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46th International Conference on Software engineering: Companion, ICSE-Companion 2024

作者： Lee, Jonathan Li, Mason Hsu, Kuo-Hsun Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Computer Science National Taichung University of Education Taichung Taiwan

ISBN: (纸本)9798400705021

In open-source development, encountering build failures is a common challenge. Addressing these issues requires analyzing the causes of errors and developing solutions for fixing them. In this work, we fine-tuned Google's BERT, a well-known language model excellent in transfer learning, to address build issues in Gradle Java projects. Our strategy utilizes this model to classify error logs and identify fixing solutions. This approach extends our previous work, Gradle ACFix, an automated build error fixing system, to explore the potential of using machine learning to classify error types and identify appropriate fixing strategies for software projects. We gathered a dataset of 11,483 open-source Gradle Java projects from GitHub for this research. The model's evaluation on the error logs of these projects demonstrated a high accuracy rate exceeding 98%. © 2024 IEEE computer Society. All rights reserved.

关键词： Errors

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Enhancing Road Safety: A Smart Accident Alert System for Rapid Emergency Response 18

Enhancing Road Safety: A Smart Accident Alert System for Rap...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Kishore, S. Nair, Rekha R. Babu, Tina Nipe Nitte University Department of Computer Applications Mangalore India Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9789380544519

Millions of lives are at risk annually due to traffic accidents, and the need for a proactive and efficient accident detection system is paramount. The traditional accident detection system faces some challenges such as limited scalability, connectivity, power consumption, and reliability of sensors. So proposed an accident detection system with Internet of Things (IoT) technologies to enhance road safety and emergency response mechanisms. The proposed system integrates advanced components such as Global Positioning System(GPS) modules, and Global System for Mobile Communication(GSM) modules to enable real-time monitoring, precise location tracking, and immediate communication with emergency services. The novelty of this work lies in the implementation of a cost-effective accident detection system that accurately provides alerts to the intended party. The system delivers alert messages containing longitude and latitude coordinates. Additionally, it initiates alerts in the form of phone calls, fostering timely communication with accident victims. This prompt interaction has the potential to save lives by facilitating on-demand treatment and immediate assistance to those in need. This methodology has the potential for future applications in industries such as Traffic Management and Analysis, Road Safety Enhancement, and Emergency Response Optimization. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Emergency Response Optimization Global Positioning System Global System for Mobile Communication Internet of Things ThingSpeak

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Production Classification in E-Commerce Based on Product Descriptions with Natural Language Processing (NLP) and Machine Learning Models 5th

Production Classification in E-Commerce Based on Product Des...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Bist, Yuvraj Gurbaxani, Paramesh Gupta, Neetu Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9783031770746

The rapid growth of the internet has increased people's reliance on it for expressing opinions on products and stores. Text sentiment analysis is now a key research area. Deep learning methods are commonly used for text classification, but they suffer from issues like information loss and weak context. This paper enhances the existing models to simplify the process, educe training time, and improve overall recall and accuracy in text sentiment classification. With the rapid development of artificial intelligence, traditional manual techniques are fading, and AI algorithms are driving the swift progress of text sentiment classification. Product categorization is critical in e-commerce since it influences search, recommendations, inventory, and consumer experience. NLP approaches and models such as TF-IDF and Word2Vec are effective for categorizing products based on their textual descriptions. This literature study investigates the research evolution, problems, and upcoming trends in using TF-IDF and Word2Vec for product classification in e-commerce. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Adversarial machine learning

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A Novel Framework for Prediction of Heart Disease using Hybrid Classifier in the Cloud Environment 10

A Novel Framework for Prediction of Heart Disease using Hybr...

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

作者： Prerna Singh, Prabhdeep Singh, Devesh Pratap Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9789380544472

This paper presents a novel framework for predicting heart diseases in the cloud environment by employing hybrid classifiers. The dataset used in this research is the Cleveland heart disease dataset. Four hybrid classifiers were tested: Naive Bayes + Random Forest, Bayes Net + Random Forest, Naive Bayes + J48, and Naive Bayes + PART. The performance of the hybrid classifiers is evaluated by comparing accuracy, precision, recall, F. Measure, and PRC area. The results showed that the accuracy of the hybrid classifiers is significantly improved compared to the individual classifiers. The Naive Bayes + Random Forest hybrid classifier achieved the best results with the highest accuracy, F. Measure, and PRC area among the four hybrid classifiers. The proposed framework provides a useful approach to predicting heart disease in the cloud environment. This framework could diagnose heart diseases more accurately and improve healthcare services. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Forecasting

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A Delay Sensitive Framework for Effective Healthcare using Machine Learning 10

A Delay Sensitive Framework for Effective Healthcare using M...

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

作者： Prerna Singh, Prabhdeep Singh, Devesh Pratap Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9789380544472

This paper proposes a delay-sensitive framework for the Healthcare Industry that can effectively predict diseases at an early stage. The framework utilizes Machine Learning (ML) techniques to integrate the Internet of Things (IoT), Cloud Computing, and ML in the medical field. The primary objective is to reduce the delay on the user's end while using Smart Healthcare services and provide the quickest response to the patient. The proposed framework can contribute to the better and optimal integration of emerging technologies in the Healthcare sector. The methodology involves collecting data from various sources, using ML algorithms, preprocessing feature extraction, and model training. The major findings indicate that the proposed framework can effectively predict diseases early and improve the quality of healthcare services. The interpretations and implications of the study highlight the importance of integrating emerging technologies in the Healthcare sector to provide better healthcare services to patients. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Internet of things

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A Specular Reflection Removal Technique in Cervigrams 1

A Specular Reflection Removal Technique in Cervigrams

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

作者： Mukku, Lalasa Thomas, Jyothi Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

Cancer detection through medical image segmentation and classification is possible owing to the advancement in image processing techniques. Segmentation and classification tasks carried out to predict and classify diseases need to be dependable and precise. Specular reflections are the high-intensity and low-saturation areas that reflect the light from the probing devices that capture the picture of the organ surface. These areas sometimes mimic the features that are key identifying factors for cancers like acetowhite lesions. This review article examines the various methods proposed for removing specular reflections from medical images, especially those captured by colposcopes. The fundamentals of specular reflection removal and its associated challenges are discussed. The paper reviews several prominent approaches for removal of specular reflections proposes a novel method to remove the specular reflections. The comprehensive review can be a strong foundation for researchers looking to decide on appropriate techniques to employ in their respective research approaches. © 2023 IEEE.

关键词： Diseases

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An IoMT data security framework with Hyperledger Fabric for smart cities

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International Journal of Information Technology (Singapore) 2024年第8期16卷 4875-4886页

作者： Samanta, Saikat Sarkar, Achyuth Kumari, Sangeeta Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Jote India School of Computer Science Engineering and Technology Bennett University U.P. Greater Noida India

There is a high-risk privacy issue related to the Internet of Medical Things (IoMT) because of a lack of security in critical and sensitive information. We propose a framework for securing medical data in Healthcare 5.0 generated by the IoMT. Our framework uses Hyperledger Fabric (HF), a permissioned blockchain platform, to provide a decentralized and tamper-proof system for data management and sharing. The proposed framework includes modules for identity management, data encryption, and access control for Healthcare 5.0. Hyperledger is a Linux Foundation-hosted open-source project. The HF is one of the IBM-developed initiatives that eventually contributed to Hyperledger. The paper presents the architecture of the framework, as well as a prototype implementation and evaluation of its performance and security specific to consumer electronics for Smart Cities. This evaluation demonstrated that the proposed framework is efficient and effective at securing medical data in the IoMT, and could be used to develop secure and scalable healthcare systems in the future. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Blockchain technology Decentralized data management Healthcare data privacy Remote patient monitoring Smart healthcare

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A Hybrid Switched Capacitor Converter Enabling Capacitive-Based Wireless Power Transfer for Battery Charging Applications 14

A Hybrid Switched Capacitor Converter Enabling Capacitive-Ba...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Sund, Jade Coday, Samantha Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science United States

ISBN: (纸本)9798331516116

This work investigates the feasibility of employing hybrid switched capacitor converters in wireless battery charging applications. A capacitively-isolated hybrid Dickson converter designed for use in low-power volume-constrained wireless power transfer applications is presented. The converter analysis, operation and design are detailed. Finally, a hardware prototype is presented and experimental results validate wireless power transfer, across varying load and gap distances. © 2025 IEEE.

关键词： Wireless charging

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Brinjal Leaf Diseases Detection Based on Deep Convolutional Neural Network 4

Brinjal Leaf Diseases Detection Based on Deep Convolutional ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Anu Priya, A. Rajaram, K. Kit - Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

Brinjal leaf diseases, such as leaf spot, mosaic virus, and wilt, would critically reduce crop yield quality if the disease is spread during an early stage. Such reasons have made early identification imperative to ensure effective prevention and management. This article, therefore, aims at coming up with an effective technique of identifying brinjal leaf diseases using VGG16 CNN model. Traditional identification methods are based on manually examining plant diseases, an effort known to be highly labour-consuming, subjective, and even unreliable. In addition, most of the available machine learning methods fail in scalability, computational cost, and specific domain problems. However, our technique makes use of the high ability of VGG16 architecture to extract automatically relevant features from leaf images and then classify them with high accuracy. Used Eggplant Leaf Disease Dataset which contains six classes. The central idea of our approach is to employ deep convolutional layers of VGG16 to extract discriminative features from the images followed by classification using fully connected layers and softmax activation. The approach obtains 98.2% accuracy for determining of Brinjalleaf diseases. This work will impact the agricultural sector by means of providing a fast automated solution that gives accurate insights about early detection of leaf disease occurrence, hence reducing losses to crops and enhancing production. © 2025 IEEE.

关键词： Convolutional neural networks

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A Novel Approach for Sentiment Analysis on social media using BERT & ROBERTA Transformer-Based Models 8

A Novel Approach for Sentiment Analysis on social media usin...

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Prasanthi, Kundeti Naga Eswari Madhavi, Rallabandi Sai Sabarinadh, Degala Naga Sravani, Battula Department of Computer Science and Engineering Mylavaram India

ISBN: (纸本)9798350334012

Sentiment analysis on social media platforms has gained immense popularity in recent years due to its ability to analyze and classify users' emotions and opinions on different topics. This paper proposes a sentiment analysis model for Twitter that utilizes two powerful transformer models BERT and ROBERTA, to classify tweets based on their sentiment. The use of BERT and ROBERTA transformers offers several advantages over traditional sentiment analysis models. These transformers can capture contextual information and understand the relationships between words in a sentence, making them well-suited for the nuanced language used in social media platforms like Twitter. To train the model, transfer learning is employed, allowing the transformers to learn from a vast dataset of tweets before fine-tuning on the sentiment analysis task. This approach enables the model to understand the nuances of informal language and slang used in tweets, improving its ability to accurately classify sentiment. Another advantage of the proposed model is its ability to handle the informal language used in tweets. However, BERT and ROBERTA transformers have been trained on a vast corpus of informal language, enabling them to accurately analyze and classify sentiment in tweets. Lastly, the proposed model's scalability and efficiency are also advantages. This model can handle large volumes of data with high efficiency and can be fine-tuned to adapt to new domains with minimal training data, making it highly versatile and applicable in various applications. © 2023 IEEE.

关键词： Sentiment analysis

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