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Deep Learning for Crime pattern recognition: A Study of Crime Hot Spots and High-Risk Areas

Deep Learning for Crime Pattern Recognition: A Study of Crim...

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2023 international conference on Recent Advances in Science and Engineering Technology, ICRASET 2023

作者： Rahul Harikumar, A. Grover, Tanay Kanchana, M. Srm Institute of Science & Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350306927

Crime is a global problem that affects individuals, communities, and societies at large. There are many different types of crime, and the rates at which they occur vary widely from country to country. While the need to be able to detect crime as it is happening is a very important part of safety and security, systems that can predict and analyze crimes are just as vital to a safer world. Understanding crime patterns and trends is crucial for effectively addressing the issue. One way to gain insights into crime is through the use of crime data analysis systems. These systems can help law enforcement agencies, policymakers, and researchers to identify patterns and trends in crime, as well as evaluate the effectiveness of different crime-prevention strategies. In our research we used four distinguished approaches: eXtreme Gradient Boosting (XGBoost), CatBoost, Tabnet, and a hybrid model 1 Dimensional Convolution Neural Network through three individual datasets to compare the results of each approach as well as a comparison between the three datasets. Our results demonstrated a very significant advantage of utilizing feature oriented datasets over a large sample size such as the Chicago dataset. We observed XGBoost provides the best results for Chicago and San Francisco 96.51% and 26.50% respectively. And as for Boston, we obtained best accuracy (20.80%) using the Bagging classifier. © 2023 IEEE.

关键词： Crime

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On the Possibilities of Using Traditional pattern recognition Techniques in Brazilian Banknote Characterization

On the Possibilities of Using Traditional Pattern Recognitio...

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international conference on Systems, Signals and Image Processing, IWSSIP

作者： Ronaldo S. Tavares Aura Conci André Gonçalves Computing Institute (IC / UFF) Universidade Federal Fluminense Niteroi Brazil Dept. of Computer Science (IC / UFF) Universidade Federal Fluminense Niteroi Brazil

ISBN: (数字)9798350391886

ISBN: (纸本)9798350391893

In Brazil there are seven kinds of banknotes where each one has a specific color associated to its correspondent value. Analysing the Brazilian banknote design, one can see that there are many different regions. In the region named Puzzle, which is formed by simple patterns present in both sides of banknotes, a simplified process of pattern recognition and classification can be proposed. That is, as each note presents a diverse pattern and color, such elements can be investigated to be used for recognizing their values. This paper aims to help with the accessibility for handling money in everyday life, studying the possibilities of classic image analysis to develop an application to improve the social inclusion of visually impaired people. To this end it is proposed to combine the use of Template Matching Technique and HSV Space Color. The Normalized Correlation Coefficient (NCC) was verified to be the best method, after the evaluation of six other methods using the OpenCV library tools and Python computer language. According to achieved results, it is possible to stipulate the value of banknotes associating the characteristic of hues and NCC using the here presented and justified approach.

关键词： Correlation coefficient Degradation Image color analysis Pigments pattern recognition Character recognition Spatial resolution

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Spark-KNN Parallel pattern recognition Technique for Substation Secondary Cable Data

Spark-KNN Parallel Pattern Recognition Technique for Substat...

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Electrical Engineering and Control Science (IC2ECS), international conference on

作者： Weizhou Lu Haiying Huang Huan Luo Guangdong Power Grid Maoming Power Supply Bureau China

With the rapid development of smart grid, the substation secondary cable condition monitoring data is growing exponentially and gradually constitutes the secondary circuit condition monitoring big data. The traditional computing architecture can no longer meet the computing performance demand. Combining Spark big data processing technology and AliCloud E-MapReduce cloud computing platform, we propose a parallel pattern recognition method for substation secondary cable condition monitoring big data, aiming to improve the ability of the secondary cable online monitoring system to quickly batch analyze the alarm monitoring data that suddenly increase in a short period of time. Spark-KNN, a parallelized K-nearest neighbor classification algorithm based on Spark, is designed to realize parallel pattern recognition of massive secondary cable monitoring data. The experimental results show that the average performance of Spark-KNN is 3.17 times higher than that of Hadoop MapReduce implementation, which is more suitable for performing real-time processing tasks of secondary cable monitoring big data.

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One-Shot Classification Is Enough for Automatic Label Mapping 27th

One-Shot Classification Is Enough for Automatic Label Mappi...

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27th international conference on pattern recognition, ICPR 2024

作者： Lin, Xiaowen Aysa, Alimjan Ubul, Kurban School of Computer Science and Technology Xinjiang University Ürümqi China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Ürümqi China Xinjiang Key Laboratory of Multilingual Information Technology Ürümqi China

ISBN: (纸本)9783031781681

In recent years, prompt-based learning has achieved some success in various natural language tasks. In text classification tasks, the construction of prompt templates and label mapping have a significant impact on the results. Therefore, many methods for constructing label mappings have emerged, such as manual and automatic construction. However, manual label construction is time-consuming, expensive, and has low scalability, while automatic label construction requires a large number of samples and other limitations. Therefore, we have explored a automatic label mapping method that only requires one sample. On the basis of only requiring one sample, we achieved good experimental results. In this paper, we propose an effective one-shot method for automatically constructing label mapping, ALaM. Our method is based on statistical algorithms and label semantic information for one to many label mapping. Our experiments demonstrate that ALaM achieves competitive performance on the GLUE benchmark without human effort or external resources. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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A 3D Printed soft Prosthetic Hand with Embedded Actuation and soft Sensing Capabilities for Directly and Seamlessly Switching Between Various Hand Gestures

A 3D Printed Soft Prosthetic Hand with Embedded Actuation an...

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IEEE/ASME international conference on Advanced Intelligent Mechatronics (AIM)

作者： Zhou, Hao Tawk, Charbel Alici, Gursel Univ Wollongong Sch Mech Mat Mechatron & Biomed Engn Appl Mechatron & Biomed Engn Res AMBER Grp Wollongong NSW 2522 Australia Univ Wollongong ARC Ctr Excellence Electromat Sci Wollongong NSW 2522 Australia Univ Wollongong Dubai Campus Dubai U Arab Emirates

ISBN: (纸本)9781665441391

In this study, a 3D printed soft robotic hand with embedded soft sensors is designed and developed to efficiently operate with new-generation myoelectric control systems, e.g., pattern recognition control (PRC) and simultaneous proportional control (SPC). The whole hand (ACES-V2) is fabricated as a monolithic structure using a low-cost and open-source 3D printer and it requires minimal post-processing for the addition of its embedded sensors. These are significant benefits for a medical device that is required to be lightweight and anthropomorphic. With the addition of position sensors, the avoidance of self-collision is obtained without returning the prosthetic hand to its neutral fully-opening position when it is commanded to switch between different gestures. This can make the transition between the hand gestures much faster, more efficient, and more intuitive as well. Further, initial contact detection of each finger is achieved for preshaping of multi-finger grasps, e.g., tripod grip and power grasps, to improve the stability of the grasps consequently.

关键词： Three-dimensional displays Medical devices Proportional control Switches soft robotics Sensors pattern recognition

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Investigation on Deployment pattern of Wi-Fi Transceivers for CSI-Based Indoor Localization and Activity recognition

Investigation on Deployment Pattern of Wi-Fi Transceivers fo...

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international conference on Mobile computing and Ubiquitous Networking (ICMU)

作者： Kiichiro Kai Hyuckjin Choi Yutaka Arakawa ISEE Kyushu University Fukuoka Japan

In recent years, many studies explore CSI-based Wi-Fi sensing produce high accurate results in device-free localization, human activity recognition. Channel state information (CSI) represents how wireless signals propagate from transmitter to receiver and provides rich information to identify human presence. By collecting CSI from the specific devices, the machine learning model can be trained to recognize the human activity. However, in multi-room residential settings where walls and furniture obstruct signals, effective coverage with a limited number of transceivers becomes a crucial challenge, underlining the importance of their optimal placement. In this paper, we deployed multiple transceivers in a smart home environment in our university and studied transceiver arrangement patterns for CSI-based indoor localization and activity recognition. We compared the accuracy of localization and activity recognition for a total of 18 patterns consisting of up to five transceivers. In the activity recognition, we used Support Vector Machine (SVM) to classify whether or not a person is moving. The results show that the pattern using only one pair of transceivers achieved an accuracy 85% and covered the entire house. Meanwhile, in the localization we used Light Gradient Boosting Machine (LGBM) to classify which room the person is. The results show that accu-racy decreases as the number of devices is reduced. Therefore, we investigated deployment pattern to achieve accuracy with smaller number of transceivers.

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Modified Expressions to Evaluate the Correlation Coefficient between Two Dual Hesitant Fuzzy soft Sets and Their Application in Decision-Making

Modified Expressions to Evaluate the Correlation Coefficient...

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2022 international conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Singh, Akanksha Department of Mathematics-UIS Chandigarh University Mohali Punjab Gharuan India

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

This paper is the study of the existing correlation coefficients (CCoefs) to evaluate the meaningful relation/dependency between two variables of the extended fuzzy sets for solving decision-making (DeM) problems. To understand the weighted CCoefs between two variables the considered domain is the dual hesitant fuzzy soft set (DHFSS) which is an extension of a fuzzy set incorporating properties of both the dual hesitant fuzzy set (DHFS) (which considers the hesitancy degree of both the membership and non-membership elements of a set) and soft set (SS) (which gives the importance to each parameter in an alternative). This strive suggests improved weighted CCoefs for solving DHFSS problems from the existing weighted CCoefs for solving DHFSS multi-criteria decision-making (MCDM) problems (DeM problems in which the rating value of each alternative over each criterion is depicted by a DHFSS) in a real-world. Also, mathematical verification is presented which clearly states the validation of the proposed expressions of weighted CCoefs for solving DHFSS MCDM problems. To show the practical application of the proposed weighted CCoefs for solving DHFSS MCDM problems, solutions to some real-world problems like- recruitment, medical diagnosis, and pattern recognition are determined, and a methodical comparison of the results are shown between the considered real-life MCDM problems by the existing approach and by the proposed approach interpretation. © 2022 IEEE.

关键词： Decision making

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Integrated Web-Based Platform for AI-Enhanced Autism Assessment

Integrated Web-Based Platform for AI-Enhanced Autism Assessm...

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Emerging Research in Computational Science (ICERCS), international conference on

作者： Yasir Abdullah R Kamesh RK Kavin Prabu S Sri Subiksha T Venkatraman P Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

This research presents a novel web-based platform that leverages facial and vocal tracking to enhance the preliminary screening process for Autism Spectrum Disorder (ASD). By incorporating advanced machine learning algorithms to analyze multimodal data, the platform offers a non-invasive, accessible, and cost-effective solution for initial ASD evaluation. The system demonstrates high diagnostic accuracy, achieving 90% in facial pattern analysis, 85% in vocal pattern analysis, and 95% accuracy when both analyses are combined. Additionally, it includes communication features designed to foster a supportive environment for individuals with ASD. This paper provides an overview of the platform’s development, functionality, and future potential, with an emphasis on its capacity to improve existing diagnostic methods and its accessibility for underserved communities.

关键词： Deep learning Autism Accuracy Machine learning algorithms Scientific computing Face recognition Medical services Speech recognition pattern analysis Artificial intelligence

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Blockchain-Based Multirole Authentication and Authorization in Smart Contracts with a Hierarchical Factory pattern

Blockchain-Based Multirole Authentication and Authorization ...

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Blockchain computing and Applications (BCCA), international conference on

作者： Muhammad Bin Saif Sara Migliorini Fausto Spoto Department of Computer Science University of Verona Verona Italy

ISBN: (数字)9798350351538

ISBN: (纸本)9798350351545

The recent spread of smart contracts in several application domains presents challenges in scalability, efficiency, management, and security. In this regard, some design patterns have been proposed to factorize common parts, prevent replicated deployment by promoting reusability, and apply some role-based access control (RBAC) techniques during smart contract execution. In particular, the factory pattern has been widely recognized as a common practice to solve the scalability issue, reducing also the increased gas costs related to both smart contract deployment and its subsequent use. However, in real-world applications, the factory pattern can be considered too limited since it only allows the instantiation of a single type or family of contracts. On the other side, no solutions have been proposed that tightly integrate a factory pattern with RBAC functionalities. In this paper, we propose an extension of the factory pattern, called the hierarchical factory pattern, which integrates a multirole authentication and authorization mechanism tailored explicitly to this pattern since it allows a secure, efficient, and role-specific interaction between the various layers of the hierarchy. The paper ends with some considerations about performance and scalability.

关键词： Authorization Costs Scalability Smart contracts Authentication Production facilities pattern recognition Blockchains Optimization

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Decomposed soft Prompt Guided Fusion Enhancing for Compositional Zero-Shot Learning

Decomposed Soft Prompt Guided Fusion Enhancing for Compositi...

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conference on Computer Vision and pattern recognition (CVPR)

作者： Xiaocheng Lu Song Guo Ziming Liu Jingcai Guo Department of Computing The Hong Kong Polytechnic University Shenzhen Research Institute The Hong Kong Polytechnic University

Compositional Zero-Shot Learning (CZSL) aims to recognize novel concepts formed by known states and objects during training. Existing methods either learn the combined state-object representation, challenging the generalization of unseen compositions, or design two classifiers to identify state and object separately from image features, ignoring the intrinsic relationship between them. To jointly eliminate the above issues and construct a more robust CZSL system, we propose a novel framework termed Decomposed Fusion with soft Prompt (DFSP) 1 1 Code is available at: https://***/Forest-art/***, by involving vision-language models (VLMs)for unseen composition recognition. Specifically, DFSP constructs a vector combination of learnable soft prompts with state and object to establish the joint representation of them. In addition, a cross-modal decomposed fusion module is designed between the language and image branches, which decomposes state and object among language features instead of image features. Notably, being fused with the decomposed features, the image features can be more expressive for learning the relationship with states and objects, respectively, to improve the response of unseen compositions in the pair space, hence narrowing the domain gap between seen and unseen sets. Experimental results on three challenging benchmarks demonstrate that our approach significantly outperforms other state-of-the-art methods by large margins.

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