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Power Network Security Situation Analysis Based on machine learning

Power Network Security Situation Analysis Based on Machine L...

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New Energy and Power Engineering (ICNEPE), international conference on

作者： Xiaolu Zhang Wuqiang Shen Lei Cui Information Center of Guangdong Power Grid Co. Ltd. Guangzhou Guangdong China

This work is using machine learning technology to ensure the safety of power grid data connection. The significance of this study depends on the grid electronic information network. The purpose of this project is to study the basic theories and key technologies of the power grid information network security knowledge localization. To achieve this goal, the project will use machine learning technology to establish a comprehensive safety assessment model for power grid information systems. This method will be used to analyze the security situation of the power plant information network and predict the potential risk. In addition, this project will develop a power grid security awareness system framework under the power grid information environment. This framework will complement the monitoring and management of the power grid information network, allowing for the timely identification and mitigation of potential security threats. Overall, the project aims to achieve security situational recognition of the power grid information environment, enabling efficient and effective management of network security and ensuring the safety and reliability of power grid operations.

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A Novel machine learning Model for Sarcasm Detection on Facebook Comments

A Novel Machine Learning Model for Sarcasm Detection on Face...

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Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： Soumya Puvvada Kusuma Mastan Valli Shaik Karthik Galla S Venkatrama Phani Kumar K Venkata Krishna Kishore Department of CSE VFSTR Deemed to be University Guntur Andhra Pradesh India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

This research focuses on identifying sarcasm in online comments using a combination of machine learning techniques, including feature extraction, pattern recognition, and analysis of emojis. Various methods were utilized to categorize comments into sarcastic and non-sarcastic categories, aiding computers in understanding the sentiment expressed. The experiments have yielded positive results, particularly with hierarchical clustering, which exhibited higher Silhouette Scores and improved cluster quality compared to other techniques. The study used a dataset containing 5,471 comments, labeled with sarcasm as (0 for sarcastic, 1 for non-sarcastic), with sentiment analysis facilitating comment categorization. By categorizing comments based on sentiment, computers can easily identify the underlying sentiment of each comment. The combination of features, including emojis and text, adds an extra layer of meaning, helping the system to identify the type of comment easily. The objective is to help computers understand human language better, enabling them to grasp the real meaning behind a joke and sarcastic sentence, there by making online communication more accurate and effective.

关键词： Computers Analytical models Sentiment analysis Accuracy Computational modeling Clustering algorithms machine learning

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Soccer Match Prediction based on Big data mining and Multimodal Features

Soccer Match Prediction based on Big Data Mining and Multimo...

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Signal Processing, Communications and Engineering Management conference (ISPCEM), international

作者： Jiacheng Zheng Hangzhou Xuejun High School Zhejiang Hangzhou China

The sports industry ushered in new development and opportunities in the context of the big data era, especially the big data mining in the sports industry provides technical support for industrial iteration, promotes the innovative development of the sports industry, and accelerates the process of sports industry technology. As a popular sport in the field of soccer, it has received extensive attention from domestic and foreign fans, while data analysis and data mining in the field of soccer are also developing rapidly. Soccer game prediction is due to its many influencing factors, and there are diversity, uncertainty, ambiguity and other problems among the factors. At present, there are various ways to analyze soccer match prediction, and the rating system is not uniform, such as odds judgment, blind guessing based on historical experience, and it is not yet possible to quantify the internal relationship between features according to a set of uniform data mining process. Therefore, this paper proposes a multimodal feature extraction network based on deep learning, which can effectively extract a fusion of low-dimensional feature representations from many different modal features provided in the raw data of soccer matches. Finally, on the basis of solving the unidimensional prediction problem through traditional machine learning models and integration models, an improved Stacking integration model oriented to multidimensional features is innovatively proposed, and ultimately achieves the goal of improving the prediction accuracy and stability of soccer matches.

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Statistical Feature Extraction and Classification of Complex data Based on Deep learning

Statistical Feature Extraction and Classification of Complex...

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Artificial Intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Dongmei Wu Chengdu College of University of Electronic Science and Technology of China Chengdu Sichuan China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

This research is focused on elucidating the extraction of statistical features and the classification methodologies for intricate data leveraging deep learning techniques. The objective is to address the challenge of proficiently handling and precisely categorizing diverse forms of complex data in practical scenarios. Within this study, a composite model has been devised, merging the functionalities of Convolutional Neural Networks (CNN) with Recurrent Neural Networks (RNN), and augmenting the framework with an Attention Mechanism (AM) to bolster the capability of the model in extracting features and classifying intricate datasets. Through extensive experimentation, this paper validates the efficacy and versatility of the proposed model across diverse data types, including images, texts, and audio. The experimental findings demonstrate that, in contrast to traditional machine learning methods, the deep learning model proposed in this study excels at capturing abstract data features, thereby enhancing classification accuracy and generalization capability. The research presented herein offers novel insights and methodologies for the domain of statistical feature extraction and classification of complex data, thus playing a pivotal role in advancing the practical applications of deep learning technology.

关键词： Deep learning Representation learning Recurrent neural networks Accuracy Merging Feature extraction data models data mining Convolutional neural networks Autonomous robots

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3rd international conference on Computing and Communication, IC3 2020

3rd International Conference on Computing and Communication,...

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3rd international conference on Computing and Communication, IC3 2020

ISBN: (纸本)9789811573934

The proceedings contain 62 papers. The special focus in this conference is on Computing and Communication. The topics include: Cycloconverter Fed Capacitor Start Capacitor Run Induction Motor Drive: Simulation Analysis;novel User Preference Recommender System Based on Twitter Profile Analysis;optimizing Energy Efficiency in Device-to-Device Communication Using Intelligent Algorithms;a Case Study and Fraud Rate Prediction in e-Banking Systems Using machine learning and data mining;implementation of Branch and Bound Method for Maximizing Network Capacity of Cognitive Radio Networks;Design and Implementation of Ploughing and Seeding of Agriculture Robot Using IOT;A Hybrid Strategy Based on Monitored Region Segregation for Redundant data Minimization (HS-MRS) in Sensor Networks;A Chaotic-Jaya Optimized OSELM Model for Cancer Classification;performance of Scalable data Linked to Internet of Things: A Case Study;comparison Between the Statistical Method Models for Better Time Series Sales Forecasting Model;Clustering Algorithms for MANETs: A Review on Design and Development;a Study on Various machine learning Algorithms Used for Prediction of Diabetes Mellitus;crime Rate Prediction Using machine learning and data mining;Heterogeneous IHRRN Scheduler Based on Closeness Centrality;a Continuous Word Segmentation of Bengali Noisy Speech;MathNET: Using CNN Bangla Handwritten Digit, Mathematical Symbols, and Trigonometric Function recognition;bengali Accent Classification from Speech Using Different machine learning and Deep learning Techniques;Bengali News Headline Generation on the Basis of Sequence to Sequence learning Using Bi-Directional RNN;in Perspective of Combining Chaotic Particle Swarm Optimizer and Gravitational Search Algorithm Based on Optimal Power Flow in Wind Renewable Energy;preface.

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Enhancing Real Estate Market Predictions: A machine learning Approach to House Valuation

Enhancing Real Estate Market Predictions: A Machine Learning...

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

作者： Pradeep Jha Manju Mathur Abhay Purohit Apoorva Joshi Anantika Johari Suhani Mathur Department of CSE Global Institute of Technology Jaipur Rajasthan India Department of IT Global Institute of Technology Jaipur Rajasthan India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

machine learning (ML) has emerged as a versatile technology, transforming various domains such as speech recognition, medical diagnostics, and real estate analytics. This research addresses the critical challenge of accurately predicting house prices in dynamic and volatile real estate markets. Traditional valuation methods often fail to capture complex relationships between features, leading to suboptimal predictions. To mitigate these limitations, this study proposes a predictive model leveraging multiple ML algorithms, including Linear Regression, Decision Tree Regression, K-Means Clustering, and Random Forest Regression. Among these, Random Forest Regression demonstrated superior accuracy, achieving a root mean square error (RMSE) of 2.913. The findings indicate that integrating advanced ML techniques can streamline the home-buying process by enhancing price prediction accuracy, reducing dependency on intermediaries, and enabling informed decision-making for stakeholders. This study provides a robust framework for applying ML in real estate and highlights opportunities for further advancements in predictive modeling.

关键词： Accuracy Speech recognition Predictive models Prediction algorithms Stakeholders Medical diagnosis Root mean square Random forests Regression tree analysis Cost accounting

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Telecom Churn Prediction Using CNN with Variational Autoencoder 3rd

Telecom Churn Prediction Using CNN with Variational Autoenco...

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3rd international conference on Smart Systems - Innovations in Computing (SSIC)

作者： Jain, Hemlata Khunteta, Ajay Srivastava, Sumit Poornima Univ Jaipur Comp Sci Sch Basic & Appl Sci Jaipur 303905 Rajasthan India Poornima Grp Coll Jaipur 303905 Rajasthan India Manipal Univ Jaipur Dept Informat Technol Jaipur 303007 Rajasthan India

ISBN: (纸本)9789811628771;9789811628764

Customer churn is a major problem across traditional and new age companies like telecom, digital services providers, online marketplace, payment banking and social media companies. Customer churn means customer is moving out from the company, hence impacting the top line of financial sheets. This is particularly more relevant for companies having subscription-based plans. Telecom industry which has sizable customer base and cut-throat competition is at higher risk of impact due to churn. With the customer database size running into petabyte and terabytes, it is a tedious and time-taking task to accurately predict customer churn based on some random logic. While machine learning appears to be an easier alternative, often such database contains large number of string attributes that are tough to use in machine learning algorithms. Various traditional machine learning and data mining techniques have been applied for handling such big data. All such techniques have leveraged different techniques for data engineering. In this study, we have demonstrated how a new and powerful technique convolutional neural network with variational autoencoder can help predict churn with higher accuracy. CNN automatically has the ability of good feature selection and representation of input data, and this study has come with a new aspect, i.e. integrating all string attributes in dataset (ISAD) for using all existing string variable of database helping in enhancing model performance. Experiment is done on three telecom companies Cell2Cell, Telco and Orange datasets of size 51,048, 7048 and 3333, respectively. Model outperformed on all three datasets and achieved a good accuracy level. ISAD model helped to enhance the performance by giving more feature option for predicting the customer behaviour correctly.

关键词： Autoencoder Categorical features CNN Deep learning machine learning Telecommunications Variational

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Real Time Automated Sign Language Detection and Translation Using AI and ML Driven Hand Gesture recognition

Real Time Automated Sign Language Detection and Translation ...

引用

Disruptive Technologies (ICDT), international conference on

作者： Khushu Jain Yuvraj Panwar Devanshu Vats Shuchi Mala Amity School of Engineering and Technology Amity University Noida India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

This paper discusses an automatically generated real-time framework towards the conversion of Indian Sign Language into text using artificial intelligence and machine learning methodologies related to hand-gesture recognition. This system uses a CNN model to classify gestures and it processes live video input from the webcam to extract hand gesture features using MediaPipe, trying to bridge this gap in communication for the hearing-impaired community with that of the mainstream society. Thorough analyses include the system architecture, methodologies of data collection, model training, and testing processes iteratively, along with challenges such as changing lighting conditions, hand placement, and gesture speed, and strategies used to overcome these challenges. Promising results show high accuracy in real-time recognition, pointing out the potential of the framework to enhance communication accessibility. Subsequent research endeavors will prioritize the augmentation of the system to facilitate word-level recognition, incorporate multilingual functionalities, and improve its robustness for utilization across varied environments, thereby promoting wider applicability and empowering individuals with special abilities.

关键词： Hands Training Sign language Translation Accuracy Text recognition Feature extraction Real-time systems Robustness data models

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Comparative Analysis on machine learning and One-Dimensional Convolutional Neural Network to Predict Surface Enhanced Raman Spectroscopy

Comparative Analysis on Machine Learning and One-Dimensional...

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Computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Nasrin Nadher Jamil Aythem Khairi Kareem Dept. of Vocational Education General Directorate of Education in Anbar Governorate Ministry of Education Hit Iraq Dept. of Heet Education General Directorate of Education in Anbar Ministry of Education Hit Iraq

Surface-enhanced Raman spectroscopy (SERS) established on machine learning (ML) techniques have been used in intelligence investigation, food safety, biological recognition, and material study. However, ML techniques typically require additional preprocessing or attribute engineering, and conducting large data employing these techniques is problematic. Deep learning (DL) techniques involve minimal input data by analyzing every probable attribute established in the network. Therefore, this study employed ML techniques. These are K-Nearest Neighbor (KNN), Naïve Bayes (NB) Support Vector machine (SVM), and Decision Trees (DT). Then, the result is compared with a one-dimensional convolutional neural network structure (1D-CNN). In this research, we utilize Rhodamine 6G (R6G) as an objective molecule. The experimental results demonstrate the effectiveness of the 1D-CNN offered.

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Cyberbullying Prediction Using K-Means Clustering with learning Methodologies for Emotion Detection in Tweets

Cyberbullying Prediction Using K-Means Clustering with Learn...

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

作者： P Privietha R Sasirekha L Melwin Akash K Punitha A Marshal Felix R Ramyabharathi Department of CSE Sathyabama Institute of Science and Technology chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The rise of social media has led to an increase in real-time data that can reveal potential cyber threats. This research explores the use of emotion detection in tweets to predict cyber attacks, employing K-Means Clustering in combination with machine learning methodologies. By analyzing emotional trends, such as fear, anger, and anxiety, this study establishes a correlation between social media sentiment and cyber attack occurrences. The results demonstrate the potential for early warning systems that harness emotional patterns for cybersecurity. Social media platforms like Twitter provide a wealth of real-time data that can reveal societal trends, including indicators of potential cyber threats. This study investigates the use of emotion detection in tweets as a predictive tool for cyber attack forecasting. By employing Natural Language Processing (NLP) techniques, emotional states such as fear, anger, and anxiety are extracted and analyzed. K-Means Clustering is utilized to group similar emotional patterns, enabling the identification of significant trends. These clusters, combined with historical cyberbullying data, are used as features in supervised learning models for cyber threat prediction. The results demonstrate the effectiveness of integrating unsupervised clustering and machine learning to correlate spikes in negative emotions with the likelihood of cyber incidents. This research highlights the potential for proactive cybersecurity strategies that leverage social media sentiment as an early warning system.

关键词： Support vector machines Emotion recognition Anxiety disorders Cyberbullying machine learning Alarm systems Market research Natural language processing Real-time systems Cyberattack

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