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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Makadia, Omikumar B. Patel, Dhaval K. Shah, Kashish D. Raval, Mehul S. Zaveri, Mukesh Merchant, S.N. School of Engineering and Applied Science Ahmedabad University India Svnit Department of Computer Science and Engineering Gujarat Surat India Iit Bombay Department of Electrical Engineering Mumbai Powai India

ISBN: (纸本)9798350383119

The rise of autonomous vehicles has generated substantial interest due to their potential to transform transportation, enhance safety, and reduce traffic congestion in urban areas. This research investigates strategies to facilitate communication among various elements in driving environments, such as vehicles, pedestrians, and emergency responders, primarily focusing on vehicle-to-infrastructure (V2I) communication. Furthermore, advancements in wireless technology, including 6G and millimetre waves, offer improved connectivity but face challenges in signal propagation. The study emphasizes the importance of beam-forming as a solution, exploring alternative, resource-efficient methods to predict optimal signal directions using user positions. Positive simulation results suggest real-world applicability, and the study addresses critical questions about utilizing future positions and their impact on overhead complexity. This research provides valuable insights into position-based approaches for enhancing wireless communication in autonomous driving scenarios. © 2024 IEEE.

关键词： 6G Autonomous vehicles beam prediction overhead complexity training overhead

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Adaptive Filtering for Low-Energy Data Aggregation 5th

Adaptive Filtering for Low-Energy Data Aggregation

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Panwar, Govind Singh Mrutyunjaya, M.S. Agarwal, Trapty Kamalraj, R. Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Information Technology Jain University Karnataka Bangalore India

ISBN: (纸本)9789819780426

Hyper Spectral Imaging (HSI) is a powerful approach used in far flung sensing packages that captures spatially contiguous set of spectrum statistics from a scene. HSI information incorporates beneficial facts about the goals within the scene;however, due to noise and various distortions, it can be tough to discover those objectives. Spatial filtering is a method used to reduce undesirable noise in images if you want to make goal identity easier and extra accurate. On this paper, we endorse a brand new technique evolved for applying spatial filters on HSI pics that utilizes a spatially adaptive sigma clear out to successfully lessen noise and improve target identity accuracy. The advanced method correctly carries spatial variability within the filter length and clear out parameters to obtains progressed goal discrimination overall performance. The proposed technique is evaluated the use of both simulated and actual HSI records sets and effects display progressed target accuracy when as compared to traditional spatial filters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wiener filtering

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Leveraging Random Forests for Ovarian Cancer Detection and Precision Prediction 7

Leveraging Random Forests for Ovarian Cancer Detection and P...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Inture, Anvita Reddy Sai Nadh, Boddu Sasi Sha, Akhbar Abhishek, S. Anjali, T. Mullapudi, Taraka Vignesh Amrita School of Computing Department of Computer Science and Engineering Amritapuri India Grainger Engineering University of Illinois Urbana Department of Computer Science Champaign United States

ISBN: (纸本)9798350340600

This study has employed machine learning techniques for the critical task of ovarian cancer detection, utilizing a comprehensive dataset: Mendeley Data, V11. Our approach involved the application of a Random Forest classifier to a dataset comprising 50 relevant features. To mitigate the risk of overfitting and enhance model robustness, we randomly divided these features into subsets of 10, iteratively training and testing the classifier. The final prediction was based on the majority vote from five classifiers. Our results demonstrated remarkable success in ovarian cancer detection. The Random Forest classifier exhibited an outstanding accuracy of 0.98 on the test dataset, indicating its proficiency in distinguishing between ovarian cancer cases and non-cancer cases. Precision-Recall analysis further underscored the model's effectiveness, with an impressive average precision (AP) of 0.98. This high AP value underscores the model's capability to provide highprecision predictions, which is particularly crucial in medical applications, where minimizing false alarms is of paramount importance. These findings underscore the potential of machine learning, specifically Random Forest, as a valuable tool in ovarian cancer detection. The combination of feature subset selection and ensemble learning techniques led to a robust and reliable predictive model. While these results are promising, ongoing validation and collaboration with domain experts are essential steps toward the translation of this research into clinical practice, where early and accurate cancer detection can have a profound impact on patient outcomes. © 2023 IEEE.

关键词： Diagnosis

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Efficient Method For Automatic Irrigation System using IOT 10

Efficient Method For Automatic Irrigation System using IOT

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10th International Conference on electrical Energy Systems, ICEES 2024

作者： Rajagopal, R. Senbagavalli, M. Sampath, P.V.V.S.S. Sanjeevi Kumar, V. Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bangalore India Alliance School of Advanced Computing Alliance University Department of CSE Bangalore India

ISBN: (纸本)9798350353778

The precise and effective management of water using Internet of Things (IoT) technology in Automatic Irrigation Systems (AIS) has revolutionized agricultural practices. The primary objective is to enhance the watering methods, reduce labor costs, and conserve water. Through the literature review, various components are used in this automatic irrigation system. Identifying gaps in existing approaches leads to the proposal of an integrated framework. This process encompasses the availability and cost-effectiveness of the product. The testing and evaluation of the system will be conducted by testing trails with different soil moisture levels. As a result, we can see an improvement in the quality of the product. The purpose of this project is to show the importance of water conservation in the modern deforested environment. Future work will focus on integrating based on weather conditions by getting the datasets from the weather station. © 2024 IEEE.

关键词： Soil moisture

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Native Nest: An E-Commerce Platform for Promoting Tribal Products and Culture 8

Native Nest: An E-Commerce Platform for Promoting Tribal Pro...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Rajendrakumar, S. Chowta, Yashasvi Tadikonda, G Harshavardhan Reddy, Sathi Lakshmi Priyatha Karthik, Mukkanti Venkata Sai Shanmugam, Selvanayaki Kolandapalayam Thangavel, Senthil Kumar Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Chemical Engineering and Materials Science Coimbatore641112 India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore641112 India Ashland University Department of Mathematics and Computer Science OH United States

ISBN: (纸本)9798350376425

This research study proposes a new online platform, Native Nest, to address the challenges faced by the commerce industry in Ooty District. This initiative provides a sustainable platform for registered tribal artisans to connect with buyers, promoting their cultural heritage and products. The platform features a user-friendly interface, adjustable layout, and reliable infrastructure, ensuring a seamless experience for both clients and administrators. QR codes strategically placed throughout Ooty facilitate easy access to the platform, expanding its reach to tourists and locals. Native Nest offers detailed product information and enables online purchasing, eliminating the need for middlemen and providing a convenient shopping experience. By empowering tribal communities and promoting their cultural heritage, Native Nest contributes to the socio-economic development of the region. This initiative enables cultural exchange and expands the market share of tribal arts and expressions, creating a more comprehensive and sustainable marketplace. © 2024 IEEE.

关键词： Marketplaces

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Finite-Time Synchronization of Complex Networks With Intermittent Couplings and Neutral-Type Delays

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IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 2026-2028页

作者： Engang Tian Yi Zou Hongtian Chen School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Dear Editor, This letter focuses on the finite-time synchronization(FTS) of neutral-type complex networks with intermittent couplings. Different from most of the existing references concerning neutral-type systems,a delay-independent dynamical event-triggering controller is considered, operating the same way as the intermittent coupling and excluding the Zeno behavior naturally.

关键词： neutral letter concerning

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Data-Driven Power Flow Estimation for MVDC Distribution Systems Based on Physics-Embedded FCN

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IEEE Transactions on Smart Grid 2025年第4期16卷 2816-2829页

作者： Sun, Pingyang Wu, Rongcheng Shen, Zhiwei Wang, Hongyi Li, Gen Khalid, Muhammad Konstantinou, Georgios School of Electrical Engineering and Telecommunications UNSW Sydney Australia University of Technology Sydney Data Science Institute Australia Aalborg University Department of AAU energy Denmark Energy Technology and Computer Science Section Department of Engineering Technology Ballerup2750 Denmark King Fahd University of Petroleum and Minerals Electrical Engineering Department Interdisciplinary Research Center for Sustainable Energy Systems Dhahran31261 Saudi Arabia

Maintaining high prediction accuracy with varying grid topologies poses a significant challenge to adopting neural network (NN)-based approaches for power flow (PF) estimation in medium-voltage direct current (MVDC) distribution systems. This paper proposes a physics-embedded fully convolutional network (PEFCN) to improve the accuracy. Physics-embedded techniques are incorporated in the proposed PEFCN through 1) architecture reconstruction, and 2) loss function reformulation. Architecture is reconstructed by input channel conversion calculation in a new physics operation layer. This process offers physical connections among the three input matrix channels (voltage, current, and line conductance). Three new physics loss terms are included in the loss function to restrict the outliers violating the limits of converter power ratings and terminal MVDC voltages. The two operations enable the FCN to achieve improved prediction accuracy and strong generalization capabilities. Five MVDC distribution systems, characterized by different dc voltage levels and topology configurations, serve to validate the superiority of the proposed PEFCN over other NNs in the PF estimation for scenarios involving both fixed and varying system structures. Moreover, a modified IEEE 69-bus distribution system is further used to demonstrate the applicability of the proposed PEFCN for larger systems. © 2010-2012 IEEE.

关键词： DC distribution systems

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Optimized Learning Model for Brain-computer Interface Using Electroencephalogram (EEG) for Neuroprosthetics Robotic Arm Design for Society 5.0

Optimized Learning Model for Brain-Computer Interface Using ...

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2024 IEEE International Conference on computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Arun Kumar, S. Anand, L. Kannur, Anil School of Computing SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu Potheri SRM Nagar Kattankulathur India Dr. P.G. Halakatti College of Engineering and Technology Department of Computer Science and Engineering Karnataka Vijayapura India

ISBN: (纸本)9798331517892

The brain activity can be represented by EEG, which does it by measuring voltage fluctuations in the brain. in this paper, the work intends to develop a Brain-computer Interface-based system that can identify and forecast the class of motor action corresponding to the left or right direction. There are two datasets used in this study, relating to motor movements - left/right-hand movement and eye open/close. The collected data is pre-processed by applying a Convolution Neuron Network to obtain a frequency domain matrix. The result computed by applying CNN provides a condensed representation of the data. The second dataset captured brain impulses corresponding to two states of the eyes opened and eyes closed, over a period of two minutes. This system is designed to effectively separate and utilize signals, with the adoption of AI techniques in these devices emerging as the most viable option for Society. The overall efficiency of this system ranges from 80% to 87%. © 2024 IEEE.

关键词： Frequency domain analysis

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Detection of Aphasia Using Speech Characteristics: A Comparative Analysis of Learning Models 5th

Detection of Aphasia Using Speech Characteristics: A Compara...

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5th International Conference on computing and Network Communications, CoCoNet 2023

作者： Surisetty, Hima Varshini Gottimukkala, Sarayu Varma Vekkot, Susmitha Gupta, Deepa Alotaibi, Yousef A. M. Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh11451 Saudi Arabia

ISBN: (纸本)9789819747108

Aphasia is one of the brain disorders that affects the part of the brain that is responsible for communication. This work deals with the detection of aphasia from speech features. The features extracted from the speech samples are MFCC, mel spectrogram, zero crossing rate, chroma, energy, spectral centroid, spectral roll-off, fundamental frequency and utterance duration. These different speech features were then ranked based on the SHapley Additive exPlanations (SHAPs), and the most important features were retained in an optimized feature set that was given to various machine learning models like support vector machine and decision trees and deep learning models like LSTM, LSTM with GRU, Bi-LSTM and Bi-LSTM with GRU. These models were then evaluated based on their train and test set accuracy, F1 score, recall, precision, plots of training loss and validation loss and confusion matrix. Based on the analysis of the performance metrics, Bi-LSTM with GRU was found to be the best model among all the models used. The model’s performance was evaluated using metrics like accuracy, F1 score, recall and precision when trained and tested with the optimal feature set which were 87.5%, 79.7%, 90% and 75%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Support vector machines

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KiAtt Fusion: Remote Sensing-Based Water Bodies Segmentation for Environmental Monitoring 4

KiAtt Fusion: Remote Sensing-Based Water Bodies Segmentation...

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4th International Conference on Digital Futures and Transformative Technologies, ICoDT2 2024

作者： Sikander, Saad Rehman, Hamood Ur Naqvi, Syed Fakhr Abbas Aslam, Waqar Perwaiz, Nazia Sir Syed CASE Institute of Technology Department of Electrical Engineering Islamabad Pakistan Sir Syed CASE Institute of Technology Department of Electrical Engineering and Computer Science Islamabad Pakistan National University of Sciences and Technology NUST School of Electrical Engineering and Computer Science Islamabad Pakistan

ISBN: (纸本)9798350391862

Monitoring the environment and managing water bodies are crucial for preserving ecosystems and ensuring sustainable resource utilization. This study aims to propose a robust approach for segmenting water bodies by combining various complementary attributes extracted from deep learning techniques. By leveraging deep contextual features learned from attention regions and enhanced edge detection, our method significantly improves the accuracy of detecting water bodies in low-resolution satellite imagery. Utilizing S1 and S2 Sentinel imagery data and exploring multi-band features, we enhance key attributes through advanced UNet architectures - specifically, Ki-UNet and Attention UNet - and integrate complementary features for downstream tasks. Additionally, we assembled a local dataset from the Khyber Pakhtunkhwa (KPK) region of Pakistan, facilitating precise water body segmentation from satellite or aerial views. Our ensemble model achieved remarkable accuracy and Intersection over Union (IoU) scores, reaching up to 99.01% and 96.2%, respectively, surpassing state-of-the-art models. This research provides automated, accurate segmentation techniques essential for environmental management and resource assessment, offering a promising solution for delineating water bodies. github link : (https://***/SaadBaloch96/Dataset/blob/main/***) © 2024 IEEE.

关键词： Environmental monitoring

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