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31st International Conference on Software engineering and Data engineering, SEDE 2022

作者： Lewis, Christopher Harris, Frederick C. Dept. of Computer Science and Engineering University of Nevada Reno RenoNV89557 United States

Virtual Reality (VR) has existed for many years;however, it has only recently gained wide spread popularity and commercial use. This change comes from the innovations in head mounted displays (HMDs) and from the work of many software engineers making quality user experiences (UX). In this work we present a brief history, current research areas, and areas for improvement in virtual reality © 2022, EasyChair. All rights reserved.

关键词： Virtual reality

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Design and analysis of key scheduling algorithm for symmetric cipher security

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3991-3997页

作者： Tiwari, Vikas Kameswari, U. Surya Singh, Ajeet Saxena, Ashutosh Acharya Nagarjuna University Andhra Pradesh Guntur 522510 India C.R. Rao Advanced Institute of Mathematics Statistics and Computer Science University of Hyderabad Campus Telangana Hyderabad 500046 India Dept of Computer Science and Engineering Acharya Nagarjuna University Andhra Pradesh Guntur 522510 India School of Computing Science and Engineering Vellore Institute of Technology VIT Bhopal University Bhopal-Indore Highway Madhya Pradesh Bhopal 466114 India CMR Technical Campus Telangana Hyderabad 501401 India

The success and safety of block cipher systems heavily depend on how efficient and secure their Key Schedule Algorithms (KSAs) are, especially when fighting against cryptanalytic attacks. This paper proposes a novel KSA architecture designed to strike an optimal balance between computational efficiency and cryptographic security. Through the utilization of advanced algorithmic constructs, the proposed KSA minimizes processing time and resource consumption, thus catering to the needs of both high-performance and resource-constrained environments. We conduct a comprehensive statistical analysis to ascertain the computational innovation of our design, employing entropy measures, autocorrelation tests, and non-linear complexity assessments. The empirical evaluation demonstrates the resilience of our KSA against a variety of cryptanalytic benchmarks, revealing significant improvements in key unpredictability and distribution. Additionally, extensive benchmarking against established cryptographic standards confirms the superior performance and security enhancements offered by our KSA. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Block cipher security Cryptographic design Encryption analysis Key schedule algorithm Secure communication

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Visible Watermark Removal with Deep Learning Technology

Visible Watermark Removal with Deep Learning Technology

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International Symposium on computer, Consumer and Control (IS3C)

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

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.

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Vehicle Classification and Counting for Traffic Video Monitoring Using YOLO-v3 1

Vehicle Classification and Counting for Traffic Video Monito...

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1st International Conference on Connected Systems and Intelligence, CSI 2022

作者： Bose, Samprit Ramesh, Chavan Deep Kolekar, Maheshkumar H. Indian Institute of Technology Patna Electrical Engineering Dept Patna India PDPM-IIITDM Computer Science and Engineering Dept Jabalpur India

ISBN: (纸本)9781665458153

Traffic has been a major concern in most of the cities. Monitoring cameras are used to track, detect and count vehicles in real-time to ensure proper management of traffic. Counting of vehicles like cars, trucks and two wheelers is important for Intelligent Transportation System (ITS) to identify the intensity of traffic flow. In this paper we proposed vision based vehicle classification and counting approach using YOLO- v3 framework. The proposed method is composed of steps like masking, detection, classification and counting of different classes of vehicles. We have tested proposed method over 2000 vehicles of different categories obtained from the CCTV camera installed at main gate of lIT Patna campus. Experimental results show that the proposed approach has achieved accuracy of 93.65 % and 87.68 % during daytime and nighttime respectively. © 2022 IEEE.

关键词： Intelligent systems

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Bridging the Modality Gap: Generative Adversarial Networks for T1-T2 MRI Image Translation

Bridging the Modality Gap: Generative Adversarial Networks f...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Mayank Pandey Naga Sai Shreya Kunda Pranave Kc Tripty Singh Rekha R Nair Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Dept. of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

CycleGAN, a significant deep learning model, has transformed medical imaging by providing a remedy for translating MRI images between different modalities. Its ability to transform T1-weighted to T2-weighted images is crucial in healthcare environments where obtaining multiple scans can be time-consuming and burdensome for patients. This can be achieved by utilizing CycleGAN, which generates images that contains vital information that will help the healthcare professionals to diagnose the patient without exposing them to further radiations. This unleashes an effective and safer way of diagnosing patients. The adaptability of CycleGAN also allows scalability and the ability to customize it for different types of medical images, where scanning can be achieved with less exposure to radiation, without loosing or compromising any important information.

关键词： Deep learning Magnetic resonance imaging Scalability Magnetic resonance Medical services Transforms Generative adversarial networks Medical diagnostic imaging

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Machine Learning and Deep Learning for Credit Card Fraud Detection: A Comparative Analysis

Machine Learning and Deep Learning for Credit Card Fraud Det...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Jyoti Bhardwaj, Komal Garima Kumar, Mukesh Verma, Rajit Kumar, Divyansh Govt. Pg College for Women Computer Science dept. Haryana Rohtak India Mullana Haryana Ambala India Chitkara University Institute of Engineering and Technology Chitkara University India

ISBN: (纸本)9798350379716

Since last couple of the years, credit card frauds have been inclining abruptly. These frauds can be stated as the illicit usage of card, uncommon transaction activity, or swapping of an inert card. In other words, illegal account access by a person for which the account was not anticipated, is called as fraud and the process of behavior recognition of transactions known as authentic or deceptive. Detection of these frauds is truly the procedure of transactions classification i.e. authorized class and fraud class. Numerous techniques have been put forth and put into practice to address credit card fraud detection, including proposed and implemented to treat of credit card fraud detection like, data mining, nature-inspired algorithms like evolutionary algorithms & swarm techniques, machine learning algorithms etc. Machine Learning (ML) possess outstanding role in the uncovering frauds in online transactions. Fraudsters are becoming increasingly smarter and accustomed so there is a need of more robust, scalable and computationally efficient prediction models like Deep Neural Networks. Machine learning includes the subfield of deep learning, which is a more comprehensive use of artificial neural networks (ANN) that use several layers of non-linear processing components for feature mining as well as transformation. Deep learning is a sort of machine learning that helps computers understand the world through a conceptual hierarchy and learn from training. Therefore, a solution to lessening losses brought on by credit card fraud is to create efficient fraud detection algorithms utilizing machine learning, and more especially, deep learning approaches. In this study the use of deep learning and machine learning techniques to combat credit card fraud, have been compared. The results show that deep learning based approach overtakes all the other ML techniques with the utmost precision and can be effectually used for fraud investigation. © 2024 IEEE.

关键词： Contrastive Learning

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TB Chest X-Ray Diagnostic Technique Using Deep Learning 3

TB Chest X-Ray Diagnostic Technique Using Deep Learning

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Goyal, Manish Kumar Yadav, Dhananjay Kumar Brar, Khushmeen Kaur Mittal, Manisha Ojha, Ananta Alzubaidi, Laith H. Pathani, Ashish Vivekananda Global University Dept. of Computer Science & Application Jaipur India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chandigarh University Dept. of Electronics and Communication Engineering Mohali India Guru Tegh Bahadur Institute of Technology New Delhi India Dept. of Computer Science and Information Technology Bangalore India The Islamic University Najaf Iraq Uttaranchal Institute of Technology Uttaranchal University Department of Civil Engineering Dehradun India

ISBN: (纸本)9798331518592

The bacteria Mycobacterium tuberculosis is responsible for the infectious illness tuberculosis (TB). Although the lungs are the primary organs affected, the kidneys, bones, and brain may also be affected. Whenever an infected person coughs, sneezes, or talks, small droplets comprising the bacteria are released into the air and spread through the airborne system. The elimination of tuberculosis (TB) remains a problem due to factors such medication resistance, co-infection with HIV, and scarce resources in high-burden configurations, even with substantial efforts to treat the disease. As a major worldwide health concern, tuberculosis (TB) necessitates quick and precise diagnosis in order to be effectively managed. This study aimed to develop a TB detection model using chest X-ray images obtained from ***, utilizing Google's Collaboration Platform. The model was trained to recognize patterns within the TB chest X-rays to efficiently recognize TB within patients in order to be treated in time. We introduce a technique for transfer learning that proves preferable to the traditional methodology of adopting ImageN et weights. The hybrid methodology utilizes best first search technique in fusion with ResNet 50 model. The SVM classifier is used for categorizing the classes. An accuracy of 93.91 % is attained. © 2024 IEEE.

关键词： Deep learning

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GridWatch: A Smart Network for Smart Grid

GridWatch: A Smart Network for Smart Grid

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IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Masoud Hemmatpour Changgang Zheng Noa Zilberman Phuong Hoai Ha Dept. Computer Science Arctic University of Norway Dept. Engineering Science University of Oxford

ISBN: (数字)9798350318555

ISBN: (纸本)9798350318562

The adoption of decentralized energy market models facilitates the exchange of surplus power among local nodes in peer-to-peer settings. However, decentralized energy transactions within untrusted and non-transparent energy markets in modern Smart Grids expose vulnerabilities and are susceptible to attacks. One such attack is the False Data Injection Attack, where malicious entities intentionally inject misleading information into the system. To address this threat, this paper proposes GridWatch, an effective real-time in-network intelligent framework to detect false data injection attacks. Gridwatch operates in a hybrid model. It deploys inference model in the programmable network devices and also on the server to detect false data injection attacks. GridWatch was evaluated using a real-world dataset from Austin, Texas, and can detect false data injection attacks with 94.8% accuracy. GridWatch on average performs 4 billions transactions per second in less than 1.8 microsecond latency.

关键词： Performance evaluation Accuracy Computational modeling Machine learning Data models Real-time systems Smart grids Peer-to-peer computing Servers Low latency communication

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Hybridization of Sparrow Search Algorithm and Sine Cosine Algorithm for Epileptic Seizure Detection 3

Hybridization of Sparrow Search Algorithm and Sine Cosine Al...

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Mohapatra, Sumant Kumar Swain, Kaliprasanna Mallik, Puspanjali Mali, Sabita Ali, Farida A. Trident Academy of Technology Odisha Bhubaneswar India Shailabala Women's Autonomous College Dept. of Computer Science Cuttack India Iter Soa University Faculty of Engineering Bhubaneswar India

ISBN: (纸本)9798350354379

An imbalance in electrical signal flow among neurons causes epilepsy, a complex brain disease that affects other parts of the body and results in seizures. Researchers and neurologists have put their efforts into devising powerful electroencephalogram devices to address the importance of health issues. In this context, machine learning algorithms are used to accurately detect the presence of a seizure signal. This experiment uses the hybridization of two meta-heuristic optimization algorithms: the sparrow search algorithm and the sine cosine algorithm. To conduct the research, this experiment uses the publicly available CHB-MIT EEG dataset, and for feature extraction, it uses the discrete wavelet transform (DWT). ANOVA We use feature selection to identify similar groups. This study uses three classifiers: support vector machines (SVM), Adaptive Neuro Fuzzy Inference System (ANFIS), and Extreme Learning ANFIS (ELANFIS). We got 99.8% accuracy, 98.35% sensitivity, 99.02% specificity, 98% positive predictive value, and 1 for area-under-curve for SSA-SCA-DWT-ELANFIS. This shows that it works better than SSA-SCA-DWT-SVM, SSA-SCA-DWT-ANFIS, and other methods that are already out there. In the future, we can integrate this study with the Internet of Things (IoT). © 2024 IEEE.

关键词： Support vector machines

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Sparse Long Short-Term Memory Approach for Energy-Efficient Adaptive Cluster Fuzzy-based Controller in Wireless Sensor Network 5

Sparse Long Short-Term Memory Approach for Energy-Efficient ...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Ramanan, K. Ramesh, S.P. Kingsly, C. Sahaya Rajkumar, G. Vinoth Ponkumar, D. David Neels Vargheese, M. Npr College of Engineering and Technology Dept. of Ece Dindigul India Amet University Department of Computer Science and Engineering Tamil Nadu Chennai India Mangalam College of Engineering Department of Computer Science and Engineering Kottayam Ettumanoor India Jp College of Engineering Department of Ece Tamil Nadu Tenkasi India Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Dept. of Ece Chennai India Psn College of Engineering and Technology Department of Cse Tamil Nadu Tirunelveli India

ISBN: (纸本)9781665474672

Wireless Sensor Network (WSN) gadgets began with limited-scope WSNs and have expanded to larger-scope and Internet of Things -based WSNs. Clustering increases WSN activity. Before picking Cluster Heads (CHs), Nodes are clustered. Nodes in a clustered WSN, transmit CH environmental variables. Current sink-selection approaches presume unlimited data transfer. Multiple CHs using the same instance, might create complications. Insufficient drop-off space causes package loss. Sinks should have airy, less-restrictive regulators. SIS reduces sink nodes. Looser regulators choose the CH. R-recommendations are used to standardize. Reductions standardize regulators. These methods reduce energy, residual energy, First Node Dead, Half Nodes Dead, Last Node Dead, packet loss, and latency. Good throughput, latency, reliability, packet loss, durability, power consumption, and end-to-end delay are accomplished. The objective of this work is to examine the accessible clustering systems for further developing sensor network execution due to different plan limits and streamlining philosophies. © 2023 IEEE.

关键词： Packet loss

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