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4th international conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th international conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249778

the proceedings contain 113 papers. the special focus in this conference is on Applied Technologies. the topics include: Automatic Evaluation of Physiotherapy Activities Using Deep learning Techniques;multivariable Control Approach Applied to the Embryo Incubation Process of Gallus Gallus Domesticus;discovering Visual Deficiencies in Pilots Candidates Using data mining;an Electronic Equipment for Measuring Color Difference Between Tissues Based on Digital Image Processing and Neural Networks;artificial Intelligence-Based Banana Ripeness Detection;U-Net vs. TransUNet: Performance Comparison in Medical Image Segmentation;access Control through Mask Detection and Estimation of People Capacity in Covered Premises;customer Segmentation in Food Retail Sector: An Approach from Customer Behavior and Product Association Rules;Bibliometric Analysis of Web of Science database STEM Fields in Engineering and Mathematics. Ecuador’s Case Study;smart Antenna Array for Optimal Electromagnetic Energy Capture;image Processing Method to Estimate Water Quality Parameter;use of the Student Engagement as a Strategy to Optimize Online Education, Applying a Supervised machine learning Model Using Facial recognition;crime data Analysis Using machine learning Models;Gene therapy as a Solution to Genetic Diseases through DNA Manipulation;Vulnerability of CAPTCHA Systems Using Bots with Computer Vision Abilities;military Leadership in the Ecuadorian Army;analysis of Key Variables for Ecuadorian Defense Industry Development;the Prioritization of External Security as a Means of Guaranteeing Multidimensional Security and Economic Growth;Comparative Study of Deep learning Algorithms in the Detection of Phishing Attacks Based on HTML and Text Obtained from Web Pages;ground Robot for Search and Rescue Management;virtual Training Module for the Production of Rubber Adhesives through the Production of Cyclopentenol.

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Automatic Vehicle Plate recognition and Overspeed Detection

Automatic Vehicle Plate Recognition and Overspeed Detection

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Shams Tavrez Kushan Kumar Himanshu Sharma Arpna Saxena Ajay Kumar Garg Engineering College Ghazipur India Ajay Kumar Garg Engineering College Muzaffarpur India Ajay Kumar Garg Engineering College Varanasi India Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Road accidents are a major issue, and their occurrence has consistently risen. therefore, implementing a system is essential in reducing these accidents. Based on statistics from highway accidents, the main reason for accidents is vehicles driving too fast. Despite the presence of speed limit signboards on all highways, many drivers opt to disregard these regulations. At present, police officers use handheld radar guns to monitor vehicle speeds and report the data to authorities manually. the goal is to develop a machine learning system capable of automatically detecting over speeding and identifying vehicle plates. this paper aims at improving road safety by precisely identifying vehicles that are speeding and by instantly recognizing license plates. the suggested system in this paper removes the necessity for human involvement and autonomously logs vehicle speeds while notifying authorities of any infractions. this paper creates a system using the machine learning model CNN that can identify speeding and record the license plate of the vehicle using a camera. the technology of image processing is utilized to capture the image and subsequently transmit it to the RTO via a server. the research paper seeks to enhance road safety by precisely detecting vehicles that surpass speed limits. During testing, the system achieved an accuracy of 80.2% in identifying overspeed and 83% in detecting license plates.

关键词： Road transportation Accuracy Snow machine learning Cameras Road safety Calibration License plate recognition Vehicles Testing

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A Framework for Daily Living Activity recognition using Fusion of Smartphone Inertial Sensors data

A Framework for Daily Living Activity Recognition using Fusi...

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international conference on Computing, Mathematics and Engineering Technologies (iCoMET)

作者： Sheharyar Khan Syed M. Adnan Shah Sadam Hussain Noorani Aamir Arsalan M. Ehatisham-ul-Haq Aasim Raheel Wakeel Ahmed University of Engineering and Technology Taxila Pakistan Fatima Jinnah Women University Rawalpindi Air University Islamabad

Recent years have seen rapid advancements in the human activity recognition field using data from smart sensor devices. A wide variety of real-world applications can be found in different domains, particularly health and security. Smartphones are common devices that let people do a wide range of everyday tasks anytime, anywhere. the sensors and networking capabilities found in modern smartphones enable context awareness for a wide range of applications. this research mainly focuses on recognizing human activities in the wild for which we selected an in-the-wild extra-sensory dataset. Six human activities i.e., lying down, sitting, standing, running, walking, and bicycling are selected. Time domain features are extracted and human activity recognition is performed using three different machine learning classifiers i.e., random forest, k-nearest neighbors, and decision trees. the proposed human activity recognition scheme resulted in the highest classification accuracy of 89.98%, using the random forest classifier. Our proposed scheme outperforms the state-of-the-art human activity recognition schemes in the wild.

关键词： Accelerometers Inertial sensors Feature extraction Gyroscopes Time-domain analysis Task analysis Smart phones

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A security situation awareness method based on the change of network security attack and defense status

A security situation awareness method based on the change of...

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Information Technology,Big data and Artificial Intelligence (ICIBA), international conference on IEEE international conference on

作者： KunSan Zhang Jie Fu JiaMing Zheng Guojie Chen Li Hong JinHuang Lin Zhangzhou Power Supply Company State Grid Fujian Electric Power Co. Ltd. Zhangzhou China State Grid FuJian Information & Telecommunication Company Fuzhou China Fujian Yili Group Co. Ltd. Fujian Yili Information Technology Co. Ltd Fuzhou China Pinghe County Power Supply Company State Grid Fujian Electric Power Co. Ltd. Zhangzhou China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

With the continuous development of cyber attack methods, traditional security situational awareness methods can no longer meet the needs of dynamic and complex network environments. this paper proposes a security situational awareness method based on changes in network security attack and defense status. By monitoring and analyzing attack behaviors and defensive measures in real-time within the network, it dynamically assesses the network security situation. Initially, this method establishes a multidimensional state change model, dividing attack and defense states into multiple layers such as attack source, attack target, and response measures. Subsequently, through data mining and machine learning techniques, it identifies correlations and change patterns between attack and defense states, enabling the prediction and early warning of potential threats. Experimental results demonstrate that this method exhibits significant advantages in enhancing situational awareness accuracy and response speed, providing more effective technical support for network security protection.

关键词： Analytical models Adaptation models Systematics machine learning Network security Real-time systems data models Time measurement Protection Monitoring

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Fault Diagnosis and Localization of Power Cables Using Bi-Directional Long Short Term Memory with Adam Optimizer

Fault Diagnosis and Localization of Power Cables Using Bi-Di...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Lingling Song Department of Intelligent Control Yantai Vocational College Yantai China

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

In recent days, the fault diagnosis and localization of power cables is one of the incredible remote ones, which have to be identified carefully and located, fixed within a short period. In existing, the Support Vector machine (SVM) is used for the diagnosis of fault in power cables. However, these machine learning (ML) models are designed for static data, which struggled to capture the temporal dependencies in a sequential time series signals. therefore, Bi-directional Long Short Term Memory (BiLSTM) with Adam Optimizer (AO) is proposed and named as BiLSTM-AO. Initially, the procedure is started with the data collection and further processed into preprocessing with Time Domain Reflectometry (TDR). After that, the features are extracted from the preprocessed data using Short Time Fourier Transform (STFT) to extract the apparent features in time domain such as frequency components and harmonics. Finally, the BiLSTM is utilized for the diagnosis of faults and AO is used to increase the performance of model. From the results, the proposed Bi-LSTM-AO achieved better accuracy (99.80%) when compared with existing Whale Gray Wolf Optimization Algorithm-Variational Mode Decomposition-Support Vector machine (WGWOA-VMD-SVM).

关键词： Location awareness Fault diagnosis Vibrations Temperature measurement Support vector machines Power cables Bidirectional long short term memory Feature extraction data mining Time-domain analysis

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Comparative Analysis on Medical Image Prediction of Breast Cancer Disease using Various machine learning Algorithms

Comparative Analysis on Medical Image Prediction of Breast C...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Ravikumar Gurusamy V. Rajmohan N Sengottaiyan P. Kalyanasundaram S.M. Ramesh Department of ECE M.P.Nachimuthu M.Jaganathan Engineering college Erode SIMATS School of Engineering Saveetha Institute of Medical And Technical Sciences (SIMATS) Chennai Computer Science Faculty of Engineering and Technology Jain Global Campus Jakkasandra Kanakapura Department of IT KSR Institute for Engineering and Technology Tiruchengode Department of ECE KPR Institute of Engineering and Technology Coimbatore

Breast Cancer disease is the utmost characterized heterogeneous illnesses consisting of various types. Apart from lung cancer, Breast cancer is spreading widely everywhere. this research work confines to accurately analyzing the benign cells and the defective malignant cells by data mining technique like Support Vector machine (SVM). To have the comparative study, a total number of 659 sample are drawn from the UCI machine learning laboratory. the G power calculation with a confidence interval of 0.8 using maximum level of acceptable error rate of 0.5 is used for this analysis. Support Vector machine offer better prediction in terms of F1 score, precision and recall as 100%, 92%, 97% for benign cells 94%, 100%, 97% for malignant cells respectively. the significance value is arrived as 0.36 for this proposed system. the SVM appears to have better results in finding the benign and malignant cells diagnosis using Wisconsin dataset.

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Performance Evaluation Of machine learning Algorithms For Rainfall Prediction Using Dimensionality Reduction Techniques

Performance Evaluation Of Machine Learning Algorithms For Ra...

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international conference on Computing, Mathematics and Engineering Technologies (iCoMET)

作者： Sapna Kumari Muhammad Owais Raza Arsha Kumari Dept. of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Hyderabad Pakistan Dept. of Information and Computing University of Sufism and Modern Sciences Bhitshah Pakistan Dept. of Electronics Engineering Mehran University of Engineering & Technology Jamshoro Pakistan

ISBN: (数字)9798350335316

ISBN: (纸本)9798350335323

In the last few decades, tremendous change is observed in rainfall patterns which are majorly influenced by two major factors 1) climate change and 2) CO2 emission. Erratic rainfall patterns caused catastrophic effects on agriculture and human life in developing countries like Pakistan, where major economic growth is largely dependent on agriculture. the main objective of this study is to evaluate a performance different machine learning algorithms for forecasting rainfall patterns using dimensionality reduction techniques on climate change indicators. For this purpose rainfall data was collected for Pakistan. Principle component analysis (PCA), Pearson correlation, and Greedy search algorithms were used for feature selection and the evolution of models was performed using Root Mean Square error (RMSE), Root Absolute Error (RAE), and Coefficient of determination metrics. Results show that features obtained using the Pearson correlation produced the least error and Bayesian linear regression performed with the highest accuracy followed by Neural Network regression.

关键词： Climate change Rain pattern recognition Predictive models machine learning Neural networks

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Convolutional Neural Network based Framework for Intelligent Evaluation of Ideological and Political Education

Convolutional Neural Network based Framework for Intelligent...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Mingxuan Wei Jing Zhang Marxist College of Marxism Harbin Normal University of Early Childhood Education Harbin China

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Assessing Ideological and Political Education (IPE) has become a key focus in modern educational reform, especially within the context of intelligent learning environments. the conventional approaches such as manual comments, fixed indicators, and subjective impressions are not adaptable, objective, and in real-time. this study addresses existing gaps by proposing a deep learning evaluation framework that integrates a hybrid model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. the model extracts both spatial and temporal characteristics from multimodal student data, such as engagement measures, sentiment scores, classroom behaviour, and feedback logs. the CNN-LSTM model differs from traditional rule-based or expert-scoring systems since it allows for automated, objective, and dynamic assessment. CNN effectively learns spatial features, whereas LSTM captures sequential dependencies; thus, the hybrid model is ideally suited to analyse intricate IPE datasets. Experimental results indicate the model has a 94.7% accuracy and a 93.8% F1-score, performing better than conventional machine learning methods. the system also features a real-time feedback analysis and performance prediction module, further improving assessment accuracy. this framework represents a significant leap in intelligent, data-driven IPE evaluation systems.

关键词： Deep learning Accuracy Computational modeling Education Real-time systems data models Convolutional neural networks data mining Integrated circuit modeling Long short term memory

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A transformer-based network for pollen particle classification

A transformer-based network for pollen particle classificati...

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international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Dezhong Xu Jianqiang Li Faculty of Information Technology Beijing University of Technology Beijing China

Pollen identification is critical in a number of fields. However, existing studies on automatic pollen identification mainly rely on image information without the consideration of the spatio and and temporal information of pollen, which can provide valuable knowledge to reduce the decision space of pollen classifier. this paper proposes a transformer-based framework to identify pollen by combining images and temporal and spatial features in the text. First, the pretrained BERT model is utilized to extract text features and the pretrained DenseNet model is used to extract image features. then, a transformer aggregator is proposed to fuse multi modal features by superposing different channels for pollen classification. Experiments with many real-world datasets show that our transformer-based pollen recognition model achieves excellent performance compared to other state-of-the-art baselines, which validates the effectiveness of our new approach.

关键词： Deep learning Visualization Image recognition machine learning algorithms Text recognition Fuses Semantics

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Research on Geomagnetic Differential Detection Algorithm of Roadside Parking Vehicles

Research on Geomagnetic Differential Detection Algorithm of ...

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international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Geming Liu Wenhai He Guangzhou College of Commerce Guangzhou China

Because of its low power consumption and costeffective performance, geo-magnetic detection technology is considered as a good roadside parking vehicle detection technology. However, due to poor anti-interference ability, the false alarm rate of recognition is high. therefore, when this technique is used alone, the probability of correct recognition is relatively low in application. Based on the characteristics of geomagnetic sensors, this paper proposes an algorithm of setting two groups of geomagnetic sensors on the detection unit, using the difference value to determine multiple discriminant features, and judging the vehicle entering and exiting state through multilevel fusion. through the experiment, the algorithm improves the anti-interference ability, and the correct recognition probability is greatly improved compared with the threshold detection algorithm of single group of geomagnetic sensors, which is equivalent to the correct recognition probability of the dualmode detection technology based on geomagnetic, and has achieved a good effect of recognizing the vehicle state on the roadside parking space.

关键词： Space vehicles Power demand machine learning algorithms Vehicle detection Signal processing algorithms machine learning Sensor phenomena and characterization

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