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Intelligent Parking System to Prevent Unauthorized Parking of Vehicles Using IoT and R-CNN with YOLO

Intelligent Parking System to Prevent Unauthorized Parking o...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Sushma Vispute Manasi Potdar Pranjal Pawara Shreya Patil Satvika Patil Dinesh Bhaskar Ahirrao Dept. of Computer Engineering Pimpri Chinchwad College Of Engineering Pune India KCES's College Of Engineering and Management Jalgaon India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In recent years., the proliferation of private vehicles has led to a significant challenge in finding adequate parking space., with unauthorized parking becoming a prevalent issue in many residential societies. To address this problem., we propose a novel solution that integrates machine learning techniques and motion detection technology to effectively manage parking spaces and curb unauthorized parking. Our approach utilizes advanced machine learning models such as RCNN for object detection and YOLOv8 for license plate detection., complemented by optical character recognition (OCR) for character recognition. Additionally., the ESP32-CAM webcam module is employed to capture real-time images., ensuring seamless integration with our system. This combination of technologies allows for the accurate detection of unauthorized parking instances in real time., providing efficient management of parking spaces within societies. Key features of the system include automated vehicle identification., real-time monitoring., and notification alerts., all of which contribute to a robust verification system for resident and guest vehicles. By leveraging these advanced technologies., our solution not only streamlines the parking process but also promotes a more organized and peaceful living environment. This innovative approach addresses a critical issue in urban settings., paving the way for more efficient and sustainable parking management practices.

关键词： YOLO Space vehicles Accuracy Webcams machine learning Real-time systems Internet of Things Character recognition Reliability License plate recognition

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Malware Detection Using Pseudo Semi-Supervised learning 3rd

Malware Detection Using Pseudo Semi-Supervised Learning

引用

3rd international conference on pattern recognition and Artificial Intelligence, ICPRAI 2022

作者： Kaur, Upinder Ma, Xin Voyles, Richard M. Min, Byung-Cheol Purdue University West LafayetteIN47907 United States

ISBN: (纸本)9783031092817

Malware, due to its ever-evolving nature, remains a serious threat. Sophisticated attacks using ransomware and viruses have crippled organizations globally. Traditional heuristic and signature-based methods have failed to keep up and are easily evaded by such programs. machine learning-based methods can alleviate this concern by detecting inherent and persistent structures in the malware unrecognized by heuristic methods. Supervised learning methods have been used previously, but they need vast labeled datasets. Semi-supervised learning can address these issues by leveraging insights gained from unlabeled data. In this paper, we present a novel semi-supervised learning framework that can identify malware based on sparsely labeled datasets. This framework leverages the global and local features learned by the combination of k-NN and CNN to generate pseudo labels for efficient training using both labeled and unlabeled samples. The combined loss of the models regularizes the neural network. The performance of this framework is compared against popular semi-supervised approaches such as LapSVM, TSVM, and label propagation on the Embers dataset. The proposed framework achieved a detection accuracy of 72% with just 10% of the labeled training samples. Hence, these results demonstrate the viability of semi-supervised methods in large-scale malware detection systems. © 2022, Springer Nature Switzerland AG.

关键词： Heuristic methods

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Boosting Synthetic data Generation with Effective Nonlinear Causal Discovery 3

Boosting Synthetic Data Generation with Effective Nonlinear ...

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3rd IEEE international conference on Cognitive machine Intelligence (IEEE CogMI)

作者： Cinquini, Martina Giannotti, Fosca Guidotti, Riccardo Univ Pisa Pisa Italy ISTI CNR Pisa Italy

ISBN: (纸本)9781665416214

Synthetic data generation has been widely adopted in software testing, data privacy, imbalanced learning, artificial intelligence explanation, etc. In all such contexts, it is important to generate plausible data samples. A common assumption of approaches widely used for data generation is the independence of the features. However, typically, the variables of a dataset depend on one another, and these dependencies are not considered in data generation leading to the creation of implausible records. The main problem is that dependencies among variables are typically unknown. In this paper, we design a synthetic dataset generator for tabular data that is able to discover nonlinear causalities among the variables and use them at generation time. State-of-the-art methods for nonlinear causal discovery are typically inefficient. We boost them by restricting the causal discovery among the features appearing in the frequent patterns efficiently retrieved by a pattern mining algorithm. To validate our proposal, we design a framework for generating synthetic datasets with known causalities. Wide experimentation on many synthetic datasets and real datasets with known causalities shows the effectiveness of the proposed method.

关键词： data Generation Causal Discovery pattern mining Synthetic datasets Explainability

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Generation and detection technology of intelligent building engineering based on digital twin and deep learning

Generation and detection technology of intelligent building ...

引用

data Science and Computer Application (ICDSCA), IEEE international conference on

作者： Liu Hongqing Kong Sheng Li Lin China Tobacco Shandong Industrial Co. Ltd Tengzhou China

With the rapid development of building information modeling and deep learning technology, as well as people's increasing demand for virtual reality and intelligent construction projects, time-consuming and inefficient manual building modeling methods are far from meeting engineering needs. Plane detection is also gradually unable to meet the needs of building earthquake resistance. This paper proposes a generation and detection technique for intelligent construction projects based on digital twins and deep learning. First, using digital twin technology, the building plane is obtained through the Boolean intersection operation between geometric objects, and then the main body, external working conditions, environment and other entities are faithfully reproduced in the building information model; Contour; Finally, based on the Boltzmann machine of deep learning, the contour outside the building plane is input into the trained anomaly detection neural network algorithm model, and the recognition result is fed back. The experimental results show that the algorithm model has greatly improved the recognition rate of the abnormal detection of concave-convex irregular building plans, and is more in line with the actual needs of intelligent building construction.

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An Improved Hand Sign recognition using Deep learning

An Improved Hand Sign Recognition using Deep Learning

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international conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Neeraj Kumar Pandey Amit Kumar Mishra Daksh Singh Abhishek Jaraut Arpit Bisht School of Computing DIT University Dehradun India Department of Computer Science & Engineering Graphic Era Hill University Dehradun India

A population of deaf and mute individuals who are unable to communicate verbally typically utilize hand sign language, but the gap between mute and spoken language is wide. This system's primary aim is to enhance communication between the two groups, hand gestures are different from person to person in terms of representation, shape, and orientation, so there is a problem of linearity which is the main problem to be flexible and arbitrary. Recent systems and software have created various methods and algorithms to solve these problems and build these systems. For hand signal interpretation and motion patterns, algorithms like K-Nearest Neighbor (KNN), Multilevel Super Vector machine (SVM), and individual experimentation are employed. This study contrasts the efficiency and reliability of CNN, SVM, and KNN algorithms to identify which one delivers the highest accuracy, as well as comprehensively develops software for pattern recognition designed to process this data using real-time hand gestures and database-filtered input. Input English alphabet and point to text output, The efficiency of this model of ours is 93.47%.

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Hybrid Model for Sentiment Analysis of Whatsapp data

Hybrid Model for Sentiment Analysis of Whatsapp Data

引用

Advances in Computing, Communication, Embedded and Secure Systems (ACCESS), international conference on

作者： Royal Kaushal Raman Chadha Department of Computer Science and Engineering Chandigarh University Mohali India

Sentiment analysis, also called opinion mining (OM), is an approach to analyzing the sentiments expressed in data. This approach uses Natural Language Processing to classify the data based on emotions in different classes. Various Social media data are analyzed to determine sentiment, and machine learning (ML) techniques classify the data. This study utilizes ML models to analyze sentiment in WhatsApp data. The sentiment analysis process includes some steps, such as to pre-process the data, extract the features, and classify the data. The initial stage contributes to the clean-up of raw data and transforms it to make it suitable for analysis. Feature extraction is a stage to retrieve a relevant feature from the pre-processed data that contribute to determining sentiment. Finally, machine learning algorithms classify data to determine the sentiments expressed in the text. This work proposes a voting classifier which is hybrid architecture comprising SVM, KNN, and a Decision tree. Python is executed to simulate the suggested algorithm, and its performance is evaluated based on accuracy, precision, and recall metrics. These parameters are useful in measuring the efficiency of the algorithm in accurately classifying the sentiments existing in the data.

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Human Activity Tracking Using Mobile Sensor data and an Optimised LSTM 3rd

Human Activity Tracking Using Mobile Sensor Data and an Opti...

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3rd international conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Anandarao, Sarvani Mastani, Shaik Geeta, Tata Vamsi, Bolleddu Reddy, Puritipati Sai Santhosh Department of Information Technology Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India

ISBN: (纸本)9789819705726

Since many years smartphones are utilised for human activity recognition (HAR), important healthcare recommendations and telemedicine. Deep learning (DL) and machine learning techniques are commonly employed in studies of statistical models of human behaviour. However, the performance of present HAR platforms is constrained by complex physical activities. In this study, we developed the Ada-HAR human activity identification and real-time monitoring system, which is able to recognise more human motions in erratic situations. The chosen hierarchical clustering and classification methods are able automatically identify and categorise 12 activities (five dynamics, six statics, and a series of transitions). Finally, actual tests were run to verify the effectiveness and reliability of the suggested methods. The results demonstrate that the DL-based classifier achieves a higher identification rate (95.15% for waist and 92.20% for pocket) in comparison to the techniques discussed in the literature. Finally, the Ada-HAR system can track human behaviour in real-time regardless of how the smartphone is pointed. Here sensor activity dataset is considered which consists of user's activity log. By applying algorithms named LSTM, Adamax, Adagrad, SGD, indentifiesExi user’s activity based on the movement of the mobile. Existing Algorithms named LSTM, K-NN, DT, ANN, SVM, NB applied on different dataset produced better result and accuracy. RNN is an updated version of LSTM. RNN stores only the current activities and fails to store previous conclusions. Fortunately, LSTM stores existing and completed work in any dynamic situations. The LSTM works with random weights which led to local optimum and high time complexity. This paper has addressed the above issue by the usage of optimization algorithms in weight updation of LSTM. This increase the accuracy of the model. Here Adamax, Adagram, Stochastic gradient descent are used for weight updation. © The Author(s), under exclusive license

关键词： Smartphones

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Nailfold Capillary Segmentation Based on U-Net and Attention Mechanism 23

Nailfold Capillary Segmentation Based on U-Net and Attention...

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3rd international conference on Bioinformatics and Intelligent Computing, BIC 2023

作者： Chen, Shupin Wei, Dan Gu, Shenming Yang, Zhangru School of Information Engineering Zhejiang Ocean University Zhoushan316022 China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University Zhoushan316022 China Department of Radiation Oncology Shanghai Chest Hospital Shanghai Jiao Tong University No. 241 West Huaihai Road Shanghai200030 China

ISBN: (纸本)9798400700200

Microcirculation refers to the blood circulation composed of arterioles and venules, which can be used as a reference indicator for doctors to diagnose diseases. The traditional manual reading method is relatively time-consuming and labor-consuming and is not conducive to the standardization and digitization of diagnosis. A feasible way to solve this problem is to use machine learning to assist in image processing and diagnosis. In this study, we proposed a method to segment the nailfold vascular images based on the U-Net backbone network combined with the residual mechanism and the attention mechanism. The results show that: 1. Adding the CBAM attention mechanism can improve the anti-noise ability of the model. 2. Using residual blocks to replace the convolution blocks of the backbone network can improve the accuracy of models. 3. Contrast enhancement can improve the accuracy of blood vessel segmentation. The optimal method in this study is Res_CBAM_Unet and the contrast enhancement uses CLAHE (clipLimit = 2). The overall accuracy is 94.31%, the Dice score is 0.8297, the IoU score is 0.7090, the VOE score is 0.2910. The generalization ability of the proposed method is verified on the public dataset DRIVE. The results show that the accuracy of the proposed method is equivalent to the state-of-the-art methods, and it has an advantage in the sensitivity. This paper provides an effective method for nailfold capillary segmentation, which lays a foundation for the automation of diagnosis based on microcirculation images. © 2023 ACM.

关键词： Microcirculation

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Mobile Forensics in Digital World using Artificial Intelligence

Mobile Forensics in Digital World using Artificial Intellige...

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Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Avani Dwivedi Ujjwal Kumar Singh Samta Kathuria Jayapal Lande Prafful Negi Law College Dehradun Uttaranchal University Dehradun India LCD Division of Research & Innovation Uttaranchal University Dehradun India School of Computer Science & Artificial Intelligence SR University Warangal India Uttaranchal Institute of Technology Uttaranchal University Dehradun India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

After the advent of industry 4.0, technologies are comprehensively unveiled to the public. Because of this, the proliferation in demand for technology in every field has begun to soar. Today, in the modern society, the pattern of crimes against women is increasing unprecedentedly. Cybercrimes are more frequent than traditional crimes. Therefore, Mobile Forensics (MF), a subset of digital forensics plays a crucial role in extracting the digital evidence. The technologies such as Artificial Intelligence (AI) is then incorporated to improve the efficiency and accuracy in mobile forensics and digital investigation. This study addresses the significance, challenges, and effects of integrating AI in Mobile Forensics and Digital Investigation (DI). In addition, this research study also emphasizes on the stages or directives of mobile forensics, which are considered as essential components in gathering the evidence. It fills the void by analyzing how various technologies and techniques such as machine learning, deep learning, and data mining can be used in mobile forensics. Eventually, this study is concluded by providing the suggestions for its advancement and avoidance while illustrating the challenges to negate the same in the future.

关键词： Deep learning Sentiment analysis Law enforcement Forensics Instruments Transforms Organizations

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Planning Marketing Strategies in Small-Scale Business Using data Analysis. 3

Planning Marketing Strategies in Small-Scale Business Using ...

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3rd international Informatics and Software Engineering conference, IISEC 2022

作者： Fernando, A.M.P. Adhikari, A.M.T.T. Wijesekara, W.H.A.T.K. Vithanage, T.V.T.I. Gamage, Anjalie Jayalath, Thilini Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

The proposed research work develops a system focused on business opportunities to enhance market returns and improve marketing strategies and new strategies by identifying how customers interact with products and their behavior. Existing research efforts attempt to identify and market consumer attraction to products and marketplace in the marketplace. Current research focuses on the challenges of identifying consumer buying patterns and how consumers interact with products, Existing research has not, however, integrated the essential elements into a single system. Consequently, the recommended study has been conducted on a number of significant issues, such as determining the high-value client base and the number of sectors, understanding the purchasing pattern of products that comprise the customers' basket, identifying customer lifetime value, and Customer Trajectory Determination for identifying customer attractive shelf. This system focuses on various machine learning algorithms. Customer segmentation and value analysis using K Mean, Agglomerative, Clustering algorithm, and Arima model. Association rules are generated using the Apriori algorithm for market basket analysis, which is built on the idea that a set of frequently purchased items is a subset of a set of frequently purchased items. Also using RFM analysis to create and prepare our data frame by using BG/NBD model and the Gamma-Gamma model to calculate the customer lifetime value standardization. Using image processing algorithms and retail video analysis algorithms, background reduction technology clearly identifies moving objects/tracks customer routes using different colors. Based on results from implementation and testing, it was determined that the suggested technique outperformed the use of CCTV to identify consumer behavior and satisfaction with the product in recognizing customer purchasing patterns. The proposed system can identify customers' buying patterns, how customers interact with products

关键词： machine learning

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