Intelligent Decision Support Tools Research Articles

Urban community governance and machine learning: practice and prospects for intelligent decision making

Abstract Urban community governance faces unprecedented challenges, but machine learning provides new ideas for it. The objective of this study is to examine the use of machine learning technology in urban community governance to enhance governance efficiency and decision-making quality. The study constructs an artificial neural network intelligent decision support model based on genetic algorithm optimization, which is based on the operational requirements of a smart decision support system. A weighted fuzzy inference network is further developed by the fusion of fuzzy logic and neural networks to enhance the system’s ability to deal with uncertainty and ambiguity. Next, prediction tests were conducted on the development levels of six communities to validate the model’s effectiveness. After 1686 training steps, the error squared SSE drops below 0.2%, according to the results. The composite index’s prediction error was 5.12%, while the minimum error was −1.79%. The predicted rankings of the communities did not change from the actual ones, which was in line with the normal trend, and the algorithmic model achieved better prediction results. This study not only provides a new intelligent decision support tool for community governance but also sets a theoretical and practical foundation for the intelligent development of urban community governance in the future.

Functional system for experimentation and testing of products for professional use - Professional risk management tool for workers performing industrial activities

The products intended for professional use must guarantee the fulfilment of the safety and health requirements from the design phase; they are being tested in the experimentation and “zero series” stage to validate the series production. The work equipment for professional use, the industrial product prototype’s functional, operating and protection properties must be defined to establish the necessary conditions for their experimentation. In this sense, it is necessary to develop specific tools for the management of professional risks in order to take the decision at the managerial level to protect the workers who carry out industrial activities. The study presents research results regarding establishing security and compliance conditions to develop a methodology for experimentation, testing and validation of product prototypes intended to protect workers and prevent occupational risks in electrical installations. As part of the research study, a managerial tool for managing professional risks was developed in order to ensure the protection of people who carry out industrial activities and the prevention of occupational risks in the energy sector, which consisted of an intelligent decision support tool necessary for designing, experimenting the product prototype, the testing of the upgraded product model and its validation, to introduce it into series production.

Intelligent decision support tool for optimizing stochastic inventory systems under uncertainty

Pricing and production policies play a key role in ensuring the added value of supply chain systems. For perishable inventory management, the pricing and production lines must be manipulated dynamically since several uncertainties are involved in the system’s behavior. This study discusses the impact of dynamic pricing and production policies on an uncertain stochastic inventory system with perishable products. The mathematical model of the inventory management system under external disturbance is formulated using a continuous differential equation in which the price and production rates are considered as control factors to optimize total profits, which is described as an objective function. An analytical solution for the optimal pricing and production rate was obtained using the Hamilton-Jacobi-Bellman equation. The unknown disturbance was approximated using an intelligent approach called radial basis function neural network. Finally, extensive numerical simulations were presented to validate the theoretical results and optimization solutions (including the efficiency of the approximation of the unknown disturbance) for the dynamic pricing and production management strategy of an uncertain stochastic inventory system against volatile markets. The performance of the proposed method was analyzed under different stock level conditions, which highlighted the importance of keeping the inventory levels at an optimal range to ensure the profitability of business operations. This management strategy can assist a business with solutions for inventory policies while supporting decision-making processes to facilitate coping with production management disruptions.

Intelligent upgrading of plant breeding: Decision support tools in the golden seed breeding cloud platform

Plant breeding is an effective guarantee of agriculture production and food security, whereas information technologies are effective ways to promote plant variety improvement. Although breeding information technologies provide a convenient and scientific means for breeding, it also leads to the problem of massive data processing with multidimensional breeding data in multiple granularities across generations. Therefore, decision support tools that help breeders extract relevant and valuable information are necessary and important. This paper introduces the decision support tools of the golden seed breeding cloud platform at three levels: (i) graphical user interfaces; (ii) statistical tools; and (iii) intelligent decision support tools. The advantages have effectively improved the breeding efficiency as well as the data management and application capabilities in more than 150 breeding institutions in China. The successful application of our system confirms the effectiveness of our work and provides data and support for future research.

Seismic Damage-Level Prediction Model of Cable-Stayed Bridge Using Nonlinear Time History Analysis for Intelligent Structural Health Monitoring under Earthquake Events

The current condition of a bridge should be given the highest priority to ensure its safety and its serviceability to the bridge users. Therefore, this paper explained the development of an intelligent decision-support tool in Bridge Health Monitoring system using a Neural Network model as a prediction of seismic damage-level for cable-stayed bridge. A total of eight earthquake loads scaled to various Peak Ground Acceleration (PGA) values. The input and output data which were fed into Feed Forward Artificial Neural Network (ANN) for damage level prediction model were developed based on acceleration responses from the Non-Linear Time History (NLTH) analysis of the cable-stayed bridge. A total of 16620 data were used as the input data. The damage-level categorization is based on FEMA 356. Data used for the ANN training are 70% for training, 15% for validation, and 15% for testing. The damage level prediction can greatly help bridge authority in order to maintain their bridge structure integrity by identifying and predicting the probability of damage occurring under earthquake loads.

Early Detection of Septic Shock Onset Using Interpretable Machine Learners

Background: Developing a decision support system based on advances in machine learning is one area for strategic innovation in healthcare. Predicting a patient’s progression to septic shock is an active field of translational research. The goal of this study was to develop a working model of a clinical decision support system for predicting septic shock in an acute care setting for up to 6 h from the time of admission in an integrated healthcare setting. Method: Clinical data from Electronic Health Record (EHR), at encounter level, were used to build a predictive model for progression from sepsis to septic shock up to 6 h from the time of admission; that is, T = 1, 3, and 6 h from admission. Eight different machine learning algorithms (Random Forest, XGBoost, C5.0, Decision Trees, Boosted Logistic Regression, Support Vector Machine, Logistic Regression, Regularized Logistic, and Bayes Generalized Linear Model) were used for model development. Two adaptive sampling strategies were used to address the class imbalance. Data from two sources (clinical and billing codes) were used to define the case definition (septic shock) using the Centers for Medicare & Medicaid Services (CMS) Sepsis criteria. The model assessment was performed using Area under Receiving Operator Characteristics (AUROC), sensitivity, and specificity. Model predictions for each feature window (1, 3 and 6 h from admission) were consolidated. Results: Retrospective data from April 2005 to September 2018 were extracted from the EHR, Insurance Claims, Billing, and Laboratory Systems to create a dataset for septic shock detection. The clinical criteria and billing information were used to label patients into two classes-septic shock patients and sepsis patients at three different time points from admission, creating two different case-control cohorts. Data from 45,425 unique in-patient visits were used to build 96 prediction models comparing clinical-based definition versus billing-based information as the gold standard. Of the 24 consolidated models (based on eight machine learning algorithms and three feature windows), four models reached an AUROC greater than 0.9. Overall, all the consolidated models reached an AUROC of at least 0.8820 or higher. Based on the AUROC of 0.9483, the best model was based on Random Forest, with a sensitivity of 83.9% and specificity of 88.1%. The sepsis detection window at 6 h outperformed the 1 and 3-h windows. The sepsis definition based on clinical variables had improved performance when compared to the sepsis definition based on only billing information. Conclusion: This study corroborated that machine learning models can be developed to predict septic shock using clinical and administrative data. However, the use of clinical information to define septic shock outperformed models developed based on only administrative data. Intelligent decision support tools can be developed and integrated into the EHR and improve clinical outcomes and facilitate the optimization of resources in real-time.

A Bayesian Network Decision Support Tool for Low Back Pain Using a RAND Appropriateness Procedure: Proposal and Internal Pilot Study.

BackgroundLow back pain (LBP) is an increasingly burdensome condition for patients and health professionals alike, with consistent demonstration of increasing persistent pain and disability. Previous decision support tools for LBP management have focused on a subset of factors owing to time constraints and ease of use for the clinician. With the explosion of interest in machine learning tools and the commitment from Western governments to introduce this technology, there are opportunities to develop intelligent decision support tools. We will do this for LBP using a Bayesian network, which will entail constructing a clinical reasoning model elicited from experts.ObjectiveThis paper proposes a method for conducting a modified RAND appropriateness procedure to elicit the knowledge required to construct a Bayesian network from a group of domain experts in LBP, and reports the lessons learned from the internal pilot of the procedure.MethodsWe propose to recruit expert clinicians with a special interest in LBP from across a range of medical specialties, such as orthopedics, rheumatology, and sports medicine. The procedure will consist of four stages. Stage 1 is an online elicitation of variables to be considered by the model, followed by a face-to-face workshop. Stage 2 is an online elicitation of the structure of the model, followed by a face-to-face workshop. Stage 3 consists of an online phase to elicit probabilities to populate the Bayesian network. Stage 4 is a rudimentary validation of the Bayesian network.ResultsEthical approval has been obtained from the Research Ethics Committee at Queen Mary University of London. An internal pilot of the procedure has been run with clinical colleagues from the research team. This showed that an alternating process of three remote activities and two in-person meetings was required to complete the elicitation without overburdening participants. Lessons learned have included the need for a bespoke online elicitation tool to run between face-to-face meetings and for careful operational definition of descriptive terms, even if widely clinically used. Further, tools are required to remotely deliver training about self-identification of various forms of cognitive bias and explain the underlying principles of a Bayesian network. The use of the internal pilot was recognized as being a methodological necessity.ConclusionsWe have proposed a method to construct Bayesian networks that are representative of expert clinical reasoning for a musculoskeletal condition in this case. We have tested the method with an internal pilot to refine the process prior to deployment, which indicates the process can be successful. The internal pilot has also revealed the software support requirements for the elicitation process to model clinical reasoning for a range of conditions.International Registered Report Identifier (IRRID)DERR1-10.2196/21804

Intelligent methods for root cause analysis behind the center line deviation of the steel strip

Abstract This article presents a statistical prediction model-based intelligent decision support tool for center line deviation monitoring. Data mining methods enable the data driven manufacturing. They also help to understand the manufacturing process and to test different hypotheses. In this study, the original assumption was that the shape of the strip during the hot rolling has a strong effect on the behaviour of the steel strip in Rolling, Annealing and Pickling line (RAP). Our goal is to provide information that enables to react well in advance to strips with challenging shape. In this article, we show that the most critical shape errors arising in hot rolling process will be transferred to critical errors in RAP-line process as well. In addition, our results reveal that the most critical feature characterizes the deviation better than the currently used criterion for rework. The developed model enables the user to understand better the quality of the products, how the process works, and how the quality model predicts and performs.

ФОРМУВАННЯ МЕРЕЖЕВОЇ БІЗНЕС-МОДЕЛІ В ПРОЦЕСІ ІНТЕГРОВАНОГО РОЗВИТКУ ПІДПРИЄМСТВА

The purpose of the current research is formation of the methodical approach for definition of optimum network business model of the enterprise taking into account possibilities of its integrated functioning. Essential filling of network business model is defined. Network business models are identified in accordance with their features. The developed classification of business models can be updated, supplemented and transformed with participation of new models. The choice of business model is considered as a strategic decision, which requires an adequate methodological basis and automation in the context of the functional module of intelligent systems to support strategic decision-making. The substantiation of a certain management decision is proposed to be considered as an engineering process. It is determined that key competencies and innovative potential of the enterprise are the main factors in the choice of network business model. Factors that hinder the implementation of the selected business model, which are determined at an early stage before making a management decision, have an impact in the form of risks. The structure of components of the network business model in the process of enterprise integrated development is formed and the methodology of its formation is justified. The proposed economic and mathematical model can be used to create an intellectual system designed for integrated development of network enterprises. The approach to business model identification is based on the similarity theory, which is widely used in data analysis systems, but in contrast to these systems, decision-making in the process of integration is not based on combinatorial methods, and is implemented through the production model. Features of this method are the use of meta-ontological classification templates, quality characteristics for evaluation and rules of fuzzy logic. The offered methodical approach allows to choose optimum network business model taking into account possibilities of integrated enterprise development. The proposed intelligent decision support tools are designed to implement effective strategic planning, support flexible organizational and economic mechanisms and optimal business process management.

The Difficulty to Break a Relational Complexity Network Can Predict Air Traffic Controllers' Mental Workload and Performance in Conflict Resolution.

To test the network disentangling model for explaining air traffic controllers' (ATCos) conflict resolution performance. The network rigidity index (NRI), and the steps to break the relational complexity network following a central-available-node-first rule, was hypothesized to explain the overall task demand, whereas marginal-effort-decrease rule was expected to explain the actual operational outcome. Understanding the conflict resolution process of ATCos is important for aviation safety and efficiency. However, linear models are insufficient. We proposed a new model that ATCos behavior can be largely considered as a process to break the relational complexity network, in which nodes represent the aircraft while links represent the cognitive complexity to understand the aircraft dyad relationship. Twenty-one professional ATCos completed 27 conflict resolution scenarios that varied in the NRI and other control variables. Multilevel regression analyses were performed to understand the influence of the NRI on the number of interventions, mental workload, and unresolved rate. A cross-validation was performed to evaluate the predictive power of the model. NRI influenced ATCos intervention number in a curvilinear manner, which further leads to ATCo's mental workload. The deviance between the number of interventions and the NRI was strongly linked with unresolved rate. Cross-validation suggests that the models predictions are robust. The network disentangling model provides a useful theory-driven way to explain controllers' conflict resolution workload and other important performance outcomes such as intervention probability. The proposed model can potentially be used for workload management, sector design, and intelligent decision support tool development.

Evaluating the performance of a neural network-based multi-criteria recommender system

Frequent use of internet applications and rapid increase in volumes of resources have made it difficult for online users to effectively make decisions on the kinds of information or items to select. Recommender systems (RSs) are intelligent decision-support tools that exploit preferences of users and suggest items that might be interesting to them. RSs are one of the various solutions proposed to address the problems of information overload. Traditionally, RSs use single rating techniques to predict and represent preferences of users for items that are not yet seen. Multi-criteria RSs use multiple ratings to various attributes of items for improving prediction and recommendation accuracy of the systems. However, one major challenge of multi-criteria RSs is the choice of an efficient approach for modelling the criteria ratings. Therefore, this paper aimed at employing artificial neural networks to model the criteria ratings and determine the predictive performance of the systems based on aggregation function approach. Seven evaluation metrics have been used to evaluate and measure the accuracy of the systems. The empirical results of the study have shown that the proposed technique has the highest prediction and recommendation than the corresponding traditional technique.

Survey of Image Processing Techniques for Brain Pathology Diagnosis: Challenges and Opportunities.

In recent years, a number of new products introduced to the global market combine intelligent robotics, artificial intelligence and smart interfaces to provide powerful tools to support professional decision making. However, while brain disease diagnosis from the brain scan images is supported by imaging robotics, the data analysis to form a medical diagnosis is performed solely by highly trained medical professionals. Recent advances in medical imaging techniques, artificial intelligence, machine learning and computer vision present new opportunities to build intelligent decision support tools to aid the diagnostic process, increase the disease detection accuracy, reduce error, automate the monitoring of patient's recovery, and discover new knowledge about the disease cause and its treatment. This article introduces the topic of medical diagnosis of brain diseases from the MRI based images. We describe existing, multi-modal imaging techniques of the brain's soft tissue and describe in detail how are the resulting images are analyzed by a radiologist to form a diagnosis. Several comparisons between the best results of classifying natural scenes and medical image analysis illustrate the challenges of applying existing image processing techniques to the medical image analysis domain. The survey of medical image processing methods also identified several knowledge gaps, the need for automation of image processing analysis, and the identification of the brain structures in the medical images that differentiate healthy tissue from a pathology. This survey is grounded in the cases of brain tumor analysis and the traumatic brain injury diagnoses, as these two case studies illustrate the vastly different approaches needed to define, extract, and synthesize meaningful information from multiple MRI image sets for a diagnosis. Finally, the article summarizes artificial intelligence frameworks that are built as multi-stage, hybrid, hierarchical information processing work-flows and the benefits of applying these models for medical diagnosis to build intelligent physician's aids with knowledge transparency, expert knowledge embedding, and increased analytical quality.

A Bayesian Network Model of the Relationships between Chronic Disease Indicators

Introduction We previous developed an informatics platform to: 1) generate large numbers of indicators of chronic conditions and determinants from heterogeneous sources, 2) present indicators in context of known causal relationships. However, the causality was defined by expert-consensus and only concerning direction. Quantitative estimates of causal effects are needed to drive public health decision-making.
 Objectives and ApproachThe objective of this work is to quantify the strength of the relationships between chronic disease indicators through empirical analysis of data for a defined population.
 Eight chronic diseases were explored and the individual data were obtained from linked administrative data for one million randomly sampled Montréal residents. We use Bayesian networks (BN) with our causal model based on expert consensus as a prior for the structure of the BN. In addition, we compare two networks estimated separately from individual-level data and data aggregated at the regional level, the latter being most commonly available to public health agencies.
 ResultsBNs were developed using constraint-based and score-based algorithms for structure learning, and maximum likelihood for parameter estimation. We found that the BN structures and parameters learned from individual-level data differed from the one estimated from data aggregated by community health centers. Specifically, the BN structure learned from individual data contained 9 more arcs between indicators and tened to fit the data better (the Bayesian factor between two network structures was 25.55), however, the results from the aggregated data matched our prior understanding of epidemiological knowledge more closely.
 Conclusion/ImplicationsConclusion: We compared BNs built using different resolutions of data as means to describe patterns among indicators for a defined population. This strategy for interpreting indicators combines prior domain knowledge with data and represents an initial step towards an intelligent decision-support tool for public health practitioners.

The Evolutionary Optimization of a Company’s Return on Equity Factor: Towards the Agent-Based Bio-Inspired System Supporting Corporate Finance Decisions

Return on Equity (ROE) is an important factor from the perspective of formulating and implementing a company’s financial strategies. It is also one of its evaluation criteria. It presents to investors the effectiveness of using their capital. Increasing profitability may be treated as a symptom of shareholder wealth, while its reduction may be a signal indicating a deterioration of the financial situation of the company. An investment in a company will be attractive to investors if the results obtained by it will enable the benefit from the dividend to be paid and if the share prices will show an upward trend. Therefore, profit and profitability are categories dependent on the company and affect the wealth of its owners. The ROE ratio is synthetic and is linked to, among others with the size of sales, asset use activity, and the size of the company’s debt. However, the decisions regarding the capital structure of a company should be made not only by purely economic and financial analyses but also should take into account the social and environmental effects of economic activities. To take into account not only short-term financial goals but also long-term sustainable development goals during the decision-making process, we need intelligent and creative multi-criteria decision support tools. Bio-inspired artificial intelligence techniques—such as evolutionary algorithms, deep neural networks, or swarm algorithms, to give only a few examples—are gaining more and more popularity in the recent years. Evolutionary algorithms are optimization techniques that are modeled on the processes of evolution that are taking place in natural populations. They can find approximate solutions to the NP-hard global, multi-modal, and multi-objective optimization problems. In this paper, we propose an innovative approach—an agent-based bio-inspired system supporting decisions in the area of corporate finance, which takes into account not only financial goals but also the sustainable development goals. The system will allow for multi-objective optimization with the use of bio-inspired algorithms. In this paper, we will concentrate on one module of the proposed system—the evolutionary algorithm optimizing the ROE factor. During the experiments, we will verify the ability of the proposed algorithm to provide decision makers with reasonable, useful, and, at the same time, also innovative and non-obvious solutions concerning the desired capital structure of a given company, which usually operates in a rapidly changing environment. The proposed system will allow for taking into account more than one criteria and perform multi-objective optimization with the use of an evolutionary algorithm or an agent-based co-evolutionary algorithm, so it will be possible to include also the long-term goals of sustainable development in the future.

Eco-intelligent Monitoring for Fouling Detection in Clean-in-place

Clean-in-place (CIP) is a widely used technique applied to clean industrial equipment without disassembly. Cleaning protocols are currently defined arbitrarily from offline measurements. This can lead to excessive resource (water and chemicals) consumption and downtime, further increasing environmental impacts. An optical monitoring system has been developed to assist eco-intelligent CIP process control and improve resource efficiency. The system includes a UV optical fouling monitor designed for real-time image acquisition and processing. The output of the monitoring is such that it can support further intelligent decision support tools for automatic cleaning assessment during CIP phases. This system reduces energy and water consumption, whilst minimising non-productive time: the largest economic cost for CIP.

Three-stage performance modeling using DEA–BPNN for better practice benchmarking

This paper proposes an innovative three-stage model using data envelopment analysis (DEA) and backpropagation neural network (BPNN) for supporting ‘better practice’ benchmarking as contrasted with the traditional ‘best practice’ benchmarking. Research has shown that DEA models have the capability of setting optimal goals, but the drawback of the standard DEA approach is its inability to propose actionable targets necessary for incremental improvement. Overcoming the shortfalls of DEA and its superiority-driven practices, the neural network approach accommodates stepwise improvement through adaptive learning and prediction capability. Consequently, the proposed three-stage model is capable of generating feasible improvement options for managers as an intelligent decision support tool. At its core, the innovative approach provides a sound methodological foundation for shaping a ‘better practice’ paradigm and contributes to the literature through methodological advancement. The effectiveness of the model is empirically tested through the use of data from the healthcare industry, and the results confirm a practical utility of the model.

A framework based on hidden Markov model with adaptive weighting for microcystin forecasting and early-warning

Harmful algal blooms during the eutrophication process produce toxins, such as microcystins (MCs), which endanger the ecosystems and human health. Accurate forecasting and early-warning of MCs can provide theoretical guidance for quick identification of risk in water management systems. The variation of MC concentration is affected by not only the status quo of numerous manifest biotic and abiotic factors, but also a hidden variable that represents the uncertainty of variations of these factors. Traditional approaches focus on fitting data precisely but less consider such a hidden variable, which would experience formidable barriers when encountering fluctuations in time-serial data. In this study, to address the forecasting problem with a hidden state variable and the problem of early-warning-of-risk, we build a novel integrated framework which is consist of three parts: 1) a forecasting model based on a Principal Component Analysis (PCA) and an improved Continuous Hidden Markov Model (CHMM) with adaptive exponential weighting (AEW), where the AEW-CHMM is proposed to forecast both the single-step-ahead concentration for general points and fluctuating points, and the three-step-ahead concentration existing immediately after the fluctuating point; 2) Bayesian hierarchical modeling for a ratio estimation; and 3) revised guidelines for the risk-level grading. The case study tests a real dataset of one shallow lake with the proposed approaches and other supervised machine learning methods. Computational results demonstrate that the proposed approaches are effective to offer an intelligent decision support tool for MC forecasting and early warning of risk by risk-level grading.

Wearable technologies - future challenges for implementation in healthcare services.

The growing use of wearable technologies increases the ability to have more information from the patient including clinical, behavioural and self-monitored data. The availability and large amounts of data that did not exist before brings an opportunity to develop new tools with intelligent analyses and decision support tools for use in clinical practice. It also opens new possibilities for the patients by providing them with more information and decision support tools specially designed for them, and empowers them in managing their own health conditions, keeping their autonomy. These new developments drive a change in healthcare delivery models and the relationship between patients and healthcare providers. It raises challenges for the healthcare systems in how to implement these new technologies and the growing amount of information in clinical practice, integrate it into the clinical workflows of the various healthcare providers. The future challenge for healthcare will be how to use the developing knowledge in a way that will bring added value to healthcare professionals, healthcare organisations and patients without increasing the workload and cost of the healthcare services. For wearable technology developers, the challenge is to develop solutions that can be easily integrated and used by healthcare professionals considering the existing constraints.

ENHANCED TIME-DEPENDENT EVOLUTIONARY FUZZY SUPPORT VECTOR MACHINES INFERENCE MODEL FOR CASH FLOW PREDICTION AND ESTIMATE AT COMPLETION

This study conducts a mechanism enhancing the time series data treatment of the time-dependent evolutionary fuzzy support vector machine inference model (EFSIMT). The enhanced model, EFSIMET, was developed particularly to treat construction management problems that contain time series data. EFSIMET is an artificial intelligent hybrid system in which fuzzy logic (FL) deal with vagueness and approximate reasoning; support vector machine (SVM) acts as supervise learning tool; and fast messy genetic algorithm (fmGA) works to optimize FL and SVMs parameters simultaneously. Moreover, to capture the time series data characteristics, the inference model develops fmGA-based searching mechanism to seek suitable weight values to weight the training data points. This random-based searching mechanism has capacity to address the complex and dynamic nature of time series data; thus, it could improve the model's performance significantly. Nowadays, construction managementis facing complex and difficult problems due to the increasing uncertainties during project implementation. Therefore, the second objective of this study is proposed for the application of EFSIMET to treat two typical problems in construction: forecasting cash flow and estimate at completion. Through performance's comparison with previous works, the effectiveness and reliability of EFSIMET are proven. Hence, this model may be used as an intelligent decision support tool to assist the decision-making process to solve the construction management's difficulties.

An integrative framework for intelligent software project risk planning

Software projects have inherent uncertainties and risks. Social software projects suffer even more requirement changes and require more attention to risk management. Risk analysis and planning are complex, making it difficult to manage risks effectively through subjective judgment. At present, ample empirical research on intelligent decision-support models for risk analysis in software projects exists. However, to the best of our knowledge, empirical models for software project risk planning, or those related to integrative software risk analysis and planning are not available. Thus, the current study proposes an integrative framework for intelligent software project risk planning (IF-ISPRP) to help in minimizing the impacts of project risks and achieving a better foreseeable project outcome. IF-ISPRP includes two core components, namely, risk analysis module and risk planning module. The risk analysis module is to predict whether a project will be successful or not. The risk planning module is to produce a cost-minimal action set for risk control based on the risk analysis module. For integrative risk analysis and planning, we propose a novel many-to-many actionable knowledge discovery (MMAKD) method for complex risk planning. We also apply the framework on a social media platform project, Guangzhou Wireless City, and demonstrate how the model can generate a cost-minimal action set to mitigate the project risk. The risk-control actions found may help develop strategies on mitigating the risks of other social software projects. We hope that the proposed framework will provide an intelligent decision-support tool for project stakeholders to effectively control project risks by integrating risk analysis and planning.