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检索条件"任意字段=International Conference on Advances in Machine Learning and Systems Engineering"

共 69114 条记录，以下是4961-4970 订阅

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Intelligent Adaptive systems and Methods Thereof for Household Energy Control and Management 3

Intelligent Adaptive Systems and Methods Thereof for Househo...

引用

3rd IEEE international conference on Electrical Power and Energy systems, ICEPES 2024

作者： Krishna, Shiva Ram Kumar, Rakesh Gaurav, Amit Singh, Nagendra Sharma, Mamta Almas, Shekh Kulsum Government Engineering College Department of Electrical Engineering Bihar Samastipur India Govt Engineering College Dept. Of Electrical Engineering Bihar Samastipur India Trinity College of Engineering & Technology Department of Electrical Engineering Karimnagar505001 India Raj Rishi Government College Department of Zoology Alwar India Sage University Department of ECE Indore India

ISBN: (纸本)9798350390728

Intelligent home energy consumption systems have been made possible by the growing need for sustainability and energy efficiency. This study describes a novel intelligent adaptive system and the related technique for effective distribution and management of home energy. The system uses sensor networks and cutting-edge technologies like IOT, AI, and machine learning to manage and distribute energy in a home in an adaptable manner. The suggested approach is to improve waste reduction, increase energy efficiency, and support a more ecologically conscious and sustainable way of living. © 2024 IEEE.

关键词： Smart homes

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Improving Intrusion Detection Using machine learning Algorithms with Feature Selection based on Information Gain 1

Improving Intrusion Detection Using Machine Learning Algorit...

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1st IEEE international conference on advances in Data-Driven Analytics and Intelligent systems, ADACIS 2023

作者： Mazighi, Abdellah Ballihi, Lahoucine Orhanou, Ghizlane Rabat Mohammed v University in Rabat Lrit Faculty of Sciences Rabat Morocco Rabat Mohammed v University in Rabat Lrit Faculty of Sciences Department of Computer Science Morocco Mohammed v University in Rabat LabMIA-SI Faculty of Sciences - Rabat Department of Computer Science Morocco

ISBN: (纸本)9798350344158

The expansion of the Internet and mobile communications has created a very large cyberspace. Thus our cyberspace has become very vulnerable to various cyber-attacks emanating from hackers and cybercriminals using increasingly sophisticated and prolonged attacks. Actually, Feature Selection and classification is applied in various domains like business, medical, cyber-security and media field. Huge datasets with a high number of attributes usually contain several insignificant features. In data analysis, those irrelevant features are time consuming, affect the efficiency and computational complexity and increase the amount of resource usage. They affect badly the machine learning performances. The main part of this article consists of many experiments we have conducted on a 20% of the CICIDS-2017 dataset. We have used four machine learning classifiers (Naive Bayes (NB), AdaBoost (AdB), Random Tree (RT) and Random Forest (RF)) and measured their performances before and after feature selection using Information Gain. We have divided the 77 attributes into four groups according to their weights and performed intrusion detection with different machine learning algorithms on each group of attributes. The results have showed that this method improves some metrics such as the global detection, execution time, the time required to learn the model. © 2023 IEEE.

关键词： Cybersecurity

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Pervasive AI and IoT in STEAM Education: Advancing Future learning Through Intelligent systems and Computational Technologies

Pervasive AI and IoT in STEAM Education: Advancing Future Le...

引用

2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Le, Nam Taherdoost, Hamed University Canada West Department of Arts Comminucations and Social Sciences Vancouver Canada University Canada West Gus Institute Global University Systems Vancouver Canada

ISBN: (纸本)9798331508685

As artificial intelligence (AI) continues to transform various sectors, its integration within education, particularly in STEAM (Science, Technology, engineering, Arts, and Mathematics) education, is gaining momentum. This paper explores how AI can enhance STEAM education for K-12 students, preparing them for a rapidly evolving digital world. A review of current literature demonstrates that AI technologies, including virtual reality, augmented reality, machine learning, and IoT, can revolutionize both teaching and learning by promoting critical thinking, creativity, and problem-solving skills. AI-driven tools like ChatGPT, learning analytics, and chatbots provide personalized learning experiences, making education more interactive and engaging. The paper also highlights AI's role in fostering inclusivity and bridging gender gaps within STEAM, particularly in arts education. Furthermore, the paper examines the challenges of AI integration, such as teacher readiness, trust in technology, and the need for professional development. The findings underscore AI's potential to empower educators and students alike, suggesting a roadmap for AI-enhanced STEAM education aligned with sustainable development goals. This research advocates for incorporating AI literacy in curricula to equip students with essential skills for the future. © 2025 IEEE.

关键词： Students

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A Review on Binary Code Analysis Datasets 18th

A Review on Binary Code Analysis Datasets

引用

18th international conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Huang, Zhijian Song, Shuguang Liu, Han Kuang, Hongyu Zhang, Jingjing Hu, Pengfei National Key Laboratory of Science and Technology on Information System Security Systems Engineering Institute Academy of Military Sciences Beijing China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (纸本)9783031714696

Binary code analysis serves as the foundation for research in vulnerability discovery, software protection, and malicious code analysis. However, analyzing binary files is challenging due to the lack of high-level semantic information, leading to heavy dependence on analysts’ expertise and significantly impacting the efficiency of binary code analysis. Recent years has witnessed the blossom of machine learning models for binary analysis, but few researches address the problem of binary code datasets. In this paper, we review all the existing and available datasets, and make classification according to their application. We set up experiments to illustrate how dataset quality could affect the performance of machine learning models in binary function recognition. Based on the experimental evaluation, we present a discussion on the ground truth as well as quality evaluation problems for binary code datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Predicting Customer Satisfaction in Service Processes Using Multilingual Large Language Models 57

Predicting Customer Satisfaction in Service Processes Using ...

引用

57th Hawaii international conference on System Sciences (HICSS)

作者： Liessmann, Annina Zilker, Sandra Weinzierl, Sven Sukhareva, Maria Matzner, Martin FAU Erlangen Nurnberg Erlangen Germany Siemens AG Munich Germany

ISBN: (纸本)9780998133171

The huge amount of data recorded during business process executions in today's organizations creates the need to leverage this data. While most existing business process monitoring methods are capable of including structured context information, the incorporation of unstructured information, for example, text, has rarely been researched. Recent advances in natural language processing offer tools to generate contextualized sentence embeddings, which capture more information in human language than ever before. Especially in service processes, such as the incident management process, a variety of unstructured information is created throughout the execution, representing a relevant use case for incorporating (multilingual) text. To close this research gap, a method exploiting multilingual text for predicting the outcome of a service process is presented. Multilingual large language models are used to generate sentence embeddings for unstructured text created during process execution. After instantiating the method, an evaluation was performed using a real-life event log to predict customer satisfaction in the incident management process at a German multinational corporation. We show that text incorporation improves predictive performance.

关键词： Predictive process monitoring natural language processing machine learning service analytics customer satisfaction

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Advancements in Automated Livestock Monitoring: A Concise Review of Deep learning-Based Cattle Activity Recognition 10

Advancements in Automated Livestock Monitoring: A Concise Re...

引用

10th international conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Deepak, D. D'Mello, Demian Antony Divakarla, Usha Canara Engineering College Department of Computer Science and Engineering Karnataka Bantwal India Nmam Institute of Technology Department of Information Science and Engineering Karnataka Nitte India

ISBN: (纸本)9798350384369

Cattle activity serves as a critical indicator of animal health and welfare, offering valuable insights into both physical and mental conditions. The livestock industry has embraced cutting-edge AI and computer vision techniques, particularly empowered by deep learning (DL) models, serving as decision-making tools. These models have successfully tackled various challenges, ranging from individual cattle identification, tracking movement, and body part recognition to cattle classification. The incorporation of cutting-edge machine learning and deep learning technologies has been instrumental in advancing precision livestock management. This includes functions like disease detection, tracking, and health monitoring. This paper conducts a review of DL based cattle recognition, focusing on the exploration and analysis of research concerning cattle activity recognition through Deep learning (DL). It provides a concise overview of DL-based approaches, organized based on a taxonomy. Drawing from our review, prominent DL models such as Convolutional Neural Network (CNN), CNN-LSTM, Faster R-CNN, and You Only Look Once (YOLO) emerge as widely adopted choices for cattle activity recognition. The insights gained from this review not only inspire the advancement of robust automated activity recognition but also contribute valuable guidance for the evolution of deep learning techniques in this domain. In conclusion, this review paves the way for future research focused on the development of effective automated systems for recognizing cattle activities © 2024 IEEE.

关键词： Cattle activity Cattle detection Cattle health Cattle identification CNN CNN-LSTM Faster R-CNN YOLO

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Early Detection of Anxiety, Depression and Stress among Potential Patients using machine learning and deep learning models 2

Early Detection of Anxiety, Depression and Stress among Pote...

引用

2nd international conference on Computational systems and Communication, ICCSC 2023

作者： Alphonsa Sini, P.J. Sherly, K.K. APJ Abdul Kalam Technological University Kerala India Rajagiri School of Engineering and Technology Kerala India

ISBN: (纸本)9781665493932

Depression, anxiety and stress are the three important mental health conditions. Only a small portion of the millions of people who experience depression, anxiety and stress each year receives a prompt treatment. The development of technology that helps clinicians detect various types of mental diseases has greatly benefited from the progress of machine learning (ML) techniques over the past few decades. This study presents a comparison of three important mental health issues (Depression, Anxiety, and stress) using machine learning and Deep learning (DL) algorithms. The proposed work also investigates about how the ML and DL techniques might help with the detection, diagnosis, and management of mental health diseases with a better effective and novel approach. As a result, three different algorithms (SVM, ANN and XgBoost) has been implemented in which Support vector machine (SVM) has achieved the better accuracy in detecting the mental health. The accuracy achieved in detecting depression, anxiety and stress are 99.32%, 99.80% and 98.44% respectively. Henceforth the proposed study was able to achieve a satisfactory result that helps to improve the decision-making and clinical practice. © 2023 IEEE.

关键词： Support vector machines

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Pesticide Recommender System for Detecting the Paddy Crop Diseases through SVM 3

Pesticide Recommender System for Detecting the Paddy Crop Di...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Jeyanathan, Josephine Selle Kumar, Manoranjan Reddy, P.Ravi Kumar Reddy, G.Chakradhar Reddy, D.Karthik Rabbani, Y.Shaik Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Virudhunagar India Karnataka Bengaluru560001 India

ISBN: (纸本)9798350361186

Rice cultivation is pivotal in India, facing various color-related issues during different growth stages. Identifying these conditions manually can be challenging, especially for less experienced farmers. Recent advancements in Deep learning (DL) demonstrate the effectiveness of automated image recognition systems. These systems utilize Convolutional Neural Networks (CNN), Transfer learning (TL), and Support Vector machine (SVM) models for similar agricultural problems. Due to the unavailability of readily accessible rice disease image datasets, a custom dataset was created. Consequently, a deep learning model was devised using transfer learning. The proposed CNN architecture, based on MobileNet, was trained and tested using datasets from paddy fields and the internet. © 2024 IEEE.

关键词： Support vector machines

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Predicting Cyberbullying on Social Media in the Big Data Era Using machine learning Algorithm 2

Predicting Cyberbullying on Social Media in the Big Data Era...

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2nd IEEE international conference on Data Science and Network Security, ICDSNS 2024

作者： Kalpana, Ponugoti Malleboina, Kavya Nikhitha, Musku Saikiran, Patanm Kumar, Satri Nithish Avn Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350373110

Before the approach of Information Communication Innovations (ICT), social intelligent essentially happened inside restricted social limits, such as neighborhood geological regions. The later headways in communication innovations have essentially outperformed the imperatives of time and space inborn in conventional communication. These innovative developments have started a insurgency in user-generated substance, online social systems, and the aggregation of broad information related to human behavior. In any case, the abuse of these social innovations, especially social media (SM) stages, has presented a unused measurement of hostility and savagery that shows only in the advanced domain. This work sheds light on a novel frame of forceful behavior illustrated on SM stages. It moreover talks about the inspirations behind creating prescient models to combat such behavior. We conduct a comprehensive survey of forecast models for cyberbullying and distinguish key challenges related with building these models on SM stages. This paper offers experiences into the generally handle of cyberbullying location, with a specific center on strategy. Whereas we expound on information collection and highlight designing forms, critical consideration is given to include choice calculations and the utilization of different machine learning calculations for anticipating cyberbullying behaviors. At last, we highlight issues and challenges that point towards unused investigate roads for researchers to investigate. © 2024 IEEE.

关键词： Adversarial machine learning

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A New Approach for Quantum Phase Estimation Based Algorithms for machine learning 8th

A New Approach for Quantum Phase Estimation Based Algorithm...

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8th international conference on Smart City Applications, SCA 2023

作者： Ouedrhiri, Oumayma Banouar, Oumayma El Hadaj, Salah Raghay, Said Department of Computer Science and Mathematics University of Quebec at Trois-Riviéres Trois-Riviéres Canada Faculty of Sciences and Techniques Laboratory of Computer Science Engineering and Systems Marrakesh Morocco

ISBN: (纸本)9783031543753

One of the greatest developments in computer science is undoubtedly quantum computing. It has demonstrated to give various benefits over the classical algorithms, particularly in the significant reduction of processing time, due to the parallelism and entanglement properties. One of the most crucial quantum computing algorithms is quantum phase estimation (QPE). It is called the eigenvalue finding module for unitary operators. It has helped to solve the order finding and the factoring problem, and to calculate the eigenvalues of unitary matrices and quantum sampling methods. In this paper, we study recent improved versions for the QPE procedure, their advantages and experimentation. We also propose a new approach for QPE based algorithms for machine learning (ML). These algorithms are the Harrow-Hassidim-Lloyd (HHL) algorithm for solving linear systems, the quantum singular value thresholding (QSVT) algorithm for matrix completion in recommender systems, and the quantum principal components analysis (QPCA) for data visualization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： machine learning

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