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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Pallapothu, Harshith Gopi Krishna, G. Sunil, Digvijay Teja Yadav, S. Nagasundari, S. Pes University Dept. of Computer Science & Engineering Bengaluru India Research Center for Information Security Forensics and Cyber Resilience Pes University Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331529833

The increasing adoption of autonomous vehicles has driven the need for robust data management solutions that support real-time operations and ensure vehicle safety and efficiency. This work introduces a cloud-based framework for management of sensor data from autonomous vehicles, focusing on optimizing payload transmission rates. The framework leverages AWS IoT Core and AWS IoT Analytics to ensure efficient data flow from vehicle sensors to cloud storage. This framework demonstrates its potential for scalable deployment in real-world autonomous vehicle networks, contributing to the evolution of connected vehicle technologies and intelligent transport systems. This framework ensures reliable data transmission up to 250 payloads per second, beyond which data loss occurs. © 2025 IEEE.

关键词： Cloud platforms

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Youtube Comments Sentiment Analysis 1

Youtube Comments Sentiment Analysis

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kant, Shashi Upadhyay, Raushan Kumar Gupta, Palak Tiwari, Raghav Kumar, Honey Sharda University Dept. of Computer Science and Engg. Greater Noida India Greater Noida Institute of Technology Dept. of Computer Science and Engg. Noida India

ISBN: (纸本)9798350356816

Over the years, there has been a significant surge in textual information, leading to a burgeoning research interest. The contemporary focus lies in the intriguing field of sentiment analysis on YouTube comments. Despite the substantial volume of user comments and reviews on many videos, limited efforts have been directed towards extracting meaningful trends due to the inherent inconsistency and variable quality of *** this study, we perform sentiment analysis on YouTube comments concerning popular topics, with a selection of various machine learning techniques and algorithms. The aim will be to highlight how sentiment analysis can show trends, seasonality, and forecasts, and offer an insightful view into the effects of real-world events on the public mood. The results display strong correlation views between users' sentiments toward corresponding keywords and the real-world *** primary aim of this research is to assist scholars in identifying high-quality research papers on sentiment analysis. Our approach involves sentiment analysis of YouTube comments using citation sentences based on an existing annotated corpus consisting of 1500 citation sentences. data cleansing involved the application of various normalization rules to eliminate noise from the comments in the corpus. © 2024 IEEE.

关键词： Adversarial machine learning

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Towards Efficient dataset Development: A Case Study of M2Q2+ in Movie QA Systems 6

Towards Efficient Dataset Development: A Case Study of M2Q2+...

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6th International Conference on Advanced Aspects of Software Engineering, ICAASE 2024

作者： Aggoune, Aicha Mihoubi, Zakaria Dept. Computer Science LabSTIC Laboratory University of 8the May 1945 Guelma Algeria Dept. Computer Science University of 8 the May 1945 Guelma Algeria

ISBN: (纸本)9798331522810

The development and availability of high-quality datasets are critical to the success of machine learning and artificial intelligence (AI) systems. datasets provide the essential foundation for training, validating, and testing models. The need for domain-specific datasets is particularly important in closed-domain systems like Movie NoSQL Question Answering (QA). A well-curated dataset enables models to translate natural language questions into NoSQL queries effectively. This paper introduces an efficient method for developing a tailored dataset for MongoDB-based Movie QA systems. The resulting dataset, M2$Q$2+, facilitates the translation of natural language questions into MongoDB queries (MQL). The dataset development process includes creating templates, performing data augmentation with the pre-Trained T5-base model, and conducting a data revision process to produce the final dataset. A combination of evaluation methods and metrics was applied to ensure that M2$Q$2+ is high quality. © 2024 IEEE.

关键词： Adversarial machine learning

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AI-Based Home Automation Using Voice Recognition and Biometric Finger Print Authentication 4

AI-Based Home Automation Using Voice Recognition and Biometr...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Pydala, Bhasha Bhargavi, M. Vyshnavi, Balaji Krishna, Machavaram Gopi Kumar, Aravadasari Lenin Bindu, K Hima dept. of Data Science Tirupati India Sree Vidyanikethan Engineering College dept. of Information Technology Tirupati India

ISBN: (纸本)9798350385793

The proposed home automation system introduces a cutting-edge solution that harnesses the power of Artificial Intelligence (AI) to upgrade residential spaces into smart, dynamic, and user-friendly environments. With a simple vocal command, the AI system swiftly and precisely executes tasks. The existing home automation system lacks in authentication so to ensure security and personalization, the proposed system utilizes fingerprint module integrated with a microcontroller like Arduino Uno and Bluetooth module. It interfaces seamlessly with a fingerprint sensor, providing reliable user authentication. This ensures that the smart home system responds only to authorized users, enhancing overall safety. The implementation includes essential components such as Arduino Uno, Bluetooth modules, regulated power supply and fingerprint module. This innovative system showcases the potential of integrating AI into homes, making them not only intelligent but also adaptable to the different requirements of each resident. With speech recognition and fingerprint authentication, this system offers a glimpse into a future where homes are not just structures but responsive companions that enhance the quality of life. © 2024 IEEE.

关键词： Bluetooth

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Slang Word Detection in the Context of User Profiling in the Social Media Platformes 4

Slang Word Detection in the Context of User Profiling in the...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Samanta, Bikram Shil, Ritika Pal, Alok Ranjan Pal, Antara College of Engg. and Management Dept. of Computer Science and Engg. Kolaghat India Atharva College of Engineering Dept. of Computer Science Engg. Mumbai India

ISBN: (纸本)9798350343670

On social media platforms, people express their views in various ways, sometimes using formal words and sometimes in informal form. The informal conversations may contain jargon words i.e. slang words which might be improper for all kinds of audience. In this work, an attempt has been made to identify slang words in a text, posted on the social media platform. The overall experiment has been carried out in 4 modules. In module-1, the suspicious words from the posted text have been identified using minimum edit distance. This experiment has been implemented by the help of a dataset of printable slang words collected from Kaggle. In module-2, the topic of discussion has been analyzed by using a knowledge based approach. This part of the experiment has been carried out by creating a newspaper archive from the online repository of the "Times of India"newspaper. But, this knowledge based strategy could not produce an appreciable accuracy at its baseline. Therefore, as a modification of this technique, in module-3, the context analysis task has been implemented combining knowledge based approach and machine learning based approach. In module-4, the module-1 and module-3 are merged to tag a suspicious word with its level of abusiveness. © 2024 IEEE.

关键词： Context Analysis Slang Word Social Media Transformer User Profiling

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Integrated and Intelligent Elder-Friendly E-commerce Framework using NLP Techniques

Integrated and Intelligent Elder-Friendly E-commerce Framewo...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Sivasanjith, G. Mohan Prabhu, N. Hemaswathi, S. Rajkumar, P. Rajeshkannan, C. Dhivya, R. Kare Dept. of Information Technology Krishnankovil India Artificial Intelligence and Data Science Indra Ganesan College of Engineering Tiruchirappalli India Mount Zion College of Engg. and Tech. Dept. of Computer Science and Engg. Pudukottai India Suguna College of Engineering Dept. of Computer Science and Engg. Coimbatore India M.Kumarasamy College of Engineering Dept. of Information Technology Karur India

ISBN: (纸本)9798331529833

All over the world, the population of elderly people is increasing a lot. There needs to be special attention to the welfare of the elderly person to make them confident in their independent quality of living. In recent years, researchers have shown much interest in smart home automation for people who live alone without any human assistance. However, the purchase of medical drugs, diapers, etc., outside the house has to be fulfilled by their family members or caretakers. In most cases, it is not feasible, as the family members may stay long away from the elders. Purchasing online is easy;however, analyzing the best product requires knowledge in each e-commerce application for better quality at a low cost. This paper proposes an integrated and intelligent e-commerce framework to avoid the drawback of purchasing items online in a single window using the NLP technique. The proposed framework provides offers, discounts, star ratings, and product reviews by analyzing other customers' review data and purchase patterns. It provides interactive data analysis at a single window for the ease of elderly persons. The performance of the proposed framework was evaluated by 120 elderly persons using a simple questionnaire with a statistical analysis of a sample purchase. © 2025 IEEE.

关键词： Recommender systems

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DivLog: Log Parsing with Prompt Enhanced In-Context Learning 24

DivLog: Log Parsing with Prompt Enhanced In-Context Learning

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Xu, Junjielong Yang, Ruichun Huo, Yintong Zhang, Chengyu He, Pinjia The Chinese University of Hong Kong Dept. of Computer Science & Engineering Hong Kong Eth Zurich Switzerland School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data China

ISBN: (纸本)9798400702174

Log parsing, which involves log template extraction from semi-structured logs to produce structured logs, is the first and the most critical step in automated log analysis. However, current log parsers suffer from limited effectiveness for two reasons. First, traditional data-driven log parsers solely rely on heuristics or handcrafted features designed by domain experts, which may not consistently perform well on logs from diverse systems. Second, existing super-vised log parsers require model tuning, which is often limited to fixed training samples and causes sub-optimal performance across the entire log source. To address this limitation, we propose Di-vLog, an effective log parsing framework based on the in-context learning (ICL) ability of large language models (LLMs). Specifically, before log parsing, DivLog samples a small amount of offline logs as candidates by maximizing their diversity. Then, during log parsing, DivLog selects five appropriate labeled candidates as examples for each target log and constructs them into a prompt. By mining the semantics of examples in the prompt, DivLog generates a target log template in a training-free manner. In addition, we design a straightforward yet effective prompt format to extract the output and enhance the quality of the generated log templates. We conducted experiments on 16 widely-used public datasets. The results show that DivLog achieves (1) 98.1% Parsing Accuracy, (2) 92.1% Precision Template Accuracy, and (3) 92.9% Recall Template Accuracy on average, exhibiting state-of-the-art performance. © 2024 ACM.

关键词： Semantics

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SARCASAM Analysis in Social Networks Using Deep Learning Algorithm 4

SARCASAM Analysis in Social Networks Using Deep Learning Alg...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Priya, M. Kumar, Vijaya K Vennila, P. Prasanna, M.A. Dept. of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam611002 India Dept. of Computer Science and Engineering Women's Engineering College Puducherry605008 India Dept. of Computer Science and Engineering K. Ramakrishnan College of Technology Trichy621112 India

Twitter is the one of the biggest social media sites, where users may share their thoughts, ideas, and opinions as well as discuss current events and live tweets. In the subject of opinion mining, creating reliable and effective algorithms for sarcasm detection on Twitter is an intriguing task. Sarcasm is the use of positive language to convey depressing emotions while speaking in opposition to one's own intentions. Sarcasm is frequently employed on social networking and micro blogging platforms, where users can offend others and find it difficult to express their true feelings. The deep learning technique utilised in the current algorithms to identify these sarcastic tweets has the limitation of not being able to predict continuous variables. A novel deep learning algorithm is proposed to identify both positive and negative terms as well as sarcasm in comments. Deep neural networks are used to classify the comments into positive and negative word categories. Customers' opinions are mined using sentiment analysis to find and extract information from the text. Sarcastically stated statements from social networking sites can be quickly categorised and recognised by using VADER (Valence Aware Dictionary and Sentiment Reasoner). © 2025 The Authors.

关键词： Tweets

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Ethereum Fraud Prevention: A Supervised Learning Approach for Fraudulent Account Recognition 1

Ethereum Fraud Prevention: A Supervised Learning Approach fo...

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1st International Conference on Trends in Engineering Systems and Technologies, ICTEST 2024

作者： Dutta, Sourav Sharma, Anjali Rajgor, Jaymin Lovely Professional University Punjab Dept. of Computer Science and Engg. Kolkata India Jss Academy of Technical Education Dept. of Computer Science and Engg. Bangalore India Shan and Anchor Kutchhi Engineering College Dept. of Computer Science and Engg. Mumbai India

ISBN: (纸本)9798350307795

In recent times, blockchain technology, especially Ethereum, has gained tremendous popularity for supporting various financial transactions. Ethereum, the second most prominent cryptocurrency platform after Bitcoin, processes over one million transactions daily, known for its high security and ease of use. However, despite its robust security features, Ethereum faces challenges with illegal activities, which hinder its widespread adoption. To address this issue, this study focuses on detecting illicit activities on the Ethereum blockchain, particularly identifying illegal accounts using machine learning techniques. It proposes a novel method combining a convolutional neural network with an XGBoost classifier, aimed at differentiating between normal and illicit accounts based on transaction history. The XGBoost model, a tree-based ensemble classifier, not only improves accuracy but also prevents overfitting and enhances the model's generalizability. Its parallel tree-building feature also speeds up training, making the model more scalable. The model is trained on a balanced dataset of over 4,000 Ethereum accounts, encompassing both normal and fraudulent accounts. The results are promising, with the model achieving an accuracy of 99.7% and an average AUC of 0.9998 (std: 0.0008), outperforming standard machine learning models. This approach shows significant potential in combating illegal activities within the Ethereum blockchain. © 2024 IEEE.

关键词： Blockchain

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Deep Learning-based Electric Kickboard Helmet Detection under Night Driving Conditions

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Transactions of the Korean Institute of Electrical Engineers 2022年第10期71卷 1411-1419页

作者： Kim, Daun Jeong, Jin-Woo Dept. of Data Science Seoul National University of Science and Technology Korea Republic of Dept. of Industrial Engineering Seoul National University of Science and Technology Korea Republic of

As the use of electric kickboards has increased recently, the frequency of safety accidents is also increasing. Even though the government amended the road traffic law to make it compulsory to wear a helmet, the helmet-wearing rate is still remarkably low. In this work, we propose a novel method to monitor and detect whether a user of an electric kickboard is wearing a helmet through a deep learning-based object detection network, thereby increasing the helmet-wearing rate. For continuous driver monitoring, the proposed method captures the user's face in a front view using the user's mobile phone attached to the kickboard. In addition, to handle both day and night driving conditions, various image conversion, enhancement, and augmentation techniques were considered and evaluated. The experimental results showed the feasibility of the proposed method. © 2022 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Object detection

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