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Advancing Heart Health with Multifaceted Deep Learning for ECG Signal Analysis

Advancing Heart Health with Multifaceted Deep Learning for E...

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7th international conference on Electronics technology, ICET 2024

作者： Ali, Sijjad Ali, Asad Hamza, Amir Jamil, Maryam Uzair, Muhammad Bari, Rana Zaki Abdul Hunza, M. Akram, Nabel Sharif, Hamid Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Shenzhen University School of Media and Communication China Shenzhen University College of Physics and Optoelectronics Engineering Shenzhen518060 China Shenzhen University School of Government Shenzhen518060 China

ISBN: (纸本)9798350363951

Cardiovascular diseases have the highest death rate in the whole world, which stresses the need for taking more preventive steps. this paper proposes a novel neural network model with multimodal machine learning techniques, able to detect cardiac arrhythmias rapidly and accurately. By using massive amounts of data, the model exhibits great performance, specifically in distinguishing normal and ventricular extra beats, which demonstrates its ability in feature extraction and classification. It is such a great achievement that helps to carry out disease surveillance and early diagnosis through an instant interpretation of complex ECG patterns. the study relies upon algorithmic modifications and real-time data processing, proposing a framework that combines wearable technology with AI diagnostics for reliable cardiac monitoring. the results contribute to the design of cardiac healthcare technology, which can be used to develop further advances in cardiac health monitoring and prevention of diseases. © 2024 IEEE.

关键词： Electrocardiography

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An Interpretable Deep Learning Model for Skin Lesion Classification 7th

An Interpretable Deep Learning Model for Skin Lesion Classif...

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Proceedings of the 7th international conference on advances in Computing and Data sciences, ICACDS 2023

作者： Jha, Avinash Ananthanarayana, V.S. Department of Information Technology National Institute of Technology Karnataka Surathkal Mangalore India

ISBN: (纸本)9783031379390

Skin Cancer is a dangerous issue in society. Early diagnosis and therapy are two of the most crucial steps in preventing the onset of a disease. Dermatologists primarily use visual methods to identify the skin lesions which may cause skin cancer. With the development of technology, methods for classifying skin lesions, like deep learning and computer vision, are gaining popularity. A hybrid model is proposed using a pre-trained DenseNet architecture integrated with Convolutional Block Attention Module (CBAM) for feature refinement. the HAM10000 dataset, which includes 10015 dermoscopic pictures with seven distinct skin disease types, was used in our research. the proposed approach outperforms the original pre-trained DenseNet models, with an average accuracy of 93%. the LIME framework is used to assess predictions further and produce visual explanations, which can assist in decreasing the drawback of black-box models in aiding medical decision-making. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diseases

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A Novel Method for Near-Duplicate Image Detection Using Global Features 1

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Proceedings of the 7th international conference on advances in Computing and Data sciences, ICACDS 2023

作者： Meena, Kunj Bihari Tyagi, Vipin Jaypee University of Engineering and Technology Guna – MP Raghogarh India

ISBN: (数字)9783031379406

ISBN: (纸本)9783031379390

the rapid growth of digital multimedia content has led to an increase in near-duplicate images. Near-duplicate image detection is a critical task in the field of multimedia forensics, which aims to detect and identify illegally distributed copies of an original image. Gaussian Hermite Moments (GHM) have been proven to be an effective global feature for image representation and analysis in various computer vision tasks, including image forgery detection. In this paper, we propose a novel near-duplicate image detection method that utilizes GHM as the global feature descriptor. We conducted experiments on the CoMoFoD image dataset. the experimental results demonstrate that the proposed method outperforms existing methods for near-duplicate image detection under various post-processing operations and geometric transformations, particularly scaling and rotation. Moreover, the proposed method is significantly faster than existing methods for near-duplicate image detection and it can potentially make the proposed method more practical for real-world applications. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Digital forensics

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Student Personality, Motivation and Sustainability of technology Enhanced Learning: A SEM-Based Approach 7th

Student Personality, Motivation and Sustainability of Techno...

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Proceedings of the 7th international conference on advances in Computing and Data sciences, ICACDS 2023

作者： Rohan, Rohani Mukherjee, Subhodeep Patra, Syamal Funilkul, Suree Pal, Debajyoti School of Information Technology King Mongkut’s University of Technology Thonburi Bangkok Thailand Andhra Pradesh Visakhapatnam India Department of Computer Science and Engineering Camellia Institute of Technology West Bengal Kolkata India

ISBN: (纸本)9783031379390

technology enhanced learning (TEL) has come into prominence and become more relevant after the onset of COVID-19 pandemic. However, it is not known that whether TEL will be socially sustainable, and what factors can affect its sustainability. therefore, in this work we propose a new research model based on UTAUT2 and the Big 5 Personality Framework that considers several motivational factors together with the different personality traits of the students. Data is collected from two Asian countries and analyzed using a Covariance-based SEM method. Results suggest that motivational factors of performance expectancy, hedonic motivation, social influence, price value and habit significantly affect the social sustainability of TEL. Likewise, the personality traits of agreeableness and neuroticism are also relevant. Suitable theoretical and practical implications are discussed based on the results. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Scanning electron microscopy

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Deep Learning Approaches for Cyber threat Detection and Mitigation 7

Deep Learning Approaches for Cyber Threat Detection and Miti...

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7th international conference on advances in Artificial Intelligence, ICAAI 2023

作者： Juyal, Aayush Bhushan, Bharat Hameed, Alaa Ali Jamil, Akhtar Department of Computer Science and Engineering School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Department of Computer Engineering Istinye University Istanbul Turkey Department of Computer Science National University of Computer and Engineering Sciences Islamabad Pakistan

ISBN: (纸本)9798400708985

Cyberspace has inflated over the past decade, primarily driven by pervasive development and widespread usage of the internet. Prolonged cyber-attacks and security vulnerabilities have become more common as a consequence. A recent study conducted by Ponemon Institute revealed that 64% of businesses have suffered web-based attacks in 2023. thus, cybersecurity measures have become mandatory in every sector, that leverages technology, to protect confidential information from malignant threats. the majority of the sectors use conventional security measures like anti-virus products to defend their systems against outside attacks, however, these products fail to work on unseen and polymorphic threats. With cutting-edge deep learning (DL) processes getting adopted in nearly every field, it has been widely exploited in cybersecurity to detect and classify cyber-attacks. this study discusses the application of common deep learning models in cybersecurity to detect malware, intrusion, phishes, and spam. this study provides a description of deep learning models and their mathematical backgrounds that are common in cybersecurity. this study also reviews and analyses the work of other researchers who experimented with deep learning models in cybersecurity. this study delineates the challenges faced by deep learning models in cybersecurity. Finally, concluding remarks and future research directions are summarized to foresee the potential improvement of deep learning in cybersecurity. © 2023 ACM.

关键词： Cybersecurity

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Leveraging Blockchain for Development of Time Limit-Based Multi-authorized Secure Digital Signature Framework 5th

Leveraging Blockchain for Development of Time Limit-Based Mu...

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5th international conference on advances in Information Communication technology and Computing, AICTC 2024

作者： Mallikharjuna Rao, K. Kumar, Ashutosh Agrawal, Deepesh Reyya, Shikhar Data Science and Artificial Intelligence International Institute of Information Technology Naya Raipur Naya Raipur India Computer Science and Engineering International Institute of Information Technology Naya Raipur Naya Raipur India

ISBN: (纸本)9789819761050

this article deeply dives into a shift in document security by introducing innovative authentication measures. Conventional digital signature methods are less secure and can easily be forged. Our research explores the integration of multiple signatures authentication and time-constrained signature features, which will boost the security of digital signatures across documents;by introducing a time-constrained signing approach, we are addressing potential issues and establishing robust defense against various threats. Also, multiple files can be uploaded using a single CID in interplanetary file system (IPFS). Besides introducing multiple signers and time-limit features, our research utilizes IPFS for secure and collaborative document management. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication Protocol

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GENERATIONAL PERSPECTIVE ON ENERGY-EFFICIENCY AWARENESS: A COMPARATIVE STUDY 18

GENERATIONAL PERSPECTIVE ON ENERGY-EFFICIENCY AWARENESS: A C...

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18th international conference on e-Learning and Digital Learning, ELDL 2024 and 12th international conference on Sustainability, technology and Education, STE 2024, Part of the 18th Multi conference on computer science and Information Systems 2024, MCCSIS 2024

作者： Pernice, Gabriella Francesca Amalia Orso, Valeria Gamberini, Luciano Departments of General Psychology University of Padova Padova Italy Human Inspired Technology Research Center University of Padova Padova Italy

ISBN: (纸本)9789898704573

Recent advances in sustainable practices in all sectors, particularly energy consumption, are critical in addressing global challenges such as climate change and resource depletion. this paper explores the crucial role of engaging the youngest generations, who are poised to become the main market influencers and consumers. Despite its potential impact, existing literature reveals a gap in integrating the perspectives and contributions of these generations to sustainable energy practices. this oversight offers a significant opportunity for further investigation and integration of their preferences and behaviours into broader sustainability frameworks. the present study collected the responses of a survey of 128 participants of different generations in Italy on their attitude and trust in energy consumption and high-energy-efficient household appliances. the results present a substantial challenge for policy makers and businesses aiming to align their strategies with the values and expectations of this key demographic. the study calls for a more nuanced understanding of these generational differences to develop effective and sustainable energy strategies that resonate with the values of the youngest demographics, aligning these efforts with Sustainable Development Goal 7 (SDG 7) to ensure access to affordable, reliable, sustainable, and modern energy for all. © 2024 e-Learning and Digital Learning. All Rights Reserved.

关键词： Clean energy

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Supervised Learning of Procedures from Tutorial Videos 7th

Supervised Learning of Procedures from Tutorial Videos

引用

7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Arunima, S. Sengupta, Amlan Krishnan, Aparna Venkata Vara Prasad, D. Venkataramana, Lokeswari Y. Department of Computer Science and Engineering Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Chennai603110 India

ISBN: (纸本)9783031699818

Online educational platforms and MOOCs (Massive Open Online Courses) have made it so that learning can happen from anywhere across the globe. While this is extremely beneficial, videos are not accessible by everyone, due to time constraints and lower bandwidths. Textual step-by-step instructions will serve as a good alternative, being less time-consuming to follow than videos, and also requiring lesser bandwidth. In this work, the authors aim to create a system that extracts and presents a step-by-step tutorial from a tutorial video, which makes it much easier to follow as per the user’s convenience. this can be mainly accomplished by using Text Recognition for academic videos and Action Recognition for exercise videos. Text Recognition is accomplished using the Pytesseract package from Python which performs OCR. Action Recognition is performed with the help of a pre-trained OpenPose model. Additional information is extracted from both exercise and academic videos with the help of speech recognition. the information extracted from the videos are then segmented into procedural instructions which is easy to comprehend. For exercise videos, a frame-wise accuracy of 79.27% is obtained. Additionally, for academic videos, an accuracy of 78.47% is obtained. © IFIP international Federation for Information Processing 2024.

关键词： Supervised learning

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Guarding the Digital Frontier: A Logistic Regression Approach to Malware Detection 7th

Guarding the Digital Frontier: A Logistic Regression Approac...

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7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Archana Jenis, M.R. Shankar, K. Srinivasa Seshadri, K. Computer Science and Engineering St. Joseph’s College of Engineering Semmencheri Tamil Nadu Chennai600119 India

ISBN: (纸本)9783031699856

Taking new computing resources, a constantly evolving field, the malware threat is so widespread that a strong detection requires a creative response. the traditional methods of malware detection that include signature or shape-based methods do not perform well against different types of malware developed. So, we propose a malware detection model consisting of two parts: one for detecting malware based on PE (Portable Executable) header features, and another for detecting malicious websites based on URL features. the model uses random forest classifier for malware detection based on PE header features, statistical method called logistic regression for malicious website detection based on URL features thereby aiming to provide a fast, reliable, and trustworthy solution for guarding the digital frontier. © IFIP international Federation for Information Processing 2024.

关键词： Logistic regression

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D3CNet: Integrating Cascade Networks for Enhanced Driver Fatigue Monitoring 7th

D3CNet: Integrating Cascade Networks for Enhanced Driver Fat...

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7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Preethi, J. Chiranjeevi, V. Rahul Surya, K. Santhosh Kumar, S. Department of Computer Science and Engineering Rajalakshmi Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9783031699818

the Real-time Drowsy Driver Recognition System is a technological breakthrough that uses the power of machine the driver’s state and caution them when indications of sleepiness are distinguished. the key elements needed to recognize the facial expression are real time data analyzer, a camera module used for live recording of learning to prevent accidents caused by drowsy driving in an era when road safety is of the utmost importance. this framework uses a mix of PC vision, facial acknowledgment, and profound learning calculations to continually screen driver face positions and some mathematical calculations. these components work simultaneously to check the system’s functionality. As the driver leaves on their excursion, the framework energetically examines their facial highlights, eye developments, and flicker designs. At the point when exhaustion is distinguished, the framework sets off ideal alarms, like discernible admonitions or vibrating seats, to guarantee the driver stays ready and centered. In order to reduce the accident percentage, this model is introduced. this Real-time Drowsy Driver Recognition System demonstrates the remarkable potential of machine learning to create a safer and more secure future on our roads by addressing the critical issue of drowsy driving. © IFIP international Federation for Information Processing 2024.

关键词： C (programming language)

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