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Cyber Bullying and Toxicity detection Using Machine Learning

Cyber Bullying and Toxicity Detection Using Machine Learning

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Ranjana Jadhav Naman Agarwal Srushti Shevate Chinmayee Sawakare Piyush Parakh Snehankit Khandare Vishwakarma Institute of Technology Pune Maharashtra INDIA

The increased use of online platforms for communication has made cyberbullying and toxicity detection a critical issue in recent times. This paper explores the topic of cyberbullying and toxicity detection and proposes potential solutions for identifying cyber violence and offensive language more effectively. According to the study of the algorithms in examined research papers on cyberbullying and toxicity detection, this research study presents a novel approach that achieved 90% accuracy in identifying bully text in social media comments. This is done by using machine learning algorithms such as SVM, Logistic Regression, Naive Bayes, KNN, and Random Forest, with SVM and Random Forest exhibiting the best performance. Additionally, the system improved the accuracy of identifying bully images in social media posts to 84.5% by using the MobileNetV2 model (DNN), which is superior to other approaches. The system is trained using a large, labeled dataset of text data to identify and classify different types of cyberbullying and toxic content. The findings suggest that the proposed models hold promise in detecting instances of cyberbullying and offensive content effectively. These results have significant implications for the development of cyberbullying and toxicity detection systems. The proposed approach can be integrated into various social media platforms and online communities to identify and mitigate cyberbullying and toxic content more efficiently. The study also highlights the need for continued research and collaboration among stakeholders to address cyberbullying and toxicity effectively.

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The simulation and analysis of second harmonic extraction of TDLAS: for SF6 decomposition components detection

The simulation and analysis of second harmonic extraction of...

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Electrical Engineering and Mechatronics technology (ICEEMT), IEEE international conference on

作者： Yufeng Lu Yubo Zhang Lei Zhang Yi Su Xiaofei Xia Xiajin Rao Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Electric Power Research Institute of Guangxi Power Grid Co Nanning China

Tunable Diode Laser Absorption Spectroscopy (TDLAS) technology is widely used in the detection of SF 6 decomposition components because of its high spectral resolution and fast response. However, although the traditional TDLAS gas detection system can measure the second harmonic signal, due to the single reference signal of the designed phase-locked amplifier, it is more susceptible to interference under complex noise environment, and the processing steps of the detection signal and the reference signal are cumbersome, the actual measurement results may be biased. In this paper, a simulation model of TDLAS absorption spectrum is built based on the virtual phase-locked amplifier in LabVIEW software, and TDLAS gas detection results are analyzed under different modulation coefficients, modulation frequencies and gas concentrations. The results show that the TDLAS gas detection simulation platform designed in this paper can effectively detect the second harmonic signal under different parameter settings. Considering the second harmonic amplitude and peak width, the modulation coefficient of the modulated signal is 2.5, and the high modulation frequency signal is used as the modulation signal. In addition, after simulating gas detection, it is found that with the increase of gas concentration, the amplitude of the second harmonic waveform also increases, and the fitting results showed that the change of the peak value of the second harmonic is highly linear with the gas concentration (R2 is 0.992). The research results can provide theoretical support for TDLAS signal extraction and trace gas detection.

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Ubiquitous Computing Integration Models Enabling Wireless Networks Of Sensors

Ubiquitous Computing Integration Models Enabling Wireless Ne...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Lovi Raj Gupta Al Smadi Khalid Ujjawal Kumar Sai Kumar Mahadevuni Hayder Al-Chilibi Malik Bader Alazzam Lovely Professional University Phagwara Faculty Of Business Department Of Business Intelligence Computer Science Jadara University Jordan Uttaranchal Institute of Technology Uttaranchal University School of Engineering SR University Warangal Telangana Computer Science and IT Al-Farahidi University Baghdad Iraq Information Technology College Ajloun National University Jordan

Proposed system, pollution monitoring, the automobile industry, and sports are just a few of the application areas that have grown as a result of ubiquitous sensing and the distinctive features (Sensor systems). As the underlying significantly expanded the number of linked things with real-time communication and data computation, WSNs have grown in importance in recent years. However, owing to the scale and accessibility of IoT, building a complex challenge, and past methodologies established for Iot technologies cannot be implemented directly. In this paper, pairwise clusters models for Iot networks in the Iot paradigm are proposed: I a resource grouping model and (ii) a business clusters model where responsibilities are allocated to individual sensor nodes depending on how well they provide services. The end-to-end latency, and communication bandwidth balancing.

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detection system implementation in streaming video with High standard Accuracy Using Single-Shot Multi-Box Detector system with CNN

Detection system implementation in streaming video with High...

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

作者： Malik Jawarneh Mohammed Q. Mohammed Hayder Al-Ghanimi Hussein Alaa Diame Alaa M. Lafta Haider Alchilibi Oman College of Management and Technology Muscat Oman Forensic sciences department Al-Esraa University College Baghdad Iraq Department of Medical Instrumentation Techniques Engineering Hilla University College Babylon Iraq Computer Engineer Department Al-Kunooze University College Basra Iraq National University of Science and Technology Dhi Qar Iraq Medical Laboratory Techniques Department Al-Farahidi University Baghdad Iraq

The purpose of this study is to compare the Single Shot Multi box Detector with the CNN algorithm approach for detecting face masks (SSD). The study consists of two groups: SSD and CNN algorithm approach, each with a sample size of 20. The pretest power used in the simulation was 0.8. accuracy values are computed to assess how well the SSD and CNN algorithm's function. The accuracy of the CNN algorithm technique and SSD was 92.65% and 81.76%, respectively. Given that the model's p (2-tailed) value is 0.000, the two groups differ significantly from one another. Inthis study, it was discovered that the CNN algorithm approach outperformed SSD substantially in terms of accuracy.

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detection Myocardial Infarction with Deep Learning Algorithms Using MRI Data

Detection Myocardial Infarction with Deep Learning Algorithm...

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Intelligent Cybernetics technology & Applications (ICICyTA), international conference on

作者： Andi Muh. Syahrul Ulum Satria Mandala Human Centric (HUMIC) Engineering School of Computing Telkom University Bandung Indonesia

Myocardial infarction (MI), also known as a heart attack, refers to a condition where the blood flow to the heart is interrupted, leading to a deficiency of blood supply. This insufficient blood flow results in damage to the heart muscle, potentially causing cell death and a loss of proper functionality. Damage or death of heart muscle tissue causes changes to the normal heart conduction system resulting in arrhythmias that can be life threatening and cause the heart to stop suddenly. So the most effective technology used to diagnose MI is by reading the patient’s Electrocardiogram (ECG) signal or performing Magnetic Resonance Imaging (MRI) tests. Currently, many studies have proposed MRI-based MI detection methods. In general, the method used for MI detection is localization heart, motion field estimation, preprocessing, and classification. From the many studies of MI detection with MRI, the accuracy value is still low compared to MI detection with ECG. This is because the classification algorithm used can still make the detection accuracy low. So in this study, MI detection with MRI will use a CNN algorithm. To solve the existing problems, this final project proposes the development of Deep Learning which is a CNN algorithm, that supports the improvement of MI detection accuracy. Last but not least, this final project also analyzes the accuracy of the proposed algorithm. The MRI data contains only 100 images so do Data Augmented for getting more data. The total data after Data Augmented is 800 images, that will be use for training, validation and testing. The highest testing classification accuracy, specificity, and sensitivity are 96.25%, 97.08%, and 94.73%, respectively. using CNN algorithm.

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A multi-source information fusion method for seismic impact analysis of cultural relics based on knowledge graph 3

A multi-source information fusion method for seismic impact ...

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3rd international conference on Electronics, Electrical and Information Engineering, ICEEIE 2023

作者： He, Lin Gong, Mengting Qu, Huiying Yang, Xiaofei Shanghai Institute of Advanced Studies Chinese Academy of Sciences Shanghai201210 China Sichuan Museum Chengdu610000 China College of artificial intelligence Tianjin University of Science &Technology Tianjin300457 China

ISBN: (纸本)9781510671423

Earthquakes are a major threat to the conservation of cultural relics, and how to accurately assess the impact of earthquakes on cultural relics in collections has become an important issue in the field of conservation. Traditional methods for assessing the seismic damage of cultural relics are mostly based on experimental data or structural dynamics theory, which cannot truly reflect the seismic damage of cultural relics. This paper proposes a method for analyzing the impact of seismic damage to cultural relics based on the fusion of multi-source information. Firstly, this paper is oriented to the real earthquake damage historical data of cultural relics using two-way long and short-term memory network to obtain the characteristics of the earthquake damage data, and using convolutional neural network to obtain the local characteristics of the data, and then using classifiers to obtain the entities and relationships contained in the earthquake damage data. Secondly, based on the obtained dataset, through multi-source information fusion and correlation analysis among the data, the seismic impact dataset of the collection can be used for the research of seismic impact analysis of the collection. Finally, relying on the knowledge of seismic impact of cultural relics, we construct a mapping of seismic impact factors of cultural relics in the collection. This provides data support and basis for subsequent research on the quantification of the impact of seismic damage to cultural relics in collections and research on the risk assessment model for seismic damage to cultural relics in collections. © 2023 SPIE.

关键词： Earthquakes

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Research on Student Physical Fitness Test Score Management system Based on Artificial intelligence

Research on Student Physical Fitness Test Score Management S...

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Information technology and Contemporary Sports (TCS), international conference on

作者： Feng Wei Pei-Pei Wang Department of Physical Education Ningxia Institute of Science and Technology Ningxia China School of Electrical Information Engineering Ningxia Institute of Science and Technology Ningxia China

The “National Physical Health Standards for Students” formulated by our country aims to promote the healthy development of students’ physical fitness and inspire students to actively engage in physical exercise. The policy provides basic technical requirements and individual evaluation criteria for college students. As a frontline group in society, college students are not only high-level professionals trained by the country, but also the main force in national construction and the leading force in enhancing the country’s comprehensive national strength. This article is based on the Java EE technology of the MVC model, using the Java language for programming. The most convenient compiler to use now is My Eclipse 2017 CI 10. Microsoft’s MySQL relational database is used to store data, and the front-end HTML+CSS technology is used to complete the student body. Development of test score management system. Realize data changes in the system through direct communication between the browser and the server, and realize the functions in the module: class management, student management, teacher management, physical test score management, authority management, etc. Combined with information technology, scientific management methods and convenient services improve work efficiency and can effectively reduce errors and omissions in data storage. It aims to help school physical education teachers more comprehensively understand and understand the physical condition of students in the school, and provide The school provides more scientific and personalized health education methods.

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Machine Learning-Powered Real-Time Motion detection system: A Review

Machine Learning-Powered Real-Time Motion Detection System: ...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Naman Khurana Madhavi Bansal Ananya Thakur Velma Sai Varshitha Tannu Er. Kirat Kaur Computer Science & Engineering Chandigarh University Punjab India

Motion detection, a fundamental technology in computer vision, is crucial for a wide array of applications, including security,surveillance, gaming and robotics. OpenCV, the open-source Computer Vision Library, has emerged as a versatile tool for developing robust and efficient motion detection systems. The article offers insights into the intricacies of motion detection using OpenCV, combining existing research findings with a unique perspective on this critical subject. It explores traditional and advanced techniques, highlights real-world applications and addresses persistent challenges in the field such as handling background clutter, adapting to changing lighting conditions, and achieving real-time processing. It presents a fundamental approach to motion detection, encompassing frame acquisition, frame differencing, thresholding, noise reduction, object tracking, and more, with a focus on the tools and technologies that can enhance these processes. The primary problem addressed in the article is the development of accurate, reliable, and real-time motion detection systems considering various challenges and objectives across different application domains. The provided references encompass a wide range of research in the field, contributing to a comprehensive understanding of motion detection. The article can serve as a valuable resource for individuals interested in motion detection and its integration with OpenCV, offering a foundation for readers to embark on their motion detection projects and foster innovation in this dynamic field.

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A Blockchain Based Decentralized Application system for Vanet FDIA detection 3rd

A Blockchain Based Decentralized Application System for Vane...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Preeti, Grover Sanjeev Kumar, Prasad School of Computing Science and Engineering Galgotias University Uttar Pradesh NCR Greater Noida India

ISBN: (纸本)9789819726707

The growing prominence of intelligent transportation systems (ITS) has led to increased research on vehicular ad hoc networks (VANETs). Blockchain technology offers promising solutions for ensuring data security, integrity, and immutability within VANETs. However, VANETs remain vulnerable to false data injection attacks (FDIAs), which can significantly disrupt network operations and endanger safety. This paper proposes a novel approach using a blockchain-based decentralized application system (Dapp) for FDIA detection in VANETs. The Dapp leverages smart contracts to manage crucial aspects like data provenance, reputation, and consensus mechanisms. It uses POS consensus algorithm to guarantee data consistency and fault tolerance even in the presence of malicious nodes. To manage the vehicular data, users can access the system via Dapp, an Ethereum—distributed application. By integration of blockchain-based IPFS-Dapps system and AI/ML/big data model with blockchain are aim of the research article for providing the solutions for false data injection attacks (FDIA). We collect the data through Radio Channels, Smart Sensors, Smart Antennas and RSU and than uploaded using Dapp and than we classified nodes from smart contract and finally send it to Ethereum. We used a machine learning SVR algorithm, i.e., vehicular network-based support vector regression (VBSVR) to identify an attacker node. We have written the pseudocode for using IPFS with Ethereum, proposed research methodology includes integration of blockchain and Dapps with smart contract, integration of AI/ML/big data models with blockchain, and integration of ML Oracle under blockchain that are ideal for detection of false injection attacks nodes, and same is logged in IPFS Decentralized Server. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ethereum

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Anomaly detection of Industrial Images Based on Instance Segmentation with Support Vector Machines Model

Anomaly Detection of Industrial Images Based on Instance Seg...

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

作者： Ramachandra A C Abbas Hameed Abdul Hussein Myasar Mundher Adnan V Malathy Muhammed Basim Jabar Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India College of Pharmacy Ahl Al Bayt University Karbala Iraq The Islamic University Najaf Iraq Department of ECE School of Engineering SR University Warangal Telangana India National University of Science and Technology Dhi Qar Iraq

In smart manufacturing systems, anomaly localization plays a crucial role in enhancing intricate production processes. However, When it comes to identifying normal zones around anomaly identification in high-dimensional points and industrial images, the models perform poorly. In this study, in order to address the aforementioned problems To find the abnormality in an industrial image, Instance Segmentation with Support Vector Machine (IS-SVM) are used. Initially, padding techniques are used to resize the input pictures of the MvTec AD dataset in order to improve segmentation. The instance segmentation approach is applied to accurately identify if a picture flaw is abnormal or not. In the end, the SVM classifier produces a prediction of the industrial image anomalies that is more accurate by Using the Histogram Of Orientation features. The developed model then gets over the restrictions by improving generalization ability and decreasing the false detection rate by using the IS-SVM architecture with a 98.97% mean ROCAUC, the constructed model outperforms the existing models like Grey Correlation-based Picture Fuzzy-Evaluation (GCPF), Patch DistrIbution Modeling (PaDiM-R18-rd100), Memory-based Segmentation Network (MemSeg), Reliable Anomaly detection and Localization system (RADL), and Vision Transformer Network Anomaly detection and Localization (VT-ADL).

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