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Care Compass: Monitoring made easy, Care made effective 3

Care Compass: Monitoring made easy, Care made effective

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3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Manhas, Prabal Chandra, Joydeep Kaur, Ramanjot Sahay, Rashi Chandigarh University Punjab India Chandigarh University Dept.of Computer Science and Engineering Punjab Mohali India Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (纸本)9798331515911

Traditional healthcare focused on hospitals and clinics proved out to be inadequate, especially during the COVID-19 pandemic and various emergency crisis, Care Compass, is an innovative and affordable smart wearable device aimed at addressing critical health challenges faced by today's youth. The 21st century witnessed a large-scale transformation with the integration of Internet of Things and smart devices ranging from healthcare to manufacturing sector. The device integrates smart IoT devices and other technologies such as cloud computing platforms, health management mobile application with machine learning algorithms, offering real-time personalized health recommendations and continuous monitoring of vital health parameters. Emphasis is placed on accessibility and affordability, making Care Compass a viable solution for a broader demographic. The integration of Care Compass with clinical workflows is facilitated through collaboration with healthcare providers, enhancing the utility and effectiveness of the device in real-world applications. The results from initial testing demonstrate the potential of Care Compass to significantly improve health outcomes and quality of life for young individuals. © 2025 IEEE.

关键词： Electronic health record

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HJCFL: Hashcash and Jaya-Based Communication Efficient Federated learning 21st

HJCFL: Hashcash and Jaya-Based Communication Efficient Feder...

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21st international conference on Distributed Computing and intelligent Technology

作者： Mondal, Sudipto Jana, Prasanta K. Indian Sch Mines Indian Inst Technol Dept Comp Sci & Engn Dhanbad Bihar India

ISBN: (纸本)9783031814037;9783031814044

Federated learning (FL) has emerged as an efficient technique to train machine learning (ML) models across decentralized devices without sharing any raw data for preserving privacy. However, it faces challenges in communication overhead and resource constraints, particularly in real-time applications of Internet of Things (IoT). To address these challenges, we propose a novel method called Hashcash and Jaya-based communication efficient Federated learning (HJCFL). It consists of three main phases: local aggregator selection, clustering of IoT devices and communication efficient FL process. It uses Hashcash for strategically selecting local aggregators and Jaya based algorithm for clustering IoT devices. The usage of Hashcash function has an added advantage over existing aggregator selection techniques because of it's simplicity, proof-of-work and adjustable difficulty level. To the best of our knowledge, none of the existing techniques uses Hashcash for the same. Moreover, unlike most of the existing schemes, the proposed method utilizes all the available devices and therefore it converges faster with significantly less number of global rounds than others. Through extensive simulations, we demonstrate the effectiveness of the proposed HJCFL as compared to two baseline techniques. The result's validity is further confirmed using a widely recognized statistical technique, i.e., analysis of variance (ANOVA), followed by a least significant difference (LSD) post-hoc analysis.

关键词： Federated learning Internet of Things Hashcash function Clustering Jaya Algorithm

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Contrastive Prototype Network for Generative Zero-Shot learning 21st

Contrastive Prototype Network for Generative Zero-Shot Lear...

引用

21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Luo, Xinxin Yin, Wei Li, Zhuang School of Cyber Science and Engineering Southeast University Nanjing China School of Economics and Management School of Cyber Science and Engineering Southeast University Nanjing China School of Foreign Languages Southeast University Nanjing China

ISBN: (纸本)9789819601189

In generative zero-shot learning, the visual distribution mismatch between synthetic and real samples is a challenge due to the lack of effective constraints for unseen class visual features. To address this, we propose the Contrastive Prototype Network (CPNet). CPNet uses prototype learning to determine the feature vector center for each category (the prototype) and classifies based on the similarity between test data and prototypes. Concurrently, contrastive learning ensures that representations of the same category are more compact, while those of different categories are more dispersed. Experimental results on three standard datasets confirm the effectiveness of our approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Application of the S-PPED-C learning Model for Developing Embedded Teaching Innovations 27th

Application of the S-PPED-C Learning Model for Developing Em...

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27th international conference on Interactive Collaborative learning, ICL 2024

作者： Inchan, Surasak Akatimagool, Somsak Department of Teacher Training in Electrical Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9783031856488

Nowadays, modern industries and smart technologies are focused on the application of intelligent technology. However, most employees still lack high ability in embedded systems. To solve this problem, the engineering education system must upskill and adapt the basic skills of embedded technology as much as possible. This research aims to apply the S-PPED-C learning model to develop embedded teaching innovations. The S-PPED-C learning model based on blended learning management was designed with 6 steps: surveying, preparing, practicing, evaluating, discussing, and concluding. The innovative learning package created includes a teacher’ manual, learning activity plan, laboratory-based learning media for embedded systems, and achievement tests. The research tool was implemented with a sample of 16 undergraduate students in the Department of Information and Communication engineering, the Faculty of engineering and Industrial Technology at Phetchaburi Rajabhat University, Thailand. The results of the research found that;1) the developed S-PPED-C learning model was effective with the E1/E2 efficiency value of 83.13/83.33 according to the standard criteria of 80/80, 2) the evaluated results of the learning achievement of the students were 81.26% according to the standard criteria, and 3) the comparison of learning achievement of student after studying was significantly higher than before studying at the.05 level. It can be seen that the developed S-PPED-C learning model can promote learners with learning skills in the 21st century and is consistent with professional standards in the Industry 4.0 era. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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PATHFINDER: intelligent CAREER DEVELOPMENT WITH SKILL GAP ANALYSIS AND PERSONALIZED UPSKILLING PATHWAYS 21

PATHFINDER: INTELLIGENT CAREER DEVELOPMENT WITH SKILL GAP AN...

引用

21st international conferences on Mobile learning, ML 2025 and 10th international conference on Educational Technologies, ICEduTech 2025

作者： Adha, Kamarul Camilleri, Anthony F. Knowledge Innovation Centre Malta

ISBN: (纸本)9789898704665

This paper presents PathFinder, an intelligent career development system that leverages large language models (LLMs) to bridge the gap between individuals' current skills and their career aspirations. The system creates sentence embeddings from user resumes and career goals, then employs vector-based matching against the European Skills, Competences, and Occupations (ESCO) database to identify skill gaps. PathFinder then generates personalised learning pathways by recommending relevant courses from educational platforms like EdX. By combining natural language processing with structured occupational data, PathFinder transforms traditional career guidance into a precise, data-driven process that aligns personal development with specific career objectives. This approach offers a scalable solution for career advancement in an increasingly dynamic job market. PathFinder was developed as part of the LCAMP Project (European Commission [EC], 2024), funded by the European Union through the ERASMUS+ Programme. © 2025 Proceedings of the international conferences on Mobile learning 2025 and Educational Technologies 2025. All rights reserved.

关键词： Embeddings

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Portable Executable Analysis for Malware Detection for Reducing Risk 1st

Portable Executable Analysis for Malware Detection for Reduc...

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1st international conference on intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Kumar, Avinash Chaudhary, Meena Department of Computer Science Engineering Mangalayatan University UP Aligarh India

ISBN: (纸本)9783031823763

In the current era where data and information is the main fuel for business, education, banking, marketing and digital applications used by people. These applications consist of personal and sensitive data. These digital applications are using various technologies that makes it heterogeneous. This heterogeneity creates vulnerabilities in many cases and thus resulting in various threats like, virus, Trojan, spyware and other malicious programs. Malware is one of the deadliest threats in cyber security and it is very challenging to detect malware when entering a system because of its intelligent coding. The reverse engineering is used for detecting malware in classical way which is a time taking process and involves maximum human interventions. Therefore, there is also need of automated defensive mechanism to detect these malwares in order to reduce the duration of mitigation. The less time for accurate detection would result in stopping malware spreading in other part of the organization once it attacks the organization. Therefore, this paper focuses on the Machine learning concept as one of the major solution for malware detection. This paper also addresses the classical reverse engineering concept that is used to analysis malware. The paper uses, Random Forest, XGBoosting Decision Tree, Gradient Boosting Classifier and SGD Classifier for analysis. Further the Machine learning models that gives highest accuracy is XGBoosting that gives accuracy of 94.86% followed by random forest which 94.66%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Reverse engineering

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A stUDY ON THE RELATIONSHIP BETWEEN HANDHELD TECHNOLOGY AND learning EFFICIENCY OF MIDDLE SCHOOL stUDENTS 21

A STUDY ON THE RELATIONSHIP BETWEEN HANDHELD TECHNOLOGY AND ...

引用

21st international conferences on Mobile learning, ML 2025 and 10th international conference on Educational Technologies, ICEduTech 2025

作者： Lu, Xixi Chen, Dongling Wei, Jiachao Nanning Normal University College of Materials and Chemistry Major in chemistry No. 508 Xinning Road Wuming District Nanning City530100 China The College of Teacher Education Wuzhou University 82 Fumin Third Road Wanxiu District Wuzhou City543002 China School of Education Science Nanning Normal University No.3 Hexing Road Qingxiu District Nanning City530299 China

ISBN: (纸本)9789898704665

Digital technology is becoming an important traction to lead the transformation of education, and secondary education as a cradle of scientific and technological talents, and further promote the development of digital technology. Based on a number of case studies on the application of digital technology in teaching and learning, this paper makes a data visualization analysis through SPAA, this paper probes into the realistic predicament of the application of mobile technology in middle school teaching at present: (1) the low penetration rate of digital technology in middle school classroom;(2) the significant regional difference in the application of intelligent technology in classroom;(3) the students' attention to study is distracted;PCCI model is proposed to design the implementation path. © 2025 Proceedings of the international conferences on Mobile learning 2025 and Educational Technologies 2025. All rights reserved.

关键词： Digital Technology Project-Based learning Social Constructivist

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Sparse Context Transformer for Few-Shot Object Detection 21st

Sparse Context Transformer for Few-Shot Object Detection

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21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Jiu, Mingyuan Mei, Jie Sahbi, Hichem Jiang, Xiaoheng Xu, Mingliang School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Engineering Research Center of Intelligent Swarm Systems Ministry of Education Zhengzhou University Zhengzhou China National SuperComputing Center in Zhengzhou Zhengzhou China Sorbonne University CNRS LIP6 Paris75005 France

ISBN: (纸本)9789819601240

Few-shot detection is a major task in pattern recognition which seeks to localize objects using models trained with few labeled data. One of the mainstream few-shot methods is transfer learning which consists in pretraining a detection model in a source domain prior to its fine-tuning in a target domain. However, it is challenging for the fine-tuned models to effectively identify new classes in the target domain, particularly when the underlying labeled training data are scarce. In this paper, we devise a novel sparse context transformer (SCT) that effectively leverages object knowledge in the source domain, and automatically learns a sparse context from only few training images in the target domain. As a result, it combines different relevant clues in order to enhance the discrimination power of the learned detectors and reduce class confusion. We evaluated the proposed method on two challenging few-shot object detection benchmarks, and empirical results show that the proposed method obtains competitive performance compared to the related state-of-the-art. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Zero-shot learning

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Enhanced Knowledge Tracing via Frequency Integration and Order Sensitivity 21st

Enhanced Knowledge Tracing via Frequency Integration and Ord...

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21st Pacific Rim international conference on Artificial Intelligence

作者： Cai, Songtao Li, Li Southwest Univ Sch Software Coll Comp & Informat Sci Chongqing Peoples R China

ISBN: (纸本)9789819601158;9789819601165

Knowledge tracing (KT) is a pivotal component of online education systems, aiming to assess and predict students' knowledge states based on their learning history. Existing knowledge tracing models have achieved considerable success, yet they have neglected the frequency of interactions between students and knowledge points. Frequency information can aid in more stable modeling of students' knowledge states. In this study, we incorporate frequency data into our model's question embeddings, enabling it to consider the frequency of student-knowledge concept interactions. Moreover, the order within students' learning sequences is crucial, but knowledge tracing models based on the transformer architecture, with their attention mechanisms, are insensitive to the sequence order. We propose the N-transformer structure that combines transformer with order-sensitive RNNs, effectively enhancing the model's sensitivity to the order in students' learning sequences. Subsequently, we employ a simpler and more effective method for prior knowledge modeling, directly extracting prior knowledge related to the current exercise from the model's predicted knowledge state at the previous time step. Finally, we have designed a decay function for the attention mechanism based on the Ebbinghaus forgetting curve to simulate the students' forgetting behavior. Ultimately, we conducted experiments on four datasets, Our model has achieved up to a 3% increase in AUC compared to the baseline on some datasets.

关键词： Knowledge tracing Deep learning intelligent tutoring systems

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Does data Balancing Impact stutter Detection and Classification? 21st

Does Data Balancing Impact Stutter Detection and Classificat...

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21st international conference on Distributed Computing and intelligent Technology

作者： Batra, Ashita Shrivastava, Pratyush Das, Pradip K. Indian Inst Technol Guwahati Gauhati 781039 India

ISBN: (纸本)9783031814037;9783031814044

stuttering, a speech impediment that significantly impacts an individual's personality and daily life, is a critical research area requiring technological solutions. Therefore, bringing our attention, we analyzed speech utterances of people who stutter, using recordings from the publicly available Sep-28k dataset, which consists of natural conversations from podcasts. A major focus has been put on analyzing the fact that data imbalance plays a role in learning the characteristics of a speech utterance. We have used a sampling technique by considering the middle value of samples calculated from the minority and the majority class and this serves as a combination of up-sampling and down-sampling. This helps us to avoid bias in the class-wise characteristic learning of the model. The proposed approach focuses on two feature extraction techniques, specifically Mel-Frequency Cepstral Coefficients (MFCCs), del-del MFCC and a comprehensive analysis using the openSMILE toolkit, selecting only the ComParE2016 features. We conducted a comparative study between various machine learning models like random forest (RF), support vector machine (SVM) and time-delayed neural network (TDNN) using both features. Our findings indicated that the stutter class outperformed the no stutter class by a significant margin for each classifier in binary classification. This prompted us to conduct two experiments: one with all classes and another with only stutter classes. The experimental analysis revealed an average F1-score with del-del MFCC features of 85.38% with RF, 90.10% with SVM when all classes are considered. Furthermore, 93.29% with RF, 93.71% with SVM when only stutter classes are considered. On the contrary, when we use ComParE2016 features we achieve an average accuracy of 86.79% with RF and 87.77% with SVM considering all classes. Whereas, 95.10% with RF, 94.34% with SVM when only stutter classes are considered. When TDNN was used with del-del MFCC, we achieved an accuracy of 79.

关键词： stuttering Speech disorder feature extraction MFCC ComParE TDNN Sep-28k

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