Artificial Intelligence-based Decision Research Articles

Artificial intelligence-based decision support system (AI-DSS) implementation in radiology residency: Introducing residents to AI in the clinical setting

PurposeThe aim of this study was to evaluate residents' real-time experiences and perceptions in using artificial intelligence-based decision support system (AI-DSS) applications in the clinical setting and provide recommendations on how to improve artificial intelligence (AI) curriculums in residency programs. MethodsWe implemented AI-DSS in our radiology workflow and integrated it into the radiology residency curriculum as a step in developing an AI-targeted curriculum. Fifteen senior residents were granted AI-DSS access for clinical use. Post-implementation, residents were anonymously surveyed to assess the utility of AI-DSS in addressing their learning needs and to determine the perceived impact of AI on their career choice and future professional development. ResultsMost residents (91.6%) support incorporating AI into the curriculum and found AI-DSS useful in supplementary roles of triaging (83.3%) and troubleshooting (66.7%), rather than for diagnostic purposes of speed (41.7%), accuracy (33.3%), or diagnosis determination (16.7%). Residents found it useful to have earlier exposure to AI (66.7%), although the exact timeline in training for when to introduce residents to AI-DSS was debated and unclear. Most residents (83.3%) had a positive outlook on the impact of AI on radiology and 50.0% were excited to further their understanding of AI. ConclusionsOur experience implementing AI-DSS in the clinical setting was a desirable and positive experience for our residents that will better prepare them as radiologists and help them capitalize on future opportunities in AI advancements. We hope our experience will provide incentive and guidance for other institutions to establish an AI program for their trainees.

Explainability does not improve biochemistry staff trust in artificial intelligence-based decision support.

Explainability, the aspect of artificial intelligence-based decision support (ADS) systems that allows users to understand why predictions are made, offers many potential benefits. One common claim is that explainability increases user trust, yet this has not been established in healthcare contexts. For advanced algorithms such as artificial neural networks, the generation of explanations is not trivial, but requires the use of a second algorithm. The assumption of improved user trust should therefore be investigated to determine if it justifies the additional complexity. Biochemistry staff completed a wrong blood in tube (WBIT) error identification task with the help of an ADS system. One-half of the volunteers were provided with both ADS predictions and explanations for those predictions, while the other half received predictions alone. The two groups were compared in terms of their rate of agreement with ADS predictions, as an index of user trust, and WBIT error detection performance. Since the AI model used to generate predictions was known to out-perform laboratory staff, increased trust was expected to improve user performance. Volunteers reviewed 1590 sets of results. The volunteers provided with explanations demonstrated no difference in their rate of agreement with the ADS system compared to volunteers receiving predictions alone (83.3% versus 81.8%, p = 0.46). The two volunteer groups were also equivalent in accuracy, sensitivity and specificity for WBIT error identification (p-values >0.78). For a WBIT error identification task, there was no evidence to justify the additional complexity of explainability on the grounds of increased user trust.

89-OR: Effective Real-World Usage of Artificial Intelligence–Based Decision Support System for Diabetes Management

Background: Artificial intelligence-based decision support system (AI-DSS) for frequent insulin dose adjustment was demonstrated to be effective in improving glycemic control in our previous clinical trials. We aimed to assess the effectiveness of AI-DSS in real world diabetes clinics. Methods: Data was collected and evaluated from routine AI-DSS usage for individuals with type 1 diabetes treated at 21 diabetes clinics (in the USA and 2 in Israel) . Two analyses were made: (1) Insulin dose recommendations were compared for rate of agreement/disagreement between those suggested by the AI-DSS and the approved recommendations by health care providers (HCP) . (2) Glycemic outcomes were compared between baseline and after 3 months of AI-DSS use. Included in this analysis were individuals who had mean baseline glucose level ≥ 182 mg/dL and at least one AI-DSS recommendation within the 3-months. Results: A total of 8 recommendations, provided to 370 individuals, were evaluated. Full agreement on the direction of insulin dose adjustments was observed in 87%, 87%, and 83% of the basal rate, carbs ratio (CR) , and correction factor (CF) pump settings parameters, respectively. Full disagreement on the direction of dose change was observed in only 0.9%, 0.5%, and 1.2% for the basal rate, CR and CF, respectively. Glycemic outcome analysis included 1 eligible individuals (mean of 1.6 recommendations within a 3-month interval) . Average sensor percentage of time in range [70-180 mg/dL] increased by 4% (p=0.003) : 43% increased time in range by more than 5% and 33% increased by 10% or more. Time in hyperglycemia [> 180 mg/dL] was reduced by 5% (p<0.01) while time in severe hypoglycemia [< 54 mg/dL] remained below 0.5%. Conclusion: A high rate of agreement with automated insulin adjustments was observed among HCPs at academic centers who used the AI-DSS in their workflow. Glycemic control was significantly improved for sub-optimally controlled individuals already after 3-months of follow up. Disclosure R.Nimri: Employee; DreaMed Diabetes, Ltd., Research Support; Abbott Diabetes, Dexcom, Inc., Insulet Corporation, Medtronic, Speaker's Bureau; Eli Lilly and Company, Novo Nordisk, Stock/Shareholder; DreaMed Diabetes, Ltd. A.Tirosh: Advisory Panel; Abbott Diagnostics, AstraZeneca, Boehringer Ingelheim International GmbH, Merck & Co., Inc., Novo Nordisk, Sanofi, Consultant; Bayer AG, DreaMed Diabetes, Ltd., Research Support; Medtronic, Speaker's Bureau; Eli Lilly and Company. I.Muller: Board Member; DreaMed Diabetes, Ltd. Y.Shtrit: Employee; DreaMed Diabetes, Ltd. M.Phillip: Advisory Panel; Dompé, Insulet Corporation, Medtronic, Pfizer Inc., Board Member; DreaMed Diabetes, Ltd., Consultant; QuLab Medical Ltd., Other Relationship; Dexcom, Inc., Dompé, DreaMed Diabetes, Ltd., Eli Lilly and Company, Medtronic, NG Solutions Ltd, Novo Nordisk, OPKO, Pfizer Inc., Sanofi, Stock/Shareholder; DreaMed Diabetes, Ltd., NG Solutions Ltd.

Advancements in Artificial Intelligence-Based Decision Support Systems for Improving Construction Project Sustainability: A Systematic Literature Review

This paper aims at evaluating the current state of research into artificial intelligence (AI)-based decision support systems (DSS) for improving construction project sustainability. The literature was systematically reviewed to explore the use of AI in the construction project lifecycle together with the consideration of the economic, environmental, and social goals of sustainability. A total of 2688 research papers were reviewed, and 77 papers were further analyzed, and the major tasks of the DSSs were categorized. Our review results suggest that the main research stream is dedicated to early-stage project prediction (50% of all papers), with artificial neural networks (ANNs) and fuzzy logic (FL) being the most popular AI algorithms in use. Hybrid AI models were used in 46% of all studies. The goal for economic sustainability is the most considered in research, with 87% of all papers considering this goal, and there is evidence given of a trend towards the environmental and social goals of sustainability receiving increasing attention throughout the latter half of the decade.

Analysis of Chinese Typical Lane Change Behavior in Car–Truck Heterogeneous Traffic Flow from UAV View

Chinese driver behavior in heterogeneous traffic is commonly available, but it is difficult to acquire due to view limitations and sensor limitations. In this study, Chinese typical lane change behavior in car-truck heterogeneous traffic flow was collected and analyzed using an Unmanned Aerial Vehicle (UAV). The velocity of ego vehicle, relative velocity and distance of ego and surrounding vehicles, time to collision (TTC), time headway (THW) at the lane change moment, and brake and accelerate reaction time during lane changes were investigated. Results showed that large differences existed in various car follow types and lane change types. Cars drove more aggressively than trucks with shorter reaction times, and cars would change lanes at a longer distance when facing trucks. Smaller TTC and THW were found for trucks when facing cars. Chinese drivers showed more frequent lane-change maneuvers and shorter reaction times compared to other countries. The driver characteristic in China provided data support for the training of artificial intelligence-based decision algorithms, the development of a localized system, and the design of roads.

Customer models for artificial intelligence-based decision support in fashion online retail supply chains

Fashion is a global, multi-trillion dollar industry devoted to producing and selling clothing, footwear, and accessories to individuals or groups of people. Its sheer numbers, together with social and environmental sustainability concerns, and the move towards digitalization of customer-centric operations, make the fashion business a prime target for Decision Support Systems (DSSs). On the other hand, decision support in fashion retail is particularly problematic and embraces all major supply chain domains. Decisions in an online fashion retail supply chain (FRSC) are highly dependent on time-varying customers' preferences and product availability, often leading to a combinatorial explosion. To address such a problem, DSSs could greatly benefit from high-quality information stored in customer models (CMs), constructed by using Artificial Intelligence techniques, allowing informed decisions on how to personalize (adapt) to match the customer's needs and preferences. Combinations of CMs with recommender systems (RSs) have been increasingly utilized in fashion e-commerce to provide personalized product recommendations. Nevertheless, works on enhancing CMs for e-commerce or other decision-making chain domains are scanty. This paper offers a systematic review of the literature on fashion CMs with applications to decision-making in FRSCs, mining topics for a research agenda. Research on the theme is relevant and urgent for the fashion business, which is still in its infancy. Work on the agenda topics could benefit distinct fashion stakeholders, not just customers, and produce well-grounded decision-making in varied FRSC contexts and dynamics.

Comparison of Insulin Dose Adjustments Made by Artificial Intelligence-Based Decision Support Systems and by Physicians in People with Type 1 Diabetes Using Multiple Daily Injections Therapy.

Objective: Artificial intelligence-based decision support systems (DSS) need to provide decisions that are not inferior to those given by experts in the field. Recommended insulin dose adjustments on the same individual data set were compared among multinational physicians, and with recommendations made by automated Endo.Digital DSS (ED-DSS). Research Design and Methods: This was a noninterventional study surveying 20 physicians from multinational academic centers. The survey included 17 data cases of individuals with type 1 diabetes who are treated with multiple daily insulin injections. Participating physicians were asked to recommend insulin dose adjustments based on glucose and insulin data. Insulin dose adjustments recommendations were compared among physicians and with the automated ED-DSS. The primary endpoints were the percentage of comparison points for which there was agreement on the trend of insulin dose adjustments. Results: The proportion of agreement and disagreement in the direction of insulin dose adjustment among physicians was statistically noninferior to the proportion of agreement and disagreement observed between ED-DSS and physicians for basal rate, carbohydrate-to insulin ratio, and correction factor (P < 0.001 and P ≤ 0.004 for all three parameters for agreement and disagreement, respectively). The ED-DSS magnitude of insulin dose change was consistently lower than that proposed by the physicians. Conclusions: Recommendations for insulin dose adjustments made by automatization did not differ significantly from recommendations given by expert physicians regarding the direction of change. These results highlight the potential utilization of ED-DSS as a useful clinical tool to manage insulin titration and dose adjustments.

Artificial intelligence-based decision support model for new drug development planning

New drug development guarantees a very high return on success, but the success rate is extremely low. Pharmaceutical companies have attempted to use various strategies to increase the success rate of drug development, but this goal has been difficult to achieve. In this study, we developed a model that can guide effective decision-making at the planning stage of new drug development by leveraging machine learning. The Drug Development Recommendation (DDR) model, we present here, is a hybrid model for recommending and/or predicting drug groups suitable for development by individual pharmaceutical companies. It combines association rule learning, collaborative filtering, and content-based filtering approaches for enterprise-customized recommendations. In the case of content-based filtering applying a random forest classification algorithm, the accuracy and area under curve were 78% and 0.74, respectively. In particular, the DDR model was applied to predict the success probability of companies developing Coronavirus disease 2019 (COVID-19) vaccines. It was demonstrated that the higher the predicted score from the DDR model, the more progress in the clinical phase of the COVID-19 vaccine development. Although our approach has limitations that should be improved, it makes scientific as well as industrial contributions in that the DDR model can support rational decision-making prior to initiating drug development by considering not only technical aspects but also company-related variables.

Value co-creation ‘gradients’: enabling human-machine interactions through AI-based DSS

Artificial Intelligence-based Decision Support Systems (AI-based DSS) are becoming increasingly important in many contexts. This work aims to define a type of human-machine interactions for new value co-creation processes' ranks, to help identify factors that can stimulate value co-creation in human-machine interactions. To understand if the outcome of a man-machine interaction can contribute to the co-creation of value, and in what way, the work carried out is epistemological and typological, also based on System Thinking. A matrix of novel gradients of the relationships between humans and non-humans has been created, and the typology of human-machine interactions has been identified for the new degrees of value co-creation processes, as well as the new specific scale of skills, in terms of language, learning, know-how, level of trust and endowment of knowledge, as a whole. The main implications concern the need to customize Decision Support Systems (DSS), to enhance different levels of intensity of relationships, and to identify insights for Decision Making AI - based users.

Development of an Intelligent Decision Support System for Attaining Sustainable Growth within a Life Insurance Company

Consumer behaviour is one of the most important and complex areas of research. It acknowledges the buying behaviour of consumer clusters towards any product, such as life insurance policies. Among various factors, the three most well-known determinants on which human conjecture depends for preferring a product are demographic, economic and psychographic factors, which can help in developing an accurate market design and strategy for the sustainable growth of a company. In this paper, the study of customer satisfaction with regard to a life insurance company is presented, which focused on comparing artificial intelligence-based, data-driven approaches to classical market segmentation approaches. In this work, an artificial intelligence-based decision support system was developed which utilises the aforementioned factors for the accurate classification of potential buyers. The novelty of this paper lies in developing supervised machine learning models that have a tendency to accurately identify the cluster of potential buyers with the help of demographic, economic and psychographic factors. By considering a combination of the factors that are related to the demographic, economic and psychographic elements, the proposed support vector machine model and logistic regression model-based decision support systems were able to identify the cluster of potential buyers with collective accuracies of 98.82% and 89.20%, respectively. The substantial accuracy of a support vector machine model would be helpful for a life insurance company which needs a decision support system for targeting potential customers and sustaining its share within the market.

Routine clinical care data from thirteen cardiac outpatient clinics: design of the Cardiology Centers of the Netherlands (CCN) database

BackgroundDespite the increasing availability of clinical data due to the digitalisation of healthcare systems, data often remain inaccessible due to the diversity of data collection systems. In the Netherlands, Cardiology Centers of the Netherlands (CCN) introduced “one-stop shop” diagnostic clinics for patients suspected of cardiac disease by their general practitioner. All CCN clinics use the same data collection system and standardised protocol, creating a large regular care database. This database can be used to describe referral practices, evaluate risk factors for cardiovascular disease (CVD) in important patient subgroups, and develop prediction models for use in daily care.Construction and contentThe current database contains data on all patients who underwent a cardiac workup in one of the 13 CCN clinics between 2007 and February 2018 (n = 109,151, 51.9% women). Data were pseudonymised and contain information on anthropometrics, cardiac symptoms, risk factors, comorbidities, cardiovascular and family history, standard blood laboratory measurements, transthoracic echocardiography, electrocardiography in rest and during exercise, and medication use. Clinical follow-up is based on medical need and consisted of either a repeat visit at CCN (43.8%) or referral for an external procedure in a hospital (16.5%). Passive follow-up via linkage to national mortality registers is available for 95% of the database.Utility and discussionThe CCN database provides a strong base for research into historically underrepresented patient groups due to the large number of patients and the lack of in- and exclusion criteria. It also enables the development of artificial intelligence-based decision support tools. Its contemporary nature allows for comparison of daily care with the current guidelines and protocols. Missing data is an inherent limitation, as the cardiologist could deviate from standardised protocols when clinically indicated.ConclusionThe CCN database offers the opportunity to conduct research in a unique population referred from the general practitioner to the cardiologist for diagnostic workup. This, in combination with its large size, the representation of historically underrepresented patient groups and contemporary nature makes it a valuable tool for expanding our knowledge of cardiovascular diseases.Trial registration: Not applicable.

Prospective study of artificial intelligence-based decision support to improve head and neck radiotherapy plan quality

Background and purposeVolumetric modulated arc therapy (VMAT) planning for head and neck cancer is a complex process. While the lowest achievable dose for each individual organ-at-risk (OAR) is unknown a priori, artificial intelligence (AI) holds promise as a tool to accurately estimate the expected dose distribution for OARs. We prospectively investigated the benefits of incorporating an AI-based decision support tool (DST) into the clinical workflow to improve OAR sparing. Materials and methodsThe DST dose prediction model was based on 276 institutional VMAT plans. Under an IRB-approved prospective trial, the physician first generated a custom OAR directive for 50 consecutive patients (physician directive, PD). The DST then estimated OAR doses (AI directive, AD). For each OAR, the treating physician used the lower directive to form a hybrid directive (HD). The final plan metrics were compared to each directive. A dose difference of 3 Gray (Gy) was considered clinically significant. ResultsCompared to the AD and PD, the HD reduced OAR dose objectives by more than 3 Gy in 22% to 75% of cases, depending on OAR. The resulting clinical plan typically met these lower constraints and achieved mean dose reductions between 4.3 and 16 Gy over the PD, and 5.6 to 9.1 Gy over the AD alone. Dose metrics achieved using the HD were significantly better than institutional historical plans for most OARs and NRG constraints for all OARs. ConclusionsThe DST facilitated a significantly improved treatment directive across all OARs for this generalized H&N patient cohort, with neither the AD nor PD alone sufficient to optimally direct planning.

A modified version of GoogLeNet for melanoma diagnosis

ABSTRACT Differential diagnosis of malignant melanoma, which is the cause of more than 75% of deaths amongst skin lesions, is vital for patients. Artificial intelligence-based decision support systems developed for the analysis of medical images are in the solution of such problems. In recent years, various deep learning algorithms have been developed to be used for this purpose. In our previous study, we compared the performances of AlexNet, GoogLeNet and ResNet-50 for the differential diagnosis of benign and malignant melanoma on International Skin Imaging Collaboration: Melanoma Project (ISIC) dataset. In this study, we proposed a CNN model by modifying the GoogLeNet algorithm and we compared the performance of this model with the previous results. For the experiments, we used 19,373 benign and 2197 malignant diagnosed dermoscopy images obtained from this public archive. We compared the performance results according to the eight different performance metrics including polygon area metric (PAM), classification accuracy (CA), sensitivity (SE), specificity (SP), area under curve (AUC), kappa (K), F measure metric (FM) and time complexity (TC) measures. According to the results, our proposed CNN achieved the best classification accuracy with 0.9309 and decreased the time complexity of GoogLeNet from 283 min 50 to 256 min 26 s.

Adjustment of Insulin Pump Settings in Type 1 Diabetes Management: Advisor Pro Device Compared to Physicians' Recommendations.

To compare insulin dose adjustments made by physicians to those made by an artificial intelligence-based decision support system, the Advisor Pro, in people with type 1 diabetes (T1D) using an insulin pump and self-monitoring blood glucose (SMBG). This was a multinational, non-interventional study surveying 17 physicians from 11 countries. Each physician was asked to provide insulin dose adjustments for the settings of the pump including basal rate, carbohydrate-to-insulin ratios (CRs), and correction factors (CFs) for 15 data sets of pumps and SMBG of people with T1D (mean age 18.4 ± 4.8 years; eight females; mean glycated hemoglobin 8.2% ± 1.4% [66 ± 11mmol/mol]). The recommendations were compared among the physicians and between the physicians and the Advisor Pro. The study endpoint was the percentage of comparison points for which there was an agreement on the direction of insulin dose adjustments. The percentage (mean ± SD) of agreement among the physicians on the direction of insulin pump dose adjustments was 51.8% ± 9.2%, 54.2% ± 6.4%, and 49.8% ± 11.6% for the basal, CR, and CF, respectively. The automated recommendations of the Advisor Pro on the direction of insulin dose adjustments were comparable )49.5% ± 6.4%, 55.3% ± 8.7%, and 47.6% ± 14.4% for the basal rate, CR, and CF, respectively( and noninferior to those provided by physicians. The mean absolute difference in magnitude of change between physicians was 17.1% ± 13.1%, 14.6% ± 8.4%, and 23.9% ± 18.6% for the basal, CR, and CF, respectively, and comparable to the Advisor Pro 11.7% ± 9.7%, 10.1% ± 4.5%, and 25.5% ± 19.5%, respectively, significant for basal and CR. Considerable differences in the recommendations for changes in insulin dosing were observed among physicians. Since automated recommendations by the Advisor Pro were similar to those given by physicians, it could be considered a useful tool to manage T1D.

Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes.

Despite the increasing adoption of insulin pumps and continuous glucose monitoring devices, most people with type 1 diabetes do not achieve their glycemic goals1. This could be related to a lack of expertise or inadequate time for clinicians to analyze complex sensor-augmented pump data. We tested whether frequent insulin dose adjustments guided by an automated artificial intelligence-based decision support system (AI-DSS) is as effective and safe as those guided by physicians in controlling glucose levels. ADVICE4U was a six-month, multicenter, multinational, parallel, randomized controlled, non-inferiority trial in 108 participants with type 1 diabetes, aged 10-21 years and using insulin pump therapy (ClinicalTrials.gov no. NCT03003806). Participants were randomized 1:1 to receive remote insulin dose adjustment every three weeks guided by either an AI-DSS, (AI-DSS arm, n = 54) or by physicians (physician arm, n = 54). The results for the primary efficacy measure-the percentage of time spent within the target glucose range (70-180 mg dl-1 (3.9-10.0 mmol l-1))-in the AI-DSS arm were statistically non-inferior to those in the physician arm (50.2 ± 11.1% versus 51.6 ± 11.3%, respectively, P < 1 × 10-7). The percentage of readings below 54 mg dl-1 (<3.0 mmol l-1) within the AI-DSS arm was statistically non-inferior to that in the physician arm (1.3 ± 1.4% versus 1.0 ± 0.9%, respectively, P < 0.0001). Three severe adverse events related to diabetes (two severe hypoglycemia, one diabetic ketoacidosis) were reported in the physician arm and none in the AI-DSS arm. In conclusion, use of an automated decision support tool for optimizing insulin pump settings was non-inferior to intensive insulin titration provided by physicians from specialized academic diabetes centers.

Artificial Intelligence in Decision Support Systems for Type 1 Diabetes.

Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin delivery systems are now available, many people choose to manage insulin delivery manually through insulin pumps or through multiple daily injections. Frequent insulin titrations are needed to adequately manage glucose, however, provider adjustments are typically made every several months. Recent automated decision support systems incorporate artificial intelligence algorithms to deliver personalized recommendations regarding insulin doses and daily behaviors. This paper presents a comprehensive review of computational and artificial intelligence-based decision support systems to manage T1D. Articles were obtained from PubMed, IEEE Xplore, and ScienceDirect databases. No time period restrictions were imposed on the search. After removing off-topic articles and duplicates, 562 articles were left to review. Of those articles, we identified 61 articles for comprehensive review based on algorithm evaluation using real-world human data, in silico trials, or clinical studies. We grouped decision support systems into general categories of (1) those which recommend adjustments to insulin and (2) those which predict and help avoid hypoglycemia. We review the artificial intelligence methods used for each type of decision support system, and discuss the performance and potential applications of these systems.

A novel hybrid artificial intelligence-based decision support framework to predict lead time

ABSTRACT Inventory and routing are the two most important elements to company’s survival in supply chain environments. Hence, solution approaches of inventory routing problem (IRP) should assure adequate inventory level and also provide an efficient route. In this case, hybrid approaches can empower researchers to solve the IRP. The aim of this study is to develop a new hybrid methodology that includes two phases to provide a generic framework for IRP. In Phase I, genetic algorithm-based simulation optimisation is used to dynamically perform inventory control and routing decisions. In Phase II, artificial intelligence (AI)-based simulation in which the lead time of supply chain members is predicted is employed to extend the functionality of the method in Phase I. The proposed hybrid methodology gives insights into the cross-fertilisation of AI, simulation, and optimisation for researchers. Therefore, this integration can be applied to different supply chain problems by using similar methods.

Inherent Bias in Artificial Intelligence-Based Decision Support Systems for Healthcare.

The objective of this article is to discuss the inherent bias involved with artificial intelligence-based decision support systems for healthcare. In this article, the authors describe some relevant work published in this area. A proposed overview of solutions is also presented. The authors believe that the information presented in this article will enhance the readers’ understanding of this inherent bias and add to the discussion on this topic. Finally, the authors discuss an overview of the need to implement transdisciplinary solutions that can be used to mitigate this bias.

Prior Recognition of Flash Floods: Concrete Optimal Neural Network Configuration Analysis for Multi-Resolution Sensing

Flash floods can demolish infrastructure and property within seconds as they are very sudden. Flash floods are the main cause of the casualties and loss of properties. Existing natural disaster prediction algorithms contains false alarms. Indefinite techniques have been applied to overcome this leading issue in many countries. A competent flood management system must have the potential and tendency to identify the flash floods and atmospheric and climatic changes on early basis with less false alarm rate. Techniques which have been designed for the flash flood investigation may be categorized into following types a. Sensors based direct measurement b. Radar images c. Satellite based X-band images. The proposed research consisted of Artificial intelligence-based decision making for multi-modal sensing (direct measurement from multi-resolution sensors). A combination of sensors like Passive infrared (PIR), water level sensor, ultrasonic sensor, temperature sensor, pressure and altimeter sensors have been integrated on a single device to investigate the flash floods. The use of most suitable pair of measurement sensors can substantially enhance the advantage of more accuracy and reliability compared to a single sensor. In recent trends Particle swarm optimization is very popular for solving stochastic global optimization problems. The data was trained and processed by modified multi-layer feed forward neural network optimized by particle swarm optimization algorithm. Hybrid Modified Particle swarm optimization has been combined with feed forward neural network for the vigorous investigation of flash floods with less false alarm rate. Simulated results showed that the proposed research algorithm Modified multi-layer feed forward neural network optimized by Particle swarm optimization for multi-modal sensing performed very well in terms of evaluation parameters compared to other existing strategies with minimum false alarm ratio. Moreover, modified multi-layer feed forward neural network optimized by article swarm optimization algorithm results have been compared with the cuckoo search, modified cuckoo search, particle swarm optimization and Multi-layer perceptron neural network configurations for the validation purpose.

Decision Support System for Management Decision in High-Risk Business Environment

Abstract As a result of substantial variations in global financial markets, constructing an enterprise risk prewarning mechanism is essential. A vast amount of related studies have implemented monetary-related indicators to depict the full spectrum of an enterprise’s operating performance. Merely considering monetary-related indicators is unable to produce an in-depth understanding of an enterprise. To fill this gap, the balanced scorecard (BSC), with the advantages of being able to capture both monetary and nonmonetary indicators, was introduced. Unfortunately, the BSC also has its own challenges, one of which is the lack of consideration given to risk exposure, which affects an enterprise’s profit variation. Thus, this study extends the original BSC by considering risk exposure and introduces an artificial intelligence-based decision support system for management decision. The inherent decision logic embedded into neural network-based mechanisms is opaque and hard to comprehend by users. To handle the challenge, this study further incorporates fit theory with a knowledge visualization technique to handle the opaque nature of the model so as to decrease the cognitive load and mental burden. The empirical results show that the introduced model is a promising alternative for management decisions in highly fluctuating financial markets.