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6th IEEE International Conference on Emerging Smart Computing and informatics, ESCI 2024

作者： Rege, P.R. Kalnawat, Aarti Dhablia, Anishkumar Sharma, Rahul Kaldoke, Rupali Sachin Ashtagi, Rashmi Computer VIIT Maharashtra Pune India Pune India Altimetrik India Pvt Ltd Maharashtra Pune India Auricle Global Society of Education and Research Bikaner Rajasthan India Nutan Maharashtra Institute of Engineering and Technology Pune Maharashtra India Vishwakarma Institute of Technology Department of Computer Engineering Pune Maharashtra 411037 India

ISBN: (纸本)9798350306613

The proliferation of network-based services has resulted in an expanded threat landscape within the domain of cybersecurity. In the realm of network security, Intrusion Detection System (IDS) assume a critical function in the protection of networks by identifying instances of unauthorized access and malicious behaviors. This research study does a comprehensive investigation into the effectiveness of different machine learning algorithms within the domain of IDS with the purpose of enhancing network security. The scope of our study centers around four key machine learning algorithms, namely Support Vector Machine (SVM), Random Forest, Naive Bayes, and ensemble techniques incorporating stacking. By utilizing the NSL-KDD dataset, which depicts a practical and demanding network traffic situation, we assess the efficacy of these algorithms in terms of accuracy, a pivotal measure for the effectiveness of IDS. The experimental findings demonstrate that the machine learning models exhibit diverse levels of effectiveness in the task of intrusion detection. Support Vector Machines (SVM) have been shown to possess the ability to effectively define intricate decision boundaries and get a high level of accuracy in detecting network anomalies. The Random Forest algorithm demonstrates resilience and flexibility, effectively managing a wide range of patterns present in the dataset. The Naive Bayes algorithm, renowned for its simplicity and computational efficiency, has a commendable level of performance in comparison to other methods. In addition, we explore ensemble strategies, with a specific focus on stacking, in order to leverage the combined capabilities of these algorithms. The results of our study demonstrate that an ensemble of machine learning models, including Support Vector Machines (SVM), Random Forest, and Naive Bayes, when combined using stacking approaches, may achieve a remarkably high accuracy rate of 98.66%. The utilization of an ensemble technique showcases enhan

关键词： Class Imbalance Feature Selection Intrusion Detection Machine Learning Network Security Stacking

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Indoors Traversability Estimation with RGB-Laser Fusion 19

Indoors Traversability Estimation with RGB-Laser Fusion

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19th IEEE International Conference on Automation Science and engineering, CASE 2023

作者： Sevastopoulos, Christos Theofanidis, Michail Hebri, Aref Konstantopoulos, Stasinos Karkaletsis, Vangelis Makedon, Fillia University of Texas at Arlington Department of Computer Science & Computer Engineering ArlingtonTX United States University of Texas Arlington Research Institute Fort WorthTX United States Institute of Informatics and Telecommunications Ncsr 'Demokritos' Agia Paraskevi Attica Greece

ISBN: (纸本)9798350320695

We propose a dual-stream, semi-supervised, attention-based approach that employs feature fusion of RGB and Laser Range Finder (LRF) modalities. Our method lever-ages the strength of two powerful transformer-based networks, i.e. Vision Transformer (ViT) and SegFormer, along with LRF information, to adequately predict whether the scene encountered in the image is safe for a robot to traverse. Towards this effort, we introduce an automated labelling system profiting from the combination of raw velocity readings and laser scanning information. Moreover, we show that overall GOINO-GO detection is enhanced by fusing RGB and laser modalities. Feature fusion is achieved through the employment of a Multi-Head Self-Attention (MHSA) module. Through cross-domain validation, we show that the proposed traversability estimation method can achieve decent amounts of transferability even with limited amount of training data. © 2023 IEEE.

关键词： Range finders

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Age-of-Information and Energy Optimization in Digital Twin Edge Networks

Age-of-Information and Energy Optimization in Digital Twin E...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Guo, Yongna Fu, Yaru Zhang, Yan Quek, Tony Q. S. Kth Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden Hong Kong Metropolitan University School of Science and Technology Hong Kong University of Oslo Department of Informatics Oslo Norway Singapore University of Technology and Design Singapore

ISBN: (纸本)9798350351255

In this paper, we study the intricate realm of digital twin synchronization and deployment in multi-access edge computing (MEC) networks, with the aim of optimizing and balancing the two performance metrics Age of Information (AoI) and energy efficiency. We jointly consider the problems of edge association, power allocation, and digital twin deployment. However, the inherent randomness of the problem presents a significant challenge in identifying an optimal solution. To address this, we first analyze the feasibility conditions of the optimization problem. We then examine a specific scenario involving a static channel and propose a cyclic scheduling scheme. This enables us to derive the sum AoI in closed form. As a result, the joint optimization problem of edge association and power control is solved optimally by finding a minimum weight perfect matching. Moreover, we examine the one-shot optimization problem in the contexts of both frequent digital twin migrations and fixed digital twin deployments, and propose an efficient online algorithm to address the general optimization problem. This algorithm effectively reduces system costs by balancing frequent migrations and fixed deployments. Numerical results demonstrate the effectiveness of our proposed scheme in terms of low cost and high efficiency. © 2024 IEEE.

关键词： Costs

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Vessels Segmentation in Angiograms Using Convolutional Neural Network: A Deep Learning Based Approach

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computer Modeling in engineering & Sciences 2023年第7期136卷 241-255页

作者： Sanjiban Sekhar Roy Ching-Hsien Hsu Akash Samaran Ranjan Goyal Arindam Pande Valentina E.Balas School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Information Engineering Asia UniversityTaichung41354Taiwan Department of Medical Research China Medical University HospitalChina Medical UniversityTaichung406Taiwan Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology School of Mathematics and Big DataFoshan UniversityFoshan528000China Medica Superspecialty Hospital Kolkata700099India Automation and Applied Informatics Aurel Vlaicu University of AradArad310130Romania

Coronary arterydisease(CAD)has become a significant causeof heart attack,especially amongthose 40yearsoldor *** is a need to develop new technologies andmethods to deal with this *** researchers have proposed image processing-based solutions for CADdiagnosis,but achieving highly accurate results for angiogram segmentation is still a *** different types of angiograms are adopted for CAD *** paper proposes an approach for image segmentation using ConvolutionNeuralNetworks(CNN)for diagnosing coronary artery disease to achieve state-of-the-art *** have collected the 2D X-ray images from the hospital,and the proposed model has been applied to *** augmentation has been performed in this research as it’s the most significant task required to be initiated to increase the dataset’s ***,the images have been enhanced using noise removal techniques before being fed to the CNN model for segmentation to achieve high *** the output,different settings of the network architecture undoubtedly have achieved different accuracy,among which the highest accuracy of the model is 97.61%.Compared with the other models,these results have proven to be superior to this proposed method in achieving state-of-the-art results.

关键词： Angiogram convolution neural network coronary artery disease diagnosis of CAD image segmentation

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University department Recommendations Using Subject-Score-Based Skyline Queries

University Department Recommendations Using Subject-Score-Ba...

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2024 International Conference on Information technology and Computing, ICITCOM 2024

作者： Damarjati, Cahya Wicaksana, Galang Suripto, Slamet Wijayanto, Heri Setyawan, Haris Chen, Hsing-Chung Universitas Muhammadiyah Yogyakarta Department of Information Technology Yogyakarta Indonesia Universitas Muhammadiyah Yogyakarta Department of Electrical Engineering Yogyakarta Indonesia Universitas Mataram Department of Informatics Engineering Mataram Indonesia Asia University China Medical University Department of Computer Science and Information Engineering Department of Medical Research Taichung Taiwan

ISBN: (纸本)9798350379839

Selecting the right university department is a critical decision-making problem for high school graduates, significantly influenced by limited guidance and personal preferences. Traditional counseling methods often do not fully utilize available academic data, resulting in less effective recommendations. This study proposed a Subject-Score-Based Skyline Queries Algorithm to enhance decision-making by recommending university departments based on candidates' high school subject scores. It used the score differences and minimum score rule in addition to the native Skyline Queries. The Algorithm identifies departments where a candidate's grades meet or exceed minimum requirements across multiple academic subjects, thereby suggesting optimal matches. This approach aims to mitigate uncertainty and enhance decision confidence among prospective students, potentially reducing dropout rates and improving academic outcomes. Experimental results demonstrate the method's effectiveness in providing personalized recommendations, underscoring its applicability beyond university admissions to various decision-making contexts. Future research could extend this approach by incorporating non-academic factors for more comprehensive guidance. © 2024 IEEE.

关键词： Students

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Extracting Behavioral Characteristics of College Students Using Data Mining on Big Data

Extracting Behavioral Characteristics of College Students Us...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Meena, R. Kavitha, T. Kumar, S. Arun Mathew, Daya Mary Anusuya, R. Karthik, G. Department of Computer Science Engineering Prathyusha Engineering College Chennai India Department of Information Technology Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Electrical and Electronics Engineering Sona College of Technology Salem India Department of Computer Science Engineering Loyola Icam College of Engineering and Technology Chennai India Department of Computer Science and Engineering Modern Institute of Technology and Research Centre Rajasthan Alwar India Department of Information Technology Karpagam College of Engineering Coimbatore India

ISBN: (纸本)9798350334357

When it comes to quickly and accurately understanding behavior trends among undergraduates, teachers may find that sophisticated analytics become useful. There has been a shift in the management of student affairs from anecdotal, qualitative information to data-driven, quantitative analysis, thanks in large part to the emergence and advancement of big data techniques that enable educators to analyze the behavior patterns of learners in a timely and effective manner, particularly to find the groups of learners that need to be concentrated on in a timely fashion. Using the clustering technique of data mining, the authors of this research suggest a Behavioral Characteristics Extraction Model (BCEM) for studying student networking habits on campus. According to the findings, there are 400 pupils who make heavy use of the Internet and are spread over four distinct categories. There is an impact on these students' academic performance and outcomes. Data mining of student network behavior was conducted for this study, which may be utilized as a real-world example of data mining in student affairs management and so offers solid empirical backing for the advancement of that field. © 2023 IEEE.

关键词： Data mining

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Big Data Bot with a Special Reference to Bioinformatics

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computers, Materials & Continua 2023年第5期75卷 4155-4173页

作者： Ahmad M.Al-Omari Shefa M.Tawalbeh Yazan H.Akkam Mohammad Al-Tawalbeh Shima’a Younis Abdullah A.Mustafa Jonathan Arnold Biomedical Systems and Informatics Engineering Department Yarmouk UniversityIrbid21163Jordan Department of Medicinal Chemistry and Pharmacognosy Yarmouk UniversityIrbid21163Jordan Department of Electrical Computer and Software EngineeringUniversity of Ontario Institute of TechnologyOshawaL1H7K4Canada Department of Mechanical Engineering University of MosulMosul41001Iraq Genetics Department University of GeorgiaAthens30602GAUSA

There are quintillions of data on deoxyribonucleic acid(DNA)and protein in publicly accessible data banks,and that number is expanding at an exponential *** scientific fields,such as bioinformatics and drug discovery,rely on such data;nevertheless,gathering and extracting data from these resources is a tough *** data should go through several processes,including mining,data processing,analysis,and *** study proposes software that extracts data from big data repositories automatically and with the particular ability to repeat data extraction phases as many times as needed without human *** software simulates the extraction of data from web-based(point-and-click)resources or graphical user interfaces that cannot be accessed using command-line *** software was evaluated by creating a novel database of 34 parameters for 1360 physicochemical properties of antimicrobial peptides(AMP)sequences(46240 hits)from various MARVIN software panels,which can be later utilized to develop novel ***,for machine learning research,the program was validated by extracting 10,000 protein tertiary structures from the Protein Data *** a result,data collection from the web will become faster and less expensive,with no need for manual data *** software is critical as a first step to preparing large datasets for subsequent stages of analysis,such as those using machine and deep-learning applications.

关键词： Bioinformatics big data data extraction bot drug design

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What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding 41

What Improves the Generalization of Graph Transformers? A Th...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Hongkang Wang, Meng Ma, Tengfei Liu, Sijia Zhang, Zaixi Chen, Pin-Yu Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute TroyNY United States Department of Biomedical Informatics Stony Brook University Stony BrookNY United States Department of Computer Science and Engineering Michigan State University East LansingMI United States MIT-IBM Watson AI Lab IBM Research MA United States Department of Computer Science and Engineering University of Science and Technology of China Anhui Hefei China IBM Thomas J. Watson Research Center Yorktown HeightsNY United States

Graph Transformers, which incorporate self-attention and positional encoding, have recently emerged as a powerful architecture for various graph learning tasks. Despite their impressive performance, the complex non-convex interactions across layers and the recursive graph structure have made it challenging to establish a theoretical foundation for learning and generalization. This study introduces the first theoretical investigation of a shallow Graph Transformer for semi-supervised node classification, comprising a self-attention layer with relative positional encoding and a two-layer perceptron. Focusing on a graph data model with discriminative nodes that determine node labels and non-discriminative nodes that are class-irrelevant, we characterize the sample complexity required to achieve a desirable generalization error by training with stochastic gradient descent (SGD). This paper provides the quantitative characterization of the sample complexity and number of iterations for convergence dependent on the fraction of discriminative nodes, the dominant patterns, and the initial model errors. Furthermore, we demonstrate that self-attention and positional encoding enhance generalization by making the attention map sparse and promoting the core neighborhood during training, which explains the superior feature representation of Graph Transformers. Our theoretical results are supported by empirical experiments on synthetic and real-world benchmarks. Copyright 2024 by the author(s)

关键词： Stochastic systems

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VILMA: A ZERO-SHOT BENCHMARK FOR LINGUISTIC AND TEMPORAL GROUNDING IN VIDEO-LANGUAGE MODELS 12

VILMA: A ZERO-SHOT BENCHMARK FOR LINGUISTIC AND TEMPORAL GRO...

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12th International Conference on Learning Representations, ICLR 2024

作者： Kesen, Ilker Pedrotti, Andrea Dogan, Mustafa Cafagna, Michele Acikgoz, Emre Can Parcalabescu, Letitia Calixto, Iacer Frank, Anette Gatt, Albert Erdem, Aykut Erdem, Erkut Koç University KUIS AI Center Turkey Koç University Department of Computer Engineering Turkey University of Pisa Department of Computer Science Italy Institute of Information Science and Technologies Italian National Council of Research Italy Hacettepe University Department of Computer Engineering Turkey Aselsan Research Turkey University of Malta Institute of Linguistics and Language Technology Malta Heidelberg University Department of Computational Linguistics Germany Amsterdam UMC University of Amsterdam Department of Medical Informatics Netherlands Amsterdam Public Health Methodology & Mental Health Amsterdam Netherlands Utrecht University Department of Information and Computing Sciences Netherlands

With the ever-increasing popularity of pretrained Video-Language Models (VidLMs), there is a pressing need to develop robust evaluation methodologies that delve deeper into their visio-linguistic capabilities. To address this challenge, we present VILMA), a task-agnostic benchmark that places the assessment of fine-grained capabilities of these models on a firm footing. Task-based evaluations, while valuable, fail to capture the complexities and specific temporal aspects of moving images that VidLMs need to process. Through carefully curated counterfactuals, VILMA offers a controlled evaluation suite that sheds light on the true potential of these models, as well as their performance gaps compared to human-level understanding. VILMA also includes proficiency tests, which assess basic capabilities deemed essential to solving the main counterfactual tests. We show that current VidLMs' grounding abilities are no better than those of vision-language models which use static images. This is especially striking once the performance on proficiency tests is factored in. Our benchmark serves as a catalyst for future research on VidLMs, helping to highlight areas that still need to be explored. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Computational linguistics

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Detection of Bicep Form Using Myoware and Machine Learning

Detection of Bicep Form Using Myoware and Machine Learning

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International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022

作者： Khan, Mohammed Abdul Hafeez Rudraraju, Rohan V. Swarnalatha, R. Department of Computer Science Engineering Birla Institute of Technology and Science Pilani Dubai Campus Dubai International Academic City Dubai United Arab Emirates Department of Electronics and Communication Engineering Birla Institute of Technology and Science Pilani Dubai Campus Dubai International Academic City Dubai United Arab Emirates Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani Dubai Campus Dubai International Academic City Dubai United Arab Emirates

ISBN: (纸本)9789819932498

Many people have been exercising at home since the beginning of the COVID-19 period. Due to this, they haven’t had the supervision of a professional trainer who could correct them, rectify their mistakes, and prevent from harmful injuries. One very common workout that people frequently do is the bicep curl exercise, yet they fail to maintain the right posture without realizing it and end up straining their back muscles and pulling their shoulders too far forward. This is very dangerous as it could result in long-term back pain and tendonitis. Considering this issue, a methodology has been proposed for the people at home to continue their regular exercises without any professional gym trainer and gym environment. This research is oriented toward the correction of bicep form during the bicep curl exercises and preventing injuries. The acquisition fragment is designed with Myoware, an open-source electromyography sensor along with a three-axis accelerometer for capturing the essential segments required for the analysis. Naïve Bayes, logistic regression, K-nearest neighbor, decision tree, and random forest classification models have been used to perform the classification of the data acquired. The analysis of the novel dataset attained has been effective for determining the model with the best results to detect the bicep form. The random forest classifier has yielded the highest individual accuracy of 90.90%. Ultimately, an app prototype has been developed using the MIT app inventor platform. It has been integrated with a Bluetooth module and google firebase for showcasing and collecting data in real time from the sensory modules to the person working out and simultaneously storing them in the database for the deployment of this test set in the machine learning model. With this, the results of the accuracy and performance of a person are then acquired on the app for expressing the nature of their workout session. © 2023, The Author(s), under exclusive license to Springe

关键词： Decision trees

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