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9th International Conference on Communication and electronics Systems, ICCES 2024

作者： Patil, Ashish Patil, Sushama Nikam, Pritam Bhosale, Akshay G. Magdum, Pravin S. Hake, Shahadev Maharashtra Kolhapur India Shivaji University Maharashtra Kolhapur India KIT's college of Engineering Department of E&tc Engineering Kolhapur India Pune India DKTE's Textile & Engineering Institute Electrical Engineering Department Ichalkaranji India SCTR's Pune Institute of Computer Technology Department of Electronics and Telecommunication Engineering Pune India

ISBN: (纸本)9798350377972

The era of 6G communication is thought to be the fusion of human being with digital and physical world. The 6G communication have started attracting many industries and academia because of its immense capabilities to drive various application. The evolution of 6G standard can be the advancement of the current 5G standards. It could provide variety of services under a single platform, hence there is a need for robust and reliable architecture that can satisfy various needs at different levels. In this article, we present various 6G services in brief, starting with the different challenge faced at the 6G operating frequencies, to the role of artificial intelligence (AI) and machine learning (ML) to provide these services in effective and efficient manner. It is expected that everything should be connected to every other thing in this new standard, providing the connectivity to remote places is a prime challenge;to address this issue, we have presented the idea of connected ocean through a sample infrastructure diagram. © 2024 IEEE.

关键词： 5G mobile communication systems

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Decision Systems in Cybersecurity: A Machine Learning Perspective 12th

Decision Systems in Cybersecurity: A Machine Learning Perspe...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Dhablia, Anishkumar Ganesh, R. Senthil Rizvi, Nuzhat Arora, Himanshu Borade, Monali Ravindra Bhujbal, Pallavi Altimetrik India Pvt. Ltd Maharashtra Pune India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Tamilnadu Coimbatore India Nagpur Campus Pune India Computer Science and Engineering Arya College of Engineering Jaipur India Artificial Intelligence and Data Science VIIT Pune India Department of Information Technology School of Computing MIT ADT University Loni Kalbhor Pune India

ISBN: (纸本)9789819601462

The evolving digital environment requires sophisticated decision systems to protect important information assets from the growing complexity and variety of cyber threats. This review paper examines the impact of machine learning (ML) on cybersecurity decision systems, with a specific focus on analyzing ML and deep learning (DL) methods. The study employs the UNSW-NB15 dataset, which is a widely used benchmark dataset in the field of network security. The paper starts with an overview of the present cybersecurity environment, emphasizing the difficulties presented by advanced and changing cyber threats. It highlights the importance of adaptive and intelligent decision systems that can efficiently identify and reduce cyberattacks as they occur. A significant part of the paper focuses on a thorough analysis of different machine learning and deep learning techniques used in cybersecurity. The review discusses conventional machine learning algorithms like decision trees, support vector machines, and ensemble methods, along with sophisticated deep learning architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Each approach is thoroughly assessed for its strengths and limitations based on factors like accuracy, interpretability, and scalability. The performance of various ML and DL models is evaluated using the UNSW-NB15 dataset as a benchmark. The dataset covers various cyberattack scenarios, enabling a thorough assessment of algorithms’ ability to detect anomalies and classify malicious activities. The paper explores the incorporation of machine learning (ML) and deep learning (DL) into decision support systems, highlighting the significance of explainability and interpretability in the realm of cybersecurity. The conversation covers the difficulties of implementing machine learning (ML) and deep learning (DL) models in practical situations, such as concerns about data privacy, adversarial attacks, and model resilience. This rev

关键词： Adversarial machine learning

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Comparative Impact of Wind and Photovoltaic Energy Integration on Isolated Microgrid Self-Sufficiency and Load Management

Comparative Impact of Wind and Photovoltaic Energy Integrati...

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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Saxena, Abhishek Yakaiah, P. Pravallika, B. Ahmed, Shaymaa Nijhawan, Ginni Rajalakshmi, B. Abes Engineering College Department Of Mechanical Engineering UP Ghaziabad201009 India Mlr Institute Of Technology Department Of Electronics And Communication Engineering Telangana Hyderabad India Institute Of Aeronautical Engineering Department. Information Technology Telangana Hyderabad50043 India Hilla University College Babylon Iraq Lovely Professional University Phagwara India New Horizon College Of Engineering Department Of Computer Science Bangalore India

ISBN: (纸本)9798350366846

The micro grid structure creates a new distribution network that includes multiple power sources in place of the conventional distribution infrastructure with a radial network and one power source. An autonomous or grid-connected micro grid may be identified based on the degree of connection it has with the main grid. Since there is no link to the main grid in an isolated micro grid, the micro grid must be self-sufficient. This kind of micro grid functions as a miniature power system, complete with generating, transmission, and distribution components. The accessibility of local energy supplies is the primary factor driving isolated micro grids. In order to improve the isolated operating of the micro grid, this research presents a comparative analysis of introducing wind or photovoltaic (PV) energy sources to the micro grid. The research compares adding the same amount of wind or photovoltaic energy to the self-sufficient microgrid. The two primary parameters that are compared are the quantity of energy that is not given to the load and the duration of time that certain loads are shredded. We'll examine in-depth a standard case analysis of the micro grid system in Jaipur, India. We will offer a simulation model that computes the electrical power production for both PV and wind energy systems based on real data collected at the site about variations in environmental conditions. MATLAB was used to develop the model so that it could be used anywhere in the globe. The evolution of the current electrical networks towards an environmentally friendly distribution network system will lead to the incorporation of the micro grid system with renewable energy resources. The final findings are shown, demonstrating how solar or wind energy affects the micro grid's isolated functioning. © 2024 IEEE.

关键词： Smart power grids

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Development of Green Computing through Power Management in Wireless Networks using Machine Learning 7

Development of Green Computing through Power Management in W...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Raja, S.R. Arya, Hemlata Ramya, D. Sivakumar, S. Arigela, Arun Kumar Venu, Nookala Saveetha Deemed University SIMATS Saveetha College of Liberal Arts And Science Department of Computer Science Chennai India Department of Computer Science & Engineering Madhya Pradesh Gwalior India Sathyabama Institute of Science And Technology Electrical And Electronics Engineering Rajiv Gandhi Salai Chennai India Vel Tech Rangarajan Dr. Sagunthala R And D Institute of Science And Technology Department of Electrical And Electronics Engineering Chennai Tamilnadu Avadi600062 India Department of Csit Telangana Hyderabad India Centre For IoT Madhya Pradesh Gwalior474 005 India

ISBN: (纸本)9798350359299

This study investigates the use of Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) to improve power utilization in green data centers. The study examines each model's individual capabilities in addressing key concerns about energy efficiency and sustainability. SVM is extremely accurate in workload prediction and resource allocation, whereas CNN excels in real-time monitoring and cooling system efficiency. RNN contributes by anticipating fluctuations in energy use and adapting to dynamic workload changes over time. The research uses ten hypothetical trials to demonstrate the models' consistent performance across multiple measures. These results suggest that SVM, CNN, and RNN have transformative potential in the development of flexible, efficient, and sustainable green data centers. The study adds to the larger discussion about green computing by emphasizing the role of powerful machine learning models in shaping the future of ecologically responsible data center operations. © 2024 IEEE.

关键词： Support vector machines

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Orthogonal Time Frequency Space Modulation: Analysis in Noisy Underwater Acoustic Channels 17

Orthogonal Time Frequency Space Modulation: Analysis in Nois...

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17th International Symposium on Ocean technology, SYMPOL 2023

作者： Varshney, Deeksha Agrawal, Monika Gupta, Ananya Sen Indian Institute of Technology Delhi Bharti School of Telecommunication Technology & Management Delhi India Indian Institute of Technology Delhi Centre for Applied Research in Electronics Delhi India University of Iowa Department of Electrical and Computer Engineering IA United States

ISBN: (纸本)9798350338744

It is prominent that underwater acoustic channel has inherently dispersive characteristics for the communication due to multipath, relative mobility of hydrophones, small-large scale fading effects, delay spread and Doppler effects. So both time and frequency selective fading are severe in the underwater acoustic channel. So reliable underwater acoustic communication (UWAC) necessitates signaling techniques that are resilient to significant Doppler shifts as an example orthogonal time frequency space (OTFS). In this work, we analyzed the OTFS modulation technique in the Bellhop-based statistical underwater channel model for the different noises namely additive white Gaussian noise (AWGN), complex noise, and real channel noise. We also compare the bit error rate (BER) performance of the OTFS based UWAC with the base technique, orthogonal frequency division multiplexing (OFDM), based UWAC system under the AWGN noise. Lastly, the simulated results show that the OTFS based underwater communication provides acceptable BER performance for the discussed three noises even with the Doppler shift approximately of 11 Hz, and from the BER comparison plot, we conclude that the OTFS scheme outmatches the underwater communication based on OFDM when it comes to the error performance and transmitter-receiver (without detection) complexity. © 2023 IEEE.

关键词： Orthogonal frequency division multiplexing

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Cloud-Enabled Isolation Forest for Anomaly Detection in UAV-Based Power Line Inspection 2

Cloud-Enabled Isolation Forest for Anomaly Detection in UAV-...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Ramasamy, Jayabharathi Srividhya, E. Vaidehi, V. Vimaladevi, S. Mohankumar, N. Murugan, S. RMK College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vinayaka Mission's Kirupananda Variyar Arts and Science College Department of Computer Science Tamil Nadu Salem India Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology School of Management Department of Commerce and Business Administration Tamil Nadu Chennai India B.V. Raju Institute of Technology Department of Electronics and Communication Engineering Telangana India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350365269

Unmanned Aerial Vehicles (UAVs) gather data efficiently for power line inspection. Anomaly detection is essential for power infrastructure dependability and security. It proposes a Cloud-Enabled Isolation Forest (CEIF) method for UAV-based power line inspection. It improves the isolation forest algorithm's efficiency and scalability in cloud computing. It can process huge UAV inspection datasets by dispersing cloud computing. The technique, which effectively isolates anomalies, is applied to the cloud for fast power line inspection and anomaly identification. It describes the CEIF system's cloud service integration and distributed computing algorithm optimization. Real-world UAV-based power line inspection datasets show it can accurately detect abnormalities with low false-positive rates. It is scalable and robust for improving power infrastructure dependability and security. It allows cloud services to deploy real-world settings to implement different inspection scales. © 2024 IEEE.

关键词： Remote sensing

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Steganographic Encryption and Decryption Using Duality Preserving GANs 22

Steganographic Encryption and Decryption Using Duality Prese...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Shah, Aaryak Kamal, Alok Kumar Neeraj Dept. of Information Technology ABV-IIITM Gwalior Gwalior India Dept. of Electrical/Electronics ABV-IIITM Gwalior Gwalior India Dept. of Computer Science and Engineering Mahindra University Hyderabad India

ISBN: (纸本)9798331527471

Image steganography has been a significant research area in the security sphere. The core idea revolves around embedding secret messages in existing images (covers) such that the resultant image displays no noticeable detectable artifacts of the secret while also being statistically similar to the original cover. Various steganographic techniques have been developed and tested. However, these traditional techniques often exploit some specific property of the image and are often easy to detect with the right tools. In recent times, generative adversarial networks, or GANs for short, have shown massive promise in their capability to modify and synthesize data. They are now ubiquitous in image synthesis and image translation tasks. With various recent advancements in this technology allowing these models to generate, in many cases, human-imperceptible data, GAN-based steganographic systems have become a significant focus of security research. This report analyzes and compares some existing models and attempts to provide a new solution that performs better on multiple well-defined metrics. A DualGAN-based architecture is proposed that trains both the encoder and decoder processes simultaneously while maintaining the underlying dual relationship of these tasks. The proposed scheme reports better results on the selected metrics (SBR, DER, PSNR) at three values of embedding capacity set at 0.3, 0.6, and 0.9. © 2024 IEEE.

关键词： Generative adversarial networks

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Employing Machine Learning to Drive Innovative Strategies for Enhanced Organizational Performance

Employing Machine Learning to Drive Innovative Strategies fo...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Gurulakshmi, A.B. Vybhavi, Gummgatta Yajaman Singh, Jay Prashant, Kumar Srinija, K. Jabbar, Fatimah Dhari Kansal, Lavish New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India Grjet Department of Cse Telangana Hyderabad India Gl Bajaj Institute of Technology and Management Dept. of Eee U.P. Gr. Noida India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Lovely Professional University Phagwara India

ISBN: (纸本)9798331527495

TalentForgeML is up against six other HR management methods that are thought to be the best in the business. People in business think these tactics are the best. Here are a few examples of these plans: the IntelliHire method and the ClassicScreen method. TalentForgeML is the name of the method that combines HR management with machine learning. The goal of this study is to investigate the methods so that we can better understand them. Our main goal is to come up with new ideas that will help our company run more efficiently. The Inclusive Workplace (IWP) program, the Competence Acquisition (PTA) method, and the Dynamic Skill Development (DSD) algorithm are thought to be three of the most important algorithms in TalentForgeML. © 2025 IEEE.

关键词： Human resource management

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Development of Healthcare Monitoring System with Pollution Control in Industrial Sectors Using the Internet of Things 5

Development of Healthcare Monitoring System with Pollution C...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Rajeswari, N Raja Arunkumar, G. Vadivu, M Senthil Gupta, Manish Sethuraman, R. Rajkumar, M. Saveetha Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Madanapalle Institute of Technology and Science Department of Computer Science and Engineering Andhra Pradesh Madanapalle India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Lovely Professional University Division of Research and Development Punjab Phagwara India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India School of Information Technology & Engineering Vellore Institute of Technology Vit University Department of Smart Computing Tamil Nadu Vellore India

ISBN: (纸本)9798350321425

Rapid improvement in industrialization and urbanization leads to various impacts on environmental conditions. This leads to a rise in pollution that causes various constraints on human health. Earlier prediction and maintenance of pollution is the solution to obtain a greener ecosystem. This is done through machine learning techniques adopting the Internet of Things. The Internet of Things helps to obtain a smarter environment through real-time monitoring with the control system. Thus machine learning helps to analyze the amount of toxic substances that are emitted from the industries and recorded periodically. They are represented as online controlling and monitoring systems. The sensors and actuators with control devices are used to record the external environmental conditions. The prior intimation of the healthcare is monitored appropriately and intimated to solve earlier. The classification and detection of infections and diseases are done through image processing techniques. Thus the proposed system helps to obtain automatic detection of healthcare and pollution detection systems through machine learning with computational systems. © 2023 IEEE.

关键词： Internet of things

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DDPG-E2E: A Novel Policy Gradient Approach for End-to-End Communication Systems

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1738-1751页

作者： Zhang, Bolun Van Huynh, Nguyen Thai Hoang, Dinh Nguyen, Diep N. Pham, Quoc-Viet University of Warwick School of Engineering CoventryCV4 7AL United Kingdom University of Liverpool Department of Electrical Engineering and Electronics LiverpoolL69 3GJ United Kingdom University of Technology Sydney School of Electrical and Data Engineering SydneyNSW2007 Australia The University of Dublin School of Computer Science and Statistics Trinity College Dublin Dublin 2 D02 PN40 Ireland

The End-to-end (E2E) learning-based approach has great potential to reshape the existing communication systems by replacing the transceivers with deep neural networks. To this end, the E2E learning approach needs to assume the availability of prior channel information to mathematically formulate a differentiable channel layer for the back-propagation (BP) of the error gradients, thereby jointly optimizing the transmitter and the receiver. However, accurate and instantaneous channel state information is hardly obtained in practical wireless communication scenarios. Moreover, the existing E2E learning-based solutions exhibit limited performance in data transmissions with large block lengths. In this article, these practical issues are addressed by our proposed deep deterministic policy gradient-based E2E communication system. In particular, the proposed solution utilizes a reward feedback mechanism to train both the transmitter and the receiver, which alleviates the information loss of error gradients during BP. In addition, a convolutional neural network-based architecture is developed to mitigate the curse of dimensionality problem when transmitting messages with large block lengths. Extensive simulations then demonstrate that our proposed solution can not only jointly train the transmitter and the receiver simultaneously without requiring prior channel knowledge but also can obtain significant performance improvement compared to state-of-the-art solutions. © 2015 IEEE.

关键词： Convolutional neural networks

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