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Utilising Deep Learning as a Law Enforcement Ally 3

Utilising Deep Learning as a Law Enforcement Ally

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3rd international conference on Smart Data Intelligence, ICSMDI 2023

作者： Ramani, D. Roja Nirmala, M. Sourabh, V.N. Chaudhary, Shreyas Kumar, Deepesh New Horizon College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665464871

Finding fugitive offenders after they have committed a crime or an illegal act takes time and effort. It is challenging for law enforcement authorities to complete this work on their own given the rising population density and the size of any nation's landmass. Thus, public participation becomes crucial, revolutionary, and beneficial. This cycle is both times and works seriously. In this paper, we tried to suggest a different framework for criminal Distinguishing & recognition using Deep learning and Heroku Cloud, i.e. Cloud computing, which, assuming it is used by our Crime Control Organizations, would help them catch criminals from CCTV images or images uploaded by the public if seen anywhere. This system is in place to assist in capturing criminals and anyone who can upload information indicating that they saw the relevant individual at a specific location and time. In India, where conditions are always changing due to things like light, weather, and specific directions, existing solutions use conventional face acknowledgement computations, which might be problematic because there is no open public contribution. Our research paper employs LBPH, Deep Learning, and Heroku Cloud technologies to construct the system. © 2023 IEEE.

关键词： Local binary pattern

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28th international conference on Computational and Experimental Engineering and Sciences, ICCES 2022

28th International Conference on Computational and Experimen...

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28th international conference on Computational and Experimental Engineering and Sciences, ICCES 2022

ISBN: (纸本)9783031020964

The proceedings contain 30 papers. The special focus in this conference is on Computational and Experimental Engineering and Sciences. The topics include: Qualitative Study of a Class of Exponential Difference Equations with Periodic Coefficient;research on the Digital Image Processing Method Based on Parallel computing;rapid pattern recognition of Electric Submersible Pump Ammeter Card Based on Artificial Neural Network;the Application of Encryption Algorithm in Information Security Reflected;multiscale Finite Element Technique for Mathematical Modelling of Multi-physics Processes in Heterogeneous Media;multilevel Modeling of Woven Composite Shell Structure;study on Rock-Breaking Mechanism of Highly Plastic Formations;the Application of Adaptive Algorithm in the Maximum Power Tracking of Power Photovoltaic System;preface;Role of Gluex in the Ion Exchange Mechanism of CLCF F−/H+ Antiporter;forest Environment Association Analysis for the Pandemic Health with Rectified Linear Unit Correlations;state and Covariance Matrix Propagation for Continuous-Discrete Extended Kalman Filter Using Modified Chebyshev Picard Iteration Method;a Novel Model to Calculate the Fluctuating Pressure in Eccentric Annulus for Bingham Fluid;interactive Restoration of Implicitly Defined Shapes;numerical Simulation Research on Seal Failure of Remedial Cement Sheath in Oil and Gas Wells;intelligent recognition of Waterline Value Based on Neural Network;compact Ultra-Wideband Antenna for Microwave Imaging Applications;medical Compound Figure Detection Using Inductive Transfer and Ensemble Learning;love Wave in a Layered Magneto-Electro-Elastic Structure with Flexomagneticity and Micro-Inertia Effect;Research on Risk Evaluation of New Infrastructure PPP Projects Based on PSO-FAHP;the Dynamic Coupling of Heterogeneous Robotic Systems for Spacecraft Motion Emulation.

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Intelligent Learning Models for Cancer Diagnosis and Treatment: A Comprehensive Analysis

Intelligent Learning Models for Cancer Diagnosis and Treatme...

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international conference on Contemporary computing and Informatics (IC3I)

作者： Shahadat Hussain Shahnawaz Ahmad Mohammed Wasid Sneh Saanvi Chawla Aakash Sangwan School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Cancer poses a significant global health and economic challenge, with $\mathbf{2 0}$ million new cases and 9.7 million deaths reported in 2022 itself. Early detection of cancer is crucial for improved survival rates, reduced mortality, making it imperative to develop advanced diagnostic tools. Intelligent Learning Models (ILMs) utilizing Deep Learning (DL) and Machine Learning (ML) by leveraging large datasets and complex pattern recognition are posing as the potential diagnostics solution for cancers. This paper reviews the current state of ILMs, evaluates existing models and case study for diagnosis prognosis and treatment of cancers.

关键词： Analytical models Reviews Biological system modeling Computational modeling Mortality Predictive models Cancer detection pattern recognition Prognostics and health management Cancer

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StressViT: Splitting and Compressing Vision Transformer Through Edge-Cloud Collaboration 27th

StressViT: Splitting and Compressing Vision Transformer Thr...

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27th international conference on pattern recognition, ICPR 2024

作者： Lin, Changyao Liu, Yi Li, Xiangyu Li, Chengxiang Zhang, Hao Jin, Jing Liu, Jie Faculty of Computing Harbin Institute of Technology Harbin China School of Astronautics Harbin Institute of Technology Harbin China International Research Institute for Artificial Intelligence Harbin Institute of Technology Shenzhen China Nanjing Research Center Huawei Technologies Co. Ltd Nanjing China

ISBN: (纸本)9783031781681

The emergence of ViT demonstrates the viability of Transformer in the field of computer vision, rivaling or even surpassing the performance of some CNN-based models. However, ViT’s substantial computation and memory demands present deployment challenges on resource-constrained devices. While various lightweight ViT techniques have been developed, they do not combine collaboration between heterogeneous devices for model splitting. Especially for large Transformer-based models, single-device deployment is infeasible, necessitating model splitting across multiple, even heterogeneous devices for distributed inference, and the optimal split point becomes crucial. Therefore, for a typical heterogeneous scenario of edge-cloud, we propose StressViT to split and compressvisual Transformer through edge-cloud collaboration. StressViT considers the disparities in computility and memory between edge and cloud environments, striving to improve inference accuracy while satisfying memory and compression constraints. Evaluations on four heterogeneous devices and two datasets confirm the efficacy of StressViT, achieving nearly 50% reduction of end-to-end latency with almost no loss of accuracy. StressViT can also be applied to homogeneous devices for edge-edge or cloud-cloud splitting, and can be easily extended to split across more than two devices. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cloud platforms

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Oracle Bone Inscription Image Retrieval Based on Improved ResNet Network 27th

Oracle Bone Inscription Image Retrieval Based on Improved Re...

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27th international conference on pattern recognition, ICPR 2024

作者： Ding, Jun Wang, Jiaoyan Aysa, Alimjan Xu, Xuebin Ubul, Kurban School of Computer Science and Technology Xinjiang University Ürümqi830046 China Xinjiang Multilingual Information Technology Key Laboratory Xinjiang University Ürümqi830046 China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Xinjiang University Urumqi830046 China

ISBN: (纸本)9783031783043

Research on oracle bone inscription image retrieval is important for applications in academic and cultural heritage areas. The current oracle bone dataset faces problems such as the low similarity between the same category, the high similarity between the different categories, and imbalanced sample distribution. In addition, due to the complex background of oracle bone images, existing network models have certain limitations in extracting image features. To address these challenges, this study first adopts a Siamese network-based image retrieval method to learn feature representations of similar and dissimilar images. Subsequently, the existing dataset was partitioned, providing a practical and usable retrieval dataset for the oracle bone image retrieval field. Finally, an improved network model based on ResNet is proposed and integrated into the Siamese network framework. The model achieves the highest retrieval MP and MAP values of 83.26% and 90.68%, respectively, which is better than the current research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Content based retrieval

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Design a Hybrid Neural Network Tracking System Using Multiple Cameras

Design a Hybrid Neural Network Tracking System Using Multipl...

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pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Ahmad Ismail Sergey Vishnyakov Mohammad Zedan Department of Computing Machines Systems and Networks Moscow Power Engineering Institute Moscow Russia Department of Diagnostic Information Technologies Moscow Power Engineering Institute Moscow Russia

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Neural networks have become a transformative technology, enabling significant advances over the past few decades. In particular, detection and tracking systems benefit from the ability of neural networks to learn and extract features, resulting in accurate object classification and localization. In this paper, we propose a hybrid neural network framework that integrates data from multiple cameras positioned along an object's path. This multi-camera configuration expands the system's field of view and improves prediction accuracy when tracking an object's position. We conduct extensive experiments to evaluate the performance of our proposed model, which demonstrates superior tracking accuracy and robustness across different environments compared to traditional single-camera systems.

关键词： YOLO Accuracy Surveillance Cameras Feature extraction Prediction algorithms Robustness pattern recognition Security Object tracking

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Inevitable Exposure: Analysing the Privacy Paradox in the Age of Digital Connectivity with Machine Learning Paradigms

Inevitable Exposure: Analysing the Privacy Paradox in the Ag...

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pattern recognition and Machine Learning (PRML), IEEE international conference on

作者： Ming Cheung Stephanie Ng James Zhang Chee Peng Lim School of Art Nanjing University of Information Science and Technology Nanjing China Institute for Intelligent Systems Research and Innovation Deakin University Geelong Australia

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

This study investigates the privacy paradox, a phenomenon whereby individuals express concerns about privacy while actively engaging in activities that compromise their personal data. Through a topic analysis of video transcripts from Douyin, China's most popular short video-sharing platform, we investigate the application of text analytics to examine the privacy paradox phenomenon. We leverage BERTopic, a state-of-the-art topic modelling technique, and the Meta Llama 3 Large Language Model to conduct a topic analysis of discourses related to privacy issues in Douyin videos. Our analysis generates useful insights into where and how the privacy paradox manifests in various aspects of daily life. The findings underscore the need to put in place proactive data protection measures to address public concerns about privacy.

关键词： Privacy Analytical models soft sensors Large language models Keyword search Data protection Machine learning Media Data models pattern recognition

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Medical Image Analysis Using soft computing Feature Selection and Classification of Skin Cancer 1

引用

international conference on Modern Electronics Devices and Communication Systems, MEDCOM 2021

作者： Saraswat, Birendra Kumar Srivastava, Shipra Singh, Samender Takuli, Arun Kumar Saxena, Aditya Department of Computer Science and Engineering G.L.A. University Mathura India Department of Information Technology Greater Noida Institute of Technology Greater Noida India Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science and Engineering Hi-Tech Institute of Engineering and Technology Ghaziabad India

ISBN: (数字)9789811963834

ISBN: (纸本)9789811963827

Adaptive resonance theory ARTI neural network (unsupervised learning) is being implemented for the classification of feature values with the objective of carrying out a comparative study with the performance of other technique including multi-layer feed forward network transform (supervised learning). A neural network is applied for the detection/identification of melanoma skin cancer. The images were collected from melanoma database is first processed and some distinguishable features were extracted by a MATLAB GUI. Two classes of the images were used: (a) benign, (b) malignant. A collection of interpreted binary input data, which is digitized from analog data after normalization is, then clustered adaptively with neural network ART-1. The goal of the project is to classify the feature dataset using supervised and unsupervised neural networks. To collect the feature set 1 implemented MATLAB image processing toolbox to create my own MATLAB functions. I created my own ARTI codes in MATLAB 701 (AKTI) to analysis which features are giving better efficiency using different vigilance parameter. Along with these supervised and unsupervised classification and pattern recognition, I am also trying to classify my own data-set of extracted features on Weka machine learning benchmarking software which is nothing but implements machine learning algorithms in JAVA. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Supervised learning

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Speech recognition using HMM and soft computing 17

Speech recognition using HMM and Soft Computing

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17th international conference on Advanced Nano Materials (ANM)

作者： Srivastava, R. K. Pandey, Digesh Dr Shakuntala Misra Natl Rehabil Univ CST UP Lucknow Uttar Pradesh India

The region of speech acknowledgment is one of the fascinating fields with regard to speech signal handling. Accomplishing precision and strength is an extremely troublesome limitation to different natural elements. Reformist work and audits in the speech acknowledgment application have been received utilizing fuzzy HMM, as one of the procedures to improve the acknowledgment exactness. This survey paper audits the different ideas of fuzzy HMM strategy and its applications to speech signal handling territory. Since the idea of a speech signal is dubious, it doesn't handle consistency at untouched stretches. To manage this dubiousness and vulnerabilities, numerous scientists have proposed fuzzy HMM is one of the better strategy to break down the speech signals. This paper presents the writing work accessible identified with speech acknowledgment utilizing fuzzy HMM procedures. (c) 2020 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 17th international conference on Advanced Nano Materials.

关键词： Speech Fuzzy HMM Strategy Consistency

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Regular Language Prediction from Complete Sample in Grammatical Inference Theory – A Literature Survey

Regular Language Prediction from Complete Sample in Grammati...

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international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Senthil Kumar.K Nishaanth Balakrishnan Pranav.N Dept. of Computing Technology SRMIST India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Inferring regular language and the corresponding finite automaton from the given set of samples (both positive and negative) is an important task in grammatical inference theory. The corresponding underlying automaton, viz., Deterministic Finite Automaton, has many applications ranging from lexical analysis of compilers, pattern recognition, image grammar, genetic engineering, etc.

关键词： Surveys Machine learning algorithms Scalability Neural networks Automata Reinforcement learning Inference algorithms Grammar pattern recognition Noise measurement

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