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4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Yunita, Reni Nababan, Erna Budhiarti Lydia, Maya Silvi Universitas Sumatera Utara Department of Data Science and Artificial Intelligence Medan Indonesia Universitas Sumatera Utara Computer Science Medan Indonesia

ISBN: (纸本)9798350376111

People with disabilities often face difficulties communicating with the general public, especially regarding sign language. Sign Language Recognition (SLR) is a technology focused on recognizing, interpreting, and translating sign language into text or speech. SLRs can be divided into two types: static and dynamic. This research focuses on dynamic sign language recognition to develop recognition for Indonesian Sign Language, using the Python programming language with libraries such as Mediapipe, cvzone, sklearn, numpy, and the Long Short-Term Memory (LSTM) technique. These findings show the potential of SLR technology in assisting communication for individuals with disabilities in Indonesia. This research significantly contributes to the development of Indonesian Sign Language SLR. © 2024 IEEE.

关键词： Disabled persons

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MOOD: Mindfulness fOr sOftware Developers 24

MOOD: Mindfulness fOr sOftware Developers

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18th ACM/IEEE International Symposium on Empirical Software engineering and Measurement, ESEM 2024

作者： Romano, Simone Scanniello, Giuseppe Marchetto, Alessandro Giorgini, Paolo Guidetti, Gloria Converso, Daniela Viotti, Sara Department of Computer Science University of Salerno Italy Department of Information Engineering and Computer Science University of Trento Italy Department of Psychology University of Turin Italy

ISBN: (纸本)9798400710476

Peopleware, which includes anything related to the role of people in Software Development (SD), has been arousing an increasing interest from both the software industry and research community. This interest is due to the current economic system that demands high-quality software products with a short time to market, staying on the budget. This exposes software developers to the risk of experiencing stress, burnout, and reduced motivation, leading, in turn, to reduced job performance, low-quality SD-related artifacts, and increased turnover. Mindfulness represents a promising intervention that might let developers do their best at work, limiting or even preventing the previously mentioned negative outcomes. This paper presents MOOD (Mindfulness fOr sOftware Developers), a research project whose overarching goal is to customize a well-known and validated group-based intervention program, Mindfulness-Based Stress Reduction (MBSR), in the context of SD-related tasks and assess whether it helps developers to improve their well-being and performance, as well as the quality of the SD-related artifacts they produce. © 2024 Owner/Author.

关键词： Budget control

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Petoria-Your Pet's Ultimate Companion

Petoria-Your Pet's Ultimate Companion

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2024 MIT Art, Design and Technology School of Computing International Conference, MITADTSoCiCon 2024

作者： Jiju, Anargh Ajayakumar, Adharsh P Paul, Edwin Shajan, John Sukumaran, Sruthy Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350362879

Pet owners often face challenges in ensuring their pet's well-being, such as receiving timely advice on health and behavior and managing complex tasks like medication schedules and grooming appointments. The lack of personalized guidance and inconsistency in sourcing high-quality pet products can contribute to suboptimal choices. Our comprehensive solution aims to simplify and enhance the pet ownership experience by bridging the gaps in advice, management, and product sourcing. By providing personalized guidance, expert advice, and a curated selection of high-quality pet products, our solution creates a more rewarding and stress-free journey for pets and their devoted owners. © 2024 IEEE.

关键词： Artificial intelligence E-commerce Pet care management

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Development of CNN-Based Feature Extraction and Multi-layer Perceptron for Eye Disease Detection 1

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3rd International Conference on Advanced Computing and Intelligent Technologies, ICACIT 2023

作者： Malakar, Antara Ganguly, Ankur Chakraborty, Swarnendu Kumar Department of Computer Science and Engineering The Assam Royal Global University Guwahati India Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Jote India

ISBN: (数字)9789819719617

ISBN: (纸本)9789819719600

Identification of multiple eye disorders utilizing a multi-label categorization methodology is an effective way. The crucial merit of this technique is that it can identify the disorders in earlier times. Ocular disease affects millions of people worldwide, and early detection and treatment of these abnormalities are crucial in preventing avoidable blindness. Diagnosing eye illnesses accurately necessitates the analysis of a diverse array of visually discernible symptoms associated with these conditions. The wide range of symptoms exhibited by various eye illnesses emphasizes the importance of a comprehensive assessment for an accurate diagnosis. Because of the moderate progression, the disorder gives several indications in the initial times, so creating disorder detection is a complex work. To achieve an effective identification system, architecture for a machine learning-assisted method is suggested. Initially, the required eye images are gathered from the standard online data sources. It is then followed by Convolution Neural Network (CNN)-based feature extraction, where the features are extracted by Visual Geometry Group 16 (VGG16). Finally, the attained features are subjected to the Multi-Layer Perceptron (MLP) for detecting the different eye disorders. The performance analysis is conducted contrast with other conventional models to prove the developed model efficacy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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Physiological Conversations For Detection and Prediction of Emotion Recognition Using Scheduling Algorithm 3

Physiological Conversations For Detection and Prediction of ...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Sathiyapriya, R. Vanitha, R. Ifet College of Engineering Department of Computer Science and Engineering Tamilnadu Villupuram India

ISBN: (纸本)9798350384598

By anticipating behavioural patterns and preventing the spread of harmful memes, machine learning enhances psychology. Cutting-edge algorithms identify meme humour and assess its influence on communities. Psychology's ability to forecast future behaviours and societal difficulties is enhanced by this combination. New tools are produced via interdisciplinary collaboration in computer science, data science, and psychology. This combination strengthens psychology's capacity for accurate prediction and practical application. The combination of machine learning and psychology is expected to advance understanding of human behaviour. This synergy promotes interventions that improve mental health and the general well-being of society in addition to helping to fathom the intricacies of the mind. © 2024 IEEE.

关键词： Behavioral patterns Harmful memes Interdisciplinary collaboration Machine learning Psychology

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Forecasting Geomagnetic Storms for Earth Using LSTM with Solar Wind and IMF Features

Forecasting Geomagnetic Storms for Earth Using LSTM with Sol...

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2024 International Conference on Innovations in science, engineering and Technology, ICISET 2024

作者： Shammi, Nusrat Jahan Ashraf, Taqia Ifty, Rashedul Arefin Rahman, Md. Ziaur International Islamic University Chittagong Kumira Department of Computer Science and Engineering Chattogram4318 Bangladesh Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chittagong4349 Bangladesh

ISBN: (纸本)9798350355499

When the solar wind interacts with Earth's field, it can cause geomagnetic storms, which pose serious risks to vital infrastructure such as satellite communication, GPS systems, and electric power transmission. We presented a novel method for forecasting the Disturbance Storm-Time Index (Dst) using a long-short-term memory (LSTM) neural network in order to meet the urgent requirement for precise predictions. Our model employs sensing data from NASA's ACE and NOAA's DSCOVR satellites to analyze the complex relationship between interplanetary magnetic field & plasma from solar wind & sunspot activity. These factors are crucial to understanding geomagnetic disturbances. The LSTM model accumulates complex patterns and temporal relationships within the space weather data by including real-time data assimilation, resulting in enhanced weather prediction capabilities. Evaluation metrics like the root mean square error (RMSE) and coefficient of determination (R2) are used to assess the model's performance and reliability. The outcomes indicate that the LSTM model is highly effective in accurately predicting geomagnetic storms. The predicted values offer vital information for operators of satellites, power grids and magnetic navigation systems, enabling them to implement preemptive actions to mitigate the effects of any disruptions caused by geomagnetic storms. The research we accomplished helps to improve the approaches employed for forecasting space weather. This provides decision-makers with a useful and timely tool to strengthen vital systems and prepare for geomagnetic disturbances. © 2024 IEEE.

关键词： NASA

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Anomaly-Focused Augmentation Method for Industrial Visual Inspection

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IEEE Access 2025年 13卷 89118-89139页

作者： Melvinlara, Melvin A. Hameed, Ibrahim A. Koziorek, Jiri Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical Engineering Ostrava70800 Czech Republic Department of Mechanical Engineering and Technology Management Ås1433 Norway

In industrial inspection, the detection of surface defects - such as scratches, dents, or other defects - is crucial for ensuring product quality. However, the limited availability of annotated images of such defects poses challenges for developing reliable detection models. This paper proposes a novel image augmentation algorithm to address this limitation by generating synthetic imperfections from a small sample set. Unlike traditional approaches that augment entire images, the proposed approach isolates defects and applies spatial transformations (e.g., rotation, shear) before blending them into background images. This allows for the creation of diverse training datasets. The algorithm employs a 2D binary map to encode the spatial relationship between defect and background regions of images and then applies an AI-driven model to generate new realistic blended images. By leveraging a generator-discriminator framework, the augmentation process is iteratively refined during training to maintain the style and texture of the original imperfections. Experimental results show that the proposed method significantly enhances the performance of defect detection models compared to existing augmentation techniques while preserving the realism of the generated images. © 2013 IEEE.

关键词： Photointerpretation

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Improving Breast Cancer Detection Accuracy Through Random Forest Machine Learning Algorithm

Improving Breast Cancer Detection Accuracy Through Random Fo...

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International Conference on Advanced engineering, Technology and Applications, ICAETA 2024

作者： Agrawal, Atul Khanday, Akib MohiUd Din Alazzawi, Esraa Mohammed Bhushan, Bharat Baniya, Pashupati Jamil, Akhtar Department of Computer Science and Engineering ITS Engineering College Greater Noida India Department of Computer Science and Engineering Sharda University Greater Noida India Department of Computer Science and Software Engineering United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Department of Computer Engineering University of Baghdad Al-Jadriya Baghdad10071 Iraq Department of Computer Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

ISBN: (纸本)9783031709234

Breast cancer continues to be a significant global health issue that greatly affects the well-being of people worldwide. Detecting breast cancer early is vital for improving the outcomes of patients. One promising method for breast cancer detection is the use of the Random Forest machine learning algorithm. In a recent research paper, we investigated how Random Forest can be used to predict breast cancer by employing explainable AI techniques. We analyzed the specific features that the algorithm relies on to classify breast cancer and highlighted the advantages of Random Forest compared to other machine learning algorithms in diagnosing breast cancer. Our model offers a well-thought-out, efficient, and easily understandable approach to predicting breast cancer using explainable machine learning techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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A Comparative Study of Image Classification Models for Terrain Recognition 3

A Comparative Study of Image Classification Models for Terra...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Krishna Kumar, V. Karthika, V. Mohana, T. Mugebur Rahman, A. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369083

Terrain recognition is vital for various applications such as autonomous navigation, environmental monitoring, military operations, and urban planning. It enables safe navigation for autonomous vehicles, aids in environmental surveillance, assists in military reconnaissance, and supports urban infrastructure development. Recent years have witnessed the augmentation of machine learning models with online feature extraction techniques and self-supervised learning frameworks. However, a notable challenge lies in managing the complexity inherent in processing extensive datasets or numerous features. In this study, this problem is addressed by employing transfer learning for classifying terrains using images. The effectiveness of three widely used pre-trained convolutional neural network models - ResNet50, VGG16, and InceptionV3 - was compared. The findings demonstrate the strong performance of the models, emphasizing the effectiveness of transfer learning in tasks focused on terrain recognition. © 2024 IEEE.

关键词： Self-supervised learning

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Advanced Natural Language Processing Techniques for Efficient Sentiment An alysis of US Airline Twitter data: A High-Performance Framework for Extracting Insights from Tweets 6

Advanced Natural Language Processing Techniques for Efficien...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Sazan, Saad Ahmed Ahmed, Mohiuddin Saad, Tayef Billah Roy, Mrittika Rajshahi University of Engineering and Technology Department of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350385779

Sentiment analysis is a critical aspect of natural language processing that can provide valuable insights into customer satisfaction and opinions. Understanding sentiment, particularly in the aviation industry, can help organizations enhance their services and improve customer experiences. To advance sentiment analysis in this field, our research analyzed Twitter data related to US airlines. We employed a data augmentation technique using a pre-trained RoBERTa model to address class imbalance and applied the fastText classifier from Facebook's AI Research (FAIR) lab to gain valuable insights. Our study aimed to comprehend and enhance customer satisfaction in the aviation industry by presenting a comprehensive approach to sentiment analysis. The results not only demonstrate the effectiveness of our methodology but also provide airlines with actionable insights to proactively improve services based on real-time customer sentiments expressed on social media. This work represents a significant stride in leveraging advanced natural language processing techniques to provide superior customer experiences. Our approach achieved an impressive F1 score of 92.05% for ternary classification and 96.23% for binary classification, surpassing state-of-the-art models. This high score highlights the robustness of our methodology, establishing it as a potent tool for extracting meaningful sentiment from social media data in the airline domain. © 2024 IEEE.

关键词： Sentiment analysis

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