BackgroundRecent statistics from the European Commission indicate that wine is one of the commodities most commonly subject to food fraud. In this context, the development of reliable classification models to differen...
详细信息
BackgroundRecent statistics from the European Commission indicate that wine is one of the commodities most commonly subject to food fraud. In this context, the development of reliable classification models to differentiate alcoholic beverages requires, besides sensitive analytical tools, the use of the most suitable data-processing methods like those based on advanced statistical tools or artificial intelligence. ResultsThe present study aims to establish a new, innovative approach for the differentiation of alcoholic beverages (wines and fruit distillates), which is able to increase the discrimination rate of the models that have been developed. A data dimensionality reduction step was applied to proton nuclear magnetic resonance (H-1-NMR) profiles. This stage consisted of the application of fuzzy principal component analysis (FPCA) prior to the development of classification models through discriminant analysis. The enhancement of the model's classification potential by the application of FPCA in comparison with principal component analysis (PCA) was discussed. ConclusionThe association of H-1-NMR spectroscopy and an appropriate statistical approach provided a very effective tool for the differentiation of alcoholic beverages. To develop reliable metabolomic approaches for the differentiation of wines and fruit distillates, H-1-NMR spectroscopic data were exploited in conjunction with fuzzy algorithms to reduce data dimensionality. The study proved the greater efficiency of using FPCA scores in comparison with those obtained through the widely applied PCA. The proposed approach enabled wines to be distinguished perfectly according to their geographical origins, cultivar, and vintage, and this could be used for wine classification. Moreover, 100% correctly classified samples were also achieved for the botanical and geographical differentiation of fruit distillates. (c) 2022 Society of Chemical Industry.
Employee silence can degrade the working environment and decrease employees' motivation and commitment to an organization. As a result, it not only affects employees but also reduces the productivity of the organi...
详细信息
Employee silence can degrade the working environment and decrease employees' motivation and commitment to an organization. As a result, it not only affects employees but also reduces the productivity of the organization. However, few studies have investigated the influencing mechanisms of employee silence empirically. This paper studies how illegitimate tasks affect employee silence based on artificial intelligence and fuzzy algorithms. We surveyed 325 employees in several medium-sized enterprises in Jiangsu and Anhui, China. According to the findings, emotional exhaustion partially mediates the relationship between illegitimate tasks and employees' silence behaviors, and leadership humor can moderate the positive effect of illegitimate tasks on emotional exhaustion. Therefore, situating the mechanisms underlying employees' silence behaviors in the context of artificial intelligence and fuzzy algorithm research helps researchers understand the relationship between illegitimate tasks and employees' silence behaviors, thus improving related research on silence behaviors.
Chloride ion exposure is one of the obstacles that reinforced concrete structures may be required to endure over the course of their lifetime. Concrete constructions may become less durable and degrade as a result of ...
详细信息
Chloride ion exposure is one of the obstacles that reinforced concrete structures may be required to endure over the course of their lifetime. Concrete constructions may become less durable and degrade as a result of this exposure, especially in coastal locations. Artificial intelligence (AI) may be used to develop models that can reliably forecast the chloride diffusion coefficient (DC) of unsteady state apparent concrete over an extended period of time by using practical field data. This method may enhance the assessment of a concrete construction's longevity by identifying the variables that matter most. The current study demonstrates the utilization of an Adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete's DC under various exposure scenarios. After being trained on a dataset including 216 data points, the forecast models were enhanced by using the Harris Hawks optimization (HHO) and Chimp optimization algorithm (COA). The results show that the ANF(COA) and ANF(HHO) models show great potential for accurately predicting the D_C of concrete in various exposure scenarios while preserving appropriate coefficient of determination (R2) values. A thorough index using a variety of metrics shows that the objective function (OF) value for the ANF(COA) was about 30% lower at 0.5235 than it was for the ANF(HHO) 0.7738. By providing reliable DC predictions, the models can inform maintenance strategies, guide material selection, and support the design of more durable concrete structures, ultimately improving infrastructure resilience and reducing long-term maintenance costs.
A smart city is a city concept who designed to help make it easy for people to access information and communication in their daily lives, with the Internet of things technology that helps to create electronic devices ...
详细信息
ISBN:
(纸本)9781728181967
A smart city is a city concept who designed to help make it easy for people to access information and communication in their daily lives, with the Internet of things technology that helps to create electronic devices connected to each other so that they can send data or do anything by reducing the function of humans. This research is to make the newest ideas in laundry services that support the development of smart cities, on IoT-based Smart Laundry on web applications that can simplify make the use of laundry more easier and more practical for users of laundry services on IoT-based Smart Laundry on web applications that can make it easier to use laundry services so that it will be saves more time and more practical for laundry service users. By using the fuzzy Algorithm as one of the Artificial Intelligence methods to support decision making in this system. This algorithm serves to make decisions in sorting which laundry will be picked up and also in this study the fuzzy Algorithm will produce the output to classify the price of laundry that will be paid by the user with the parameters of weight, humidity, and color. So, the laundry owner doesn't need to calculate manually again.
Traffic congestion in urban areas is a global challenge - leading to stifled economic growth, increased road accidents and atmospheric pollution, among other negative trends. Existing traffic management solutions have...
详细信息
ISBN:
(纸本)9781728134642
Traffic congestion in urban areas is a global challenge - leading to stifled economic growth, increased road accidents and atmospheric pollution, among other negative trends. Existing traffic management solutions have proven to be largely ineffective in medium-size African cities. Wireless sensor networks have emerged as possible cost-effective solutions, especially in under-developed countries. In this paper, we present a solution for detection and quantification of traffic congestion at signaled isolated four-way junctions in order to optimize traffic flow. The research utilizes optical sensors to collect road parameters and fuzzy logic to quantify and then prioritize entry. Simulations are used to compare strategies employed by traffic signals;the interest being to observe which of the two traffic light management schemes is more effective. The two schemes compared in this paper implement fairly weighted round-robin and fuzzy algorithms.
Future combat vehicles will implement hybrid energy management systems comprised of complex multivariable non-linear algorithms. In this paper, fuzzy algorithms based on neural networks (NNs) were applied to a hybrid ...
详细信息
ISBN:
(纸本)9781538674130
Future combat vehicles will implement hybrid energy management systems comprised of complex multivariable non-linear algorithms. In this paper, fuzzy algorithms based on neural networks (NNs) were applied to a hybrid energy management system. In addition, a combination of fuzzy theory and NN computing theory was analyzed in detail. fuzzy control algorithms were also used to resolve the run mode switch and power distribution issues of energy management systems. Furthermore, the proposed energy management control strategy was validated using MATLAB. The results of the simulation indicated that the proposed control strategy and algorithms could improve the working conditions and adaptability of future combat vehicles.
Modern sports need a great physical and psychological effort from young athletes to reach high performance levels. Stature length, leg length, foot length, arm length, hand length, shoulder width, hip width, chest wid...
详细信息
Modern sports need a great physical and psychological effort from young athletes to reach high performance levels. Stature length, leg length, foot length, arm length, hand length, shoulder width, hip width, chest width and weight are the anthropometric characteristics that affect swimmers' performances. This paper introduces new techniques to select promising junior swimmers in Egypt. It develops two automated algorithms to select junior swimmer depending on their anthropometric measurement. The first technique uses Canny filter to develop the first algorithm, while the second one uses the fuzzy concepts. The proposed algorithms make use of the image processing technique to handle the anthropometric measurements, by detecting the human body feature points automatically from the front and side images. The 101 feature points extract automatically from 36 human body measurements, while swimming games needs only 8 body dimensions from these 36 human body measurements. Therefore, the proposed system is not limited to swimming sports but can also be applied to other sports. Moreover, the experimental results and the corresponding statistical analysis show the high accuracy and advantages of the proposed algorithms. The first algorithm improved the results that could have obtained using the well-known fully vision-based automatic human body method by 22.23%, 15.9%,29.41%, and 27.5%, for stature length, arm length, leg length, and shoulder width, respectively. Also, it gave the best results for Leg Length, and Shoulder width, while the second one yielded the best result for Stature Length.
An essential requirement for modern industrial plants in the Industry 4.0 vision is to guarantee cybersecurity and safety related to the presence of faults. Additionally, the increasing complexity of the control syste...
详细信息
An essential requirement for modern industrial plants in the Industry 4.0 vision is to guarantee cybersecurity and safety related to the presence of faults. Additionally, the increasing complexity of the control systems and the digital transformation in industries demand a more integrative vision in supervision services improving interoperability. Regularly, research in fault diagnosis and cybersecurity has been developed separately despite having many elements in common. This paper presents a novel fuzzy-based strategy that integrates the early detection and location of cyber-attacks and faults. This holistic approach promotes the necessary and important interrelation between the technical groups of Operational Technology (OT) and Information Technology (IT) in industrial plants allowing for the simplification of the computational solution of the condition monitoring system. The proposal was assessed with two known benchmarks showing robust behavior in the presence of noise and disturbances in the measurements and outstanding performance.
Transportation networks are essential to the operation of societies and economies. Protecting the privacy of sensitive information is a meaningful conception in sustainable transport when mining the transportation dat...
详细信息
Transportation networks are essential to the operation of societies and economies. Protecting the privacy of sensitive information is a meaningful conception in sustainable transport when mining the transportation data. In data mining, differential privacy (DP) has provable privacy guarantees for releasing sensitive data by introducing randomness into query results. However, it suffers from significant accuracy loss of outputs when the query has high sensitivity (e.g., triangle counting). The reason is that the range of random perturbation to each query result in DP is too large. It consists of all possible output values for a query that forms a large or even unbounded interval. However, when impose perturbation only in a small neighborhood of the true query result, the similarity measure based on randomness in DP fails. Thereupon, we introduce fuzziness into DP to formulate new models which have smaller disturbance via fuzzy similarity measures. In this article, we establish a novel and general theory of private data analysis, fuzzy differential privacy (FDP). The new theory FDP aims to acquire a more flexible tradeoff between the accuracy of outputs and the privacy-preserving level of data. FDP combines DP with fuzzy set theory by introducing fuzziness into the query results and characterizing similarities between outputs via multiple fuzzy similarity measures. From this perspective, DP can be viewed as a special case of FDP with probabilistic similarity measure. Compared with DP, FDP has three superiorities: 1) most fuzzy similarity measures in FDP support sliding window perturbation strategies we proposed, which refer to perturbation in a small neighborhood of the query results;2) FDP adds noise to the query results only according to a fraction of all possible neighboring datasets;and 3) the fuzzy similarity with valued in [0,1] quantifies the privacy protection level intuitively. These three points enable more accurate outputs while providing provable and intui
Construction or renovation of distribution network plays an important role in the developing of smart grid. It is necessary to evaluate the investment projects in order to promote scientific development and management...
详细信息
ISBN:
(纸本)9781479987313
Construction or renovation of distribution network plays an important role in the developing of smart grid. It is necessary to evaluate the investment projects in order to promote scientific development and management of distribution network in smart grid. In this paper, we propose a lifecycle evaluation scheme for distribution network projects, which includes three steps: previous evaluation, mid-term evaluation, and post evaluation of the investment projects. The previous evaluation is carried out based on the type-2 fuzzy algorithm to analyze the feasibility of the proposed investment projects. The mid-term evaluation aims to monitor and assess the execution process of the project by comparing the designed and real statutes of the executing project. The post evaluation can be implemented with a specific or comprehensive traditional project evaluation methods according to different types of distribution network projects. Finally, a specific case study verifies the effectiveness of our proposed evaluation scheme.
暂无评论