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Analysis of Fictional Character Backstories Using Natural Language Processing and Deep Learning 26

Analysis of Fictional Character Backstories Using Natural La...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Tanvir, Sifat Khan, Md. Rafid Jahan Oyshi, Ishmam Ishan Tonmoy, Md. Sahil Zaman Rafi, Mashrafi Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

With the overwhelming successes of industries like Marvel and DC over the years, fictional characters have become more and more popular among the general mass. However, generation of new fictional characters do not seem to be as appealing as before. This paper revolves around the idea of generating fictional character backstories using GPT-3.5. A dataset consisting of AI generated pre-existing and newly created fictional characters has been prepared. A sequential neural network model was designed by the authors and pre-trained using the popular Superheroes NLP dataset from Kaggle. The proposed model and some state-of-the-art pre-trained text classification BERT models have been compared side by side. The AI generated dataset has been used as the test data to see how the models perform in identifying the moral alignment of the characters. A comparative analysis is also done between the two datasets. © 2023 IEEE.

关键词： BERT Neural Network NLP RoBERTa Sequential Model

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Efficient Support System for Hearing Disabled using CNN 5

Efficient Support System for Hearing Disabled using CNN

引用

5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Sharma, Mayank Goswami, Puneet Srm University Dept. Of Computer Science And Engineering Delhi India

ISBN: (纸本)9798350330861

Objective: In today's world communication plays a major role in a person's life in many ways. Communication is a process of conveying or transferring your feelings, knowledge or ideas to the rest of the world. This becomes very difficult for a person who cannot speak or listen. To overcome this problem we aim to develop a sign language system that is able to detect the hand gestures of the Deaf and Dumb from the captured images and convert them into a text format so that a person who does not have any knowledge of the sign language can understand them easily. Scope: This project aims at detecting the hand gestures performed by the deaf and dumb person and translate those signs into readable form so that the communication barrier between the physically challenged and ordinary people can be removed. Method: Deep learning and computer vision for detecting the signs of the sign language performed by the deaf and dumb people have proved to be very efficient. The dataset contains images of different signs in the Indian sign language which is used to train a neural network. Convolutional Neural Network (CNN) is used for the classification of the signs for the alphabet and numbers. Findings: This research has immense capability to detect the hand gestures performed by Deaf and Dumb people and to remove the communication gap between deaf and dumb people and the ordinary people. Therefore, our paper will provide a convenient way for ordinary people to understand and easily understand the deaf and dumb people. © 2023 IEEE.

关键词： computer vision

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Youth Unemployment Mitigation: Leveraging Machine Learning and Exploratory Data Analysis for Evidence-Driven Strategies

Youth Unemployment Mitigation: Leveraging Machine Learning a...

引用

2023 IEEE Conference on engineering Informatics, EI 2023

作者： Aarthi, S. Dharmita, A. Pragathi, G. Elakkiya, S. Srm Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Srm Institute of Science and Technology Dept. Computer Science Engineering Specialization with Big Data Analytics Chennai India

ISBN: (纸本)9798350338522

Youth unemployment represents an urgent global socioeconomic challenge. This study combines machine learning and exploratory data analysis to understand and solve this problem comprehensively. Traditional approaches often rely on qualitative assessments and historical data requiring a data-driven transition to more effective solutions. Our approach integrates machine learning algorithms and Exploratory Data Analysis techniques to analyze diverse data sets, identify influencing factors, and recommend targeted interventions. The analysis process includes data collection, preprocessing, feature selection, machine learning modeling, and Exploratory Data Analysis techniques to understand the dynamics of the Better understanding unemployment among young people. Preliminary results reveal precise predictive patterns and previously unrecognized relationships, providing evidence-based solutions to inform policy and support the economic well-being of younger generations. Further research and validation are needed to increase applicability. © 2023 IEEE.

关键词： Data visualization

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Early Detection of Alzheimer's Disease using CNN with PSO for parameter optimization 1

Early Detection of Alzheimer's Disease using CNN with PSO fo...

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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Harwalkar, Jayant Hemankith Reddy, M. Shravani, M. Samptur, Neelesh Kamatchi Priya, L. PES University Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

Alzheimer's disease (AD) is a progressive neurological disorder that causes the brain to shrink and brain cells to die. Machine learning models play an important role in early diagnosis so the patient can have an opportunity to get the treatment. This paper contains a comprehensive analysis of different machine learning methods used to detect AD. The paper proposes a model for the early detection of AD using Convolutional Neural Network (CNN) and Particle Swarm optimization (PSO) for parameter optimization. The model showed an accuracy of 94.94% which was better than the other models in comparison. © 2023 IEEE.

关键词： Neurodegenerative diseases

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Review Machine Learning Models for Managing Hotel Cancellations in the Tourism Industry 3

Review Machine Learning Models for Managing Hotel Cancellati...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Sekhon, Gursimran Ahuja, Sachin Chandigarh Univercity Dept. of Computer Science Engineering Mohali India

ISBN: (纸本)9798350338607

This survey paper is one-stop-shop for keeping up with the latest research on predicting hospitality booking cancellations using machine learning and deep learning techniques. With cancellations having such a significant impact on hospitality revenue and reputation, it's crucial to develop effective prediction models, and this paper highlights the various techniques used in recent studies. But accurate and reliable cancellation forecasting is more than just a theoretical exercise;it's essential for successful revenue management in the tourism and hospitality industry. The paper delves into the challenges associated with demand forecasting, particularly in the context of last-minute bookings and changes in customer behavior. To help navigate these challenges, this survey paper suggests the neural network method as a promising approach for predicting reservation cancellations, enabling hospitality management to make informed decisions about which reservations to accept or reject. Although cancellation policies have been put in place by hospitality management to mitigate the effects of cancellations, issues such as demand and reputation persist. The paper underscores the significance of segregating data sets and feature engineering in predictive models, which is supported by the studies reviewed. Moreover, the paper examines the importance of extracting insights from extensive databases and leveraging predictive analytics to identify the factors that influence the likelihood of booking cancellations. © 2023 IEEE.

关键词： Predictive analytics

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N-Queens Convergence: A Tactical Framework for Efficient Target-Directed Drone Formations and Attacks 7

N-Queens Convergence: A Tactical Framework for Efficient Tar...

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7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Kumar, Preethika Ajay Pes University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350343144

This study introduces a novel methodology for enhancing the efficiency of Unmanned Aerial Vehicle (UAV) formations in order to achieve accurate target engagements. The concept draws inspiration from the N-Queens puzzle. By incorporating unmanned aerial vehicle (UAV) positioning into a simulated chessboard and expanding it to include a target in the (N + 1)th column, the algorithm produces arrangements that guarantee effective engagement with the target. Significantly, this particular technique ensures the absence of overlapping pathways among UAVs during strikes, hence augmenting operational safety. The methodology employed in this study involves the construction of formations through the generation of permutations for all possible queen positions. Subsequently, the eligible queen configurations are determined by filtering based on the diagonal lines of attack. The method has been tested on N values as high as 10. This study explores the intersection of combinatorial problem-solving with military applications, providing novel insights and tactics for deploying UAVs that minimize collisions. © 2023 IEEE.

关键词： Drones

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Oversample minority classes in Lung Ultrasound using Generative Adversarial Network

Oversample minority classes in Lung Ultrasound using Generat...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Fatima, Noreen Mento, Federico Demi, Libertario University of Trento Dept. of Information Engineering and Computer Science Italy

ISBN: (纸本)9798350346459

The development of a strong methodological strategy focusing on class imbalance issues for Lung Ultrasound (LUS) data is lacking in the state of the art. Traditional oversampling methods such as geometric and affine transformations generate quasi-duplicates of the original data. This problem could be resolved through generative adversarial networks (GANs). However, in classic GANs the generator cannot handle mode collapse issues and the model's performance remains unstable when images in different classes look similar. To address this problem, we proposed a novel approach based on a supervised autoencoder generative adversarial network (SA-GAN), which could help to oversample the minority classes. At first, we build a latent space with the help of a supervised autoencoder (SA) to handle the mode collaspe issues. Then the trained weights of the SA are used to construct the generator and discriminator models. During the adversarial training of both models, the loss function is calculated for real and generated images through a customized gradient penalty loss function. This also allows classifying each image into real or generated, and its corresponding classes. This approach was tested on a dataset comprising 5500 LUS images, which were collected from 35 COVID-19 patients. As a result, SA-GAN provided the 900 generated LUS samples in minority classes to balance with the majority class. The proposed approach significantly improved the classification accuracy by up to 5% using synthetic augmentation through SA-GAN over the classic data augmentation method. © 2023 IEEE.

关键词： Generative adversarial networks

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Mask Region-Based Convolutional Neural Network for Disease Detection in Rice Leaf 3

Mask Region-Based Convolutional Neural Network for Disease D...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Putta, Narsaiah Kumari, T. Suvarna Naik, K Jairam Vasavi College of Engineering Dept. of Computer Science & Engg. Telangana Hyderabad India Chaitanya Bharathi Institute of Technology Dept. of Computer Science & Engg. Telangana Hyderabad India National Institute of Technology Raipur Dept. of Computer Science & Engg. Chhattisgarh India

ISBN: (纸本)9798350354171

Many approaches have been proposed for the problems, like manual diagnosis of crops and soil by experts. But this one is actually a very time-consuming activity. With the advancement of technology, many approaches have been made using machine learning, like image processing techniques, deep convolutional neural networks, segmentation techniques based on neutrosophic logic, etc. However, most of these methods were slow or had implemented complex algorithms that made them unsuitable for being used as an Android app. So, in this paper, a rice leaf disease detection and classification model using region-based convolutional neural networks and deep learning has been proposed. Using this approach, we have successfully developed a model to detect and classify diseases with an accuracy of 94.4%. Determining the type of disease in crops may help the farmers take timely measures and, therefore, avoid the loss. © 2024 IEEE.

关键词： Convolutional neural networks

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IoT-driven automatic vehicle accident rescue system implemented with Proteus 1

IoT-driven automatic vehicle accident rescue system implemen...

引用

1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Hemalatha, K. Ganga Devi, S.V.S. Deepika, V. Madanapalle Institute of Technology and Science Dept. of Computer Science & Engineering-Artificial Intelligence Andhra-Pradesh Madanapalle India Madanapalle Institute of Technology and Science Dept. of Computer Science & Engineering-Cyber Security Andhra-Pradesh Madanapalle India Sri Venkateswara College of Engineering Dept. of Computer Science & Engineering Andhra-Pradesh India

ISBN: (纸本)9798350328042

IoT-driven automatic vehicle accident rescue system implemented with Proteus is an inventive solution aimed at enhancing emergency response and rescue operations following vehicle accidents. Given the rising vehicle usage alongside population growth, the frequency of accidents has also surged over time, leading to unfortunate loss of life due to delays in ambulance arrival at accident scenes. Swift transportation of accident victims to medical facilities is of paramount importance. Upon occurrence of an accident, the proposed "IoT-driven automatic vehicle accident rescue system implemented with Proteus"immediately transmits accident details to the ambulance service, investigation unit, and designated emergency contacts. Simultaneously, the accident's precise location is shared via a Google Maps link to the users' smart devices equipped with mobile network connectivity. This facilitates efficient location identification for both ambulance services and police departments. Comprising elements such as accelerometers, GPS (Global Positioning System) modules, GSM (Global System for Mobile Communication) modules, motors, buzzers, LEDs, and more, the system seamlessly integrates these components with a central microcontroller unit using Proteus software. The core objective of this initiative revolves around two key goals: rescuing drivers involved in accidents and expediting victim recovery during the critical early stages of mishaps. © 2023 IEEE.

关键词： Global system for mobile communications

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Machine Learning Based Prediction of Psychiatric Disorder 4

Machine Learning Based Prediction of Psychiatric Disorder

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Sharma, Mayank Goswami, Puneet SRM University Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350306118

The foundation of the education systems all around the world were shaken up when Covid-19 started spreading its roots worldwide. India of course was no exception to it. While the education sector in India was already undergoing a transformation with the NEP 2020 policy, all the schools and colleges had to be shut down urgently due to the rapid spread of Covid-19. Almost overnight students and teachers had to switch to online learning. At the onset of lockdown around 250 million students were affected due to closure of educational institutes in India. While there was a massive decline in learning outcomes, the physical and mental well-being of the students was also severely impacted. Students lost touch with being socially physical and alternated to being present on social media. However humans are social animals. With deprivation, came a sense of loneliness and that's when physical and mental wellness issues started taking over leading to psychiatric disorders. Our generation are the leaders of the next and ensuring the wellbeing of young minds is a must. Thus there is a need to study and find out just how much Covid-19 is impacting our students and their lives using Artificial Intelligence cum Machine Learning and furthermore act upon it to find solutions to help them. In this paper, we have approached the above problem using the Binary Classification Predictive Model with different logical neural networks. © 2023 IEEE.

关键词： Students

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