Search results for: risk classification

Authors: Debarati Bhaumik, Diptish Dey

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Multilevel models, increasingly deployed in industries such as insurance, food production, and entertainment within functions such as marketing and supply chain management, need to be transparent and ethical. Applications usually result in binary classification within groups or hierarchies based on a set of input features. Using open-source datasets, we demonstrate that popular explainability methods, such as SHAP and LIME, consistently underperform inaccuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution (negative versus positive contribution to the outcome). Besides accuracy, the computational intractability of SHAP for binomial classification is a cause of concern. For transparent and ethical applications of these hierarchical statistical models, sound audit frameworks need to be developed. In this paper, we propose an audit framework for technical assessment of multilevel regression models focusing on three aspects: (i) model assumptions & statistical properties, (ii) model transparency using different explainability methods, and (iii) discrimination assessment. To this end, we undertake a quantitative approach and compare intrinsic model methods with SHAP and LIME. The framework comprises a shortlist of KPIs, such as PoCE (Percentage of Correct Explanations) and MDG (Mean Discriminatory Gap) per feature, for each of these three aspects. A traffic light risk assessment method is furthermore coupled to these KPIs. The audit framework will assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit businesses deploying multilevel models to be future-proof and aligned with the European Commission’s proposed Regulation on Artificial Intelligence.

Keywords: audit, multilevel model, model transparency, model explainability, discrimination, ethics

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Authors: Khitem Amiri, Mohamed Farah

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Image classification is a challenging task and is gaining lots of interest since it helps us to understand the content of images. Recently Deep Learning (DL) based methods gave very interesting results on several benchmarks. For Hyperspectral images (HSI), the application of DL techniques is still challenging due to the scarcity of labeled data and to the curse of dimensionality. Among other approaches, Deep Belief Network (DBN) based approaches gave a fair classification accuracy. In this paper, we address the problem of the curse of dimensionality by reducing the number of bands and replacing the HSI channels by the channels representing radiometric indices. Therefore, instead of using all the HSI bands, we compute the radiometric indices such as NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), etc, and we use the combination of these indices as input for the Deep Belief Network (DBN) based classification model. Thus, we keep almost all the pertinent spectral information while reducing considerably the size of the image. In order to test our image representation, we applied our method on several HSI datasets including the Indian pines dataset, Jasper Ridge data and it gave comparable results to the state of the art methods while reducing considerably the time of training and testing.

Keywords: hyperspectral images, deep belief network, radiometric indices, image classification

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Authors: I. A. Farhat, M. Bin Hasan

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A developed approach for the protection of power transmission lines using Support Vector Machines (SVM) technique is presented. In this paper, the SVM technique is utilized for the classification and isolation of faults in power transmission lines. Accurate fault classification and location results are obtained for all possible types of short circuit faults. As in distance protection, the approach utilizes the voltage and current post-fault samples as inputs. The main advantage of the method introduced here is that the method could easily be extended to any power transmission line.

Keywords: fault detection, classification, diagnosis, power transmission line protection, support vector machines (SVM)

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Authors: Zakari Mustapha, Clinton Aigbavboa, Wellington Didi Thwala

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Majority of the incidents and accidents in complex high-risk systems that exist in the construction industry and other sectors have been attributed to unsafe acts of workers. The purpose of this paper was to asses Human Factors Analysis and Classification System (HFACS) in construction accident prevention. The study was conducted through the use of secondary data from journals, books and internet to achieve the objective of the study. The review of literature looked into details of different views from different scholars about HFACS framework in accidents investigations. It further highlighted on various sections or disciplines of accident occurrences in human performance within the construction. The findings from literature review showed that unsafe acts of a worker and unsafe working conditions are the two major causes of accident in the construction industry.Most significant factor in the cause of site accident in the construction industry is unsafe acts of a worker. The findings also show how the application of HFACS framework in the investigation of accident will lead to the identification of common trends. Further findings show that provision for the prevention of accident will be made based on past accident records to identify and prioritize where intervention is needed within the construction industry.

Keywords: accident, construction, HFACS, unsafe acts

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Authors: Francesco Venuti, Simona Alfiero

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The aim of this paper is to develop an empirical research on the nature and consequences of corporate governance on Eurozone Insurance Industry risk taking attitude. More particularly, we analyzed the effect of public ownership on risk taking with respect to privately held Insurance Companies. We also analyzed the effects on risk taking attitude of different degrees of ownership concentration, directors compensation, and the dimension/diversity of the Board of Directors. Our results provide quite strong evidence that, coherently with the Agency Theory, publicly traded insurance companies with more concentrated ownership are less risky than the corresponding privately held.

Keywords: agency theory, corporate governance, insurance companies, risk taking

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Authors: Queen Suraajini Rajendran, Sai Hung Cheung

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Statistical down scaling models are required to connect the global climate model outputs and the local weather variables for climate change impact prediction. For reliable climate change impact studies, the uncertainty associated with the model including natural variability, uncertainty in the climate model(s), down scaling model, model inadequacy and in the predicted results should be quantified appropriately. In this work, a new approach is developed by the authors for statistical classification, statistical down scaling and uncertainty assessment and is applied to Singapore rainfall. It is a robust Bayesian uncertainty analysis methodology and tools based on coupling dependent modeling error with classification and statistical down scaling models in a way that the dependency among modeling errors will impact the results of both classification and statistical down scaling model calibration and uncertainty analysis for future prediction. Singapore data are considered here and the uncertainty and prediction results are obtained. From the results obtained, directions of research for improvement are briefly presented.

Keywords: statistical downscaling, global climate model, climate change, uncertainty

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Authors: Y. K. Park, B. K. Kim, J. W. Lee, S. J. Lee

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To assess the risk of the underground structures and surrounding ground, we collect basic data by the engineering method of measurement, exploration and surveys and, derive the risk through proper analysis and each assessment for urban railroad underground structures and surrounding ground including station inflow. Basic data are obtained by the fiber-optic sensors, MEMS sensors, water quantity/quality sensors, tunnel scanner, ground penetrating radar, light weight deflectometer, and are evaluated if they are more than the proper value or not. Based on these data, we analyze the risk level of urban railroad underground structures and surrounding ground. And we develop the risk management system to manage efficiently these data and to support a convenient interface environment at input/output of data.

Keywords: urban railroad, underground structures, ground subsidence, station inflow, risk

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Authors: Tudor Barbu

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An automatic moment-based texture segmentation approach is proposed in this paper. First, we describe the related work in this computer vision domain. Our texture feature extraction, the first part of the texture recognition process, produces a set of moment-based feature vectors. For each image pixel, a texture feature vector is computed as a sequence of area moments. Second, an automatic pixel classification approach is proposed. The feature vectors are clustered using some unsupervised classification algorithm, the optimal number of clusters being determined using a measure based on validation indexes. From the resulted pixel classes one determines easily the desired texture regions of the image.

Keywords: image segmentation, moment-based, texture analysis, automatic classification, validation indexes

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Authors: A. Shirazibeheshti, T. Radwan, A. Ettefaghian, G. Wilson, C. Luca, Farbod Khanizadeh

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Anticholinergic medication has been associated with events such as falls, delirium, and cognitive impairment in older patients. To further assess this, anticholinergic burden scores have been developed to quantify risk. A risk model based on clustering was deployed in a healthcare management system to cluster patients into multiple risk groups according to anticholinergic burden scores of multiple medicines prescribed to patients to facilitate clinical decision-making. To do so, anticholinergic burden scores of drugs were extracted from the literature, which categorizes the risk on a scale of 1 to 3. Given the patients’ prescription data on the healthcare database, a weighted anticholinergic risk score was derived per patient based on the prescription of multiple anticholinergic drugs. This study was conducted on over 300,000 records of patients currently registered with a major regional UK-based healthcare provider. The weighted risk scores were used as inputs to an unsupervised learning algorithm (mean-shift clustering) that groups patients into clusters that represent different levels of anticholinergic risk. To further evaluate the performance of the model, any association between the average risk score within each group and other factors such as socioeconomic status (i.e., Index of Multiple Deprivation) and an index of health and disability were investigated. The clustering identifies a group of 15 patients at the highest risk from multiple anticholinergic medication. Our findings also show that this group of patients is located within more deprived areas of London compared to the population of other risk groups. Furthermore, the prescription of anticholinergic medicines is more skewed to female than male patients, indicating that females are more at risk from this kind of multiple medications. The risk may be monitored and controlled in well artificial intelligence-equipped healthcare management systems.

Keywords: anticholinergic medicines, clustering, deprivation, socioeconomic status

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Authors: Ritsuko Kawasaki, Takeshi Hiromatsu

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Management is required to understand all information security risks within an organization, and to make decisions on which information security risks should be treated in what level by allocating how much amount of cost. However, such decision-making is not usually easy, because various measures for risk treatment must be selected with the suitable application levels. In addition, some measures may have objectives conflicting with each other. It also makes the selection difficult. Therefore, this paper provides a model which supports the selection of measures by applying multi-objective analysis to find an optimal solution. Additionally, a list of measures is also provided to make the selection easier and more effective without any leakage of measures.

Keywords: information security risk treatment, selection of risk measures, risk acceptance, multi-objective optimization

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Authors: Marielcio Lacerda, Angelo Paulino, Elcio Shiguemori, Alvaro Damiao, Lamartine Guimaraes, Camila Anjos

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Accurate information about the terrain is extremely important in disaster management activities or conflict. This paper proposes the use of the Unmanned Aircraft Systems (UAS) at the identification of Airdrop Zones (AZs) and Helicopter Landing Zones (HLZs). In this paper we consider the AZs the zones where troops or supplies are dropped by parachute, and HLZs areas where victims can be rescued. The use of digital image processing enables the automatic generation of an orthorectified mosaic and an actual Digital Surface Model (DSM). This methodology allows obtaining this fundamental information to the terrain’s comprehension post-disaster in a short amount of time and with good accuracy. In order to get the identification and classification of AZs and HLZs images from DJI drone, model Phantom 4 have been used. The images were obtained with the knowledge and authorization of the responsible sectors and were duly registered in the control agencies. The flight was performed on May 24, 2017, and approximately 1,300 images were obtained during approximately 1 hour of flight. Afterward, new attributes were generated by Feature Extraction (FE) from the original images. The use of multispectral images and complementary attributes generated independently from them increases the accuracy of classification. The attributes of this work include the Declivity Map and Principal Component Analysis (PCA). For the classification four distinct classes were considered: HLZ 1 – small size (18m x 18m); HLZ 2 – medium size (23m x 23m); HLZ 3 – large size (28m x 28m); AZ (100m x 100m). The Decision Tree method Random Forest (RF) was used in this work. RF is a classification method that uses a large collection of de-correlated decision trees. Different random sets of samples are used as sampled objects. The results of classification from each tree and for each object is called a class vote. The resulting classification is decided by a majority of class votes. In this case, we used 200 trees for the execution of RF in the software WEKA 3.8. The classification result was visualized on QGIS Desktop 2.12.3. Through the methodology used, it was possible to classify in the study area: 6 areas as HLZ 1, 6 areas as HLZ 2, 4 areas as HLZ 3; and 2 areas as AZ. It should be noted that an area classified as AZ covers the classifications of the other classes, and may be used as AZ, HLZ of large size (HLZ3), medium size (HLZ2) and small size helicopters (HLZ1). Likewise, an area classified as HLZ for large rotary wing aircraft (HLZ3) covers the smaller area classifications, and so on. It was concluded that images obtained through small UAV are of great use in calamity situations since they can provide data with high accuracy, with low cost, low risk and ease and agility in obtaining aerial photographs. This allows the generation, in a short time, of information about the features of the terrain in order to serve as an important decision support tool.

Keywords: disaster management, unmanned aircraft systems, helicopter landing zones, airdrop zones, random forest

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Authors: Sanaa Rashed Abdallah, Yasser F. Hassan

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The paper will introduce an approach where a rough sets, gene expression programming and rough neural networks are used cooperatively for learning and classification support. The Objective of gene expression programming rough neural networks (GEP-RNN) approach is to obtain new classified data with minimum error in training and testing process. Starting point of gene expression programming rough neural networks (GEP-RNN) approach is an information system and the output from this approach is a structure of rough neural networks which is including the weights and thresholds with minimum classification error.

Keywords: rough sets, gene expression programming, rough neural networks, classification

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Authors: Hasan Vural

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Turkey is a favorable country to produce a great variety of agricultural products because of her different geographic and climatic conditions which have been used to divide the country into four main and seven sub regions. This classification into seven regions traditionally has been used in order to data collection and publication especially related with agricultural production. Afterwards, nine agricultural regions were considered. Recently, the governmental body which is responsible of data collection and dissemination (Turkish Institute of Statistics-TIS) has used 12 classes which include 11 sub regions and Istanbul province. This study aims to evaluate these classification efforts based on the acreage of ten main crops in a ten years time period (1996-2005). The panel data grouped in 11 subregions has been evaluated by cluster and multivariate statistical methods. It was concluded that from the agricultural production point of view, it will be rather meaningful to consider three main and eight sub-agricultural regions throughout the country.

Keywords: agricultural region, factorial analysis, cluster analysis,

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Authors: I. Gusti A. A. Rai Asmiwyati, Robert J. Corner, Ashraf M. Dewan

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Change on land use/cover (LULC) dominantly affects spatial structure and function. It can have such impacts by disrupting social culture practice and disturbing physical elements. Thus, it has become essential to understand of the dynamics in time and space of LULC as it can be used as a critical input for developing sustainable LULC. This study was an attempt to map and monitor the LULC change in Bali Indonesia from 2003 to 2013. Using object based classification to improve the accuracy, and change detection, multi temporal land use/cover data were extracted from a set of ASTER satellite image. The overall accuracies of the classification maps of 2003 and 2013 were 86.99% and 80.36%, respectively. Built up area and paddy field were the dominant type of land use/cover in both years. Patch increase dominantly in 2003 illustrated the rapid paddy field fragmentation and the huge occurring transformation. This approach is new for the case of diverse urban features of Bali that has been growing fast and increased the classification accuracy than the manual pixel based classification.

Keywords: land use/cover, urban, Bali, ASTER

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Authors: Qing Cao

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Generally speaking, China has a relatively high level of intellectual property (IP) infringement. Risk assessment is indispensable in the strategic planning process. To complement the current literature in international business, the paper sheds the light on how to assess IP risk for foreign companies in planning market entry to China. Evaluating internal and external IP environment, proposed in the paper, consists of external analysis, internal analysis and further internal analysis. Through position the company’s IP environment, the risk assessment approach enables the foreign companies to either build the corresponding IP strategies or abort the entry plan beforehand to minimize the IP risks.

Keywords: intellectual property, IP environment, risk assessment

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Authors: Diyaolu Babajide Olufemi, Oyerinde Oyewole Olusesan

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This study examined the effect of sex education training programs on the knowledge and practice of sexual risk behavior among secondary school adolescents in Ibadan North Local Government area of Oyo State. A total of 105 students were sampled from two schools in the Local Government area. Seventy students (70) constituted the experimental group while thirty-five (35) constituted the control group. Pretest-Posttest control group quasi-experimental design was adopted. A self-developed questionnaire was used to test participants’ knowledge and practice of sexual risk behavior before and after the training (α=.62, .82 and .74). Analysis indicated a significant effect of sex education training on participants’ knowledge and practice of sexual risk behavior, a significant gender difference in knowledge of sexual risk behavior but no significant age and gender difference in the practice of sexual risk behavior. It was thus concluded that sex education should be taught in schools and emphasized at homes with no age or gender restrictions.

Keywords: early adolescent, health risk, sexual risk behavior, sex education

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Authors: Willie Eselebor

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Environmental governance is not new, but may consist of a series of actions taken to establish sanity and ensure sustainable environment. While there is a growing accord linking disaster risk reduction with the management of environment and natural resources, little is known about failure to act which constitute vulnerability and how improved governance reduces risk globally. The paper reviews emerging trends in the field of application of governance tools and approaches for reducing disaster risk. The Hyogo Framework for Action (HFA) enjoin all stakeholders to stimulate the sustainable use and management of ecosystems, which promote the implementation of integrated environmental and natural resource planning that incorporate disaster risk reduction, including structural and non-structural measures, such as integrated management of fragile ecosystems. The methodology adopted is a case study of disaster-prone sites, prompting guided analysis on which hazards are traceable to environmental degradation, why a degraded environment reduces community resilience; how healthy ecosystems provide natural defense, and which opportunities exist to address gaps in reduction of disasters in Nigeria. The paper further analyses the interaction between disaster risk and environmental change. It is established that environmental governance remains a challenge; which implies that there is the need for a shift in traditional approaches to disaster risk management; exploring new initiatives and allowing environmental managers to be docketed as disaster risk managers in context, potentially opening up a window of dialogue on disaster risk management.

Keywords: disaster, ecosystem, environment, risk

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Authors: Olga A. Momot, Boris I. Synzynys, Alla A. Oudalova

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Obninsk is situated in the Kaluga region 100 km southwest of Moscow on the left bank of the Protva River. Several enterprises utilizing nuclear energy are operating in the town. A special attention in the region where radiation-hazardous facilities are located has traditionally been paid to radioactive gas and aerosol releases into the atmosphere; liquid waste discharges into the Protva river and groundwater pollution. Municipal intakes involve 34 wells arranged 15 km apart in a sequence north-south along the foot of the left slope of the Protva river valley. Northern and southern water intakes are upstream and downstream of the town, respectively. They belong to river valley intakes with mixed feeding, i.e. precipitation infiltration is responsible for a smaller part of groundwater, and a greater amount is being formed by overflowing from Protva. Water intakes are maintained by the Protva river runoff, the volume of which depends on the precipitation fallen out and watershed area. Groundwater contamination with tritium was first detected in a sanitary-protective zone of the Institute of Physics and Power Engineering (SRC-IPPE) by Roshydromet researchers when realizing the “Program of radiological monitoring in the territory of nuclear industry enterprises”. A comprehensive survey of the SRC-IPPE’s industrial site and adjacent territories has revealed that research nuclear reactors and accelerators where tritium targets are applied as well as radioactive waste storages could be considered as potential sources of technogenic tritium. All the above sources are located within the sanitary controlled area of intakes. Tritium activity in water of springs and wells near the SRC-IPPE is about 17.4 – 3200 Bq/l. The observed values of tritium activity are below the intervention levels (7600 Bq/l for inorganic compounds and 3300 Bq/l for organically bound tritium). The risk has being assessed to estimate possible effect of considered tritium concentrations on human health. Data on tritium concentrations in pipe-line drinking water were used for calculations. The activity of 3H amounted to 10.6 Bq/l and corresponded to the risk of such water consumption of ~ 3·10-7 year-1. The risk value given in magnitude is close to the individual annual death risk for population living near a NPP – 1.6·10-8 year-1 and at the same time corresponds to the level of tolerable risk (10-6) and falls within “risk optimization”, i.e. in the sphere for planning the economically sound measures on exposure risk reduction. To estimate the chemical risk, physical and chemical analysis was made of waters from all springs and wells near the SRC-IPPE. Chemical risk from groundwater contamination was estimated according to the EPA US guidance. The risk of carcinogenic diseases at a drinking water consumption amounts to 5·10-5. According to the classification accepted the health risk in case of spring water consumption is inadmissible. The compared assessments of risk associated with tritium exposure, on the one hand, and the dangerous chemical (e.g. heavy metals) contamination of Obninsk drinking water, on the other hand, have confirmed that just these chemical pollutants are responsible for health risk.

Keywords: radiation-hazardous facilities, water intakes, tritium, heavy metal, health risk

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Authors: Ya-Fen Lee, Yun-Yao Chi

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The study aims to explore the relationship between risk perceptions of rockfall and revisit intention using a Structural Equation Modelling (SEM) analysis. A total of 573 valid questionnaires are collected from travelers to Taroko National Park, Taiwan. The findings show the majority of travellers have the medium perception of rockfall risk, and are willing to revisit the Taroko National Park. The revisit intention to Taroko National Park is influenced by hazardous preferences, willingness-to-pay, obstruction and attraction. The risk perception has an indirect effect on revisit intention through influencing willingness-to-pay. The study results can be a reference for mitigation the rockfall disaster.

Keywords: risk perception, rockfall, revisit intention, structural equation modelling

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Authors: Lei Zhang

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The carbon cycle greatly influences global change, and the land cover changes contribute to the status and rate of the carbon budget in ecosystems. This paper proposes a land cover classification system for mapping land cover, the national ecological environment assessment, and estimating carbon storage in ecosystems. The classification system consists of basic land cover classes at levels Ⅰ and Ⅱ and auxiliary features at level III. The basic 38 classes characterizing land cover features are derived from 19 criteria referring to composition, structure, pattern, phenology, etc. The basic classes reflect the status of carbon storage in ecosystems. The auxiliary classes at level III complement the attributes of higher levels by 9 criteria. The 5 environmental criteria of temperature, moisture, landform, aspect and slope mainly reflect the potential and intensity of carbon storage in ecosystems. The disturbance of vegetation succession caused by land use type influences the vegetation carbon budget. The other 3 vegetation cover criteria, growth period, and species characteristics further refine the vegetation types. The hierarchical structure of the land cover map (the classes of levels Ⅰ and Ⅱ) is independent of the products of level III, which is helpful for land cover product management and applications. The classification system has been adopted in the Chinese national land cover database for the carbon budget in ecosystems at a 30 m scale.

Keywords: classification system, land cover, ecosystem, carbon storage, object based

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Authors: Shahabeddin Sotudian, M. H. Fazel Zarandi, I. B. Turksen

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Hepatitis is one of the most common and dangerous diseases that affects humankind, and exposes millions of people to serious health risks every year. Diagnosis of Hepatitis has always been a challenge for physicians. This paper presents an effective method for diagnosis of hepatitis based on interval Type-II fuzzy. This proposed system includes three steps: pre-processing (feature selection), Type-I and Type-II fuzzy classification, and system evaluation. KNN-FD feature selection is used as the preprocessing step in order to exclude irrelevant features and to improve classification performance and efficiency in generating the classification model. In the fuzzy classification step, an “indirect approach” is used for fuzzy system modeling by implementing the exponential compactness and separation index for determining the number of rules in the fuzzy clustering approach. Therefore, we first proposed a Type-I fuzzy system that had an accuracy of approximately 90.9%. In the proposed system, the process of diagnosis faces vagueness and uncertainty in the final decision. Thus, the imprecise knowledge was managed by using interval Type-II fuzzy logic. The results that were obtained show that interval Type-II fuzzy has the ability to diagnose hepatitis with an average accuracy of 93.94%. The classification accuracy obtained is the highest one reached thus far. The aforementioned rate of accuracy demonstrates that the Type-II fuzzy system has a better performance in comparison to Type-I and indicates a higher capability of Type-II fuzzy system for modeling uncertainty.

Keywords: hepatitis disease, medical diagnosis, type-I fuzzy logic, type-II fuzzy logic, feature selection

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Authors: Muhammad Wasif, Asif Ahmed Shaikh, Sarosh Hashmat Lodi, Muhammad Aslam Bhutto, Riazuddin

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Risk Management System is quite popular in profit- based organizations, health and safety and project management fields since the last few decades. But due to rapidly changing environment and requirement of ISO 9001:2015 standards, public-sector institution, especially higher education institutes are also performing risk assessment to monitor the performance of the institution and aligning it with the latest benchmark. In this context, NED University of Engineering and Technology performed research and developed a Standard Operating Procedure (SOP) for the risk assessment, its monitoring and control. In this research, risks are broken into the four sources, namely; Internal Academics Risks, External Academics Risks, Internal Non-academic Risks, External Non-academic Risks. Risks are identified by the management at all levels. Severity and likelihood of the risks are assigned based on the previous audit results and the customer complains. Risk Ratings are calculated to orderly arrange the risk according to the Risk Rating, and controls for the risks are designed, which are assigned to the responsible person. At the end of the article, result and analysis on the different sources of risk are discussed in details and the conclusion is drawn. Discussion on few sample risks are presented in this article. Hence it is presented in the research that the Risk Management System can be applied in a Higher Education Institute to effectively control the risks which might affect the scope and Quality Management System of an organization.

Keywords: higher education, quality management system, risk assessment, risk management

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Authors: Joseph George, Anne Kotteswara Roa

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Skin disease is one of the most common and popular kinds of health issues faced by people nowadays. Skin cancer (SC) is one among them, and its detection relies on the skin biopsy outputs and the expertise of the doctors, but it consumes more time and some inaccurate results. At the early stage, skin cancer detection is a challenging task, and it easily spreads to the whole body and leads to an increase in the mortality rate. Skin cancer is curable when it is detected at an early stage. In order to classify correct and accurate skin cancer, the critical task is skin cancer identification and classification, and it is more based on the cancer disease features such as shape, size, color, symmetry and etc. More similar characteristics are present in many skin diseases; hence it makes it a challenging issue to select important features from a skin cancer dataset images. Hence, the skin cancer diagnostic accuracy is improved by requiring an automated skin cancer detection and classification framework; thereby, the human expert’s scarcity is handled. Recently, the deep learning techniques like Convolutional neural network (CNN), Deep belief neural network (DBN), Artificial neural network (ANN), Recurrent neural network (RNN), and Long and short term memory (LSTM) have been widely used for the identification and classification of skin cancers. This survey reviews different DL techniques for skin cancer identification and classification. The performance metrics such as precision, recall, accuracy, sensitivity, specificity, and F-measures are used to evaluate the effectiveness of SC identification using DL techniques. By using these DL techniques, the classification accuracy increases along with the mitigation of computational complexities and time consumption.

Keywords: skin cancer, deep learning, performance measures, accuracy, datasets

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Authors: A. Pontrandolfi, G. Enjolras, F. Capitanio

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The study analyses the strategies Italian farmers use to cope with the risks that face their production. We specifically explore the potential and the limitations of the economic tools for climatic risk management in agriculture of the Common Agricultural Policy 2014-2020, that foresees contributions for economic tools for risk management, in relation to farms’ needs, exposure and vulnerability of agricultural areas to climatic risk. We consider at the farm level approaches to hedge risks in terms of the use of technical tools (agricultural practices, pesticides, fertilizers, irrigation) and economic/financial instruments (insurances, etc.). We develop cross-sectional and longitudinal analyses as well as analyses of correlation that underline the main differences between the way farms adapt their structure and management towards risk. The results show a preference for technical tools, despite the presence of important public aids on economic tools such as insurances. Therefore, there is a strong need for a more effective and integrated risk management policy scheme. Synergies between economic tools and risk reduction actions of a more technical, structural and management nature (production diversification, irrigation infrastructures, technological and management innovations and formation-information-consultancy, etc.) are emphasized.

Keywords: agriculture and climate change, climatic risk management, insurance schemes, farmers' approaches to risk management

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Authors: Somaiyeh Dehghan, Mohammad Reza Kheirkhahan Haghighi

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The rapid growth of domains that have data with a large number of features, while the number of samples is limited has caused difficulty in constructing strong classifiers. To reduce the dimensionality of the feature space becomes an essential step in classification task. Random subspace method (or attribute bagging) is an ensemble classifier that consists of several classifiers that each base learner in ensemble has subset of features. In the present paper, we introduce Random Subspace Ensemble of CMAC neural network (RSE-CMAC), each of which has training with subset of features. Then we use this model for classification task. For evaluation performance of our model, we compare it with bagging algorithm on 36 UCI datasets. The results reveal that the new model has better performance.

Keywords: classification, random subspace, ensemble, CMAC neural network

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Authors: Iqra Yaseen

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One of the well-known areas of computer science and engineering, image processing in the context of computer vision has been essential to automation. In remote sensing, medical science, and many other fields, it has made it easier to uncover previously undiscovered facts. Grading of diverse items is now possible because of neural network algorithms, categorization, and digital image processing. Its use in the classification of agricultural products, particularly in the grading of seeds or grains and their cultivars, is widely recognized. A grading and sorting system enables the preservation of time, consistency, and uniformity. Global population growth has led to an increase in demand for food staples, biofuel, and other agricultural products. To meet this demand, available resources must be used and managed more effectively. Image processing is rapidly growing in the field of agriculture. Many applications have been developed using this approach for crop identification and classification, land and disease detection and for measuring other parameters of crop. Vegetation localization is the base of performing these task. Vegetation helps to identify the area where the crop is present. The productivity of the agriculture industry can be increased via image processing that is based upon Unmanned Aerial Vehicle photography and satellite. In this paper we use the machine learning techniques like Convolutional Neural Network, deep learning, image processing, classification, You Only Live Once to UAV imaging dataset to divide the crop into distinct groups and choose the best way to use it.

Keywords: image processing, UAV, YOLO, CNN, deep learning, classification

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Authors: Lamia Hammadi, Abdellah Ait Ouhman, Aomar Ibourk

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It is widely assumed that the case of Customs Supply Chain is classified as a complex system, due to not only the variety and large number of actors, but also their complex structural links, and the interactions between these actors, that’s why this system is subject to various types of Risks. The economic, political and social impacts of those risks are highly detrimental to countries, businesses and the public, for this reason, Risk management in the customs supply chain is becoming a crucial issue to ensure the sustainability, security and safety. The main characteristic of customs risk management approach is determining which goods and means of transport should be examined? To what extend? And where future compliance resources should be directed? The purposes of this article are, firstly to deal with the concept of customs supply chain, secondly present our risk management approach based on Cross Activity Based Costing (ABC) Method as an interactive tool to support decision making in customs risk management. Finally, analysis of case study of Moroccan customs to putting theory into practice and will thus draw together the various elements of a structured and efficient risk management approach.

Keywords: cross ABC method, customs supply chain, risk, risk management

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Authors: Musarrat Jabeen Khan, Uzma Azam, Kainat Umar, Jazba Amber Satti, Aiman Shehzadi, Nimo Omer

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This study aimed to examine the comparison between substance abusers and non-abusers on anxiety, peer pressure, and risk-taking behavior among young adults. The sample consisted of 138 individuals including 64 female and 71 males, age range from 17-35 years, drawn from non-clinical population through convenient sampling. Questionnaire technique was used for the information assortment and the scales were susceptibility to peer pressure (Dieman, Pamella, Shope & Butchart, 1987), Zung self-rating anxiety scale (Zung, 1971), and risk-taking questionnaire (Gullone, Moore, Moss & Boyd, 2000) having alpha reliability of .54, .88, and .80 respectively. Results showed that anxiety negatively correlates with the risk-taking behavior. High level of anxiety stops an individual to involve himself in risk taking activities. Peer pressure have positive correlation with risk-taking behavior. Females are more susceptible to peer pressure irrespective of being abusers or non-abusers as compared to male abusers and non-abusers. Substance abusers have less anxiety as compared to non-abusers but are more susceptible to peer pressure and risk-taking behaviors.

Keywords: substance, substance abuse, anxiety, peer pressure, risk-taking behavior

Procedia PDF Downloads 132

Authors: Mike Tsionas

Abstract:

The paper develops new indices of financial stability based on an explicit model of expected utility maximization by financial institutions subject to the classical technology restrictions of neoclassical production theory. The model can be estimated using standard econometric techniques, like GMM for dynamic panel data and latent factor analysis for the estimation of co-variance matrices. An explicit functional form for the utility function is not needed and we show how measures of risk aversion and prudence (downside risk aversion) can be derived and estimated from the model. The model is estimated using data for Eurozone countries and we focus particularly on (i) the use of the modeling approach as an “early warning mechanism”, (ii) the bank- and country-specific estimates of risk aversion and prudence (downside risk aversion), and (iii) the derivation of a generalized measure of risk that relies on loan-price uncertainty.

Keywords: financial stability, banking, expected utility maximization, sub-prime crisis, financial crisis, eurozone, PIIGS

Procedia PDF Downloads 318

Authors: Refilwe Moeletsi

Abstract:

Dimension stone quarrying around Brits and Belfast areas started in the early 1930s and has been growing rapidly since then. Environmental impacts associated with these quarries have not been documented, and hence this study aims at detecting any change in the environment that might have been caused by these activities. Landsat images that were used to assess land use/land cover changes in Brits quarries from 1998 - 2015. A supervised classification using maximum likelihood classifier was applied to classify each image into different land use/land cover types. Classification accuracy was assessed using Google Earth™ as a source of reference data. Post-classification change detection method was used to determine changes. The results revealed significant increase in granite quarries and corresponding decrease in vegetation cover within the study region.

Keywords: remote sensing, GIS, change detection, granite quarries

Procedia PDF Downloads 287