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A Multi-Source Approach to Film Recommendations using Social Media, Search Data, and Streaming History 5

A Multi-Source Approach to Film Recommendations using Social...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Raj, J. Relin Francis Karthikeyan, M. Saravana Rajkumar, G. Vinoth Shankar, S. Vijay Isaac, R. Augustian Anushya, S. Thanga Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India J. P. College of Engineering Department of Electronics and Communication Engineering Tenkasi India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Thandalam Chennai India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India

ISBN: (纸本)9798331530013

In the rapidly evolving OTT platforms, providing highly personalized and relevant film recommendations has become a critical challenge. Traditional recommendation systems primarily rely on user ratings and watch history, neglecting the vast potential of other data sources such as social media interactions and search engine queries. This research presents a multi-source film recommendation framework, integrating data from social media posts, search behaviour, and OTT streaming history to deliver enhanced personalization. A novel algorithm, CoFiSent (Collaborative Filtering with Sentiment Analysis), is proposed, combining collaborative filtering techniques with sentiment analysis of user-generated content from social media. By analysing user sentiments and preferences, the algorithm refines recommendations to match users' dynamic interests more effectively. The performance of CoFiSent is evaluated against existing algorithms, including standard collaborative filtering and content-based filtering models. Simulations are conducted using real-world datasets, and the system's effectiveness is measured through precision, recall, F1-score, and mean reciprocal rank (MRR). Results demonstrate that CoFiSent outperforms traditional methods, particularly in capturing emerging trends and contextual preferences reflected in social media activities. The integration of multiple data sources enhances the overall accuracy and relevance of recommendations, offering a significant improvement in user satisfaction. The research findings suggest that incorporating social media sentiment and search behaviour into film recommendation systems represents a promising approach for next-generation entertainment platforms. © 2024 IEEE.

关键词： Collaborative filtering

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Optimal Renewable Energy Wireless Power Management System for Electric Vehicles Using Predictive Analytics

Optimal Renewable Energy Wireless Power Management System fo...

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Network, Multimedia and Information technology (NMITCON) International Conference on

作者： Narendra Kumar P M Shivakumarswamy N Nikhil Nagaraj Moger M.Vijay Raghav B M Kavya N Yuvaraj H N Mahendra S Mallikarjunaswamy Artificial Intelligence and Machine Learning RNS Institute of Technology Bengaluru India Dept of Electronics and Communication Engineering JSS Academy of Technical Eduction Bengaluru India Dept of Electronics and Communication Engineering KS Institute of Technology Bengaluru India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

This paper introduces the Optimal Renewable Energy Wireless Power Management System (OREWPMS) for electric vehicles (EVs), designed to significantly enhance charging efficiency and sustainability by integrating innovative wireless charging technology with renewable energy sources. In contrast to conventional EV charging methods, which predominantly rely on direct electrical connections involving cables and plugs prone to wear and environmental damage, and heavily dependent on non-renewable energy, the proposed system capitalizes on an integrated network of solar and wind energy. This renewable grid powers a series of strategically placed wireless charging pads in parking areas and along roadways, offering a more adaptable and less intrusive solution. The OREWPMS utilizes resonant inductive coupling for efficient and low-interference power transfer, and it is governed by a predictive analytics algorithm that dynamically adjusts power distribution based on real-time vehicle demand, weather forecasts, and renewable energy availability. Preliminary testing has shown that the OREWPMS improves charging efficiency by approximately 0.40% and increases the utilization of renewable energy sources in EV charging by up to 0.65% compared to these conventional wired charging systems. Additionally, it reduces overall energy costs by about 0.30%, presenting a substantial improvement in both environmental impact and operational costs, thereby supporting a more sustainable and economically feasible transition to electric vehicle usage.

关键词： Wireless communication Renewable energy sources Costs Power system management Inductive charging Power distribution Prediction algorithms Electric vehicle charging Real-time systems Predictive analytics

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Prediction of Air Quality using Deep learning based Model with CRO 4

Prediction of Air Quality using Deep Learning based Model wi...

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4th International Conference on Emerging Research in Electronics, Computer Science and technology, ICERECT 2022

作者： Pareek, Piyush Kumar Gollagi, S.G. Parameshachari, B.D. Karamthoti, Mohandas Nitte Meenakshi Institute of Technology Artificial Intelligence and Machine Learning Bengaluru India KLE College of Engineering and Technology Computer Science and Engineering Belagavi India Nitte Meenakshi Institute of Technology Department of Electronics and Communications Engineering Bengaluru India Edu Infotech International Res Inc AtlantaGA United States

ISBN: (纸本)9781665456357

As their populations rapidly urbanise, many emerging nations also have a severe issue with air pollution. The ability to foresee changes in air quality is increasingly valuable to both policymakers and citizens. In this study, we usage of air quality and weather forecast data to make predictions for the following 48 hours at each monitoring station. This work creates a feature selection-based classifier for forecasting based on expertise in the field of air pollution. Three forms of information-air contaminants, meteorological data, and weather data-are offered as prerequisites for making air quality forecasts. In order to facilitate more accurate categorization, the raw data are split and standardised during the pre-processing stage. Therefore, it is essential to identify the best characteristics from the pre-processed data and eliminate the extraneous ones that might lead to incorrect categorization. In this research, an unique optimization-based approach was used to pick features from coral reefs. An optimization approach called the coral reefs optimization (CRO) procedure is able to find optimal solutions by modelling coral behaviours relevant to reef location and development. Each possible solution to the issue is analogized to a coral that is always searching for a suitable spot in which to settle and flourish in the reefs in the suggested approach. At each stage, the solutions are processed with the help of the coral reefs optimization algorithm's unique operators. A better solution is more likely to flourish on the reefs after a number of iterations. The issue is solved by selecting the best solution. In the end, a Network is used to make predictions (DNN). As can be seen from the findings, the suggested model is almost as accurate as the state-of-the-art alternatives, at 96%. © 2022 IEEE.

关键词： Deep neural networks

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EEG-Based Emotion Recognition Using Morlet Dual-Level Wavelet Contextual Neural Network with Snow Geese Algorithm 4

EEG-Based Emotion Recognition Using Morlet Dual-Level Wavele...

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4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Sivaramkrishnan, M. Siva Ramkumar, M. Kannaiyan, Mathi Kanan, Mohammad Baig, Rahmath Ulla Giri, Jayant Karpagam College of Engnieering Dept of Eee India SNS College of Technology Dept of Ece India University College of Kancheepuram Anna University Department of Mechanical Engineer Kanchipuram631552 India University of Business and Technology College of Engineering Department of Industrial Engineering Jeddah21448 Saudi Arabia Zarqa University College of Engineering Department of Mechanical Engineering Zarqa Jordan Cambridge Institute of Technology Department of Artificial Intelligence and Machine Learning K R Puram Bangalore560086 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798331523923

EEG-based Emotion Recognition (ER) works by analyzing brainwave patterns from EEG signals to understand and identify a person's emotional state. The problem of emotion identification from EEG signals remains challenging because brain activity presents both non-linear dynamic properties and individual-specific EEG patterns along with biological noise. Traditional approaches to emotion detection become inefficient due to EEG signal complexity thereby complicating accuracy and reliability measures. To tackle the challenges in EEG-based emotion recognition, this study presents a new approach called the Morlet Dual-level Wavelet Contextual Neural Network with Snow Geese Algorithm (MorDWCNNet+SGA). The method uses the SEED dataset, starting with preprocessing through the Observability-Constrained Resampling-Free Cubature Kalman Filter (OCRCKF) to enhance important EEG patterns. For feature extraction, the Multi-Discrete Wavelet Transform (MDWT) is applied to capture key EEG characteristics. Emotion classification is then performed using the Morlet Dual-level Wavelet Contextual Neural Network (MorDWCNNet), which is further optimized by the Snow Geese Algorithm (SGA) to improve accuracy. Implemented in Python and tested on the SEED dataset, the MorDWCNNet+SGA framework achieves remarkable results, with 99.9% accuracy and 99.3% sensitivity, significantly outperforming existing methods. The outcomes of the proposed method achieved high accuracy in effectively distinguishing emotional states through brainwave patterns. This method demonstrates how advanced techniques can combine synergistically to enhance ER performance resulting in improved real-world application accuracy and speed. © 2025 IEEE.

关键词： Kalman filters

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An Elitist artificial-Electric-Field-Algorithm-Based artificial Neural Network for Financial Time Series Forecasting 1

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2nd International Conference on Biologically Inspired Techniques in Many Criteria Decision Making, BITMDM 2021

作者： Nayak, Sarat Chandra Sanjeev Kumar Dash, Ch. Behera, Ajit Kumar Dehuri, Satchidananda Department of Artificial Intelligence and Machine Learning CMR College of Engineering & Technology Hyderabad501401 India Department of Computer Science Silicon Institute of Technology Bhubaneswar India Department of Information and Communication Technology Fakir Mohan University Vyasa Vihar Odisha Balasore756019 India

ISBN: (数字)9789811687396

ISBN: (纸本)9789811687389

This article introduces the concept of elitism in the recently developed artificial electric field algorithm (AEFA) and termed it as eAEFA. The elitism method helps AEFA to preserve the best individuals from iteration to iteration through directly placing best fit particles into the population for the next generation and thus, strengthening the optimization capacity of AEFA. The proposed eAEFA is then used to find the optimal parameters of an artificial neural network (ANN) to form a hybrid model eAEFA + ANN. Financial time series (FTS) data behaves arbitrarily, highly volatile in nature, and possess nonlinearity, hence difficult to predict. We evaluated AEFA + ANN on predicting four FTS such as NASDAQ index, IND/USD exchange rate, WTI crude oil prices, and Bitcoin closing prices through four error statistics. Analysis of experimental outcomes are in favour of proposed model when compared with other. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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Performance Analysis of Underwater Acoustic MIMO Systems with Channel Estimation Errors

Performance Analysis of Underwater Acoustic MIMO Systems wit...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kumar, M. Dilip Aswini, Kilaru Reddy, Uma Tayal, Ankit Nagpal, Amandeep Shnawa, Ammar H. Mlr Institute of Technology Department of Electrical and Electronics Engineering Telangana Hyderabad India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India Gl Bajaj Institute of Technology & Management Dept. of Eee U.P. Greater Noida India Lovely Professional University Phagwara India College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350357769

Underwater Acoustic (UWA) communication systems have gained significant attention due to their pivotal role in marine applications, including deep-sea exploration, environmental monitoring, and naval operations. Multiple Input Multiple Output (MIMO) systems have emerged as a promising solution to enhance the capacity and reliability of UWA channels. However, the performance of MIMO systems in UWA environments can be severely affected by channel estimation errors. This research paper presents a comprehensive analysis of the impact of channel estimation errors on the performance of UWA MIMO systems. The study employs a realistic UWA channel model, considering the unique propagation characteristics and environmental factors. The results elucidate the degradation in system performance due to imperfect channel knowledge, quantified in terms of bit error rate, channel capacity, and signal-to-noise ratio. Furthermore, the paper provides insights into the trade-offs between system complexity, estimation accuracy, and overall performance. The findings of this research offer valuable guidelines for the design and optimization of robust UWA MIMO systems in the presence of channel estimation imperfections. © 2023 IEEE.

关键词： Bit Error Rate Channel Estimation Errors MIMO Systems Signal-to-Noise Ratio.Top of Form Underwater Acoustic Communication

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Anomaly Detection in Industrial Quality Control with Computer Vision and Deep learning

Anomaly Detection in Industrial Quality Control with Compute...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Mary, A. Jenita Umesh, V. Khairnar, Prerana Nilesh Sahana, R. Sangeethaa, S.N. Shanthi, H.J. Srm Institute of Science and Technology Faculty of Science and Humanities Vadapalani City Campus Department of Computer Science and Applications Chennai India Ksou Mysore Department of It Mysuru India Visvesvaraya Institute of Technology Department of Electronics and Telecommunication Engineering Chincholi Nashik India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi Department of Artificial Intelligence and Machine Learning Chennai India Bannari Amman Institute of Technology Department of Computer Science & Engineering Sathyamangalam India Hindustan Institute of Technology and Science Deemed to Be University Department of Computer Applications Padur India

ISBN: (纸本)9798350375442

This study examines the impact of the dynamic manufacturing environment on companies' efforts to improve customer satisfaction through product quality enhancement, production cost reduction, and sustainability realisation. The research focuses on the current trends and challenges companies face in adapting in response to the dynamic nature of the industrial industry. This study seeks to offer practical recommendations by analyzing these factors for companies to meet the evolving demands of their customers while ensuring long-term success and competitiveness. This study examines the impact of anomaly detection on product quality, specifically focusing on the commonly employed method of visual quality inspection. This study examines the methods of visual quality inspection for products, comparing manual and automatic approaches. This study examines the limitations of manual inspection as a monotonous task, which can result in the oversight of errors and subjective assessments. In order to keep up with the needs of smart production and the advent of Industry 4.0, manufacturing as a whole is actively seeking computerized quality inspection solutions. Innovative inspection systems can be created through the effective use of big data. This research abstract discusses the widespread use of machine learning in automated quality inspection systems to enhance performance and support data analytics methods. This study uses a convolutional neural network architecture to extract complex features and patterns from images. This enables the model to differentiate between normal and abnormal conditions effectively. This research presents a proposed model trained on a diverse dataset, including a comprehensive collection of potential defects and irregularities. This research abstract presents a proposed framework for validating effectiveness through extensive experiments on datasets from diverse industrial domains. This study presents the findings of a research project that evaluated

关键词： Smart manufacturing

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Automated Digitally Modulated Signal Recognition and Classification using machine learning with Multimodal Information 2

Automated Digitally Modulated Signal Recognition and Classif...

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2nd International Conference on Augmented intelligence and Sustainable Systems, ICAISS 2023

作者： Kalaiarasi, N. Walid, Md. Abul Ala Das, Shyamali Nanmaran, R. Srimathi, S. Beschi, I.S. Rmk College of Engineering and Technology Department of Ece India Department of Computer Science and Engineering India Cmr Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India Saveetha School of Engineering Simats Department of Bio Technology Tamilnadu Chennai India Department of Computer Applications Chennai India

ISBN: (纸本)9798350325799

Recently, the pattern detection of communication modulation signals takes obtained important attention in many application regions like civilian field, military, and so on. It develops vital to proposal a safe and robust feature extraction (FE) system to effectively detect the many signal modulation varieties in a difficult platform. Many works have developed novel approaches for extracting the feature parameters like instant features, fractal features, etc. Besides, machine learning (ML) algorithms are generally utilized for modulation signal classification. This study develops an Automated Digitally Modulated Signal Recognition and Classification using machine learning (ADMSR-ML) technique. The aim of the ADMSR-ML approach lies in the proficient recognition and categorization of various kinds of digitally modulated signals. At the initial stage, Sevcik Fractal Dimension (SFD) model is employed to produce a set of feature vectors. For modulation detection and classification, deep extreme learning machine (DELM) model is applied. Eventually, artificial Bee Colony (ABC) optimization algorithm was used for the optimal parameter selection of the DELM model. To examine the classification results of the ADMSR-ML approach, series of experiment are made. The extensive outcomes indicate the superior performance of the ADMSR-ML technique over other models. © 2023 IEEE.

关键词： Fractal dimension

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LDWPSO based Bi-LSTM Model for Predicting the Missing Data in PHRs

LDWPSO based Bi-LSTM Model for Predicting the Missing Data i...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Piyush Kumar Pareek Anoop N Prasad Gangambika G Nayana C P Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India

In medical and health datasets, it's common for some information to be missing. When you look at datasets that are missing some information, you might get skewed results or less statistical power. Because of this, some data may be missing, which hurts the analysis of the data. These missing data should be replaced with appropriate values. This article suggests a way to estimate missing data using an optimised Bi-Directional Long Short-Term Memory (OBi-LSTM) applied to big data in healthcare. A memory gate is used in the suggested method to estimate missing data that comes up when collecting and processing data. In this study, information from the Korean National Health Nutrition Examination Survey (KNHNES) by the Korea Centers for Disease Control and Prevention (KCDC) is used. As typical South Korean health care records, they have a lot in common with personal health records (PHRs). On the basis of this, models can be made to estimate the missing data in PHRs. PHRs are also multimodal, which means that data can be collected from many different sources for a single object. The Bi-LSTM model is used in a multimodal setting because of this. By preprocessing, a KNHNES data set is made that has no missing values. In data set-based learning, a label is set as the first piece of data. The parameters of Bi-computational LSTM affect how fast the model works. Linearly decreasing weight particle swarm optimization is used in the research to deal with these problems (LDWPSO). There is a big difference in the number of parameters between the two models, but there isn't much difference in how accurate they are. A multi-modal approach can also cut down on the time it takes to process large amounts of data in places like hospitals and institutions.

关键词： Support vector machines Radio frequency Noise reduction machine learning Predictive models Data models Particle swarm optimization

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Clustering Based Segmentation with 1D-CNN Model for Grape Fruit Disease Detection

Clustering Based Segmentation with 1D-CNN Model for Grape Fr...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Piyush Kumar Pareek Ramya I M Jagadeesh B N LeenaShruthi H M Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India

There has been a lot of focus on agricultural image dispensation research in the previous years. Computers employ image processing procedures to do picture analysis. Recent technological advances in the fields of picture imprisonment and data dispensation have greatly facilitated the solution of several agricultural issues. Identification and categorization of crop diseases are essential to the agricultural sector's technical and economic health. The first step in agricultural image processing is acquiring a digital colour photograph of a sick leaf. Grapes have the highest global per capita fruit consumption. It is unacceptable to let the spread of grape leaf contamination in the grapefruit crop to go unchecked, since this would be detrimental to the industry's growth. Farmers in rural regions sometimes lack the skills to spot these illnesses early on, which leads to poor crop yields. In order to detect diseases in grapefruit leaves, the suggested system employs a deep learning-based categorization method. K-means clustering is first used to segregate the backgrounds of the input datasets. The Convolutional Neural Network (CNN) is then fed the segmented regions to get a disease classification. The suggested work utilises the hyperparameter tuning operation with the assistance of Firefly with cyclic randomization to enhance the classification accuracy (FCR). The accuracy, f-measure, recall, and precision of the proposed model are evaluated in light of the experimental data. Finally, compared to the current models, which obtained accuracies of 88% to 90% for grape fruit disease detection, the suggested CNN model achieved 95% accuracy and 92% precision.

关键词： Computers Image segmentation Computational modeling Pipelines Crops Real-time systems Convolutional neural networks

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