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2024 International Conference on computer, Electronics, electrical engineering and their Applications, IC2E3 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Dept. of Electronics and Engineering Dehradun India

ISBN: (纸本)9798350388534

This study had conducted an intensive exploration of the possibilities of VGG-16 and VGG-19 Convolutional Neural Network models for picture categorisation. Using a thorough method comprising substantial training and severe testing, we may observe both steady loss decrease and significant model accuracy improvements. It is assumed that the models may appropriately keep track of subtle variations in data to which they are adversarial while identifying regular patterns they may generalize. The VGG models' architecture is quite sophisticated, but as seen above, they give strong classification accuracy, with performance in nearly ideal 99 percent in many trials. This integration proves that extremely advanced deep learning instruments are competent to handle and complete even very sophisticated picture recognition assignments. Additionally, our experiments identify a few essential aspects of model adjustment and tuning identifying important techniques considerably enhancing CNN outcomes in application scenarios. This study is pioneering for the automatic picture identification discipline since it provided a strong understanding of the generality and model quicker than many real-world models. Additionally, it exhibits that the CNN structures enhance artificial intelligence's power to recognize visual input and could improve many sectors like the surveillance and health systems. The findings defined here may now open the door to more progress in deep learning and picture process analyses while reiterated the importance of CNN in modern technology. © 2024 IEEE.

关键词： Convolutional neural networks

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Sentient Sound waves: Elevating Emotional Communication with AI-Generated Speech Technology 5

Sentient Sound waves: Elevating Emotional Communication with...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Roshan, Rahul G. Roshan, Rohit Sohan, M.H. Raj, S.M. Sutharsan Prasad, V.R. Badri Dept. of Computer Science and Engineering PES University Bangalore India

ISBN: (纸本)9798350361155

This research-based project is about a new way to put feelings into computer-generated speech. It has two stages: text emotion detection and emotional speech synthesis. In the former part, labeled text data is used to build models to spot the emotions. This model learns to change pitch, volume, and rhythm based on the given emotion. During the latter phase, the text is changed into neutral speech using text-to-speech (TTS) methods. The model is used for practice to find the emotion in the text, tagging the text with that emotion. Also, a deep-learning model known as a tacotron is employed to make an emotional-sounding speech. The model perfectly uses machine learning strategies to blend feelings into speech. This opens up possibilities for turning plain text into speech with feelings of varied emotions. The proposed system makes a novice and important contribution to natural language processing for emotion identification and deep learning for emotional speech synthesis. © 2024 IEEE.

关键词： Speech synthesis

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Unveiling Insights: Vision-Based Data Mining Analysis of Webpages in Transition from HTML 3 to HTML 5 5

Unveiling Insights: Vision-Based Data Mining Analysis of Web...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Chaudhary, Meena Pooja Chandwani, Gunjan Manav Rachna University Dept. of Computer Science and Technology Faridabad India Galgotias University Dept. of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

Deep web material is accessed with web database requests, and returned information is packaged into dynamically generated web pages known as deep Web pages. Due to their intricate structure, it is tedious task to retrieve organized data from deep web pages. A great many approaches to address this problem are yet to be proposed, but all of them have inherent drawbacks because they are based on the website and the programming language. The contents of Sites are always shown for users to search daily, like the common two-dimensional media. This motivates us to search for an alternative way to extract deep Web information, by using some interesting popular visual features from deep Webpages, to address the limitations of past works. A novel blog vision approach is proposed in this paper to overcome these issues. This technique uses visual features in deep web pages specifically to conduct deep web data extraction. This paper also recommends a new appraisal measure to capture the required human activity to make maximum mining. The proposed methodology is highly efficient in the deep network data extraction, according to our studies with a large set of web-based data bases. © 2024 IEEE.

关键词： Data mining HTML 3 HTML5 Vision based technique Web Content Mining

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eXtended Reality Services for Outdoor Cultural Spaces: System Design and Evaluation Framework Using Unbiased Biometric Data 15

eXtended Reality Services for Outdoor Cultural Spaces: Syste...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Koukopoulos, Zois Koukopoulos, Dimitrios Fidas, Christos University of Patras Interactive Technologies Lab Dept. of Electrical & Computer Engineering Patras Greece University of Patras Dept. of History-Archaeology Patras Greece University of Patras Dept. of Electrical & Computer Engineering Patras Greece

ISBN: (纸本)9798350368833

The concept of enriching the visible reality with more information has led to the development of the eXtended Reality technology, where digital elements like text, images, 3D representations and virtual scenarios, are embedded to the real world and become visible to humans via mobile devices. Cultural Heritage is embracing eXtended Reality and tries to find new ways to harvest its merits in an endless struggle to disseminate cultural information to the public and the next generations. In this paper, we are presenting an innovative intelligent Environment, operating in outdoor cultural spaces and offering personalized eXtended Reality cultural experiences to visitors. The Environment aims to support the financial sustainability of cultural heritage institutes by attracting visitors and especially younger ones, as well as create new sources of revenue for cultural heritage professionals by allowing them to create XR experiences, without the help of IT professionals and exploiting them in an online market of XR cultural experiences. We propose a novel framework to evaluate the user experience by collecting biometric data (brain activity and eye behavior) from the visitors of outdoor cultural spaces and exploring their cognition and emotions while using the new Environment. © 2024 IEEE.

关键词： Biometric Data eXtended Reality Outdoor Cultural Spaces Sustainability

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Enhanced Level Brain Tumor Identification Using CNN, VGG16 and ResNet Models 2

Enhanced Level Brain Tumor Identification Using CNN, VGG16 a...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Sambandam, Rakoth Kandan Jyoshna, Mallela Christ University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350383652

The comprehension of brain growths is significantly improved through the identification and categorization of these disorders. Still, their discovery is relatively grueling due to their variability in terms of position, shape, and size. Fortunately, deep literacy has revolutionized the field and significantly improved recognition, prediction, and opinion in various healthcare areas, including brain excrescences. The main goal of this study is to thoroughly review exploration that utilizes CNN, VGG16, and RESNET infrastructures to classify brain excrescences using MRI images. The performance of these models varied significantly, with CNN, VGG16, and RESNET achieving an emotional delicacy of 99.6. Additionally, ResNet and VGG16 achieved rigor of 92.4 and 89.7 independently. Likewise, the visualization of the decision-making processes of these models has provided valuable insight into the features they prioritize. By incorporating these models into their practice, healthcare professionals have the opportunity to enhance their individual capabilities, eventually leading to improved patient outcomes. © 2024 IEEE.

关键词： Deep learning

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ENHANCING E-HEALTH CYBERSECURITY AND RESILIENCE: SHIFTING FROM MONOLITHIC TO MICROSERVICES ARCHITECTURE

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: electrical engineering and computer science 2025年第1期87卷 21-34页

作者： Contașel, Cristian Rughiniș, Răzvan Trancă, Dumitru Cristian Țurcanu, Dinu Dept. of Computer Science and Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Dep. of Software Engineering and Automatics Technical University of Moldova Chișinău Moldova

This study provides an innovative architectural model for e-Health systems that aims to improve cyber resilience while maintaining high availability under fluctuating traffic loads. We examined typical cybersecurity incidents in the field of e-Health, their correlations with architectural defects, and frequent design patterns in currently operational systems. A testing approach based on our research finds those weaknesses and proposes viable fixes. In this paper, a comprehensive support strategy for transitioning from conventional monolithic architectures to microservices is presented. This change makes use of cloud computing's vertical and horizontal scalability to maximize resource utilization while ensuring system reliability. We also discuss deployment ideas for the new microservices, focusing on operational resilience and cybersecurity in e-Health environments. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Electronic health record

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Machine Learning Aided Spectrum Sensing

Machine Learning Aided Spectrum Sensing

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2023 IEEE International Conference on electrical, Electronics, Communication and computers, ELEXCOM 2023

作者： Jabbal, Raunaq Singh Srivastava, Vishwas Prasad, Binod Dept. of Computer Science and Engineering MP Gwalior India Dept. of Electrical and Electronics Engineering MP Gwalior India

ISBN: (纸本)9798350305111

Spectrum sensing (SS) is a key concept that determines the effectiveness of cognitive radio (CR). Although multiple sensing practices have been proposed in the literature, out of them energy detector (ED) have been widely adopted. However, the performance of ED relies on pre-existing knowledge of noise distribution and threshold. Therefore, efficient spectrum sensing remains a challenge in wireless communication technologies. This paper aims to analyze the sensing performance of various machine learning (ML) models in different fading scenarios. We carefully model a spectrum sensing environment and generate a large-scale comprehensive dataset. We then train and test several well-established classical and ML techniques for spectrum sensing. The performance of classical spectrum sensing and ML-based spectrum sensing are compared under various parameters like the number of secondary user (SU), the distance between primary user (PU) and SU;and different fading environments such as Rayleigh, Rician, and Nakagami. The Simulation result shows that machine learning models outperform classical sensing techniques. Furthermore, it is found that support vector machine (SVM) outperformed other ML techniques in predicting channel availability. © 2023 IEEE.

关键词： Cognitive radio

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EMC and EMF Safety Aspects of Wireless Power Transfer Systems for e-Mobility

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IEEE Electromagnetic Compatibility Magazine 2024年第3期13卷 74-92页

作者： Maradei, Francesca Ahn, Seungyoung Campi, Tommaso Covic, Grant Cruciani, Silvano Hirata, Akimasa Wang, Shuo Feliziani, Mauro Sapienza University Dept. of Astronautic Electrical and Energy Engineering Rome Italy Daejeon Korea Republic of The University of Auckland Auckland New Zealand Tor Vergata University Dept. of Industrial Engineering Rome Italy Nagoya Institute of Technology Dept. of Electrical and Mechanical Engineering Nagoya Japan University of Florida Dept. of Electrical & Computer Engineering GainesvilleFL United States University of L'Aquila Dept. of Industrial and Information Engineering L'Aquila Italy

This paper intends to provide a comprehensive over-view of the current state-of-the-art practices in wireless power transfer (WPT) technology within the automotive sector, while addressing pertinent issues related to electromagnetic compatibility (EMC) and electromagnetic field (EMF) safety compliance, as well as highlighting ongoing challenges and areas for further exploration. © 2012 IEEE.

关键词： Inductive power transmission

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Cybersecurity: Threats, Requirements, Deployments, Techniques, System Architecture, and Future Research

Cybersecurity: Threats, Requirements, Deployments, Technique...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Yadav, Ajay Karnatak, Vijay Mishra, Amit Kumar Verma, Shalini Aswal, Pallaw Singh Tripathi, Neha University Institute of Computing Chandigarh University Punjab Mohali India THDC Institute of Hydropower Engineering and Technology Dept. of Computer Science & Engineering Tehri India Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Doon Group Of Institution Dept. of Computer Science & Engineering Rishikesh India THDC Institute of Hydropower Engineering and Technology Computer Science and Engineering Tehri India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350394412

Cybersecurity safeguards computer systems, net- works, and digital information from a wide variety of cyber-attacks. Its major purpose is to protect the privacy of users' data and resources while maintaining their integrity and making them accessible in the digital world. Cybersecurity is an area that receives investment from businesses, individuals, and governments all around the world in order to secure sensitive information, key infrastructure, and digital assets. Protection of Sensitive Information Both individuals and businesses store huge amounts of sensitive information digitally, including personal data, financial records, intellectual property, and trade secrets. This information must be protected in order to prevent unauthorized access or disclosure. The protection provided by cybersecurity prevents this information from being stolen, exposed, or accessed without authorization. Cyberattacks can result in severe financial losses, including theft of funds, fraud, and the expenditures associated with recovering from a cyber incident. It is possible to prevent these types of losses by taking preventative measures. Implementing these strategies helps prevent losses of this nature. In this paper, we propose a system architecture of a generic secure system, which can be deployed for various applications, i.e., smart healthcare, Intelligent Transport Systems (ITS), smart city, etc. We discuss the threats and attacks in cyberspace. Then, we explain the requirements of cybersecurity. Further, we discuss the devastation in the absence of cyber security. We also explain the cybersecurity deployments and techniques under cybersecurity. Then, in the end, some future research directions for cybersecurity are provided. © 2024 IEEE.

关键词： Cyber attacks

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A Framework for Evaluating Model Trustworthiness in Classification of Very High Resolution Histopathology Images

A Framework for Evaluating Model Trustworthiness in Classifi...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Nouyed, Mohammad Iqbal Doretto, Gianfranco Adjeroh, Donald A. West Virginia University Lane Dept. of Computer Science and Electrical Engineering MorgantownWV26506 United States

ISBN: (纸本)9798350386226

In computer vision, one approach to explaining a deep learning model's decision is to show regions of visual evidence upon which the model makes a decision. Typically, this evidence is represented in the form of a saliency map which conveys how much an image region is contributing to the model's decision. For a model to be trustworthy, it is expected that this saliency region should provide relevant information. In this work, we use model "trustworthiness"or "rationale"to describe how much relevant information the model is using to determine the image class. For medical images, this information connects to biological relevance. For very high resolution histopathology image applications, such as gigapixel whole-slide image classification, where patch-based multiple-instance based learning approach is taken to determine the patch label, this biological relevance has to be determined both at the patch and the image level. In this work, we present a novel patch-based model trustworthiness evaluation framework for very high resolution histopathology images. Our trustworthiness framework takes two approaches: spatial overlap based and feature based evaluation. For the overlap based approach, we check overlap with the annotation provided with the database to see if they have biological relevance, since for tumor positive patches only high probability regions from within the annotated regions are likely to be relevant. For feature based approach, we train an interpretability model using the sub-patches of the training set, extract features and cluster them. Then based on the distance from these clusters we determine if there is any biological rationale behind the prediction. Finally, we propose four patch-level and four image-level rationale metrics that evaluate the biological relevance of the information used by the classifier to decide on the patch class. Our experiment using the CAMELYON16 dataset shows the efficacy of this approach for model trustworthiness evaluation

关键词： Deep learning

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