1,720,982 research outputs found
Forecasting of CO2 Emission According to data analysis
No full textThe effects of global warming are being felt more and more today. One of the most
important factors affecting global warming is carbon emission. Global warming and climate
change are the problems of the world and humanity that concern sectors such as industry,
commerce, and agriculture. The increasing amount of carbon in the world is due to many
reasons. Carbon emissions are also called greenhouse gas emissions. Since most of these gases
are based on carbon molecules, they are called carbon emissions. The purpose of this article is
to find important factors that affect carbon emissions and make predictions for the future. Many
reasons such as increasing population, increasing use of fossil fuel vehicles, increasing
industrialization in the world after the industrial revolution, waste materials and urbanization
affect carbon emissions. Carbon emission estimation is important to raise awareness between
states and the public and to learn about measures to be taken. Carbon emission measurements
can be made with sensors and devices with Internet of Things (IoT) systems. Regions, where
carbon emissions are high, can be determined by IoT. Real-time monitoring of carbon dioxide
emissions can be done with IoT systems. Each of the factors affecting carbon emission was
estimated to estimate. Factor estimates were used in the regression equation. In addition, we
aim to clarify the measures that can be taken for the factors used in the regression equation. In
the regression equation, forest areas, waste, the number of incoming tourists, the use of
renewable energy sources and the number of vehicles used have an important effect. In this
article, Moving Average, Single and Double Exponential Smoothing and Winter Method are
the prediction methods. As a result, the amount of carbon emission will increase in the future.
The results obtained to prove this diagnosis. Measures need to be taken for CO2 emissions.
Although global warming cannot be eliminated, slowing down can be achieved with IoT based
systems and applications.No sponso
A multi criteria decision making methodology based novel model for supplier selection
Full text linkSupplier selection and measurement of supplier performance are multi-criteria decision making
(MCDM) problems and have strategic importance for all industries. The study contains analyzed factors that are
affecting the process of the supply chain concerning supplier performance. Supplier performance measures is a
tool to determine whether suppliers are doing their job as expected. The importance of supplier performance
measurement should not be underestimated due to direct and indirect productivity-related consequences. Supplier
evaluation is a complex multiple criteria decision-making problem that is affected by several conflicting factors.
Therefore, the measurement of supplier performance has been becoming crucial and critical throughout the
world. The purpose of this paper is to investigate the MCDM methods and propose a novel method to check how
the performance of suppliers is being measured using three different methods. Qualification and final selection of
the supplier can be done with a proposed novel model. In the study, the criteria are weighted with the Analytical
Hierarchy Process, while TOPSIS and VIKOR methods are used to evaluate and rank the suppliers. Evaluating
supplier performance, derive the importance of the main criteria and sub-criteria applied in decision-matrix to
sort the suppliers according to the measurement of supplier performance criteria.No sponso
İnsani yardım tedarik zinciri depo yer seçimi: ÇKKV metodolojisi temelli bir örnek olay incelemesi
Full text link"Disaster" is a general name given to events that cause physical, economic, and social losses for people that will disrupt the functioning of a community or society. Disasters that occur largely or completely beyond the control of people cause a mass loss of life and property. Turkey is in one of the most effective earthquake zones which is the Mediterranean-Alpine-Himalayan belt. Almost, every 5 years, one big earthquake is happened and causes loss of life and property. Disaster management requires complex logistic activities and it is an unpredictable marketplace, they must be managed appropriately to achieve faster and more efficient results. In this study, evaluation of the factors which is affecting the location selection of the humanitarian supply chain warehouses (HSCW) at the local and regional
levels is done with multi-criteria decision making (MCDM) based methods. Main and subcriteria weights are Main criteria and subcriteria were calculated with AHP. The ranking of criteria and alternatives was carried out with the TOPSIS method. In this study, AHP-TOPSIS integrated criteria assessment is conducted for the HSCW selection problem. This study intends to explore the humanitarian supply chain warehouse selection problem and evaluate criteria to improve humanitarian supply chain management and location selection implementation."Afet", bir topluluğun veya toplumun işleyişini bozacak kişiler için fiziksel, ekonomik ve sosyal kayıplara neden olan olaylara verilen genel bir addır. Büyük ölçüde veya tamamen insanların kontrolü dışında meydana gelen afetler, kitlesel can ve mal kaybına neden olur. Türkiye, Akdeniz-Alp-Himalaya kuşağı olan en etkili deprem bölgelerinden biridir. Neredeyse her 5 yılda bir büyük bir deprem meydana gelir ve can ve mal kaybına neden olur. Afet yönetimi, karmaşık lojistik faaliyetler gerektirir ve öngörülemeyen bir pazardır, daha hızlı ve daha verimli sonuçlar elde etmek için uygun şekilde yönetilmeleri gerekir. Bu çalışmada, insani yardım tedarik zinciri depolarının (HSCW) yerel ve bölgesel düzeydeki lokasyon seçimini etkileyen faktörlerin değerlendirilmesi, çok kriterli karar verme (MCDM) tabanlı yöntemlerle yapılmıştır. Temel ve alt kriter ağırlıkları Ana kriterler olup, alt kriterler AHP ile hesaplanmıştır. Kriter ve alternatiflerin sıralaması TOPSIS yöntemi ile yapıldı.No sponso
AHP-TOPSIS integration extended with Pythagorean fuzzy sets for information security risk analysis
Full text linkRisk analysis (RA) contains several methodologies that object to ensure the protection and safety of occupational stakeholders. Multi attribute decision-making (MADM) is one of the most important RA methodologies that is applied to several areas from manufacturing to information technology. With the widespread use of computer networks and the Internet, information security has become very important. Information security is vital as institutions are mostly dependent on information, technology, and systems. This requires a comprehensive and effective implementation of information security RA. Analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) are commonly used MADM methods and recently used for RA. In this study, a new RA methodology is proposed based on AHP-TOPSIS integration extended with Pythagorean fuzzy sets. AHP strengthened by interval-valued Pythagorean fuzzy numbers is used to weigh risk parameters with expert judgment. Then, TOPSIS with Pythagorean fuzzy numbers is used to prioritize previously identified risks. A comparison of the proposed approach with three approaches (classical RA method, Pythagorean fuzzy VIKOR and Pythagorean fuzzy MOORA) is also provided. To illustrate the feasibility and practicality of the proposed approach, a case study for information security RA in corrugated cardboard sector is executed.No sponso
Occupational health, safety and environmental risk assessment in textile production industry through a Bayesian BWM-VIKOR approach
Full text linkOccupational risk assessment (ORA) is a process that consists of evaluating, ranking, and classifying the hazards and associated risks arising in any workplace from the viewpoint of occupational health and safety. Many ORA methods have been proposed in the literature, from a single independent expert to participatory methodologies made by group decision
and simple to complex ones. In this paper, a holistic ORA is presented, which uses two important multi-attribute decision methods named Bayesian Best-Worst Method (Bayesian BWM) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). Bayesian BWM is used to determine the importance weights of six different assessment criteria, which are the probability of hazardous event (P), frequency (F), severity (S), detectability (D), cost (C) and sensitivity not to use personal protective equipment (SNP). Since the classical BWM finds solution to the weights of a number of criteria from only one expert’s judgment, Bayesian BWM is preferred in this paper (1) to enable participation of a group of experts, (2) to aggregate the preferences of these multiple experts into consensus without loss of information and (3) to follow a probabilistic way for solving the ORA problem. The hazards are then ranked by VIKOR. The approach is implemented in the ORA process of a textile production plant. Results of risk analysis showed that electricity hazard and associated risks constitute the highest risk ratings. These hazards arise from the product, process, human and working environment. The associated risks are evaluated, prioritized, and detailed control measures are proposed. This study made comparisons with the classical BWM-VIKOR approach to demonstrate the proposed approach’s difference and practicality. Results can also help practitioners and risk analysts in formulating the improvement measures to increase the overall safety of the working environment further.No sponso
A comparative analysis of breast cancer detection and diagnosis using data visualization and machine learning applications
Full text linkIn the developing world, cancer death is one of the major problems for humankind.
Even though there are many ways to prevent it before happening, some cancer types still do not have
any treatment. One of the most common cancer types is breast cancer, and early diagnosis is the
most important thing in its treatment. Accurate diagnosis is one of the most important processes
in breast cancer treatment. In the literature, there are many studies about predicting the type of
breast tumors. In this research paper, data about breast cancer tumors from Dr. William H. Walberg
of the University of Wisconsin Hospital were used for making predictions on breast tumor types.
Data visualization and machine learning techniques including logistic regression, k-nearest neighbors,
support vector machine, naïve Bayes, decision tree, random forest, and rotation forest were applied to
this dataset. R, Minitab, and Python were chosen to be applied to these machine learning techniques
and visualization. The paper aimed to make a comparative analysis using data visualization and
machine learning applications for breast cancer detection and diagnosis. Diagnostic performances of
applications were comparable for detecting breast cancers. Data visualization and machine learning
techniques can provide significant benefits and impact cancer detection in the decision-making
process. In this paper, different machine learning and data mining techniques for the detection of
breast cancer were proposed. Results obtained with the logistic regression model with all features
included showed the highest classification accuracy (98.1%), and the proposed approach revealed
the enhancement in accuracy performances. These results indicated the potential to open new
opportunities in the detection of breast cancer.No sponso
Control measure prioritization in Fine - Kinney-based risk assessment: a Bayesian BWM-Fuzzy VIKOR combined approach in an oil station
No full textThe Fine - Kinney is a risk assessment method widely used in many industries due to its ease of use and quantitative risk evaluation. As in other methods, it is a method that recommends taking a series of control measures for operational safety. However, it is not always possible to implement control measures based on the determined priorities of the risks. It is considered that determining the priorities of these measures depends on many criteria such as applicability, functionality, performance, and integrity. Therefore, this study has studied the prioritization of control measures in Fine - Kinney-based risk assessment. The criteria affecting the prioritization of control measures are hierarchically structured, and the importance weights of the criteria are determined by the Bayesian Best-Worst Method (BBWM). The priorities of control measures were determined with the fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (FVIKOR) method. The proposed model has been applied to the risk assessment process in a petrol station's liquid fuel tank area. According to the results obtained with BBWM, the most important criterion affecting the prioritization of control measures is the applicability criterion. It has an importance weight of about 42%. It is followed by performance with 31%, functionality with 18%, and integrity with 10%, respectively. FVIKOR results show that the "Periodic control of the ventilation device" measure is the top priority for Fine - Kinney risk assessment. "The absence of any ducts or sewer pits that may cause gas accumulation in the tank area and near the dispenser; Yellow line marking of entry and exit and vehicle roads; Placing of speed limit warning signs" has been determined as a secondary priority. On conclusion, this proposed model is expected to bring a new perspective to the work of occupational health and safety analysts, since the priority suggested by Fine - Kinney risk analysis methods is not always in the same order as the one in the stage of taking action, and the source, budget, and cost/benefit ratio of the measure affect this situation in practice
A Neuro-fuzzy-Based Multi-criteria Risk Evaluation Approach: A Case Study of Underground Mining
No full textUnderground mining is considered as one of the most hazard-prone industries, and serious work-related fatalities have arisen as a consequence of processes related to it; this chapter deals with occupational hazards and related risk factors. Artificial neural network-based risk assessment approach in underground copper and zinc mine case study is proposed. Occupational health and safety (OHS) history dates back to ancient human history ever. Mankind date was obliged to do business in order to sustain life. OHS studies aim to increase the safety standard with reducing risk level in an acceptable degree. Safe workplaces with respect to OHS increase health, safety, and welfare standards of whole workers. Throughout the world major hazards categorized as physical, chemical, biological, psychosocial, and ergonomic risks can be observed. Although technological developments provide rapid growth in almost all industries, it can be observed that there is a lack of attention being paid and advanced occupational safety practices in the mining industry. A case study is carried out in one of the largest underground mining companies using neuro-fuzzy approach. Neuro-fuzzy logic-based risk assessment study supplies opportunity to provide more adequate decision-making process and gives meaningful classifications of hazard. Neuro-fuzzy approach is a combination of advantages of artificial neural networks and fuzzy logic. It gives more appropriate and comprehensive risk assessment in OHS. After all the neuro-fuzzy approach is applied for classification of risk types in each department of the copper and zinc mine, the necessary control measures for each department and for a whole system are presented. In the study, adaptive neuro-fuzzy inference system (ANFIS)-focused model is applied to the copper and zinc mine risk analysis problem based on three-step neuro-fuzzy approach. Improvements are shown on the study to show the efficiency and flexibility of the method. The main target by integrating the neuro-fuzzy logic application into the risk analysis is to obtain a more effective risk assessment and getting better results than the conventional models used. In conclusion, besides its theoretical contribution, obtained results of this study contribute toward improving occupational safety levels of copper and zinc mine with more comprehensive risk assessment process.No sponso
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