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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Bandodkar, Priyanka Fernandes, Aisha Goa College of Engineering Goa College of Engineering Dept. of Information Technology Goa India

ISBN: (纸本)9798331505745

Current human collapse detection methods are plagued by several issues. These methods often rely on manual monitoring or simplistic threshold-based sensor systems, which can be slow to respond and prone to human errors. Traditional systems frequently struggle with high false alarm rates, where non-critical events are mistaken for collapses. Conversely, they might miss actual collapses due to their limited detection capabilities. The proposed method aims to detect falls in video footage by using pose estimation techniques. The project utilizes the OpenPifPaf library to extract keypoints of a person and Shufflenetv2k30, which is a pre-trained model that can be used to extract relevant features from input images, essential for accurate human pose estimation. It focuses on calculating the velocity change at the neck keypoints to identify falls. The velocity threshold is fine-tuned for the current dataset. When a fall is detected, the system continuously monitors the person to determine whether they remain motionless. If the person remains still for a predefined period, an alarm is triggered. The alarm service can be customized, allowing the system to connect with different types of alert mechanisms. Here, a mobile computing system and application are utilized for the same. The method is evaluated with the Le2i public dataset. © 2025 IEEE.

关键词： Error correction

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WellBe: An Intelligent Elderly Care and Well-Being Monitoring System Using Deep Learning 4

WellBe: An Intelligent Elderly Care and Well-Being Monitorin...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Raotole, Atharv Shirodkar, Sailee Sanjay Shukla, Rahul Sisodia, Jignesh Devadkar, Kailas Patel Institute of Technology Dept. of Information Technology Sardar Mumbai India Patel Institute of Technology Dept. of Computer Science and Engineering Sardar Mumbai India

ISBN: (纸本)9798350349900

Deep Learning refers to a subset of Machine Learning that utilizes deep neural networks to simulate the complex decision-making process of the human brain. In recent years, DL has made remarkable progress in addressing multiple unique challenges faced by most elderly individuals. While there have been significant contributions to independent systems catered for the elderly, there is the existence of an overall integrated AI platform that unifies the use of individual modules for an optimized solution. This paper introduces 'WellBe,' an innovative AI system that encompasses an alarm-based fall detection component, a facial emotion recognition (FER) system and a built-in personalized chatbot all under a user-friendly website. The novelty of the work extends beyond the aforementioned components, owing to the implementation of an optimized FER system able to handle wrinkles and changes in skin texture, thereby tailored exclusively for the elderly demographic. The system leverages a custom feature extraction module, fusing facial and wrinkle features, in addition to extending a convolutional neural network (CNN) architecture for accurate emotion classification, achieving promising performance with precision, recall, and F1-scores of 0.87, 0.91, and 0.89 for the 'Happy' (one of the predicted emotions) class, respectively. The fall detection component, on the other hand, utilizes a pre-trained YOLOv7 Pose model for the real-time identification of falls, sitting at a performance accuracy of 95.3%. Finally, the personalized chatbot is implemented using a rule-based approach enhanced with the Jaccard similarity algorithm, to facilitate prompt assistance and a sense of companionship to the end user. Overall, the integration of these components into a cohesive website offers a comprehensive solution for enhancing the quality of life for elderly individuals living alone, addressing loneliness, safety, and overall well-being. © 2024 IEEE.

关键词： Convolutional neural networks

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Improving Fake News Detection with a Mixture of Experts: Leveraging Sentiment Analysis and Sarcasm Detection as Core Expert Models 6

Improving Fake News Detection with a Mixture of Experts: Lev...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Gunasekara, Tharindu Thihan, Disira Kodithuwakku, Sansika Dissanayake, Dinuka Fernando, Harinda Vidhanaarachchi, Samitha Sindhujan, Archchana Sri Lanka Institute of Information Technology Malabe Dept. of Software Engineering Sri Lanka Sri Lanka Institute of Information Technology Malabe Dept. of Computer Computer Systems Engineering Sri Lanka University of Surrey Dept. of Computer Science and Electronic Engineering United Kingdom

ISBN: (纸本)9798331517878

Fake news continues to proliferate, posing an increasing threat to public discourse. The paper proposes a framework of a Mixture of Experts, Sentiment Analysis, and Sarcasm Detection experts for improved fake news detection. This approach captures the emotional cues in the text through a Sentiment Analysis expert, which is based on bidirectional encoder representations from Transformers (BERT) models with sentiment vectors generated using SentiWordNet and Integrated Gradients. It combines a sarcasm detection expert based on BERT, recognizing sarcasm and its type to help classify fake news. By fusing these experts through a Mixture of Experts gateway, subtle linguistic cues often found in fake news are more effectively analyzed, leading to improved accuracy in detecting misinformation. Experimental results are presented as 96% for the Sarcasm expert with the BERT base model and 83% for the Sentiment Analysis expert with the distilled version of the BERT (DistilBERT) base model, proving the effectiveness of the proposed approach in beating traditional methods. © 2024 IEEE.

关键词： fake news detection gateway mixture of experts sarcasm detection sentiment analysis

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Leveraging Sentiment Analysis and Negation Phrase Identification for Mitigating information Overload in E-commerce 3

Leveraging Sentiment Analysis and Negation Phrase Identifica...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Ranganatha, K. Rao, Abhishek S. Shilpa, B. Nayak, Vasanth Shet, Nakul N Canara Engineering College Dept. of Information Science & Engineering Benjanapadavu India NMAM Institute of Technology Dept. of Information Science & Engineering Nitte India

ISBN: (纸本)9798350317022

In the era of e-commerce, online reviews have become crucial for both consumers and merchants. However, the overwhelming volume of reviews has resulted in information Overload (10). To address this, we employ Sentiment Analysis (SA) on user comments, utilizing machine learning to classify sentiments. Our study focuses on the challenge of accurately predicting sentiment orientation, particularly noting that adjectives and adverbs with negative prefixes can convey opposite sentiments. We introduce a negation phrase identification algorithm to address this issue. By combining sentiment analysis and negation phrase identification, our research aims to alleviate information overload, providing valuable insights for e-commerce stakeholders. Our model, utilizing the Bayes classifier, achieves an 89% accuracy, showcasing the effectiveness of machine learning in understanding customer sentiment. The research findings indicate that employing machine learning algorithms, particularly the Bayes algorithm, on textual data will allow businesses to gain valuable insights into customer sentiment, surpassing the limitations of manual efforts. The combined approach positions the sentiment analysis methodology as a forefront contender in natural language understanding, promising more reliable and contextually accurate assessments of sentiment from user-generated text data. © 2023 IEEE.

关键词： e-commerce natural language processing opinion mining product reviews sentiment analysis

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Developing a Dynamic Multi-factor Authentication System for Securing Smart Organizations 19

Developing a Dynamic Multi-factor Authentication System for ...

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19th Iranian Conference on Intelligent Systems, ICIS 2024

作者： Saffar, Mohsen Naji, Hamid Reza K. N. Toosi University of Technology Dept. of Industrial Engineering Tehran Iran Graduate University of Advanced Technology Dept. of Computer Engineering and Information Technology Kerman Iran

ISBN: (纸本)9798331507565

As smart organizations increasingly rely on technologies like AI, big data, and IoT, securing these systems is crucial. This research introduces a dynamic multi-factor authentication (MFA) system within the Zero Trust Architecture (ZTA) framework, which enforces continuous verification. The system randomly selects biometric authentication factors, such as fingerprint, iris, and face recognition using a Markov chain model to ensure adaptability and reduce predictability in security measures. Using CASIA biometric datasets and AES-128 encryption, the system securely manages user data and authentication. Performance analysis shows that the dynamic selection process balances security and efficiency, making it suitable for smart organizations, particularly in e-government contexts. This MFA system strengthens cybersecurity by providing a flexible, real-time defense against evolving threats. © 2024 IEEE.

关键词： Authentication Protocol

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Dynamic Optimization of Provider-Based Scheduling for HPC Workloads 31

Dynamic Optimization of Provider-Based Scheduling for HPC Wo...

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31st International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2023

作者： Marino, Jacopo Risso, Fulvio Bighi, Mauro Politecnico di Torino Dept. of Control and Computer Engineering Torino Italy PUNCH Torino S.p.A Dept. of Information Technology Torino Italy

ISBN: (纸本)9798350301076

The vast array of cloud providers present in today's market proffer a suite of High-Performance Computing (HPC) services. However, these offerings are characterized by significant variations in execution times and cost structures. Consequently, selecting the optimal cloud provider and configuring the features of the chosen computing instance (e.g. virtual machines) proves to be a challenging task for users intending to execute HPC workloads. This paper introduces a novel component designed for effortless integration with existing HPC scheduling systems. This module's primary function is to facilitate the selection of the most appropriate cloud provider for each distinct job, thereby empowering dynamic and adaptive cost-minimization strategies. Through the application of data augmentation techniques and the employment of Continuous Machine Learning, the system is endowed with the capability to operate efficiently with cloud providers that have not been previously utilized. Furthermore, it is capable of tracking the evolution of jobs over time. Our results show that this component can achieve consistent economic savings, based on the quality of the data used in the training phase. © 2023 University of Split, FESB.

关键词： Machine learning

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NSS-ML: a Novel spectrum sensing framework using machine learning for cognitive radio IoT networks

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International Journal of information technology (Singapore) 2024年第7期16卷 4599-4604页

作者： Marriwala, Nikhil Kumar Shukla, Vinod Kumar Shanbhog, Manjula Panda, Sunita Kaushik, Ruchi Rathore, Deepak Dept. of Electronics and Communication Engineering University Institute of Engineering and Technology Kurukshetra University Information Technology Head of Academics-School of Engineering Architecture Interior Design Amity University School of Sciences Christ (deemed to be university) Dept. of Electrical Electronics and Communication Engineering GITAM School of Technology Dept. of Electronics and Communication Engineering Guru Ghasidas Vishwavidyalaya

A key component of cognitive radio systems is spectrum sensing, which reduces coexistence problems and maximises spectrum efficiency. However, the introduction of multiple situations with distinct characteristics brought about by 5G communication presents problems for spectrum sensing to support a wide range of applications with high performance and flexible implementation. Inspired by these difficulties, a new method with a multi-layer extreme learning machine optimised for bats is presented in this study. This technique makes use of a variety of input vectors, such as channel ID, energy, distance, and received signal intensity, to enhance user categorization and sensing capabilities. Moreover, we compare the proposed method with the state-of-the-art spectrum sensing approaches in order to evaluate its effectiveness in 5G situations, especially in healthcare applications. Evaluation metrics including channel detection probability, sensitivity, and selectivity are carefully examined. The findings unequivocally prove the suggested spectrum sensing approach’s superiority over current methods and highlight its potential for smooth incorporation into a variety of 5G applications. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： 5G communications BAT algorithm Cognitive radio Spectrum sensing

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Research on Suspended Sediment Concentration Detection Based on the ISSA-RF Model

Research on Suspended Sediment Concentration Detection Based...

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2024 OES China Ocean Acoustics, COA 2024

作者： Ning, Gengxin Lei, Zihan Li, Yuan Xiao, Ruojun Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural Resources South China University of Technology Dept. of Electronic and Information Engineering Guangzhou China South China University of Technology Dept. of Electronic and Information Engineering Guangzhou China South China University of Technology Dept. of Civil Engineering and Transportation Guangzhou China

ISBN: (纸本)9798350340297

Traditional methods for detecting suspended sediment concentration, such as the weighing method, optical method, and remote sensing monitoring method have high equipment requirements and complex detection processes. To address these issues, a multi-frequency acoustic suspended sediment concentration detection device is constructed. Based on the characteristics of the received signals, the use of frequency domain features is proposed to improve the random forest algorithm for detecting the suspended sediment concentration, namely the Random Forest Based on Improved Sparrow Search Algorithm (ISSA-RF) algorithm. This algorithm enhances the traditional sparrow search algorithm by optimizing its initialization process to mitigate issues related to reduced population diversity and convergence to zero during the search process. Additionally, this improved algorithm is utilized to select the optimal parameters of the random forest, thereby reducing the time required for constructing the random forest model. We use the ISSA-RF algorithm to simulate the detection of suspended sediment concentration, and the results show that the algorithm effectively improves the efficiency of constructing the random forest model and the accuracy of detection. © 2024 IEEE.

关键词： Suspended sediments

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Cutting Tool Condition Monitoring Based on a Machine Learning Approach by Using Vibration Signals 1

Cutting Tool Condition Monitoring Based on a Machine Learnin...

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1st International Conference on Robotics, engineering, Science, and technology, RESTCON 2024

作者： Tiwari, Saurabh Gunasegaran, V. Amarnath, M. Chelladurai, H. India Institute of Information Technology Design and Manufacturing Dept. of Mechanical Engineering Jabalpur India

ISBN: (纸本)9798350348842

In metal cutting operations, tool wear could result in higher energy utilization, excessive tool vibration, and deteriorated surface finish quality. Therefore, tool wear monitoring is critical to enhance manufacturing productivity and quality. This study proposes a new approach for monitoring and classification of tool wear with 192 data sets of turning experimental tests at different cooling approaches. The Daubechies wavelet transform is used to state the level of flank wear at different cooling approaches. The acquired vibration signals were processed in both the time and frequency domain with various features extracted. An artificial neural network (ANN) model was applied to detect the flank wear at dry, flood, and minimum quantity lubrication(MQL) cooling states. The results confirmed the ability of the monitoring system to detect the flank wear of the cutting insert by utilizing the tool vibration signals. © 2024 IEEE.

关键词： Neural networks

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Federated Learning Architectures for Credit Risk Assessment: A Comparative Analysis of Vertical, Horizontal, and Transfer Learning Approaches 3

Federated Learning Architectures for Credit Risk Assessment:...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Pingulkar, Shriya Pawade, Dipti K.J. Somaiya College of Engineering Dept. of Information Technology Mumbai India

ISBN: (纸本)9798350354348

The rapid evolution of financial services, spurred by the ascent of FinTech, has significantly transformed how we assess credit risk. Traditional centralized approaches to credit risk modelling, now struggle with issues surrounding data privacy, security, and the ability to scale. Against this backdrop, federated learning (FL) has come to the fore as a revolutionary strategy, enabling collaborative model crafting while ensuring data remains decentralized and private. This paper delves deeply into a comparative assessment of three key federated learning architectures - vertical federated learning (VFL), horizontal federated learning (HFL), and federated transfer learning (FTL), in the context of credit risk assessment in FinTech. We assess the structural aspects of each federated learning architecture, discussing their strengths, limitations, and key performance indicators within the credit risk context. Furthermore, the paper identifies key research gaps, open problems, and future research directions, emphasizing the need for advanced privacy-preserving techniques, handling data heterogeneity, ensuring fairness and explainability, improving scalability and efficiency, integrating with emerging technologies, and developing appropriate governance and regulatory frameworks. The comparative analysis presented in this paper provides valuable insights for researchers, practitioners, and policymakers working at the intersection of federated learning and credit risk assessment in FinTech. By understanding the strengths and limitations of different federated learning architectures, stakeholders can make informed decisions when designing and implementing collaborative credit risk modelling solutions. Lastly, the paper proposes an enhanced Adaptive Hierarchical Hybrid Federated Learning (HHFL) architecture, tailored to address these challenges and advance the field of credit risk assessment through federated learning. © 2024 IEEE.

关键词： Federated learning

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