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Aspect-Adaptive Knowledge-based Opinion Summarization 20th

Aspect-Adaptive Knowledge-based Opinion Summarization

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20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Wang, Guan Li, Weihua Lai, Edmund M.-K. Bai, Quan Auckland University of Technology Auckland1010 New Zealand University of Tasmania HobartTAS7005 Australia

ISBN: (纸本)9789819600250

the increase in online information has overwhelmed users with opinions and comments on various products and services, making decision-making a daunting task. Text summarization can help by distilling long or multiple documents into concise, relevant content. Recent advances in Large Language Models (LLM) have shown great potential in this area. the existing text summarization approaches often lack the "adaptive" nature required to capture diverse aspects in opinion summarization, which is particularly detrimental to users with specific preferences. In this paper, we introduce an Aspect-adaptive Knowledge-based Opinion Summarization model for product reviews. this model generates summaries that highlight specific aspects of reviews, providing users with targeted, relevant information quickly. Our extensive experiments with real-world datasets explicitly demonstrate that our model surpasses current state-of-the-art methods. It effectively adapts to user needs, producing efficient, aspect-focused summaries that help users make informed decisions based on their unique preferences. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Self-supervised learning

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Dynamic Talking-Head Generation with Speech Emotion Recognition and Intensity Detection 13

Dynamic Talking-Head Generation with Speech Emotion Recognit...

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13th international conference on Electronics, Communications and Networks, CECNet 2023

作者： Fu, Chen Yu, Yang Zhang, Zhiqiang Weng, Wei Zhou, Jinjia Graduate School of Science and Engineering Hosei University Japan School of Computer Science and Technology Southwest University of Science and Technology China Institute of Liberal Arts and Science Kanazawa University Japan

ISBN: (纸本)9781643684802

In recent years, speech emotion recognition (SER) and emotive talking head generation have drawn significant attention. However, existing methods either focus predominantly on emotion recognition without considering its intensity or produce emotionally consistent talking heads, neglecting the rich expressiveness of human interactions. Addressing these limitations, our study aims to bridge these two areas, proposing an innovative talking-head generation approach that further enhances the realism of the generated video. Within the SER domain, we develop a dual-level model that not only improves the accuracy of identifying emotions in speech data but also assesses their intensity. this dual-capacity model significantly enhances the depth and subtlety of emotional recognition, achieving an industry-leading accuracy rate of 73.5%. On the talking head front, this work generates emotionally expressive talking heads that reflect both emotional state and intensity. Beyond this, the model also simulates human facial dynamics like blinking, thus offering a more comprehensive and realistic representation of human speech. these advances effectively address the shortcomings of existing methods and provide a new avenue for the accurate portrayal of emotional intensity in synthesized talking heads. Extensive experimental results demonstrate the effectiveness and superiority of our approach over existing methodologies in both SER and talking head generation. © 2024 the authors and IOS Press.

关键词： Emotion Recognition

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Lightweight Deep Learning Models for Robust Hand Gesture Recognition 5th

Lightweight Deep Learning Models for Robust Hand Gesture Rec...

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5th international conference on advances in Information Communication technology and Computing, AICTC 2024

作者： Nisha Sonu Narayan, Satya Gajrani, Jyoti Department of Computer Science and Engineering Engineering College Ajmer Rajasthan Ajmer India

ISBN: (纸本)9789819761050

Hand gesture recognition is a burgeoning area of study with wide-ranging applications, including human–computer interaction, sign language translation, and virtual reality control. In this work, we introduce an innovative lightweight DL model designed for robust HGR and provide an in-depth analysis of its classification performance. We meticulously evaluate key performance metrics such as precision, detection rate, Jaccard index, and false positive rate and compare these results with existing methodologies. Our proposed model stands out by surpassing the performance of current models. It exhibits higher accuracy rates during both training and validation, a substantially improved detection rate, and an almost perfect Jaccard index. Most impressively, our model achieves a remarkable false positive rate of zero, showcasing its exceptional reliability in distinguishing between positive and negative instances. these findings underscore the effectiveness of our approach in addressing challenges related to overlapping gestures, unbalanced class distributions, and precise HGR. In our conclusion, we identify areas for potential enhancement and further research. To clarify the novelty and research gap of our proposed model, we emphasise its lightweight nature and articulate how it differs from existing methods. this distinction lies in its capacity to deliver accurate and efficient HGR while imposing minimal computational burden, setting it apart from more resource-intensive approaches. In summary, our study and potential to revolutionise practical applications that demand dependable and effective hand gesture recognition systems, bridging existing gaps in the area. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Palmprint recognition

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Interconnected through Robotic System in English Foreign Language Education 5th

Interconnected Through Robotic System in English Foreign Lan...

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5th international conference on advances in Information Communication technology and Computing, AICTC 2024

作者： Muthmainnah, M. de Barros Cardoso, Luís Miguel Oliveira Kaur, Dalwinder Burhamzah, Muftihaturrahmah Aeni, Nur Al Yakin, Ahmad Sharma, Aditi Teacher Training and Education Faculty Indonesian Language Study Program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Portugal Department of Languages and Communication Sciences Polytechnic Institute of Portalegre Portalegre Portugal Manipal Globalnxt University Kuala Lumpur Malaysia English Study Program Universitas Negeri Makassar Makassar Indonesia Teacher Training and Education Faculty Civic Education Study Program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International [Deemed University Pune India

ISBN: (纸本)9789819761050

Robots have become increasingly important in a variety of life domains as a result of the rapid advancement of technology. Additionally, robots are employed in education as teaching assistants in science, art, and language courses. this research aims to explore the potential of robotic systems as a multipurpose tool for increasing cultural awareness, language proficiency, and communicative competence in the GenAI era. Language games, conversation simulations, and real-time feedback mechanisms are just a few examples of interactive scenarios enabled by virtual robot systems or tutor robots that enable immersive language experiences that go beyond the limitations of conventional schooling. this research contributes knowledge that explores how AI can be used to personalize language teaching according to the needs and learning styles of each student in higher education. the total population of undergraduate students involved in this research was 350 for the 2023 academic year, with 24 students selected purposefully based on sample characteristics from this group who were exposed to using AI for one semester. the research method used is a mix method with quantitative analysis of the results of surveys and interviews with students who are known to have given positive responses and proposes a comprehensive strategy for using AI robot technology in EFL classrooms, which can significantly improve effective learning outcomes. the survey results also show that if educators have a solid understanding of the relationship between linguistic acquisition and technological advances, they will be better prepared to design engaging, individualized lessons that prepare their students to use the language effectively in real-world contexts. the robot-assisted language learning (RALL) approach used in this study, with the integration of robots in the classroom, has the potential to change the way people learn languages to make it more interesting, fun, and motivating, increase engagement

关键词： Students

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Advancing Real-Time Land Cover Classification for Biomass Density and Carbon Stocks Estimation in Google Earth Engine 11

Advancing Real-Time Land Cover Classification for Biomass De...

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11th international conference on Geographical Information Systems theory, Applications and Management, GISTAM 2025

作者： Abudu, Dan Bastin, Lucy Chong, Katie Röder, Mirjam Energy and Bioproducts Research Institute Aston University BirminghamB4 7ET United Kingdom School of Computer Science and Digital Technologies Aston University BirminghamB4 7ET United Kingdom

ISBN: (纸本)9789897587412

Addressing climate change requires timely and accurate biomass and carbon stocks information. Traditional biomass estimation techniques rely on infrequent ground surveys and manual processing, limiting their scalability. this study proposes a novel framework that advances land cover classification to estimate biomass and carbon stocks using machine learning algorithms in Google Earth Engine. By integrating remote sensing data, machine learning algorithms, and allometric models, the framework automates above-ground biomass (ABG) and below-ground biomass (BGB) calculations, facilitating large-scale carbon stock assessments. the methodology leverages Landsat imagery, alongside derived Normalized Difference Vegetation Indices, to classify seven land cover types and estimate biomass. Equations are applied to derive AGB, with BGB calculated as a fraction of AGB. Carbon stock is estimated using a standard conversion factor of 0.47. Real-time processing capabilities of GEE ensure continuous monitoring and updates, enhancing accuracy and scalability. Findings demonstrate the potential for real-time biomass mapping and the identification of carbon-dense regions. the proposed approach is vital for sustainable land practices, carbon accounting, and forest conservation initiatives, to provide policymakers with accurate, real-time data, that supports climate mitigation efforts and contribute to realizing the Sustainable Development Goals 13 and 15. © 2025 by SCITEPRESS - science and technology Publications, Lda.

关键词： Global warming

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Cryptocurrency Price Prediction Using Deep Learning 9th

Cryptocurrency Price Prediction Using Deep Learning

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9th international conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： tharun, S.V. Saranya, G. Tamilvizhi, T. Surendran, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SaveethaInstitute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9783031440830

As technology advances daily, are advancing our lives into the digital sphere. the introduction of these cryptocurrencies aims to prevent the financial crisis. Due to its decentralized nature, high level of security, and restrictions on the number of coins that may be created, cryptocurrencies have attracted investors. Predicting the future price includes several limits and determinants because it involves capital. It varies according to market share. Using block chain technology and encryption, the transactions are encrypted from the beginning to end. Predicting prices to encourage consumers to invest during a specific period and earn a profit. they include a variety of elements, such as market analysis, sentiment analysis on Twitter, trading volume, and open and closing prices. Typical models that can be used to forecast bitcoin prices include regression techniques, neural networks, and support vector machines. Predictions of cryptocurrency prices based on their closing prices give investors additional insight into whether to wait until the closing period if prices are low for the entire day or to invest the following day. Using deep learning and bidirectional Long Short-Term Memory (LSTM) suggested this model to anticipate the price of digital currencies including Bitcoin, Litecoin, Ethereum, and Cardano. In this model, predictions are made using historical price statistics, and the graph is created by evaluating several performance indicators. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Long short-term memory

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Crime Analysis and Prediction in 7 States of India Using Statistical Software RStudio 1

引用

4th international conference on advances in Information Communication technology and Computing, AICTC 2022

作者： Sharma, Aditi Sharma, Chandani Sharma, Rahul Panchal, K.D. School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan Department of Computer Science and Engineering Quantum University Roorkee India Department of Computer Science and Engineering Parul Institute of Technology Parul University Vadodara India Department of Mechanical Engineering Dr. S. & S.S. Ghandhy Government Engineering College Surat India

ISBN: (数字)9789811998881

ISBN: (纸本)9789811998874

Crime analysis is a function of law enforcement. It comprises systematized analysis for understanding the movement of crime patterns. Crime is an increasing factor all over the world and the condition in India is the same. Common people feel insecure as they feel there is no safe place left in this world. It is also well known that crime increases with population and by looking at any year’s population count of India it can be said how crime rate goes a step forward from the previous year. In this paper, data for the top 3 crimes in India namely Rape, Dowry death and Domestic violence is collected and pattern is analyzed using Holt’s Linear Trend method (aka Double Exponential Smoothing). through this paper, the respective crime patterns and future values are forecasted. the statistical computations and graphical presentation have been done with the help of the statistical software RStudio. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Crime

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Performance Comparison of Different Machine Learning Classifiers for Diabetes Prediction 6th

Performance Comparison of Different Machine Learning Classif...

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6th international conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Ghosh, Dipayan Ganguly, Abhik Chakraborty, Rounak Singh, Pawan Kumar Li, Aimin Department of Information Technology Jadavpur University West Bengal Kolkata India Shinawatra University Pathum Thani Thailand School of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an China

ISBN: (纸本)9789819780891

this paper presents a thorough evaluation of machine learning algorithms for assessing the risk of having diabetes with the help of the Pima Indian Diabetes dataset. In view of the global diabetes epidemic, timely and precise risk assessment is imperative. Our study involves an in-depth exploration of the data, uncovering a robust correlation between glucose levels and the likelihood of diabetes. We deploy a diverse set of nine machine learning models, encompassing logistic regression, decision trees, random forests, AdaBoost, support vector machine, K-nearest neighbors, Naive Bayes, XGBoost, and an artificial neural network approach. the results illuminate the strengths and weaknesses of each model, providing valuable insights for potential clinical applications. the logistic regression approach, in particular, showcases its ability to capture intricate patterns within the dataset, underscoring its effectiveness in diabetes risk assessment. In addition to traditional classifiers, we introduce an ensemble model that combines the strengths of five best performing classifiers. these findings not only improve the accuracy of diabetes risk assessment but also establish a benchmark for future research in medical diagnosis. Identifying the most effective model assists healthcare practitioners in early intervention and tailored treatment strategies. this research advances the field of healthcare analytics, facilitating more informed decisions in diabetes prevention and management. the integration of a robust ensemble model approach broadens the scope of potential applications, marking a significant contribution to the field. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Literature Review on Human Behavioural Analysis Using Deep Learning Algorithm 5th

Literature Review on Human Behavioural Analysis Using Deep L...

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5th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2022

作者： Poorni, R. Madhavan, P. Department of Computer Science and Engineering Sri Venkateswara College of Engineering Pennalur Tamil Nadu Sriperumbudur Tk. India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India

ISBN: (纸本)9783031163630

Human behaviour analysis is the active area of research in computer science and engineering which determines the behaviour of humans using various algorithms. the input can be taken from the real time environment to analyze the human predictions. Deep learning plays a vital role as the input data involves a lot of computational images and spatial and temporal information upon which the predictions can be made. In this paper, we discuss the various techniques, concepts and algorithms that are implemented on a various field of image analysis and on real world input data to visualize the behaviour of a human. © 2022, IFIP international Federation for Information Processing.

关键词： Learning algorithms

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Optimization of Multi-Channel Quartz Crystal Microbalance for Gas Sensing Application 8

Optimization of Multi-Channel Quartz Crystal Microbalance fo...

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8th IEEE international conference on Recent advances and Innovations in Engineering, ICRAIE 2023

作者： Zainuddin, Ahmad Anwar Bhattacharjee, Soumik Md Ralib, Aliza Aini Mohd Sukri, Nurain Sufi Sabreena Binti Kamarudin, Saidatul Izyanie Subramaniam, Krishnan Handayani, Dini Reyasudin Basir Khan, M. Muhammad Saifudin, Nik Nor International Islamic University Malaysia Computer Science Kuliyyah of Information Communication Technology Kuala Lumpur Malaysia Universitat Bremen Digital Media Masters Germany International Islamic University Malaysia Department of Electrical and Computer Engineering Kuala Lumpur Malaysia Selangor Shah Alam Malaysia Mila University Department of Mechanical Engineering Nilai Negeri Sembilan Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798350315516

Mass detection sensor Quartz Crystal Microbalance (QCM) measures mass sensitivity by piezoelectric, high frequency shaving on nanograms these sensors detect with high precise geometry, low cost and ease of production. Conventional QCM sensors are limited to one electrode structure that minimizes the range of sensing. On a single quartz crystal substrate, three new QCM Multi-Channel sensors are developed and optimized that include 2-channel, 3-channel and 4-channel QCM sensors the COMSOL Multiphysics software is used to design and simulate the multi-channel sensors to enhance the device structure for applications with less interference frequency. From the results, it shows that the least interferences exhibit frequency values of 9.58MHz as both QCMs for the 5mm centre-to-centre (c2c). However, due to small c2c distances that decrease accuracy and operating disadvantages, this design was not selected. For 3 channel sensors, however, three resonant peaks show a similar value of 9.58MHz when c2c is maintained at 7mm. With the highest c2c distance this design shows less interference. Finally, for 4-channel sensors, a distance of 6 mm c2c also has a minimum interference with a 9.58 MHz resonance frequency. this shows that 4 channel sensors with the highest c2c can provide greater operational convenience and less interference at frequencies and thus maximize measurement accuracy. Multi-sensor arrays are advantageous when detecting a large number of analytes at the same time or when only selective coatings (coatings that respond to more than one analyte at the same time) are available these sensor arrays, which use only one sensor and combine it with enhanced signal evaluation methods or prior sample separation to achieve the results of multi-sensor arrays, are becoming increasingly popular. © 2023 IEEE.

关键词： Quartz crystal microbalances

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