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Image Caption Generation in Kannada using Deep Learning Frameworks 1

Image Caption Generation in Kannada using Deep Learning Fram...

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1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Chethas, K. Ankita, V. Apoorva, B.S. Sushma, H. Jayashree, R. Pes University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350348057

The official language of the state of Karnataka and one of the most frequently spoken languages in India, Kannada is spoken by more than 57 million people worldwide. In this modern era, one of the most important tasks in the field of Natural Language Processing in the present times is the development of image captions. In order to provide a textual description of an image intended to highlight its key qualities, Image Caption generation has long been a study of significant interest to academics in the fields of Artificial Intelligence and Machine Learning. While a great deal of research is being conducted in English, resources in regional languages like Kannada, are severely lacking. Given that 72% of the Karnataka population are Kannada speakers, Image Caption generation in Kannada is the realization of human-computer interaction for native language speakers, enabling the development of regional language virtual assistants which is a breakthrough in this line of work. Through our research, we present a model that generates semantic, syntactic and structured captions in Kannada. © 2023 IEEE.

关键词： Recurrent neural networks

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Investigating ChatGPT's Potential to Assist in Requirements Elicitation Processes 49

Investigating ChatGPT's Potential to Assist in Requirements ...

引用

49th Euromicro Conference on Software engineering and Advanced Applications, SEAA 2023

作者： Ronanki, Krishna Berger, Christian Horkoff, Jennifer University of Gothenburg Dept. of Computer Science and Engineering Gothenburg Sweden

ISBN: (纸本)9798350342352

Natural Language Processing (NLP) for Requirements engineering (RE) (NLP4RE) seeks to apply NLP tools, techniques, and resources to the RE process to increase the quality of the requirements. There is little research involving the utilization of Generative AI-based NLP tools and techniques for requirements elicitation. In recent times, Large Language Models (LLM) like ChatGPT have gained significant recognition due to their notably improved performance in NLP tasks. To explore the potential of ChatGPT to assist in requirements elicitation processes, we formulated six questions to elicit requirements using ChatGPT. Using the same six questions, we conducted interview-based surveys with five RE experts from academia and industry and collected 30 responses containing requirements. The quality of these 36 responses (human-formulated + ChatGPT-generated) was evaluated over seven different requirements quality attributes by another five RE experts through a second round of interview-based surveys. In comparing the quality of requirements generated by ChatGPT with those formulated by human experts, we found that ChatGPT-generated requirements are highly Abstract, Atomic, Consistent, Correct, and Understandable. Based on these results, we present the most pressing issues related to LLMs and what future research should focus on to leverage the emergent behaviour of LLMs more effectively in natural language-based RE activities. © 2023 IEEE.

关键词： Requirements engineering

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Unique Approach of Contextual Uniqueness Assessment of Research Papers Using Multi-Label Classification and Similarity Detection 26

Unique Approach of Contextual Uniqueness Assessment of Resea...

引用

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

作者： Tanvir, Sifat Islam, Md. Zihadul Moon Taher, Sidrat Muztahid, Shaikh Adib Biswas, Arpon Shopnil, Md. Shahariar Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Research papers should possess a significant amount of novelty. However, proper paraphrasing of entire paper may not be identified as plagiarism. Therefore, in this work a unique approach of similarity detection technique has been proposed. At first the abstracts are used to classify which area the paper belongs to, then papers of the same area are extracted and then contextual similarity is detected using cosine similarity. For training and testing purposes, two datasets have been created using data from IEEE Xplore. Since, almost every paper has some elements of paraphrased contents, therefore, a maximum acceptable threshold has also been proposed in this paper. Maintaining the maximum threshold, how well cosine similarity was able to distinguish the novel, paraphrased, and fully plagiarised works has also been analyzed. © 2023 IEEE.

关键词： Contextual Similarity Detection Cosine Similarity Deep Learning Multi-Label Classification

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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...

引用

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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Oversample minority classes in Lung Ultrasound using Generative Adversarial Network

Oversample minority classes in Lung Ultrasound using Generat...

引用

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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Fuzzy Clustering based Cluster Head Selection for IoT enabled WSNs 10

Fuzzy Clustering based Cluster Head Selection for IoT enable...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Gangwar, Saumitra Prasad, Ikkurthi Bhanu Pal, Vipin Yogita Yadav, Satyendra Singh Nit Meghalaya Dept. of Computer Science and Engineering Shillong India

ISBN: (纸本)9789380544472

This work presents a fuzzy mechanism for selecting the optimum cluster head (CH) inside a cluster. During the preliminary simulations, different parameters with different weights are taken into account to make sure that the best CH is chosen, which ameliorates the wireless sensor network (WSN) lifetime. Fuzzy c-means (FCM) clustering is used in the proposed work to select a CH based on the remaining energy ratio, the distance to the sink, the node history, and the node degree. By varying the weights for the above parameters using a brute-force approach, the best-performing weights have been acquired that extend the longevity of a WSN. The simulation results confirm that by incorporating the above-mentioned parameters, network stability is improved by 140.10% and 45.09% as compared to LEACH and FCM, respectively. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Energy efficiency

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Reviewing the Landscape of Deep Learning Approaches for Breast Cancer Detection 4

Reviewing the Landscape of Deep Learning Approaches for Brea...

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

作者： Maiti, Souvik Juneja, Sonam Goyal, Reema Chaudhry, Navneet Chandigarh University Dept. of Computer Science and Engineering Gharuan India

ISBN: (纸本)9798350306118

Rapid and precise identification of breast cancer is crucial in order to improve patient outcomes for this prevalent and life-Threatening disease. The emergence of AI and deep learning techniques has opened new avenues for enhancing breast cancer screening and diagnosis. CNNs and ensemble models, among other deep learning architectures, have demonstrated promising results in the analysis of medical pictures and the extraction of useful characteristics for precise diagnosis. Additionally, the use of transfer learning and pretrained CNN models has demonstrated superior performance in classifying breast tumor images. Studies investigating the usage of one-dimensional data conversion into pictures and utilizing stacked ensemble models for increased classification accuracy show that the use of deep learning extends beyond image-based research. These techniques offer potential solutions for addressing the challenges in breast cancer detection, such as distinguishing between benign and malignant tumors and predicting prognosis. By automating the diagnosis process, deep learning algorithms can assist medical professionals in making informed decisions and providing personalized treatment plans. Enhancing the accuracy and reliability of breast cancer prediction models necessitates further research into the incorporation of larger datasets, processing multi-modal data, and advancements in image generation methods. Overall, AI and deep learning hold great promise in revolutionizing breast cancer screening and diagnosis, ultimately leading to improved survival rates and better patient care. © 2023 IEEE.

关键词： Support vector machines

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Enhancing Emergency Response Through Speech Emotion Recognition: A Machine Learning Approach 26

Enhancing Emergency Response Through Speech Emotion Recognit...

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

作者： Deb, Priom Mahrin, Habiba Bhuiyan, Asibur Rahman BRAC University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Applications of interaction between humans and computers, such as emergency response systems, heavily depend on emotion recognition. In this experiment, we present an investigation into Speech Emotion Recognition (SER) specifically in the context of emergency calls using a meticulously curated dataset. This dataset comprises audio recordings from 18 speakers, each expressing four distinct emotions (angry, drunk, painful, and stressful) while reading predefined emergency scenarios. We conducted comprehensive preprocessing, which involved feature extraction using Mel-Frequency Cepstral Coefficients (MFCCs), Chroma (Pitch Classes), and Mel Spectrogram Frequency. The performance of many machine learning models, such as the KNeighbors Classifier, MLP Classifier, Random Forest Classifier, Gradient Boost Classifier, SVM, and Logistic Regression, was then assessed by dividing the dataset into training and testing sets. Our results reveal that the KNeighbors Classifier outperforms other models, achieving an accuracy of 67.06% and maintaining balanced performance metrics. These results offer insightful information about whether SER is practical for emergency call applications. This research contributes to the understanding of emotion recognition in critical situations and can enhance the efficiency of emergency response systems by automating emotion assessment in distress calls. Our findings have practical implications for the development of intelligent systems that can better assist emergency service providers. © 2023 IEEE.

关键词： Emergency Call Emotion Classification Feature Extraction Human-computer Interaction Machine Learning Models Speech Emotion Recognition

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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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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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