Hasil untuk "Management information systems"

Wil M.P. van der Aalst

Business Process Management (BPM) research resulted in a plethora of methods, techniques, and tools to support the design, enactment, management, and analysis of operational business processes. This survey aims to structure these results and provide an overview of the state-of-the-art in BPM. In BPM the concept of a process model is fundamental. Process models may be used to configure information systems, but may also be used to analyze, understand, and improve the processes they describe. Hence, the introduction of BPM technology has both managerial and technical ramifications and may enable significant productivity improvements, cost savings, and flow-time reductions. The practical relevance of BPM and rapid developments over the last decade justify a comprehensive survey.

Walaa N. Ismail, Mona A. S. Ali

Artificial intelligence (AI) and machine learning (ML) have made significant advances in the early detection and diagnosis of autism spectrum disorder (ASD), overcoming the limits of previous screening methods. These AI-based technologies offer more objective, scalable, and efficient methods for identifying risk behaviors associated with ASD. This article presents a novel approach for enhancing the detection and classification of ASD by integrating squeeze-and-excitation, multiscale attention mechanisms, and convolutional neural networks (CNNs) with automated hyperparameter optimization using the white shark optimization (WSO) algorithm. By leveraging attention mechanisms to focus on relevant facial features across multiple scales, this method enhances feature extraction, improves classification accuracy, and provides a robust framework for analyzing complex facial imaging data. An extensive autism dataset, encompassing both facial and multimodal datasets, was utilized in this study, including subjects from the non-ASD control (NC) group and individuals diagnosed with ASD. Experimental results demonstrate that the proposed model significantly outperforms state-of-the-art methods, achieving a high accuracy of 95.36%, precision of 92.62%, and an F1-score of 95.5% for ASD detection and classification. This proposed model is a promising tool for the accurate and early identification of ASD, which is crucial for effective treatment and management. By providing deeper insights into distinctive facial patterns and morphological features associated with ASD, the model enables physicians to make more informed decisions and develop targeted treatment plans, ultimately improving patient outcomes.

Hichem Yakoub Khemmal, Azzedine Hani, Kaddour Benmarce

Abstract Groundwater is a vital resource, especially in semi-arid regions where water scarcity poses a major challenge. The northern upper Boussellam region, with its unique hydrogeological conditions, requires effective groundwater management for sustainability. This study introduces a novel approach for mapping groundwater recharge potential zones across a 1,100 $$\hbox {km}^2$$ km 2 area using geographic information systems, the analytic hierarchy process, total dissolved solids levels, and the receiver operating characteristic curve. The study incorporates the precipitation data from NASA LaRC POWER to address the spatial and temporal limitations of local meteorological data and enhance model performance. The results revealed a clear spatial distribution of recharge potential, with a maximum of 8.66 observed in the northern mountainous areas and a gradual decrease toward the southern region. A key innovation of this study is the use of total dissolved solids levels for model validation, revealing a strong relationship with recharge dynamics. The receiver operating characteristic curve analysis yielded an area under the curve of 0.835, indicating high model reliability. The approach incorporates easily accessible data, including NASA LaRC POWER, the digital elevation model, Landsat imagery, and total dissolved solids, offering a replicable and adaptable framework, particularly useful in regions with limited hydrogeological data. These findings provide valuable insights into groundwater recharge processes and highlight the importance of total dissolved solids as a practical tool for model validation. The study advances the identification and spatial distribution of groundwater recharge zones, providing a foundation for more effective water resource management and sustainable resource planning in semi-arid regions facing similar challenges.

Zidu Xu, Yaowen Gu, Xiaowei Xu et al.

BackgroundChina’s older population is facing serious health challenges, including malnutrition and multiple chronic conditions. There is a critical need for tailored food recommendation systems. Knowledge graph–based food recommendations offer considerable promise in delivering personalized nutritional support. However, the integration of disease-based nutritional principles and preference-related requirements needs to be optimized in current recommendation processes. ObjectiveThis study aims to develop a knowledge graph–based personalized meal recommendation system for community-dwelling older adults and to conduct preliminary effectiveness testing. MethodsWe developed ElCombo, a personalized meal recommendation system driven by user profiles and food knowledge graphs. User profiles were established from a survey of 96 community-dwelling older adults. Food knowledge graphs were supported by data from websites of Chinese cuisine recipes and eating history, consisting of 5 entity classes: dishes, ingredients, category of ingredients, nutrients, and diseases, along with their attributes and interrelations. A personalized meal recommendation algorithm was then developed to synthesize this information to generate packaged meals as outputs, considering disease-related nutritional constraints and personal dietary preferences. Furthermore, a validation study using a real-world data set collected from 96 community-dwelling older adults was conducted to assess ElCombo’s effectiveness in modifying their dietary habits over a 1-month intervention, using simulated data for impact analysis. ResultsOur recommendation system, ElCombo, was evaluated by comparing the dietary diversity and diet quality of its recommended meals with those of the autonomous choices of 96 eligible community-dwelling older adults. Participants were grouped based on whether they had a recorded eating history, with 34 (35%) having and 62 (65%) lacking such data. Simulation experiments based on retrospective data over a 30-day evaluation revealed that ElCombo’s meal recommendations consistently had significantly higher diet quality and dietary diversity compared to the older adults’ own selections (P<.001). In addition, case studies of 2 older adults, 1 with and 1 without prior eating records, showcased ElCombo’s ability to fulfill complex nutritional requirements associated with multiple morbidities, personalized to each individual’s health profile and dietary requirements. ConclusionsElCombo has shown enhanced potential for improving dietary quality and diversity among community-dwelling older adults in simulation tests. The evaluation metrics suggest that the food choices supported by the personalized meal recommendation system surpass autonomous selections. Future research will focus on validating and refining ElCombo’s performance in real-world settings, emphasizing the robust management of complex health data. The system’s scalability and adaptability pinpoint its potential for making a meaningful impact on the nutritional health of older adults.

Joleha, Safridatul 'Audah, Yohanna Lilis Handayani et al.

The problem of flooding in Tuah Madani District, Pekanbaru City, needs attention. Drainage and flood control systems need to be made to accommodate excess water and hold water somewhere. Groundwater management is directed at creating a balance between groundwater utilization and soil conservation efforts through the application of ecodrain which affects the conditions and characteristics of the area which provides an overview of land hydrology in parcels by comparing the height of the land with the receiving water bodies. as a drainage outlet. This research aims to create a map of groundwater depth and soil permeability based on Geographic Information Systems (GIS) for the feasibility of infiltration well locations in Tuah Madani District in implementing ecodrain. The research method was carried out by collecting data on the coordinates of infiltration wells at 30 points in five areas of Tuah Madani District. then measure the depth of the groundwater table and test the permeability in the field. Data is processed using ArcGIS. The results of mapping the depth of the groundwater table in the area are classified as shallow (1.28m–3.58m). Only 4 locations (Tuah Karya and West Sidomulyo) out of 30 sample locations did not meet the standards. Meanwhile, the permability value obtained was 2.61–11.07 cm/hour. Technically, the procedure for planning infiltration wells (SNI03-2453-2002) fulfills two conditions, namely a minimum groundwater depth of 1.50 m and soil permeability ≥ 2.0 cm/hour. Based on the contour lines of Tuah Madani District, it displays a pattern of groundwater flow from high flow to low flow at each point of the wellbore, the highest points on the map are colored blue (Air Putih and Sidomulyo Barat), while the lowest points on the map are Air Putih and Sidomulyo Barat. red (Tuah Madani, Luah Karya and Sialangmunggu).

Faisal Fiaz, Syed Muhammad Sajjad, Zafar Iqbal et al.

The Metaverse brings together components of parallel processing computing platforms, the digital development of physical systems, cutting-edge machine learning, and virtual identity to uncover a fully digitalized environment with equal properties to the real world. It possesses more rigorous requirements for connection, including safe access and data privacy, which are necessary with the advent of Metaverse technology. Traditional, centralized, and network-centered solutions fail to provide a resilient identity management solution. There are multifaceted security and privacy issues that hinder the secure adoption of this game-changing technology in contemporary cyberspace. Moreover, there is a need to dedicate efforts towards a secure-by-design Metaverse that protects the confidentiality, integrity, and privacy of the personally identifiable information (PII) of users. In this research paper, we propose a logical substitute for established centralized identity management systems in compliance with the complexity of the Metaverse. This research proposes a sustainable Self-Sovereign Identity (SSI), a fully decentralized identity management system to mitigate PII leaks and corresponding cyber threats on all multiverse platforms. The principle of the proposed framework ensures that the users are the only custodians and proprietors of their own identities. In addition, this article provides a comprehensive approach to the implementation of the SSI principles to increase interoperability and trustworthiness in the Metaverse. Finally, the proposed framework is validated using mathematical modeling and proved to be stringent and resilient against modern-day cyber attacks targeting Metaverse platforms.

Caner Tanış, Mahmoud M. Mansour, Enayat M. Abd Elrazik et al.

In this study, we propose a new zero-inflated regression model as an alternative to zero-inflated regression models, such as the zero-inflated Poisson, zero-inflated negative binomial, zero-inflated hurdle-Poisson, and zero-inflated hurdle negative binomial models. In this regard, we take benefit of the flexibility of the Poisson–Bilal distribution and some of its notable properties. More concretely, it is employed as the baseline distribution to generate a new regression model called the zero-inflated Poisson-Bilal regression model. It is designed to be a good alternative for modeling overdispersed data quite effectively. This aspect is emphasized using two real-world data sets from the medicine and education fields. Furthermore, these data sets are analyzed to compare the goodness-of-fit of the suggested zero-inflated regression model with some of its direct competitors.

Vittal Srinivasan, Nak-seung Patrick Hyun

In nature, different species of smaller animals produce ultra-fast movements to aid in their locomotion or protect themselves against predators. These ultra-fast impulsive motions are possible, as often times, there exist a small latch in the organism that could hold the potential energy of the system, and once released, generate an impulsive motion. These types of systems are classified as Latch Mediated Spring Actuated (LaMSA) systems, a multi-dimensional, multi-mode hybrid system that switches between a latched and an unlatched state. The LaMSA mechanism has been studied extensively in the field of biology and is observed in a wide range of animal species, such as the mantis shrimp, grasshoppers, and trap-jaw ants. In recent years, research has been done in mathematically modeling the LaMSA behavior with physical implementations of the mechanism. A significant focus is given to mimicking the physiological behavior of the species and following an end-to-end trajectory of impulsive motion. This paper introduces a foundational analysis of the theoretical dynamics of the contact latch-based LaMSA mechanism. The authors answer the question on what makes these small-scale systems impulsive, with a focus on the intrinsic properties of the system using bifurcations. Necessary and sufficient conditions are derived for the existence of the saddle fixed points. The authors propose a mathematical explanation for mediating the latch when a saddle node exists, and the impulsive behavior after the bifurcation happens.

Weizhen Bian, Siyan Liu, Yubo Zhou et al.

Faced with the burgeoning volume of academic literature, researchers often need help with uncertain article quality and mismatches in term searches using traditional academic engines. We introduce IntellectSeeker, an innovative and personalized intelligent academic literature management platform to address these challenges. This platform integrates a Large Language Model (LLM)--based semantic enhancement bot with a sophisticated probability model to personalize and streamline literature searches. We adopted the GPT-3.5-turbo model to transform everyday language into professional academic terms across various scenarios using multiple rounds of few-shot learning. This adaptation mainly benefits academic newcomers, effectively bridging the gap between general inquiries and academic terminology. The probabilistic model intelligently filters academic articles to align closely with the specific interests of users, which are derived from explicit needs and behavioral patterns. Moreover, IntellectSeeker incorporates an advanced recommendation system and text compression tools. These features enable intelligent article recommendations based on user interactions and present search results through concise one-line summaries and innovative word cloud visualizations, significantly enhancing research efficiency and user experience. IntellectSeeker offers academic researchers a highly customizable literature management solution with exceptional search precision and matching capabilities. The code can be found here: https://github.com/LuckyBian/ISY5001

Nadiia Davydenko, Yuliya Lutsyk, Alina Buriak et al.

The article is devoted to the modern development of high technologies and computer technology greatly enhanced the development of automated banking systems of banking sector organizations and allowed the synthesis of information and communication technologies for their formation.The main purpose of the article is to select the main indicators for assessing the level of financial security of the banking system of the state and identify promising areas of its development using forecasting models. In the process of research such analytical functions have been used: polynomial, exponential, power and logarithmic. The authors believe that the information and analytical provision of the financial security of the bank is an information provision that combines, on the one hand, information work, that is, ways, means and methods of collecting the necessary information, and on the other - analytical work, which includes forms and methods of information analysis and processing, which ensures an objective assessment of the situation and the adoption of a balanced management decision. As a result, forecast models were built for each of the indicators and also, it has been found that most indicators of the banking system of Ukraine in 2021-2023 will remain at “unsatisfactory” and “critical” levels. In conclusions it was proposed to introduce measures that would be aimed at improving the reliability and stability of the banking system of Ukraine.

Richard Emmanuel Adrian Sinaga, Jonathan Tristan Santoso, Irene Sonbay et al.

Finding a secure and comfortable parking space is one of the needs of automobile owners when they visit retail centers. The ability of parking lot managers to provide information on sufficient capacity and access presents a barrier to delivering parking facilities in retail centers. Due to this, a website-based parking facility management information system is required, allowing drivers to make reservations and payments online based on when they plan to visit the shopping center. The method used in the design of ParkHere information systems is design thinking which consists of empathizing, defining, ideating, prototyping, and testing stages. The output of this study shows that at the empathize stage, motorists need information about the capacity of parking lots in various shopping centers before visiting the location. At the defined stage, this shows the need for motorists for website-based and real-time parking facility information. At the idea stage, it is necessary to design an information system based on the website that provides information on the capacity of parking facilities based on the operational time of the shopping center. At the prototype and testing stages, the ParkHere information system can answer the user's needs in choosing a place and location for vehicle parking based on the specified time.

Teresa M. Coque, Rafael Cantón, Ana Elena Pérez-Cobas et al.

Antimicrobial resistance (AMR) is one of the Global Health challenges of the 21st century. The inclusion of AMR on the global map parallels the scientific, technological, and organizational progress of the healthcare system and the socioeconomic changes of the last 100 years. Available knowledge about AMR has mostly come from large healthcare institutions in high-income countries and is scattered in studies across various fields, focused on patient safety (infectious diseases), transmission pathways and pathogen reservoirs (molecular epidemiology), the extent of the problem at a population level (public health), their management and cost (health economics), cultural issues (community psychology), and events associated with historical periods (history of science). However, there is little dialogue between the aspects that facilitate the development, spread, and evolution of AMR and various stakeholders (patients, clinicians, public health professionals, scientists, economic sectors, and funding agencies). This study consists of four complementary sections. The first reviews the socioeconomic factors that have contributed to building the current Global Healthcare system, the scientific framework in which AMR has traditionally been approached in such a system, and the novel scientific and organizational challenges of approaching AMR in the fourth globalization scenario. The second discusses the need to reframe AMR in the current public health and global health contexts. Given that the implementation of policies and guidelines are greatly influenced by AMR information from surveillance systems, in the third section, we review the <i>unit of analysis</i> (“the what” and “the who”) and the indicators (the “operational units of surveillance”) used in AMR and discuss the factors that affect the validity, reliability, and comparability of the information to be applied in various healthcare (primary, secondary, and tertiary), demographic, and economic contexts (local, regional, global, and inter-sectorial levels). Finally, we discuss the disparities and similarities between distinct stakeholders’ objectives and the gaps and challenges of combatting AMR at various levels. In summary, this is a comprehensive but not exhaustive revision of the <i>known unknowns</i> about how to analyze the heterogeneities of hosts, microbes, and hospital patches, the role of surrounding ecosystems, and the challenges they represent for surveillance, antimicrobial stewardship, and infection control programs, which are the traditional cornerstones for controlling AMR in human health.

Mert Ozcan, Serhat Peker

Heart disease remains the leading cause of death, such that nearly one-third of all deaths worldwide are estimated to be caused by heart-related conditions. Advancing applications of classification-based machine learning to medicine facilitates earlier detection. In this study, the Classification and Regression Tree (CART) algorithm, a supervised machine learning method, has been employed to predict heart disease and extract decision rules in clarifying relationships between input and output variables. In addition, the study’s findings rank the features influencing heart disease based on importance. When considering all performance parameters, the 87% accuracy of the prediction validates the model’s reliability. On the other hand, extracted decision rules reported in the study can simplify the use of clinical purposes without needing additional knowledge. Overall, the proposed algorithm can support not only healthcare professionals but patients who are subjected to cost and time constraints in the diagnosis and treatment processes of heart disease.

Yutong Tian, Yan Zhang, Qingyun Cheng et al.

Background: Integrated care is an important initiative to respond positively to the ageing of society and information and communication technology(ICT) plays an important role in facilitating the integration of functional and normative health and social care. The scoping review aims to synthesize evidence on the experience and practice of ICT-based implementation of integrated care for older adults. Methods: This study followed the research framework developed by Arksey and O’malley for the scoping review and systematically searched for relevant studies published between 1 January 2000 and 30 March 2022 from nine electronic databases, three specialist journals, three key institutional websites, 11 integrated care project websites, google scholar and references of the studies to be included. Two reviewers independently screened and extracted data and used thematic analysis to sort out and summarize the core elements, hindrances and facilitators of ICT-based integrated care. Results: A total of 77 studies were included in this study, including 36 ICT-based practice models of integrated care with seven core elements of implementation including single entry point, comprehensive geriatric assessment, personalized care planning, multidisciplinary case conferences, coordinated care, case management and patient empowerment, which generally had a positive effect on improving quality of life, caregiver burden and primary care resource utilization for older adults, but effectiveness evaluations remained Heterogeneity exists. The barriers and facilitators to ICT-based implementation of integrated care were grouped into four themes: demand-side factors, provider factors, technology factors and system factors. Conclusion: The implementation of ICT-based integrated care for the elderly is expected to improve the health status of both the supply and demand of services, but there is still a need to strengthen the supply of human resources, team training and collaboration, ICT systems and financial support in order to promote the wider use of ICT in integrated care.

Jingrui Hou, Georgina Cosma, Axel Finke

Continual learning refers to the capability of a machine learning model to learn and adapt to new information, without compromising its performance on previously learned tasks. Although several studies have investigated continual learning methods for information retrieval tasks, a well-defined task formulation is still lacking, and it is unclear how typical learning strategies perform in this context. To address this challenge, a systematic task formulation of continual neural information retrieval is presented, along with a multiple-topic dataset that simulates continuous information retrieval. A comprehensive continual neural information retrieval framework consisting of typical retrieval models and continual learning strategies is then proposed. Empirical evaluations illustrate that the proposed framework can successfully prevent catastrophic forgetting in neural information retrieval and enhance performance on previously learned tasks. The results indicate that embedding-based retrieval models experience a decline in their continual learning performance as the topic shift distance and dataset volume of new tasks increase. In contrast, pretraining-based models do not show any such correlation. Adopting suitable learning strategies can mitigate the effects of topic shift and data augmentation.

Sagnik Bhattacharya, Prakash Narayan

Shared information is a measure of mutual dependence among multiple jointly distributed random variables with finite alphabets. For a Markov chain on a tree with a given joint distribution, we give a new proof of an explicit characterization of shared information. The Markov chain on a tree is shown to possess a global Markov property based on graph separation; this property plays a key role in our proofs. When the underlying joint distribution is not known, we exploit the special form of this characterization to provide a multiarmed bandit algorithm for estimating shared information, and analyze its error performance.

Petra Schubert, Johannes H. Glitsch

In recent years we have seen the emergence of a new type of collaboration software, the so-called “Enterprise Social Software”. The “social features” of this software type have stimulated a renewed interest in Enterprise Collaboration Systems (ECS). In this article we present findings from a longitudinal research project on the introduction and use of ECS in companies. We argue that ERP Systems and ECS are inherently different and that the process-paradigm that is common to ERP cannot be applied identically to ECS. To address this issue, we suggest the two concepts use case and collaboration scenario for the analysis and description of collaboration activity in companies. From the literature and 26 case studies we identified typical use cases and collaboration scenarios that can serve as blueprints for ECS introduction projects. The longitudinal objective of our research is to assist companies with their ECS initiatives and to provide them with a catalog of existing use cases and collaboration scenarios from various industry settings.

Manjusha G Warrier, Priya Treesa Thomas, Arun Sadasivan et al.

Background: The caregivers of persons with motor neuron disease (MND) have several needs that are usually not voiced on any platform. Aim: To explore the lived experience of spouses of persons with MND, identify their needs and develop guidelines for better home-based care. Method: An exploratory study with 3-point in-depth interviews among 13 participants was conducted. Participants were the spouses of persons with MND under treatment at a national quaternary referral center. The interpretative phenomenological analysis identified participants’ needs. These needs, with literature review synthesis, guided the drafting of guidelines, which was validated by experts. Results: The needs were emotional, social, care systems, and skills. The guidelines developed contained 2 sections (1) Information for the spouses: understanding MND, communication, symptom management, marital relationship, palliative care, and everyday life made easier . (2) Well-being of the spouse caregivers: well-being of the spouse caregivers, self-care, mental health, supporting carers in palliative care, where to find help. Conclusion: The guidelines can be developed as a manual for the caregivers and for training healthcare professionals working with neurodegenerative conditions.

A. Smirne, D. Tamascelli, J. Lim et al.

We determine the conditions for the equivalence between the multi-time expectation values of a general finite-dimensional open quantum system when interacting with, respectively, an environment undergoing a free unitary evolution or a discrete environment under a free evolution fixed by a proper Gorini-Kossakowski-Lindblad-Sudarshan generator. We prove that the equivalence holds if both environments are bosonic and Gaussian and if the one- and two-time correlation functions of the corresponding interaction operators are the same at all times. This result leads to a non-perturbative evaluation of the multi-time expectation values of operators and maps of open quantum systems interacting with a continuous set of bosonic modes by means of a limited number of damped modes, thus setting the ground for the investigation of open-system multi-time quantities in fully general regimes.

Ignacio E. Sánchez, Ezequiel A. Galpern, Martín M. Garibaldi et al.

We propose an application of molecular information theory to analyze the folding of single domain proteins. We analyze results from various areas of protein science, such as sequence-based potentials, reduced amino acid alphabets, backbone configurational entropy, secondary structure content, residue burial layers, and mutational studies of protein stability changes. We found that the average information contained in the sequences of evolved proteins is very close to the average information needed to specify a fold ~2.2 $\pm$ 0.3 bits/(site operation). The effective alphabet size in evolved proteins equals the effective number of conformations of a residue in the compact unfolded state at around 5. We calculated an energy-to-information conversion efficiency upon folding of around 50%, lower than the theoretical limit of 70%, but much higher than human built macroscopic machines. We propose a simple mapping between molecular information theory and energy landscape theory and explore the connections between sequence evolution, configurational entropy and the energetics of protein folding.