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Multi-Person Key Points Detection for Abnormal Human Behavior Analysis Using The ConvLSTM-AE Method

Multi-Person Key Points Detection for Abnormal Human Behavio...

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International Conference on computer engineering, Network and Intelligent Multimedia (CENIM)

作者： Sofia Ariyani Eko Mulyanto Yuniarno Mauridhi Hery Purnomo Dept. Of Electrical Engineering Dept. of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Faculty of engineering Institut Teknologi Sepuluh Nopember Universitas Muhammadiyah Jember Surabaya Indonesia Dept. Of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember University Center of Excellence on Artificial Intelligence for Healthcare and Society Surabaya Indonesia

ISBN: (纸本)9781665476515

The detection of abnormal human behavior is an interesting issue to consider in computer vision. The problem of detecting abnormal activities carried out in this study is a problem formulated in the process of monitoring human activities. In understanding the nature of human activities, a system is needed that is applied to data training which is specifically proposed based on ConvLSTM-AE for the detection of moving objects in motion-based events. Detection of anomalies and localization that arise due to camera jitter and movement of objects in the area. With the unusual motion of the video frame, connected component analysis is exploited to provide bounding boxing to find out whether human movements whose activities are based on human poses will be classified as normal or abnormal in a set of activity data. The learning and training process so that the proposed model can capture different duration of time and more optimal processes.

关键词： Training Location awareness computer vision Surveillance Multimedia systems Computational modeling Estimation

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A Method for Predicting Cardiovascular Disorder using Machine Learning Techniques

A Method for Predicting Cardiovascular Disorder using Machin...

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Recent Advances in Science and engineering technology (ICRASET), International Conference on

作者： Ramakrishna Hegde D R Pavithra S Shivashankara G Prasanna Kumar S M Soumyasri S Nagashree Computer Science and Engg. Vidyavardaka College of Engineering Affiliated to Visvesvaraya Technological University Belagavi India Electronics & Communication Engg. Sri Jayachamarajendra College of Engineering JSS Science and Technology University Mysuru India Computer Science and Engg. KR Pete Krishna Government Engineering College Krishnarajapet India Information Science & Engg. The National Institute of Engineering Mysore India Dept. of Computer Applications Vidya Vikas Inst. of Engg. and Tech Affiliated to Visvesvaraya Technological University Belagavi India Electronics and communication Engg Vidya Vikas Institute of Engineering and Technology Mysuru India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

Drastic increase of cardiovascular disease has led to a lot of adult’s death. As per “News 18” very year, 12 million young people in India die from heart disease. In most of these cases people experience cardiac arrest, some of which are normal. First, families of patients are vulnerable because it takes only a few minutes for a person to die of a heart attack, and it is difficult to get medical help in time. Second, most victims are under the age of 45. According to Dr. Maninder Sandhu, cardiologist and cardiologist at Artemis Hospital Gurgaon, 30 percent of people who suffer from heart disease are under the age of 45. This paper proposes a method that maybe used by laboratory technicians, doctors and users to understand whether there are any chances of having cardiovascular disease or not. The proposed system takes live data of patients and their lifestyle attributes these data are processed by the trained model to produce the output. In this model we haveused random forest classifier, linear regression, $\mathbf{k}$-neighbors classifier & x gradient boost classifier. This model has attained maximum accuracy and least type one error in “X- Gradient Boost”. We have concentrated more on type one error. The developed system shows whether the patient has any chances of cardiovascular disease or not.

关键词： Heart Accuracy Linear regression Cardiac arrest Predictive models Nearest neighbor methods Feature extraction Prediction algorithms Classification algorithms Random forests

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Advancing Fetal Surveillance with Physiological Sensing: Detecting Hypoxia in Fetal Sheep

Advancing Fetal Surveillance with Physiological Sensing: Det...

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IEEE SENSORS

作者： Weitao Tang Nhi Tran Nasim Katebi Reza Sameni Gari D. Clifford David Walker Vaishnavi Horlali Callum Taylor Robert Galinsky Faezeh Marzbanrad Electrical and Computer System Engineering Monash University Melbourne Australia The Ritchie Centre Hudson Institute of Medical Research Melbourne Australia Dept. of Biomedical Informatics Emory University Atlanta USA Dept. of Biomedical Engineering Georgia Institute of Technology Emory University Atlanta USA Department of Obstetrics and Gynaecology Monash University Melbourne Australia

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

Accurate detection of hypoxia during fetal monitoring is critical for timely intervention and prevention of brain injury. This study investigated hypoxia detection in fetal sheep by developing a classifier to differentiate between baseline, during acute hypoxia (umbilical cord occlusion; UCO), and post-hypoxia recovery (post-UCO), using physiological signals and an XGBoost machine learning model. A multi-modal approach integrating electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) signals was compared to a model based on the ECG signal only. We included recordings from 14 fetal sheep, each of which underwent acute hypoxia via a single UCO procedure. Performance of hypoxia detection was measured by Marco F1 score (F1 m ), Accuracy (Acc), and standard deviation of validation error (SDE). Our multi-modal model achieved F1 m = 0.88, Acc = 0.90, while the ECG-based approach achieved F1 m = 0.82, Acc = 0.85. These results indicate that the multi-modal approach was superior to the ECG-only model for overall classification. While ECG and multimodal models correctly classified over 92% and 97% of acute hypoxia intervals, respectively, the classification of post- UCO intervals proved more challenging. The complexity of interpreting physiological signals underscores the need to consider temporal dynamics and physiological context in fetal monitoring.

关键词： Accuracy Machine learning Electrocardiography Hypoxia Brain modeling Feature extraction Electromyography Electroencephalography Complexity theory Biomedical monitoring

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Exploring the Potential of DeepSeek-R1 Model in Transforming Healthcare Solutions: An Overview

Exploring the Potential of DeepSeek-R1 Model in Transforming...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Muhammad Raheel Raza Shahbaz Ahmed Fahad Ahmed Khokhar Asaf Varol Peter Faber Business School Australian Catholic University North Sydney Australia Department of Computer Science Universitat Koblenz Koblenz Germany Department of Informatics & Mathematics University of Florence Firenze FI Italy Department of Engineering Management & Technology College of Eng. & Comp. Science Uni. of Tennessee at Chattanooga Chattanooga TN US Dept. of Computer Engineering College of Eng. & Nat. Sciences Maltepe University Istanbul Turkiye

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

Over the past few decades, artificial intelligence (AI) has become more integrated into healthcare, with Large Language Models (LLMs) being a key component in improving healthcare decision-making. These LLMs' capacity to produce and interpret human-like texts has the potential to revolutionize healthcare procedures, where efficient data processing and communication are crucial. DeepSeek-Rl, created by DeepSeek AI, has drawn notice globally for its reasoning-based design that uses reinforcement learning (RL) to enhance problem-solving skills. Keeping this in view, the paper emphasizes the significance of the DeepSeek- Rl model and its application in the healthcare industry. Highlighting some hypothetical potential scenarios, we believe that DeepSeek models can playa crucial role in transforming existing medical approaches and revolutionizing the future of AI in health care.

关键词： Industries Biological system modeling Large language models Digital forensics Decision making Medical services Reinforcement learning Data processing Security Problem-solving

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A Machine Learning Approach to Classify Network Traffic 13

A Machine Learning Approach to Classify Network Traffic

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13th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2021

作者： Jadav, Nilesh Dutta, Nitul Sarma, Hiren Kumar Deva Pricop, Emil Tanwar, Supeep Marwadi University Dept. of Computer Engineering Rajkot India Sikkim Manipal Inst. of Technology Dept. of Information Technology Majitar India Petroleum-Gas University of Ploiesti Automatic Control Comp and Elect. Dept Ploiesti Romania Nirma University Dept. of Computer Science and Eng. Ahmedabad India

ISBN: (纸本)9781665425346

There is a significant increase of cloud and networking-enabled applications, leading to an exponential growth of network traffic. Monitoring all these applications and their generated traffic is a challenging and complex task, especially in regard to anonymous networks used to access the Dark Web or darknet. Manual investigation of the network traffic to determine patterns of malicious activity is a very difficult task and the results might not be satisfactory. The usage of machine learning is the most viable approach for the classification of the traffic as normal or malign activity. This paper analyzes CIC-Darknet 2020 dataset to classify the benign and darknet traffic. Before applying any classifiers to our dataset, we have balanced it using Synthetic Minority Oversampling Technique (SMOTE). We have applied PCA to reduce dimensionality, furthermore, ensemble techniques, logistic classifiers, tree-classifiers, and Naive Bayes have been compared and evaluated thoroughly with various evaluation metrics Accuracy, Precision, Recall, F1-Score, and Mathew's Correlation Coefficient (MCC). © 2021 IEEE.

关键词： Machine learning

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Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees

Enhanced Outsourced and Secure Inference for Tall Sparse Dec...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Andrew Quijano Spyros T. Halkidis Kevin Gallagher Kemal Akkaya Nikolaos Samaras Dept. of Computer Science and Engineering New York University New York NY USA Dept. of Applied Informatics University of Macedonia Thessaloniki Greece Nova Lincs Nova School of Science and Technology Lisbon Portugal Advanced Wireless and Security Lab Florida International University Miami FL USA

ISBN: (数字)9798350367942

ISBN: (纸本)9798350367959

A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user’s input from the classifier. On the other hand, with the rise of cloud computing, data owners are keen to reduce risk by outsourcing their model, but want security guarantees that third parties cannot steal their decision tree model. To address these issues, Joye and Salehi introduced a theoretical protocol that efficiently evaluates decision trees while maintaining privacy by leveraging their comparison protocol that is resistant to timing attacks. However, their approach was not only inefficient but also prone to side-channel attacks. Therefore, in this paper, we propose a new decision tree inference protocol in which the model is shared and evaluated among multiple entities. We partition our decision tree model by each level to be stored in a new entity we refer to as a "level-site." Utilizing this approach, we were able to gain improved average run time for classifier evaluation for a non-complete tree, while also having strong mitigations against side-channel attacks.

关键词： Resistance Hands Privacy Protocols Prevention and mitigation Computational modeling Side-channel attacks Data models Timing Decision trees

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Lossy Source Coding of Binary Memoryless Source by Trained LDGM Encoder

Lossy Source Coding of Binary Memoryless Source by Trained L...

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International Symposium on Information Theory and Its Applications, ISITA

作者： Kazuki Ogawa Hiromu Takata Motohiko Isaka Dept. of Informatics Graduate School of Science and Technology Kwansei Gakuin University Sanda Japan Dept. of Computer Science School of Engineering Kwansei Gakuin University Sanda Japan

ISBN: (数字)9784885523410

ISBN: (纸本)9781665499071

Use of low-density generator matrix codes as codebook and encoding with message-passing algorithm is an efficient approach for lossy coding of the i.i.d. binary source. We propose lossy source encoder which is trained on the associated unfolded Tanner graph for improving the rate-distortion performance and/or reducing computational cost.

关键词： Codes Source coding Rate-distortion Generators Computational efficiency

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Generating Automated Assistance Mechanism in Android Programming Self-learning System Using Automatic Testing Tools

Generating Automated Assistance Mechanism in Android Program...

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Innovation and Intelligence for informatics, Computing, and Technologies (3ICT), International Conference o

作者： Yan Watequlis Syaifudin Dewi Yanti Liliana Andi Baso Kaswar Triana Fatmawati Nobuo Funabiki Indrazno Siradjuddin Dept. of Information Technology State Polytechnic of Malang Malang Indonesia Dept. of Informatics Engineering State Polytechnic of Jakarta Jakarta Indonesia Dept. of Computer Engineering State University of Makassar Makassar Indonesia Dept. of Elec. and Comm. Engineering Okayama University Okayama Japan Dept. of Electrical Engineering State Polytechnic of Malang Malang Indonesia

Nowadays, computer-assisted learning (CAL) has experienced a significant increase in demand for self-learning purposes with the mechanism of automated assistance to facilitate assessments and provide instant feedback to learners. Using automatic testing, test cases can be executed repeatedly, providing informative feedback to assist students in correcting the written code referring to the assignment specifications. This research experiences an automated testing mechanism in an Android programming self-learning system to serve several important functions for learning experience enhancement. By adopting the TDD method and utilizing automatic software testing tools, the testing process combines unit testing with JUnit and integration testing with Robolectric. It ensures the quality and functionality of the application under test based on learning assignment’s specifications. Utilizing the assertion methods realizes the code verification referring to expected behavior and potentially produces informative feedback that can be captured to provide automated assistance. Evaluation through the test code implementation in the Basic Application learning topic shows that automatic testing tools cover the automated assistance feature in the validations of project configuration and UI components.

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A Secure and Trustworthy Biometric Data Ecosystem for Cross-border Suspect Identification

A Secure and Trustworthy Biometric Data Ecosystem for Cross-...

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IEEE International Conference on Big Data

作者： Katerina Kyriakou Apostolos Apostolaras Polychronis Velentzas Georgios Benos Konstantinos Koutsoukos Chrysostomos Symvoulidis Kaitai Liang Zeshun Shi Asterios Leonidis Kyriaki Miniadou Eleni Veroni Spyridon Evangelatos Georgios Th. Papadopoulos Thanasis Korakis Department of Electrical and Computer Engineering University of Thessaly Volos Greece CERTH The Centre for Research & Technology Hellas Volos Greece Research & Development Thridium Ltd. London United Kingdom Delft University of Technology Delft The Netherlands Institute of Computer Science (ICS) Foundation for Research and Technology - Hellas (FORTH) Crete Greece Research & Innovation Development Netcompany-Intrasoft S.A. Luxembourg Luxembourg Dept. of Electronic Engineering Hellenic Mediterranean University Crete Greece Department of Informatics and Telematics Harokopio University of Athens Athens Greece

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

This paper introduces the Biometrics Data Space framework, which is a secure ecosystem built on Data Spaces technology and it is designed to address the challenges of suspect identification during cross-border crime investigation. Apart from Data Spaces technology, the proposed framework innovates by leveraging also Privacy Enhancing Technologies (PETs) and blockchain to enable secure, trustworthy, and sovereign data exchange between Law Enforcement Agencies (LEAs) across borders. Specifically, it utilizes advanced PETs, including Large-Scale Biometric Data Indexing based on deep hashing techniques and Homomorphic Encryption to allow for suspect identification without disclosing sensitive information of personal biometric data. Thus, it enables LEAs to securely compare and exchange encrypted sensitive biometric data, including facial images, fingerprints and voiceprints, while maintaining data privacy and data sovereignty. LEAs define the usage rules for the biometic data they own and these rules are enforced to and respected by the other LEAs participating in the Biometrics Data Space. The proposed architecture is designed to be scalable, allowing the incorporation of additional biometric modalitiies and the easy expansion and integration with new participant LEAs.

关键词： Biometrics Law enforcement Space technology Terrorism Ecosystems Regulation Blockchains Resource management Spectrogram Indexing

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Machine Vision Approach Using Multi Features for Detection of Oil Palm Stem Disease

Machine Vision Approach Using Multi Features for Detection o...

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Advanced engineering and Technologies (ICONNIC), International Conference on

作者： Anindita Septiarini Hamdani Hamdani Encik Akhmad Syaifudin Emy Setyaningsih Damar Nurcahyono Noor Alam Hadiwijaya Dept. of Informatics Mulawarman University Samarinda Indonesia Faculty of Agriculture Mulawarman University Samarinda Indonesia Department of Computer System Engineering Institut Sains Teknologi AKPRIND Yogyakarta Indonesia Dept. of Information Technology Politeknik Negeri Samarinda Samarinda Indonesia

Indonesia is a significant palm oil producer, widely cultivated due to its status as a prominent vegetable oil-producing plant. Palm oil is a highly sought-after crop in agriculture due to its profitability and the demand for substantial quantities of high-quality oil. Early diagnosis of oil palm diseases is crucial for prompt prevention and eradication measures essential for maintaining high-quality palm oil production. Hence, there is a requirement for a machine vision-based method to classify diseases on plant stems. The proposed method consists of several main processes: pre-processing, feature extraction, and classification. Several machine learning algorithms and feature extraction using color and texture features were performed to develop a machine vision for detecting oil palm stem disease. It applied cross-validation with a K-fold value of 10. Performance evaluation was carried out using three parameters: precision, recall, and accuracy. The Linear Discriminant Analysis (LDA) method achieves the optimum results in testing scenarios combining color features based on HSV color space and textures features, achieving an accuracy value of 90.50%.

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