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检索条件"机构=Computer Applications in Science and Engineering dept."

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Project or Factorize? A Case Study of Multiview CCA and PARAFAC2 Tensor Factorization 58

Project or Factorize? A Case Study of Multiview CCA and PARA...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Chen, Jia Papalexakis, Evangelos E. University of California Dept. of Electrical and Computer Engineering Riverside United States Uuniversity of Californiav Dept. of Computer Science and Engineering Riverside United States

ISBN: (纸本)9798350354058

Consider learning the shared representations from multiple unlabeled views. Previous work either projects different views to the same space while enforcing the agreement among the projected views such as multiview canonical correlation analysis (MCCA), or factorizes different views while ensuring the common latent components across the views such as PARAFAC2, a tensor decomposition method. In this paper we first investigate a fundamental question: 'Do these two approaches learn different representations?' Preliminary numerical results suggest that in practice they do, which, in turn, begs the question of how we can leverage this observation in order to compute a superior representation. In this paper, we present a simple proof-of-concept scheme which augments MCCA with PARAFAC2 representations and vice-versa, and we demonstrate on multiple real datasets that such scheme can improve upon the baseline representation, paving the way for future research on optimally combining the strengths of projection and factorization methods for multiview representation learning. © 2024 IEEE.

关键词： Tensors

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Role of IoT in Smart Cities: A Review, applications, Open Challenges and Solutions 3

Role of IoT in Smart Cities: A Review, Applications, Open Ch...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Vivek, Kolla Swetha, Gurrala Kumar M, V. T. Ram Pavan Veni, Anusuri Krishna Praveen, Neti Kale, Masthan Rao Dept. of Cse Andhra Pradesh Ongole India Dept. of Cse Telangana India Kakaraparti Bhavanarayana College Dept. of Computer Science Andhra Pradesh Vijayawada India Madanapalle Institute of Technology & Science Dept. of Computer Science & Engineering - Data Science Andhra Pradesh Madanapalle517325 India Srkr Engineering College Dept. of Computer Science and Design Andhra Pradesh Bhimavaram India Dept. of Cse Andhra Pradesh Ongole India

ISBN: (纸本)9798331509675

Smart cities have emerged as a response to the fast expansion of metropolitan areas;these places strive to improve the lives of their residents while simultaneously promoting environmentally responsible growth. To maximize urban services, the Internet of Things (IoT) is an essential technology that allows for the seamless integration of sensors, devices, and systems. This allows smart cities to become a reality. With an emphasis on smart transportation, energy management, healthcare, environmental monitoring, and public safety, this paper explores the critical role of IoT in smart cities. Data security, privacy, scalability, and interoperability are some of the problems that arise when smart cities use IoT, despite the revolutionary potential it has. These obstacles are thoroughly examined in this work, along with possible solutions to these problems. This study gives a thorough overview of the IoT and its impact on smart city development, innovation, and urban resilience by focusing on the present situation, applications, and unanswered questions. © 2025 IEEE.

关键词： Digital Technology Drainage Monitoring Fire detection Smart City Waste Management

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Microwave Marvels: Design and Computational Analysis of an Ultra-thin Metamaterial Absorber for C, X and K Band 6

Microwave Marvels: Design and Computational Analysis of an U...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Tasnim, Nowshin Rattre, Naziya Hasan Shawon, Sarowar Morshed Alam, Mohammed Shamsul Alam, Touhidul Chittagong University of Engineering & Technology Dept. of Computer Science & Engineering Chittagong4349 Bangladesh International Islamic University Chittagong Dept. of Computer Science & Engineering Chittagong4318 Bangladesh University of Science & Technology Chittagong Dept. of Computer Science & Engineering Chattogram4202 Bangladesh Universiti Kebangsaan Malaysia Bangi43600 UKM Malaysia

ISBN: (纸本)9798350385779

Metamaterial absorbers are an advancement in material science as they provide more advantages in comparison to conventional materials. Achieving miniaturization with a multilayered structure is the primary challenge here. In this study, a novel metamaterial absorber design is proposed for microwave application. The proposed design is shaped as a squad of four segments where each segment contains an octagonal split ring and a central ring. The multi-layered design of the absorber helps to secure a high absorption rate. An extensive range of research is done to determine particular measurement units, and geometric shapes with the help of CST Studio Suit 2019. Here both ground and patch are designed using copper and the separator is designed with Roger. Moreover, the thickness of the absorber is only 3.4 mm which ensures its ultra-thin and lightweight nature. With an elaborated numerical analysis, it has been shown that the proposed absorber provides 90% to 99% absorption at the C, X, and K bands. Moreover, a concise computational analysis is accomplished with FR4 lossy, Rogers RT6010LM lossy, and Rogers RT5870LM lossy as dielectric constant. The key contribution of this research is the high absorption rate, coupled with minimal thickness and simple structure. © 2024 IEEE.

关键词： Metamaterials

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Attention Mechanism with Bayesian Decision Theory for Drug Prediction

Attention Mechanism with Bayesian Decision Theory for Drug P...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Sankaran, A. Sathiyamurthy, K. Puducherry Technological University Dept. of Computer Science and Engineering India Puducherry Technological University Faculty of Computer Science and Engineering India

ISBN: (纸本)9798331510022

Biomedical literature lacks integration of dynamic drug interaction data for personalized prescriptions. This proposed work is a new hybrid model that enhances drug safety prescriptions by taking into account individual patient features and drug interactions. It does this by hybrid attention mechanisms with Bayesian Decision Theory (BDT), which employs Bayesian statistical thinking instead of the dot-product approach. This approach is driven by the growing complexity of patient profiles and the limitations of current algorithms, which often fail to dynamically change the relevance of individual aspects. This method seeks 94% accuracy, effectively prioritizing safer drug combinations and personalizing drug recommendations. This integration leads to more consistent and customized drug prescriptions, which ultimately improve patient outcomes and advance the field of personalized healthcare. © 2024 IEEE.

关键词： Drug interactions

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Evaluating the Performance of Machine Learning Models in Handwritten Signature Verification

Evaluating the Performance of Machine Learning Models in Han...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Akter, Tahmina Akter, Mst. Sharmin Mahmud, Tanjim Chakma, Rishita Hossain, Mohammad Shahadat Andersson, Karl Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati4500 Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350361537

Handwritten signatures remain a widely used method for personal authentication in various official documents, including bank checks and legal papers. The verification process is often labor-intensive and time-consuming, necessitating the development of efficient methods. This study evaluates the performance of machine learning models in handwritten signature verification using the ICDAR 2011 Signature and CEDAR datasets. The investigation involves preprocessing, feature extraction using CNN architectures, and optimization techniques. The most effective models undergo a rigorous evaluation process, followed by classification using supervised ML algorithms, such as linear SVM, random forest, logistic regression, and polynomial SVM. The results indicate that the VGG16 architecture, optimized with the Adam optimizer, achieves the satisfactory performance metrics. This study demonstrates the potential of ML methodologies to enhance the efficiency and accuracy of signature verification, offering a robust solution for document authentication. © 2024 IEEE.

关键词： Decision trees

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Predictive Modeling of Cryptocurrency Prices Using Machine Learning Algorithms 15

Predictive Modeling of Cryptocurrency Prices Using Machine L...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mahmud, Tanjim Akter, Tahmina Chakma, Rishita Barua, Tikle Hossain, Mohammad Shahadat Andersson, Karl Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati4500 Bangladesh Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong4331 Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå97187 Sweden

ISBN: (纸本)9798350370249

Cryptocurrencies, such as Bitcoin, Binance, Ethereum, FTX, and XRP, are decentralized digital assets known for their volatile nature and potential as investment instruments. Accurate price prediction is crucial for informed investment decisions. This study explores the feasibility of various modeling techniques on diverse data structures and features for predicting the prices of these cryptocurrencies. We utilize daily and high-frequency price data to classify and predict prices using deep learning and machine learning techniques, including LSTM, Bi-LSTM, GRU, linear regression, and SGD regression. Our findings indicate that daily price projections achieve an accuracy of 0.99, outperforming more complex deep learning and machine learning models. Compared to benchmark results, our approach demonstrates superior performance, with the highest scores achieved by the applied statistical methods and advanced algorithms. This research highlights the effectiveness of deep learning and machine learning models in cryptocurrency price forecasting, offering a foundation for further exploration in the field and emphasizing the significance of sample size in predictive modeling. ©2024 IEEE.

关键词： Adversarial machine learning

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Enhancing OCR Accuracy Using Convolutional Neural Networks: A Study on Spelling Correction in Handwritten Numerals 1

Enhancing OCR Accuracy Using Convolutional Neural Networks: ...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, Priyanka Dewal, Inder Singh Singh Rathore, Sajjan Singh, Vinay Kumar, Sani Bhavani, P.M. Sindhu Chandigarh University Dept. of Computer Science Engineering Chandigarh India Madhav University Dept. of Humanities & S.Science Pindwara India Usha Martin University Dept. of Com. & Information Technology Jharkhand India Mangalayatan University Dept. of Computer Eng. & Applications Aligarh India Meenakshi A.H.Edu. & Research Dept. of Humanities & Science Chennai India

ISBN: (纸本)9798350356816

Artificial neural networks have transformed machine learning and had a significant impact on a wide range of industries, including robotics, deep learning, healthcare, sports, and surveillance. Convolutional Neural Networks (CNNs) are unique among these developments because they combine cutting-edge deep learning methods with conventional Artificial Neural Networks (ANNs). CNNs are essential for tasks like data analysis, text classification, speech and face recognition, and pattern recognition in a variety of fields. In order to improve spelling correction, this article uses a dataset that was upgraded from the National Institute of Standards & Technology (NIST) and includes 70,000 grayscale pictures of handwritten numbers. © 2024 IEEE.

关键词： Convolutional neural networks

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Smart Vehicle Procurement System using Blockchain Technology 3

Smart Vehicle Procurement System using Blockchain Technology

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kumar, Boddu Manoj Rajeswari, G. Duraipandian, Kavitha David, Shiela Dept. of Computer Science and Engineering with specialization in Blockchain Technology Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9798331527549

Blockchain technology has become more widespread in our lives revolutionizing various industries through improved security, transparency, and operational efficiency. Its decentralized ledger system is particularly effective in areas like finance and supply chain management ensuring data integrity and reducing the need for middlemen. This study explores how blockchain technology is used in the process of buying vehicles highlighting its benefits compared to methods. Traditional vehicle purchases involve a network of dealers, banks and regulators leading to inefficiencies, higher costs and potential fraud. The usual process involves steps like financing checking vehicle history and changing ownership. Each requiring extensive paperwork and third-party intervention. In contrast using blockchain for vehicle purchases can simplify things through contracts that automate tasks like verifying history securely transferring ownership instantly settling payments. The unchangeable nature of records ensures that all transaction data remains secure against tampering fostering trust between buyers and sellers. Furthermore, blockchain can cut costs significantly by removing the need for middlemen such, as escrow services and title companies while reducing burdens. The advanced security elements built into technology help reduce the chances of fraudulent actions like altering odometer readings or selling stolen cars. This study showcases a comparison illustrating how blockchain can simplify the process of buying vehicles offering an economical and secure experience. © 2025 IEEE.

关键词： Decentralized finance

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Masked Ciphertext Comparison without Masking Conversions using Register Rotation and Chinese Remainder Theorem 15

Masked Ciphertext Comparison without Masking Conversions usi...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Kim, Geumtae Seo, Eunyoung Lee, Yongwoo No, Jong-Seon Seoul National University Dept. Electrical and Computer Engineering Seoul Korea Republic of Dept. Electrical Engineering and Computer Science Daegu Korea Republic of Inha University Dept. Electrical and Computer Engineering Incheon Korea Republic of

ISBN: (纸本)9798350364637

In this paper, we propose a novel method for masked comparison using register rotation technique without masking conversions. In key encapsulation mechanisms (KEMs), ciphertext comparison is essential to ensure the security, but it is one of the most computationally expensive operations in masking environment. Previous works on masked ciphertext comparison have mainly focused on masking conversion and its post-processing. However, the proposed method introduces another register-based approach, which eliminates the need for time-consuming masking conversion and solely relies on register rotation using Chinese remainder theorem (CRT). © 2024 IEEE.

关键词： Ciphertext

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A Study of Landmark-Augmented and Landmark-Free Approaches: Yoga Pose Recognition 9

A Study of Landmark-Augmented and Landmark-Free Approaches: ...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Marasani, Jayasurya Yerramreddy, Dhanvanth Reddy Karthik, Abburi Sai Karthik, Maddala H. S. M. K. Jayakumar, T.V. Nedungadi, Prema Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Amritapuri India Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Applications Amritapuri India Amrita Vishwa Vidyapeetham Amrita School of Computing Amritapuri India

ISBN: (纸本)9798350394474

In the realm of yoga pose recognition, this project delves into a comprehensive investigation of two distinct methodologies, namely landmark-augmented and landmark-free approaches, to enhance the accuracy and efficacy of pose recognition systems. The first approach leverages state-of-the-art pose estimation techniques employing MoveNet, MediaPipe, and YOLO V8 to precisely detect pivotal anatomical landmarks that define yoga postures. Subsequently, these detected landmarks are utilized as foundational cues for the classification process. On the other hand, the landmark-free approach directly employs cutting-edge deep learning architectures, specifically Vision Transformer, ResNet50, and VGG16, to classify yoga poses without the explicit utilization of detected landmarks. By juxtaposing these two methodologies, the project endeavors to uncover the comparative advantages, challenges, and limitations associated with each approach in the domain of yoga pose recognition. Beyond performance evaluation, this study aims to provide valuable insights into the most effective and robust techniques for accurately identifying and classifying yoga poses. The findings are poised to contribute to the development of sophisticated yoga pose recognition systems that hold promise in assisting yoga practitioners in achieving proper alignment and posture during their practice, ultimately promoting their overall well-being and practice effectiveness. © 2024 IEEE.

关键词： Deep learning

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