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Text Sentiment Analysis of Film Reviews Using Bi-LSTM and GRU 4

Text Sentiment Analysis of Film Reviews Using Bi-LSTM and GR...

引用

4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Mouthami, K. Yuvaraj, N. Thilaheswaran, K.K. Lokeshvar, K.J. Kpr Institute of Engineering and Technology Dept. of Computer Science Engineering Coimbatore India Kpr Institute of Engineering and Technology Dept. of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350300093

Sentiment analysis is a vast subject to explore in natural language processing (NLP) techniques. The film reviews were analyzed and segregated into positive, neutral, and negative reviews. The proposed model examines two distinct datasets, one with multi-class labels and the other with binary-type class labels. The preprocessing is done by using the bag of words and skip-gram word2vec, variety of classifiers, including Two state Gated recurrent units (TS-GRU), was used for binary classification, and the Bidirectional Long Short-Term Memory (Bi-LSTM for the multi-class situation was implemented. Bi-LSTM was utilized in the model, in which symmetric Bi-LSTM replaces the LSTM approaches to get around the high computational cost of training the normal LSTM. The Bi-LSTM has equivalent accuracy to the LSTM while significantly reducing computational costs. Sentiment analysis offers a glimpse of how consumers feel about certain movies. This endeavor is undertaken via the study of natural language processing, in which sentiment analysis is a branch which examines how words are arranged and utilized to extract meaning from user writing. This helps to investigate further the methods for deciphering such data thanks to significant advancements in machine learning techniques;with Keras API and the Internet Movie database (IMDb) dataset, this study compares single and multi-branch CNN with Bidirectional LSTMs with different kernel sizes. GRU achieved 98.24% of accuracy, and bi-LSTM achieved 98.65% of accuracy. © 2023 IEEE.

关键词： Sentiment analysis

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Explainable AI for Enhanced Intrusion Detection Systems 3

Explainable AI for Enhanced Intrusion Detection Systems

引用

3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Ananya, G. Siri, N. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Electronic and Communication Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798350354171

As everything is turning digital there is a hike in the invasion of digital space, because of which advanced tools are needed. One such tool is the Intrusion Detection System (IDS). In this work, we have tried to detect and classify suspicious connections accurately. This is done by incorporating advanced computing into Intrusion Detection Systems. To achieve this, we have used the Coburg Intrusion Detection dataset. To achieve this, we used the Coburg intrusion detection dataset. We investigate the effectiveness of Single Classifiers and Bagging and Boosting in this context. We proposed a four-stage Ensemble model to classify the connections using its attributes. The proposed model uses four Single Classification models: Logistic Regression, KNN, Decision Tree, and SVM. Before applying the algorithms, the data was Preprocessed. Later, the models were hyper-tuned to increase the accuracy. The results showed that Random Forest outperforms other single classifiers and machine learning algorithms, with a mean accuracy of 99.99%, a precision of 99.99%, and an F1-score of 99.99%. Furthermore, as models are usually hard to understand by using Explainable AI we can increase the transparency of the model. Explainable AI techniques are used to make the advanced computing models reliable and more understandable. This study aims to develop advanced and dependable intrusion detection systems. © 2024 IEEE.

关键词： Network intrusion

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Anodyne for Piracy by Hue Anatomization 2

Anodyne for Piracy by Hue Anatomization

引用

2nd IEEE International Conference on data, Decision and Systems, ICDDS 2023

作者： Srinivasan, K N Pranav Sujatha, D.N. New Horizon College of Engineering Dept. of Information Science and Engineering Bangalore India B M S College of Engineering Dept. of Computer Applications Bangalore India

ISBN: (纸本)9798350314168

Piracy is a malignant and deleterious parasite in the world of digital content. Here we are specific to piracy that occurs in movies through cam recorders and mobiles. Our solution aims to find the source of piracy among screening theaters to remediate the film industry and expunge piracy altogether. The method we use to solve this problem is hue anatomization, which uses hue and its properties to locate a theater distinctively from other theaters. The proposed method is studied with four progressions, Further with Structural Similarity Index Measure (SSIM), the results are accurate only if the window size it searches for is correct, this needs to be rechecked manually. © 2023 IEEE.

关键词： Theaters

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Enhancing Quantum Operations: Random Forests for Anomaly Detection and Alert Generation

Enhancing Quantum Operations: Random Forests for Anomaly Det...

引用

International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： M. Anoop B. Nithya Dept. of Computer Science and Applications SRM Institute of Science and Technology Chennai India Dept. of Computer Applications Hindustan Institute of Technology and Science Chennai India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

A promising approach for enhancing the dependability and security of quantum processes is the integration of Random Forests, a traditional machine learning technique, with quantum anomaly detection and alert production. This approach involves training Random Forest models to inspect quantum data and spot abnormalities such as variations in quantum states, entanglement patterns, and the results of quantum processes. When anomalies are found, alarms are activated, categorized by severity, and sent out via several notification channels. Operators of quantum systems can act quickly by applying techniques for adaptive control, error correction, or system reinitialization. The rigorous preservation of historical data makes post-incident study and ongoing system improvement possible. This novel strategy strengthens security while simultaneously improving the performance of quantum systems, particularly in situations involving quantum communication. To guarantee its efficiency in the continually changing quantum landscape, careful design and ongoing monitoring are essential.

关键词： Quantum system Adaptation models Quantum computing Quantum entanglement data models Error correction Security

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Earthquake Prediction Through Machine Learning Approach 4

Earthquake Prediction Through Machine Learning Approach

引用

4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Shrote, Pratik Dasarwar, Priya Dongre, Snehlata Khobragade, Prashant Intern Center of Excellence Data Science Gh Raisoni College of Engineering Nagpur India Nagpur Campus Pune India Gh Raisoni College of Engineering Dept. of Computer Science and Engineering Nagpur India H Raisoni College of Engineering Dept. of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350387490

The population of earthquake-prone areas is growing, as is the number of people killed and injured by earthquakes. The problem of the increasing number of populated regions that are vulnerable to earthquakes is being addressed by a shortage of experts in earthquake engineering. This implies the requirement for automated intelligence-monitored mitigation and warning systems. Predicting earthquakes is still a major problem for disaster management around the globe. The objective of this work is to build a machine learning (ML) approach that uses geographic coordinates (latitude, longitude) and elevation (height) as inputs to forecast earthquake magnitude. The estimated magnitude is used to classify the seismic risk level for a given site into high, low, or no risk categories using Richter's scale. The study offers a thorough analysis of the methodology, covering feature engineering, data collecting, preprocessing, and model construction. A variety of machine learning methods are investigated, and relevant measures are used to assess their effectiveness. © 2024 IEEE.

关键词： Adversarial machine learning

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Secrecy Capacity Optimization in RF/FSO Systems: Impact of Mixed Rayleigh and Log-Normal Fading with Atmospheric Turbulence

Secrecy Capacity Optimization in RF/FSO Systems: Impact of M...

引用

2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India

ISBN: (纸本)9798350379525

This paper investigates the secrecy capacity optimization in RF/FSO systems with mixed Rayleigh and log-normal fading, while masking eavesdroppers channel state information () as well as suffering from atmospheric turbulence. We explore the impact of several parameters including beamforming angles, modulation schemes and hybrid RF/FSO link configurations through a combination of simulations as well as analytical models. Secrecy capacity is enhanced for an improved SNR and vice versa under higher turbulence conditions and cooler influents eavesdropped scenarios. Performance of the hybrid RF/FSO systems dominates considerably over single-link solutions, especially at longer distances. All these mechanisms appreciably increase the secrecy capacity, while beamforming and advanced modulation techniques offer most of the improvement. The study also demonstrates the interdependencies of system parameters, providing guidelines for designing secure communication systems under different environmental and operational conditions. © 2024 IEEE.

关键词： Beamforming

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Deep Learning for Channel Prediction in Non-Stationary Wireless Fading Environments

Deep Learning for Channel Prediction in Non-Stationary Wirel...

引用

2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Data Science and Quantum Computing ABV Indian Institute of Information Technology and Management Gwalior India

ISBN: (纸本)9798350379525

In non-stationary wireless fading channels deep learning models are effective in predicting channel states as indicated by this study. This work compared the LSTM and GRU models with autoregressive methods or Kalman filter, implying that deep learning is indeed able to capture temporal dependence between variables and was likely more adapted than linear method when dealing with many changes in dynamics. From the perspective of pre-processing data, keys like attention mechanism and transfer learning were decisive elements in helping them to improve prediction scores and make models more robust. These results showed that these enhanced procedures allow the models to generalise over a wide range of non-stationary situations, generating consistent performance in various diverse environments. This study indicates the promise of deep learning for enhancing wireless network operation, which could lead to greater self-awareness and utilization features in emerging future-generation wireless systems. © 2024 IEEE.

关键词： Prediction models

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Machine Learning Approach for Complaint Prediction in the Telecom Industry 2

Machine Learning Approach for Complaint Prediction in the Te...

引用

2nd International Conference of Intelligent Methods, Systems and applications, IMSA 2024

作者： Gewaly, Mohamed Mahmoud Saeed, Mahmoud Samy Ammar, Ammar Mohammed FGSSR Cairo University Dept. of Data Science Cairo Egypt Telecom Egypt Cairo Egypt FGSSR Cairo University Dep. of Computer Science Cairo Egypt

ISBN: (纸本)9798350362633

The quality of service of the individual user of telecommunication services is one of the most critical criteria in the telecom industry that affects customer satisfaction. This paper aims to predict customer complaints before escalation and validate current escalated complaints in fixed broadband from telecommunication companies via historical and real-time customer data, including service logs and previous complaints, to optimize resource allocation and maximize customer satisfaction as the challenges are related to the volume of physical complains and churn. The dataset is real-world data provided by a single Egyptian mobile network operator and consists of 12 features pertaining to 100,000 customers. We represent 12 predictive models developed using machine learning algorithms via advanced techniques such as classification algorithms like Random Forest Classifier (RFC), AdaBoost Classifier with a Decision Tree (DT), Logistic Regression (LR), -Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), Decision Tree Classifier (DTC), Support Vector Machine (SVM) to analyze various features and patterns within the data. The results show that the AdaBoost with a Decision Tree model did well using a variety of assessment indicators, including Accuracy, Precision, Recall, and F1-score;the model aims to classify customers, indicating their likelihood of raising complaints in the future. The results and insights obtained from this study can provide telecom service providers with valuable guidance for implementing data-driven strategies to improve customer experience and take proactive maintenance that will reduce cost and churn. © 2024 IEEE.

关键词： Customer satisfaction

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Enhancing Weather Forecasting Accuracy: Integrating Machine Learning Algorithms with Numerical Weather Prediction Models 1

Enhancing Weather Forecasting Accuracy: Integrating Machine ...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sharma, Madhuri Singh, Rana Pratap Sharma, Usha Arivukkodi, R. Roy, Ranjeet Kumar Verma, Kartik Dept. of Life Sciences CHRI Campus India Madhav University Dept. of Humanities and S.Science Pindwara India Meenakshi A.H.E.R Dept. of Computer Applications Chennai India Lingayas Vidyapeeth University Dept. of Mathematics Faridabad India Chandigarh University Dept. of Com. Science Engineering Chandigarh India

ISBN: (纸本)9798350356816

Many industries, including agriculture, transportation, & energy, heavily rely on weather forecasts. Earlier weather forecasting techniques were built on numerical weather prediction models, which use physical equations to simulate atmospheric conditions. However, these models have limitations due to measurement uncertainty & the complexity of atmospheric systems. Machine learning techniques have emerged as a promising advancement in weather forecasting. This paper introduces a novel method for weather forecasting using Naive Bayes & Chi-Square algorithms. The theoretical foundations of these algorithms & their application in weather forecasting are discussed. Experimental results on real weather datasets demonstrate the effectiveness of the proposed method. © 2024 IEEE.

关键词： Prediction models

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ABC-DE-WOA: A New Hybrid Algorithm for Optimization Problems 4

ABC-DE-WOA: A New Hybrid Algorithm for Optimization Problems

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4th International Conference on Circuits, Control, Communication and Computing, I4C 2022

作者： Pradeep Kumar, D. Sowmya, B.J. Kanavalli, Anita Vincent, Annvin Dept. of Computer Science and Engineering Bangalore India Dept. of Artificial Intelligence and Data Science Bangalore India

ISBN: (纸本)9798350397475

One of the most effective swarm intelligence-based algorithms for many global optimization issues is the Artificial Bee Colony (ABC) strategy. Despite the fact that there are many Artificial Bee Colony (ABC) variants, the algorithm normally has a low convergence rate. Therefore, it is still essential to moderate an algorithm's intensity and diversity. In this instance, the standard Artificial Bee Colony (ABC) algorithm has been combined with the Whale Optimization Algorithm (WOA) and Differential Evolution Algorithm (DE) to generate a novel Hybrid Artificial Bee Colony algorithm (ABC), Artificial Bee Colony-Differential Evolution-Whale Optimization Algorithm (ABC-DE-WOA). For simple benchmark problems with up to 100 dimensions, 50 dimensions, 30 dimensions, and 10 dimensions, the proposed hybrid technique is compared with Artificial Bee Colony (ABC) variants like Artificial Bee Colony-Whale Optimization Algorithm (ABC-WOA), Artificial Bee Colony-Differential Evolution Algorithm (ABC-DE), and original Artificial Bee Colony Algorithm (ABC). The results show that the proposed technique performs better than its competitors in terms of convergence speed. © 2022 IEEE.

关键词： Global optimization

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