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Multi-criteria Radio Frequency Identification Approach for Manufacturing Company Selection Based on Partitioned Maclaurin Symmetric Mean Operators Under Complex Intuitionistic Fuzzy Set Environment

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International Journal of Fuzzy Systems 2024年第5期26卷 1730-1748页

作者： Azam, Muhammad Jana, Chiranjibe Khan, Muhammad Sajjad Ali Pal, Madhumangal Xin, Qin Yang, Shilin Sarkar, Biswajit Faculty of Science Beijing University of Technology Beijing100022 China Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University Midnapore721102 India Department of Mathematics Khushal Khan Khattak University Karak Khyber Pakhtunkhwa Karak27200 Pakistan Faculty of Science and Technology University of the Faroe Islands Tórshavn Faroe Islands Department of Industrial Engineering Yonsei University 50 Yonsei-ro Sinchon-dong Seodaemun-gu Seoul03722 Korea Republic of Center for Global Health Research Saveetha Medical College Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu 600077 Chennai India Department of Industrial Engineering and Management National Taipei University of Technology Taipei City 106344 Taiwan Chennai602105 India

The theory of intuitionistic fuzzy sets (IFSs) plays an essential role to deal with uncertainty and ambiguity. However, the IFSs deal only with anticipation, not periodicity. But, complex IFSs (CIFS) can handle both uncertainties and periodicity at a time. Therefore, this paper focuses on a new multi-criteria decision making (MCDM) approach using Maclaurin symmetric mean (MSM) operator in connection with the CIFS setting. Then, we develop some CIF partitioned based MSM (CIFPMSM) operators and their weighted form by considering that all the criteria can be arranged into some groups. The proposed operators not only deal with the interrelationship among criteria but also deal with the partitioned relationship among criteria. We discuss the properties of these proposed operators and investigate their cases. Finally, a decision-making approach for radio frequency identification (RFID) for manufacturing companies is developed based on the proposed operators and compared the results with the existing method to show the application and feasibility of the proposed method. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2024.

关键词： Decision making

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Decentralised Blockchain-based Solutions for Electronic Healthcare Record with Interacting Social Networking Components

Decentralised Blockchain-based Solutions for Electronic Heal...

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2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021

作者： Munoz, Daniel-Jesus Constantinescu, Denisa-Andreea ITIS Software CAOSD Group Universidad de Málaga Andalucía Tech Málaga Spain HPACuma Parallel Programming Models and Compilers Group Universidad de Málaga Computer Architecture Department Málaga Spain

ISBN: (纸本)9781665440325

The healthcare industry is a conjunction of monolithic applications based on neutral and time-stamped diagnostics. Hence, it is particularly interested in decentralised technologies to achieve global and social-driven electronic health records. Blockchain is a decentralised networking system where multiple copies of immutable data records are distributed and validated among different independent nodes. While traditionally used to create currently famous digital cryptocurrencies like Bitcoin, its worldwide fever helped to extend its applications beyond the financial industry. However, given how fast completely new technologies evolve, healthcare stakeholders are struggling to find the correct usage of blockchain ecosystem developments. Our goal is to provide a summary of the state-of-the-art efforts in the development of decentralised blockchain-based solutions for electronic healthcare records, in which social networking and wearable technology data is considered in order to provide patient-driven diagnostics - proven to be critical in the current SARS-CoV-2 pandemic. In this paper, we present a comprehensive guide on blockchain technologies competing in the healthcare market, including emerging blockchain-based initiatives and trends. Additionally, we discuss the main caveats of blockchain applications development in this industry based on practical experience. © 2021 IEEE.

关键词： Blockchain

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On One Method of Constructing a Decision Rule in Pattern Recognition

On One Method of Constructing a Decision Rule in Pattern Rec...

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2021 International Conference on Information Science and Communications Technologies, ICISCT 2021

作者： Kasimova, Shoista Kasimov, Saidaminxon Tashkent University of Information Technologies after Name Mukhammad Al-Khwarizmi Information and Computer Technologies and Programming Department Tashkent Uzbekistan Tashkent University of Information Technologies after Name Mukhammad Al-Khwarizmi Information Security Department Tashkent Uzbekistan

ISBN: (纸本)9781665432580

The general problem of pattern recognition is to establish the theory and principles of building systems that share complex situations and events into classes. In this case, usually the number of possible situations in the classes is great and they are almost impossible to be remembered. The method of estimates' calculating contributes to a variety of scoring functions' building, which in turn, allows us to formulate and solve a wide class of multivariate data processing. In recent years more and more widely distributed methods of pattern recognition, in which the primary step is to calculate the balance of information and consideration of evidence. Based on the latest evidence being implemented, all further procedures until the distinction of classes or assign any object to one of the classes. © 2021 IEEE.

关键词： Pattern recognition

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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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An Approach for Parallel Reading in Multiple Sequence Alignment

An Approach for Parallel Reading in Multiple Sequence Alignm...

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2020 International Conference Automatics and Informatics, ICAI 2020

作者： Ko, Soon-Heum Gancheva, Veska Linkoping University National Supercomputing Center Linkoping Sweden Technical University of Sofia Programming and Computer Technologies Department Sofia Bulgaria

ISBN: (纸本)9781728193083

We propose an approach for faster file reading of multiple sequence alignment input through the use of MPI-I/O over a subset of MPI cores. The idea is to allow a subset of MPI cores that perform I/O operations and to broadcast locally on individual neighbors, so the code is less sensitive to the stability of the parallel file system. It is achieved by creating a number of subgroups under a global MPI communicator. The size of each subgroup and the buffer size of each reading operation are tuned through the synthetic benchmark. We verify the performance of our approach by comparing it with the traditional way of "sequential file reading and global broadcast", and apply it to the existing MPI version of multiple sequence alignment software ClustalW. In the production run over 8192 BlueGene/Q cores, the current approach provides 6.8 times acceleration of the existing ClustalW-MPI implementation. © 2020 IEEE.

关键词： bioinformatics ClustalW-MPI MPI-I/O multiple sequence alignment parallel reading

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Data poisoning: issues, challenges, and needs

Data poisoning: issues, challenges, and needs

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7th IET Smart Cities Symposium (SCS 2023)

作者： Mohammad Aljanabi Alaa Hamza Omran Maad M Mijwil Mostafa Abotaleb El-Sayed M. El-kenawy Sahar Yousif Mohammed Abdelhameed Ibrahim Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq 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 Science Department Computer Science & Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence.

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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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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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Correction to: Estimation of most effected cycles and busiest network route based on complexity function of graph in fuzzy environment

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Artificial Intelligence Review 2023年第12期56卷 15567-15567页

作者： Ghorai, Ganesh Poulik, Soumitra Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University Midnapore India

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Speaker's Emotions Recognition Module Based on the GoogleLeNet Neural Network

Speaker's Emotions Recognition Module Based on the GoogleLeN...

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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Tereikovskyi, Ihor Tereikovska, Liudmyla Kryvoruchko, Olena Tereikovskyi, Oleh Tyshchenko, Dmytro Franchuk, Tamara National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of System Programming And Specialized Computer Systems Kyiv Ukraine Kyiv National University Of Construction And Architecture Dept. Of Information Technology For Design And Applied Mathematics Kyiv Ukraine Kyiv National University Of Trade And Economics Dept. Of Software Engineering And Cyber Security Kyiv Ukraine National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of Department Of Computer Engineering Kyiv Ukraine

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The article is devoted to the development of means for recognition of the emotions of the speaker, based on the neural network analysis of fixed fragments of the voice signal. The possibility of improving recognition tools through the use of a convolutional neural network of the GoogLeNet type is shown. The expediency of training the network on the examples of the TESS database is determined. A procedure for representing a voice signal in the form of a square grayscale image has been developed. Based on the use of the specified database, the modification of the GoogLeNet network was carried out. The architecture and software implementation of the recognition module have been developed. With the help of computer experiments, it has been established that after 150 epochs of training on a fairly limited training sample, the modified GoogLeNet makes it possible to achieve an accuracy of recognition of the speaker's emotions of approximately 0.97 on test examples. The obtained accuracy is comparable to the accuracy of the best modern systems of this purpose and confirms the promise of using neural network models like GoogLeNet for emotion recognition based on voice signal analysis. The need for further research aimed at reducing the resource intensity of the neural network model is shown. © 2022 IEEE.

关键词： Emotion Recognition

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