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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Singh, Nripendra Jaiswal, Anand Singh, Prakhar Sharma, Dilip Kr. PennWest University Dept. of Management and Marketing Clarion United States New Delhi Institute of Management Dept. of Operations and Scm Delhi India PennWest University Computer Information Science Department Clarion United States GLA University Dept. of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350346961

Digital marketers use a number of strategies, including custom audiences, lookalike audiences, ad creative, and A/B testing, to organize and enhance how they present advertisements to consumers. The purpose of this research is to show an effective method and process for running advertisements on Facebook more efficiently. The study has taken a case study of a company- Great Deal Tires and analyzed the Facebook analytics data and insights with A/B testing and audience creation approach to assess the digital marketing impact. The study has implemented three test strategies for designing and placing Facebook ads for Great Deal Tires in order to assess the marketing impact. In the first test, a custom audience was chosen, and an identical Facebook video ad's impact was evaluated based on the duration of the ad. In the second test, A/B testing was used to test Facebook ads, and the results of the tests were compared across three different audience groups to determine how well the ads performed. The third test evaluated on the audience conversion in the terms of subscribing to the Page, in which three different ads were shown to three distinct audience groups in order to compare the effects of the various ads on the diverse audiences. The study's findings provided important information about Facebook ad placement, its effects on various audience segments, and digital marketing strategies to increase audience outreach. The study is important in demonstrating that Facebook advertisements are an effective marketing medium and a digital marketer can achieve good outreach with a modest investment of funds.. © 2023 IEEE.

关键词： Marketing

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A Hierarchical conv-LSTM and LLM Integrated Model for Holistic Stock Forecasting

arXiv

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arXiv 2024年

作者： Chakraborty, Arya Basu, Auhona Dept. of Computer Science and Engineering Birla Institute of Technology Mesra Ranchi India Dept. of Computer Engineering York University Toronto Canada

The financial domain presents a complex environment for stock market prediction, characterized by volatile patterns and the influence of multifaceted data sources. Traditional models have leveraged either Convolutional Neural Networks (CNN) for spatial feature extraction or Long Short-Term Memory (LSTM) networks for capturing temporal dependencies, with limited integration of external textual data. This paper proposes a novel Two-Level Conv-LSTM Neural Network integrated with a Large Language Model (LLM) for comprehensive stock advising. The model harnesses the strengths of Conv-LSTM for analyzing time-series data and LLM for processing and understanding textual information from financial news, social media, and reports. In the first level, convolutional layers are employed to identify local patterns in historical stock prices and technical indicators, followed by LSTM layers to capture the temporal dynamics. The second level integrates the output with an LLM that analyzes sentiment and contextual information from textual data, providing a holistic view of market conditions. The combined approach aims to improve prediction accuracy and provide contextually rich stock advising. © 2024, CC BY-SA.

关键词： Prediction models

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Multispectral Images and IoT Based Tea Plantation Monitoring: A Proposed System Architecture 5

Multispectral Images and IoT Based Tea Plantation Monitoring...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Kariyawasam, K.P.W.D.V. Kahandagamage, P.N. Fernando, M.R.R. Fernandopulle, J.M. Jayasinghearachchi, Vishan Dissanayaka, Kapila Dept. of Software Engineering Sri Lanka Dept. of Information Technology Sri Lanka Dept. of Computer Science Sri Lanka

ISBN: (纸本)9798331530983

In recent years, the Sri Lankan tea industry has fallen behind its competitors in the global tea market. This decline is caused by the challenges in productivity and resource management due to the limitations of traditional crop monitoring methods. This study presents a prototype system architecture that integrates multispectral imagery and IoT technologies to enhance plantation monitoring. The proposed system uses drones to capture high-resolution multispectral images and IoT devices to collect real-time environmental data, providing a comprehensive approach to assessing plant yield and detecting stress. Built on a hexagonal architecture, the system emphasizes modularity, reliability, and scalability by ensuring a clear separation of core functionalities from external components. This design facilitates easy adaptation to various crop types and agricultural contexts, enabling flexibility in deployment and use. This flexible framework can serve as a blueprint for improving decision-making and management practices across diverse plantation environments. © 2025 IEEE.

关键词： Risk perception

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Revolutionizing Campus Exploration with GikiLenS: A Deep Learning-Powered Object Detection App 18

Revolutionizing Campus Exploration with GikiLenS: A Deep Lea...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Mueed, Iqra Arshad, Usama Ali, Raja Hashim Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

For newcomers and tourists, navigating university campuses can be difficult, resulting in aggravation and lost time. We respond by introducing 'GikiLenS', an object identification application driven by deep learning that revolutionizes campus exploration by accurately identifying buildings and landmarks while enhancing user experience. GikiLenS is a comprehensive and user-friendly smartphone application that precisely caters to the demands of newcomers and visitors, unlike previous studies in campus navigation. Our study intends to close this gap and develop a reliable method for identifying campus buildings, optimizing navigation, and enhancing user experience. Through rigorous testing, GikiLenS has proven to have remarkable real-time building detection accuracy, highlighting its potential as an important tool for campus exploration. The app's outcomes and conclusions demonstrate how well it works to give users specific building information, promoting a more knowledgeable and enjoyable campus experience. The importance of our findings lies in the development of a unique, user-centered app that offers a cutting-edge method of campus mobility. Our contribution consists of a game-changing method for building detection that improves campus exploration and user pleasure. © 2023 IEEE.

关键词： Object detection

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Advancing Phishing Detection with Random Forest: A Machine Learning Approach for Enhanced Cybersecurity

Advancing Phishing Detection with Random Forest: A Machine L...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Sucheta Chandra Priyanka Das Ankita Mandal Sriparno Chakroborty Ishika Chowdhury Kathamrita Ghosh Dept. of Computer Application and Science Institute of Engineering & Management University of Engineering & Management Kolkata India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Phishing attacks remain a critical threat to cybersecurity, leveraging deception to steal sensitive information from individuals and organizations. Traditional detection methods often fail to keep up with evolving phishing tactics, highlighting the need for advanced solutions. This paper investigates the use of the Random Forest algorithm, a powerful machine learning technique, to enhance phishing detection systems. By leveraging the ensemble learning approach of Random Forest, we improve accuracy and efficiency in classifying phishing attempts, while reducing false positives. The methodology includes data preprocessing, feature extraction, and model implementation, showcasing its effectiveness in real-time detection. The study also emphasizes the importance of continuous learning, allowing the model to adapt to new phishing strategies, thereby offering a robust defense against emerging threats. This research contributes to advancing cybersecurity by providing a dynamic, scalable approach to phishing detection.

关键词： Support vector machines Uniform resource locators Accuracy Machine learning algorithms Phishing Feature extraction Ensemble learning Noise measurement Random forests Overfitting

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Emotional Behavior Trees for Empathetic Human-Automation Interaction 23

Emotional Behavior Trees for Empathetic Human-Automation Int...

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23rd Workshop "From Objects to Agents", WOA 2022

作者： Dell’Acqua, Pierangelo Costantini, Stefania Dept. of Science and Technology Linköping University Sweden Department of Information Engineering Computer Science and Mathematics University of L’Aquila Italy

For the tasks of improving caregiving in medicine and other sectors (i.e., teaching) and of constructing effective human-AI teams, agents should be endowed with an emotion recognition and management module, capable of empathy, and of modelling aspects of the Theory of Mind, in the sense of being able to reconstruct what what someone is thinking or feeling. In this paper, we propose an architecture for such a module, based upon an enhanced notion of Behavior Trees. We illustrate the effectiveness of the proposed architecture on a significant example, and on a wider case study. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Forestry

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A Framework for Smart & Secure Vehicle Intelligent Life Monitoring System 1

A Framework for Smart & Secure Vehicle Intelligent Life Moni...

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2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Dhulipudi, Ramesh Dangeti, Srinivasa Rao Pynam, Venkateswarlu Daniya, T. Das, Prasenjit Maram, Balajee Godavari Institute of Engineering and Technology Department of Cse Rajahmundry India Shri Vishnu Engineering College for Women Department of Information Technology India Jntugv Cev Dept. of It Vizianagaram India AP Rajam India Chandigarh University Dept. of Computer Science and Engineering Punjab Mohali140413 India School of Computer Science and Artificial Intelligence Sr University Telangana Warangal506371 India

ISBN: (纸本)9798350338423

In today's world, vehicle safety has become one of the most concerns for private. Nowadays, existing alarm systems of vehicles can be easily replaced as the necessary tools are equally available. While traveling safety is everyone's primary concern. This analysis explains the model of an efficient data model that can monitor and secure the condition of the vehicle / car. The use of IoT for tracking and monitoring is the components. Internet of Things (IoT) is significant for observing the vehicles, medical treatment and manufacturing approaches. By the uninterrupted growth of Internet of Things, many 'things' are capable. In this framework, a new IoT based framework depended on Field Programmable Gate Array (FPGA) and System on Chip (SoC) that gives unified access for IoT is low as well as high-speed devices with extensibility and configurability. Hence, this model performs better accuracy and security. © 2023 IEEE.

关键词： System-on-chip

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Advanced Machine Learning Techniques for Optimizing Renewable Energy Systems in Healthcare Facilities 4

Advanced Machine Learning Techniques for Optimizing Renewabl...

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

作者： Pradeep, S. Kalpana, P. Sreeram, G. Adgaonkar, Amarja Roy, Devika Rani Namdev, Arpit Dept. of Cse Telangana Secunderabad India Christ University Dept. of Computer Science Karnataka Bangalore India Vignana Bharathi Institute of Technology Dept. of Cse Telangana Hyderabad India K.C. College of Engineering & Management Studies & Research Thane India University Institute of Technology Rgpv Dept. of It MP Bhopal India

ISBN: (纸本)9798350387490

Optimizing renewable energy systems in healthcare facilities via use of modern ML algorithms to improve energy efficiency along with sustainability is goal of this research. Integrating machine learning provides a potential solution in case of optimizing energy consumption along with management, which is particularly relevant for healthcare institutions that are looking to decrease their operating expenses and carbon footprint by using renewable energy sources. Optimal deployment of renewable ES like solar along with wind may be achieved by the analysis of consumption patterns, prediction of future energy demands, and the investigation of different ML methods, such as neural networks, reinforcement learning, and predictive analytics. Research in this area aims to increase hospital renewable energy systems' efficiency and dependability by creating models that take use of environmental factors, real-time energy consumption indicators, and historical data. The results imply that healthcare facilities may save money, have less of an effect on the environment, and be more resilient if they use sophisticated machine learning methods to perk upcompetence of their renewable energy systems. The study emphasizes the importance of hyperparameter configuration in improving machine learning model accuracy, with a proposed approach achieving 99 % training and 96 % testing accuracy, outperforming conventional methods and outperforming other techniques. © 2024 IEEE.

关键词： Advanced Machine Learning Energy Optimization Healthcare Facilities Neural Networks Reinforcement Learning Renewable Energy Systems

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ATL Model Checking for Analysis of Ethereum Smart Contracts

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Transactions of the Korean Institute of Electrical Engineers 2021年第12期70卷 2006-2014页

作者： Nam, Wonhong Kil, Hyunyoung Dept. of Software Korea Aerospace University Korea Republic of Dept. of Computer Science and Engineering Konkuk University Korea Republic of

A blockchain is a growing list of cryptographically secured blocks to maintain shared data on decentralized systems, in order to archive transactions between untrusted participants. Smart contracts are computer programs that automatically execute legal events according to the terms of contracts. Although the Ethereum blockchain has been successfully applied to a number of interesting applications, there have been several events that subtle flaws in smart contracts induce a huge amount of financial loss such as the DAO attack. Accordingly, to analyze smart contracts, we propose a novel formal verification technique to employ ATL (Alternating-time Temporal Logic) model checking. Our methodology represents the interaction between users and smart contracts with a two-player game and verify game properties by using MCMAS that is an efficient ATL model checker. © The Korean Institute of Electrical Engineers

关键词： Smart contract

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Early Detection of Type 2 Diabetes Using Oversampling-Based Ensemble Learning for Classification of Imbalanced dataset

Early Detection of Type 2 Diabetes Using Oversampling-Based ...

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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： P Rajasekhar Sridhar Mourya Narasimha Reddy Bathala G Lavanya S Gayatri Munni Shaik Dept. of Computer Science Engineering Vignan's Institute of engineering for women Visakhapatnam India Dept. of Information Technology Malla Reddy University Hyderabad India Dept. of Computer Science Engineering Malla Reddy University Hyderabad India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Diabetes is marked by high blood glucose levels and adversely affects various body systems, notably cardiovascular, renal, and visual. Early diagnosis via automated screening is vital for operative treatment. Advances in machine learning (ML), particularly have enhanced diabetes diagnosis but one of the major issues when working with medical datasets is class imbalance. Oversampling is crucial in the medical field to address class imbalance, ensuring that models can effectively learn patterns from minority classes, such as rare diseases or conditions. While feature selection and hyperparameter tuning optimize model performance, oversampling directly improves the model's ability to detect and predict critical, but less frequent, medical events, reducing bias toward the majority class. This paper introduces an oversampling based approach for using ensemble learning (EL) with decision tree as meta-estimators to address data imbalance. To balance the classes and enhance the accuracy of classification performance KMeans SMOTE Boost, SMOTE Bagging, Over Boost, and Over Bagging classifier strategies are been used on training data. This study identifies the most effective combinations of oversampling techniques and classifiers, and finds that using oversampling methods consistently leads to better classification results. These results highlight the effectiveness of oversampling with EL system in diabetes classification on Type 2 Diabetes dataset.

关键词： Visualization Accuracy Training data Signal processing algorithms Predictive models Diabetes Ensemble learning Medical diagnostic imaging Bagging Tuning

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