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engineering and Technology (PICET), Parul International Conference on

作者： Rakesh Bhavsar Vishvjit Thakar Dept. of Computer Science Engineering School of Engineering Indrashil University Mahesana Gujarat India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

From small businesses to major corporations, Cloud computing is quickly taking over as the preferred platform. due to the obligation to guarantee that cloud apps are safe and can shield data from unauthorized attackers. According to the joint responsibility model of Cloud Security, organizations who utilize these services are also responsible for maintaining their degree of security while service providers are responsible for making sure that their services are safe. A shared responsibility Paradigm for identity and access management transfers part of the client's responsibility for application security. Setting up procedures for identity management and access control as well as carrying out ethical hacking and penetration testing are required to guarantee the greatest degree of data security. Since Cloud Security has developed into a shared duty between the user and the service provider, clients are now required to take accountability for the security of their applications in the cloud. IaaS and PaaS services are the major beneficiaries of this. Advanced security procedures like ethical hacking and penetration testing are necessary to provide a safe virtual environment. Organizations will be able to avoid harmful assaults and data leaks thanks to these efforts. Raising risk visibility, identifying vulnerabilities, minimizing risks, and gathering crucial telemetry data are the greatest ways to enhance security. The primary goal of this study article is to assess the techniques for designing a framework or checklist to identify associated risks & vulnerabilities while at the same time attacking cloud-based applications. As a consequence, malicious actors will be kept out of cloud-based programs.

关键词： Cloud computing Ethics Scalability Computational modeling Cloud computing security Companies Safety Security computer crime Penetration testing

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A Dual-Port, and Single-Element Planar Inverted-F Antenna for Current 4G/5G Portable Applications 21

A Dual-Port, and Single-Element Planar Inverted-F Antenna fo...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Alja'Afreh, Saqer S. Altakhaineh, Amjaad T. Al-Shamaileh, Mallak H. Matarneh, Aser M Daoud, Omar R. Al-Khawaldah I, Mohammad School of Engineering and Computing American International University Electrical and Computing Engineering Al-Jahra Kuwait Pioneers International Academy Data Science and Machine Learning Dept. Amman Jordan Al-Ahliyya Amman University Communications and Computer Engineering Amman Jordan

ISBN: (纸本)9798350374131

A first-hand, low-profile, two-port planar inverted-F antenna is proposed for current 4G/5G sub-3 GHz hand-portable multiple-input and multiple-output (MIMO) applications. The design features a profile of about 5 mm and dual-coplanar feeds, which are isolated using only a modified top plate, while the shared ground plane is not perturbed as in previous designs. An open-edge slot on the top of the PIFA provides a 3λ/4 current path. The proposed antenna covers approximately a 15% impedance bandwidth fraction in the range of 2.45-2.85 GHz. The simulated results, including scattering parameters, far-field radiation patterns, and the envelope correlation coefficient, all exhibit excellent features. © 2024 IEEE.

关键词： Antenna feeders

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Deepfake Image Detection using Deep Learning

Deepfake Image Detection using Deep Learning

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Artificial Intelligence and Data engineering (AIDE), International Conference on

作者： Arun Kumar S Anand Kumar M Robin Rohit Vincent Uttam Upendra Hegde Anusha M S N Nauman Pasha Dept. of Computer Studies Symbiosis Institute of Computer Studies and Research (SICSR) Pune India Dept. of Information Science and Engineering Bio-Intelligence Lab Presidency University Bangaluru India Dept. of Computer Science and Engineering Bio-Intelligence Lab Presidency University Bangaluru India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Deepfake images are generated by modifying existing visuals and are frequently exploited in harmful ways. When executed proficiently, these images can be almost indistinguishable from authentic ones. The increasing development of deep learning techniques has largely fueled the increase in deepfake content. While numerous techniques are available for creating deepfake images, the most commonly employed are GANs and autoencoders. This paper presents a way to make deepfake detection more accurate by using a combination of the YOLOv8 model and a Recurrent Neural Network (RNN). YOLOv8 extracts spatial details from the images, while the RNN identifies temporal patterns and subtle irregularities across image frames, signaling potential manipulation. This methodology presents an efficient solution for identifying deepfake images and reducing their misuse.

关键词： YOLO Deep learning Deepfakes Adaptation models Analytical models Visualization Recurrent neural networks Accuracy Computational modeling Faces

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Futuristic Authentication: Development of an Artificial Intelligence based Enhanced Face Detection and Recognition System 7

Futuristic Authentication: Development of an Artificial Inte...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Rama Satish, K.V. Jadhav, Dattatraya Arun Siva Prasad, M.V. Gopal Thatipudi, Jayendra Tejasri, V. Divya, J. Rnsit Dept of Ai Ml Bengaluru India Keystone School of Engineering Electronic And Telecommunication Department Pune India Anurag Engineering College Department of Cse Kodad India University of North Texas Dept. of Computer Science India S.R.K.R.Engineering College Department of It Chinaamiram Andhra Pradesh Bhimavaram India R.M.K.Enginnering College Dept of Computer Science And Design India

ISBN: (纸本)9798350359299

This research presents the design and implementation of an advanced Face Detection and Recognition System employing an Artificial Intelligence Assisted Face Recognition Model (AIFRM). The proposed system leverages the powerful Cascaded VGG16 architecture to enhance the accuracy of face detection and recognition tasks. The methodology involves a two-step approach, beginning with robust face detection using the cascaded VGG16 model. This is followed by feature extraction and recognition using the same architecture, resulting in a highly efficient and accurate facial recognition system. The cascaded VGG16 model is trained on diverse datasets to ensure adaptability to various scenarios and lighting conditions, enhancing the system's real-world applicability. The experimental results demonstrate the effectiveness of the proposed system in achieving state-of-the-art accuracy in face detection and recognition. The cascaded VGG16 model proves to be a formidable backbone for the AIFRM, contributing to the system's success in handling complex scenarios. The system's high accuracy, reaching 97%, positions it as a reliable solution for security, surveillance, and authentication applications in diverse environments. © 2024 IEEE.

关键词： Face recognition

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An Effective Method to Detect DHCP Starvation Attack using Port Scanning

An Effective Method to Detect DHCP Starvation Attack using P...

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Next-Generation Computing, IoT and Machine Learning (NCIM), International Conference on

作者： Asaduzzaman Jony Abu Saleh Musa Miah Muhammad Nazrul Islam Dept. of Computer Science and Engineering Bangladesh University of Professionals (BUP) Dhaka Bangladesh Dept. of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan Dept. of Computer Science and Engineering Military Institute of Science and Technology (MIST) Dhaka Bangladesh

Nowadays, the DHCP starvation attack is a crucial attack that denied the services for the legitimate network user where the attacker mainly occupies all the IP addresses in the DHCP server pool. Most of the existing studies proposed ICMP (Internet Control Message Protocol) and the ARP (Address Resolution Protocol) based techniques to detect the DHCP starvation attack. However, the ICMP-based detection is ineffective if the client has an active host-based firewall that blocks the ICMP ECHO REQUEST, while the ARP-based detection does not work for DHCP networks separated by a DHCP relay agent and works only within the same or local network. Therefore, the objective of this research is to propose an effective technique to detect DHCP starvation attack detection through port scanning to address the limitations of ICMP and ARP-based detection methods. To attain this objective, this study first highlights the limitations of existing detection methods demonstrating insightful facts. Secondly, the study proposed a port scan-based DHCP starvation attack detection method for local and remote (relay) DHCP networks. Finally, the effectiveness and validation of the proposed techniques were demonstrated using the Multivendor Network Emulation Software (EVE-NG). The study showed that the proposed port scanning-based technique could accurately detect the DHCP starvation attack while overcoming the limitations of the ICMP and ARP techniques.

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An Overview of Real-Time Electricity Market and Green Energy Trading in India

An Overview of Real-Time Electricity Market and Green Energy...

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Zacharias, Annu Mary Dikaiakos, Christos Anto, Robins University of London School of Mathematics Computer Science and Engineering City United Kingdom Indian Institute of Technology Dept. of Hydro and Renewable Energy Roorkee India

ISBN: (纸本)9798350331790

To manage the high load fluctuations arising from renewable energy integration, the Ministry of Power Govt. of India has launched real-time trading in the energy market. In this paper, we discuss the real-time market process, regulatory authorities, system balancing, prerequisites of RTM of India, and one of the prominent real-time energy markets in the world. The market process is based on bidding, and Indian market has a zone wised bidding system. In India, the energy is traded in two market platforms named Energy Exchange of India and Power Exchange of India Ltd. The energy market operations are unavoidably connected with the load dispatch centers as the market has to be cleared by considering the transmission corridor availability within the system. The power exchanges of India and the load dispatch centers are monitored and guided by the central electricity regulatory authority and Power Grid which is formerly known as the Power system operation corporation. The real-time power trading is done for 30 minutes, and the auction is open for 15 minutes, followed by market clearing after gate closure. In addition to the real-time market, this paper gives a brief overview of the green energy market window in Indian energy exchanges. © 2023 IEEE.

关键词： Renewable energy resources

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Evaluating Low-Resource Lane Following Algorithms for Compute-Constrained Automated Vehicles

arXiv

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arXiv 2024年

作者： Froemming-Aldanondo, Beñat Rastoskueva, Tatiana Evans, Michael Machado, Marcial Vadella, Anna Johnson, Rickey Escamilla, Luis Jostes, Milan Butani, Devson Kaddis, Ryan Chung, Chan-Jin Siegel, Josh Dept. of Computer Science & Engineering University of Minnesota Minneapolis United States Dept. of Computer Science The University of Arizona Tucson United States Dept. of Computer Science Old Dominion University Norfolk United States Dept. of Computer Science & Engineering The Ohio State University Columbus United States Dept. of Computer Science SWE Butler University Indianapolis United States Dept. of Computer Science North Carolina A&T State University Greensboro United States Dept. of Computer Science New Mexico State University Las Cruces United States Dept. of Math & Computer Science Lawrence Technological University Southfield United States DeepTech Lab Michigan State University East Lansing United States

Reliable lane-following is essential for automated and assisted driving, yet existing solutions often rely on models that require extensive computational resources, limiting their deployment in compute-constrained vehicles. We evaluate five low-resource lane-following algorithms designed for real-time operation on vehicles with limited computing resources. Performance was assessed through simulation and deployment on real drive-by-wire electric vehicles, with evaluation metrics including reliability, comfort, speed, and adaptability. The top-performing methods used unsupervised learning to detect and separate lane lines with processing time under 10 ms per frame, outperforming compute-intensive and poor generalizing deep learning approaches. These approaches demonstrated robustness across lighting conditions, road textures, and lane geometries. The findings highlight the potential for efficient lane detection approaches to enhance the accessibility and reliability of autonomous vehicle technologies. Reducing computing requirements enables lane keeping to be widely deployed in vehicles as part of lower-level automation, including active safety systems. © 2024, CC BY.

关键词： Unsupervised learning

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Predicting Lifespan of Ground-to-Air Multipath Components in mmWave UAV Channels

Predicting Lifespan of Ground-to-Air Multipath Components in...

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IEEE Conference on Wireless Communications and Networking

作者： Wahab Khawaja Rune H. Jacobsen Sajid Hussain Ismail Guvenc Dept. Electrical and Computer Engineering Aarhus University Aarhus Denmark Dept. Computer Systems Engineering Mirpur Univ. Science and Technology Mirpur AJK Pakistan Dept. Electrical and Computer Engineering North Carolina State University Raleigh NC USA

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In mobile ground-to-air (GA) propagation channels, the birth and death of multipath components (MPCs) are frequently observed, and the wide-sense stationary uncorrelated scattering (WSSUS) assumption does not always hold. Several methods exist for tracking the birth and death of MPCs, however, to the best of knowledge of authors, there is no existing literature that addresses the prediction of the lifespan of the MPCs in non-WSSUS GA propagation channels. In this work, we consider the GA channel as non-WSSUS and individual MPCs across receiver positions are represented as time series based on the Euclidean distance between channel parameters of the MPCs. These time series representations, referred to as path bins, are analyzed using a semi-Markov chain model. The channel parameter variations and dependencies between path bins are used to predict the lifespan of path bins using weighted sum method, machine learning classifiers, and deep neural networks. For comparison, the birth and death of path bins are also modeled using a Poisson distribution and a Markov chain. Simulation results demonstrate that deep neural networks offer highly accurate predictions for the lifespan (including death) of MPC path bins in the considered GA propagation scenario.

关键词： Analytical models Simulation Time series analysis Scattering Artificial neural networks Machine learning Receivers Euclidean distance Autonomous aerial vehicles Millimeter wave communication

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Singer Separation for Karaoke Content Generation 27

Singer Separation for Karaoke Content Generation

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27th Conference on the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of Speech Databases and Assessment Techniques, O-COCOSDA 2024

作者： Lin, Hsuan-Yu Chen, Xuanjun Jang, Jyh-Shing Roger Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan National Taiwan University Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798331506032

Due to the rapid development of deep learning, we can now successfully separate singing voice from mono audio music. However, this separation can only extract human voices from other musical instruments, which is undesirable for karaoke content generation applications that only require the separation of lead singers. For this karaoke application, we need to separate the music containing male and female duets into two vocals, or extract a single lead vocal from the music containing vocal harmony. For this reason, we propose in this article to use a singer separation system, which generates karaoke content for one or two separated lead singers. In particular, we introduced three models for the singer separation task and designed an automatic model selection scheme to distinguish how many lead singers are in the song. We also collected a large enough data set, MIR-SingerSeparation11, which has been publicly released to advance the frontier of this research. Our singer separation is most suitable for sentimental ballads and can be directly applied to karaoke content generation. As far as we know, this is the first singer-separation work for real-world karaoke applications. © 2024 IEEE.

关键词： Source separation

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Detection of Brain Tumor Using a Deep Learning Approach

Detection of Brain Tumor Using a Deep Learning Approach

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Artificial Intelligence and Data engineering (AIDE), International Conference on

作者： Arun Kumar S Anand Kumar M Robin Rohit Vincent Anusha M S Uttam Upendra Hegde N Nauman Pasha Dept. of Computer Studies Symbiosis Institute of Computer Studies and Research (SICSR) Pune India Dept. of Information Science and Engineering Networks & IoT Lab Presidency University Bangaluru India Dept. of Computer Science and Engineering Bio-Intelligence Lab Presidency University Bangaluru India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

This study introduces a deep learning-based method for classifying brain tumors using a pre-trained VGG19 convolutional neural network (CNN). By leveraging transfer learning, we adapted the VGG19 model with custom fully connected layers and trained it on MRI images to differentiate between tumor and non-tumor cases. The model’s architecture includes dropout layers to minimize overfitting and enhance generalization while maintaining the robust feature extraction capabilities of the VGG19 base. We developed a Flask web application interface to make the model accessible via web-based user input, allowing users to upload MRI images and receive immediate predictions on tumor presence. Preliminary results show that proposed model can accurately classify brain tumor images with high accuracy, highlighting the potential of deep learning in automating brain tumor diagnostics. This tool could significantly aid clinicians in making rapid and accurate diagnoses, ultimately improving patient outcomes.

关键词： Deep learning Adaptation models Accuracy Magnetic resonance imaging Transfer learning Brain tumors Predictive models Brain modeling Convolutional neural networks Reliability

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