18 results on '"Ullah, Ihsan"'
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2. Multi-level Federated Learning for Industry 4.0 - A Crowdsourcing Approach.
- Author
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Ullah, Ihsan, Hassan, Umair Ul, and Ali, Muhammad Intizar
- Subjects
INDUSTRY 4.0 ,CROWDSOURCING ,COMPUTER science ,ECOLOGICAL impact ,MACHINE learning - Abstract
Federated learning is one of the emerging areas of research in computer science. It has shown great potential in some application areas and we are witnessing evidence of new approaches where millions or even billions of IoT devices can contribute collectively to achieve a common goal of machine learning through federation. However, existing approaches are primarily suitable for single-task learning with a single objective in a single task owner where it is assumed that the majority of devices contributing to federated learning have a similar design or device type and restrictions. We argue that the true potential of federated learning can only be realised if we have a dynamic and open ecosystem where devices, industrial units, machine manufacturers, non-governmental agencies, and governmental entities can contribute toward learning for multiple tasks and objectives in a crowdsourced manner. In this article, we propose a multi-level framework that shows how federated learning, IoT, and crowdsourcing can come hand-in-hand with each other to make a robust ecosystem of multi-level federated learning for Industry 4.0. This helps build future intelligent applications for Industry 4.0 such as predictive maintenance and fault detection for systems in smart manufacturing units. In addition, we also highlight several use-cases of multi-level federated learning where this approach can be implemented in Industry 4.0. Moreover, if the approach is implemented successfully, besides enhancement in performance it will also help towards a greater common goal e.g. UN Sustainable Goal No 13 i.e. reduction in carbon footprint. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
3. Wireless Location Verification and Acquisition Using Machine Learning
- Author
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Ullah, Ihsan
- Subjects
Machine Learning ,IoT ,Neural Networks ,Location Acquisition ,Wireless Networks Security ,VANETs ,ITS ,400899 Electrical engineering not elsewhere classified ,Location Verification - Abstract
Traditional wireless location verification (authentication) is only feasible under the assumption that radio propagation is described by simple time-independent mathematical models. A similar situation applies to location acquisition, albeit to a lesser extent. However, in real-world situations, channel conditions are rarely well-described by simple mathematical models. In this thesis, novel location verification and acquisition techniques that integrate machine learning algorithms into the decision process are designed, analysed, and tested. Through the use of both simulated and experimental data, it is shown how the novel solutions developed remain operational in unknown time-varying channel conditions, thus making them superior to existing solutions, and more importantly, deployable in real-world scenarios. Location verification will be of growing importance for a host of emerging wireless applications in which location information plays a pivotal role. The location verification solutions offered in this thesis are the first to be tested against experimental data and the first to invoke machine learning algorithms. As such, they likely form the foundation for all future verification algorithms.
- Published
- 2021
- Full Text
- View/download PDF
4. Graph convolutional networks: analysis, improvements and results.
- Author
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Ullah, Ihsan, Manzo, Mario, Shah, Mitul, and Madden, Michael G.
- Subjects
MACHINE learning ,COMPLEX organizations - Abstract
A graph can represent a complex organization of data in which dependencies exist between multiple entities or activities. Such complex structures create challenges for machine learning algorithms, particularly when combined with the high dimensionality of data in current applications. Graph convolutional networks were introduced to adopt concepts from deep convolutional networks (i.e. the convolutional operations/layers) that have shown good results. In this context, we propose two major enhancements to two of the existing graph convolutional network frameworks: (1) topological information enrichment through clustering coefficients; and (2) structural redesign of the network through the addition of dense layers. Furthermore, we propose minor enhancements using convex combinations of activation functions and hyper-parameter optimization. We present extensive results on four state-of-art benchmark datasets. We show that our approach achieves competitive results for three of the datasets and state-of-the-art results for the fourth dataset while having lower computational costs compared to competing methods. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
5. Machine Learning-based Stable P2P IPTV Overlay.
- Author
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Iqbal, Muhammad Javid, Ullah, Ihsan, Ali, Muhammad, Ahmed, Atiq, Noor, Waheed, and Basit, Abdul
- Subjects
STREAMING video & television ,MULTICASTING (Computer networks) ,CONTENT delivery networks ,MACHINE learning ,SCALABILITY - Abstract
Live video streaming is one of the newly emerged services over the Internet that has attracted immense interest of the service providers. Since Internet was not designed for such services during its inception, such a service poses some serious challenges including cost and scalability. Peer-to-Peer (P2P) Internet Protocol Television (IPTV) is an application-level distributed paradigm to offer live video contents. In terms of ease of deployment, it has emerged as a serious alternative to client server, Content Delivery Network (CDN) and IP multicast solutions. Nevertheless, P2P approach has struggled to provide the desired streaming quality due to a number of issues. Stability of peers in a network is one of the major issues among these. Most of the existing approaches address this issue through older-stable principle. This paper first extensively investigates the older-stable principle to observe its validity in different scenarios. It is observed that the older-stable principle does not hold in several of them. Then, it utilizes machine learning approach to predict the stability of peers. This work evaluates the accuracy of several machine learning algorithms over the prediction of stability, where the Gradient Boosting Regressor (GBR) out-performs other algorithms. Finally, this work presents a proof-of-concept simulation to compare the effectiveness of older-stable rule and machine learning-based predictions for the stabilization of the overlay. The results indicate that machine learning-based stability estimation significantly improves the system. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
6. Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation.
- Author
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Ullah, Ihsan, Rios, Andre, Gala, Vaibhav, and Mckeever, Susan
- Subjects
DEEP learning ,CREDIT card fraud ,COMPUTER vision ,FEATURE selection ,SUBSET selection ,MACHINE learning - Abstract
Trust and credibility in machine learning models are bolstered by the ability of a model to explain its decisions. While explainability of deep learning models is a well-known challenge, a further challenge is clarity of the explanation itself for relevant stakeholders of the model. Layer-wise Relevance Propagation (LRP), an established explainability technique developed for deep models in computer vision, provides intuitive human-readable heat maps of input images. We present the novel application of LRP with tabular datasets containing mixed data (categorical and numerical) using a deep neural network (1D-CNN), for Credit Card Fraud detection and Telecom Customer Churn prediction use cases. We show how LRP is more effective than traditional explainability concepts of Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) for explainability. This effectiveness is both local to a sample level and holistic over the whole testing set. We also discuss the significant computational time advantage of LRP (1–2 s) over LIME (22 s) and SHAP (108 s) on the same laptop, and thus its potential for real time application scenarios. In addition, our validation of LRP has highlighted features for enhancing model performance, thus opening up a new area of research of using XAI as an approach for feature subset selection. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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7. Machine Learning and Location Verification in Vehicular Networks
- Author
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Ullah Ihsan, Robert Malaney, and Shihao Yan
- Subjects
Signal Processing (eess.SP) ,Vehicular ad hoc network ,business.industry ,Wireless network ,Computer science ,RSS ,05 social sciences ,050801 communication & media studies ,Context (language use) ,computer.file_format ,Machine learning ,computer.software_genre ,Base station ,0508 media and communications ,0502 economics and business ,FOS: Electrical engineering, electronic engineering, information engineering ,050211 marketing ,Artificial intelligence ,Electrical Engineering and Systems Science - Signal Processing ,business ,computer ,Intelligent transportation system ,5G ,Communication channel - Abstract
Location information will play a very important role in emerging wireless networks such as Intelligent Transportation Systems, 5G, and the Internet of Things. However, wrong location information can result in poor network outcomes. It is therefore critical to verify all location information before further utilization in any network operation. In recent years, a number of information-theoretic Location Verification Systems (LVSs) have been formulated in attempts to optimally verify the location information supplied by network users. Such LVSs, however, are somewhat limited since they rely on knowledge of a number of channel parameters for their operation. To overcome such limitations, in this work we introduce a Machine Learning based LVS (ML-LVS). This new form of LVS can adapt itself to changing environments without knowing the channel parameters. Here, for the first time, we use real-world data to show how our ML-LVS can outperform information-theoretic LVSs. We demonstrate this improved performance within the context of vehicular networks using Received Signal Strength (RSS) measurements at multiple verifying base stations. We also demonstrate the validity of the ML-LVS even in scenarios where a sophisticated adversary optimizes her attack location., Comment: 5 pages, 3 figures
- Published
- 2019
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8. Machine Learning-Enabled Power Scheduling in IoT-Based Smart Cities.
- Author
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Awan, Nabeela, Khan, Salman, Imam Rahmani, Mohammad Khalid, Tahir, Muhammad, Alam MD, Nur, Alturki, Ryan, and Ullah, Ihsan
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SMART cities ,PARTICLE swarm optimization ,TELECOMMUNICATION ,MACHINE learning ,INTERNET of things ,ENERGY management ,SMART power grids - Abstract
Recent advancements in hardware and communication technologies have enabled worldwide interconnection using the internet of things (IoT). The IoT is the backbone of smart city applications such as smart grids and green energy management. In smart cities, the IoT devices are used for linking power, price, energy, and demand information for smart homes and home energy management (HEM) in the smart grids. In complex smart grid-connected systems, power scheduling and secure dispatch of information are the main research challenge. These challenges can be resolved through various machine learning techniques and data analytics. In this paper, we have proposed a particle swarm optimization based machine learning algorithm known as a collaborative execute-before-after dependency-based requirement, for the smart grid. The proposed collaborative execute-before-after dependency-based requirement algorithm works in two phases, analysis and assessment of the requirements of end-users and power distribution companies. In the first phases, a fixed load is adjusted over a period of 24 h, and in the second phase, a randomly produced population load for 90 days is evaluated using particle swarm optimization. The simulation results demonstrate that the proposed algorithm performed better in terms of percentage cost reduction, peak to average ratio, and power variance mean ratio than particle swarm optimization and inclined block rate. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
9. Automated Meter Reading Detection Using Inception with Single Shot Multi-Box Detector.
- Author
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Iqbal, Arif, Basit, Abdul, Ali, Imran, Babar, Junaid, and Ullah, Ihsan
- Subjects
CONVOLUTIONAL neural networks ,SIGNAL convolution ,DETECTORS ,GERMANIUM radiation detectors ,MACHINE learning - Abstract
Automated meter reading has recently been adopted by utility service providers for improving the reading and billing process. Images captured during meter reading are incorporated in consumer bills to prevent reporting false reading and ensure transparency. The availability of images captured during the meter reading process presents the potential of completely automating the meter reading process. This paper proposes a convolutional network-based multi-box model for the automatic meter reading. The proposed research leverages the inception model with a single shot detector to achieve high accuracy and efficiency compared to the existing state-of-the-art machine learning methods. We tested the multi-box detector with Mobile-Net and Faster Region-based Convolutional Neural Networks (R-CNN). The results depict that the proposed method not only outperforms the two baseline methods, but also requires less iterations (epochs) to train and rapidly improve precision with 96% accuracy. The dataset used for this research has been collected, preprocessed, and made publicly available to encourage further research and to serve as a baseline for the comparison. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
10. Efficient data aggregation with node clustering and extreme learning machine for WSN.
- Author
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Ullah, Ihsan and Youn, Hee Yong
- Subjects
- *
MACHINE learning , *RADIAL basis functions , *KALMAN filtering , *DATA transmission systems - Abstract
Wireless sensor network is effective for data aggregation and transmission in IoT environment. Here, the sensor data often contain a significant amount of noises or redundancy exists, and thus, the data are aggregated to extract meaningful information and reduce the transmission cost. In this paper, a novel data aggregation scheme is proposed based on clustering of the nodes and extreme learning machine (ELM) which efficiently reduces redundant and erroneous data. Mahalanobis distance-based radial basis function is applied to the projection stage of the ELM to reduce the instability of the training process. Kalman filter is also used to filter the data at each sensor node before transmitted to the cluster head. Computer simulation with real datasets shows that the proposed scheme consistently outperforms the existing schemes in terms of clustering accuracy of the data and energy efficiency of WSN. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
11. Intelligent Data Fusion for Smart IoT Environment: A Survey.
- Author
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Ullah, Ihsan and Youn, Hee Yong
- Subjects
MULTISENSOR data fusion ,DATA transmission systems ,ARTIFICIAL intelligence ,ACQUISITION of data ,WIRELESS sensor networks - Abstract
Efficient data collection and communication are key tasks in smart IoT environment consisting of a large number of devices. Here imprecise data are generated due to the interferences between the devices and harsh operation condition, and therefore data fusion is needed to gather and extract useful data from multiple sources. A number of approaches for data fusion have been proposed which are based on probability, artificial intelligence, or evidence theory to efficiently aggregate the data. The techniques allow the system to be cognitive and intelligent in terms of decision-making under the uncertainty of data and limited resource. In this paper a comprehensive survey on the data fusion techniques for smart IoT system is presented. The challenges and opportunities with data fusion are also delineated. It will be useful for the researchers in developing the applications and services based on smart IoT environment, which require intelligent decision making. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
12. Predicting the Session of a P2P IPTV User through Support Vector Regression (SVR).
- Author
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Ali, Muhammad, Ullah, Ihsan, Noor, Waheed, Sajid, Ahthasham, 1., Abdul Basit, and Baber, Junaid
- Subjects
COMPUTER network architectures ,PEER-to-peer architecture (Computer networks) ,STREAMING video & television ,QUALITY of service ,MACHINE learning ,FORECASTING - Abstract
Scalability and ease of implementation make Peer-to-Peer (P2P) infrastructure an attractive option for live video streaming. Peer end-users or peers in these networks have extremely complex features and exhibit unpredictable behavior, i.e. any peer may join or exit the network without prior notice. Peers' dynamics is considered one of the key problems impacting the Quality of Service (QoS) of the P2P based IPTV services. Since, peer dynamics results in video disruption to consumer peers, for smooth video distribution, stable peer identification and selection is essential. Many research works have been conducted on stable peer identification using classical statistical methods. In this paper, a model based on machine learning is proposed in order to predict the length of a user session on entering the network. This prediction can be utilized in topology management such as offloading the departing peer before its exit. Consequently, this will help peers to select stable provider peers, which are the ones with longer session duration. Furthermore, it will also enable service providers to identify stable peers in a live video streaming network. Results indicate that the SVR based model performance is superior to an existing Bayesian network model. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
13. Un classifieur du comportement des utilisateurs dans les applications pair-à-pair de streaming vidéo
- Author
-
Ullah, Ihsan, Bonnet, Grégory, Doyen, Guillaume, Gaïti, Dominique, Environnement de Réseaux Autonomes (ERA), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS), Equipe MAD - Laboratoire GREYC - UMR6072, Groupe de Recherche en Informatique, Image et Instrumentation de Caen (GREYC), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU), UTC, Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-Université de Caen Normandie (UNICAEN), and Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] ,machine learning ,user behavior ,P2P networks ,multimedia applications ,ACM: C.: Computer Systems Organization/C.2: COMPUTER-COMMUNICATION NETWORKS - Abstract
Session QoS; International audience; Depuis quelques années, les applications de streaming vidéo pair à pair sont devenues de plus en plus populaires. Cependant, ces systèmes souffrent toujours de problèmes de performance du fait de la dépendance mutuelle des pairs pour la fourniture du contenu. De ce fait, le comportement individuel des utilisateurs qui les contrôlent influence directement la performance du service offert. L'étude du comportement des utilisateurs se présente alors comme une piste prometteuse pour définir des mécanismes de contrôle adaptatifs. Toutefois, la littérature ne propose que des modèles globaux qui considèrent des utilisateurs homogènes en comportement. Dans cet article, nous proposons un classifieur bayésien qui permet de rattacher un utilisateur à une classe de comportement. Ce classifieur est construit sur les relations de dépendances mesurées dans des implantations opérationnelles de systèmes de streaming vidéo pair à pair, et les classes de comportement que nous proposons sont issues de mesures individuelles. Afin de valider notre modèle, nous présentons les résultats de simulations effectuées sur un millier de pairs sur une période de cent jours. Enfin, nous montrons un exemple d'application de ce classifieur pour la construction des topologies virtuelles robustes à la dynamique du réseau.
- Published
- 2011
14. Classification of the Confocal Microscopy Images of Colorectal Tumor and Inflammatory Colitis Mucosa Tissue Using Deep Learning.
- Author
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Jeong, Jaehoon, Hong, Seung Taek, Ullah, Ihsan, Kim, Eun Sun, and Park, Sang Hyun
- Subjects
CONFOCAL microscopy ,DEEP learning ,COLON tumors ,COLITIS ,DATA augmentation - Abstract
Confocal microscopy image analysis is a useful method for neoplasm diagnosis. Many ambiguous cases are difficult to distinguish with the naked eye, thus leading to high inter-observer variability and significant time investments for learning this method. We aimed to develop a deep learning-based neoplasm classification model that classifies confocal microscopy images of 10× magnified colon tissues into three classes: neoplasm, inflammation, and normal tissue. ResNet50 with data augmentation and transfer learning approaches was used to efficiently train the model with limited training data. A class activation map was generated by using global average pooling to confirm which areas had a major effect on the classification. The proposed method achieved an accuracy of 81%, which was 14.05% more accurate than three machine learning-based methods and 22.6% better than the predictions made by four endoscopists. ResNet50 with data augmentation and transfer learning can be utilized to effectively identify neoplasm, inflammation, and normal tissue in confocal microscopy images. The proposed method outperformed three machine learning-based methods and identified the area that had a major influence on the results. Inter-observer variability and the time required for learning can be reduced if the proposed model is used with confocal microscopy image analysis for diagnosis. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
15. A Bayesian approach for user aware peer-to-peer video streaming systems
- Author
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Ullah, Ihsan, Doyen, Guillaume, Bonnet, Grégory, and Gaïti, Dominique
- Subjects
- *
BAYESIAN analysis , *COMPUTER users , *PEER-to-peer architecture (Computer networks) , *STREAMING video & television , *COMPUTER systems , *PERFORMANCE evaluation , *SIMULATION methods & models , *MACHINE learning - Abstract
Abstract: Peer-to-Peer (P2P) architectures for live video streaming has attracted a significant attention from both academia and industry. P2P design enables end-hosts to relay streams to each other overcoming the scalability issue of centralized architectures. However, these systems struggle to provide a service of comparable quality to that of traditional television. Since end-hosts are controlled by users, their behavior has a strong impact on the performance of P2P streaming systems, leading to potential service disruption and low streaming quality. Thus, considering the user behavior in these systems could bring significant performance improvements. Toward this end, we propose a Bayesian network that captures all the elements making part of the user behavior or related to it. This network is built from the information found in a cross-analysis of numerous large-scale measurement campaigns, analyzing the user behavior in video streaming systems. We validate our model through intensive simulations showing that our model can learn a user behavior and is able to predict several activities helping thus in optimizing these systems for a better performance. We also propose a method based on traces collection of the same user type that accelerates the learning process of this network. Furthermore, we evaluate the performance of this model through exploring its applications and comparison with non-contextual models. [Copyright &y& Elsevier]
- Published
- 2012
- Full Text
- View/download PDF
16. Wireless Location Verification and Acquisition Using Machine Learning
- Author
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Ullah, Ihsan ; https://orcid.org/0000-0003-2900-1165
- Subjects
- Location Verification, Location Acquisition, Machine Learning, Neural Networks, VANETs, ITS, IoT, Wireless Networks Security, anzsrc-for: 400899 Electrical engineering not elsewhere classified
- Abstract
Traditional wireless location verification (authentication) is only feasible under the assumption that radio propagation is described by simple time-independent mathematical models. A similar situation applies to location acquisition, albeit to a lesser extent. However, in real-world situations, channel conditions are rarely well-described by simple mathematical models. In this thesis, novel location verification and acquisition techniques that integrate machine learning algorithms into the decision process are designed, analysed, and tested. Through the use of both simulated and experimental data, it is shown how the novel solutions developed remain operational in unknown time-varying channel conditions, thus making them superior to existing solutions, and more importantly, deployable in real-world scenarios. Location verification will be of growing importance for a host of emerging wireless applications in which location information plays a pivotal role. The location verification solutions offered in this thesis are the first to be tested against experimental data and the first to invoke machine learning algorithms. As such, they likely form the foundation for all future verification algorithms.
- Published
- 2021
17. A new approach to neural network via double hierarchy linguistic information: Application in robot selection.
- Author
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Zhang, Yang, Abdullah, Saleem, Ullah, Ihsan, and Ghani, Fazal
- Subjects
- *
GREY relational analysis , *INDUSTRIAL robots , *AGGREGATION operators , *DIGITAL technology , *FUZZY neural networks , *TOPSIS method , *MACHINE learning - Abstract
Robotization is necessary to keep up with the constant changes in production, which calls for a staff with robotics expertise. A manufacturing business must also have the ability to swiftly change its production method. But today the procedure is drawn-out and complicated. In this study, inverse kinematics functionality and a machine learning model have been used to simulate an industrial robot's movement in a digital environment. By using machine learning, less time and money must be invested in developing the procedure and determining the robot's route. In this article, feed-forward double hierarchy linguistic neural networks with estimation information for double hierarchy linguistic term sets are proposed. First defined were the Yager operational rules and Yager aggregation operators for the double hierarchy linguistic terms set. Following that, we'll discuss fuzzy neurons, feed-forward neural networks, simple neural networks, hybrid neural networks, and the sigmoid function. After that, explain feed-forward, double-hierarchy linguistic neural networks, including how their output is calculated. The weight vector of expert's information is calculated by using the entropy measure with the help of Yager aggregation operators. Finally, we use the Yager t-norms to determine the output date of feed-forward double hierarchy linguistic neural networks and also find the output data. Linguistic neural network with Yager T-norms apply to the Robot selection for manufacturing bussing. The proposed approach of linguistic neural network are compared with Extended TOPSIS methods and GRA method for ranking. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
18. Improved spectral clustering using three-way decisions.
- Author
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Khan, Shahzad, Khan, Omar, Azam, Nouman, and Ullah, Ihsan
- Subjects
- *
MACHINE learning , *NOISE control , *TERNARY system , *MEASUREMENT errors , *APPROXIMATE reasoning - Abstract
Spectral clustering is an unsupervised machine learning algorithm that groups similar data points into clusters. The method generally works by modeling pair-wise data points as input similarity matrices, and then performs their eigen-decomposition. Clustering is then carried out from this high-dimensional representation by utilizing spectral properties. Here, several eigen-points are mapped and merged to a lower dimensional sub-space iteratively. In contrast to traditional methods, spectral clustering is well poised to solve problems involving complex patterns. However, the approach is sensitive to outliers, measurement errors, or perturbations in the original data. These then appear in the form of increased levels of spectral noise, especially in the higher ordered eigen-vectors. Consequently, the application of pre-processing and noise reduction techniques are important for its performance. In this article, we address this issue by introducing a three-way decision based approach to spectral clustering in order to make it insensitive to noise. Three-way decisions are classically applied to problems involving uncertainty and follow a ternary classification system involving actions of acceptance, rejection, and non-commitment. The proposed approach is tested on various standard datasets for verification and validation purposes. Results on the basis of these datasets demonstrate that the proposed approach outperforms classical spectral clustering by an average of 30%. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
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