Efficient nonlinear classification via low-rank regularised least squares
- Authors: Fu, Zhouyu , Lu, Guojun , Ting, Kaiming , Zhang, Dengsheng
- Date: 2013
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 22, no. 7-8(2013), p. 1279-1289
- Full Text: false
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- Description: We revisit the classical technique of regularised least squares (RLS) for nonlinear classification in this paper. Specifically, we focus on a low-rank formulation of the RLS, which has linear time complexity in the size of data set only, independent of both the number of classes and number of features. This makes low-rank RLS particularly suitable for problems with large data and moderate feature dimensions. Moreover, we have proposed a general theorem for obtaining the closed-form estimation of prediction values on a holdout validation set given the low-rank RLS classifier trained on the whole training data. It is thus possible to obtain an error estimate for each parameter setting without retraining and greatly accelerate the process of cross-validation for parameter selection. Experimental results on several large-scale benchmark data sets have shown that low-rank RLS achieves comparable classification performance while being much more efficient than standard kernel SVM for nonlinear classification. The improvement in efficiency is more evident for data sets with higher dimensions.
Automatic image search based on improved feature descriptors and decision tree
- Authors: Hou, Jin , Chen, Zeng , Qin, Xue , Zhang, Dengsheng
- Date: 2011
- Type: Text , Journal article
- Relation: Integrated Computer-Aided Engineering Vol. 18, no. 2 (2011), p. 167-180
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- Description: There has been a growing interest in implementing image search engine at the semantic level. However, most existing practical systems including popular commercial image search engines like Google and Yahoo! are either text-based or a simple hybrid of texts and visual features. This paper proposes a novel image search system based on automatic image annotation. We develop a technology which learns semantic image concepts from image contents and transforms unstructured images into textual documents, so that images are indexed and retrieved in the same way as textual documents. Existing database management systems can be used to effectively manage image contents, and image search can be as efficient as text search by transforming images into textual documents through machine learning. Experiments in both the Corel dataset and real Web dataset are performed to validate our system and the results are promising. This system suggests a new combination of texts and visual features in order to achieve a semantic image search, and is expected to become a re-ranking system to the existing image search result via the Internet.
Learning sparse kernel classifiers for multi-instance classification
- Authors: Fu, Zhouyu , Lu, Guojun , Ting, Kaiming , Zhang, Dengsheng
- Date: 2013
- Type: Text , Journal article
- Relation: IEEE Transactions on Neural Networks and Learning Systems Vol. 24, no. 9 (2013), p. 1377-1389
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- Description: We propose a direct approach to learning sparse kernel classifiers for multi-instance (MI) classification to improve efficiency while maintaining predictive accuracy. The proposed method builds on a convex formulation for MI classification by considering the average score of individual instances for bag-level prediction. In contrast, existing formulations used the maximum score of individual instances in each bag, which leads to nonconvex optimization problems. Based on the convex MI framework, we formulate a sparse kernel learning algorithm by imposing additional constraints on the objective function to enforce the maximum number of expansions allowed in the prediction function. The formulated sparse learning problem for the MI classification is convex with respect to the classifier weights. Therefore, we can employ an effective optimization strategy to solve the optimization problem that involves the joint learning of both the classifier and the expansion vectors. In addition, the proposed formulation can explicitly control the complexity of the prediction model while still maintaining competitive predictive performance. Experimental results on benchmark data sets demonstrate that our proposed approach is effective in building very sparse kernel classifiers while achieving comparable performance to the state-of-the-art MI classifiers.
A review on automatic image annotation techniques
- Authors: Zhang, Dengsheng , Islam, Md , Lu, Guojun
- Date: 2012
- Type: Text , Journal article
- Relation: Pattern Recognition Letters Vol. 45, no. 1 (2012), p. 346-362
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- Description: Nowadays, more and more images are available. However, to find a required image for an ordinary user is a challenging task. Large amount of researches on image retrieval have been carried out in the past two decades. Traditionally, research in this area focuses on content based image retrieval. However, recent research shows that there is a semantic gap between content based image retrieval and image semantics understandable by humans. As a result, research in this area has shifted to bridge the semantic gap between low level image features and high level semantics. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) which extracts semantic features using machine learning techniques. In this paper, we focus on this latest development in image retrieval and provide a comprehensive survey on automatic image annotation. We analyse key aspects of the various AIA methods, including both feature extraction and semantic learning methods. Major methods are discussed and illustrated in details. We report our findings and provide future research directions in the AIA area in the conclusions
Music classification via the bag-of-features approach
- Authors: Fu, Zhouyu , Lu, Guojun , Ting, Kaiming , Zhang, Dengsheng
- Date: 2011
- Type: Text , Journal article
- Relation: Pattern Recognition Letters Vol. 32, no. 14 (2011), p. 1768-1777
- Full Text: false
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- Description: A central problem in music information retrieval is audio-based music classification. Current music classification systems follow a frame-based analysis model. A whole song is split into frames, where a feature vector is extracted from each local frame. Each song can then be represented by a set of feature vectors. How to utilize the feature set for global song-level classification is an important problem in music classification. Previous studies have used summary features and probability models which are either overly restrictive in modeling power or numerically too difficult to solve. In this paper, we investigate the bag-of-features approach for music classification which can effectively aggregate the local features for song-level feature representation. Moreover, we have extended the standard bag-of-features approach by proposing a multiple codebook model to exploit the randomness in the generation of codebooks. Experimental results for genre classification and artist identification on benchmark data sets show that the proposed classification system is highly competitive against the standard methods.
Vehicle trajectory clustering based on dynamic representation learning of internet of vehicles
- Authors: Wang, Wei , Xia, Feng , Nie, Hansong , Chen, Zhikui , Gong, Zhiguo
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 22, no. 6 (2021), p. 3567-3576
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- Description: With the widely used Internet of Things, 5G, and smart city technologies, we are able to acquire a variety of vehicle trajectory data. These trajectory data are of great significance which can be used to extract relevant information in order to, for instance, calculate the optimal path from one position to another, detect abnormal behavior, monitor the traffic flow in a city, and predict the next position of an object. One of the key technology is to cluster vehicle trajectory. However, existing methods mainly rely on manually designed metrics which may lead to biased results. Meanwhile, the large scale of vehicle trajectory data has become a challenge because calculating these manually designed metrics will cost more time and space. To address these challenges, we propose to employ network representation learning to achieve accurate vehicle trajectory clustering. Specifically, we first construct the k-nearest neighbor-based internet of vehicles in a dynamic manner. Then we learn the low-dimensional representations of vehicles by performing dynamic network representation learning on the constructed network. Finally, using the learned vehicle vectors, vehicle trajectories are clustered with machine learning methods. Experimental results on the real-word dataset show that our method achieves the best performance compared against baseline methods. © 2000-2011 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Feng Xia” is provided in this record**
Structural image retrieval using automatic image annotation and region based inverted file
- Authors: Zhang, Dengsheng , Islam, Md , Lu, Guojun
- Date: 2013
- Type: Text , Journal article
- Relation: Journal of Visual Communication and Image Representation Vol. 24, no. 7 (2013), p. 1087-1098
- Full Text: false
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- Description: Image retrieval has lagged far behind text retrieval despite more than two decades of intensive research effort. Most of the research on image retrieval in the last two decades are on content based image retrieval or image retrieval based on low level features. Recent research in this area focuses on semantic image retrieval using automatic image annotation. Most semantic image retrieval techniques in literature, however, treat an image as a bag of features/words while ignore the structural or spatial information in the image. In this paper, we propose a structural image retrieval method based on automatic image annotation and region based inverted file. In the proposed system, regions in an image are treated the same way as keywords in a structural text document, semantic concepts are learnt from image data to label image regions as keywords and weight is assigned to each keyword according to spatial position and relationship. As the result, images are indexed and retrieved in the same way as structural document retrieval. Specifically, images are broken down to regions which are represented using colour, texture and shape features. Region features are then quantized to create visual dictionaries which are similar to monolingual dictionaries like English or Chinese dictionaries. In the next step, a semantic dictionary similar to a bilingual dictionary like the English–Chinese dictionary is learnt to mapping image regions to semantic concepts. Finally, images are then indexed and retrieved using a novel region based inverted file data structure. Results show the proposed method has significant advantage over the widely used Bayesian annotation models.
An annotation rule extraction algorithm for image retrieval
- Authors: Chen, Zeng , Hou, Jin , Zhang, Dengsheng , Qin, Xue
- Date: 2012
- Type: Text , Journal article
- Relation: Pattern Recognition Letters Vol. 33, no. 10 (2012), p.1257-1268
- Full Text: false
- Reviewed:
- Description: Automatic image annotation can be used to facilitate semantic search in large image databases. However, retrieval performance of the existing annotation schemes is far from the users’ expectation. In this paper, we propose a novel method to automatically annotate image through the rules generated by support vector machines and decision trees. In order to obtain the rules, we collect a set of training regions by image segmentation, feature extraction and discretization. We first employ a support vector machine as a preprocessing technique to refine the input training data and then use it to improve the rules generated by decision tree learning. The preprocessing can effectively deal with the similar regions in an image as well. Moreover, we integrate the original rules to the modified ones, so as to formulate the complete and effective annotation rules. We can translate an unknown image into text by this algorithm, and the proposed system can retrieve images queried by both images and keywords. Experiments are carried out in a standard Corel dataset and images collected from the Web to test the accuracy and robustness of the proposed system. Experimental results show the proposed algorithm can annotate and retrieve images more efficiently than traditional learning algorithms.
Semantic image retrieval using region based inverted file
- Authors: Zhang, Dengsheng , Islam, Md , Lu, Guojun , Hou, Jin
- Date: 2009
- Type: Text , Journal article
- Relation: Journal of Visual Communication and Image Representation Vol. 24, no. 7 (2009), p.242-249
- Full Text: false
- Reviewed:
- Description: Image retrieval has lagged far behind text retrieval despite more than two decades of intensive research effort. Most of the research on image retrieval in the last two decades are on content based image retrieval or image retrieval based on low level features. Recent research in this area focuses on semantic image retrieval using automatic image annotation. Most semantic image retrieval techniques in literature, however, treat an image as a bag of features/words while ignore the structural or spatial information in the image. In this paper, we propose a structural image retrieval method based on automatic image annotation and region based inverted file. In the proposed system, regions in an image are treated the same way as keywords in a structural text document, semantic concepts are learnt from image data to label image regions as keywords and weight is assigned to each keyword according to spatial position and relationship. As the result, images are indexed and retrieved in the same way as structural document retrieval. Specifically, images are broken down to regions which are represented using colour, texture and shape features. Region features are then quantized to create visual dictionaries which are similar to monolingual dictionaries like English or Chinese dictionaries. In the next step, a semantic dictionary similar to a bilingual dictionary like the English–Chinese dictionary is learnt to mapping image regions to semantic concepts. Finally, images are then indexed and retrieved using a novel region based inverted file data structure. Results show the proposed method has significant advantage over the widely used Bayesian annotation models.
MODEL : motif-based deep feature learning for link prediction
- Authors: Wang, Lei , Ren, Jing , Xu, Bo , Li, Jianxin , Luo, Wei , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 7, no. 2 (2020), p. 503-516
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- Description: Link prediction plays an important role in network analysis and applications. Recently, approaches for link prediction have evolved from traditional similarity-based algorithms into embedding-based algorithms. However, most existing approaches fail to exploit the fact that real-world networks are different from random networks. In particular, real-world networks are known to contain motifs, natural network building blocks reflecting the underlying network-generating processes. In this article, we propose a novel embedding algorithm that incorporates network motifs to capture higher order structures in the network. To evaluate its effectiveness for link prediction, experiments were conducted on three types of networks: social networks, biological networks, and academic networks. The results demonstrate that our algorithm outperforms both the traditional similarity-based algorithms (by 20%) and the state-of-the-art embedding-based algorithms (by 19%). © 2014 IEEE.
Detecting outlier patterns with query-based artificially generated searching conditions
- Authors: Yu, Shuo , Xia, Feng , Sun, Yuchen , Tang, Tao , Yan, Xiaoran , Lee, Ivan
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 8, no. 1 (2021), p. 134-147
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- Description: In the age of social computing, finding interesting network patterns or motifs is significant and critical for various areas, such as decision intelligence, intrusion detection, medical diagnosis, social network analysis, fake news identification, and national security. However, subgraph matching remains a computationally challenging problem, let alone identifying special motifs among them. This is especially the case in large heterogeneous real-world networks. In this article, we propose an efficient solution for discovering and ranking human behavior patterns based on network motifs by exploring a user's query in an intelligent way. Our method takes advantage of the semantics provided by a user's query, which in turn provides the mathematical constraint that is crucial for faster detection. We propose an approach to generate query conditions based on the user's query. In particular, we use meta paths between the nodes to define target patterns as well as their similarities, leading to efficient motif discovery and ranking at the same time. The proposed method is examined in a real-world academic network using different similarity measures between the nodes. The experiment result demonstrates that our method can identify interesting motifs and is robust to the choice of similarity measures. © 2014 IEEE.
Tracing the Pace of COVID-19 research : topic modeling and evolution
- Authors: Liu, Jiaying , Nie, Hansong , Li, Shihao , Ren, Jing , Xia, Feng
- Date: 2021
- Type: Text , Journal article
- Relation: Big Data Research Vol. 25, no. (2021), p.
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- Description: COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial intelligence (AI) has greatly expedited the development of human society. In this paper, we investigate and visualize the on-going advancements of early scientific research on COVID-19 from the perspective of AI. By adopting the Latent Dirichlet Allocation (LDA) model, this paper allocates the research articles into 50 key research topics pertinent to COVID-19 according to their abstracts. We present an overview of early studies of the COVID-19 crisis at different scales including referencing/citation behavior, topic variation and their inner interactions. We also identify innovative papers that are regarded as the cornerstones in the development of COVID-19 research. The results unveil the focus of scientific research, thereby giving deep insights into how the academic society contributes to combating the COVID-19 pandemic. © 2021 Elsevier Inc. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Jing Ren and Feng Xia" is provided in this record**
- Description: COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial intelligence (AI) has greatly expedited the development of human society. In this paper, we investigate and visualize the on-going advancements of early scientific research on COVID-19 from the perspective of AI. By adopting the Latent Dirichlet Allocation (LDA) model, this paper allocates the research articles into 50 key research topics pertinent to COVID-19 according to their abstracts. We present an overview of early studies of the COVID-19 crisis at different scales including referencing/citation behavior, topic variation and their inner interactions. We also identify innovative papers that are regarded as the cornerstones in the development of COVID-19 research. The results unveil the focus of scientific research, thereby giving deep insights into how the academic society contributes to combating the COVID-19 pandemic. © 2021 Elsevier Inc.
Data-analytically derived flexible HbA1c thresholds for type 2 diabetes mellitus diagnostic
- Authors: Stranieri, Andrew , Yatsko, Andrew , Jelinek, Herbert , Venkatraman, Sitalakshmi
- Date: 2015
- Type: Text , Journal article
- Relation: Artificial Intelligence Research Vol. 5, no. 1 (2015), p. 111-134
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- Description: Glycated haemoglobin (HbA1c) is now more commonly used as an alternative test to the fasting plasma glucose and oral glucose tolerance tests for the identification of Type 2 Diabetes Mellitus (T2DM) because it is easily obtained using the point-of-care technology and represents long-term blood sugar levels. According to WHO guidelines, HbA1c values of 6.5% or above are required for a diagnosis of T2DM. However outcomes of a large number of trials with HbA1c have been inconsistent across the clinical spectrum and further research is required to determine the efficacy of HbA1c testing in identification of T2DM. Medical records from a diabetes screening program in Australia illustrate that many patients could be classified as diabetics if other clinical indicators are included, even though the HbA1c result does not exceed 6.5%. This suggests that a cutoff for the general population of 6.5% may be too simple and miss individuals at risk or with already overt, undiagnosed diabetes. In this study, data mining algorithms have been applied to identify markers that can be used with HbA1c. The results indicate that T2DM is best classified by HbA1c at 6.2% - a cutoff level lower than the currently recommended one, which can be even less, having assumed the threshold flexibility, if additionally to HbA1c being high the rule is conditioned on oxidative stress or inflammation being present, atherogenicity or adiposity being high, or hypertension being diagnosed, etc.
Integrated generalized zero-shot learning for fine-grained classification
- Authors: Shermin, Tasfia , Teng, Shyh , Sohel, Ferdous , Murshed, Manzur , Lu, Guojun
- Date: 2022
- Type: Text , Journal article
- Relation: Pattern Recognition Vol. 122, no. (2022), p.
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- Description: Embedding learning (EL) and feature synthesizing (FS) are two of the popular categories of fine-grained GZSL methods. EL or FS using global features cannot discriminate fine details in the absence of local features. On the other hand, EL or FS methods exploiting local features either neglect direct attribute guidance or global information. Consequently, neither method performs well. In this paper, we propose to explore global and direct attribute-supervised local visual features for both EL and FS categories in an integrated manner for fine-grained GZSL. The proposed integrated network has an EL sub-network and a FS sub-network. Consequently, the proposed integrated network can be tested in two ways. We propose a novel two-step dense attention mechanism to discover attribute-guided local visual features. We introduce new mutual learning between the sub-networks to exploit mutually beneficial information for optimization. Moreover, we propose to compute source-target class similarity based on mutual information and transfer-learn the target classes to reduce bias towards the source domain during testing. We demonstrate that our proposed method outperforms contemporary methods on benchmark datasets. © 2021 Elsevier Ltd
Diagnostic with incomplete nominal/discrete data
- Authors: Jelinek, Herbert , Yatsko, Andrew , Stranieri, Andrew , Venkatraman, Sitalakshmi , Bagirov, Adil
- Date: 2015
- Type: Text , Journal article
- Relation: Artificial Intelligence Research Vol. 4, no. 1 (2015), p. 22-35
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- Description: Missing values may be present in data without undermining its use for diagnostic / classification purposes but compromise application of readily available software. Surrogate entries can remedy the situation, although the outcome is generally unknown. Discretization of continuous attributes renders all data nominal and is helpful in dealing with missing values; particularly, no special handling is required for different attribute types. A number of classifiers exist or can be reformulated for this representation. Some classifiers can be reinvented as data completion methods. In this work the Decision Tree, Nearest Neighbour, and Naive Bayesian methods are demonstrated to have the required aptness. An approach is implemented whereby the entered missing values are not necessarily a close match of the true data; however, they intend to cause the least hindrance for classification. The proposed techniques find their application particularly in medical diagnostics. Where clinical data represents a number of related conditions, taking Cartesian product of class values of the underlying sub-problems allows narrowing down of the selection of missing value substitutes. Real-world data examples, some publically available, are enlisted for testing. The proposed and benchmark methods are compared by classifying the data before and after missing value imputation, indicating a significant improvement.
Intelligent energy prediction techniques for fog computing networks
- Authors: Farooq, Umar , Shabir, Muhammad , Javed, Muhammad , Imran, Muhammad
- Date: 2021
- Type: Text , Journal article
- Relation: Applied Soft Computing Vol. 111, no. (2021), p.
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- Description: Energy Efficiency is a key concern for future fog-enabled Internet of Things (IoT). Since Fog Nodes (FNs) are energy-constrained devices, task offloading techniques must consider the energy consumption of the FNs to maximize the performance of IoT applications. In this context, accurate energy prediction can enable the development of intelligent energy-aware task offloading techniques. In this paper, we present two energy prediction techniques, the first one is based on the Recursive Least Square (RLS) filter and the second one uses the Artificial Neural Network (ANN). Both techniques use inputs such as the number of tasks and size of the tasks to predict the energy consumption at different fog nodes. Simulation results show that both techniques have a root mean square error of less than 3%. However, the ANN-based technique shows up to 20% less root mean square error as compared to the RLS-based technique. © 2021 Elsevier B.V.
Extraction and processing of real time strain of embedded FBG sensors using a fixed filter FBG circuit and an artificial neural network
- Authors: Kahandawa, Gayan , Epaarachchi, Jayantha , Wang, Hao , Canning, John , Lau, Alan
- Date: 2013
- Type: Text , Journal article
- Relation: Measurement: Journal of the International Measurement Confederation Vol. 46, no. 10 (2013), p. 4045-4051
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- Description: Fibre Bragg Grating (FBG) sensors have been used in the development of structural health monitoring (SHM) and damage detection systems for advanced composite structures over several decades. Unfortunately, to date only a handful of appropriate configurations and algorithm sare available for using in SHM systems have been developed. This paper reveals a novel configuration of FBG sensors to acquire strain reading and an integrated statistical approach to analyse data in real time. The proposed configuration has proven its capability to overcome practical constraints and the engineering challenges associated with FBG-based SHM systems. A fixed filter decoding system and an integrated artificial neural network algorithm for extracting strain from embedded FBG sensor were proposed and experimentally proved. Furthermore, the laboratory level experimental data was used to verify the accuracy of the system and it was found that the error levels were less than 0.3% in predictions. The developed SMH system using this technology has been submitted to US patent office and will be available for use of aerospace applications in due course. © 2013 Elsevier Ltd. All rights reserved.
Image preprocessing in classification and identification of diabetic eye diseases
- Authors: Sarki, Rubina , Ahmed, Khandakar , Wang, Hua , Zhang, Yanchun , Ma, Jiangang , Wang, Kate
- Date: 2021
- Type: Text , Journal article
- Relation: Data Science and Engineering Vol. 6, no. 4 (2021), p. 455-471
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- Description: Diabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model’s development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity. © 2021, The Author(s).
Automated unsupervised authorship analysis using evidence accumulation clustering
- Authors: Layton, Robert , Watters, Paul , Dazeley, Richard
- Date: 2013
- Type: Text , Journal article
- Relation: Natural Language Engineering Vol. 19, no. 1 (2013), p. 95-120
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- Description: Authorship Analysis aims to extract information about the authorship of documents from features within those documents. Typically, this is performed as a classification task with the aim of identifying the author of a document, given a set of documents of known authorship. Alternatively, unsupervised methods have been developed primarily as visualisation tools to assist the manual discovery of clusters of authorship within a corpus by analysts. However, there is a need in many fields for more sophisticated unsupervised methods to automate the discovery, profiling and organisation of related information through clustering of documents by authorship. An automated and unsupervised methodology for clustering documents by authorship is proposed in this paper. The methodology is named NUANCE, for n-gram Unsupervised Automated Natural Cluster Ensemble. Testing indicates that the derived clusters have a strong correlation to the true authorship of unseen documents. © 2011 Cambridge University Press.
- Description: 2003010584
A comparison of bidding strategies for online auctions using fuzzy reasoning and negotiation decision functions
- Authors: Kaur, Preetinder , Goyal, Madhu , Lu, Jie
- Date: 2017
- Type: Text , Journal article
- Relation: IEEE Transactions on Fuzzy Systems Vol. 25, no. 2 (2017), p. 425-438
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- Description: Bidders often feel challenged when looking for the best bidding strategies to excel in the competitive environment of multiple and simultaneous online auctions for same or similar items. Bidders face complicated issues for deciding which auction to participate in, whether to bid early or late, and how much to bid. In this paper, we present the design of bidding strategies, which aim to forecast the bid amounts for buyers at a particular moment in time based on their bidding behavior and their valuation of an auctioned item. The agent develops a comprehensive methodology for final price estimation, which designs bidding strategies to address buyers' different bidding behaviors using two approaches: Mamdani method with regression analysis and negotiation decision functions. The experimental results show that the agents who follow fuzzy reasoning with a regression approach outperform other existing agents in most settings in terms of their success rate and expected utility.