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A Multiple Branch Fusion Network for Feature Learning and Hyperspectral Image Classification 14

A Multiple Branch Fusion Network for Feature Learning and Hy...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Kanthi, Murali Rao, K. Venkateshwara Pavani K, Sudha Bhaskar, Nuthanakanti Bhukya, Shankar Nayak Hitendra Sarma, T. Cmr Technical Campus Telangana Hyderabad India CMR College of Engineering & Technology Dept. of Computer Science & Engineering Telangana Hyderabad India Dept. of Computer Science & Engineering Cmr Technical Campus Telangana Hyderabad India Vasavi College of Engineering Dept. of Information Technology Telangana Hyderabad India

ISBN: (纸本)9798350335095

Hyperspectral image (HSI) classification has made extensive use of deep learning approaches, specifically Convolutional Neural Network (CNN). CNN's ability to fully capture local contextual aspects of HSI makes it an excellent HSI classifier. Although, a significant factor in the poor classification performance is the scarcity of training data. Conventional CNN-based methods don't use the integrate-band relationships of HSI as much as they could because they typically employ 2D-CNNs to extract features. On the other hand, 3D-CNNs use a similar procedure to get both spectral and spatial data. This gets around the problem with 2D-CNNs, but it could mean that not enough features are extracted. As a solution, we offer a multi-scale 3D-CNN with multi branch feature fusion, which we call multi branch fusion CNN (MBF-CNN), for HSI classification. We begin by applying 3D-CNN on multiple scales to extract HSI features. After that, the multiple distinct characteristics are streamlined and combined together. The fused features then go through several fully connected layers to get the classification outcomes. The experiments are executed on both the new data sets and the publicly accessible benchmark data sets to expose the efficiency and durability of the proposed model. Empirically, it has been demonstrated that the presented model succeeds over the remaining state-of-the-art approaches in terms of classification accuracy. © 2023 IEEE.

关键词： Classification (of information)

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Movie Recommendation System Using Euclidean Distance

Movie Recommendation System Using Euclidean Distance

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Khan, Muneeb Sami Hussain, Zunnurain Amaad, Muhammad Irtaza Hasan, Muhammad Zulkifl Khalid, Aqsa Chuhan, Saad Hussain Awan, Rimsha Yaqub, Muhammad Atif Ashraf, Farhan Information Technology University Department of Computer Engineering Lahore Pakistan Bahria University Lahore Campus Dept. of Computer Science Pakistan University of Central Punjab Faculty of Information Technology India National College of Business Administration and Economics Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350373783

In this study, we pivot from the commonly used cosine similarity to the Euclidean distance as the foundation for our film recommendation algorithm. Initially, data pertaining to users, films, and ratings are loaded using the panda's library. The algorithm then computes normalized ratings, tabulates the number of films in each genre, and visualizes this distribution. The frequency of ratings for each genre is similarly plotted. A dictionary is maintained to store movie IDs for each genre. These values are then integrated into the normalized rating matrix. For each genre, the normalized ratings of movies are cataloged in a lexicon. Using the normalized ratings matrix and movie IDs, a Data Frame is generated. Subsequently, the movie and ratings datasets are merged. A pivot table captures the number of films each user has watched per genre. The study also be happy with their purchase if these recommendations are tailored to their needs. As a result, the client would utilize this application once more. Because of the significant money gained determines the average number of films a user watches in each genre. This pivot table is refined to display movies in each genre that a user has watched, but only those exceeding the genre's average viewership. Instead of cosine similarity, the Euclidean distance is computed to gauge the similarity between movies, offering a fresh perspective on recommendation systems. © 2024 IEEE.

关键词： ContentBased Filtering Data Normalization Euclidean Distance Genre Analysis Machine Learning Algorithms Movie Recommendation System Personalized Recommendations Similarity Measurement User Preferences User Rating Analysis

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EFFICIENT PARALLEL SIMULATION OF NETWORKED SYNCHRONOUS DISCRETE-EVENT SYSTEMS

EFFICIENT PARALLEL SIMULATION OF NETWORKED SYNCHRONOUS DISCR...

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2024 Winter Simulation Conference, WSC 2024

作者： Karanjkar, Neha Desai, Madhav Kushe, Akhil Natekar, Anish School of Mathematics and Computer Science IIT Goa Goa India Dept. of Electrical Eng IIT Bombay Mumbai India Dept. of Electronics and Telecomm Eng Goa College of Engineering Goa India

ISBN: (纸本)9798331534202

We present Sitar, an open-source, general-purpose modeling framework consisting of a custom modeling language and a simulation kernel, designed for efficient parallel simulation of networked Synchronous Discrete-event Systems (SDES) in applications such as communication networks and computer systems design. A unique feature of Sitar is its two-phase, cycle-based simulation algorithm that allows an efficient, race-free parallel execution on shared-memory systems. This is achieved by imposing a mild restriction on the set of SDES that can be described within the framework. The modeling language is designed for describing complex, networked systems with a static interconnection structure. We demonstrate Sitar’s modeling capability, performance and scalability through a detailed performance evaluation and a comparison against SystemC and SimPy. Our results show that Sitar’s single-threaded performance is better than that of SimPy and comparable to that of SystemC, whereas a multi-threaded execution shows near-linear scaling for several benchmark configurations. © 2024 IEEE.

关键词： Discrete event simulation

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A Comprehensive Review of Leap Motion Controller-Based Hand Gesture Datasets

A Comprehensive Review of Leap Motion Controller-Based Hand ...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Chakravarthi, Bharatesh Prabhu Prasad, B.M. Imandi, Raju Pavan Kumar, B.N. School of Computing and Augmented Intelligence Arizona State University TempeAZ United States Indian Institute of Information Technology Dept. of Computer Science and Engineering Dharwad India Indian Institute of Information Technology Sri City Dept. of Computer Science and Engineering Chittoor India

ISBN: (纸本)9798350319088

This paper comprehensively reviews hand gesture datasets based on Ultraleap's leap motion controller, a popular device for capturing and tracking hand gestures in real-time. The aim is to offer researchers and practitioners a valuable resource for developing and evaluating gesture recognition algorithms. The review compares various datasets found in the literature, considering factors such as target domain, dataset size, gesture diversity, subject numbers, and data modality. The strengths and limitations of each dataset are discussed, along with the applications and research areas in which they have been utilized. An experimental evaluation of the leap motion controller 2 device is conducted to assess its capabilities in generating gesture data for various applications, specifically focusing on touchless interactive systems and virtual reality. This review serves as a roadmap for researchers, aiding them in selecting appropriate datasets for their specific gesture recognition tasks and advancing the field of hand gesture recognition using leap motion controller technology. © 2023 IEEE.

关键词： Gesture recognition

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Evaluation of Meteorological Factors’ Effect on Daily Heart Rate Variability Over Five Years 12

Evaluation of Meteorological Factors’ Effect on Daily Heart...

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12th International Conference on Awareness science and Technology, iCAST 2023

作者： Kokubun, Kota Li, Tianhui Chen, Wenxi Dept. of Computer and Information Systems The University of Aizu Aizu-Wakamatsu Japan Division of Information System The University of Aizu Aizu-Wakamatsu Japan Dept. of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Japan

ISBN: (纸本)9798350324693

The aim of this study is to investigate the effect of meteorological factors on heart rate variability (HRV) derived from daily electrocardiogram (ECG) data. Daily meteorological data were obtained from the Japan Meteorological Agency’s database. Daily ECG data were measured while bathing for five y ears. T he s ubject w as a h ealthy m ale i n h is fi fties. After collecting ECG data, R-R intervals were calculated to perform HRV analysis in time domain and frequency domain. The evaluation of link between meteorological factors and HRV parameters was realized by utilizing meteorological factors’ importance calculated by Random Forest, and Steel-Dwass significant difference analysis of the values. Additionally, the evaluation was also performed by analyzing the obtained meteorological factors’ importance values using Granger Causality test. In Steel-Dwass test, several significant d ifferences (p © 2023 IEEE.

关键词： Electrocardiograms

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Navigating the Privacy and Cybersecurity Risks of Smart Homes 24

Navigating the Privacy and Cybersecurity Risks of Smart Home...

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24th International Arab Conference on information Technology, ACIT 2023

作者： Alghamdi, Fatimah Abdulrahman Alghamdi, Abdulrahman Abdullah College of Computing and Artificial Intelligence Jeddah University Dept. Computer Science and Engineering Jeddah Saudi Arabia College of Computing and Information Technology Shaqra University Dept. Computer Science Shaqra Saudi Arabia

ISBN: (纸本)9798350384307

The increasing popularity of smart home devices has led to a growing concern over cyberthreats to these devices. This study aims to investigate the perceived cyberthreats to smart home devices and identify ways to protect privacy and countermeasure against these threats. Five experts in the field of smart home technology were selected for this study. Semi-structured interviews were conducted with the selected experts over the telephone. The transcripts of the interviews were analyzed using thematic analysis. The main findings of the study indicate that the most significant cyberthreats to smart home devices are: Voice recordings (35.5%), Camera and microphone access (30.1 %), Location tracking (24.9%). Personal information (20.1%), Data breaches (15.1 %), Third-party access (7.4 %), Privacy invasion (7.4%), Lack of control over personal information (5.0%), Cyber-attacks (4.8%), Security vulnerabilities in smart devices (4.8%) were also identified as potential threats. The study suggests that protecting personal data and ensuring device security are key considerations for safeguarding smart home devices and that it is important to take the necessary precautions to protect smart home devices from cyber threats, and to be aware of the potential risks that these devices pose to personal privacy and security. © 2023 IEEE.

关键词： Countermeasures Cyberthreats Data breaches IOT security Privacy invasion Smart home devices

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Enhancing Power Grid Resilience: Advanced Strategies for Detecting and Analyzing Faults in Three-Phase Transmission Lines

Enhancing Power Grid Resilience: Advanced Strategies for Det...

引用

2024 IEEE International Conference on information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Ahmed, Khandakar Rabbi Shayer, Khandokar Shadman Shuvo, Jahidul Islam Haque, F.M. Mahmudul Salam, Towfika Miraz, Shafiun Noman, Abdullah Al Sobuj, Md. Shohanur Islam Arafat, Md. Yaqub Rial, Md Istiak Hasan Miyan Research Institute International University of Business Agriculture and Technology Uttara Model Town Dhaka Bangladesh Dept. of Mechatronics Engineering Kazla Rajshahi6204 Bangladesh American International University-Bangladesh Dept. of Electrical & Electronic Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Dept. of Electrical & Electronic Engineering Dhaka Bangladesh Dept. of Computer Science and Engineering Rajshahi Bangladesh Jahangirnagar University Dept. of Institute of Information Technology Saver Dhaka1342 Bangladesh Hajee Mohammad Danesh Science and Technology University Dept. of Electrical and Electronic Engineering Dhaka Bangladesh International University of Business Agriculture and Technology Dept. of Mechanical Engineering Dhaka Bangladesh Dept. of Computer Science and Engineering Andhra Pradesh Kakinada533291 India

ISBN: (纸本)9798350382693

Power system fault analysis is a process that establishes the bus voltages and line currents when various fault types occur. Three-phase balanced faults and unbalanced faults are two categories of power system faults. Single line-to-ground, line-to-line, and double line-to-ground faults are three different types of unbalanced faults that can occur on transmission lines for power systems. Fault analysis is used to choose and set the proper protective devices and switchgear. Determining the bus voltages and line currents is critical while investigating power system issues. Numerous mathematical calculation techniques are shown in the process, which is challenging to do by hand. The calculation can be performed quickly on a computer using a Simulink created using MATLAB. However, an error occurs when inverting an extensive bus system's Y-bus to Z-bus in the conventional short circuit study;this paper offers a solution that is solved using 'MATLAB SIMULINK' to reduce error. © 2024 IEEE.

关键词： Power system analysis

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Post Quantum Blockchain Based Lightweight Edge Server Handover Authentication Key Agreement Protocol for Vehicular Communication System 14

Post Quantum Blockchain Based Lightweight Edge Server Handov...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sharma, Tannu Durgarao, M.S.P. Reddy, Kethamreddy Karthikeya Kaushal, Sachin Sreedevi, A.G. Chaudhary, Dharminder Lovely Professional University Dept. of Mathematics Jalandhar India Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350335095

We have designed a safe and portable vehicle to infrastructure handover authentication using blockchain technology. In this protocol, blockchain technology provides benefits for data sharing since it can guarantee data quality, transparency, and provenance. To get information for handover authentication, road side units can act nodes of the consortium blockchain. Furthermore, transactions on the blockchain cannot be falsified since they include an road side unit signature and cannot be updated due to blockchain technological features. As a result, other road side units may verify the authenticity of vehicles by utilizing the data stored on the blockchain. For authentication phase, we have used lattice based cryptography that plays a very important role in authentication and key exchanges that protects against the threat of quantum attacks. The proposed protocol ensures both securities against quantum attacks and efficiency due to simple algebraic operations that are polynomial addition and multiplications. © 2023 IEEE.

关键词： Blockchain

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Dengue Cases Prediction and Analysis using LSTM and Region-specific Data

Dengue Cases Prediction and Analysis using LSTM and Region-s...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Ud Deen Shafique, Hafiz Noor Ali, Nauman Hussain, Muhammad Zunnurain Hasan, Muhammad Zulkifl Mustafa, Muzzamil Khalid, Aqsa Awan, Rimsha Ashraf, Farhan Khan, Zohaib Ahmed Javaid, Arslan University Lahore Department of Computer Engineering Information Technology Punjab Pakistan Bahria University Lahore Campus Dept. of Computer Science Zunnurain Pakistan University of Central Punjab Lahore Faculty of Information Technology Department of Computer Science Pakistan National College of Business Administration and Economics Department of Computer Science Lahore Pakistan Information Technology University Lahore Pakistan

ISBN: (纸本)9798350373783

Dengue Fever presents a substantial global health challenge, necessitating the development of accurate predictive models for effective disease management. This research paper conducts a comprehensive analysis of a Long ShortTerm Memory (LSTM) neural network-based model for forecasting Dengue cases in the Philippines. The study includes data preprocessing, model development using TensorFlow and Keras, and thorough evaluation using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Rsquared (R2). The dataset, 'Dengue Cases in the Philippines,' provides a detailed historical record of Dengue fever cases and fatalities across various regions, enabling a thorough analysis of temporal and spatial dynamics. Data preprocessing involves label encoding, normalization, and careful data splitting to ensure accurate model training and testing. The LSTM neural network architecture is optimized through hyperparameter tuning, including learning rate, batch size, and epochs, to improve prediction accuracy. Model performance is rigorously evaluated, with an MAE of 913, indicating areas for further improvement. While the model shows promise, there is room for refinement. This study contributes to the field by exploring the feasibility of LSTM models for Dengue prediction, with insights that may inform future research in disease forecasting and public health strategies. Limitations, ethical considerations, and recommendations for further work are discussed. © 2024 IEEE.

关键词： Electronic health record

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Visible Watermark Removal with Deep Learning Technology 6

Visible Watermark Removal with Deep Learning Technology

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6th International Symposium on computer, Consumer and Control, IS3C 2023

作者： Lin, Chia-Chen Wang, Pei-Yu Lin, Yan-Heng Huang, Hsuan-Chao Saberikamposhti, Morteza National Chin-Yi University of Technology Dept.of Computer Science and Information Engineering Taichung Taiwan Universiti Kebangsaan Malaysia Dept. of Information Science & Technology Selangor Malaysia

ISBN: (纸本)9798350301953

Watermarking is a technique used to assert ownership over an image, and can be categorized into visible and invisible forms based on the detectability of the watermark. Visible watermarking is more user-friendly and intuitive than invisible methods since it allows individuals to identify image ownership with their own eyes rather than relying on machine-based watermark decoders. To enhance the visual quality of watermarked images and ensure the original images can be fully recovered after visible watermark authentication, a visible watermark removal approach using deep learning-based inpainting is proposed in this paper. Experimental results demonstrate that the watermarked images carrying the visible watermark and auxiliary information achieve peak signal-to-noise ratios (PSNRs) ranging from 41.89 dB to 43.17 dB and structural similarity indices (SSIMs) up to 0.97 to 0.98. Furthermore, our hybrid recovery operations enable the complete restoration of the original images, making them easily readable. © 2023 IEEE.

关键词： Watermarking

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