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Meteorological Progress: A Comprehensive Review of Weather Prediction 3

Meteorological Progress: A Comprehensive Review of Weather P...

引用

3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Singh, Gaurav Kumar Ughara, Pawandeep Singh Ishaq, Malik Muzamil Singh, Aryan Pratap Chauhan, Kanak Chandigarh University Dept. of Computer Science & Engineering Mohali India

ISBN: (纸本)9798350343632

Weather forecasting is a crucial and essential part of modern life, spanning many industries and fulfilling a variety of functions. Weather forecasting is essential for the safety, wellbeing, and economic stability of individuals and society as a whole. Accurate predictions help us mitigate the impacts of adverse weather events and make informed decisions in various aspects of our lives. The agricultural industry also relies on weather forecasts to help farmers make decisions about planting, harvesting, and irrigation in order to maximize crop yields and lower the possibility of crop failure due to unfavourable weather conditions. Weather forecasts play a critical role in transportation planning, safety, and accident avoidance in the aviation, marine, and automotive industries. In the energy industry, weather forecasts are also essential for the effective management of renewable energy sources and the production of power. For the purpose of safety, economic planning, environmental monitoring, scientific advancement, and emergency response, weather forecasting is essential. It is essential to many facets of our life since it guarantees readiness, security, and well-informed choices. © 2023 IEEE.

关键词： Artificial Neural Network (ANN) Back Propagation Electronic Neural Network (ENN) Mean Square Error (MSE) Multilayer Feed-Forward Neural Network(MLFNN) Soft Computing

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CNN Autoencoder Resizer: A Power-Efficient LoS/NLoS Detector in MIMO-enabled UAV Networks 35

CNN Autoencoder Resizer: A Power-Efficient LoS/NLoS Detector...

引用

35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Akhtarshenas, Azim Ayoobi, Navid Lopez-Perez, David Toosi, Ramin Amoozadeh, Matin Universitat Politècnica de València Dept. of Telecommunication Engineering Valencia Spain University of Houston Dept. of Computer Science Houston United States University of Tehran Dept. of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350362244

Optimizing the design, performance, and resource efficiency of wireless networks (WNs) necessitates the ability to discern Line of Sight (LoS) and Non-Line of Sight (NLoS) scenarios across diverse applications and environments. Unmanned Aerial Vehicles (UAVs) exhibit significant potential in this regard due to their rapid mobility, aerial capabilities, and payload characteristics. Particularly, UAVs can serve as vital nonterrestrial base stations (NTBS) in the event of terrestrial base station (TBS) failures or downtime. In this paper, we propose CNN autoencoder resizer (CAR) as a framework that improves the accuracy of LoS/NLoS detection without demanding extra power consumption. Our proposed method increases the mean accuracy of detecting LoS/NLoS signals from 66% to 86%, while maintaining consistent power consumption levels. In addition, the resolution provided by CAR shows that it can be employed as a preprocessing tool in other methods to enhance the quality of signals. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Performance Evaluation of Machine Learning Algorithms for Lung Nodule Detection using Multi-Modal Imaging - A Hybrid Approach 2

Performance Evaluation of Machine Learning Algorithms for Lu...

引用

2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Puneeth, R.P. Amogha Mayya, K.S. Shetty, Agnesh Rao, Akash Sooda, Aditya A. Dept. of Computer Science and Engineering Karnataka Nitte574110 India

ISBN: (纸本)9798350347579

Lung cancer is a major contributor to global mortality rates and identification is critical to improve patient outcomes. In recent years, machine learning algorithms have demonstrated promising results in identifying lung nodules from medical images. The most compelling area of research for scientists is the early detection of lung cancer. This study is a method for lung nodule detection using CT images. The study incorporates a hybrid model that combines multiple machine learning algorithms including CNN, SVM, DTC, ANN, and KNN to improve the accuracy of nodule detection. The hybrid model demonstrated high accuracy in identifying various types of lung nodules, including Adenocell carcinoma, squamous cell carcinoma, and large cell carcinoma. Specifically, the model achieved an accuracy rate of over 90% in detecting and differentiating normal lung tissue and Adenocell carcinomas. Accuracy graphs and priority setting were utilized to assess the model's capability in accurately predicting the presence of lung cancer. Additionally, the efficiency of the hybrid model was compared with other machine learning algorithms, including SVM, Random Forest, and Decision Trees. A large dataset of CT scans was collected for training and evaluation purposes. The results demonstrated the advantages of the suggested hybrid model in terms of accuracy and efficiency. This study highlights the importance of early lung nodule identification using CT scans and demonstrates the effectiveness of the hybrid model in accurately identifying different types of lung nodules. © 2023 IEEE.

关键词： computerized tomography

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Image Caption Generation in Kannada using Deep Learning Frameworks 1

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

引用

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 ...

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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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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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Finding Type 2 Diabetes Using Sparse Balanced Support Vector Machine in Electronic Health Records

Finding Type 2 Diabetes Using Sparse Balanced Support Vector...

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Kaur, Ranjanpal Sekhon, Er. Navjot Kaur Chandigarh University Dept. of Computer Science & Engineering Mohali India

ISBN: (纸本)9798350331790

Early Type 2 Diabetes (T2D) diagnosis is crucial for optimal integrated therapy and patient follow-up. There has been a huge growth in the quantity of accessible Electronic Health Record (EHR) data, and developments in Machine Learning (ML) methodologies have been as amazing. Maintaining and modelling a huge data set can cause over fitting, model interpretability, and computational expenditure. We used Sparse Balanced Support Vector Machine (SB-SVM) to detect type 2 diabetes in an original EHR dataset (named FIMMG dataset). In particular, we have chosen just those aspects of the EHR collected from a homogeneous group of participants before the diagnosis of T2D, including exemptions, examinations, and medicine prescriptions. The proposed solution is trustworthy compared to other ML and Deep Learning methods utilised by the state-of-the-art to handle this problem. The SB-SVM offers the best prediction performance-computation time tradeoff, according to the data. In addition, the induced sparsity makes the model more interpretable while automatically handling high-dimensional data and the normally uneven distribution of classes. © 2023 IEEE.

关键词： Support vector machines

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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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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...

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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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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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