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Principles and Potentials of F5G: The Fifth Generation Fixed Network

Principles and Potentials of F5G: The Fifth Generation Fixed...

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Circuits, Power and Intelligent Systems (CCPIS), International Conference on

作者： Sudhir K. Routray Abhishek Javali KP Sharmila M. Pappa Mahesh K. Jha Susanta K. Sarangi Department of Computer Science and Engineering CMR University Bangalore India Department of Electronics and Communication Engineering CMR Institute of Technology Bangalore India Department of Electronics and Communication Engineering SoA (Deemed-to-be) University Bhubaneswar India

In general, 5G is understood to be the fifth generation of mobile communications. However, there is also a fifth generation of wired communications which is led by the optical fiber technologies. This technology too has several attractive features like the wireless mobile 5G. In this article, we present the basic concepts of Fifth Generation Fixed Network (F5G). We show its characteristics and explain how it is going to change the communication landscape. We present the key benefits of F5G and show the main motivations behind its development. There are several applications of F5G and many emerging applications are also being introduced every year. Therefore, this newly developed technology has a lot of commercial importance in the modern world. In addition to the above, F5G is going to be one of the main rivals of 5G in the fixed environment. It is expected that F5G will outperform all its contemporary access area technologies in the fixed domain in the next few years. In this article, we briefly present its applications and future economic prospects.

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Machine Learning-based Alzheimer's Disease Prediction using Personalized Methods 3

Machine Learning-based Alzheimer's Disease Prediction using ...

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3rd International Conference on Smart electronics and communication, ICOSEC 2022

作者： Lavanya, M. Chandan, Radha Raman Rajasekar, P. Rham, P. Rethik Deivakani, M. Mahesh Kumar, A.S. Master of Computer Applications Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India Shambhunath Institute of Engineering and Technology Department of Computer Science & Engineering Uttar PradeshJhalwa Prayagraj211015 India Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India Saveetha Medical College Tamil Nadu Chennai602105 India Psna College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India Pes College of Engineering Department of Electronics and Communication Engineering Karnataka Mandya571401 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Alzheimer's disease (AD) is a neurological illness which occurs over time. Early detection reduces the likelihood of disease worsening condition. Unfortunately, the present research focuses on defining disease processes in their existing stages rather than forecasting disease progression. The Generative Personalized embedding model (GPE) is a multi-layer perceptron that is proficient of relating prior knowledge to the prediction process by realizing the patient's behavioural data and helps in forecasting the disease progression. This work suggest a prediction process based on GPE. As a result, a GPA network is built with the connectivity layers and activated layers to observe the possible correlation among information and next generation of Alzheimer's disease. Experiments reveal that the outcomes significantly outperformed the majority of previous designs. © 2022 IEEE.

关键词： Machine learning

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NLP-Driven Detection of Cyber-Bullying Comments in Instagram Social Network

NLP-Driven Detection of Cyber-Bullying Comments in Instagram...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： C. Valarmathi Anitha Velu A. Prasanth Rajesh Kumar Dhanaraj Department of Computer Science and Engineering Sri Sairam College of Engineering Bengaluru India Department of Electronics and Communication Engineering Sri Sairam College of Engineering Bengaluru India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

People's attention towards usage of social media is growing continuously, particularly youngsters are paying special attention by means of advancing their professions and expanding network. Cyber-bullying, includes posting derogatory comments on other people's posts, fabricating an identity, and disseminating embarrassing photos or videos, is also on the rise on social media. Victims of cyber-bullying may result in low self-esteem, increased suicidal thoughts, and other negative emotions like anxiety, frustration, fury, or sadness of the person. Undoubtedly numerous calculations like Machine Learning (ML) methods have been utilized in recognizing cyber-bullying exercises which remain totally frozen. The primary focus of the proposed work is to integrate Term Frequency-Inverse Document Frequency (TF-IDF) information retrieval method with a Random Forest (RF) classifier. TF-IDF decreases the dimensionality of the dataset by turning textual data into numerical features and concentrating on informative words. RF classifier handles the words with high TF-IDF scores in a corpus and classifies the comments as “Low” and “High” depending on the severity flagged as potential indicators of bullying behavior. This framework develops software which automatically identifies and prevents hateful comments and hides them from the victim's page. Compared to other classifiers, RF adapts many linguistic patterns, manages copious data generated on social platform and handles imbalances in cyberbullying comments, particularly when paired with class weighting or sampling approaches. This work mainly focusses on Instagram social media page where it accomplishes 88% of accuracy, 0.89 of precision, 0.94 of recall and 0.91 of F-measure in detection of harmful words.

关键词： Linguistics Turning Natural language processing Software Web sites Multimedia communication Information technology Random forests Classification tree analysis Videos

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RMS: Real-time Motion Segmentation over the Internet of Vehicles

RMS: Real-time Motion Segmentation over the Internet of Vehi...

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2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2023

作者： Zhan, Lei Hu, Kai Zou, Longhao Yuan, Zhenhui Muntean, Gabriel-Miro Institue of Future Networks Southern University of Science and Technology Shenzhen China Peng Cheng Laboratory Department of Broadband Communication Shenzhen China Northumbria University Department of Computer and Information Sciences Newcastle United Kingdom Dublin City University School of Electronic Engineering Dublin Ireland

ISBN: (纸本)9798350321524

In the context of autonomous driving, moving objects such as vehicles and pedestrians are of critical importance as they primarily influence the maneuvering and braking of cars. Unfortunately, due to the limited detection range of sensors, some distant and blocked objects cannot be detected, leading to slow responses when some unexpected situations occur during driving. To address this problem, a real-time motion segmentation multi-task model (RMS), running on an individual vehicle, is introduced to provide motion segmentation of moving objects within its field of view. RMS consists of a shared encoder, a multi-modal fusion module, and a dual decoder. An enhanced High Definition (HD) map constructed with the proposed RMS in line with the recommendations of the 3rd Generation Partnership Project (3GPP) Vehicle-to-everything (V2X) communication standard is produced. Extensive experiments demonstrate how RMS outperforms existing state-of-the-art motion segmentation methods in terms of multiple metrics, including mean Intersection over Union (mIoU). Additionally, Internet of Vehicles (IoV) simulation experiments show how the time required to update the map is better than the times achieved when using other methods. © 2023 IEEE.

关键词： Autonomous vehicles

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Apple Leaf Disease Detection and Classification Using L1 Regularized Multi Head Vision Transformer 3

Apple Leaf Disease Detection and Classification Using L1 Reg...

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3rd International Conference on Integrated Circuits and communication Systems, ICICACS 2025

作者： Math, Gayatri Vishwanath, P. Balsem, Zayd Ajsan Supriya Jena, Suvendu Kumar Pda College of Engineering Department of Electronics and Communication Engineering Kalaburagi India Vtu Belagavi India Hke Society's Sir M Visvesvaraya College of Engineering Department of Electronics and Communication Engineering Raichur India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Bengaluru India Indira Gandhi Institute of technology Department of Computer Science Engineering and Applications Sarang Dhenkanal India

ISBN: (纸本)9798331508456

Timely identification of apple leaf diseases is important to avoid disease spread and ensure healthy growth of apple industry. Recent developments in digital cameras and electronic devices integrated with Machine Learning (ML) and Deep Learning (DL) based apple leaf disease detection has emerged as efficient to traditional visual inspection models. Therefore, this research proposes a L1 regularized Multi Head Vision Transformer (L1-MHViT) for apple leaf disease detection and classification. The L1-MHViT utilized multi-head attention to capture complex patterns and improve classification performance. Furthermore, it incorporates advantages of convolution-based and transformer-based models to enhance the detection and classification accuracy. Furthermore, the Median Filter (MF) is applied for preprocessing which removes noise and enhance the image quality thereby helps to enhance the accuracy of the consequent steps. Then, EfficientNet is applied for feature extraction which is known for higher accuracy and effective scaling of model size. The effectiveness assessment of proposed L1-MHViT uses standard metrics which include precision (Pre), recall (Rec), F1 score (F1) and accuracy (Acc). The L1-MHViT achieved 99.46% pre, 99.54% rec, 99.50% F1 and 99.82% are for Apple dataset which is better than baseline models. © 2025 IEEE.

关键词： Feature Selection

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Antennas and Other Peripherals for IoT

Antennas and Other Peripherals for IoT

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International Conference on communication and electronics Systems (ICCES)

作者： Sudhir K. Routray Mahesh K. Jha Abhishek Javali M. Pappa Department of Computer Science and Engineering CMR University Bangalore India Department of Electronics and Communication Engineering CMR Institute of Technology Bangalore India

Internet of things (IoT) is predominantly wireless in nature and thus it utilizes the main principles of wireless communication and networking. In order to design and deploy IoT networks, several wireless network components are needed. The common among them are the antennas, antenna support systems, edge computing facilities, gateways, and data centers. However, IoT nodes and sensors are small in size and they are exposed to limited resource environments. Keeping that in mind, the wireless components needed for IoT are designed according to their matching applications and demanded characteristics. This study analyzes the antennas and other peripheral devices required in IoT deployment. Also, this study discusses about the issues related to the architecture used for large scale and small scale deployments.

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Network Traffic Virtualization Using Wireshark and Google Maps

Network Traffic Virtualization Using Wireshark and Google Ma...

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Distributed Computing and Electrical Circuits and electronics (ICDCECE), IEEE International Conference on

作者： Sudha Arvind S. Arvind Vamshi Kumar Silveri Govardhan Potey Parameshwar Nunavath Ranadheep Podishetty Department of Electronics and Communication Engineering CMR Hyderabad India Department of Computer Science and Engineering Hyderabad Institute of Technology and Management Hyderabad India

Security analysts have the knowledge necessary to identify the types of traffic that are present in a network, identify instances of abnormally high traffic, and identify the network nodes responsible. Assisting users in locating servers and the destinations of data flows, network traffic visualization can also help researchers identify online threats. With Wireshark’s capabilities as a packet tracer and capture tool, network analysis starts and ends with the packet capture parameters selected in this study. Wireshark is used in this study to create a network tracker. In order to identify the source Internet Protocol (IP) and destination IP on a Google map, will capture the data packets using packet tracer, analyze them, and then translate the IP addresses into latitude and longitude values. This will let users visualize network traffic on a Google map and track it.

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Comprehensive Survey on Internet of Medical Things (IoMT) - Applications and Challenges

Comprehensive Survey on Internet of Medical Things (IoMT) - ...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： V Sai Anusha R Kiran Kumar G Charan Kumar Palagiri Mabjan Department of Electronics and Communication Engineering Madanapalle Institute of Technology & Science Madanapalle India Department of Computer Applications Annamacharya University Rajampet India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The Internet of Medical Things (IoMT) is changing healthcare through the use of the Internet to connect medical devices, apps, and systems. This helps improve patient care, makes healthcare more efficient, and improves health outcomes. This paper deals with the edge-fog-cloud computing architectures for Internet of Health applications. Remote patient monitoring is done by collecting the data from wearable health devices, processing at different communication levels, applying intelligent algorithms and providing insights to the patient and healthcare professionals. This paper reviews the IoMT architecture and how the entire workflow is managed at device, edge, fog, and cloud levels. The paper also discusses IoMT challenges, such as data security and privacy concerns due to the interconnectedness and interoperability of devices at each layer and hurdles for seamless data integration within healthcare networks.

关键词： Surveys Patient monitoring Data privacy Medical devices Reviews Data security Data integration Internet of Medical Things computer architecture Interoperability

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A Joint Scheme on Spectrum Sensing and Access with Partial Observation: A Multi-Agent Deep Reinforcement Learning Approach

A Joint Scheme on Spectrum Sensing and Access with Partial O...

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2023 IEEE/CIC International Conference on communications in China, ICCC 2023

作者： Zhang, Yulong Li, Xuanheng Ding, Haichuan Fang, Yuguang Dalian University of Technology School of Information and Communication Engineering Dalian China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing China City University of Hong Kong Department of Computer Science Hong Kong

ISBN: (纸本)9798350345384

Dynamic spectrum access (DSA) has been regarded as a promising solution to mitigate the serious spectrum shortage problem in the 6G networks, in which secondary users (SUs) are allowed to opportunistically access the licensed bands when primary users (PUs) are inactive. Due to the hardware limitation, partial spectrum sensing with a suitable sensing window (SW) is considered as an effective way to find the idle bands to access. It is noteworthy that the SW selection could determine how many bands are available to access, and the network performance after the access could be used to guide the SW selection. Thus, a sophisticated joint design on both spectrum sensing and access is necessary, which, however, is not an easy task considering the uncertainty and dynamics of the spectrum environment, and also the mutual impacts among SUs. In this paper, we propose a joint partial spectrum sensing and power allocation (PA) scheme to help each SU make the best SW and PA decisions that can optimize the network throughput. To achieve the best decision under the dynamic and uncertain of the environment, considering the mutual interference issue, we develop a multi-agent deep reinforcement learning approach to enable each SU to obtain the best SW and PA decisions autonomously and adaptively. © 2023 IEEE.

关键词： Reinforcement learning

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Advancing Security: Exploring AI-driven Data Encryption Solutions for Wireless Sensor Networks

Advancing Security: Exploring AI-driven Data Encryption Solu...

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Advances in Computing, communication and Applied Informatics (ACCAI), International Conference on

作者： L Arulmurugan Sangeeta Thakur R. Dayana S Thenappan Banavath Nagesh R. Kalaivani Sri Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam Tamil Nadu India Department of Electronics and Communication Engineering Garhwa Polytechnic Hasker Jharkhand India Department of Computer Science and Engineering Jeppiaar Institute of Technology Sriperumbudur Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Cyber Security Marri Laxman Reddy Institute of Technology and Management Dundigal Telangana India Department of Computer Science and Engineering Arunai Engineering College Tiruvannamalai Tamil Nadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The research thoroughly examines encryption techniques in Wireless Sensor Networks (WSNs), emphasizing machine learning-based, conventional, and cutting-edge AI-driven approaches. Important conclusions are drawn from a comparative analysis of speed, overhead, and energy use. The machine learning approach shows significant flexibility and increased security, whereas the conventional approaches show subtle trade-offs between energy use and network latency. This study presents a revolutionary AI-driven encryption architecture and reveals improved performance measures, such as reduced overhead, maximum energy efficiency, and faster speed. The outcomes demonstrate how AI can completely transform WSN security by providing a flexible and reliable solution. Graphics improves the findings’ comprehensibility and offers a more sophisticated view of performance differences. This study establishes a standard for current practices and opens the door for further developments in WSN security through AI integration.

关键词： Graphics Wireless communication Wireless sensor networks Data security Machine learning Transforms Energy efficiency

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