Decision Support System Tool Research Articles

The Use of Generative AI for Scientific Literature Searches for Systematic Reviews: ChatGPT and Microsoft Bing AI Performance Evaluation.

A large language model is a type of artificial intelligence (AI) model that opens up great possibilities for health care practice, research, and education, although scholars have emphasized the need to proactively address the issue of unvalidated and inaccurate information regarding its use. One of the best-known large language models is ChatGPT (OpenAI). It is believed to be of great help to medical research, as it facilitates more efficient data set analysis, code generation, and literature review, allowing researchers to focus on experimental design as well as drug discovery and development. This study aims to explore the potential of ChatGPT as a real-time literature search tool for systematic reviews and clinical decision support systems, to enhance their efficiency and accuracy in health care settings. The search results of a published systematic review by human experts on the treatment of Peyronie disease were selected as a benchmark, and the literature search formula of the study was applied to ChatGPT and Microsoft Bing AI as a comparison to human researchers. Peyronie disease typically presents with discomfort, curvature, or deformity of the penis in association with palpable plaques and erectile dysfunction. To evaluate the quality of individual studies derived from AI answers, we created a structured rating system based on bibliographic information related to the publications. We classified its answers into 4 grades if the title existed: A, B, C, and F. No grade was given for a fake title or no answer. From ChatGPT, 7 (0.5%) out of 1287 identified studies were directly relevant, whereas Bing AI resulted in 19 (40%) relevant studies out of 48, compared to the human benchmark of 24 studies. In the qualitative evaluation, ChatGPT had 7 grade A, 18 grade B, 167 grade C, and 211 grade F studies, and Bing AI had 19 grade A and 28 grade C studies. This is the first study to compare AI and conventional human systematic review methods as a real-time literature collection tool for evidence-based medicine. The results suggest that the use of ChatGPT as a tool for real-time evidence generation is not yet accurate and feasible. Therefore, researchers should be cautious about using such AI. The limitations of this study using the generative pre-trained transformer model are that the search for research topics was not diverse and that it did not prevent the hallucination of generative AI. However, this study will serve as a standard for future studies by providing an index to verify the reliability and consistency of generative AI from a user's point of view. If the reliability and consistency of AI literature search services are verified, then the use of these technologies will help medical research greatly.

A web GIS based integrated water resources assessment tool for Javeh Reservoir

The creation of tools and services for tracking, estimating, and predicting water resource indices that might provide authorities access to information in close to real-time is essentially a must. The considerable development of Web-Geographic Information System Decision Support System (Web-GIS DSS) allows for the analysis of geospatial data and scenarios via the internet without a traditional desktop GIS. The web GIS technology is coupled to techniques which determine water resources Carlson, and Organization for Economic Corporation and Development (OECD) Trophic Status Indices (TSIs), the water resource vulnerability index (WRVI), and social water stress index (SWSI). The water quality indices were determined by helping machine learning (ML) techniques, Decision Tree, Random Forest, and XGBoost. The performances of aforementioned MLs were compared, and the best efficient one, XGBoost, was included in the Web-GIS DSS tool. The developed Web-GIS DSS was examined in Javeh Reservoir, Kurdestan, Iran.The developed Web-GIS DSS allows the users to define the relevant features of the Javeh Reservoir system in pre-defined time horizons such as meteorological, hydrological, inflow water quality, reservoir operation strategies, downstream water demands, geographical external water transfers, etc. The results indicate that the Javeh Reservoir experiences eutrophication status according to the Carlson and OECD indices over time horizons. The water quantity assessment reveals the Javeh Reservoir's susceptibility based on WRVI, although no stress is indicated by SWSI. This susceptibility may be attributed to limited precipitation and water inflow during the simulation period, suggesting a need for comprehensive water resources management.The conclusion drawn from this research underscores the essential role of the developed Web-GIS DSS in decision-making processes for water and environment management, substantiating its practicality with accurate and user-friendly water and environmental data analysis. The tool exemplifies the potential of integrating ML and Web-GIS technologies to promote sustainable environmental practices, thereby establishing a model for future research and policy-making initiatives.

Clinical Decision Support System for Guidelines-Based Treatment of Gonococcal Infections, Screening for HIV, and Prescription of Pre-Exposure Prophylaxis: Design and Implementation Study.

The syndemic nature of gonococcal infections and HIV provides an opportunity to develop a synergistic intervention tool that could address the need for adequate treatment for gonorrhea, screen for HIV infections, and offer pre-exposure prophylaxis (PrEP) for persons who meet the criteria. By leveraging information available on electronic health records, a clinical decision support (CDS) system tool could fulfill this need and improve adherence to Centers for Disease Control and Prevention (CDC) treatment and screening guidelines for gonorrhea, HIV, and PrEP. The goal of this study was to translate portions of CDC treatment guidelines for gonorrhea and relevant portions of HIV screening and prescribing PrEP that stem from a diagnosis of gonorrhea as an electronic health record-based CDS intervention. We also assessed whether this CDS solution worked in real-world clinic. We developed 4 tools for this CDS intervention: a form for capturing sexual history information (SmartForm), rule-based alerts (best practice advisory), an enhanced sexually transmitted infection (STI) order set (SmartSet), and a documentation template (SmartText). A mixed methods pre-post design was used to measure the feasibility, use, and usability of the CDS solution. The study period was 12 weeks with a baseline patient sample of 12 weeks immediately prior to the intervention period for comparison. While the entire clinic had access to the CDS solution, we focused on a subset of clinicians who frequently engage in the screening and treatment of STIs within the clinical site under the name "X-Clinic." We measured the use of the CDS solution within the population of patients who had either a confirmed gonococcal infection or an STI-related chief complaint. We conducted 4 midpoint surveys and 3 key informant interviews to quantify perception and impact of the CDS solution and solicit suggestions for potential future enhancements. The findings from qualitative data were determined using a combination of explorative and comparative analysis. Statistical analysis was conducted to compare the differences between patient populations in the baseline and intervention periods. Within the X-Clinic, the CDS alerted clinicians (as a best practice advisory) in one-tenth (348/3451, 10.08%) of clinical encounters. These 348 encounters represented 300 patients; SmartForms were opened for half of these patients (157/300, 52.33%) and was completed for most for them (147/300, 89.81%). STI test orders (SmartSet) were initiated by clinical providers in half of those patients (162/300, 54%). HIV screening was performed during about half of those patient encounters (191/348, 54.89%). We successfully built and implemented multiple CDC treatment and screening guidelines into a single cohesive CDS solution. The CDS solution was integrated into the clinical workflow and had a high rate of use.

Decision Support Systems in Forestry and Tree-Planting Practices and the Prioritization of Ecosystem Services: A Review

In this study, tree-selection/plantation decision support systems (DSSs) were reviewed and evaluated against essential objectives in the available literature. We verified whether existing DSSs leverage multiple data sources and available online resources such as web interfaces. We compared the existing DSSs, and in this study mainly focused on five main objectives that DSSs can consider in tree selection, including (a) climate resilience, (b) infrastructure/space optimization, (c) agroforestry, (d) ecosystem services, and (e) urban sustainability. The climate resilience of tree species and urban sustainability are relatively rarely taken into account in existing systems, which can be integrated holistically in future DSS tools. Based on this review, deep neural networks (DNNs) are recommended to achieve trade-offs between complex objectives such as maximizing ecosystem services, the climate resilience of tree species, agroforestry conservation, and other benefits.

Quo vadis, smallholder forest landscape? An introduction to the LPB-RAP model.

The impacts of the Anthropocene on climate and biodiversity pose societal and ecological problems that may only be solved by ecosystem restoration. Local to regional actions are required, which need to consider the prevailing present and future conditions of a certain landscape extent. Modeling approaches can be of help to support management efforts and to provide advice to policy making. We present stage one of the LaForeT-PLUC-BE model (Landscape Forestry in the Tropics-PCRaster Land Use Change-Biogeographic & Economic model; in short: LPB) and its thematic expansion module RAP (Restoration Areas Potentials). LPB-RAP is a high-resolution pixel-based scenario tool that relies on a range of explicit land use types (LUTs) to describe various forest types and the environment. It simulates and analyzes future landscape configurations under consideration of climate, population and land use change long-term. Simulated Land Use Land Cover Change (LULCC) builds on dynamic, probabilistic modeling incorporating climatic and anthropogenic determinants as well as restriction parameters to depict a sub-national regional smallholder-dominated forest landscape. The model delivers results for contrasting scenario settings by simulating without and with potential Forest and Landscape Restoration (FLR) measures. FLR potentials are depicted by up to five RAP-LUTs. The model builds on user-defined scenario inputs, such as the Shared Socioeconomic Pathways (SSP) and Representative Concentration Pathways (RCP). Model application is here exemplified for the SSP2-RCP4.5 scenario in the time frame 2018-2100 on the hectare scale in annual resolution using Esmeraldas province, Ecuador, as a case study area. The LPB-RAP model is a novel, heuristic Spatial Decision Support System (SDSS) tool for smallholder-dominated forest landscapes, supporting near-time top-down planning measures with long-term bottom-up modeling. Its application should be followed up by FLR on-site investigations and stakeholder participation across all involved scales.

Water-Ecosystems-Food nexus security achievement in the context of climate change: the case study of an agricultural Mediterranean Basin, Greece

Natural resources conservation is considered indispensable for a sustainable future. A thorough managerial analysis of the current and future conservation and availability to meet future demands is both necessary and challenging. As water of adequate quantity and good quality is required for a favourable condition of natural ecosystems and for agricultural production, a comprehensive analysis which would consider hydrological, environmental and agricultural dimensions is needed to properly address their interactions and potential impacts. This study presents a Water-Ecosystems-Food (WEF) nexus methodological flamework aiming at identification and mitigation of critical challenges. The framework is tested in a highly productive water basin in north Greece, the Kokkinorema River Bain, which is also characterised by intense agriculture practices. The presented methodological approach was developed in the context of a natural resources sustainability scheme adapted by the national funded AgroClim project. The selection and prioritisation of the most efficient measures, including Nature-based Solutions, would be driven by a Decision Support System (DSS) tool which will feed upon ecological, social, economic and legislative information. The proposed DSS will also incorporate future climate scenarios to evaluate and address expected future water scarcity, ecosystems degradation and reduced agricultural productivity issues. The proposed methodology for addressing nexus challenges could be transferred to any other natural resources-stressed water basin with similar characteristics.

Supporting clinical decision making in the emergency department for paediatric patients using machine learning: A scoping review protocol.

Machine learning as a clinical decision support system tool has the potential to assist clinicians who must make complex and accurate medical decisions in fast paced environments such as the emergency department. This paper presents a protocol for a scoping review, with the objective of summarising the existing research on machine learning clinical decision support system tools in the emergency department, focusing on models that can be used for paediatric patients, where a knowledge gap exists. The methodology used will follow the scoping study framework of Arksey and O'Malley, along with other guidelines. Machine learning clinical decision support system tools for any outcome and population (paediatric/adult/mixed) for use in the emergency department will be included. Articles such as grey literature, letters, pre-prints, editorials, scoping/literature/narrative reviews, non-English full text papers, protocols, surveys, abstract or full text not available and models based on synthesised data will be excluded. Articles from the last five years will be included. Four databases will be searched: Medline (EBSCO), CINAHL (EBSCO), EMBASE and Cochrane Central. Independent reviewers will perform the screening in two sequential stages (stage 1: clinician expertise and stage 2: computer science expertise), disagreements will be resolved by discussion. Data relevant to the research question will be collected. Quantitative analysis will be performed to generate the results. The study results will summarise the existing research on machine learning clinical decision support tools in the emergency department, focusing on models that can be used for paediatric patients. This holds the promise to identify opportunities to both incorporate models in clinical practice and to develop future models by utilising reviewers from diverse backgrounds and relevant expertise.

Distribution Channel Selection Using FUCOM-ADAM: A Novel Approach

The selection of the appropriate distribution channel is crucial for the success of any business dealing with physical goods. When dealing with this selection, it is crucial to have an effective decision support system (DSS) that can assist with such decisions. While various DSS approaches exist in the literature, not all are suitable for real-world applications. This research aims to address this gap by developing practical DSS tools that can aid decision-makers in making optimal decisions even in situations of uncertainty. The paper explores six different distribution channels (retailer’s warehouse, wholesaler’s warehouse, manufacturer’s warehouse, cross-dock, 3PL services, and direct delivery) in order to select the optimal one based on nine established criteria (inventory costs, distribution costs, delivery speed, service level, market coverage, product availability, order consolidation capability, reverse logistics, and order tracking) by using the FUCOM (Full Consistency Method) and ADAM (Axial-Distance-Based Aggregated Measurement) methods. After applying the FUCOM method, C1 (inventory costs) had the highest value when observing criteria weights, whereas C9 (order tracking) had the lowest. The results of the ADAM method showed that A5 (3PL services) was the best-ranked alternative, whereas A4 (cross-dock) was ranked as the worst. Based on the results, a model validation, and sensitivity analysis was conducted to determine whether the final ranking of the alternatives will change. This research provides decision makers with the necessary tools for better decision making, leading to improved distribution operations and increased profitability for the business.

Web-based eHealth Clinical Decision Support System as a tool for the treat-to-target management of patients with systemic lupus erythematosus: development and initial usability evaluation

BackgroundTreat-to-target (T2T) is a therapeutic strategy currently being studied for its application in systemic lupus erythematosus (SLE). Patients and rheumatologists have little support in making the best treatment decision in...

Assessments of an Innovative Fertilizer Management Tool (Nutrient Expert®) for Improved Nutrient Management of Spring Maize at Rani Jamara Kulariya Irrigation Command Area of Tikapur, Kailali

A multi-locational field trials were assessed to know the innovative fertilizer management tool (Nutrient Expert®) for improved nutrient management of Spring Maize in Rani Jamara Kulariya Irrigation Project Command Area (RJKIPCA) of Tikapur, Kailali during spring season of 2021. The layout and planting of Spring Maize for NE® - Hybrid Maize model for 42 farmers’ fields (Tikapur-18, Janaki-18 and Lamkichuha-6)], and Nutrient Omission Plot Technique (NOPT) maize trials for 21 farmers’ fields (Tikapur-9, Janaki-9 and Lamkichuha-3) were grown using baby trail/ diamond trials in Randomized Complete Block Design (RCBD) considering farmers as replications. Data recording, tabulation, and analysis and interpretation of the data was performed by using ANOVA through the use of R Stat-software. Data analysis was also done for the yield gap analysis using percentage change in yield of Spring Maize over the location due to changes in crop cultivars and NOPT. The experimental results highlighted that most of the soils in the project site were deficit in N followed by K, with the minimum yield loss due to omission of P. The treatment; farmers' fertilizer practices (FFP) with their own cultivar has shown more decreasing trend in Spring Maize yields and it was more prominently seen within the farmers growing hybrid maize than the open pollinated variety (OPV). With huge percentage of yield gaps in Spring Maize denoted that the soil of Janaki and Tikapur were found to be more prone to N and K nutrients deficient than the soils of Lamkichuha. NE®- Hybrid Maize model has fairly predicted the Spring Maize yields and predicted the sound results on profitability with assured actual attainable yield over the FFP. Therefore, NE-model for Spring Maize is suggested to adopt as a recommended decision support system (DSS) tool in the project command areas of RJKIPCA, Tikapur, Kailali.

Environmental decision support system development for natural distribution prediction ofFestuca ovinain restoration of degraded lands

AbstractAnthropogenic activities, species invasions, and ecological factors are driving rapid changes in rangeland ecosystems. For ensuring the richness and sustainability of plant species habitats, there is a pressing need for a reliable prediction model that can accurately forecast and map species distribution under varying ecological conditions. We aimed to compare the performance of three widely used machine learning methods: Multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) in predicting the distribution ofFestuca ovinain mountainous protected rangelands. We conducted our investigation by analyzing the distribution ofF. ovinain 305 randomly selected plant sample plots. In each plot, we recorded 10 ecological variables. Three machine learning models were developed to predict the likelihood ofF. ovinadistribution. Our results demonstrated that the RBF model had a higher number of misclassifications (11 samples) compared to MLP and SVM models (10 samples), indicating that MLP and SVM models were more accurate for distribution modeling. Additionally, MLP showed a higher R‐squared value (0.87) compared to SVM (0.85), suggesting that MLP was the most accurate model for restoring degraded lands. Hence, we developed theFestuca ovinaDistribution Model (FODM) using the MLP model. Sensitivity analyses revealed that soil texture, soil depth, electrical conductivity (EC), pH, and vegetation density significantly influencedF. ovinadistribution, with respective sensitivity coefficients of 0.48, 0.47, 0.45, 0.41, and 0.41. Based on the finalized FODM, we designed an Environmental Decision Support System (EDSS) tool to assist rangeland managers in mappingF. ovinadistribution. By applying the EDSS tool, we demonstrated its practicality in using the FODM for effective decision‐making and land management. The EDSS tool serves as a valuable resource for rangeland managers, enabling them to make informed decisions regardingF. ovinarestoration and effectively use the predictive capabilities of the FODM in real‐world applications.

Analyzing the Effects of Planting Date on the Uncertainty of CERES-Maize and Its Potential to Reduce Yield Gap in Arid and Mediterranean Climates

Decision support system tools such as crop models and considering the uncertainties associated with them are important for making an informed decision to fill the yield gap in farms and increase food security. This study’s objective was to identify and quantify the degree to which crop management practices, as well as climate and soil, affected the uncertainty of total biomass, evapotranspiration, and water productivity of silage maize by using a crop model and spatiotemporal input data. Using a calibrated crop model (DSSAT) and pSIMS platform, three planting dates by considering ten ensemble weather data and three soil profile data were simulated for the time period between 2002 and 2017 with a 2 km × 2 km resolution across maize production areas with arid and Mediterranean climates in Isfahan province, Iran. Additionally, the findings were used to determine the yield gap in the studied area to identify opportunities to boost food production. Our results showed larger uncertainty in Mediterranean climates than in arid climates, and it was more affected by planting date than weather parameters and soil profile. The accuracy of total biomass prediction by using pSIMS-CERES-Maize based on the spatiotemporal input data was 1.9% compared to field experimental data in the dry climate, and the yield gap based on the comparison of modified-pSIMS-CERES-Maize and reported biomass was 6.8 to 13 tons ha−1 in the arid and Mediterranean climate. Generally, all results represented the importance of using crop models and considering spatiotemporal data to increase reliability and accuracy, especially in Mediterranean climates, and their potential to increase food production in developing countries with limited water resources and poor agriculture management.

User Engagement Testing with a Pilot Decision Support Tool Aimed to Support Species Managers

Abstract Species status assessments (SSAs) are required for endangered species by the U.S. Fish and Wildlife Service and focus on the resiliency, redundancy, and representation of endangered species. SSAs must include climate information, because climate is a factor that will impact species in the future. To aid in the inclusion of climate information, a decision support system (DSS) entitled Climate Analysis and Visualization for the Assessment of Species Status (CAnVAS) was developed by the State Climate Office of North Carolina using a coproduction approach. In this study, users viewed a mock-up version of the CAnVAS interface displaying a sample layout of future projections for three key climate variables (average precipitation, average maximum temperature, and occurrence of maximum temperature) at a location of interest. This assessment of the pilot version of the CAnVAS DSS was the first step in refining CAnVAS for species-manager use. This research analyzed the differences in usability between two pilot versions of the CAnVAS DSS through eye tracking and subsequent interviews with novice users. The two pilot versions of CAnVAS differed in the way data were displayed on graphs and the color ramps used on regional maps. We found that graphically displaying temporal climate information through box-and-whisker plots and spatially through a sequential color ramp from white to purple were more effective than alternative displays at communicating climate information on endangered species. The results of this research will be used to further develop the CAnVAS DSS tool for future implementation. Significance Statement A decision support system was developed for U.S. Fish and Wildlife Service biologists to incorporate more climate information in species status assessments for endangered species. This tool was tested through eye tracking and interviews with a novice undergraduate student sample to best refine the tool for stakeholder use. This work was able to discover that graphically displaying data in box-and-whisker format and spatially displaying data with a sequential color scheme of white to purple was best for usability purposes. The authors provide these recommendations for those who are producing usable products.

Screening and routine diagnosis of mental disorders among migrants in primary care: A cross-sectional study

BackgroundMigrants in host countries are at risk for the development of mental health conditions. The two aims of the study were to describe routine diagnoses of mental disorders among migrant patients at primary healthcare level and the associated risk factors, and to test the utility of an innovative migrant mental health assessment by evaluating whether the health professionals followed the recommendations proposed by the clinical decision support system (CDSS) tool. MethodsA cross-sectional study was carried out in eight primary care centres (PCCs) in four non-randomly selected health regions of Catalonia, Spain from March to December 2018. Routine health data and mental health diagnoses based on the International Classification of Diseases (10th edition), including mental, behavioural and neuro developmental disorders (F01-F99), symptoms and signs involving emotional state (R45), and sleep disorders (G47), were extracted from the electronic health records. The proportion of mental health conditions was estimated and logistic regression models were used to assess any possible association with mental health disorders. The utility of the mental health assessment was assessed with the proportion of questionnaires performed by health professionals for migrants fulfilling the mental health screening criterion (country of origin with an active conflict in 2017) and the diagnoses given to the screened patients. ResultsOf 14,130 migrants that visited any of the PCCs during the study period, 7,358 (52.1 %) were women with a median age of 38.0 years-old. There were 520/14,130 (3.7 %) migrant patients diagnosed with a mental disorder, being more frequent among women (342/7,358; 4.7 %, p-value < 0.001), migrants from Latin-America (177/3,483; 5.1 %, p < 0.001) and those who recently arrived in Spain (170/3,672; 4.6 %, p < 0.001). A lower proportion of mental disorders were reported in migrants coming from conflicted countries in 2017 (116/3,669, 3.2 %, p = 0.053).Out of the 547 mental health diagnoses reported in 520 patients, 69/14,130 (0.5 %) were mood disorders, 346/14,130 (2.5 %) anxiety disorders and 127/14,130 (0.9 %) sleeping disorders. Mood disorders were more common in migrants from Eastern Europe (25/2,971; 0.8 %, p < 0.001) and anxiety disorders in migrants from Latin-America (126/3,483; 3.6 %, p < 0.001), while both type of disorders were more often reported in women (p < 0.001).In the adjusted model, women (aOR: 1.5, [95 % CI 1.2–1.8, p < 0.001]), migrants with more than one visit to the health center during the study period (aOR: 4.4, [95 %CI 2.8–6.8, p < 0.001]) and who presented an infectious disease (aOR: 2.1, [95 %CI 1.5–3.1, p < 0.001]) had higher odds of having a mental disorder.Lastly, out of the 1,840 migrants coming from a conflicted country in 2017 who were attended in centres where the CDSS tool was implemented, 29 (1.6 %) had a mental health assessment performed and the tool correctly identified one individual. ConclusionsMental health is a condition that may be overlooked in migrants at primary healthcare. Interventions at this level of care must be reinforced and adapted to the needs and circumstances of migrants to ensure equity in health services.

Decision Support System to Determine Higher Education Resource Needs Using Accreditation Assessment Standards

Accreditation is an activity to evaluate the eligibility of programs and academic units based on predetermined criteria. In an educational institution, accreditation is an important thing to do to improve quality in the field of education because the aim is to improve quality which includes all aspects of education in the form of science, administration, as well as teaching, and educational staff. College managers really need a decision support system that can produce several alternative strategic decisions quickly and accurately. The existence of decision support system tools used to make strategic decisions makes the manager's work lighter and the results are more accurate. In this study, a decision support system approach was applied to determine the resource requirements of tertiary institutions using accreditation assessment standards consisting of a management subsystem model, data management subsystem, and user interface management subsystem. The result of the research is a design of a decision support system that can be used in an educational institution for determining the need of higher education resources.

Dependency Visualization Tool for Decision Support Systems with Preferential Dependencies

Large-scale and long-term projects often consist of subtasks linked to each other with certain dependencies that need to be completed. Dependency evaluation and visualization tools can help identify project bottlenecks, prioritize tasks, and plan resource allocation. Hence, visualization tools can improve the efficiency of project interdependency management by dynamically displaying the project constraints and task dependencies. Many of the dependency visualization tools that are being used in the industry are designed to manage internal dependencies and task dependencies. In this work, we introduce an online tool for creating and maintaining visualizations of logical dependencies and preferential dependencies. The proposed online tool can be used with any process that is defined in terms of completing a well-defined logical sequence of activities that must be completed in order to reach a specific target, such as project planning, or with any process that can be completed by different sequences of activities, such as college degree planning (due to elective courses) or interest-aligned career planning. Keywords: academic decision making, decision support system, data visualization, dependency visualization, project management

Oncology Informatics for Lynch Syndrome Research and Care: A Literature Review.

This study aims to review and evaluate available informatics platforms for research and management purposes of Lynch syndrome (LS) to identify gaps and needs for future development. LS informatics tools were identified through literature search in four publication databases (1 and Scopus). First, the LS and functional elements of every informatics tools for LS were introduced. Then, current existing LS informatics tools were reviewed and explained. A detailed review of implemented studies shows that many types of informatics platforms are available for LS management (ie, prediction model, clinical decision support system, database website, and other tools for research and management purposes of LS). Moreover, several dimensions of existing LS informatics tools were discussed and features and positive findings were reported. Reviewing the literature reveals that several LS informatics tools were focused on gene-specific estimate, cancer risk prediction, identifying/screening patients, supporting personalized care of individuals with LS, and storing mismatch repair mutations information. Nevertheless, these platforms do not fully cover the care and research purposes. For instance, future developments of LS tools require more attention to dynamic knowledgebase, extra-colonic lynch-related cancers on the basis of precision medicine, variants of unknown significance, and support from diagnosis to surveillance for patient follow-up. Insights and recommendations provided in this study could help researchers and developers to meet the existing challenges in future developments.

OffshoringDSS: An Automated Tool of Application Maintenance Offshoring

The rapid spread of the internet over the last two decades has prompted more and more companies to deploy their work internationally. The offshoring strategy enables organizations to cut down costs, boost shareholder value, acquire a competitive advantage, reduce cycle time, increase workforce flexibility, generate revenue and focus on their core business. The number of worldwide software development projects has increased due to globalization. Global Software Development (GSD) projects are forecast to grow by 20% to 30% in countries like India and China. The outsourcing experts choose one of the suitable models from the available global delivery options to deliver services in the global software paradigm. However, adopting the appropriate model for application maintenance is a complicated process. In addition, the right model is selected based on various influencing factors, type of the project and client requirements. Additionally, sufficient domain expertise is necessary for the decision making of offshore outsourcing. Currently, there is no dynamic and automated tool for the decision making of application maintenance offshoring. Therefore, this study presents an Offshoring Decision Support System (OffshoringDSS), an automated and novel tool to make the offshoring decisions of application maintenance. The suggested tool is based on the Analytic Hierarchy Process (AHP) technique. The tool automatically performs all the calculations involved in the decision making and ranks the sourcing models.

Stock Prediction Using Machine Learning Algorithms

In the recent times, the stock markets have emerged as one of the top investment destinations for individual and retail investors due to the lure of huge profits that are possible with stock investments compared to more traditional and conservative forms of investments such as bank deposits, real estate and gold. The stock markets unlike other forms of investment are highly dynamic due to the various variables involved in stock price determination and are complex to understand for a common investor. Individual and small-time investors have to generate a portfolio of common stocks to reduce the overall risk and generate reasonable returns on their investment. This phenomenon has given way too many individual and retail investors incurring huge losses because their decisions are based on speculation and not on sound technical grounds. While there are financial advisory firms and online tools where individual investors can get professional stock investment advice, the reliability of such investment advice in the recent past has been inconsistent and not meeting the rigor of quantitative and rational stock selection process. Many of such stock analysts and the tools mostly rely on short term technical indicators and are biased by the speculation in the market leading to huge variances in their predictions and leading to huge losses for individual investors. While the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques is widely adopted in the financial domain, integration of AI/ML techniques with fundamental variables and long-term value investing is a lacking in this domain. Some of the stock portfolio tools available in the market use AI/ML techniques but are mostly built using technical indicators which makes them only suitable for general trend predictions, intraday trading and not suitable for long term value investing due to wide variances and reliability issues. The availability of a Financial Decision Support System which can help stock investors with reliable and accurate information for selecting stocks and creating an automated portfolio with detailed quantitative analysis is lacking. A Financial Decision Support System (DSS) that can establish a relationship between the fundamental financial variables and the stock prices that can VII automatically create a portfolio of premium stocks shall be of great utility to the individual investment community. As part of this thesis, the researcher has designed and developed a Financial Decision Support System (DSS) for selecting stocks and automatically creating portfolios with minimal inputs from the individual investors. The Financial DSS is based on a System Architecture combining the advantages of Artificial Intelligence (AI), Machine learning (ML) and Mathematical models. The design and development of the Financial DSS is based on the philosophy to combine various independent models and not rely on a single stock price model to increase the accuracy and reliability of the stock selections and increase the overall Return on Investment (ROI) of the stock portfolio. The Machine learning models are used to establish the relationship between fundamental financial variables and the price of the stock, a mathematical model is developed to calculate the intrinsic value of the stock taking in to account the full lifecycle of the stock which involves various phases and a comprehensive model to analyze the financial health of the stocks. The AI/ML stock models are independently trained using historical financial data and integrated with the overall Financial DSS. Finally, the Financial DSS tool with a graphical user interface is built integrating all the three models which shall be able to run on a general-purpose desktop or laptop. To reliably validate the Financial DSS, it has been subjected to wide variety of stocks in terms of market capitalization and industry segments. The Financial DSS is validated for its short term and long-term Return on Investment (ROI) using both historical and current real-time financial data. The researcher has reported that the accuracy of the AI/ML stock price models is greater than 90% and the overall ROI of the stock portfolios created by the Financial DSS is 61% for long term investments and 11.74% for short term investments. This system has the potential to help millions of individual investors who can make their financial decisions on stocks using this system for a fraction of cost paid to corporate financial consultants and value eventually may contribute to a more efficient financial system.

FPGA Implementation of Expert System for Medical Diagnosis of Disc Hernia Diagnosis Based on Bayes Theorem

The aim of this research is to create a medical expert system based on Bayes theorem to diagnose level of disc hernia based on real foot force measurement signals obtained using sensors and implement the whole system on field programable gate array (FPGA). We have created a database of attributes based on recorded foot force values of 33 patients pre-diagnosed with herniated disc on levels L4/L5 or L5/S1 on the left or right side. The results obtained by software (Matlab) and hardware (FPGA simulation) are matching well, achieving high accuracy, which shows that VHDL implementation of Naïve Bayes theorem for disc hernia diagnostics is adequate. The output on FPGA is easy to understand for any user, as it is implemented as four-bit output where the position of bit value 1 indicates the level of disc herniation. The system is able to distinguish between the healthy subjects and subjects with disc herniation and is able to detect if improvement in stability is present after surgery or physical therapy. Our proposed measurement platform can be coupled with FPGA to create a portable and not expensive tool for real time signal acquisition, processing and decision support system in disc hernia diagnosis and post-surgical recovery.