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E-commerce Product Sentiment Assessment and Aspect Analysis 1st

E-commerce Product Sentiment Assessment and Aspect Analysis

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1st international Research conference on computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Praveen, M. Vijay, R.R. Aaditya Shreeram, R.S. Manohar, S. Computer Science and Engineering SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9783031823824

This research paper advances Sentiment Analysis and Aspect-Based Sentiment Analysis for e-commerce product reviews, delivering valuable insights for users and product creators. We extend beyond typical sentiment analysis by focusing on specific aspects of products such as features and qualities to provide a comprehensive analysis. Our methodology involves rigorous data preprocessing, including stemming, stop words removal, lemmatization, lower casing, contraction expansion, and tokenization, ensuring organized text data for efficient and accurate analysis. Central to our approach is the Aspect Term Extraction (ATE) step, which employs techniques such as POS Tagging, Noun Combination, Dependency Parsing, and Stop Word Removal to extract aspects from the text. These customized rules identify linguistic patterns and grammatical structures, revealing detailed facets of products. We fine-tune a BERT model for ABSA, processing the dataset to classify aspect sentiments as positive, neutral, or negative, and assigning a sentiment score ranging from –1 (extreme negativity) to +1 (extreme positivity). By leveraging advanced machine learning, we enhance the accuracy and depth of sentiment analysis, providing detailed insights into customer sentiments. This structured approach empowers businesses to extract actionable insights from extensive textual data and sets a new standard for sentiment analysis in digital retail. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Natural language processing systems

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Real Time Voice Language Interaction 1st

Real Time Voice Language Interaction

引用

1st international conference on Innovation and Emerging Trends in computing and Information Technologies, IETCIT 2024

作者： Yadav, Gagan S. Vimala Devi, J. Jain, Tanmai Harshith, H.D. Neha, B.N. CSE Dayananda Sagar College of Engineering Bangalore India

ISBN: (纸本)9783031808388

Communication across linguistic boundaries is critical in an era of increased global connectivity and diverse cross-cultural interactions. This paper discusses the difficulties presented by traditional language translation techniques, emphasizing the importance of real-time voice interaction to overcome delays and facilitate natural speech flow. Recognizing the complexities of adult language acquisition, we proposed a Speech-to-Speech Translation (S2ST) system that integrates advanced machine learning models seamlessly via a socket-based communication application. We have used the three key models: OpenAI Whisper for Speech-to-Text, Google Deep Translator for Text-to-Text translation, and Google Translator for Text-to-Speech translation. Together with a sophisticated socket-based application, these models form an innovative real-time voice language interaction platform. Users can join rooms, select their preferred language, and engage in multilingual conversations with speech that is seamlessly translated in real time. The proposed S2ST system aims to bridge linguistic gaps in a variety of fields, including business, tourism, education, and administration. Unlike traditional manual translation methods that are limited to specific content types, proposed approach addresses a wide range of communication requirements, fostering a more inclusive and accessible global dialogue. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Linguistics

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Instructional Scaffolding in Online learning Environment: A Meta-Analysis

Instructional Scaffolding in Online Learning Environment: A ...

引用

international conference on teaching and learning in computing and engineering (LaTiCE)

作者： Jumaat, Nurul Farhana Tasir, Zaidatun Univ Teknol Malaysia Fac Educ Dept Educ Sci Math & Creat Multimedia Skudai 81310 Johor Malaysia

ISBN: (纸本)9781479935918

Instructional scaffolding or simply known as scaffolding in education is defined as a guidance or support from teachers, instructors or other knowledgeable persons that facilitate students to achieve their goals in learning. Conceptually, scaffolding means providing students with instructions during the early stage of learning before slowly shifting the responsibility to them as they develop their own understanding and skills. As technology extends learning from classroom to learning communities, same goes to the concept of scaffolding. The scaffolding is no longer implemented via face-to-face instruction that literally exists between a teacher and students in a classroom. Currently, the form of instructions that emerges between teachers and students is mediated through technology and the learning communities exist in the online settings. Thus, it is important to acknowledge the suitable form of support required for the students, especially in an online learning environment. The aim of this meta-analysis is to investigate the types of scaffolding that could be implemented in an online learning environment together with its potential in validating students' success in an online learning setting.

关键词： Instructional scaffolding Online learning environment Meta-analysis

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MTMixAD: Metric-Trace Mixed Anomaly Detection Framework for Microservice Systems with Limited and Mislabeled Data 3rd

MTMixAD: Metric-Trace Mixed Anomaly Detection Framework for...

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3rd international conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： Tang, Yixiang Tan, Zhuanglun Chen, Jian Yao, Xi Zhong, Guoxiang Liu, Fagui School of Computer Science and Engineering South China University of Technology Guangzhou China Peng Cheng Lab Shenzhen China

ISBN: (纸本)9789819645022

Given the significant security challenges posed to microservice systems in cloud computing, efficient anomaly detection is crucial for maintaining reliable microservice systems. Most existing anomaly detection methods require significant amounts of accurately labeled monitoring data or pure normal data. However, in the real-world scenario, the volume of labeled data is usually limited due to high cost, and the data often contains mislabeling noises owing to false alarms. To address the above problem, we propose MTMixAD, a microservice anomaly detection framework with limited and mislabeled monitoring data. Specially, MTMixAD uses Mixup for data augmentation, confident learning to filter mislabeled data, and a novel loss function to enhance model discrimination. Additionally, MTMixAD integrates metrics and trace data from microservice systems to better capture system characteristics. Experiments verify the effectiveness of our method and each part of MTMixAD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anomaly detection Data scarcity Microservice Mislabeled data

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Sign Language Recognition Using Convolutional Neural Network 4th

Sign Language Recognition Using Convolutional Neural Network

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4th international conference on Advanced Network Technologies and Intelligent computing, ANTIC 2024

作者： Potluri, Tejaswi Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering &Technology Vignana Jyothi Nagar Pragathi Nagar Telangana Nizampet Hyderabad500090 India

ISBN: (纸本)9783031837920

Sign language detection is essential for improving accessibility, communication, and inclusion for individuals who are deaf or hard of hearing. This technology enables smooth communication across various environments, including schools, workplaces, healthcare settings, and everyday interactions. By accurately interpreting sign language gestures, it bridges communication barriers, providing equal access to information and services. In education, sign language detection supports language learning and academic success for deaf students, while in healthcare, it ensures that medical information is conveyed accurately, enhancing care quality. In the workplace, it fosters inclusivity by enabling communication between deaf employees and their colleagues, promoting equal opportunities and professional growth. Additionally, during emergencies, it can save lives by enabling swift communication with emergency responders. This study presents a robust sign language detection system based on convolutional neural networks (CNNs), trained on a dataset of 2,515 images representing 36 distinct ASL gestures. The system employs a comprehensive preprocessing pipeline, with images standardized, resized, and split for training and validation, resulting in an accuracy rate of 85.87% across ASL letters and numerals. Real-time testing, facilitated by OpenCV’s video capture, demonstrates the model’s effectiveness in live gesture recognition with minimal delay. Despite minor errors in misclassification and occlusion, the system shows strong potential to support inclusive communication across settings and to further advance sign language recognition technology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Adoption of Quantum NLP for Improved Healthcare System 1st

Adoption of Quantum NLP for Improved Healthcare System

引用

1st international conference on Computation of Artificial Intelligence and Machine learning, ICCAIML 2024

作者： Arora, Chandani Sandhu, Ramandeep School of Computer Science and Engineering Lovely Professional University Bengaluru India

ISBN: (纸本)9783031714832

With the incorporation of Quantum Natural Language Processing (NLP) techniques into the critical field of patient-clinical trial matching, the healthcare sector is undergoing a profound transformation. Through the adaption of pre-trained language models for Quantum NLP, this research envisions a paradigm where patients and clinical trials synergize, altering the way healthcare is provided and medical research is carried out. The ground-breaking method of modifying pre-trained language models for Quantum NLP to improve the complex process of matching patients to clinical trials is at the core of this change. Quantum computing principles promise to improve the accuracy and speed of patient-trial alignment by bringing previously undiscovered computational efficiency and complexity handling capabilities. In order to overcome the gap between those looking for the best healthcare options and cutting-edge research projects, this study examines the potential of quantum NLP in patient-centric clinical trial matching. Quantum Embedding’s are expected to be added, strengthening the relationship between patients and trials and allowing for a deeper representation of patient profiles and trial characteristics. The distinctions of language and meaning are captured using quantum feature extraction techniques, allowing for a greater comprehension of trial needs and patient eligibility standards. This study aims to accelerate the patient-trial matching process by rapidly and precisely extracting crucial data from enormous datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Clinical research

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REVIEW OF learning MODELS FOR PRODUCTION BASED EDUCATION TRAINING IN TECHNICAL EDUCATION

REVIEW OF LEARNING MODELS FOR PRODUCTION BASED EDUCATION TRA...

引用

international conference on teaching and learning in computing and engineering (LaTiCE)

作者： Isa, Mohd Halimudin Mohd Masrom, Maslin Salim, KamilahRadin Univ Teknol Malaysia Johor Baharu Johor Malaysia

ISBN: (纸本)9781479935918

Lack of real problem or industrial exposure causes the students in technical education to face difficulties in understanding their lessons and also cause inadequate practical hands-on skill of the students. Since they are the future generation of the country and will become the employees in the industry, lacking in knowledge, practical hands-on skills and generic skills will affect the development of the country. A study in several countries has shown that institutions which implement industrial experience programs to simulate a real working environment produce students with real experience and have better skills and expertise. The aims of this study are to review the existing learning models for PBET, and to examine the components of PBET model in technical education. The identified components can be used to propose a strategic framework for promoting active learning which can enhance students' competency and professional skill.

关键词： Technical education Production Based Education Training (PBET) learning model hands-on skills

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Leveraging Social Media Data to Improve Disaster Response and Recovery Efforts Using Artificial Intelligence Techniques: A Comprehensive Review 3rd

Leveraging Social Media Data to Improve Disaster Response an...

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

作者： Pimpalkar, Shilpa P. Sai Madhavi, D. Rao Bahadur Y. Mahabaleswarappa Engineering College Ballari Visvesvaraya Technological University Karnataka Belagavi590018 India AISSMS Institute of Information Technology Pune India

ISBN: (纸本)9783031862984

The increased use of social media platforms, which provide user-generated data in real-time, has significantly impacted the disaster response and recovery field. The application of artificial intelligence (AI) techniques to parse multimodal datasets from social media has emerged as a practical and feasible strategy to enhance disaster management efficacy in response to this paradigm shift. This research provides a thorough overview of the most recent advancements in AI-driven social media data analysis methods to aid in disaster response and recovery operations. The study explains the wide range of available AI-driven solutions, covering state-of-the-art techniques such as computer vision, deep learning, machine learning, and natural language processing (NLP). This study presents a review based on 44 recent papers, 32% of which are survey papers, 64% on an implementation approach and 4% case study. Survey papers selected for the comprehensive review provide valuable insights covering 1715+ papers. The findings offer valuable input related to the dataset used in recent research, the proposed methodology, and future directions. The research makes a significant contribution to synthesizing the findings for future work in the proposed area. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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MPC-Pipe: an Efficient Pipeline Scheme for Semi-honest MPC Machine learning 24

MPC-Pipe: an Efficient Pipeline Scheme for Semi-honest MPC M...

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Proceedings of the 29th ACM international conference on Architectural Support for Programming Languages and Operating Systems, Volume 4

作者： Yongqin Wang Rachit Rajat Murali Annavaram Department of Electrical & Computer Engineering University of Southern California Los Angeles California United States of America

ISBN: (纸本)9798400703911

Multi-party computing (MPC) has been gaining popularity as a secure computing model over the past few years. However, prior works have demonstrated that MPC protocols still pay substantial performance penalties compared to plaintext, particularly when applied to ML algorithms. The overhead is due to added computation and communication costs. Prior studies, as well as our own analysis, found that most MPC protocols today sequentially perform communication and computation. The participating parties must compute on their shares first and then perform data communication to allow the distribution of new secret shares before proceeding to the next computation step. In this work, we show that serialization is unnecessary, particularly in the context of ML computations (both in Convolutional neural networks and in Transformer-based models). We demonstrate that it is possible to carefully orchestrate the computation and communication steps to *** propose MPC-Pipe, an efficient MPC system for both training and inference of ML workloads, which pipelines computations and communications in an MPC protocol during the online phase. MPC-Pipe proposes three pipeline schemes to optimize the online phase of ML in the semi-honest majority adversary setting. The three pipeline schemes are 1) inter-linear pipeline, 2) inner-layer pipeline, and 3) inter-batch pipeline. Inter-linear pipeline focuses on linear layers; inner-layer pipeline focuses on non-linear layers; inter-batch pipeline focuses on communication and computation overlaps in different input batches. We implement MPC-Pipe by augmenting a modified version of CrypTen, which separates online and offline phases. We evaluate the end-to-end system performance benefits of the online phase of MPC using deep neural networks (VGG16, ResNet50) and Transformers using different network settings. We show that MPC-Pipe can improve the throughput and latency of ML workloads.

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A Neural Network-Based Facial Expressions Detection Technique Using CK+ Dataset 2nd

A Neural Network-Based Facial Expressions Detection Techniqu...

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2nd international conference on Cyber Warfare, Security and Space computing, SpacSec 2024

作者： Jat, Subhash Chandra Naaz, Sadaf Chaudhary, Shikha Rajasthan College of Engineering for Women Jaipur India Manipal University Jaipur India

ISBN: (纸本)9783031734939

A person's facial expression is a great window into their emotional state. Around half of what we say to one another is conveyed nonverbally, while the other half is expressed vocally. One of the biggest challenges in computer science right now is learning to automatically recognise people's faces. There are several uses for facial expression recognition (FER), and they aren't restricted to gauging a person's mood or state of mind. Additionally, it is being used in a variety of industries, including criminal investigation, holographic as well as smart healthcare, security & education systems, robotics, and entertainment. Medical professionals are finding that facial expressions help patients with bipolar disease, whose emotions fluctuate often. In this study, a method for automatic face detection is developed by combining a pretrained VGG-16 convolutional neural network (FD-VGG-16 with CNN) with four convolutional layers and two hidden layers. The findings of this research employ a Cohn-Kanade dataset that includes extra pictures of male and female faces expressing different emotions, including happiness, anger, fear, disgust, contempt, neutrality, sadness, and surprise. Python is utilized as the implementation tool for this project, which uses datasets from the Kaggle repository. Preprocessing, feature extraction, as well as classification are all components of this study's FD-VGG-16 with CNN procedure. The accuracy of 98 percent of the suggested method is shown in both tabular and graphical forms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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