocessing method to divide the fuzzy intervals of a medical quantitative data set, which enhances the fuzziness and interpretability of the data. Next, in the process of IF-HUPM, both fuzzy tree and list structures are employed to calculate fuzzy high-utility values. By combining the characteristics of the one-stage and two-stage algorithms of HUPM, an adaptive-phase Fuzzy HUPM hybrid frame is proposed. The experimental results demonstrate that the proposed IF-HUPM algorithm enhances both accuracy and efficiency and the mining process requires less time and space on average.
... Managers use artificial intelligence technology as an auxiliary decision-making system, which can carry out strategic planning more comprehensively. Technologies such as data mining and knowledge discovery are needed to collect global information and combine with existing internal and external information (Jia et al., 2018;Khan & Shaheen, 2023). ...
The main objective of this research is to carefully describe the role of Expert Systems (ES) in human resource management discourse. What logical constructs must be built when there is a desire to empower the capabilities of computational expert systems in their pragmatic use. Human Resource Expert Systems (HRES) can mimic the decision-making capabilities of a Human Resource Expert (HRE). The design and development of HRES depends on top management initiative and support. The implementation of HRES should be holistic, with maximum participation from all departments in the organization. This study uses a qualitative descriptive method, with a literature study approach. The main data is drawn from a variety of selected journal papers, taken from reputable journals. Other sources that are compatible with the research, such as news, academic papers, videos and other documents. The study found that there is a challenge to reduce the fear of computers in HR functions and resistance to change. HRES can increase productivity levels by assisting HR managers in making rational decisions to solve unstructured HR related problems. The emphasis is on organizational change and bold initiatives to further implement ES in the HR domain and overcome challenges. This paper aims to analyse the focus areas of HRES and the functions where HRES faces limitations and challenges.
... Te work in reference [21] incorporated frequent itemsets with domain knowledge in the form of a taxonomy to mine negative association rules. Shaheen and Abdullah developed a series of algorithms for diferent felds, such as exploring positive and negative context-based association rules for conventional/characteristic data [24,25], and mining context-based association rules on microbial databases to extract interesting and useful associations of microbial attributes with existence of hydrocarbon reserve [26][27][28][29]. It should be noted that some contradictory rules may be mined when positive and negative rules are mined simultaneously, such as A ⇒ B and A ⇒ ¬B are both strong rules [30][31][32]. ...
Association rules mining with the Chinese social insurance fund dataset can effectively discover different kinds of errors, irregularities, and illegal acts by providing auditors with relationships among the items and therefore improve auditing quality and efficiency. However, traditional positive and negative association rules (PNARs) mining algorithms inevitably produce too many meaningless or contradictory rules when these two types of rules are mined simultaneously, which brings a huge challenge to auditors retrieving decision information. Aimed to reduce the quantity of low-reliability PNARs without missing interesting rules, this paper first proposes an improved PNARs mining algorithm with minimum correlation and triple confidence threshold to control the mined rules number by narrowing the range of confidence settings. Then, a novel pruning algorithm based on the inclusion relation of the rule’s antecedent and consequent is given to remove those redundant rules. After that, the proposed optimized PNARs mining approach is applied to the Chinese social insurance fund dataset starting with audit features influence factors mining using the Hash table. The experimental results with different datasets show that the proposed framework not only can ensure effective and interesting rules extraction but also has better performance than traditional approaches in both accuracy and efficiency, reducing the number of redundant PNARs by over 70.1% with experimental datasets and average 78.5% with auditing data mining, respectively.
... Centralized databases allow for organization, standardization and management of hazard and exposure data with data accessibility being a core component. FAIRification (Findable, Accessible, Interoperable and Reusable) promotes data sharing principles for ecotoxicology and emphasizes scientific research that is accessible, interoperable, and interpretable for multiple stakeholders [22,23]. Data frameworks establish platforms for data transparency, allowing original experimental data to be reused and applied to risk governance. ...
After 20 years of assessing ecotoxicological risks of engineered nanomaterials, data gaps limit the efficacy of regulatory guidelines. Presently, there are efforts to compile historical data on nanomaterial research into online data platforms that follow FAIR (findable, accessible, interoperable, and reusable) principles. FAIR data practices for alternative testing strategies such as mesocosms are needed as standard testing strategies and regulatory platforms do not appropriately capture the mobility and bioavailability of nanomaterials in an ecosystem, limits their ability to define environmental risk. The study created a FAIR dataset for mesocosm research from three European projects with data conforming to standard ontologies modified to accommodate mesocosms. Data ranked well on the FAIRness maturity indicator proposed by the European Union’s Horizon 2020 initiative, with data on physicochemical properties being a major limitation for reusability. Statistical analysis demonstrated that chemical elements were a dominant descriptor of the data. FAIR data were achieved in the present study; however, the research highlights questions surrounding data reporting guidelines for alternative testing strategies. Considerations around data usage for historical data are also necessary to meet stakeholder needs.
... Hence, it results in good synergy with the Islamic organization leadership (Jhuji et.al., 2020). As a supporting factor in decision-making, knowledge management includes documentation related to historical activities and experiences, which is one of essential references in decision making (Khan & Shaheen, 2021). ...
Islamic youth development and da'wah organizations frequently face constraints in the decision-making process, both those carried out in the secondary level education sector. This is caused by the low effectiveness of the knowledge sharing process carried out between members, the lack of direction and support from the leadership or strategy, and the low quality of information management are the main factors for failure in the knowledge management process in a work program as well as the overall activities of Islamic youth da'wah organizations. The lack of facilities in managing the historical activity track record has hampered the decision-making process in organizational knowledge. Misunderstandings and lack of knowledge also lead to the loss of ghirah (da'wah motivation) and ukhuwah (familial relationships) in Islamic youth da'wah organizations in the education sector. This study aims to prove how knowledge management activities carried out in a structured manner by da'wah organizations, which aim to guide Islamic youth in high school (SMA/K) and junior high school (SMP) can transform information into valuable assets in managing Islamic da'wah operations and decision making that need to be carried out by the organization in the long term based on the determining factors, including managing aspects of knowledge management and Islamic-based leadership character. The methods and data processing carried out in this study used a quantitative-confirmatory approach and used multiple linear regression analysis, through the distribution of questionnaires to members of the XYZ high school Muslim youth development organization in Bandung Regency. The results and conclusions of this study prove that there is a significant positive influence from the application of knowledge management carried out in a structured manner up to 47.1% and Islamic-based leadership abilities up to 41.9%, on the decision-making process of Muslim youth development organizations in secondary schools, as well as Islamic-based education in general.
... If there is a big volume of data accessible for analysis, the quality of the derived knowledge will be higher. Artificial intelligence, data visualization, database technology and machine learning are all components of DM as a multidisciplinary field of science 12,106 . ...
Creativity, i.e., the process of generating and developing fresh and original ideas or products that are useful or effective, is a valuable skill in a variety of domains. Creativity is called an essential 21st-century skill that should be taught in schools. The use of educational technology to promote creativity is an active study field, as evidenced by several studies linking creativity in the classroom to beneficial learning outcomes. Despite the burgeoning body of research on adaptive technology for education, mining creative thinking patterns from educational data remains a challenging task. In this paper, to address this challenge, we put the first step towards formalizing educational knowledge by constructing a domain-specific Knowledge Base to identify essential concepts, facts, and assumptions in identifying creative patterns. We then introduce a pipeline to contextualize the raw educational data, such as assessments and class activities. Finally, we present a rule-based approach to learning from the Knowledge Base, and facilitate mining creative thinking patterns from contextualized data and knowledge. We evaluate our approach with real-world datasets and highlight how the proposed pipeline can help instructors understand creative thinking patterns from students' activities and assessment tasks.
... For this reason, the process of DIKW usability in the medical domain is expressed through the applications of ehealthcare, where clinical associates with patients who are dissatisfied with the system utilization and evaluations of electronic health records. International medical standards categorize clinical trials into three main portions for the sake of usability: efficiency, effectiveness, and increased satisfaction level in terms of medical data processing, scheduling, and management [28,29]. Include the patients' experience, historical ledger, and incorporated principles for designing clinical applications [29]. ...
... International medical standards categorize clinical trials into three main portions for the sake of usability: efficiency, effectiveness, and increased satisfaction level in terms of medical data processing, scheduling, and management [28,29]. Include the patients' experience, historical ledger, and incorporated principles for designing clinical applications [29]. To protect patients' records' safety 3 Wireless Communications and Mobile Computing Table 1: Management of medical data, information, knowledge, and wisdom, as well as blockchain security related literature reviews. ...
This paper presents a layered hierarchy that depicts the progressive relationship between data, information, knowledge, and wisdom. To begin with, data is gathered and organized into information. Information is gathered, filtered, refined, and put through an investigation process to create knowledge. Wisdom is attained after knowledge discovery through the process of filtration and aggregation through experience. The layered hierarchy in the domain of e-healthcare necessitates higher scheduling costs for data collection, processing wisdom, and management, which is also an insecure and untrustworthy process for progressive medical service. The medical industry faces a difficult problem in providing collected data integrity, information reliability, and knowledge trustworthiness for the service of progressive medical relationships in the face of an increasing number of day-today records. The blockchain consortium hyperledger (fabric) has been used in this paper to act as a bridge that bridges the gap between electronic data, information, knowledge, and wisdom (DIKW) movement and processes by enabling the process of the layered hierarchy of schedule information and management and providing security and transparency. For e-healthcare information management and privacy, the DIKW-ledger, such as patients' consultancy information, availing medical services, personal records, appointments, treatment details, and other health-related transactions, a consortium hyperledger fabric-enabled efficient architecture is proposed. This proposed architecture creates two networks: a public network for medical stakeholders to exchange and agree on specific medical activities before being preserved on distributed storage (read-only after record registration) and a private network for complete DIKW process scheduling and management. We designed and created smart contracts for this purpose, as well as use-case diagrams to describe the overall execution process. The proposed architectural solution provides more efficient information integrity, provenance, and storage procedures to immutably preserve the medical ledger in a permissioned hash-encrypted structure.
... Local wisdom exists in various aspects of life so it is expected to anticipate negative influences that degrade existing norms and customs. (Agatha, 2016;Cockburn, 2020;Demaio, 2011;Genilo, 2010;Khan & Shaheen, 2021;Maulidzy & Dwijayanti, 2016;Pesurnay, 2018;Sugito et al., 2019). ...
Rural communities have ways to maintain the health of their families, and the community environment by processing plants and herbal products during the Corona Virus Disease-2019 (Covid-19) pandemic. The study aims to design a community empowerment program base on the green economy in preserving herbs as local wisdom. The study used Participatory Learning and Action (PLA) is relevant to the design of empowerment by collecting data through interview dialogue, observation, documentation, and focus group discussions. Research informants were determined through purposive sampling, namely stakeholders in the government of Tegal Regency, Central Java Province in Indonesia, such as the head of the health department and regional technical implementing unit for the Herbal Medicine Tourism (HMT) area in Kalibakung, the Headman of the Kalibakung Village, herbal practitioners, and empowerment groups. Participatory research analysis was used to identify, and categorize problems, prepare action plans, evaluate the entire process, and implement actions. The results of the study showed: (1) The community around the HMT area has not been empowered to cultivate plants and herbal products, so mutually beneficial partnerships have not been established. (2) HMT area in Kalibakung and the surrounding community land have the potential for cultivating herbal plants that can realize health independence for families, and communities, so there need to be empowerment programs. (3) The empowerment programs for the cultivation of plants and herbal products could be started by increasing the motivation and inspiration for the benefits of herbs, knowledge, understanding, and skills of products, and the cultivation of herbal plants in a sustainable manner.
