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Multilabel Machine Learning System for Predicting Gender and Age Group from Human Gait 2

Multilabel Machine Learning System for Predicting Gender and...

2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Qomariyah, Nunung Nurul Nurul Fajar, Ahmad Yono, Vincent Kazakov, Dimitar Bina Nusantara University School of Computing and Creative Arts Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Information System Management Information Systems Management Department Jakarta11480 Indonesia University of York Computer Science Department YO10 5GH United Kingdom

ISBN: (纸本)9798350360523

This paper explores the development of a multilabel machine learning system for predicting both gender and age from human gait patterns. Gait analysis, a non-intrusive method of identifying subtle nuances in human movement, has proven to be a rich source of information related to demographic characteristics. The research extends beyond traditional single-label classification approaches, adopting a multilabel framework to simultaneously predict gender and age *** study evaluates various multilabel machine learning algorithms, with the Random k-labeLsets (RAKEL) algorithm demonstrating superior performance in predicting gender and age labels from human gait datasets. The accuracy of the algorithm can reach up to 87%. We also compared the multilabel approach with several multiclass algorithms such as Decision Tree, Random Forest, Gradient Boosting, K-Neighbors and XGBoost. However, when we also considering the training time, Classifier Chain algorithm showed the best trade off with the accuracy of 86% and the training time is twice faster than the RAKEL algorithm. © 2024 IEEE.

关键词： Gait analysis

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Automated Access Control via License Plate Recognition using Neocognitron Neural Network 36

Automated Access Control via License Plate Recognition using...

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36th International Conference on Information Technologies, InfoTech 2022

作者： Rothkrantz, Leon Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Department of Intelligent Systems Delft2628 CD Netherlands

ISBN: (数字)9781665468701

ISBN: (纸本)9781665468701

In 1979 Fukushima developed a hierarchical, multilayered neural network called Neocognitron and used it for the automatic recognition of handwritten Japanese symbols. We combined the Neocognitron classifier with a special image and segment processor and applied the system in 2001 for automatic recognition of license plates in laboratory experiments. In this paper we report about a special image acquisition module and a postprocessor. We tested the system in real life conditions in an application of automated access control. © 2022 IEEE.

关键词： Image segmentation

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Experimental evaluation of MU-MIMO in TDD environment for 5G NR using exploiting channel reciprocity

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4651-4666页

作者： Kumar, Rajesh Sinwar, Deepak Singh, Vijander Department of Computer and Communication Engineering Manipal University Jaipur Rajasthan Jaipur 303007 India Department of IoT and Intelligent Systems Manipal University Jaipur Rajasthan Jaipur 303007 India Department of ICT and Natural Sciences Faculty of Information Technology and Electrical Engineering NTNU - Norwegian University of Science and Technology Ålesund Norway

5G New Radio (NR) operates in two frequency ranges viz. Frequency Range 1 (sub-6 GHz band) and Frequency Range 2 (millimeter wave communication). It utilizes Time Division Duplex (TDD) communication, allowing the same frequency band for uplink and downlink transmission using sounding reference signals. These signals help estimate channel conditions for scheduling downlink traffic. Multi-User Multiple Input Multiple Output (MU-MIMO) technology enables simultaneous data transmission to multiple users using multiple antennas. This study examines MU-MIMO performance in TDD by analyzing variables like radio conditions, modulation schemes, code rates, precoding algorithms, and sub-carrier spacing. Simulation results show that Zero Forcing (ZF) precoding outperforms Block Diagonalization (BD) and Regularized Zero Forcing (RZF) in good radio conditions. However, in bad radio conditions, RZF performs better than ZF. Choosing higher code rates in 16 and 64-QAM and lower code rates in 256-QAM from the MCS table improves throughput. This indicates that MCS and user selection are crucial for higher throughput in TDD-based MU-MIMO for 5G NR. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： 5G new radio Beamforming MU-MIMO Precoding Sounding reference signal Time division duplex

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GACP:graph neural networks with ARMA flters and a parallel CNN for hyperspectral image classification

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International Journal of Digital Earth 2023年第1期16卷 1770-1800页

作者： Jing Yang Jie Sun Yaping Ren Shaobo Li Shujie Ding Jianjun Hu School of Mechanical Engineering Guizhou UniversityGuiyangPeople’s Republic of China State Key Laboratory of Public Big Data Guizhou UniversityGuiyangPeople’s Republic of China School of Intelligent Systems Science and Engineering Jinan UniversityZhuhaiPeople’s Republic of China Department of Computer Science and Engineering University of South CarolinaColumbiaSCUSA

In recent years,the use of convolutional neural networks(CNNs)and graph neural networks(GNNs)to identify hyperspectral images(HSIs)has achieved excellent results,and such methods are widely used in agricultural remote sensing,geological exploration,and marine remote *** many generalization classification algorithms are designed for the purpose of learning a small number of samples,there is often a problem of a low utilization rate of position information in the empty spectral *** on this,a GNN with an autoregressive moving average(ARMA)-based smoothingfilter samples the node information in the null spectral domain and then captures the spatial information at the pixel level via spatial feature convolution;then,the null spectral domain position information lost by the CNN is located by a coordinate attention(CA)***,autoregressive,spatial convolution,and CA mechanisms are combined into multiscale features to enhance the learning capacity of the network for tiny *** conducted on the widely used Indian Pines(IP)dataset,the Botswana(BS)dataset,Houton 2013(H2013),and the WHU-Hi-HongHu(WHU)benchmark HSI dataset demonstrate that the proposed GACP technique can perform classification work with good accuracy even with a small number of training examples.

关键词： Graph neural networks word convolutional neural networks hyperspectral image classification attention mechanisms

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Gaussian Process-based Traversability Analysis for Terrain Mapless Navigation

Gaussian Process-based Traversability Analysis for Terrain M...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Abe Leininger Mahmoud Ali Hassan Jardali Lantao Liu Department of Computer Science Indiana University Bloomington IN USA Department of Intelligent Systems Engineering Indiana University Bloomington

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Efficient navigation through uneven terrain remains a challenging endeavor for autonomous robots. We propose a new geometric-based uneven terrain mapless navigation framework combining a Sparse Gaussian Process (SGP) local map with a Rapidly-Exploring Random Tree* (RRT*) planner. Our approach begins with the generation of a high-resolution SGP local map, providing an interpolated representation of the robot’s immediate environment. This map captures crucial environmental variations, including height, uncertainties, and slope characteristics. Subsequently, we construct a traversability map based on the SGP representation to guide our planning process. The RRT* planner efficiently generates real-time navigation paths, avoiding untraversable terrain in pursuit of the goal. This combination of SGP-based terrain interpretation and RRT* planning enables ground robots to safely navigate environments with varying elevations and steep obstacles. We evaluate the performance of our proposed approach through robust simulation testing, highlighting its effectiveness in achieving safe and efficient navigation compared to existing methods. See the project GitHub 1 for source code and supplementary materials, including a video demonstrating experimental results.

关键词： Uncertainty Navigation Source coding Vegetation Gaussian processes Planning Vehicle dynamics

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Disaster recovery plan implementation evaluation model at the corporation 7

Disaster recovery plan implementation evaluation model at th...

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7th Information systems International Conference, ISICO 2023

作者： Sardjono, Wahyu Perdana, Widhilaga Gia Putra, Gustian Rama Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia Post Graduate Program of Management Science State University of Jakarta Jl. R.Mangun Muka Raya No.11 Jakarta Timur Jakarta13220 Indonesia Computer Science Study Program Faculty of Mathematics & Natural Sciences Pakuan University Bogor Indonesia

The application of information technology in addition to making internal business processes more effective and efficient, is also to improve customer service. This role is important for companies to maintain the sustainability of information systems as a strategic tool for competitive advantage so system availability is absolutely essential. Research related to the Disaster Recovery Plan (DRP) needs to be carried out to see the readiness of a corporation to build a backup system in different places and locations. By using the method of factor analysis and regression analysis to form an evaluation model, it will obtain an overview of what factors must be considered and what the current conditions are. The results of the study show that there are Core Technical DRP, Organizational Preparation, and Costing factors which are the main variables in supporting DRP readiness. The conclusion from this study, through these three new factors a mathematical model can be built as a reference for improving DRP implementation in corporations. © 2023 The Authors. Published by Elsevier B.V.

关键词： Factor analysis

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Systematic Literature Review of Topic Labeling

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IEEE Access 2025年 13卷 93124-93147页

作者： Salma Mekaoui Ilham Chaker Arsalane Zarghili Nikola S. Nikolov Department of Computer Science and Information Systems University of Limerick Limerick Ireland Laboratory of Intelligent Systems and Applications Faculty of Sciences and Technology Sidi Mohamed Ben Abdellah University Fes Morocco

The rapid growth of textual data on the web has led researchers to develop methods in Natural Language Processing (NLP) to process, understand, and identify topics. Among these methods, Topic Modeling helps extract relevant topics, represented as clusters of words. However, interpreting these clusters into meaningful topics remains a challenge. This limitation has led to further research into topic labeling, an approach for assigning comprehensive and semantically meaningful labels to topic modeling results, ensuring that they are interpretable and understandable from a human perspective. In this paper, we present a Systematic Literature Review (SLR) on topic labeling. This review explores its definition, geographical and time distribution, methodologies, datasets, evaluation methods, successes, and challenges. This paper presents an SLR on topic labeling, synthesizing insights from 41 high-quality studies. It serves as a rich source of information for researchers interested in investigating different approaches for discovering topics within textual data. It addresses the various aspects of topic labeling and includes discussions that highlight the challenges of this approach, encouraging further research in this field.

关键词： Labeling Systematic literature review Search problems Probabilistic logic Computational modeling Databases Data models Context modeling Usability Semantics

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Optimizing One-Stage Object Detection for URLLC: A Two-Stage Pipeline with Oculi SPU 25

Optimizing One-Stage Object Detection for URLLC: A Two-Stage...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Moghlebey, Adam Bou Moubayed, Abdallah Injadat, MohammadNoor Lebanese American University Electrical & Computer Engineering Department Byblos Lebanon King Fahd University of Petroleum & Minerals Computer Engineering Department Interdisciplinary Research Center for Intelligent Secure Systems Dhahran Saudi Arabia Zarqa University Data Science & Artificial Intelligence Department Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

Pedestrian-related accidents account for an estimated 20-25% of the approximately 1.19 million road fatalities occurring annually, highlighting the urgent need for enhanced detection systems in surveillance and smart transportation to mitigate such incidents. The escalating demand for Ultra-Reliable Low-Latency Communications (URLLC) in modern surveillance and transportation systems has become a solution to that problem but highlighted significant challenges in reducing inference times of machine learning models, particularly on constrained hardware. The maximum permissible latency for most vehicular applications ranges from 6 to 20 milliseconds based on the application. When tested on an 8GB RAM setup using the NTU, LLVIP, and VIRAT datasets, YOLOv8n achieved a minimum inference time of approximately 200 milliseconds per frame, making it unsuitable for URLLC applications despite 8GB RAM being an adequate hardware setup for edge devices. This research introduces a new two-stage pipeline that integrates Oculi's Sensing and Processing Unit (SPU) simulation with the YOLOv8n one-stage object detection model to enhance pedestrian detection, thereby addressing these challenges. By leveraging Regions of Interest (ROIs) generated by Oculi's SPU, our approach optimizes the input data for the YOLO model, substantially reducing computational overhead and thus making it suitable for URLLC applications. This integration can lead to a reduction in inference time by up to 93% in the tested frames when being compared to YOLOv8n pre-trained model. © 2024 IEEE.

关键词： Digital storage

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Comparing the Effect of Attention Mechanism and Aspect Embedding on the Performance of Long Short-term Memory Model for Aspect-Based Sentiment Analysis

Comparing the Effect of Attention Mechanism and Aspect Embed...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Heryadi, Yaya Wijanarko, Bambang Dwi Murad, Dina Fitria Tho, Cuk Hashimoto, Kiyota BINUS Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Information Systems Department Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket83120 Thailand

ISBN: (纸本)9798350320954

The attention mechanism is one of the key enablers which have positioned transformer models as the state-of-the-art models in Natural Language Processing. By having the attention mechanism, the first version (vanilla) transformer model can retrieve the hidden state but at the same time can maintain a low requirement on the context vector dimension by selectively choosing to which encoder hidden states to give priority attention. The objective of this study is to evaluate the effect of modified Long Short-term Memory models architecture on the model performance as a predictive model. The first model is modified by adding an attention mechanism to its architecture;whilst, the second model is modified by adding additional input to its architecture. The performance of both models is compared by assigning each model the same task: to address the aspect-based sentiment analysis task. The experiment results show that adding an attention mechanism is not significantly increase the performance of the Long Short-term Memory model in comparison to adding input to the same model. In particular, the average training accuracy of the former model is 0.885;whilst, the average training accuracy of the latter model is 0.942. © 2023 IEEE.

关键词： Sentiment analysis

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Proposing a visualized comparative review analysis model on tourism domain using Naïve Bayes classifier 8

Proposing a visualized comparative review analysis model on ...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Sano, Albert Verasius Dian Stefanus, Adriel Anderson Madyatmadja, Evaristus Didik Nindito, Hendro Purnomo, Agung Sianipar, Corinthias P.M. Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia Kyoto University Kyoto606-8502 Japan

This study, using the Naïve Bayes classifier, proposes a new descriptive model for conducting a comparative review analysis on the tourism domain. The proposed model seeks to improve the understanding of tourists' experiences by visually representing their opinions between various tourist destinations. The model predicts the sentiment of tourist reviews using the Naive Bayes classifier, which is then used to generate visualizations that facilitate comparative analysis. The comparison takes two tourism sites as experimental examples. The results, in terms of accuracy, show 80% and 64% for the first and the second site respectively. The study discusses the model's implementation, effectiveness, and potential applications in the tourism industry. The findings of the study suggest that the proposed model can provide valuable insights into tourist experiences and help tourism stakeholders, such as the managements of tourism sites, the future or soon coming tourists to make informed decisions based on comparative analysis. © 2023 The Authors. Published by Elsevier B.V.

关键词： Comparative review analysis Naïve Bayes classifier tourism

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