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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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Anti-Accident Mechanism to Detect Driver Drowsiness Integrated with Alerting Mechanism: An IoT-based Model

Anti-Accident Mechanism to Detect Driver Drowsiness Integrat...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： Srujan Katukam Sai Vinay Potluri Sunell Dutt Abburi Rahul Cherukuri C Sreeka Parinishta Mohith Peddineni R. P. Ram Kumar Sanjeeva Polepaka Department of AI & ML Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Artificial Intelligence and Machine Learning Engineering Gokaraju Rangaraju Institute of Engineering and Technology (GRIET) Hyderabad India

According to the report released by the National Safety Council (NSC), about 1,00,000 accidents, 71,000 injuries, and 1,550 deaths are caused due to Drowsiness while driving each year. According to a study by the AAA Foundation for Road Safety, Drowsiness is responsible for up to 9.5% of accidents and 10.8% of those involving airbag deployment, injury, or significant property damage. The paper aims to build a real-time system to prevent accidents caused due to Drowsiness. It implements the features of Computer Vision, Deep learning, and Internet of Things (IoT) tools to detect Drowsiness and alert the Driver. The various alerting techniques adopted to alert the Driver includes Alarming, Vibration, and Water Sprinkler are implemented using IoT.

关键词： Vibrations Road safety Real-time systems Safety Internet of Things Brakes Injuries

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Empirical Evaluation of Deep learning based Models for Time Series Datasets 3

Empirical Evaluation of Deep Learning based Models for Time ...

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3rd International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Champaneria, Malav Panchal, Dhyani Bhalavat, Jiten Yadav, Hemant Bhatt, Nirav Prajapati, Purvi Bhatt, Nikita Smt. K. D. Patel Department of Information Technology CSPIT Charotar University of Science and Technology Gujarat Changa India Department of Artificial Intelligence and Machine Learning CSPIT Charotar Universityof Science and Technology Gujarat Changa India U & P U. Patel Department of Computer Engineering CSPIT Charotar University ofScience and Technology Gujarat Changa India

A deep learning-based method for predicting numerous time series is suggested inthis study. For example, this paper contrasts the effectiveness of DeepAR, N- Beats, Transformer, Temporal Fusion Transformer, and other deep learning models. Our test findings demonstrate that deep learning models can anticipate with excellent accuracy across a variety of time series datasets. Moreover, our results indicate that the ideal input representation differs among datasets and that it plays a significant impact in predicting accuracy. Overall, our work gives practical advice for choosing the appropriate model and input representation, as well as insights into the use of deep learning models for predicting numerous timeseries. © 2023 Elsevier B.V.. All rights reserved.

关键词： Time series

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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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A Review on the Applications of Natural Language Processing in Healthcare

A Review on the Applications of Natural Language Processing ...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Mohan K. Warbhe Prateek Verma Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

In healthcare, Natural Language Processing (NLP) is a technique to improve clinical decision support, patient care, and medical research. By automating clinical recording, NLP enhances the calibre of medical records and professional communication. It makes Electronic Health Records (EHRs) easier to use by facilitating quick and easy data entry, retrieval, and access to vital patient data that helps with precise diagnosis and efficient treatment planning. Through the analysis of patient-specific data, NLP supports clinical decision support by delivering prompt recommendations. Chatbots and virtual assistants managing repetitive duties like booking appointments also improve patient connection. NLP makes it easier to analyse vast amounts of literature for medical research, facilitating the extraction and summary of important discoveries. Healthcare data processing requires text preparation, sentiment analysis, Named Entity Recognition (NER), semantic parsing, and Natural Language Generation (NLG). However, there are obstacles to overcome, like bias, ethical concerns, system integration, privacy, and data quality. The healthcare industry can use NLP to enhance patient outcomes, promote personalized medication, and streamline healthcare delivery by guaranteeing interoperability and setting standards.

关键词： Sentiment analysis Ethics Data privacy Virtual assistants Semantics Natural language generation Medical services Natural language processing Medical diagnostic imaging Interoperability

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A Hybrid learning-Based Q-Commerce Framework Evolving from E-Commerce 2

A Hybrid Learning-Based Q-Commerce Framework Evolving from E...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Presskila, X. Arogya Raj, J. Relin Francis Kumar, P. Selva Sankar, G. Ram Rajkumar, G. Vinoth Sundaravadivel, P. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Francis Xavier Engineering College Department of Management Studies Tirunelveli India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India J. P. College of Engineering Department of Electronics and Communication Engineering Tenkasi India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331505745

The rapid evolution of E-Commerce has led to the emergence of Quick Commerce (Q-Commerce), a model that prioritizes ultra-fast deliveries and real-time decision-making. This paper proposes a Hybrid learning-based framework that enhances Q-Commerce operations by integrating Self-Supervised, Supervised, Reinforcement, and Unsupervised learning techniques. The model leverages BERT for self-supervised pretraining, enabling context-aware customer personalization and improved search relevance. LSTMs and CNNs are employed for demand forecasting and product classification, ensuring optimized inventory management. GNNs enhance personalized recommendations by analyzing user-product relationships. For logistics optimization, DQN and PPO are implemented to dynamically adjust last-mile delivery routes, reducing delivery times and operational costs. To ensure fraud prevention, AEs and GANs detect anomalous transaction behaviors, improving security while minimizing false positives. The proposed hybrid deployment strategy, combining cloud-based AI inference and Edge AI at micro-fulfillment centers (MFCs), enhances system responsiveness and scalability. The framework is evaluated using MAE, RMSE, HR, Fl-Score, and ROC curves, ensuring accurate demand prediction, optimized logistics, and effective fraud detection. Future advancements in Autonomous Deliveries, AI-Powered Warehousing, and Blockchain Integration will further revolutionize Q-Commerce, making it an indispensable extension of E-Commerce. © 2025 IEEE.

关键词： Marketplaces

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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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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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Leveraging Digital Twin technology to Combat Cardiovascular Disease: A Comprehensive Review 2

Leveraging Digital Twin Technology to Combat Cardiovascular ...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Weerarathna, Induni Nayodhara Kumar, Praveen Verma, Prateek Raymond, David Luharia, Anurag Mishra, Gaurav Datta Meghe Institute of Higher Education and Research Department of Biomedical Sciences Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Department of Radiotherapy Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Department of Radiodiagnosis Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

The innovative Digital Twin (DT) technology, which constructs a copy of objects, has emerged as a groundbreaking approach in various sectors, including the healthcare industry. This thorough examination delves into the potential of utilizing DT technology to address disease (CVD), a prominent cause of illness and death globally. By combining real-time data, sophisticated analytics and machine learning algorithms, DTs provide a platform for monitoring, diagnosing and enhancing health. The document explores the background and evolution of DT technology, its role in precision medicine, and its transformative impact on managing CVD. It also sheds light on how digital twins replicate, forecast and analyze the functions and performance of systems to offer insights into prevention, therapy options and personalized care. Furthermore, it discusses the obstacles and constraints of incorporating DT technology in healthcare settings, such as data security concerns, interoperability challenges, and ethical considerations. The paper showcases case studies that highlight the applications of DTs in research and treatment to underscore the potential of this technology in transforming patient outcomes. The evaluation concludes with prospects stressing the importance of research efforts and collaborative initiatives to surmount existing hurdles and fully leverage the advantages of DT technology in addressing cardiovascular disease. © 2024 IEEE.

关键词： Patient treatment

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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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