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Twitter Trolling Detection Using machine learning 7th

Twitter Trolling Detection Using Machine Learning

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7th international conference on soft computing: theories and Applications, SoCTA 2023

作者： Ghosh, Shubhra Bhunia Kumar, Horesh Joshi, Aditya Kumar, Anshul Jain, Tarun Department of CSE Manipal University Jaipur Jaipur India G.L. Bajaj Institute of Technology and Management Greater Noida India

ISBN: (纸本)9789819720309

the increased usage of media has resulted in an increase, in cyberbullying and online trolling, and this study aims to identify and address trolling behavior through the application of machine learning algorithms also the research incorporates seven algorithms, Decision Tree, KNN, Logistic Regression, Naive Bayes Random Forest, Simple ANN and SVM. Additionally, four feature extraction techniques are employed: Bag-of-Words, TF IDF, Word2Vec and Glove. the analysis returned 28 combinations, with Glove word embeddings attaining the best accuracy at 89.77%. Bag-of-Words fared ideally when partnered with Logistic Regression and SVM, whereas TF-IDF excelled with Simple ANN and Naive Bayes. Word2Vec exhibited proficiency alongside Decision Tree and KNN. the Twitter dataset, characterized by unorthodox linguistic formulations, changes in syntax, and liberal use of slang, eludes typical text preprocessing techniques. the dataset also confronts us with symbols replacing letters, making typical symbol removal ineffective. this research underlines the relevance of customizing techniques to unique data sources and setting the way for future efforts to increase trolling detection. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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A Comparative Study of machine learning Techniques for Predicting Student Academic Performance 1

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Niranjala, Shyama Heshini Alobaedy, Mustafa Muwafak Goyal, S.B. Faculty of Information Technology City University Petaling Jaya46100 Malaysia

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

this study examines the effectiveness of different machine learning algorithms, specifically Decision Trees, Random Forests, and Support Vector machines (SVM), in forecasting the academic performance of students. By utilizing a dataset that includes demographic information, past academic records, and socioeconomic factors, the study seeks to determine the most efficient algorithm for educational data analysis. Our approach involves employing feature selection techniques to refine the dataset and employing cross-validation to evaluate algorithmic effectiveness. the findings reveal that while SVM demonstrated the highest accuracy in predicting performance, Decision Trees and Random Forests offered valuable insights into the importance of features. these findings hold significant implications for educators and policymakers aiming to implement data-driven strategies to enhance student achievement. Future research directions involve the assessment of additional machine learning algorithms and feature selection methods to optimize prediction accuracy. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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the Hustlee Credit Card Fraud Detection using machine learning 7

The Hustlee Credit Card Fraud Detection using Machine Learni...

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7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Velicheti, Sri Sandhya Pavan, Arepalli Sri Hari Tirapathi Reddy, B. Vanisrikala, N. Pranay, R. Kannaiah, Sathish Kumar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9781665464086

It is extremely crucial for financial institutions to actually acknowledge the fraudulent purchases. Clients are really not charged for products they did not order. Such problems can be solved by Digital Marketing, and also its relevance, as well as Functional learning, can indeed be overshadowed. the said initiative aims to showcase the prediction large dataset used during machine learning identifying fraud. Credit thing wrong of order to detect data theft entails designing past debt card data and transactions from accounts that appear to be illegitimate. the whole framework then serves to evaluate if either the work being actually achieved is innovative or untruthful. the proposed research objective is to reduce the number of types of fraudulent fraud while finding 100% dishonest employment. A standard sample separation for detecting credit card fraud. Designers are starting to focus on examining and prioritizing data sets, while also submitting the several bewildering locating algorithms, including this Small Form Major consideration and Data on credit card transactions were tailored by the PCA's isolation forest technique. this study examines the effectiveness of logistic regression, decision tree, XGBoost, Naive Bayes and random forest for detecting credit card fraud. Dataset, which consists of credit card transactions made by European cardholders has been collected from Kaggle. © 2023 IEEE.

关键词： Decision trees

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Enhancing Android Security: Static Analysis for Robust Protection and Resilient Defences using Deep learning 7

Enhancing Android Security: Static Analysis for Robust Prote...

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7th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Abhishek, S. Anjali, T. Prakash, Prathiba Nair, Arun Karunakaran Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India University of Greenwich Old Navel College Department of Computer Science LondonSE10 9LS United Kingdom

ISBN: (纸本)9798350341485

Android has been a popular target for criminal actions due to its widespread use as a prominent mobile operating system, requiring the creation of sufficient security measures. Android has grown exposed to illegal activities due to its open-source design and ability to install third-party apps without centralized oversight. the suggested study thoroughly examines the rights that programs ask for to see whether there are any noticeable differences between the permissions used by malware samples and their benign equivalents. Based on complex authorization patterns, various machine learning methods are applied to distinguish between the two groups effectively. the discoveries spur the creation of solid security frameworks, eventually reducing the dangers of potential malware attacks. © 2023 IEEE.

关键词： Mobile security

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machine learning Approach to Predict Trends in Future for Options 7

Machine Learning Approach to Predict Trends in Future for Op...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Tarle, Sahil Balasaheb Paulkar, Pratik Parmeshwar Phad, Karan Govind Jadhav, Shreyas Vishwanath Chaudhari, Archana Y. Patil, Pramod D. Ahire, Prashant Dr. D. Y. Patil Institute of Technology Pimpri Computer Engineering Department Pune18 India Pimpri Chinchwad College of Engineering Computer Engineering Department Pune India

ISBN: (纸本)9798350304268

It is challenging to make predictions about the stock market since it requires in-depth expertise to weed out news stories, look at past data, and determine how news stories affect stock price movements. there is a drastic increase in challenges because of the fluctuating nature of stock values. the stock market prediction helps investors, policymakers, and businesses make informed decisions based on the expected future performance of the stock market. this paper presents an application of the Prophet algorithm in the stock market, to forecast stock prices. Prophet is a time series forecasting algorithm that takes into account seasonality, trends, and holidays. the data source for this investigation is downloaded from Yahoo Finance includes daily High, Low, Open and Close prices of a selected stock from January 2015 to April 2023. the model is trained on the 70 percent of historic data, and the performance is evaluated on the test data which is remaining 30 percent of data. the research results indicate that the Prophet An algorithm is likely to accurately foresee the stock prices with an average MAPE of 20.8 percent. Additionally, the model sheds light into the trends and seasonality of the stock market, which can be useful for investors and traders. Overall, the Findings indicate that the Prophet algorithm can be a valuable tool for stock market prediction. © 2023 IEEE.

关键词： Equities Prediction Facebook Prophet machine learning Time Series Variance

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Age and Gender Detection using Deep learning 7

Age and Gender Detection using Deep Learning

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Singla, Riya Singh, Gurpreet Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350304268

Automatic age and gender prediction from face photos have received a lot of attention due to its use in a variety of facial analysis applications, including access control, marketing intelligence, enforcement, and visual surveillance. these are also the two facial attributes that play a pivotal part in social relations. Different facial features are uprooted using the Deep Convolution neural network. It works efficiently indeed with limited data. In a way, CNNs are regularized multilayer feedforward. In recent years, CNNs have been successfully applied to the task of age and gender detection. the ability of CNNs to automatically learn features relevant to the job at hand eliminates the need for manual feature engineering, which is one of their key advantages. Additionally, CNNs can be trained on large datasets, which allows them to generalize well to new, unseen data. Purpose of this research paper is to develop an algorithm that estimates the age and gender of a person correctly. In this research, we propose the use of a CNN for the task of age and gender detection. To train the network, we used a dataset consisting of images of faces along with their corresponding age and gender labels. this paper is divided into 5 sections, Section-1 contains introduction, Section-2 contains Literature Review/ Previous work, Section-3 contains Methodology, Section-4 Result and Discussion and finally section-5 contains Conclusion and Future Scope © 2023 IEEE.

关键词： Age CNN Deep learning Gender gender issues machine learning marginalization

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Detection of Cardiovascular Disease Using Extreme learning machine and Artificial Neural Network 1

引用

7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Tanjil, Ashraful Reza Hasan, Md. Ashraful Bhuiyan, Fahim Mohammad Adud Reza, Ahmed Wasif Department of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (数字)9783031508875

ISBN: (纸本)9783031508868

Early detection and treatment of heart disease are crucial for improving patient outcomes and lowering healthcare costs since it is a prominent factor in fatalities. Worldwide. this research uses patient characteristics to analyze machine learning models to detect and forecast the risk of diseases involving the human heart. Age, sex, cholesterol, and blood pressure were just a few of the 14 distinct factors that comprised the collection, including 270 patient records. this study compared artificial neural networks (ANN) and Extreme learning machines (ELM) and obtained 98.77% and 86.42% accuracy, respectively. ANNs are versatile for different heart disease patterns because they are excellent at capturing complex nonlinear correlations and feature learning. Conversely, ELM is relatively less complicated to implement and offers a much faster training pace than other traditional ML techniques. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Modeling of Temperature-dependent Joint Friction in Industrial Robots Using Neural Networks 17

Modeling of Temperature-dependent Joint Friction in Industri...

引用

7th IEEE international conference on Robotic computing (IRC)

作者： Trinh, Minh Yadav, Ritesh Schwiedernoch, Ruben Gruendel, Lukas Petrovic, Oliver Brecher, Christian Rhein Westfal TH Aachen Lab Machine Tools & Prod Engn Aachen Germany

ISBN: (纸本)9798350395747

the application of industrial robots (IR) in machining offers many potential advantages such as flexibility and a large workspace. However, IR show weaknesses in absolute and path accuracy compared to machine tools due to their functional structure. Model-based compensation techniques can be a possible solution, which requires precise modeling of the robot dynamics and its influences. Friction causes a large portion of the total torque, particularly at low speeds showing highly nonlinear behavior. Furthermore, friction is caused by many influencing variables that are not considered in simple analytical models, such as temperature or load. the aim of this work is to propose analytical and data-driven friction models that adapt to the change in joint temperatures due to prolonged robot movements. As observed with experiments, the effect of temperature rise is significant on friction when the robot is operated over a long duration. Robot trajectories, with varying velocities and temperature rise rates inside the joints, are generated and implemented on the first axis of the MABI MAX 100 robot for training of the proposed friction models. While trajectory execution, additional sensors on the robot are used to record the robot housing temperature and the environment temperature in the robot cell. As a result, the temperature-dependent as well as data-driven models outperformed currently used models.

关键词： Temperature-dependent friction modeling industrial robotics machine learning

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IoT Based Fruit Quality Inspection and Lifespan Detection System 7

IoT Based Fruit Quality Inspection and Lifespan Detection Sy...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Saha, Ashim Ali, Lubaina Rahman, Rudrita Hossain, Toushif Mohammad Bin Shamim, Shah Jalal Supto, Md. Al Abid Shidujaman, Mohammad Uddin, Mohammad Rejwan Hasan, Mahady Independent University Bangladesh Fab Lab Iub Dhaka Bangladesh

ISBN: (纸本)9798350304268

Fruits are an essential source of nutrients, but their quality depends heavily on their freshness and shelf life, which can be impacted by various external factors. this paper presents a comprehensive study on the decay of fruits and the development of a lifespan detection system that integrates a Raspberry Pi Pico, a DHT-11 sensor for capturing real-time temperature and humidity of fruits, and an MQ-4 methane gas detector sensor to estimate methane gas emissions from spoiled fruits. then data storage and machine learning (ML) algorithms are used to accurately predict the expiration date of fruits. the proposed system focuses on measuring some crucial elements that essentially identify the spoilage state of fruit and are transmitted to a local database using a NodeMCU ESP8266. the collected data is then used to predict the expiration date of the fruit using a suggested equation with high accuracy and reliability. By accumulating necessary information about the temperature, humidity, and methane emission of fruit, this paper illustrates a graphical representation of real-time data with an approximate lifespan of the fruit. the system is cost- effective, user-friendly, and useful in warehouses, cold storage, and food quality inspection sectors. With further research and development, the proposed system can be extended to detect the lifespan and expiration of other perishable items, thus providing a comprehensive solution for food preservation and safety. © 2023 IEEE.

关键词： Embedded System IoT(Internet of things) machine learning (ML) Methane Gas Sensor (MQ-4) Raspberry Pi Pico

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7th international conference on Big Data and Cloud computing Challenges, ICBCC 2022

7th International Conference on Big Data and Cloud Computing...

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7th international conference on Big Data and Cloud computing Challenges, ICBCC 2022

ISBN: (纸本)9789819910502

the proceedings contain 24 papers. the special focus in this conference is on Big Data and Cloud computing Challenges. the topics include: Early Planning of Virtual machines to Servers in Cloud Server Farms is an Approach for Energy-Efficient Resource Allocation;performance Analysis of Distributed Algorithms for Big Data Classification;achieving Sustainability by Rectifying Challenges in IoT-Based Smart Cities;framework for Implementation of Smart Driver Assistance System Using Augmented Reality;ioT-Based Mental Health Monitoring System Using machine learning Stress Prediction Algorithm in Real-Time Application;fish Feeder System Using Internet of things;an Efficient and Recoverable Symmetric Data Aggregation Approach for Ensuring the Content Privacy of Internet of things;Implementation and Comparative Analysis of Various Energy-Efficient Clustering Schemes in AODV;secure Web Gateway on Website in Cloud;adaptive End-To-End Network Slicing on 5G Networks;zero Trust Framework in Integrated Cloud Edge IoT Environment;o2Q: Deteriorating Inventory Model with Stock-Dependent Demand Under Trade Credit Policy;embedding (K9-C9)n into Certain Necklace Graphs;Lyrics Generation Using LSTM and RNN;design a Quantum Cryptography Algorithm and Evaluate the Risks of Quantum-Based Nano computing;human Odor Security Using E-nose;automated Road Surveillance System Using machine learning;an Artificial Intelligence-Based Technique to Optimize Hybrid Vehicle Using Renewable Energy Sources;stock Market Prediction Using machine learning Techniques: A Comparative Study;comparative Study on Different Intrusion Detection Datasets Using machine learning and Deep learning Algorithms;mitigating Postnatal Depression: A Big Data Self-help therapy.

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