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Deep Sentiment: “An Effective Sentiment Analysis of Twitter Data using BERT+GRU Hybrid Model”

Deep Sentiment: “An Effective Sentiment Analysis of Twitter...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： K. Sudhakar T. Jatin Pratap Singh M. Thanuj B. Praveen Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Sentiment Analysis (SA) has been an active area of research for over a decade. It involves an in-depth study of opinions, sentiments, and subjectivity expressed in text. Among various platforms, Twitter is one of the most popular microblogging services and serves as a significant source for sentiment analysis and data exploration. Organizations globally utilize SA on Twitter data to gain insights with applications across various domains. However, while SA is effective for understanding public opinion, bias in tweets can distort analysis, leading to inaccurate results and potentially influencing users to make flawed decisions. Such biased tweets may originate from both authentic but biased individuals and automated social bots that disseminate skewed opinions on specific topics. To address this challenge, this research proposes a statistical model designed to detect users and bots responsible for spreading biased content on Twitter. Using an annotated Twitter dataset, the study evaluates sentiment analysis results with and without biased tweets, analysing the impact of biased users at both micro and macro levels. Experimental results demonstrate that the proposed approach effectively distinguishes biased users and bots from genuine users, thereby enhancing the accuracy of sentiment analysis.

关键词： Sentiment analysis Analytical models Accuracy Social networking (online) Computational modeling Blogs Organizations Control systems Data models Data mining

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A Predictive Analytics Approach to Traceability in Digital Forensics Using Time Series Analysis 2

A Predictive Analytics Approach to Traceability in Digital F...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Raju, K Srujan Ahila, R. Al-Farouni, Mohammed Karuna, Gotlur Yadav, Kanchan Kumar, Neelam Sanjeev Department of CSE CMR Technical Campus Telangana Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of CSE Tamil Nadu Chennai600073 India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq GRIET Bachupally Department of AI&ML Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India SRM Institute of Science & Technology Vadapalani Department of Computer Science & Engineering Chennai26 India

ISBN: (纸本)9798350368109

Digital forensics is essential to cybersecurity and the examination of virtual activities -particularly criminal ones. This research outlines a predictive analytical technique in his based on the time series to improve digital forensics traceability. The performance of the research is assessed by using the Digital Forensic Investigation Dataset (DFID) which contains application logs, network traffic files and file system images derived from inv You can download simulated forensic cases which are time stamped application logs, network traffic records and file system snapshots which are used to evaluate the performance of the research through Digital Forensic Investigation Dataset (DFID). In this regard, machine learning and deep learning algorithms such as Random Forest, Support Vector Machines, Long Short-Term Memory Networks were used to anticipate and detect the notion of suspicious action. Concerning the results of the Random Forest model, it yielded a high accuracy of 95% and precision of 94%, along with a recall of 96%, F1-score of 95%, and AUC of 0. 98. The feature importance analysis emphasized the most important features in the forensic process such as time, Internet protocol addresses, file hashes, and types of events. This research contributes to enhancing the knowledge base in the field of digital forensics by proposing the use of predictive analytics for early identification and mitigation of threats;this forms part of making Investigations efficient. The results support the notion that informational knowledge, as well as currently practiced and emerging computational methods, should play a central role in forensic practices. Some of the areas of further work are the attempt to combine the approaches and the refinement of the deep learning schemes to faces more complicated circumstances that can be encountered in forensics, as well as the integration of legal and ethical issues to the use of predictive models within digital environment of forensic invest

关键词： Predictive analytics

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An Ensemble Approach of CNN and GRU Models for Network and Server Intrusion

An Ensemble Approach of CNN and GRU Models for Network and S...

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Emerging Technology (INCET), International Conference for

作者： Aniketh Reddy Adireddy Vishnu Kurnala Sindu Patlolla Gagandeep Arora Artificial Intelligence & Machine Learning Vardhaman College of Engineering Hyderabad India Computer Science & Engineering (AI&ML) Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

This research paper delves into the realm of cybersecurity by proposing an innovative ensemble model that blends Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN) for network and server intrusion detection. CNNs excel in spatial feature extraction, particularly suited for analyzing network traffic patterns, while GRUs are adept at capturing temporal dependencies within sequential data. The integration of these models aims to leverage their complementary strengths, enhancing the overall robustness of intrusion detection systems. Through comprehensive training and evaluation on diverse datasets, the ensemble model showcases promising results, demonstrating improved accuracy and resilience against adversarial attacks compared to standalone CNN and GRU models. In addition to making a significant contribution to the rapidly developing field of deep learning in cybersecurity, this research offers practical implications for the development of more secure network and server environments in the face of dynamic cyber threats.

关键词： Training Intrusion detection Telecommunication traffic Logic gates Feature extraction Robustness Convolutional neural networks

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Proactive Purity: Predicting Water Quality with Precision

Proactive Purity: Predicting Water Quality with Precision

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： K. Sandhya G. Ashitha B. Sharath Kumar B. Varun Kumar T. Bharadwaj Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Water pollution, driven by industrialization and urbanization, threatens public health, aquatic ecosystems, and livelihoods worldwide. Rivers, being the primary source of water for drinking, agriculture, and industry, are highly vulnerable to contamination from untreated sewage, industrial discharge, and agricultural runoff. Traditional water quality monitoring methods are expensive, time-consuming, and lack real-time *** study proposes a technology-driven water quality monitoring system capable of processing large data volumes in real-time, identifying pollution patterns, and supporting timely interventions. By integrating advanced monitoring techniques, the system enhances decision-making for water management, ensuring sustainability and ecological balance. The research highlights the critical role of technology in addressing global water pollution balance, further underlining the very important linkage between technology and *** data volumes from source water can be processed by this system, identifying patterns and trends that indicate fluctuation in water quality. this system is particularly beneficial in areas that experience seasonal and man-made causes of change in water quality. Besides its technological achievements, this project is a giant leap toward the management of water *** technology provides authority to decision makers. who together with environmentalists and enables easy accessibility for various tasks to make sensible decisions for drinking water supply as well as irrigation systems and pollution prevention measures for water sources. measures. It has great importance as it combines science and technology in saving vital natural resources and maintaining long-term ecological balance by tackling one of the most urgent worldwide environmental issues.

关键词： Machine learning algorithms Water quality Prediction algorithms Market research Water pollution Real-time systems Pollution measurement Public healthcare Water resources Monitoring

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A Novel Hybrid Approach Integrating Machine Learning and ANN for Stroke Risk Assessment 4

A Novel Hybrid Approach Integrating Machine Learning and ANN...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Vullam, Nagagopiraju Jagadeesh, S.V.V.D. Pavani, K. Siva Janakidevi, Maddala Lakshmanarao, A. Chandreswari, Y.S.N. Chalapathi Institute of Engineering and Technology Department of Cse A.P Guntur India LakiReddy Bali Reddy College of Engineering Department of Ai&ds Mylavaram India Aditya University Department of Ai&ml Surampalem India Srkr Engineering College Department of Cse A.P Bhimavaram India Aditya University Department of It Surampalem India Bonam Venkata Chalamayya institute of technology and science Department of Mca Batlapalem India

ISBN: (纸本)9798331529635

Brain stroke is a life-threatening condition that needs to be detected early and correct precision to improve patient wellness. This paper presents a method for predicting stroke using ml and ANN using a publicly available data from Kaggle. The dataset has class imbalance. This can be solved using SMOTE and ADASYN to guarantee a balanced class distribution. After preprocessing, six traditional ml models including Logistic Regression, DTC, Random Forest, KNN, SVM and Gradient Boosting were used to predict the risk of stroke. To further increase the accuracy of the forecast. Later, ensemble models also applied to further enhance performance. Finally, ANN was applied to capture complex patterns in the data. The results shown that the ensemble model significantly improves the prediction performance compared to the individual ml models. Later a hybrid ml and DL ANN is proposed. The proposed hybrid model combining ml best model and ANN technique improved the accuracy in predicting stroke and provide a more reliable system for early detection of stroke. It is observed that the hybrid method will outperform both individual ml models and ensemble models. © 2024 IEEE.

关键词： Adversarial machine learning

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PaySafe Al: Intelligent Fraud Detection for UPI Transactions using Machine Learning

PaySafe Al: Intelligent Fraud Detection for UPI Transactions...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Markala Anjali Maddela Ajay Mukkerla Saiteja Baddula Omkar Yadav Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

In the financial world, it is impoartant to have a sound understand of systems against fraud, as they help detect activities such as anomalous and malicious transactions, through the analysis of activity engagement. Decision Tree are the recent form of deep learning models which, in more recent years, have shown advanced capability in the identification of fraudulent transactions, however, sensitivity and accuracy when implemented in real time systems remain an issue. In this research, we provide a new idea of fraud detection of UPI transactions that utilizes a Decision Tree based sequence modeling framework. The model focuses on normalization sets and typical cardholder activities to effectively learn how to detect anomalies in UPI transactions. It then analyzes the sequences of a person’s activities and adds on the probabilities, to then classify if the transaction is fraudulent or not. Several measures are established, however, in order to aid in minimizing false positives, because the aim is to ensure valid transactions are still processed. The experimental results support the proposed fraud detection system’s potential usefulness in monitoring actual UPI transactions and it also shows how it outperforms current systems in related studies. Our model facilitates the UPI transaction environment equally and addresses the fine balance in detection efficiency, accuracy and transaction speed by proposing the integration of deep learning techniques.

关键词： Support vector machines Deep learning Logistic regression Accuracy Sensitivity Predictive models Fraud Decision trees Security Standards

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Invisible Shield: Real-Time Industrial Threats Detection for IoT-based Systems

Invisible Shield: Real-Time Industrial Threats Detection for...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Kadaganchi Sudhakar Pasula Manish Mavillapally Rohit Shaik Kaleem Ahmed Batharaju Sneha Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Hyderabad Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

The Industrial Internet of Things' (IIoT) explosive growth has significantly changed industrial environments by linking smart devices via Supervisory Control and Data Acquisition (SCADA) systems. This integration also introduces significant cyber security vulnerabilities even though it brings about flexibility, resource efficiency, and operational agility. Current IDS using traditional machine learning fail to classify cyber attacks precisely because of complexity in data, limited availability, and mislabeling issues. To overcome these challenges, this study presents a scalable and effective ensemble detection framework that utilizes Pyramidal Recurrent Units (PRUs) and Decision Tree (DT) models. This framework aims to detect and counter cyber attacks across large IIoT networks, especially in SCADA-based environments. The propose of developing this project is to ensure scalable and efficient Deep Learning and Decision Tree based ensemble cyber attack detection framework to resolve trustworthiness issues in the SCADA based IIoT networks. Our proposed detection method can be applied to various IIoT domains. It is easy to implement and deploy, improving efficiency and accuracy while addressing the limitations of earlier *** framework enhances the security of SCADA-based IIoT systems, making industrial networks more reliable, trustworthy, and resilient. The results indicates that the system performs well across different network settings, showcasing its adaptability and robustness in detecting IIoT-based SCADA systems.

关键词： Deep learning Accuracy SCADA systems Feature extraction Threat assessment Complexity theory Security Decision trees Cyberattack Industrial Internet of Things

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Solar Radiation Prediction Enhancement Via Ensemble Learning Method

Solar Radiation Prediction Enhancement Via Ensemble Learning...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： P. Ravi Kishore G. Sai Kiran Reddy A. Shinjith P. Vikas Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Efficient forecasts of solar radiation are very important for optimizing solar energy systems, enhancing energy management, and ensuring the reliability of all renewable energy solutions. This study scrutinizes the performance evaluation of machine-learning algorithms as Decision Trees (DT), Support Vector Machines (SVM) and K - Nearest Neighbors (KNN), Linear Regression (LR), and Logistic Regression (LRg) to forecast solar radiation in relation to urban energy planning. Logistic Regression, usually used in binary classification problems, is modified in this study to demonstrate its regressive capacity. Its simplicity and modeling of linear relationships provide in-depth hungry comparative insights in conjunction with an ensemble. The analysis is made applying the year 2020 data at a high resolution sourced from the Solar Radiation Research Laboratory (SRRL) at the National Renewable Energy Laboratory (NREL). And the dataset considered metropolitan cities, including Delhi, Bangalore, and Hyderabad in the year 2020, where Global Horizontal Irradiance (GHI) is the core predictor variable in this study. Metrics of performance such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Squared Error (MSE), are similarly utilized to appraise the accurate model of the highest predictive power. Further, the current study incorporates weather parameters, such as temperature, humidity, and wind speed, to augment the forecasting operation and make a thoroughly vast analysis. The findings present an exhaustive comparison of the algorithms along with a highlighter showing their strengths and limitations within an urban energy planning context. The research has brought to light the potential for implementing machine learning algorithms in forecasting solar energy while offering key insights to enhance the efficiency, scalability, and reliability of solar energy systems in highly populated areas.

关键词： Support vector machines Logistic regression Renewable energy sources Machine learning algorithms Wind speed Solar energy Planning Solar radiation Reliability Forecasting

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Titanic Survivors Prediction Analysis Using Machine Learning Algorithms 1

Titanic Survivors Prediction Analysis Using Machine Learning...

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1st International Conference on Computational Intelligence for Security, Communication and Sustainable Development, CISCSD 2024

作者： Devamane, Shridhar B. Abhijith, B.H. Purushotham, Bhushan Ravindranath, R.C. Lakshmikantha, G.C. Udayaprasad, P.K. Global Academy of Technology Department of Computer Science and Engineering Bangalore India Global Academy of Technology Department of Artificial Intelligence and Machine Learning Bangalore India Global Academy of Technology Department of Ai and Ml Bangalore India

ISBN: (纸本)9798350365405

Machine learning algorithms are computational models that allow computers to acquire knowledge and improve performance on a task by automatically learning patterns and rules from input data provided by the user. In this work we are predicting the Titanic disaster survivors based on a particular dataset. Based on characteristics such as age, sex, ticket class, etc., we applied the Support Vector Machine, K-Nearest Neighbors, Decision Tree, and Random Forest algorithm to identify which passenger would survive. © 2024 IEEE.

关键词： Adversarial machine learning

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Advanced Detection of Machine Failure in Automated Industries using Long-Short Term Memory and LightGBM

Advanced Detection of Machine Failure in Automated Industrie...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： Muppidi Mahesh Ponnagani Sreeja Sunnapu Swetha Madala Ritwik Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

In the rapidly evolving industrial landscape, companies are using various technologies to optimize their operations which thereby reduce costs, and they focus on increasing their profits. Proactive maintenance has emerged as an important strategy that enable industries to foresee and prevent machine failures beforehand. This proactive approach not only extends the machine lifespan but also leads to cost savings. Machine failure detection is very essential in automated industries, medical fields, aerospace, transportation and many more industries where machinery is central for the production process. Traditional reactive maintenance methods only focus on repairing the machines after the occurrence of the failure which greatly affects the production. Machine learning, with it’s ability to analyze large datasets and identify the unusual patterns based on the historical data. This paper mainly proposes using a hybrid approach of the ml algorithms to increase the accuracy and efficiency of the detection of machine failures. The algorithms are Long-Short Term Memory (LSTM) networks and Light Gradient Boosting Machine (LightGBM). This approach is mainly used for early fault detection in machines. We combine the features of LSTM to find temporal patterns in sensor data with LightGBM’s capacity to refine predictions and analyze feature importance. This approach accurately finds the failure predictions, then classify failure severity that can be visualized using graphs and trigger actionable maintenance alerts. By this proposed approach we achieved a result of 98% accuracy on real sensor data when compared to traditional methods, outperforming them by 7%. This research fills the gap between the traditional reactive maintenance and the current predictive maintenance.

关键词： Industries Machine learning algorithms Costs Accuracy Transportation Production Predictive models Maintenance Long short term memory Predictive maintenance

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