Integrated Information System Research Articles

Reviewing the availability and integration of community health information system for HIV/AIDS in Lesotho

Background: Universal Health Coverage (UHC) is an integral part of the Sustainable Development Goals, with community-based services playing a crucial role. Various stakeholders contribute to human immunodeficiency virus (HIV) interventions, which must be documented and shared with others for informed decision-making.Aim: This study aims to review the availability of a community health information system (CHIS) in Lesotho and its integration between the Ministry of Health (MOH) and the National AIDS Commission (NAC).Setting: The study is based on the Ministry of Health and the National AIDS Commission in Maseru, Lesotho.Method: A scoping review used peer-reviewed articles, documents from MOH and NAC, and other relevant reports from non-governmental organisations. The community information systems were examined for inclusion of clinical and non-clinical information. Possible linkages of information between MOH and NAC were reviewed.Results: Clinical information from CSOs is recorded in DHIS2 at the MOH facilities, while non-clinical information is reported in LOMSHA. However, clinical information from VHWs is currently not included in DHIS2 and formal reporting tools are being developed. There are no links between MOH and NAC, which limits information sharing.Conclusion: Although a CHIS exists, it lacks the necessary linkages. Community-based information from VHWs is not reported through DHIS2. Therefore, the country does not benefit fully from community-based health information.Contribution: Community health information systems review has never been conducted in Lesotho before. Therefore, this review will raise awareness about its importance and use in decision-making.

An Analysis of How Logistics Integration Affects Organizational Performance in Kenya's Clearing and Forwarding Sector. A Structural Equation Modelling Approach.

The performance of logistics service providers in Kenya is marred by poor customer satisfaction, stemming from delays experienced in port clearance, disintegrated clearance systems, complex regulations, inefficient document processing and lack of integrated information systems. This study examined the effect of logistics integration on organizational performance among the Clearing and Forwarding firms in Kenya. It adopted a cross-sectional design and collected primary data through structured questionnaires. Simple and stratified random sampling were used to select respondents and ethical approvals obtained before collecting data using structured questionnaires. Data were collected from 298 employees from the operations, customer service, and finance departments. Using SPSS and SPSS AMOS 29, the study used descriptive statistics and structural equation modelling. The study findings revealed a disparity in the level of logistics integration among different Clearing and Forwarding firms in Kenya. This variance in logistics integration significantly impacts the overall organizational performance of these firms. From SEM analysis, the study established that logistics integration has a positive and significant effect (Path coefficient = 0.791, C.R = 5.664, P-value < 0.001) on the organizational performance of C&F companies in Kenya. The study concludes that enhancing logistics integration will lead to a substantial improvement in the organizational performance of C&F companies in Kenya. Thus, managers responsible for strategy should aim to collaborate with strategic partners such as state corporations managing ports and customs processes (KRA and Kenya Ports Authority), shipping lines, transportation companies, and other key stakeholders in the C&F supply chain in integrating logistics systems and processes.

Spatial and Temporal Analysis of Road Traffic Accidents in Major Californian Cities Using a Geographic Information System

Road traffic accidents have increased globally, which has led to significant challenges to urban safety and public health. This concerning trend is also evident in California, where major cities have seen a rise in accidents. This research conducts a spatio-temporal analysis of traffic accidents across the four major Californian cities—Los Angeles, Sacramento, San Diego, and San Jose—over five years. It achieves this through an integration of Geographic Information System (GIS) functionalities (space–time cube analysis) with non-parametric statistical and spatial techniques (DBSCAN, KDE, and the Getis-Ord Gi* method). Our findings from the temporal analysis showed that the most accidents occurred in Los Angeles over five years, while San Diego and San Jose had the least occurrences. The severity maps showed that the majority of accidents in all cities were level 2. Moreover, spatio-temporal dynamics, captured via the space–time cube analysis, visualized significant accident hotspot locations. The clustering of accidents using DBSCAN verified the temporal and hotspot analysis results by showing areas with high accident rates and different clustering patterns. Additionally, integrating KDE with the population density and the Getis-Ord Gi* method explained the relationship between high-density regions and accident occurrences. The utilization of GIS-based analytical techniques in this study shows the complex interplay between accident occurrences, severity, and demographic factors. The insight gained from this study can be further used to implement effective data-driven road safety strategies.

Zagros Grass Index—A New Vegetation Index to Enhance Fire Fuel Mapping: A Case Study in the Zagros Mountains

Annually, the oak forests of the Zagros Mountains chains in western Iran and northeastern Iraq face recurring challenges posed by forest fires, particularly in the Kurdo–Zagrosian forests in western Iran and northeastern Iraq. Assessing fire susceptibility relies significantly on vegetation conditions. Integrating in situ data, Remote Sensing (RS) data, and Geographical Information Systems (GIS) integration presents a cost-effective and precise approach to capturing environmental conditions before, during, and after fire events, minimizing the need for extensive fieldwork. This study refines and applies the Zagros Grass Index (ZGI), a local vegetation index tailored to discern between grass-covered surfaces and tree canopies in Zagros forests, identifying the grass masses as the most flammable fuel type. Utilizing the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) product as input from 2013 to 2022, the ZGI aims to mitigate the influence of tree canopies by isolating NDVI values solely attributable to grass cover. By incorporating phenological characteristics of forest trees and grass species, the ZGI outperforms NDVI in mapping grass-covered areas crucial for the study region’s fire susceptibility assessment. Results demonstrate a substantial overlap between ZGI-based maps and recorded fire occurrences, validating the efficacy of the index in fire susceptibility estimation.

Human Papilloma Virus Vaccination and Cervical Screening in the Italian Regions: An Overview of the Current State of the Art.

Human Papilloma Virus (HPV) infection and HPV-related cancers can be prevented through vaccinations and mass cervical screening programmes. The Ministry of Health in Italy provides recommendations on primary and secondary prevention of HPV-related diseases, but the 19 Italian regions and 2 autonomous provinces have organisational and decision-making autonomy, with differences in the strategies for offering prevention. The aim of this study is to describe the HPV vaccination and cervical screening offered in all Italian regions. Regional official documents up until 31 December 2021 were first identified. Subsequently, primary and secondary prevention experts from each region were interviewed to validate the previously collected data. The National Immunisation Plan (NIP) 2017-2019 recommends HPV vaccination from the age of 11 for both sexes, with a coverage target of 95%. HPV vaccination is offered free of charge or co-payment. All regions have screening programmes for cervical cancer, using PAP or HPV-DNA tests every three to five years. All regions have an electronic registry for vaccination and screening status. All regions have developed awareness-raising campaigns. It is important to harmonise regional policies with the implementation of information systems integration. The collected data could enhance both regional and national public health initiatives, bolstering the resilience of vaccination programs.

Landscape Mapping: Investigating the Scope of Information Systems and Technology in Contemporary Organizations

In today's fast-paced business environment, the integration of Information Systems (IS) and technology is crucial for organizational success. This research aims to explore the landscape of Information Systems and Technology (IST) within contemporary organizations, investigating its multifaceted dimensions across diverse organizational contexts. Through a systematic examination, this study seeks to shed light on the intricate relationships between technology and organizational functionality, offering insights into IST deployment, utilization, and management strategies. By employing bibliometric analysis, thematic mapping, and author collaboration network analysis, this research provides a comprehensive understanding of IST research dynamics, trends, and future directions. The findings not only contribute to scholarly discourse but also offer practical implications for organizational stakeholders, policymakers, and technology practitioners.

Designing an Integrated Information System with a Fast (Sister Fani) and Attractive Interface to Improve Performance and User Experience: Case Study at SMK PGRI 1 Tangerang

The Integrated Information System (Sister Fani) is an important innovation in the educational environment of SMK PGRI 1 Tangerang. Considering the need for a fast and attractive interface, this study aims to design and implement Sister Fani that meets high standards of performance and user experience. This study adopted a qualitative approach, collecting data through observation, interviews, and documentary research. Research results show that fast and attractive interface design can improve users' efficiency in accessing and using information and provide a better user experience. The practical implication of this study is that the adoption of Sister Fani at SMK PGRI 1 Tangerang can improve the productivity and quality of educational services

Artificial Intelligence-Driven Platform: Unveiling Critical Hepatic Molecular Alterations in Hepatocellular Carcinoma Development.

Since most Hepatocellular Carcinoma (HCC) typically arises as a consequence of long-term liver damage, the hepatic molecular characteristics are closely related to the occurrence of HCC. Gaining comprehensive information about the location, morphology, and hepatic molecular alterations related to HCC is essential for accurate diagnosis. However, there is a dearth of technological advancements capable of concurrently providing precise HCC diagnosis and discerning the accompanying hepatic molecular alterations. In this study, an integrated information system is developed for the pathological-level diagnosis of HCC and the revelation of critical molecular alterations in the liver. This system utilizes computed tomography/Surface-enhanced Raman scattering combined with an artificial intelligence strategy to establish connections between the occurrence of HCC and alterations in hepatic biomolecules. Employing artificial intelligence techniques, the SERS spectra from both healthy and HCC groups are successfully classified into two distinct categories with a remarkable accuracy rate of 91.38%. Based on molecular profiling, it is identified that the nucleotide-to-lipid signal ratio holds significant potential as a reliable indicator for the occurrence of HCC, thereby serving as a promising tool for prevention and therapeutic surveillance.

Geographical Information Systems (GIS) in Municipal Planning and Management: A Pathway to Improve South African Service Delivery

In the years after 1994, the democratic government of South Africa worked to transfer and extend basic service provision to practically all sections of the country. The delivery of public services continues to be an embarrassment to humanity and the developmental state. Without proper basic services such as water, sanitation, power, infrastructure, health facilities, and housing, a large section of South Africa’s people would continue to live and die. As a result, society remains disenfranchized and exposed to socioeconomic shocks, contributing to the economy's decline and destruction. To support successful service delivery, the integration of Geographical Information Systems (GIS) as a shift from human-led operations to computerized operations is now recognized and required in municipal planning and management. The central purpose of this study is to evaluate the feasibility of applying GIS as a crucial planning tool infused within integrated development planning in municipal planning and management. The current state of service delivery in existing municipal planning and management procedures, as well as the factors that influence it and the effect it has on society, will be examined. This conceptual study employs secondary data and follows a certain methodology. Using the Critical Discourse Analysis (CDA) methodology, secondary data were examined. The results demonstrate that political influence and manipulation, a lack of openness and accountability, insufficient citizen participation, a lack of personnel capability, and poor planning, monitoring, and evaluation contribute to poor public service delivery in South Africa. Furthermore, the findings indicate that GIS functions as a tool for visualization and collation of spatial information, map production, and data capturing and storage in local municipalities with the aim of enhancing public service delivery.

The materials design ontology

In the materials design domain, much of the data from materials calculations is stored in different heterogeneous databases with different data and access models. Therefore, accessing and integrating data from different sources is challenging. As ontology-based access and integration alleviates these issues, in this paper we address data access and interoperability for computational materials databases by developing the Materials Design Ontology. This ontology is inspired by and guided by the OPTIMADE effort that aims to make materials databases interoperable and includes many of the data providers in computational materials science. In this paper, first, we describe the development and the content of the Materials Design Ontology. Then, we use a topic model-based approach to propose additional candidate concepts for the ontology. Finally, we show the use of the Materials Design Ontology by a proof-of-concept implementation of a data access and integration system for materials databases based on the ontology.11 This paper is an extension of (In The Semantic Web – ISWC 2020 – 19th International Semantic Web Conference, Proceedings, Part II (2000) 212–227 Springer) with results from (In ESWC Workshop on Domain Ontologies for Research Data Management in Industry Commons of Materials and Manufacturing 2021 1–11) and currently unpublished results regarding an application using the ontology.

Quality assessment using water quality indicators with geospatial analysis of groundwater quality El-Oued region, Northern Sahara, Algeria

ABSTRACT The present study was carried out to assess groundwater quality in two layers (Albian and complex terminal) intended for irrigation, to identify their original chemical composition and overall risk by different attack techniques in the province of El-Oued. Eleven physico-chemical parameters were measured at 16 locations. Emphasis was placed on the selection of irrigation water quality index (IWQI) inputs through the application of multivariate statistical analysis, the estimation of various classical irrigation indices, and the integration of geographic information systems. The results of the IWQI, sodium adsorption ratio, Kelly index, permeability index, residual sodium carbonate and magnesium hazard showed that the majority of groundwater samples were classified for irrigation purposes in the following categories: severely restricted (100%), low sodium and excellent (100%), good (93.75%), marginally safe (100%), good (100%) and suitable (81.25%), respectively. In addition, the majority of selected samples were classified as “good to eligible for irrigation” based on %Na as shown in the distribution map. Whereas the principal component analysis results show that the four factors together explain 72.74% of the total variance according to the Kaiser rule. Cluster analysis divides the parameters into three groups. In summary, the combination of the geographic system and irrigation quality indices is useful for identifying irrigation zones.

Systematic application of traffic‐signal‐control system architecture design and selection using model‐based systems engineering and Pareto frontier analysis

AbstractThe global population rise has increased vehicles on roads, complicating traffic management. Inefficient traffic control systems cause significant economic losses owing to commuter time wastage, high energy consumption, and greenhouse gas emissions. Traffic signal control systems (TSCSs) are vital in traffic management, impacting traffic flow significantly; therefore, studies are exploring new optimization approaches that adapt to changing traffic conditions. However, they concentrate on either new technology infusion or on control algorithm optimization, and do not holistically address the architectural configuration of the system. In this study, we presented a unique case study by applying an existing systematic framework to the TSCS system architecture design and selection process. This application demonstrates that TSCS enhancement is a multifaceted process that requires a comprehensive assessment of not only technical aspects, such as the control algorithm, but also factors including system architecture, security, and data integrity. Because of the increasing reliance of TSCSs on data exchange between their various subsystems, this case study also adopted a cybersecurity perspective of the system and introduced cyber resiliency as a crucial metric for evaluating TSCS architecture performance. Furthermore, through the applied framework, an optimal TSCS architectural configuration with executable options was identified by generating multiple TSCS architectural configurations using decision option patterns and identifying those on the Pareto frontier to understand the architectural decision‐making process. Traffic engineers and transportation planners can use this case study application as a guide to optimize TSCSs employed in existing transportation networks and design more efficient transportation networks for future urban development.

A comprehensive review on the integration of geographic information systems and artificial intelligence for landfill site selection: A systematic mapping perspective.

Properly selecting landfill sites for waste disposal is crucial for mitigating environmental and public health risks. Geographic Information Systems (GISs) and Artificial Intelligence (AI) techniques have emerged as valuable tools for identifying suitable landfill locations. This study presents a systematic mapping study (SMS) that investigates the usage of GIS and AI in landfill site selection. We searched six databases (IEEE Xplore, ACM Digital Library, Science Direct, Emerald Insight, Taylor & Francis Online and Web of Science) using predefined keywords related to landfills, GIS and AI. From 858 initially retrieved articles, we selected 48 relevant articles for in-depth analysis. Our research aimed to answer various questions, such as publication trends, the geographic distribution of case studies, criteria for assessing landfill suitability, tools and techniques employed, preliminary site screening methods, decision-making processes, limitations and future research directions. We used bubble charts, bar charts and tables to visualize the results. The findings of our study highlight the growing interest in using GIS and AI for landfill site selection and emphasize the importance of incorporating multi-criteria decision-making techniques. Furthermore, the results reveal the need for developing more advanced AI models, addressing the limitations of current approaches and exploring novel visualization techniques for enhancing landfill site selection processes. This study provides valuable insights for researchers and practitioners in waste management, environmental science and geoinformatics. It sets the groundwork for future research on improving GIS- and AI-based landfill site selection methodologies.

Correlation Aware Relevance-Based Semantic Index for Clinical Big Data Repository.

In this paper, we focus on indexing mechanisms for unstructured clinical big integrated data repository systems. Clinical data is unstructured and heterogeneous, which comes in different files and formats. Accessing data efficiently and effectively are critical challenges. Traditional indexing mechanisms are difficult to apply on unstructured data, especially by identifying correlation information between clinical data elements. In this research work, we developed a correlation-aware relevance-based index that retrieves clinical data by fetching most relevant cases efficiently. In our previous work, we designed a methodology that categorizes medical data based on the semantics of data elements and merges them into an integrated repository. We developed a data integration system for medical data sources that combines heterogeneous medical data and provides access to knowledge-based database repositories to different users. In this research work, we designed an indexing system using semantic tags extracted from clinical data sources and medical ontologies that retrieves relevant data from database repositories and speeds up the process of data retrieval. Our objective is to provide an integrated biomedical database repository that can be used by radiologists as a reference, or for patient care, or by researchers. In this paper, we focus on designing a technique that performs data processing for data integration, learn the semantic properties of data elements, and develop a correlation-aware topic index that facilitates efficient data retrieval. We generated semantic tags by identifying key elements from integrated clinical cases using topic modeling techniques. We investigated a technique that identifies tags for merged categories and provides an index to fetch data from an integrated database repository. We developed a topic coherence matrix that shows how well a topic is supported by a corpus from clinical cases and medical ontologies. We were able to find more relevant results using an annotation index from an integrated database repository, and there was a 61% increase in a recall. We evaluated results with the help of experts and compared them with naive index (index with all terms from the corpus). Our approach improved data retrieval quality by providing most relevant results and reduced data retrieval time as we applied correlation-aware index on an integrated data repository. Topic indexing approach proposed in this research work identifies tags based on a correlation between different data elements, improves data retrieval time, and provides most relevant cases as an outcome of this system.

Critical Watershed Prioritization through Multi-Criteria Decision-Making Techniques and Geographical Information System Integration for Watershed Management

The Precambrian hard rock topography of the Manbhum-Singhbhum plateau, which is well known for its semi-arid climates prone to drought, is often seen in Purulia district in West Bengal, India. Despite the district’s middling groundwater capacity, 17 out of 20 blocks have exorbitant fluoride pollution in the groundwater that negatively impacts the health of local residents. Approximately 13% of the whole area suffers from severe erosion. It is evident that the river Kangsabati and its tributaries are not well fed by rainwater and thereby there is always a dearth of ground water. The aim of this study was to identify and prioritize integral watersheds in the Purulia area using Multi-Criteria Decision Making (MCDM) and Geographic Information Systems (GIS). The evaluation was carried out in the Bandu sub-watershed, which contains five micro watersheds: 2A2B5m, 2A2B5k, 2A2B5h, 2A2B5b, and 2A2B5j. The analysis considered five major factors: lithological properties, land use and land cover, soil erosion, groundwater recharge, and hydrogeomorphology. The weights of these criteria were determined by the Analytical Hierarchy Process (AHP) model, which was then prioritized using the Techniques for Order of Preference by Similarity to the Ideal Solution (TOPSIS) technique. This study emphasized an integrated approach to assess watershed hazards and to establish rational conservation goals. The Central Ground Water Board (CGWB) of India report was referred during data analysis. As a result of this study, the 2A2B5k watershed emerged as the most critical due to its susceptibility across the analyzed parameters. This thorough plan demonstrated the usefulness of identifying watershed threads and prioritizing conservation efforts.

Integration of Geographic Information Systems and Spatial Data Analysis in Location Decision Making for Manufacturing Industries

Integration of Geographic Information Systems and Spatial Data Analysis in Location Decision Making for Manufacturing Industries

Remote Sensing-Enabled Urban Growth Simulation Overlaid with AHP-GIS-Based Urban Land Suitability for Potential Development in Mersin Metropolitan Area, Türkiye

This study delves into the integration of analytic hierarchy process (AHP) and geographic information system (GIS) techniques to identify suitable areas for urban development in six districts within the Mersin Metropolitan Area of Turkey. The specific aim is to generate an urban land use suitability map, in order to facilitate informed decision-making for urban development. Drawing on open Landsat satellite imagery and employing the random forest (RF) algorithm, the study spans a fifteen-year period, over which land use/land cover (LULC) changes are measured. Furthermore, a novel approach is introduced by incorporating the urban land use suitability map into an urban growth simulation model developed using a logistic regression (LR) algorithm. This simulation forecasts urban growth up to 2027, enabling planners to evaluate potential development areas against suitability criteria. Findings reveal spatial patterns of land suitability and projected urban growth, aiding decision-makers in selecting optimal areas for development while preserving ecological integrity. Notably, the study emphasizes the importance of considering various factors such as topography, accessibility, soil capability, and geology in urban planning processes. The results showcase significant proportions of the study area as being moderately to highly suitable for urban development, alongside notable shifts in LULC classes over the years. Additionally, the overlay analysis of simulated urban growth and land suitability maps highlights areas with contrasting suitability levels, offering valuable insights for sustainable urban growth strategies. By overlaying the urban land suitability map with a simulated LULC map for 2027, it is revealed that 2247.3 hectares of potential new urbanization areas demonstrate very high suitability for settlement, while 7440.12 hectares exhibit very low suitability. By providing a comprehensive framework for assessing urban land suitability and projecting future growth, this research offers practical implications for policymakers, urban planners, and stakeholders involved in Mersin’s development trajectory, ultimately fostering more sustainable and resilient urban landscapes.

Reliability analysis of intelligent manufacturing systems based on improved FMEA combined with machine learning

Along with the booming of intelligent manufacturing, the reliability management of intelligent manufacturing systems appears to be becoming more significant. Failure mode and effects analysis (FMEA) is a prospective reliability management instrument extensively utilized to manage failure modes of systems, products, processes, and services in various industries. However, the conventional FMEA method has been criticized for its inherent limitations. Machine learning can handle large amounts of data and has merits in reliability analysis and prediction, which can help in failure mode classification and risk management under limited resources. Therefore, this paper devises a method for complex systems based on an improved FMEA model combined with machine learning and applies it to the reliability management of intelligent manufacturing systems. First, the structured network of failure modes is constructed based on the knowledge graph for intelligent manufacturing systems. Then, the grey relation analysis (GRA) is applied to determine the risk prioritization of failure modes. Hereafter, the k-means algorithm in unsupervised machine learning is employed to cluster failure modes into priority classes. Finally, a case study and further comparative analysis are implemented. The results demonstrate that failure modes in system security, production quality, and information integration are high-risk and require more resources for prevention. In addition, recommendations for risk prevention and monitoring of intelligent manufacturing systems were given based on the clustering results. In comparison to the conventional FMEA method, the proposed method can more precisely capture the coupling relationship between the failure modes compared with. This research provides significant support for the reliability and risk management of complex systems such as intelligent manufacturing systems.

Healthcare Integrated Information System in Indonesia

The United States (US), leads the world in medical science and technology. Lead by clear definitions in almost all sectors of clinical research, training and governance practices. Likewise, the pharmaceutical and medical device (medical equipment) manufacturers are considered one of the leading in the world. Therefore, many of the definitions and rules have also been adopted into global regulations by the world health organization, the World Health Organization (WHO). Currently, these rules have been implemented into the health information system (HIS), which further rapidly being adopted and used in many countries. Nowadays, health facilities (HF) around the world have used HIS, which are basic or have reached an advanced level. This paper will describe how the development of HIS and applied products from utilization of information technology (IT) in Indonesia, along with the opportunities and challenges in the future.

Artificial intelligence in accounting

Artificial Intelligence (AI) technologies open up broad horizons for enhancing business efficiency and advancing various professional domains, boosting their productivity and compe­titiveness. There is an active exploration of approaches to incorporating AI technologies in the accounting sphere, promising a seamless transition from human to machine involvement. The aim of this article is to summarize the acquired experience, identify perspectives, constraints, and risks associated with the use of AI technologies in the professional activities of accountants. The research is based on the hypothesis that widespread use of AI in the professional activity of an accountant with an insufficient level of professional skepticism and caution carries significant threats and risks for both the accountant and the business as a whole. Scientific search methods, comparative and critical analysis, theoretical generalization, and synthesis were used. A prerequisite for imple­menting AI technologies in accounting is expert information systems and ERP systems. The analysis of AI technology implementation experience in various industries demonstrates their relevance in the accounting field for performing routine tasks (automated recognition of primary documents, processing incoming signals, and other standard operations with a simultaneous reduction in the probability of errors), analyzing large datasets, and providing information support for decision-making (pro­ces­sing business data and regulatory docu­ments), training professionals, and organi­zing internal and external communication (parti­cularly between humans and machines). Identi­fied potential risks include breaches of privacy and data security, misinterpretation of output data, and the disregard of activity context, external and internal environments, especially due to the absence of emotional intelligence, which influences the trust level in integrated information systems. The requirement for the application of professional assessments and judgments, mandated by regulatory documents, limits the scope of AI technology utilization in accounting. Future research should focus on exploring the possibilities of widespread integ­ration of AI technologies in information systems for accounting and improving legislation based on the principle of risk assessment.