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5th International Conference on Innovative Trends in Information Technology (ICITIIT)

作者： Sateesh, Nayani Kaur, Kuljeet Lakshminarayana, M. Vekariya, Vipul Patil, Harshal Maranan, Ramya CVR Coll Engn Dept Comp Sci & Engn Hyderabad 501510 Telangana India Graph Era Deemed Be Univ Dept Management Dehra Dun 248002 Uttarakhand India MS Ramaiah Inst Technol Dept Med Elect Engn Bengaluru 560054 Karnataka India Parul Univ Parul Inst Engn & Technol Dept Comp Sci & Engn Waghodia 391760 Gujarat India Symbiosis Int Deemed Univ Symbiosis Inst Technol Dept Comp Sci & Engn Pune 412115 Maharashtra India SIMATS Saveetha Sch Engn Dept Res & Innovat Chennai 602105 Tamil Nadu India

ISBN: (纸本)9798350386813;9798350386820

The ever-expanding volume of scientific literature necessitates innovative solutions for efficient information organization and retrieval. This final qualifying work focuses on the development of a robust algorithm for the automated categorization of articles in a scientific journal. The primary objective is to streamline the process of classifying diverse research contributions, thereby enhancing accessibility and knowledge discovery within scholarly domains. The goal of the final qualifying work is to develop an algorithm for automated categorization of articles in a scientific journal. To achieve this goal, an application is developed for the administrator of a scientific journal, allowing for the preparation, classification and visualization of data.

关键词： Automated Categorization Scientific Journal Articles Natural Language Processing Machine Learning Textual Analysis Information Retrieval Knowledge Management classification algorithm

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Comparative Performance Exploration of Different Machine Learning and Deep Learning algorithms for classification of Hand Wrist Gestures 2

Comparative Performance Exploration of Different Machine Lea...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Tiwari, Bhawna Gupta, Sindhu Hak Balyan, Vipin Amity University Noida India Gl Bajaj Institute of Technology & Management Greater Noida India Amity University Department of Electronics and Communications Noida India Cape Peninsula University of Technology Department of Electrical Electronics and Computer Engineering Cape Town South Africa

ISBN: (纸本)9798350371055

In the current scenario, reliable recognition and classification of hand wrist gestures are gaining high demand for numerous applications including health care application, for Sign language recognition, and in robotics for managing mobile robots etc. This paper highlights the comparative evaluation of various machine learning and deep learning algorithms for classification of hand wrist gestures in terms of classification accuracies, confusion matrix, recall, and precision. This research presents the experiment conducted on ten different subjects demonstrates six different hand wrist gestures for creation of dataset based on variation in scattering parameters of designed wearable Antennas. © 2024 IEEE.

关键词： Body worn antenna classification algorithm deep learning hand wrist gestures Machine learning

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Research on Online Shopping Mall Product classification Recommendation Based on Graph Neural Network 3

Research on Online Shopping Mall Product Classification Reco...

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3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Yi, Jucai Deng, Ye Software College Jiangxi Agricultural University Jiangxi Nanchang China

ISBN: (纸本)9798350380989

The practical application of graph neural networks in online shopping malls is a significant area within real-life scenarios. As neural network technology continually advances, an increasing emphasis is being placed on how to efficiently leverage graph data for enhancing the functionality and performance of these e-commerce platforms. Nonetheless, prevailing methodologies commonly employ conventional adjacency matrices or basic attention mechanisms to consolidate and model data pertaining to items, users, and their interactions. To some extent, these approaches overlook the intricacy of graph structures and prove challenging to implement effectively on a grand scale. Running on graph data requires a large amount of computing resources, leading to performance issues. To address these issues, we propose a new method based on hierarchical transformation aggregation model to process graph data in online shopping malls more efficiently. This method first selects nodes at various levels and regions through random walks, and then uses graph convolutional neural networks (GCN) for information aggregation. Upon acquiring node features for each layer, a transformed attention mechanism is further introduced to more effectively consolidate the node features from every layer, thereby obtaining the ultimate representation of each node. This method better captures the local and global characteristics of graph data by considering node information at different levels, improving the performance and user experience of the mall. Ultimately, we substantiate the dependability of this model through multiple real-world dataset validations, thereby evidencing its practical application feasibility in online shopping malls. This method based on the hierarchical transformation aggregation model provides a more efficient way for malls to process and analyze graph data, and is expected to improve the performance of recommendation systems, personalized recommendations, and user shopping experienc

关键词： classification algorithm GCN neural network online mall

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Building an accurate hardware Trojan detection technique from inaccurate simulation models and unlabelled ICs

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IET COMPUTERS AND DIGITAL TECHNIQUES 2019年第4期13卷 348-359页

作者： Xue, Mingfu Bian, Rongzhen Wang, Jian Liu, Weiqiang Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Jiangsu Peoples R China

Most of prior hardware Trojan detection approaches require golden chips for references. A classification-based golden chips-free hardware Trojan detection technique has been proposed in the authors' previous work. However, the algorithm in that work is trained by simulated ICs without considering a shift between the simulation and silicon fabrication. In this study, a co-training based hardware Trojan detection method by exploiting inaccurate simulation models and unlabeled fabricated ICs is proposed to provide reliable detection capability when facing fabricated ICs, which eliminates the need of golden chips. Two classification algorithms are trained using simulated ICs. These two algorithms can identify different patterns in the unlabelled ICs during test-time, and thus can label some of these ICs for the further training of the other algorithm. Moreover, a statistical examination is used to choose ICs labelling for the other algorithm. A statistical confidence interval based technique is also used to combine the hypotheses of the two classification algorithms. Furthermore, the partial least squares method is used to preprocess the raw data of ICs for feature selection. Both EDA experiment results and field programmable gate array (FPGA) experiment results show that the proposed technique can detect unknown Trojans with high accuracy and recall.

关键词： classification-based golden chips-free hardware Trojan detection technique invasive software pattern classification statistical confidence interval based technique embedded systems electronic design automation simulation labelled ICs fabricated ICs authors ICs labelling inaccurate simulated models detection performance Hardware Trojans classification algorithm field programmable gate arrays unlabelled ICs prior hardware Trojan detection approaches reliable detection capability integrated circuit industry co-training based hardware Trojan detection method simulated ICs IC co-training method final detection decision unknown Trojans electronic design automation inaccurate simulation models accurate hardware Trojan detection technique silicon fabrication

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Assessment of Driver Mental Fatigue Using Facial Landmarks

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IEEE ACCESS 2019年 7卷 150423-150434页

作者： Cheng, Qian Wang, Wuhong Jiang, Xiaobei Hou, Shanyi Qin, Yong Beijing Inst Technol Sch Mech Engn Dept Ind Engn Beijing 100081 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China

Driver fatigue is one of the causal factors for traffic accidents. Actions of facial units convey various information from our brains. This paper proposed a comprehensive approach to explore whether pattern of sequences of the drivers facial landmarks changes from the alert start to the fatigue state. A driving-simulator-based experiment was designed and conducted. Videos of 21 participants faces were recorded during the experiment, together with subjective and others assessment of the level of alertness. Sequences of eye aspect ratio (EAR) and mouth aspect ratio (MAR) were calculated based on facial landmarks. Totally 21 feature candidates including Percent eye-closure over a fixed time window (PERCLOS), blink rate, statistics of blink duration, closing speed, reopening speed and number of yawns were extracted. A mental fatigue assessment model is proposed after feature selection. Four machine learning algorithms were used to build the assessment model of fatigue. The best performance was achieved by logistic regression, with cross-validation accuracies of 83.7 and 85.4. This study may contribute to the development of driver fatigue monitoring system for automotive ergonomics.

关键词： Fatigue Roads Vehicles Circadian rhythm Electroencephalography Support vector machines classification algorithm driver's mental fatigue eye blink parameters facial landmarks

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Research and Application of Digital Media Object classification Method Based on Large Interval Distribution Learning 3

Research and Application of Digital Media Object Classificat...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Pu, Li'e Ru, Huasuo Tang, Yufang Yunnan Land and Resources Vocational College Yunnan Kunming650500 China

ISBN: (纸本)9798350319125

Digital media object classification plays an important role in digital media, but there is a problem of low classification accuracy. Deep learning algorithms cannot improve the accuracy of digital media object classification. Therefore, this paper proposes a large-interval distribution learning algorithm to classify digital media objects. Digital media objects are analyzed through distributed learning, and indicators are divided according to classification requirements. The large interval distribution learning algorithm forms the digital media object classification scheme, and the formed classification scheme is comprehensively analyzed. The simulation results show that the classification accuracy and classification efficiency of the proposed algorithm are better than those of the deep learning algorithm. © 2023 IEEE.

关键词： arge interval distribution learning classification algorithm digital media object classification

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A multiple classifiers time-serial ensemble pruning algorithm based on the mechanism of forward supplement

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APPLIED INTELLIGENCE 2023年第5期53卷 5620-5634页

作者： Shen, Yan Jing, Luyi Gao, Tian Song, Zizhao Ma, Ji Jiangsu Univ Dept Informat Management & Informat Syst Zhenjiang 212013 Jiangsu Peoples R China Jiangsu Univ Sch Management Zhenjiang 212013 Jiangsu Peoples R China

Because there are lots of typical applications and urgent needs, the research on the efficient classification learning about accumulated big data in nonstationary environments has become one of the hot topics in the field of data mining recently. The LearnNSE algorithm is an important research result in this field. For the long-term accumulated big data, the LearnNSE-Pruned-Age, a pruning version of LearnNSE, was given, which has received widespread attentions. However, it is found that the pruning mechanism of the LearnNSE-Pruned-Age algorithm is not perfect, which lost the core ability of the LearnNSE algorithm to reuse the learned classification knowledge. Therefore, the ensemble mechanism of LearnNSE is adjusted in this paper, and a novel ensemble mechanism is designed. The new mechanism uses the integration of the latest base-classifiers to track the changes of the data generation environment, and then selects the old base-classifiers that contribute to the current classification for forward supplementary integration. On this basis, a new pruned algorithm named FLearnNSE-Pruned-Age is proposed. The experiment results show that the FLearnNSE-Pruned-Age algorithm has the ability to reuse the learned classification knowledge and it can achieve the very close classification accuracy compared to LearnNSE, even better in some scenes. In addition, it improves the efficiency of ensemble learning and is suitable for the fast classification learning of accumulated big data.

关键词： classification algorithm Big data mining Ensemble learning Nonstationary environment

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Optimizing unsupervised classifications of remotely sensed imagery with a data-assisted labeling approach

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COMPUTERS & GEOSCIENCES 2008年第12期34卷 1877-1885页

作者： Lang, Ruili Shao, Guofan Pijanowski, Bryan C. Farnsworth, Richard L. Purdue Univ Dept Forestry & Nat Resources W Lafayette IN 47907 USA

The quality of remotely sensed land use and land cover (LULC) maps is affected by the accuracy of image data classifications. Various efforts have been made in advancing Supervised or unsupervised classification a methods to increase the repeatability and accuracy of LULC mapping. This study incorporates a data-assisted labeling approach (DALA) into the unsupervised classification of remotely sensed imagery. The DALA-unsupervised classification algorithm consists of three steps: (1) creation of N spectral-class maps using Iterative Self-Organizing Data Analysis Technique algorithm (ISODATA);(2) development of LULC maps with assistance of reference data;and (3) accuracy assessments of all the LULC maps using independent reference data and selection or one LULC map with the highest accuracy. classification experiments with a composite image of a Landsat Thematic Mapper (TM) image and tin Enhanced Thematic Mapper Plus (ETM +) image suggest that DALA was effective in making unsupervised classification process more objective, automatic, and accurate. A comparison between the DALA-unsupervised classifications and some conventional classifications suggests that the DALA-unsupervised classification algorithm yielded better classification accuracies compared to these conventional approaches. Such a simple, effective approach has not been systematically examined before but has great potential for many applications in the geosciences. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： LULC mapping Automated classification Landsat classification accuracy classification algorithm

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A radiosity-based method to avoid calibration for indoor positioning systems

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 105卷 89-101页

作者： Belmonte-Fernandez, Oscar Montoliu, Raul Torres-Sospedra, Joaquin Sansano-Sansano, Emilio Chia-Aguilar, Daniel Jaume I Univ Inst New Imaging Technol Castellon De La Plana 12071 Spain

Due to the widespread use of mobile devices, services based on the users current indoor location are growing in significance. Such services are developed in the Machine Learning and Experst Systems realm, and ranges from guidance for blind people to mobile tourism and indoor shopping. One of the most used techniques for indoor positioning is WiFi fingerprinting, being its use of widespread WiFi signals one of the main reasons for its popularity, mostly on high populated urban areas. Most issues of this approach rely on the data acquisition phase;to manually sample WiFi RSSI signals in order to create a WiFi radio map is a high time consuming task, also subject to re-calibrations, because any change in the environment might affect the signal propagation, and therefore degrade the performance of the positioning system. The work presented in this paper aims at substituting the manual data acquisition phase by directly calculating the WiFi radio map by means of a radiosity signal propagation model. The time needed to acquire the WiFi radio map by means of the radiosity model dramatically reduces from hours to minutes when compared with manual acquisition. The proposed method is able to produce competitive results, in terms of accuracy, when compared with manual sampling, which can help domain experts develop services based on location faster. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Indoor positioning Radiosity classification algorithm Machine learning

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Spatio-temporal K-NN prediction of traffic state based on statistical features in neighbouring roads

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第5期40卷 9059-9072页

作者： Priambodo, Bagus Ahmad, Azlina Kadir, Rabiah Abdul Univ Kebangsaan Malaysia IIR4 0 Bangi 43600 Selangor Malaysia Univ Mercu Buana Fac Comp Sci Jakarta Indonesia

Traffic congestion on a road results in a ripple effect to other neighbouring roads. Previous research revealed existence of spatial correlation on neighbouring roads. Similar traffic patterns with regards to day and time can be seen amongst roads in a neighbouring area. Presently, nonlinear models of neural network are applied on historical data to predict traffic congestion. Even though neural network has successfully modelled complex relationships, more time is needed to train the network. A non-parametric approach, the k-nearest neighbour (K-NN) is another method for forecasting traffic condition which can capture the nonlinear characteristics of traffic flow. An earlier study has been done to predict traffic flow using K-NN based on connected roads (both downstream and upstream). However, impact of road congestion is not only to connected roads, but also to roads surrounding it. Surrounding roads that are impacted by road congestion are those having 'high relationship' with neighbouring roads. Thus, this study aims to predict traffic state using K-NN by determining high relationship roads within neighbouring roads. We determine the highest relationship neighbouring roads by clustering the surrounding roads by combining grey level co-occurrence matrix (GLCM) with k-means. Our experiments showed that prediction of traffic state using K-NN based on high relationship roads using both GLCM and k-means produced better accuracy than using k-means only.

关键词： classification algorithm clustering algorithm machine learning algorithm nearest neighbour search intelligent transportation system

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