Early warning and control of food safety risk using an improved AHC-RBF neural network integrating AHP-EW

A safety risk assessment method based on conditionally constrained game theory and adaptive ensemble learning: Application to wheat flour and rice

Food safety risk control and comprehensive assessment are crucial measures to ensure food safety. However, existing food safety risk assessment methods face challenges, such as unreasonable weight distribution of hazard factors and poor adaptability. Therefore, a safety risk assessment model based on conditionally constrained game theory and adaptive ensemble learning is proposed in this paper. Firstly, new constraints are established on the traditional game theory combination weighting method and solved using the augmented Lagrange multiplier method to obtain the optimal linear combination coefficients and actual composite risk values of the samples, which are taken together with the hazard factor detection data as inputs to the adaptive ensemble learning model. Then, an adaptive ensemble learning model is constructed, which prefers the base learner based on the combined measure of stability and accuracy, and predicts the composite risk value by using robust weighted random forest as the meta-learner. Finally, the model's validity was verified using wheat flour and rice hazard factor detection data. The experimental results indicate that the model's fit on the two datasets is 0.996 and 0.991, respectively, demonstrating strong generalization ability and high prediction accuracy. Meanwhile, unqualified products in wheat flour and rice can be effectively identified through risk thresholds, which helps to provide early warning of potential safety risks.

Optimizing entropy weight model to accurately predict variation of pharmaceuticals and personal care products in the estuaries and coasts of the South China

Pharmaceuticals and personal care products (PPCPs) have raised increasing concern worldwide due to their continuous release and potential hazards to the ecosystem and human health. This study optimized the entropy weight model (EW-WRSR) that combines entropy weight with multi-criteria decision analysis to investigate pollution patterns of PPCPs in the coasts and estuaries. The results revealed that occurrences of PPCPs from the 1940s to the present were consistent with using PPCPs, different types of human activities, and local urban development. This helped better understand the history of PPCP contamination and evaluate the uncertainty of EW-WRSR. The model predicted hotspots of PPCPs that were consistent with the actual situation, indicating that PPCPs mainly enter the nearshore ecosystem by the form of sewage discharge and residual aquaculture. This study can provide method that identifying highly contaminated regions on a global scale.

A novel framework for urban flood risk assessment: Multiple perspectives and causal analysis

Risk assessment and adaptation have become key focuses in the examination of urban flooding risk. In recent decades, global climate change has resulted in a high incidence of extreme weather events, notably flooding. This study introduces a spatial multi-indicator model developed for assessing flood risk at the urban agglomeration scale. A crucial addition to the model is the incorporation of an adaptive capacity within the IPCC risk framework. The model systematically considers various flood risk indicators related to the economic, social, and geographic environments of the central and southern Liaoning urban agglomeration (CSLN). It generates a spatial distribution map of integrated flood risk for multiple scenario combinations. Furthermore, the intricate relationship between different risk indicators and flood risk was analyzed using correlation analysis and the Light Gradient Boosting Machine model (Light GBM). The findings reveal notable variations in flood risk under different scenarios. The inclusion of vulnerability indicators increased flood risk by 33 %, while the subsequent inclusion of adaptive indicators decreased flood risk by 45 %. Dense populations and assets contribute to high flood risk, while adaptive capacity significantly mitigates urban flood risk. The framework adopted in this paper can be applied to other areas where urban agglomeration-scale flood risk assessment is needed, and can contribute to advancing scientific research on flood forecasting and mitigation.

A comprehensive risk assessment method for hot work in underground mines based on G1-EWM and unascertained measure theory

A risk assessment method for hot work based on G1-EWM and unascertained measurement theory was proposed to prevent hot work accidents in underground mines. Firstly, based on the risk influencing factors and classification criteria for underground hot work operations in mines, a single indicator measurement matrix was constructed using unascertained measurement theory; Secondly, a risk assessment index system for mine underground hot work operations was established. The combination weight coefficient of each index was determined using the order relationship analysis method (G1) and entropy weight method (EWM) and coupled with the single index measurement evaluation vector to calculate the multi-index comprehensive evaluation vector of the evaluation object; Finally, the model was validated and examined using engineering examples, and the evaluation level was determined using confidence identification criteria. The results showed that the proposed method, when used to evaluate the risk of hot work operations in tunnels and vertical shafts in metal mines, produces risk levels that are in line with reality III (Moderate Risk) for the vertical shaft and IV (High Risk) for the tunnels. The evaluation model results are consistent with the risk evaluation results the whole process of on-site hot work, which verifies the model feasibility. A unique strategy and method for risk management in hot work operations in underground mines is provided by the combination of weighting and unascertained measure models, which has theoretical and practical value. Future research could focus on refineing this model by exploring the applicability in diverse mining environments and integrating advanced analytical techniques to enhance the predictive accuracy and operational efficiency.

An explainable unsupervised risk early warning framework based on the empirical cumulative distribution function: Application to dairy safety

Efficient food safety risk assessment significantly affects food safety supervision. However, food detection data of different types and batches show different feature distributions, resulting in unstable detection results of most risk assessment models, lack of interpretability of risk classification, and insufficient risk traceability. This study aims to explore an efficient food safety risk assessment model that takes into account robustness, interpretability and traceability. Therefore, the Explainable unsupervised risk Warning Framework based on the Empirical cumulative Distribution function (EWFED) was proposed. Firstly, the detection data's underlying distribution is estimated as non-parametric by calculating each testing indicator's empirical cumulative distribution. Next, the tail probabilities of each testing indicator are estimated based on these distributions and summarized to obtain the sample risk value. Finally, the "3σ Rule" is used to achieve explainable risk classification of qualified samples, and the reasons for unqualified samples are tracked according to the risk score of each testing indicator. The experiments of the EWFED model on two types of dairy product detection data in actual application scenarios have verified its effectiveness, achieving interpretable risk division and risk tracing of unqualified samples. Therefore, this study provides a more robust and systematic food safety risk assessment method to promote precise management and control of food safety risks effectively.

Advancing food safety risk assessment in China: development of new approach methodologies (NAMs)

Novel techniques and methodologies are being developed to advance food safety risk assessment into the next-generation. Considering the shortcomings of traditional animal testing, new approach methodologies (NAMs) will be the main tools for the next-generation risk assessment (NGRA), using non-animal methodologies such as in vitro and in silico approaches. The United States Environmental Protection Agency and the European Food Safety Authority have established work plans to encourage the development and application of NAMs in NGRA. Currently, NAMs are more commonly used in research than in regulatory risk assessment. China is also developing NAMs for NGRA but without a comprehensive review of the current work. This review summarizes major NAM-related research articles from China and highlights the China National Center for Food Safety Risk Assessment (CFSA) as the primary institution leading the implementation of NAMs in NGRA in China. The projects of CFSA on NAMs such as the Food Toxicology Program and the strategies for implementing NAMs in NGRA are outlined. Key issues and recommendations, such as discipline development and team building, are also presented to promote NAMs development in China and worldwide.

Review of visual analytics methods for food safety risks

With the availability of big data for food safety, more and more advanced data analysis methods are being applied to risk analysis and prewarning (RAPW). Visual analytics, which has emerged in recent years, integrates human and machine intelligence into the data analysis process in a visually interactive manner, helping researchers gain insights into large-scale data and providing new solutions for RAPW. This review presents the developments in visual analytics for food safety RAPW in the past decade. Firstly, the data sources, data characteristics, and analysis tasks in the food safety field are summarized. Then, data analysis methods for four types of analysis tasks: association analysis, risk assessment, risk prediction, and fraud identification, are reviewed. After that, the visualization and interaction techniques are reviewed for four types of characteristic data: multidimensional, hierarchical, associative, and spatial-temporal data. Finally, opportunities and challenges in this area are proposed, such as the visual analysis of multimodal food safety data, the application of artificial intelligence techniques in the visual analysis pipeline, etc.

Prediction and Visual Analysis of Food Safety Risk Based on TabNet-GRA

Food safety risk prediction is crucial for timely hazard detection and effective control. This study proposes a novel risk prediction method for food safety called TabNet-GRA, which combines a specialized deep learning architecture for tabular data (TabNet) with a grey relational analysis (GRA) to predict food safety risk. Initially, this study employed a GRA to derive comprehensive risk values from fused detection data. Subsequently, a food safety risk prediction model was constructed based on TabNet, and training was performed using the detection data as inputs and the comprehensive risk values calculated via the GRA as the expected outputs. Comparative experiments with six typical models demonstrated the superior fitting ability of the TabNet-based prediction model. Moreover, a food safety risk prediction and visualization system (FSRvis system) was designed and implemented based on TabNet-GRA to facilitate risk prediction and visual analysis. A case study in which our method was applied to a dataset of cooked meat products from a Chinese province further validated the effectiveness of the TabNet-GRA method and the FSRvis system. The method can be applied to targeted risk assessment, hazard identification, and early warning systems to strengthen decision making and safeguard public health by proactively addressing food safety risks.

Changes in Lipids and Proteins of Common Carp (Cyprinus carpio) Fillets under Frozen Storage and Establishment of a Radial Basis Function Neural Network (RBFNN)

Storage via freezing remains the most effective approach for fish preservation. However, lipid oxidation and protein denaturation still occur during storage, along with nutritional loss. The extent of lipid alteration and protein denaturation are associated with human health defects. To precisely predict common carp (Cyprinus carpio) nutritional quality change during frozen storage, here, we first determined lipid oxidation and hydrolysis and protein denaturation of common carp fillets during 17 weeks of frozen preservation at 261 K, 253 K, and 245 K. Results showed that the content of thiobarbituric acid reactive substances (TBARS) and free fatty acids (FFA) were significantly increased. However, salt-soluble protein (SSP) content, Ca²⁺-ATPase activity, and total sulfhydryl (SH) content kept decreasing during frozen storage, with SSP content decreasing by 64.82%, 38.14%, and 11.24%, respectively, Ca²⁺-ATP enzyme activity decreasing to 12.50%, 18.52%, and 28.57% Piμmol/mg/min, and SH values decreasing by 70.71%, 64.92%, and 56.51% at 261 K, 253 K, and 245 K, respectively. The values at 261 K decreased more than that at 253 K and 245 K (p < 0.05). Ca²⁺-ATPase activity was positively correlated (r = 0.96) with SH content. Afterwards, based on the results of the above chemical experiments, we developed a radial basis function neural network (RBFNN) to predict the modification of lipid and protein of common carp fillets during frozen storage. Results showed that all the relative errors of experimental and predicted values were within ±10%. In summary, the quality of common carp can be well protected at 245 K, and the established RBFNN could effectively predict the quality of the common carp under frozen conditions at 261-245 K.

Novel risk assessment model of food quality and safety considering physical-chemical and pollutant indexes based on coefficient of variance integrating entropy weight

Food safety is important for sustainable social and economic development and people's health. The traditional single risk assessment model is one-sided to the weight distribution of food safety factors including physical-chemical and pollutant indexes, which cannot comprehensively assess food safety risks. Therefore, a novel food safety risk assessment model combining the coefficient of variation (CV) integrating the entropy weight (EWM) (CV-EWM) is proposed in this paper. The CV and the EWM are used to calculate the objective weight of each index with physical-chemical and pollutant indexes effecting food safety, respectively. Then, the weights determined by the EWM and the CV are coupled by the Lagrange multiplier method. The ratio of the square root of the product of two weights and the weighted sum of the square root of the product are regarded as the combined weight. Thus, the CV-EWM risk assessment model is constructed to comprehensively assess the food safety risk. Moreover, the Spearman rank correlation coefficient method is used to test the compatibility of the risk assessment model. Finally, the proposed risk assessment model is applied to evaluate the quality and safety risk of sterilized milk. By analyzing the attribute weight and comprehensive risk value of physical-chemical and pollutant indexes effecting the sterilized milk quality, the results show that this proposed model can scientifically obtain the weight of physical-chemical and pollutant indexes to objectively and reasonably evaluate the overall risk of food, which has certain practical value for discovering the influencing factors of risk occurrence to risk prevention and control of food quality and safety.

CSGNN: Contamination Warning and Control of Food Quality via Contrastive Self-Supervised Learning-Based Graph Neural Network

Effective contamination warning and control of food quality can significantly reduce the likelihood of food quality safety incidents. Existing food contamination warning models for food quality rely on supervised learning, do not model the complex feature associations between detection samples, and do not consider the unevenness of detection data categories. In this paper, To overcome these limitations, we propose a Contrastive Self-supervised learning-based Graph Neural Network framework (CSGNN) for contamination warning of food quality. Specifically, we structure the graph for detecting correlations between samples and then define the positive and negative instance pairs for contrastive learning based on attribute networks. Further, we use a self-supervised approach to capture the complex relationships between detection samples. Finally, we assessed each sample's contamination level based on the absolute value of the subtraction of the prediction scores from multiple rounds of positive and negative instances obtained by the CSGNN. Moreover, we conducted a sample study on a batch of dairy product detection data in a Chinese province. The experimental results show that CSGNN outperforms other baseline models in contamination assessment of food quality, with AUC and recall of unqualified samples reaching 0.9188 and 1.0000, respectively. Meanwhile, our framework provides interpretable contamination classification for food detection. This study provides an efficient early warning method with precise and hierarchical contamination classification for contamination warning of food quality work.

Evaluation of the integration of industrialization and information-based entropy AHP–cross-efficiency DEA model

Purpose

The purpose of this paper is to provide scientific guidance for the integration of industrialization and information (TIOII). In recent years, TIOII has promoted the development of intelligent manufacturing in China. However, many enterprises blindly invest in TIOII, which affects their normal production and operation.

Design/methodology/approach

This study establishes an efficiency evaluation model for TIOII. In this paper, entropy analytic hierarchy process (AHP) constraint cone and cross-efficiency are added based on traditional data envelopment analysis (DEA) model, and entropy AHP–cross-efficiency DEA model is proposed. Then, statistical analysis is carried out on the integration efficiency of enterprises in Guangzhou using cross-sectional data, and the traditional DEA model and entropy AHP–cross-efficiency DEA model are used to analyze the integration efficiency of enterprises.

Findings

The data show that the efficiency of enterprise integration is at a medium level in Guangzhou. The efficiency of enterprise integration has no significant relationship with enterprise size and production type but has a low negative correlation with the development level of enterprise integration. In addition, the improved DEA model can better reflect the real integration efficiency of enterprises and obtain complete ranking results.

Originality/value

By adding the entropy AHP constraint cone and cross-efficiency, the traditional DEA model is improved. The improved DEA model can better reflect the real efficiency of TIOII and obtain complete ranking results.

Tunnel collapse risk assessment based on improved quantitative theory III and EW-AHP coupling weight

It is a multi-criteria decision issue to conduct a risk assessment of the tunnel. In this paper, a tunnel collapse risk assessment model based on the improved theory of quantification III and the fuzzy comprehensive evaluation method is proposed. According to the geological conditions and the construction disturbance classification method, the evaluation factors are selected, and the tunnel collapse risk level is divided into 5 levels according to the principle of maximum membership degree. The three groups of scores with the largest correlation ratio are calculated by the theory of quantification III to form the X, Y, and Z axes of the spatial coordinate system, The spatial distance of each evaluation factor is optimized by the Kendall correlation coefficient combined with the empirical formula, so that it can be used to judge the probability of the occurrence of the evaluation factor; taking the coupling of the objective entropy weight method (EW) and the subjective analytic hierarchy process (AHP) as the weight. Finally, the fuzzy comprehensive evaluation method is used to determine the possibility classification of tunnel collapse. Taking the Ka-Shuang water diversion tunnel as a case study, the comparison between the evaluation results of 10 tunnel samples and the status quo of the actual engineering area verifies the reliability of the method.

Financial Risk Early Warning Model of Listed Companies Under Rough Set Theory Using BPNN

In order to reduce the risk of enterprise management, the financial risk early warning methods of listed companies are mainly studied. The financial risk characteristics of listed companies are analysed. With the help of rough set theory, the financial risk indicators are selected, and the financial risk early warning index system is established. The financial risk early warning model is constructed by using back propagation neural network (BPNN) algorithm based on deep learning. Finally, the accuracy and feasibility of the constructed neural network model are verified. The results show that rough set theory can be used to screen financial risk indicators and select important indicators, which can simplify the data and reduce the complexity of calculation. BPNN can calculate the simplified data and identify and evaluate the financial risk. Empirical analysis shows that the proposed method can shorten the training time of the model to a certain extent, and improve the accuracy of financial risk prediction.

Anomaly Score-Based Risk Early Warning System for Rapidly Controlling Food Safety Risk

Food safety is a high-priority issue for all countries. Early warning analysis and risk control are essential for food safety management practices. This paper innovatively proposes an anomaly score-based risk early warning system (ASRWS) via an unsupervised auto-encoder (AE) for the effective early warning of detection products, which classifies qualified and unqualified products by reconstructing errors. The early warning analysis of qualified samples is carried out by early warning thresholds. The proposed method is applied to a batch of dairy product testing data from a Chinese province. Extensive experimental results show that the unsupervised anomaly detection model AE can effectively analyze the dairy product testing data, with a prediction accuracy and fault detection rate of 0.9954 and 0.9024, respectively, within only 0.54 s. We provided an early warning threshold-based method to conduct the risk analysis, and then a panel of food safety experts performed a risk revision on the prediction results produced by the proposed method. In this way, AI improves the panel’s efficiency, whereas the panel enhances the model’s reliability. This study provides a fast and cost-effective, food safety early warning method for detection data and assists market supervision departments in controlling food safety risk.

A novel and controllable SERS system for crystal violet and Rhodamine B detection based on copper nanonoodle substrates

Copper nanostructures have attracted more and more attention due to low preparation cost, similar thermal conductivity and optical characteristics to silver nanostructures. A novel macroscopic dendritic copper nanonoodles with the length of 3-50 mm prepared by solid-state ionics method at 10 μA direct current electric field (DCEF) using fast ionic conductor RbCu₄Cl₃I₂ films was reported. The surface-enhanced Raman scattering (SERS) performance of prepared copper nanonoodles was detected by crystal violet (CV) and rhodamine B (RB) aqueous solution as analyte molecules. The results present that the copper nanonoodles assembled by short-range order copper nanowires and the diameters of nanowires changed from 20 nm to 80 nm, many regularly arranged nanoparticles with the diameter from 5 to 10 nm existed on the prepared copper nanonoodles, which lead to the nanonoodles have high surface roughness. The copper nanonoodles contain only Cu element, no O element and the fractal dimension of copper nanonoodles is 1.355 because of macroscopic dendritic structures. The prepared copper nanonoodles composed of pure Cu have high surface roughness and the free electrons on the rough copper nanonoodles resonate with the atomic nuclei inside the copper nanonoodles to form a locally enhanced electromagnetic field under the excitation of incident light, so the limiting concentrations for CV and RB detected by the prepared copper nanonoodles are as low as 1 × 10^-11 mol/L and 1 × 10^-12 mol/L, respectively. The centimeter-scale copper nanonoodles with low limiting concentration of analyte molecules can be used to detect harmful food additives.

Assessment of agricultural prospects in relation to land use change and population pressure on a spatiotemporal framework

The urbanisation process moves quickly in emerging nations like India and Bangladesh, transforming natural landscapes into unsustainable landscapes. Consequently, growing development has had a significant impact on agricultural land as a natural environment. Moreover, there is a scarcity of research on fragmentation probability modelling in the extant literature. Thus, by combining random forest (RF) and bagging with the datasets which are multi-temporal in a GIS framework, the probability of fragmentation of LULC at Jangipur subdivision in India and Bangladesh can be modelled. Parallelepiped, Mohalnobis distance, support vector machines (SVM), spectral angle mapper (SAM), and artificial neural networks (ANN) classifiers were used for LULC classification, where SVM (Kappa coefficient: 0.87) surpassed other classifiers. The LULC maps for 1990, 2000, 2010, and 2020 were created using the best classifier (SVM). During this time, the built-up area grew from 23.769 to 158.125 km². Then, using an ANN-based cellular automata model, the future LULC map for 2030 was predicted (CA-ANN). In 2030, the built-up area would be 201.58 km². Then the matrices of class and landscape were taken out of the LULC maps utilising FRAGSTAT software and included the patch number (NP), largest patch index (LPI), edge density (ED), contagion index (percentage) (CONTAG), perimeter and area (P/A), aggregation index (AI), landscape percentage (PLAND), the area of class (CA), patch density (PD), edge in total (TE), total core area (TCA), and largest shape index (LSI). The validation results revealed that bagging (0.915 = AUC) and RF (0.874 = AUC) are capable of assessing fragmentation probability, with the bagging model having the greatest precision level of the two. Almost 20% of the total LULC was in a high and very high zone of fragmentation vulnerability, necessitating the use of direct measures to safeguard it. As a result, adequate LULC management is required.

Phytoremediation potential evaluation of multiple Salix clones for heavy metals (Cd, Zn and Pb) in flooded soils

Climate-induced flooding makes soil more vulnerable to heavy metal contamination, posing challenges for soil remediation. Salix has the potential to cope with flooding stress and environmental contamination, but its effectiveness in flooded soils with multiple heavy metals has not yet been well assessed. Thus, the present work tested fifteen Salix clones grown in multimetal (Cd, Zn and Pb) contaminated soils under non-flooded versus flooded conditions. The results indicated that all tested Salix clones withstood long-term (90 d) flooding. Compared to the non-flooded condition, the flooded condition reduced the Cd (11.7-90.1%) contents in all organs but dramatically increased the Zn and Pb contents in the roots. The bioconcentration factor values of heavy metals in the aboveground organs were in the order of Cd > Zn > Pb. The tested Salix clones were characterized by high phytoextraction capacity for Cd and Zn under non-flooded condition and phytostabilization trait for Pb under flooded condition. To assess the overall performance of phytoremediation potentials, we attempted to use an analytic hierarchy process-entropy weight (AHP-EW) model, which considered the growth performance, photosynthetic parameters, accumulation, and mobility of toxic metals. Three Salix clones (J1010, P54 and P667) exhibited significant potential for multimetal remediation capacities. The current study provided valuable insights into the phytomanagement of woody plants, and the AHP-EW model is helpful for screening suitable trees for the phytoremediation of heavily multimetal contaminated wetlands.

A Voting-Based Ensemble Deep Learning Method Focused on Multi-Step Prediction of Food Safety Risk Levels: Applications in Hazard Analysis of Heavy Metals in Grain Processing Products

Grain processing products constitute an essential component of the human diet and are among the main sources of heavy metal intake. Therefore, a systematic assessment of risk factors and early-warning systems are vital to control heavy metal hazards in grain processing products. In this study, we established a risk assessment model to systematically analyze heavy metal hazards and combined the model with the K-means++ algorithm to perform risk level classification. We then employed deep learning models to conduct a multi-step prediction of risk levels, providing an early warning of food safety risks. By introducing a voting-ensemble technique, the accuracy of the prediction model was improved. The results indicated that the proposed model was superior to other models, exhibiting the overall accuracy of 90.47% in the 7-day prediction and thus satisfying the basic requirement of the food supervision department. This study provides a novel early-warning model for the systematic assessment of the risk level and further allows the development of targeted regulatory strategies to improve supervision efficiency.

A novel method based on machine vision system and deep learning to detect fraud in turmeric powder

Assessing the quality of food and spices is particularly important in ensuring proper human nutrition. The use of computer vision method as a non-destructive technique in measuring the quality of food and spices has always been taken into consideration by researchers. Due to the high nutritional value of turmeric among the spices as well as the fraudulent motives to gain economic profit from the selling of this product, its quality assessment is very important. The lack of marketability of grade 3 chickpeas (small and broken chickpeas) and their very low price have made them a good choice to be mixed with turmeric in powder form and sold in the market. In this study, an improved convolutional neural network (CNN) was used to classify turmeric powder images to detect fraud. CNN was improved through the use of gated pooling functions. We also show with a combined approach based on the integration of average pooling and max pooling that the accuracy and performance of the proposed CNN has increased. In this study, 6240 image samples were prepared in 13 categories (pure turmeric powder, chickpea powder, chickpea powder mixed with food coloring, 10, 20, 30, 40 and 50% fraud in turmeric). In the preprocessing step, unwanted parts of the image were removed. The data augmentation (DA) was used to reduce the overfitting problem on CNN. Also in this research, MLP, Fuzzy, SVM, GBT and EDT algorithms were used to compare the proposed CNN results with other classifiers. The results showed that prevention of the overfitting problem using gated pooling, the proposed CNN was able to grade the images of turmeric powder with 99.36% accuracy compared to other classifiers. The results of this study also showed that computer vision, especially when used with deep learning (DL), can be a valuable method in evaluating the quality and detecting fraud in turmeric powder.

A Novel Strategy for the Assessment of Radon Risk Based on Indicators

Among the physical pollutants affecting indoor air, the radioactive gas radon may turn out to be the most hazardous. Health effects related to radon exposure have been investigated for several decades, providing major scientific evidence to conclude that chronic exposures can cause lung cancer. Additionally, an association with other diseases, such as leukemia and cancers of the extra-thoracic airways, has been advanced. The implementation of a strategy to reduce the exposure of the population and minimize the health risk, according to the European Directive 59/2013/Euratom on ionizing radiations, is a new challenge in public health management. Starting from an understanding of the general state-of-the-art, a critical analysis of existing approaches has been conducted, identifying strengths and weaknesses. Then, a strategy for assessing the radon exposure of the general population, in a new comprehensive way, is proposed. It identifies three main areas of intervention and provides a list of hazard indicators and operative solutions to control human exposure. The strategy has been conceived to provide a supporting tool to authorities in the introduction of effective measures to assess population health risks due to radon exposure.

EKNN: Ensemble classifier incorporating connectivity and density into kNN with application to cancer diagnosis

In the microarray-based approach for automated cancer diagnosis, the application of the traditional k-nearest neighbors kNN algorithm suffers from several difficulties such as the large number of genes (high dimensionality of the feature space) with many irrelevant genes (noise) relative to the small number of available samples and the imbalance in the size of the samples of the target classes. This research provides an ensemble classifier based on decision models derived from kNN that is applicable to problems characterized by imbalanced small size datasets. The proposed classification method is an ensemble of the traditional kNN algorithm and four novel classification models derived from it. The proposed models exploit the increase in density and connectivity using K¹-nearest neighbors table (KNN-table) created during the training phase. In the density model, an unseen sample u is classified as belonging to a class t if it achieves the highest increase in density when this sample is added to it i.e. the unseen sample can replace more neighbors in the KNN-table for samples of class t than other classes. In the other three connectivity models, the mean and standard deviation of the distribution of the average, minimum as well the maximum distance to the K neighbors of the members of each class are computed in the training phase. The class t to which u achieves the highest possibility of belongness to its distribution is chosen, i.e. the addition of u to the samples of this class produces the least change to the distribution of the corresponding decision model for class t. Combining the predicted results of the four individual models along with traditional kNN makes the decision space more discriminative. With the help of the KNN-table which can be updated online in the training phase, an improved performance has been achieved compared to the traditional kNN algorithm with slight increase in classification time. The proposed ensemble method achieves significant increase in accuracy compared to the accuracy achieved using any of its base classifiers on Kentridge, GDS3257, Notterman, Leukemia and CNS datasets. The method is also compared to several existing ensemble methods and state of the art techniques using different dimensionality reduction techniques on several standard datasets. The results prove clear superiority of EKNN over several individual and ensemble classifiers regardless of the choice of the gene selection strategy.