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A new economic order quantity model for deteriorated items under the joint effects of stock dependent demand and inflation

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Decision Analytics Journal 2023年 8卷

作者： Mallick, Rabin Kumar Patra, Kartik Mondal, Shyamal Kumar Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University W.B. Midnapore 721 102 India Department of Mathematics Vivekananda Satavarshiki Mahavidyalaya Manikpara W.B. Jhargram 721513 India

In the present market scenario, it is seen that commodity is attracted by the stock of the items. Considering this fact in this present work, the demand for the item is considered as stock dependent in an inventory control system, where the items are deteriorating in nature. When the number of items in the stock increases, the number of deteriorations also increases. Also, this is important to know for a retailer that when an order will be placed and when the order will be received from the supplier. So, the lead time has an important role in the inventory control system in this stock-dependent demand inventory model. Also, it is seen that the market price of any item may increase gradually with time. Considering this phenomena, the inflation has been incorporated in this work. Here, the proposed model has been analyzed in a finite time horizon. From our analysis, the optimal profit has been calculated for optimal replenishment period of each cycle. Also, depending on the changes in the parameters such as demand, stock, and deterioration, the changes in optimal profit have been analyzed numerically and graphically in the numerical section and the effects of inflation also has been analyzed in this section. As the parameters associated with the system are not always deterministic, so uncertainty arises in the model. To remove this uncertainty, the model has been extended to a fuzzy model considering the q-fuzzy membership function. The fuzzy model has been defuzzified using the Center of Gravity (COG) method and fuzzy extension principle. Also, the fuzzy model has been analyzed by numerical illustration and some sensitivity analysis have been shown corresponding to different values of q and different membership values of the fuzzy parameters. © 2023 The Author(s)

关键词： Deterioration Inflation Inventory Lead time Markup price q-fuzzy number

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A recommender system for WordPress themes using item-based collaborative filtering technique 29

A recommender system for WordPress themes using item-based c...

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29th International Scientific Conference Electronics, ET 2020

作者： Ujkani, Berat Minkovska, Daniela Stoyanova, Lyudmila Technical University of Sofia Faculty of Computer Systems and Technologies Department of Programming and Computer Technologies 8 Kliment Ohridski blvd Sofia1000 Bulgaria

ISBN: (纸本)9781728174266

Recommender systems allow making personalized recommendations for items or products while browsing online, using numerous algorithms and techniques to predict and recommend potentially useful items to a specific user. This paper presents a system built for the purpose of filtering out WordPress themes trough the ratings or reviews of other similar users using item-based collaborative filtering technique and provide user-specific recommendations for the largest online marketplace of web templates called Themeforest, part of Envato marketplaces. © 2020 IEEE.

关键词： Collaborative filtering

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Advanced IOT Traffic Light Control System

Advanced IOT Traffic Light Control System

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International Conference on Advances in Computational Tools for Engineering Applications, ACTEA

作者： Martin Abou Hamad Antonio El Hajj Roy Abi Zeid Daou Ali Hayek Gaby Abou Haidar Biomedical Technologies Department Faculty of Public Health Lebanese German University Lebanon MART Learning Education and Research Center Lebanon Department of Computer Architecture and System Programming University of Kassel Kassel Germany Engineering Faculty American University of Science and Technology Zahle Lebanon

For several years, traffic congestion has been a major problem in big cities where the number of cars and different means of transportation has been increasing significantly. The problem of congestion is becoming more and more critical, and if not treated smartly this issue will negatively affect drivers by wasting time and fuel gas while waiting for hours in lanes. This paper presents a new and smart way to mitigate this issue in an affordable cost, minimum processing power, and low power consumption. This concept takes into consideration the majority of the cases that may cause congestion and presents a smart and accurate outputs to ease traffic flow leading to the prediction of the peak hours of traffic congestion for smarter control. A model is designed to study the case of a four lanes crossroad with two traffic lights and two LCD monitors. The strategy in reading data is divided into two parts: real data from sensors and pre collected data from google maps to create a kind of a predicted pattern over a certain time interval. The responsiveness of the system is analyzed thoroughly, and the accuracy of all possible cases is carefully considered and evaluated. Each part of the system was tested alone, and the overall system is still in an ongoing testing phase. The results have shown minimum faulty errors and accepted outputs that can lead to safe traffic control decisions. Finally, integrating more IoT devices and sensors between V2V, V2P, V2I with the help of artificial intelligence will definitely optimize this system with higher accuracy.

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Vehicle: Interfacing Neural Network Verifiers with Interactive Theorem Provers

arXiv

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arXiv 2022年

作者： Daggitt, Matthew L. Kokke, Wen Atkey, Robert Arnaboldi, Luca Komendantskya, Ekaterina Department of Computer Science Heriot-Watt University Edinburgh United Kingdom Mathematically Structured Programming Group University of Strathclyde Glasgow United Kingdom Laboratory for Foundations of Computer Science University of Edinburgh Edinburgh United Kingdom

Verification of neural networks is currently a hot topic in automated theorem proving. Progress has been rapid and there are now a wide range of tools available that can verify properties of networks with hundreds of thousands of nodes. In theory this opens the door to the verification of larger control systems that make use of neural network components. However, although work has managed to incorporate the results of these verifiers to prove larger properties of individual systems, there is currently no general methodology for bridging the gap between verifiers and interactive theorem provers (ITPs). In this paper we present Vehicle, our solution to this problem. Vehicle is equipped with an expressive domain specific language for stating neural network specifications which can be compiled to both verifiers and ITPs. It overcomes previous issues with maintainability and scalability in similar ITP formalisations by using a standard ONNX file as the single canonical representation of the network. We demonstrate its utility by using it to connect the neural network verifier Marabou to Agda and then formally verifying that a car steered by a neural network never leaves the road, even in the face of an unpredictable cross wind and imperfect sensors. The network has over 20,000 nodes, and therefore this proof represents an improvement of 3 orders of magnitude over prior proofs about neural network enhanced systems in ITPs. © 2022, CC BY.

关键词： Problem oriented languages

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Sensing metric-based hybrid underlay/overlay transmission framework for CR-IoT networks for smart city applications

Sensing metric-based hybrid underlay/overlay transmission fr...

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7th IET Smart Cities Symposium (SCS 2023)

作者： Indu Bala Maad M. Mijwil Mostafa Abotaleb El-Sayed M. El-kenawy Abdelhameed Ibrahim School of Electrical and Electronics Engineering Lovely Professional University Punjab India Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

With the increasing number of IoT devices, there is a growing need for bandwidth to support their communication. Unfortunately, there is a shortage of available bandwidth due to preallocated bands for various services. To address this issue, Cognitive Internet of Things (CR-IoT) enables devices to optimize their efficiency and enhance their communication capabilities by intelligently accessing available bandwidth. This is achieved through the use of soft sensing metrics, where devices continuously monitor the RF environment and transmit data opportunistically in overlay mode if a free channel is detected, or in underlay mode if not. In this paper, a soft sensing metric based hybrid transmission framework is proposed for CR-IoT devices to meet the data rate requirement for the smart city applications. The efficacy of this approach is demonstrated through simulation results.

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Comparative Study for Classification Methods to Predict and Detect Epilepsy Seizure 5

Comparative Study for Classification Methods to Predict and ...

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5th IEEE Middle East and Africa Conference on Biomedical Engineering, MECBME 2020

作者： El Halabi, Nashaat Abi Zeid Daou, Roy Achkar, Roger Hayek, Ali Boercsoek, Josef American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany

ISBN: (纸本)9781728123585

Epilepsy is a medical problem that tackles lots of patients. It limits the life activity of such patients due to the seizures that occur anytime and anywhere. Thus, creating a monitoring system that could make their life easier and allow them to perform daily life activities as safe as possible has been studied. This paper is a continuity for a project where a system monitors epileptic patients using a set of wearable sensors connected to several processing units, the main sensors are Electroencephalogram (EEG) for the electrical activity of the brain, electrocardiogram (ECG) for the electrical activity of the heart, and accelerometers to monitor the posture of the patient, features extracted from EEG and ECG signals are used for prediction/detection of seizures. Thus, the objective of this paper is to present a comparative study in order to choose the appropriate classification method for seizure prediction and detection. Five classification methods were studied: Decision Trees, Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Learning. For each of the classification methods, the accuracy, the specificity, the sensitivity, the precision, the false detection rate and the Fmeasure were studied and compared. The results for seizure prediction system show that the highest accuracy, specificity and F-Measure, and lowest false detection rate were achieved with Bagged Trees Ensemble Learning, the highest sensitivity and precision were achieved with Quadratic Support Vector Machine. However, for seizure detection system, the highest accuracy and F-Measure were achieved with Linear Support Vector, the highest sensitivity and specificity were achieved with Fine Gaussian Support Vector Machine, and the highest precision and lowest false detection rate were achieved with Subspace Discriminant Ensemble Learning. © 2020 IEEE.

关键词： Support vector machines

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Lookback Period, Epochs and Hidden States Effect on Time Series Prediction Using a LSTM based Neural Network 28

Lookback Period, Epochs and Hidden States Effect on Time Ser...

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28th National Conference with International Participation, TELECOM 2020

作者： Koparanov, Kiril Andreev Georgiev, Krasin Krasimirov Shterev, Vasil Aleksandrov Technical University of Sofia Faculty of Computer Systems and Technologies Department of Programming and Computer Technologies 8 Kliment Ohridski blvd. Sofia1000 Bulgaria

ISBN: (纸本)9781728187174

Forecasting time series problem occurs in various subject areas. Recently neural network techniques have been used for solving such tasks. However, they have not been sufficiently studied. The article explores the influence of the lookback period, the training epochs, and hidden state dimensionality in forecasting time series using long short-term memory. Numerical experiments with example financial data show that using more lags does not improve the results. Such a study of model parameters is important for their proper selection. © 2020 IEEE.

关键词： Time series

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Storage of key Features of Images of Sulfur Imprints in the Mathematical Structure Form

Storage of key Features of Images of Sulfur Imprints in the ...

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2020 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2020

作者： Bagaev, I.I. Logunova, O.S. Institute of Energy and Automated Systems Nosov Magnitogorsk State Technical University Department of Computer Engineering and Programming Magnitogorsk Russia

ISBN: (纸本)9781728169514

The aim of the study is to develop a method for storing images of sulfur imprints. In the framework of the study, a more effective method of representing images of sulfur imprints by using knowledge of the specificity of these images was developed compared to universal methods. In particular, a method for storing images of sulfur imprints has been developed, which stores information about objects that are significant for technological analysis in the image. The developed method was tested for images of sulfur imprints of continuously cast billets for the conditions of a large metallurgical enterprise in Russia. To conduct a computational experiment, a software product was designed and developed that allows you to convert the image of a sulfur imprint into a mathematical representation, as well as the process of restoring the main image. Using the array of retrospective information of 2011-2018 and the developed software product, the calculation of the effectiveness of the presentation of graphic information in the form of a structural unit of information without directly saving the image was performed. The average share of stored information from the original volume is 0.53 percent © 2020 IEEE.

关键词： Sulfur

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Analysis of Road Map Design Based on Multigraph with Picture Fuzzy Information

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International Journal of Applied and Computational Mathematics 2020年第3期6卷 1-17页

作者： Das, Sankar Ghorai, Ganesh Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University Midnapore 721 102 India Department of Mathematics Kharagpur College Kharagpur 721 305 India

In this paper, the picture fuzzy planar graph is defined along with some basic properties of picture fuzzy graph. The notion of picture fuzzy planar graphs is introduced and the terms such as strong (weak) edges, strong (weak) picture fuzzy planar graphs, strength of an edge, degree of planarity, picture fuzzy faces, strong (weak) picture fuzzy faces, picture fuzzy dual graphs and picture fuzzy combinatorial dual graphs are defined. Some theorems on degree of planarity and picture fuzzy combinatorial dual graphs have been established. An application of picture fuzzy planar graph in the design of road maps is given. © 2020, Springer Nature India Private Limited.

关键词： Picture fuzzy combinatorial dual graph Picture fuzzy dual graphs Picture fuzzy graphs Picture fuzzy planar graphs Strong (weak) picture fuzzy planar graphs

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Novel Approach of a Smart Medical Mirror System for Medical Applications 5

Novel Approach of a Smart Medical Mirror System for Medical ...

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5th IEEE Middle East and Africa Conference on Biomedical Engineering, MECBME 2020

作者： Halaby, Shady Khoury, George Zeid Daou, Roy Abi Hayek, Ali Boercsoek, Josef Lebanese German University Biomedical Technologies Department Jounieh Lebanon Lebanese German University Biomedical Technologies Department Jounieh Lebanon Lebanese German University Biomedical Technologies Department Jounieh Lebanon University of Kassel Faculty of Electrical Engineering/Computer Science Department of Computer Architecture and System Programming Germany University of Kassel Faculty of Electrical Engineering/Computer Science Department of Computer Architecture and System Programming Germany

ISBN: (纸本)9781728123585

Humans always face problems when it comes to medical check-up due to lack of time or laziness. However, lots of medical problems and fatal illnesses can be detected at earlier stages if the person is subject to continuous check-up. Hence, the idea of the proposed system is to design a tool enabling the measurement of the basic vital signs with the smallest effort possible from the person's side. Thus, this paper proposes a smart medical mirror system that acts as a normal mirror and also delivers the basic vital signs of the user such as the heart rate, SPO2, body temperature, and Body Mass Index (BMI). The system takes five seconds for initialization and starts the measurements and displays the results. The user will not lose time as this check-up can be performed every morning while the person is wearing his clothes and getting ready to start his daily activities. The system was tested by multiple users having different body statuses. The recorded values showed very small differences with the exact ones. At the end, the system was able to meet the user specifications concerning its portability, sensitivity, accuracy and, most importantly, its low cost. © 2020 IEEE.

关键词： Mirrors

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