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application of fuzzy clustering based on improved whitening weight function in vocational education evaluation 23

Application of fuzzy clustering based on improved whitening ...

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proceedings of the 2023 4th international conference on computer Science and Management Technology

作者： Yijun Feng Wanning Zhu Lei Hong Youfeng Li Shaoyu Tang School of Software Engineering Jinling Institue of Technology China

ISBN: (纸本)9798400709517

In recent years, the importance of vocational education development has increased significantly. the key to improving vocational education lies in addressing the evaluation problem of vocational education schools. However, previous solutions to this problem suffered from issues like subjective evaluation methods and distorted data. this paper presents a vocational education evaluation method that combines gray clustering and fuzzy clustering, while also improving the form of the whitening weight function. the initial membership degree is calculated using the whitening weight function, followed by optimization using fuzzy C-means clustering. Experimental results demonstrate that this algorithm effectively combines the advantages of gray clustering and fuzzy C-means clustering, enabling more accurate clustering of vocational education schools in the evaluation process. this approach facilitates more objective evaluation and provides a quantitative basis for vocational education evaluation.

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QuACK: accelerating gradient-based quantum optimization with koopman operator learning 23

QuACK: accelerating gradient-based quantum optimization with...

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proceedings of the 37th international conference on Neural Information Processing Systems

作者： Di Luo Jiayu Shen Rumen Dangovski Marin Soljačić Center for Theoretical Physics Massachusetts Institute of Technology Cambridge MA and Department of Physics Harvard University Cambridge MA and The NSF AI Institute for Artificial Intelligence and Fundamental Interactions Department of Physics University of Illinois Urbana-Champaign Urbana IL and Illinois Quantum Information Science and Technology Center Illinois Center for Advanced Studies of the Universe Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA Department of Physics Massachusetts Institute of Technology Cambridge MA

Quantum optimization, a key application of quantum computing, has traditionally been stymied by the linearly increasing complexity of gradient calculations with an increasing number of parameters. this work bridges the gap between Koopman operator theory, which has found utility in applications because it allows for a linear representation of nonlinear dynamical systems, and natural gradient methods in quantum optimization, leading to a significant acceleration of gradient-based quantum optimization. We present Quantum-circuit Alternating Controlled Koopman learning (QuACK), a novel framework that leverages an alternating algorithm for efficient prediction of gradient dynamics on quantum computers. We demonstrate QuACK's remarkable ability to accelerate gradient-based optimization across a range of applications in quantum optimization and machine learning. In fact, our empirical studies, spanning quantum chemistry, quantum condensed matter, quantum machine learning, and noisy environments, have shown accelerations of more than 200x speedup in the overparameterized regime, 10x speedup in the smooth regime, and 3x speedup in the non-smooth regime. With QuACK, we offer a robust advancement that harnesses the advantage of gradient-based quantum optimization for practical benefits.

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application of integrated energy data processing based on real-time stream processing platform 23

Application of integrated energy data processing based on re...

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proceedings of the 2023 4th international conference on computer Science and Management Technology

作者： Xinyuan Jiao Zheng Hou Mimi Zhang Dechao Feng Zhencong Cai State Grid Information and Telecommunication Group Co. Ltd. China Beijing Energy Group Co. Ltd. China

ISBN: (纸本)9798400709517

Integrated energy is an important component of the new power system, and its IoT components need to collect and process energy equipment measurement data in real time. In order to solve the problems of large batches of equipment measurement data, measurement data requiring multiple calculations based on the relationship between equipment, and timely data processing requirements, distributed real-time data synchronization and processing technology is introduced to build a real-time stream processing platform, thereby ensuring stable operation of the system and efficient use of computing resources. First, the key technologies of the real-time stream processing platform are introduced, then the design of the integrated energy data collection and processing architecture is explained, and finally the real-time stream processing platform is built and functionally verified.

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WannaLaugh: A Configurable Ransomware Emulator - learning to Mimic Malicious Storage Traces 24

WannaLaugh: A Configurable Ransomware Emulator - Learning to...

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proceedings of the 17th ACM international Systems and Storage conference

作者： Dionysios Diamantopoulos Roman Pletka Slavisa Sarafijanovic A.L. Narasimha Reddy Haris Pozidis IBM Research Europe - Zürich Lab Säumerstrasse 4 Rüschlikon Switzerland Department of Electrical and Computer Engineering Texas A&M University College Station Texas USA

ISBN: (纸本)9798400711817

Ransomware, a fearsome and an evolving cybersecurity threat, continues to inflict severe consequences on individuals and organizations worldwide. Traditional detection methods, reliant on static signatures and application behavioral patterns, are challenged by the dynamic nature of these threats. this paper introduces two primary contributions to address this challenge. First, we introduce the WannaLaugh ransomware emulator. this tool is designed to safely mimic ransomware attacks without causing actual harm or spreading malware, making it a unique solution for studying ransomware behavior. Second, we show how this emulator can be used to mimic the I/O behavior of existing ransomware. Experimental results show that WannaLaugh can mimic six real ransomware with high accuracy. Both the emulator and its mimicking application aim to represent significant steps forward in ransomware detection in the era of machine-learning-driven cybersecurity.

关键词： Ransomware

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Deep learning Methods for Precise Sugarcane Disease Detection and Sustainable Crop Management 13th

Deep Learning Methods for Precise Sugarcane Disease Detectio...

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13th international Advanced Computing conference, IACC 2023

作者： Sharma, Davesh Kumar Punhani, Akash Department of Computer Science and Engineering SRM Institute of Science and Technology NCR Campus Modinagar Ghaziabad India

ISBN: (纸本)9783031567025

In the agricultural domain, sugarcane crops, like many others, are susceptible to diseases, posing a significant threat to both quality and quantity of production. Identifying and mitigating these diseases in their early stages are critical to averting financial losses for farmers. In response, researchers have turned to Artificial Intelligence (AI) techniques such as machine learning (ML) and Deep learning (DL) to analyze diverse agricultural data, including yield prediction, climate patterns, and soil quality, with disease prevention being a prime focus. this paper presents a thorough exploration of the effectiveness of a Deep learning-based Convolutional Neural Network (CNN) algorithm tailored for the detection of prevalent sugarcane diseases in India. Motivated by the rapid evolution of disease classes and farmers’ limited diagnostic skills, this study employs advanced deep learning and computer vision techniques. through image categorization into healthy and diseased groups, the trained model achieves an impressive 98.69% accuracy rate in sugarcane disease detection. Furthermore, to empower farmers, a web-based application is developed for ongoing disease monitoring. the paper suggests future research avenues, including user feedback integration and exploring the intersection of disease detection with agricultural productivity enhancement and price forecasting, thus enriching farmers’ decision-making processes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Crops

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An Advanced Deep learning Approach for Dietary Recommendations using ROBERTA

An Advanced Deep Learning Approach for Dietary Recommendatio...

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Intelligent Technologies (CONIT), international conference on

作者： Bajjuri Usha Rani Terli Joshnavalli Betha Srikanth Reddy Atluri Sreelaasya Department of Computer Science and Engineering Lakireddy Bali Reddy College of Engineering (Autonomous) Mylavaram India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

this project introduces a novel Food Recommendation System empowered by the Roberta model, a state-of-the-art transformer-based architecture in natural language processing. Leveraging Roberta’s advanced language understanding capabilities, our system aims to revolutionize the domain of dietary recommendations by providing personalized and context-aware suggestions to users. the Roberta model plays a pivotal role in capturing intricate textual nuances related to nutritional content, dietary preferences, and individual health profiles, thereby enhancing the accuracy and relevance of the recommendations. the methodology involves the integration of Roberta into the recommendation system, detailing the fine-tuning process and adaptation of the model to the unique challenges posed by dietary recommendation tasks. We explore the incorporation of relevant nutritional databases, ensuring that the Roberta model is well-versed in the domain-specific knowledge required for effective food suggestions. the system’s performance is evaluated through various metrics, showcasing its ability to outperform traditional rule-based and machine learning-based approaches in providing tailored dietary advice. Furthermore, this project presents insightful curves and analyses derived from the model’s training process, illustrating the learning trajectory, and highlighting key milestones in its proficiency. the experimental results demonstrate the system’s effectiveness in adapting to diverse user preferences and evolving dietary trends, establishing its potential to positively impact users’ health and well-being. Ultimately, our Food Recommendation System, driven by the Roberta model, signifies a significant advancement in the fusion of deep learning and nutritional science. the methodology curves presented herein offer a comprehensive understanding of the model’s learning dynamics, emphasizing its role in revolutionizing the landscape of personalized dietary recommendations. the promising res

关键词： Measurement Training Deep learning Adaptation models Accuracy Semantics Natural language processing

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Deep-learning algorithm for rib fracture detection in low-dose chest CT images 22

Deep-learning algorithm for rib fracture detection in low-do...

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proceedings of the 2022 9th international conference on Biomedical and Bioinformatics Engineering

作者： Hui Yu Yongzheng Ding Jinglai Sun Xuyao Yu Fengling Zhu Huanming Li Xiaoyun Liang Dagong Jia Key Laboratory of Biomedical Detecting Techniques and Instruments Tianjin University China Tianjin Medical University Cancer Institute & Hospital China Tianjin Hospital China Tianjin Joint Laboratory of intelligent medicine Tianjin 4TH Centre Hospital China School of Precision Instruments and Optoelectronics Engineering Tianjin University China

ISBN: (纸本)9781450397223

the detection of rib fractures is of great significance for the diagnosis and treatment of patients with blunt chest trauma. the while application of low-dose CT has become a trend. But there are few studies focusing on automatic fracture detection under low-dose CT. In this paper, a deep-learning model was proposed based on Mask R-CNN and its performance was evaluated by public dataset RibFrac containing 480 chest CT images with 3D rib fracture annotations. In addition, another dataset including 100 cases were collected from Tianjin Hospital for extensibility *** curve was used to compare the effects of different doses of CT images while the precision, recall, F1 score, IoU index and Dice coefficient were calculated. On the rib detection task,the detection rates achieved 93.5%, 90.8%, and 90.5% at normal dose, 10% mAs, and 5% mAs, respectively. On the external dataset,the recall rates of detection achieved of 95.7%, 93.9% and 92.7%.Our proposed network can effectively realize the task of detecting rib fractures in low-dose CT, and provide help for the application of deep learning for rib fracture detection in low-dose CT .

关键词： Object detection Low-dose CT Rib fracture Deep learning

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Prediction of COVID-19 Epidemiology with Chest computerized Tomography Images Using machine learning Techniques

Prediction of COVID-19 Epidemiology with Chest Computerized ...

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international Multidisciplinary Information Technology and Engineering conference (IMITEC)

作者： Khokhoni Innocentia Mpho Ramaphosa Tranos Zuva Temidayo Otunniyi Faculty of Applied and Computer Sciences Vaal University of Technology Vanderbijlpark South Africa

ISBN: (数字)9798350387988

ISBN: (纸本)9798350387995

the COVID-19 pandemic placed immense pressure healthcare systems, emphasizing the need for efficient prediction methods. this research proposes a deep learning-based solution using Convolutional Neural Networks (CNNs) to classify chest CT scan images as either COVID-19 positive or negative. A dataset of 400 normal and 399 COVID-19 CT scan images was used to evaluate CNN models, including VGG19, DenseNet201, Xception, InceptionV3, and VGG16, over 25 and 50 epochs. At 25 epochs, DenseNet201 achieved the highest accuracy of 97%, followed by VGG16 at 96%, InceptionV3 at 93%, and VGG19 and Xception at 91%. At 50 epochs, accuracy improved, with VGG16 and DenseNet201 both reaching 98%, and VGG19 at 97%. Xception and Inception V3also saw improvements, reaching 96%. VGG16 consistently achieved 100% for both Precision and Recall across all epochs, while DenseNet201 maintained 100% Precision at 25 epochs but dropped to 95% at 50 epochs. Xception showed significant improvement in Recall, rising from 85% to 97%. Enhanced preprocessing, including noise reduction and contrast adjustment, further boosted model performance. A COVID-19 Prediction Web application (CPWA) was developed using the fine-tuned VGG19 model to offer a practical tool for healthcare professionals. this study demonstrates the potential of AI-driven diagnostics to streamline COVID-19 screening and improve early diagnosis, providing scalable solutions for clinical settings.

关键词： COVID-19 Accuracy Pandemics Computed tomography Noise reduction Medical services machine learning Streaming media Predictive models Convolutional neural networks

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A New Feature Selection Method for Internet Traffic Classification Using ML

A New Feature Selection Method for Internet Traffic Classifi...

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2010 4th international conference on Intelligent Information Technology application (IITA2010)

作者： Liu Zhen School of computer science and engineering South China University of Technology Guangzhou, China Liu Qiong School of software South China University of Technology Guangzhou, China

If 248 statistical features are used to characterize network traffic flows, the computation cost of classifier will be overlarge. the feature selection methods referenced here improve the accuracy of majority classes and meanwhile decrease the accuracy in minority classes as the cost. As a result, it brings about the multi-class imbalance problem. In this paper, main contributions include two aspects below. 1) An evaluation criterion based on information theory was proposed to assess how much do one feature bias towards one class. 2) A new feature selection method named BFS was proposed to reduce features and alleviate multi-class imbalance. BFS was compared with fast correlation-based filter (FCBF) and full feature set using Nave Bayes and ten skewed datasets. the results show that 1) BFS is more advantage to maintain the balance of multi-class classification results than FCBF, such as the reduction of g-mean is just about 8% using BFS, 2) classification accuracy of Nave Bayes using BFS can achieve to 90%.

关键词： machine learning feature selection multiclass imbalance Internet traffic classification

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An Novel Based Mobile Dropping Sesing to Protect using Inertia Sensor and Integration of DL Technology

An Novel Based Mobile Dropping Sesing to Protect using Inert...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： S.R. Barkunan J. Radha Raju Egala Ritesh Kumar Singhal Mohammed Ayad Alkhafaji Ban Sefeer Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi India Department of CSE Nandha Engineering College Perundurai Tamilnadu Department of ECE Gayatri Vidya Parished College for Degree and Pg Courses(A) Visakhapatnam Andhra Pradesh Ajay Kumar Garg Institute of Management Ghaziabad College of Technical Engineering National University of Science and Technology Dhi Qar Iraq Al-Farahidi university Baghdad Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

With all such technologies, the recent advances in health monitoring due to smartphone sensor technologies provide an opportunity to quantify an individual’s functional performance and deviations from normality. Falling and drowning are two major unnatural reasons for accidental silent death, and it requires an ambient approach to detect the same. this paper presents a novel ambient assistive framework, FaDD (Falling and Drowning Detection), designed for identifying and distinguishing incidents of falls or drowning. Input data are taken through smartphone sensors like the accelerometer, gyroscope, magnetometer, and GPS, which capture the movement of a person’s body for effective recognition. By adopting a hierarchical approach, FaDD effectively detects dropping and choking actions using machine learning models. the framework is also integrated with the emergency alert notifications embedded in a smartphone application and, in the case of drowning, immediately alerts the respective stakeholders of the guardians, rescue team, and close community circle. With an accuracy rate of ${9 8 \%}$, FaDD is proficient in detecting falls, drowning, and normal activities. Coordination of all stakeholders through the FaDD framework is bound to enhance the provision of an effective and efficient health service to the people.

关键词： Accelerometers Accuracy Magnetometers Computational modeling machine learning Gyroscopes Stakeholders

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