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Special issue on 2020 international conference on machine learning and big data analytics for IoT security and privacy (SPIoT-2020)

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NEURAL computing & APPLICATIONS 2021年第9期33卷 3869-3870页

作者： Zhao, Jinghua Zhou, Qingyuan Univ Shanghai Sci & Technol Sch Business Shanghai 200093 Peoples R China Changzhou Vocat Inst Mechatron Technol Sch Econ & Management 26 Mingxinzhong Rd Changzhou 213164 Jiangsu Peoples R China

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Analyzing machine learning Frameworks for Detection of Monkeypox Virus Across the Globe 5

Analyzing Machine Learning Frameworks for Detection of Monke...

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5th international conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Sodhi, Navdeep Singh Nikita Kant, Vishnu Singla, Sofia Kaur, Upinder Bharany, Salil University Institute of Computing Chandigarh University Mohali India School of CSE Lovely Professional University Phagwara India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Lovely Professional University Department of CSE Punjab Phagwara144411 India

ISBN: (纸本)9798331518097

Recently, Monkeypox has spread to numerous nations, causing a life-threatening health concerns to both humans as well as animals. This sudden appearance of Monkeypox cases in different countries has shuttered the entire world. In this view, researchers are kept on proposing novel frameworks based on machine learning in order to ensure timely as well as efficient detection of Monkeypox virus across the world. This study has shown a comparative analysis of numerous existing frameworks based on machine learning in terms of four different metrics i.e. accuracy, precision, recall and Fl-score. Further, graphical analysis shows that researchers are kept on trying to provide best performance in the detection of Monkeypox across the globe. It is also found that some of the frameworks are only able to provide best performance with smaller datasets and without considering any other relevant attributes like security. In the current year, this research gap of utilizing smaller datasets and non-considering of other attributes like security of patients' data is seen to be decreasing gradually. © 2024 IEEE.

关键词： Adversarial machine learning

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Quantum machine learning in the latent space of high energy physics events 20

Quantum machine learning in the latent space of high energy ...

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20th international Workshop on Advanced computing and Analysis Techniques in Physics Research (ACAT)

作者： Wozniak, Kinga Anna Belis, Vasilis Pierini, Maurizio Vallecorsa, Sofia Dissertori, Gunther Barkoutsos, Panagiotis Tavernelli, Ivano CERN European Org Nucl Res CH-1211 Geneva Switzerland Univ Vienna A-1090 Vienna Austria ETH Zurich Switzerland IBM Res Zurich IBM Quantum CH-8803 Ruschlikon Switzerland

We investigate supervised and unsupervised quantum machine learning algorithms in the context of typical data analyses at the LHC. To accommodate the constraints on the problem size, dictated by limitations on the quantum hardware, we concatenate the quantum algorithms to the encoder of a classical convolutional autoencoder, used for dimensionality reduction. We present results for a quantum classifier and a quantum anomaly detection algorithm, comparing performance to corresponding classical algorithms.

关键词： High energy physics

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machine learning Based Integrated Audio and Text Modalities for Enhanced Emotional Analysis 5

Machine Learning Based Integrated Audio and Text Modalities ...

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5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Agarkhed, Jayashree Vishalakshmi Pda College of Engineering Computer Science and Engineering Kalaburagi Karnataka585102 India

ISBN: (纸本)9798350321425

Emotions play a crucial role in human communication, and the ability to automatically recognize them has broad applications in various domains, including human-computer interaction, affective computing, and virtual reality. Bimodal speech emotion recognition which aims to accurately recognize and interpret emotions expressed in speech signals. This article reviews the state-of-the-art techniques and methodologies employed in multimodal speech emotion recognition, highlighting the advancements in feature extraction, fusion strategies, and machine learning algorithms. Additionally, emerging trends and future directions in this field are discussed, focusing on the challenges and opportunities for improving the performance and robustness of multimodal speech emotion recognition systems. © 2023 IEEE.

关键词： Feature extraction

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Human Activity Recognition with Smartphones using machine learning Algorithm

Human Activity Recognition with Smartphones using Machine Le...

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2024 international conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Ghamya, K. Prema, K. Reddy, D. Abhinay Varaprasad, K. Sai, K. Navyasri Tejesh, S. School of Computing Tirupati India Sree Vidyanikethan Engineering College Department of Cse Tirupati India

ISBN: (纸本)9798350359688

Human Activity Recognition with Smartphones (HAR) leverages the ubiquitous nature of smartphones equipped with sensor technology to identify and classify daily human activities from the generated data. This paper explores the field of HAR, focusing on the use of smartphone sensors like magnetometers, gyroscopes, and accelerometers to capture movement patterns. This provides a comprehensive exploration of HAR, delving into methodologies such as feature extraction data acquisition, and the application of various machine learning algorithms for activity classification. The discussion encompasses challenges and limitations intrinsic to smartphone-based HAR, addressing issues like data variability and device heterogeneity. Promising research directions are highlighted, with an emphasis on context awareness, multi sensor fusion, and deep learning approaches, aiming to enhance the robustness and accuracy of activity recognition systems. The abstract concludes by emphasizing the potential applications of HAR in healthcare, where it could revolutionize patient monitoring, fitness monitoring, optimizing routines, and personalized user experiences, showeasing its role in shaping the future of health-related technologies. This model contributes valuable insights into the dynamic field of HAR, considering the synergy between smartphones, sensor technologies, and machine learning algorithms to propel innovative applications and personalized user experiences. © 2024 IEEE.

关键词： Gyroscopes

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Design of a Dining Coupon Usage Prediction and Distribution System Based on machine learning 24

Design of a Dining Coupon Usage Prediction and Distribution ...

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4th international conference on Artificial Intelligence, Automation and High Performance computing, AIAHPC 2024

作者： Qin, Jilin Aviles, Joey S. Graduate School Angeles University Foundation Angeles City2009 Philippines

ISBN: (纸本)9798400710049

The aim of this study is to design a system for predicting and distributing dining coupons based on machine learning methods for the dining population in urban areas of China. Firstly, by analyzing the consumption habits and behavioral data of diners, we have established a predictive model to forecast users’ dining intentions at specific times and locations. Secondly, combining users’ historical consumption records and personal preferences, we have designed a strategy for distributing dining coupons, and developed and tested the application system using the WeChat platform. Finally, through the validation and analysis of actual data, we have demonstrated the effectiveness and feasibility of this system. This study provides a new marketing method and management approach for the catering industry in urban areas of China, with practical significance and application value for improving the operational efficiency and user experience of the catering industry. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Chronic Kidney Disease Prediction using Optimized machine learning and Ensemble learning Techniques 9

Chronic Kidney Disease Prediction using Optimized Machine Le...

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9th international conference on Communication and Electronics Systems, ICCES 2024

作者： Devi, M. Uma Teja, Madaka Hima Shanmuk Sai Seshagiri Rao, K. Srm Institute of Science and Technology Department of computing technologies Kattankulathur India

ISBN: (纸本)9798350377972

Chronic Kidney Disease (CKD) is a progressive health condition caused by the gradual decline of kidney function over time. Such a disorder negatively impacts a wide range of individuals and can lead to significant health problems if not identified and treated early. Early detection and diagnosis of CKD are critical for timely intervention, preventing the disease from progressing to more advanced and difficult-to-treat stages, which can severely impair a patient's quality of life. This study focuses on analyzing patient data using machine learning (ML) methods to forecast chronic kidney disease based on a specific grouping of salient medical factors. The purpose of this study is to assess the efficiency and performance of various machine learning methods such as K-Nearest Neighbors (KNN), Random Forest (RF), Logistic Regression (LR), and Gradient Boosting (GB). This evaluation aims to identify the most ideal and reliable strategy for CKD prediction. During this process, challenges related to handling imbalanced datasets, selecting/extracting appropriate features, and improving model accuracy were addressed. These enhancements aim to improve the models' prediction performance and reliability, ensuring robust results even when dealing with challenging datasets. Notably, Cat Swarm Optimization (CSO) was used for feature selection, leveraging its unique seeking and tracing modes to identify an optimal subset of features. By incorporating CSO, the study refined the models' performance, achieving higher accuracy and reliability, thereby showcasing the potential of integrating machine learning with advanced optimization techniques for effective CKD prediction. © 2024 IEEE.

关键词： Adversarial machine learning

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machine learning for Ensuring Sustainable Development: Predicting Employee Attrition in the Workplace 3

Machine Learning for Ensuring Sustainable Development: Predi...

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3rd international conference on Advanced computing Technologies and Applications, ICACTA 2023

作者： Krishna, Shobhanam Shobhitanshu Borah, Debajyoti Ob and Hrm Iim Shillong Shillong India Hrm Gl Bajaj Institute of Management and Research Noida India Defence Institute of Advanced Technology Technology Management Pune India

ISBN: (纸本)9798350348347

The present research provides a machine learning technique that uses Logistic Regression to predict employee turnover rates while accounting for critical parameters. The purpose is to prevent employees from leaving because it has a negative impact on organizational productivity, costs, and employee well-being. By analyzing data on employee demographics, work satisfaction, and other relevant variables, logistic regression is used to identify factors that impact attrition. In addition, the research investigates the effects of projecting employee attrition on long-term development goals like decent employment and economic growth, gender equality, excellent health and wellbeing, and reduced inequities. Organizations can promote long-term economic success while contributing to a more sustainable future by fostering a safe and productive workplace. © 2023 IEEE.

关键词： machine learning

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Privacy Preservation in Federated learning: its Attacks and Defenses 3

Privacy Preservation in Federated Learning: its Attacks and ...

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3rd international conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Maurya, Jyoti Prakash, Shiva Madan Mohan Malaviya University of Technology Department of Information Technology and Computer Application Gorakhpur273010 India

ISBN: (纸本)9798350322842

In the context of artificial intelligence, the traditional centralized server-based machine learning model training approaches have significant security and privacy issues. The federated learning (FL) approach was created to address these problems. FL uses a privacy-by-design architecture, in which several devices work together to develop any machine learning system that does not reveal users' personal information under the control of a single server. In federated learning, privacy preservation is achieved through several techniques that involve adding noise, secure aggregation, local training, and model compression. These techniques aid in making sure that raw data is not transmitted over the network and that only the required information is shared to train a global model. This research study has discussed about the federated learning-related privacy threats and defenses. The existing research works are divided into two types as attack methods and corresponding defense mechanism and further analyzed the relevant research papers from the last few years, and then further discussed the research challenges, and finally concluded the research study. © 2023 IEEE.

关键词： machine learning

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A machine learning Based Approach to Predicting Flight Fares for Indian Airlines

A Machine Learning Based Approach to Predicting Flight Fares...

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2023 international conference on Innovations in Engineering and Technology, ICIET 2023

作者： Dixit, Vageesh Srinivasan, R. SRM Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350309980

Flight ticket prices fluctuate drastically depending upon a number of factors such as fuel prices, government regulations, routes taken, festival seasons, airline policies, etc. Flight aggregator websites fail to educate the consumers on an optimally effective timing for purchasing tickets without any data analyses backing them up. The proposed work helps to predict the flight ticket based on various factors using machine learning (ML) approaches. The proposed work analyses the Indian Domestic Airlines data on various machine learning models for computing expected future prices of Indian Airlines. The proposed model can be used by airlines to reduce human error in ticket pricing, manage price offerings to meet revenue targets, and increase profits. Experimental results helps to identify the best ML approach and results in higher statistical evaluation scores. © 2023 IEEE.

关键词： Forecasting

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