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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Demir, Özlem Tuǧfe Björnson, Emil Tobb University of Economics and Technology Department of Electrical-Electronics Engineering Ankara Turkey Kth Royal Institute of Technology Department of Computer Science Stockholm Sweden

ISBN: (纸本)9798350351255

A large-scale MIMO (multiple-input multiple-output) system offers significant advantages in wireless communication, including potential spatial multiplexing and beamforming capabilities. However, channel estimation becomes challenging with multiple antennas at both the transmitter and receiver ends. The minimum mean-squared error (MMSE) estimator, for instance, requires a spatial correlation matrix whose dimensions scale with the square of the product of the number of antennas on the transmitter and receiver sides. This scaling presents a substantial challenge, particularly as antenna counts increase in line with current technological trends. Traditional MIMO literature offers alternative channel estimators that mitigate the need to fully acquire the spatial correlation matrix. In this paper, we revisit point-to-point MIMO channel estimation and introduce a reduced-subspace least squares (RS-LS) channel estimator designed to eliminate physically impossible channel dimensions inherent in uniform planar arrays. Additionally, we propose a cluster-aware RS-LS estimator that leverages both reduced and cluster-specific subspace properties, significantly enhancing performance over the conventional RS-LS approach. Notably, both proposed methods obviate the need for fully/partial knowledge of the spatial correlation matrix. © 2024 IEEE.

关键词： Mean square error

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Examining the Application of Machine Learning to Improve Organizational Performance in the Medical Setting 15

Examining the Application of Machine Learning to Improve Org...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shelke, Ganesh C. Dutta, Abhishek Katariya, Jitendra Kumar Sakhare, Nitin N. Anandhasilambarasan, D. Mahalakshmi, V. Vishwakarma Institute of Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350370249

This paper examines the capacity utility of machine gaining knowledge of within the clinical putting to enhance organizational overall performance and affected person effects. The paper begins by means of offering a top level view of ways gadget gaining knowledge of is being utilized in other industries and the way it is able to be applied inside the scientific placing. It then goes on to speak about the important thing challenges related to growing and deploying gadget learning inside the clinical putting which include statistics privateness and security, records availability, and they want for comprehensive expertise of clinical contexts. It examines the modern-day literature on machine studying inside the scientific placing to perceive satisfactory practices and proposes regions of destiny studies. In the end, the paper summarizes the implications from the studies, highlighting the capacity advantages and demanding situations that want to be addressed earlier than machine gaining knowledge of may be efficiently implemented in the medical field. Universal, this paper offers an informative evaluation of the capability programs of machine gaining knowledge of inside the clinical placing and emphasizes the need for greater studies in this place. © 2024 IEEE.

关键词： clinical gaining greater satisfactory situations

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Prediction of Lung Infections with Deep Learning Techniques: A Systematic Review 3

Prediction of Lung Infections with Deep Learning Techniques:...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Thangamuthu, Vaishnavi Kamaraj, K. Hezekiah, James Deva Koresh KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350343632

This research study systematically examines the application of deep learning techniques in predicting lung infections including cancer, pneumonia, tuberculosis, Covid 19 and so on. The analysis includes a range of deep learning models, including convolutional neural networks (CNNs) and various architectures like ResNet, VGG, AlexNet, and others. The review assesses the efficacy of these techniques in forecasting lung infections on their computational complexity and accuracy. This study also diversified by utilizing both chest CT images and X-ray images on the predictions. The insights derived from this systematic review contribute to a better understanding of the potential and limitations of employing deep learning in the early detection of lung infections. © 2023 IEEE.

关键词： 2019-nCoV AlexNet CNN GoogleNet LeNet ResNet SARS-CoV-) VGG

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Communicate or Sense? AP Mode Selection in mmWave Cell-Free Massive MIMO-ISAC 58

Communicate or Sense? AP Mode Selection in mmWave Cell-Free ...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Yan, Weixian Topal, Ozan Alp Behdad, Zinat Demir, Özlem Tuǧfe Cavdar, Cicek Kth Royal Institute of Technology Department of Computer Science Kista Sweden Tobb University of Economics and Technology Department of Electrical-Electronics Engineering Ankara Turkey

ISBN: (纸本)9798350354058

Integrated sensing and communication (ISAC) is a promising technology for future mobile networks, enabling sensing applications to be performed by existing communication networks, consequently improving the system efficiency. Millimeter wave (mmWave) signals provide high sensing resolution and high data rate but suffer from sensitivity to blockage. Cell-free massive multiple-input multiple-output (MIMO), with a large number of distributed access points (APs), can overcome this challenge by providing macro diversity against changing blockages and can save energy consumption by deactivating unfavorable APs. Thus, in this work, we propose a joint dynamic AP mode selection and power allocation scheme for mmWave cell-free massive MIMO-ISAC, where APs are assigned either as ISAC transmitters, sensing receivers, or shut down. Due to the large size of the original problem, we propose three different sub-optimal algorithms that minimize the number of active APs while guaranteeing the sensing and communication constraints. Numerical results demonstrate that assigning ISAC transmitters only satisfying communication constraints, followed up by sensing receiver assignment only for sensing constraint achieves the best performance-complexity balance. © 2024 IEEE.

关键词： Transmitters

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Performance Evaluation of Federated Learning in Edge Computing Environment 2

Performance Evaluation of Federated Learning in Edge Computi...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kotecha, Prajay Dhoka, Tanvi Bhatia, Jitendra Kumhar, Malaram Gupta, Rajesh Tanwar, Sudeep Jadav, Nilesh Kumar Department of Electronics and Communications Institute of Technology Nirma University Ahmedabad India Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India

In the rapidly changing world of new technologies, AI and ML have been delivered to the network's edge, allowing edge devices to compute simple models that can be applied later in practical applications. Edge computing allows the deployment of cloudlike services to the network edge. Moreover, it allows network devices to contribute to Federated Learning (FL) by using their processing power. Federated learning was developed when privacy and security concerns emerged among people. Here, multiple clients work together to solve machine-learning problems. Instead of collecting users' raw data, this technique protects user privacy by aggregating model parameters from each client to a central server. Even though FL techniques have several benefits, such as scalability and data privacy, when used with heterogeneous devices, they can introduce significant problems in processing complexity and speed. This paper thoroughly surveyed the important and efficient technological advancements based on edge federated learning. We review the widely used FL frameworks that effectively facilitate communication between clients and servers. We also present a case study on handwritten digit recognition using the MNIST dataset to state the effectiveness of federated learning. On applying this approach, we were able to achieve 94.56% accuracy. © 2024 Elsevier B.V.. All rights reserved.

关键词： Edge computing

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Twitter Based Emotion Recognition Using Bi-LSTM 2

Twitter Based Emotion Recognition Using Bi-LSTM

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Anbazhagan, K. Kurlekar, Supriya Brindha, T.V. Sudhish Reddy, D. Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Department of Electronics and Telecommunication Engineering JSPM Narhe Technical Campus Pune India Sri Venkateshwara College of Engineering and Technology Department of Computer Science and Engineering Tiruvallur India Rmd Engineering College Department of Computer Science and Engineering Tiruvallur India

ISBN: (纸本)9798350384277

Twitter and other social media platforms have evolved into crucial sources of information and communication for billions of people across the world. The massive amount of data collected on these platforms enables insights into people's emotions and feelings. Emotion identification, in particular, has gotten a lot of attention because of its potential uses in industries like marketing, healthcare, and social study. "Emotional states of individuals, also known as moods, are central to expressing thoughts, ideas, and opinions, which influence attitudes and behavior."In this study, we present a method for identifying the emotion or mood of a tweet and classifying it into the appropriate emotional category. The work mentioned in this paper aims to identify and analyze the mood and emotion displayed by people in their text tweets to offer ideas. We gathered tweets and replies on a particular set of themes to create a dataset with text, user, emotion, sentiment, and other data. We used the dataset to compute user impact ratings based on various user and tweet-related factors to analyze feelings and emotions in tweets and their replies. Our model used a brand-new bidirectional long short-term memory (BiLSTM) and previously learned word embedding vectors. Finally, using the latest data, we made generalized and specific consumer suggestions based on Twitter's conduct. According to the experimental findings, the suggested model has 95.56% and 99.16% prediction accuracy. Modern performance improvement was achieved for the model. © 2024 IEEE.

关键词： Tweets

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Deep Learning Model to Detect HTTP-Based Attack on Internet of Things 4th

Deep Learning Model to Detect HTTP-Based Attack on Internet ...

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4th International Conference on Data, engineering, and Applications, IDEA 2022

作者： Dhillon, Sumeet Mishra, Nishchol Shakya, Devendra Kumar Department of Computer Science and Engineering SATI Vidisha India School of Information Technology RGPV Bhopal India Department of Electronics Engineering SATI Vidisha India

ISBN: (纸本)9789819700363

The information exchange between devices and remote servers occurs through the Application Programming Interface (API), which was created based on Internet technology (commonly referred to as web technology), in most of edge computing applications, including Internet of Things (IoT) devices. The majority of users cannot use the edge device APIs directly;hence, they experience relatively fewer threats than users of typical web applications. However, as IoT devices have gained popularity, attacks targeting APIs have started to acquire attraction. In order to identify online traffic received by IoT services and mine dangerous traffic, this paper provides a web attack vector identification approach that will deliver security information to the Security Operation Center (SOC). This approach is based on the feature extraction, targeting the mostly constant message format of the API request, in combination with the Bidirectional Long Short-Term Memory (BLSTM) attack vector identification for online traffic. The experimental findings demonstrate that, in comparison to standard machine learning approaches and rule-based web application firewalls (WAF), the suggested approach is more successful at identifying attacks against IoT service APIs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： HTTP

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High data-rate two-three inputs all-optical and gate based on FWM in highly nonlinear fiber

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Journal of Optical Communications 2024年第3期45卷 513-522页

作者： Sabbar, Adnan Abdulnabi, Saif Hasan Fakhruldeen, Hassan Falah Department of Electrical Engineering Faculty of Engineering University of Kufa Kufa Iraq Computer Techniques Engineering Department Faculty of Information Technology Imam Ja'afar Al-Sadiq University Al-Najaf Al-Ashraf Iraq Department of Electronics and Communications Engineering Faculty of Engineering University of Kufa Kufa Iraq Department of Electrical Engineering College of Engineering University of Baghdad Baghdad Iraq

In this paper, two-and-three channel all-optical AND logic gates based on four-wave mixing in a highly nonlinear fiber for 120 Gbps on-off keying signals were designed, simulated, and investigated. Simulation results show low bit error rate and consequently fairly high Q-factor at the receiving end in both two and three inputs AND gates, as well as the logic operation, is confirmed by the open eye diagrams with moderate power penalties for both designs. © 2022 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Four wave mixing

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Automated Energy Monitoring System for Smart grids and Microgrids

Automated Energy Monitoring System for Smart grids and Micro...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Revathi, R. Keerthi Ragavendran, S. Dhamodharan, B. Harish, P.S. Thaiyub A, Ahamed Kpr Institute of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350308167

The technical advancements in modernizing the world in every way possible have increased the usage of power and energy. Due to this, an increase in tariff, decrease in power quality, and frequent blackouts have increased every day because of traditional methods used in transmission lines. The proposed methodology addresses this drawback by offering the usage of advanced energy meters in smart grids and microgrids for efficient transmission of power and energy across every household and industry, improving power quality and reducing the chance of blackouts [1] significantly. This allows both the user and the utility to continuously monitor the energy usage, power factor, power quality, and load usage in real-time, to maintain the load balance in the grid. © 2023 IEEE.

关键词： Automated Energy Monitoring System (AEMOS) Facebook Prophet Micro Grid (MG) Smart Grid (SG)

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Innovative Machine Learning Techniques for Accurate Detection of Bacterial Blight in Rice Agriculture 8

Innovative Machine Learning Techniques for Accurate Detectio...

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8th International Conference on electronics, Communication and Aerospace technology, ICECA 2024

作者： Singh, Gurdeep Mittal, Sonam Chitkara University Institute of Engineering and Technology Chitkara University Department of Electronics and Communication Engineering Punjab India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350367904

Bacterial blight is a devastating disease that can significantly impact rice production. Early and accurate detection of the disease is crucial for effective management. This research proposes a deep learning-based approach, specifically utilizing Convolutional Neural Networks (CNNs), to identify and classify different severity levels of bacterial blight in rice plants. The proposed model involves a multi-step process, including data preprocessing, feature extraction, and classification. The CNN architecture is designed to capture the visual characteristics of infected plants, enabling accurate disease diagnosis. The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1-score. By implementing this automated disease detection system, farmers can make timely decisions regarding disease management strategies, leading to improved crop yields and reduced economic losses. This research contributes to the advancement of precision agriculture and sustainable food production. © 2024 IEEE.

关键词： Convolutional neural networks

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