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31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Sahu, Pratham Malakar, Preeti Department of Computer Science and Engineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9798331509118

Performance variability is common in HPC applications, due to factors such as job scheduling, resource contention, and network traffic. We propose a machine learning-based model to predict performance variability in high performance computing applications. Our model integrates classification and regression techniques to identify jobs with high variability and accurately predict their performance fluctuations. The model incorporates job-specific and system-wide factors such as network counters to enhance prediction accuracy. © 2024 IEEE

关键词： Prediction models

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Real-Time Sound Detection of Rose-Ringed Parakeet Using LSTM Network with MFCC and Mel Spectrogram

Real-Time Sound Detection of Rose-Ringed Parakeet Using LSTM...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Jose, Theresa Mayan, J. Albert Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350303452

Nowadays, machine learning, deep learning, and artificial intelligence are essential in identifying bird presence in rice fields and other agricultural applications. Automated identification techniques make it easier for farmers to monitor bird populations and identify potential threats to rice crops without human intervention. Using sound-based analysis, artificial intelligence (AI) - driven systems can distinguish between bird species that benefit the environment and those that might cause harm. This research presents a unique approach to realtime vocalization identification of Rose-Ringed Parakeets. A Long Short-Term Memory (LSTM) neural network is used in the proposed approach, and it is improved by Mel Frequency Cepstral Coefficients (MFCC) and Mel Spectrogram features. The audio data was processed to extract Mel Spectrogram and MFCC features, which were then utilized to capture the unique spectrum attributes shown by parakeet sounds. The LSTM framework for identifying Rose-ringed Parakeets from their auditory signals was developed and implemented using a Raspberry Pi 4 B single-board computer. Because of its adaptability, our method is ideal for real-world use in situations like the prompt detection and control of parakeet populations. Automating AI procedures reduces the possibility of human error and ensures a continuous and accurate monitoring system. Farmers and researchers can make data-driven decisions that enhance crop yields, preserve biodiversity, and promote sustainable agricultural practices with these technologies. © 2023 IEEE.

关键词： Long short-term memory

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An E-learning System Model to Enhance User Experience with Content Recommendation 10

An E-learning System Model to Enhance User Experience with C...

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10th International Multidisciplinary engineering Research Conference on Moratuwa engineering Research, MERCon 2024

作者： Udugahapattuwa, D.P.D. Fernando, M.S.D. University of Moratuwa Department of Computer Science & Engineering Moratuwa Sri Lanka

ISBN: (纸本)9798331529048

The rise of E-learning platforms has created a requirement to monitor and evaluate student performance while delivering interactive content, ultimately improving student learning. This research project focuses on studying the use of various data mining algorithms to extract user interactions from E-learning systems and identify patterns for recommending personalized content. The study will explore manipulating content through translations and formatting across different media to maintain high student interest. Additionally, it highlights the benefits of personalized learning, increased satisfaction, and early intervention in extracting student behavior. Moreover, it emphasizes best practices for formatting E-learning management system content, such as using headings, shorter paragraphs, images for illustration, and consistent style. Ultimately, this research concludes a model that helps to create an intelligent E-learning system that leverages data mining algorithms and machine learning techniques to generate personalized content recommendations based on user performance ratings to improve engagement and learning outcomes. In the initial testing of the model, it was given around 63.16% accuracy. After retraining the model, it was given a 78.90% accuracy in testing. Finally, the content will be arranged using the SCORM standard. © 2024 IEEE.

关键词： Contrastive Learning

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Analysis on Feature Selection and Classification of Cardiovascular Disease using Machine Learning 3

Analysis on Feature Selection and Classification of Cardiova...

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

作者： Kavya, B. Jayasri, B.S. Dayananda Sagar University Department of Computer Science and Engineering Bangalore India The National Institute of Engineering Department of Computer Science and Engineering Mysuru India

ISBN: (纸本)9798350317022

Cardiovascular disease has emerged as the leading cause of death in recent years, with a significant number of individuals unknowingly affected by this condition. Cardiovascular disease (CVD) disrupts the proper functioning of our heart and blood vessels, often resulting in death or physical disability. Thus, the timely and automated detection of CVD can potentially save numerous lives. Various studies have been conducted with the aim of accomplishing this goal, yet there remains scope for enhancing performance and dependability. This research represents another stride in pursuing this objective. The objective of this paper is to provide an overview of the progress made thus far and assess the future hurdles that need to be tackled, taking into account the obtained results. Additionally, it offers insights into the features and algorithms that should be considered for the early detection of cardiovascular disease (CVD). © 2023 IEEE.

关键词： Cardiovascular disease Early detection Machine Learning

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An Adaptive Event-Related Component Identification (AERCI)based Technique for SSVEP-BCIs 2

An Adaptive Event-Related Component Identification (AERCI)ba...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Darshan, H. Swetha, K.R. Department of Computer Science & Engineering BGSIT Adichunchanagiri University Mandya India

ISBN: (纸本)9798350383867

SSVEPs (Steady State Visual-Evoked-Potentials) captures brain responses to the visual stimuli from outside sources, detectable through scalp recordings. Brain-computer Interface (BCI) technique facilitates direct brain-to-device interaction, enabling users to engage with the external environment using brain signals. This study introduces Adaptive Event-Related Component Identification (AERCI), aimed at enhancing SSVEP reproducibility across trials and improving SSVEP data SNR by eliminating EEG background events and optimizing the feature extraction process. Notably, the feature extraction process is tailored to prioritize crucial data relevant for effective classification, with the primary objective of maximizing classification accuracy. This involves reducing the feature space dimensionality or eliminating redundant features. During classification, Power Spectral Density (PSD) is employed to forecast higher magnitudes for every stimulation frequency. © 2024 IEEE.

关键词： Electroencephalography

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ResumAI: Revolutionizing Automated Resume Analysis and Recommendation with Multi-Model Intelligence

ResumAI: Revolutionizing Automated Resume Analysis and Recom...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Jacob, Prisly Mary Jacob, Susan Cheriyan, Jo Nair, Lekshmi S Saintgits College of Engineering Department of Computer Science and Engineering Kerala India Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350308167

Finding and organizing suitable candidates for a vacant job position can pose challenges, especially when there is a high volume of submissions. This can impede team growth as it becomes challenging to identify the most suitable individual in a timely manner. To address this issue, an automated system called "Resume Recommendation and Classification"can expedite the selection process and aid in decision-making, while also reducing the time-consuming task of fair screening and shortlisting. This research utilizes Natural Language Processing to collect resumes and extract the necessary information. Subsequently, the resumes are ranked using BERT and TF-IDF word embeddings to obtain similarity scores. A classification model is then developed to categorize resumes based on job designations. To categorize the resumes, various techniques were employed, including Multinomial Naive Bayes, Linear Support Vector Classifiers, and k-NN Classifiers. These approaches enable the classification of resumes based on specific criteria. © 2023 IEEE.

关键词： BERT Classification NLP Recommendation Similarity Score Word Embedding

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The Intelligent Information Integrity Model to Ensure the Database Protection Using Blockchain in Cloud Networking 2

The Intelligent Information Integrity Model to Ensure the Da...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Raja Praveen, K.N. Rajat Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

In the era of cloud networking, database protection is a problem that is becoming more and more crucial, and blockchain technology is emerging as a potent remedy. Blockchain is a digital ledger technology that uses an immutable, distributed, and decentralized record of transactions to build a secure, transparent, and auditable data protection system. The integrity of data saved in the cloud is further ensured by using blockchain technology. By using cryptographic hashes, data stored on the blockchain can be verified for accuracy and authenticity. This means that any changes or modifications to the data will be detected and addressed. Furthermore, blockchain technology provides a permanent and immutable record of transactions, making it difficult for data to be tampered with or corrupted. In addition to its security benefits, blockchain technology also offers scalability, efficiency, and cost savings. Blockchain-based databases are much more efficient than traditional databases, as they require fewer resources and are able to process a larger volume of transactions. Furthermore, since blockchain technology is decentralized, it can work across multiple devices and networks, allowing for a more efficient and cost-effective solution. © 2023 IEEE.

关键词： Blockchain

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Person Image Generation To Enhance The Input Training Image For Re-Identification 14

Person Image Generation To Enhance The Input Training Image ...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Anjali, P.S. Rahamathulla, K. Swaraj, K.P. Government Engineering College Department of Computer Science and Engineering Thrissur India Government Engineering College Sreekrishnapuram Department of Computer Science and Engineering Palakkad India

ISBN: (纸本)9798350335095

The process of recognizing a person from photos or videos taken with a different camera is called re-identification (reID). In today's world, the widespread use of extensive networks of CCTV and security cameras makes it difficult for a human operator to trace a person. This is where re-identification technology comes into play. However, person re-identification in real-world applications is very challenging due to the size of the existing dataset, the lack of human motions, and the inability to supply enough training samples. In this paper, proposed a method for person image generation using Generative Adversarial Networks (GANs). GANs allow us to build additional person images that look as realistic as possible. © 2023 IEEE.

关键词： Generative adversarial networks

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Adaptive finite-time command-filtered backstepping sliding mode control for stabilization of a disturbed rotary-inverted-pendulum with experimental validation

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JVC/Journal of Vibration and Control 2023年第5-6期29卷 1431-1446页

作者： Mofid, Omid Alattas, Khalid A Mobayen, Saleh Vu, Mai The Bouteraa, Yassine Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia School of Intelligent Mechatronics Engineering Sejong University Seoul Korea Republic of Department of Computer Engineering College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia University of Sfax Sfax3038 Tunisia

In this paper, the finite-time stabilization of the disturbed and uncertain rotary-inverted-pendulum system is studied based on the adaptive backstepping sliding mode control procedure. For this purpose, first of all, the dynamical equation of the rotary-inverted-pendulum system is obtained in the state-space form in the existence of external disturbances and model uncertainties with unknown bound. Afterward, a novel command filter is defined to enhance the control strategy by consideration of a virtual control input. Therefore, the differential signal is replaced by the output of the command filter to reduce the complicated computing in the control process. Hence, the finite-time convergence of the sliding surface to the origin is attested by using the backstepping sliding mode control scheme according to the Lyapunov theory. Besides, the unknown upper bound of the exterior perturbation and uncertainty is approximated providing the adaptive control technique. Finally, simulations and experimental results are done to demonstrate the impression and proficiency of the suggested method. © The Author(s) 2022.

关键词： Inverted pendulum

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Quality-Driven Web Service Selection: Machine Learning Based Approach 11th

Quality-Driven Web Service Selection: Machine Learning Based...

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11th International Symposium on Applied Computing for Software and Smart Systems, ACSS 2024

作者： Kumar, Dewashish Sarkar, Anirban Department of Computer Science and Engineering National Institute of Technology Durgapur Durgapur India

ISBN: (纸本)9789819797615

The task of choosing a suitable web service has become increasingly difficult in today’s landscape due to the growing number of services offering similar functionalities. This paper addresses the critical need for efficient web service selection based on Quality of Service parameters. A minimal set of discrete QoS characteristics were carefully chosen as inputs for several machine learning models targeted at finding and suggesting suitable web services. The performance of these models is systematically evaluated and compared. The machine learning techniques used for this work include a Support Vector Machine, Decision Tree, and Random Forest. Among these, the results show that the proposed Random Forest model consistently outperforms other techniques in terms of accuracy and effectiveness. This study contributes to the advancement of web service selection methodologies by providing insights into the comparative performance of various machine learning approaches, ultimately offering a valuable guide for decision-makers in navigating the intricate landscape of web service options. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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