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10th International Conference on communication and Signal Processing, ICCSP 2024

作者： Azath, H. Sivanantham, E. Suganthi, B. Swarnalatha, E. Suresh, M. Elango, B. Vit Bhopal University School of Computing Science and Engineering Bhopal India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Rvs College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Guru Nanak Institute of Technology Department of Electronics and Communication Engineering Hyderabad Telangana Ibrahimpatnam India Vinayaka Missions Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

This research explores real-time Internet of Things (IoT) methods to track and mitigate construction site hazards. The construction industry must innovate due to its high accident rate to protect workers. It uses IoT to create a system that watches the construction site and discovers real-time safety issues. The system uses sensors across the construction site to monitor worker location, equipment operation, ambient conditions, and structural integrity. In real-time, the IoT identifies potential threats from this data. Workers, supervisors, and other users receive timely alerts about dangers, enabling quick and focused mitigation. The system's proactive approach to building site safety reduces accidents, injuries, and deaths. With real-time IoT solutions, potential hazards can be discovered early and addressed quickly. Data collection and analysis allow the system to improve safety protocols and practices over time. It highlights the importance of real-time IoT technologies in construction site safety management. IoT devices improve hazard tracking and identification and safety culture by fostering proactive actions and informed decision-making. As buildings embrace digital transformation, this study enables safer and more efficient procedures. © 2024 IEEE.

关键词： Risk management

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Experimental Investigation of 3D-Printed Lightweight Waveguide Port Antenna 11

Experimental Investigation of 3D-Printed Lightweight Wavegui...

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11th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2023 - Proceedings

作者： Wang, LiBo Lin, Shu Wang, Yingqiao Xu, Tong Zhang, Xinyue Zhang, Xingqi Harbin Institute of Technology School of Electronics and Information Engineering Harbin China Jilin University Department of Communication Engineering Changchun China Maynooth University Department of Electronic Engineering Maynooth Ireland University of Alberta Department of Electrical and Computer Engineering Edmonton Canada

ISBN: (纸本)9798350326277

In order to make the antenna lightweight, this paper proposed a rectangular aperture stepped waveguide model working in the S band based on the 3D printing technology of ABS (Acrylonitrile Butadiene Styrene) plastic dielectric materials. The antenna incorporates a copper foil coating to provide the necessary electrical properties of a metal surface. The waveguide is fed by an SFT-50-3 semi-steel cable, and tested using an Agilent E8363B vector network analyzer. Experimental results demonstrate that the waveguide exhibits a reflection coefficient below -10 dB within the frequency range of 3.44 GHz to 3.8 GHz. Furthermore, to enhance the antenna's gain, a prism lens with a trapezoidal axis cross-section printed in 3D using ABS plastic dielectric material is integrated at the waveguide's front end. This configuration results in an increased gain of 6.41 dBi at 3.6 GHz. Compared to an all-metal model, the proposed antenna achieves an 86.5% reduction in weight while maintaining performance, making it suitable for various outdoor elevated antenna applications. © 2023 IEEE.

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A Machine Learning Framework for Integrating Multi-Omics Data for Early Leukemia Detection 8

A Machine Learning Framework for Integrating Multi-Omics Dat...

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

作者： Nikshya, J. Ebens Karthikeyan, M. Saravana Prasad, Shalini Krishnan, R. Santhana Balamurugan, S. Raj, J. Relin Francis Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India City Engineering College Department of Electronics and Communication Engineering Doddakalasandra India Velammal College of Engineering and Technology Department of Mathematics Madurai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350376425

The early detection of leukemia remains a critical challenge in medical diagnostics due to the complexity and variability of omics data. This research introduces a novel machine learning framework that integrates multi-omics data for the early detection of leukemia, utilizing the proposed CCA-RFE Selector (CCARS) algorithm. The CCARS algorithm combines Canonical Correlation Analysis (CCA) with Recursive Feature Elimination (RFE) to enhance feature selection and classification accuracy. The effectiveness of CCARS is evaluated through extensive simulation analyses, comparing its performance with existing algorithms, including Principal Component Analysis (PCA), Lasso Regression (LR), and Canonical Correlation Analysis (CCA). The evaluation metrics used include accuracy, F1-score, and computational time. Results demonstrate that the CCARS algorithm significantly improves accuracy and F1-score while reducing computational time compared to the existing methods. The CCARS algorithm achieves notable performance enhancements, validating its potential for integrating multi-omics data in leukemia detection. © 2024 IEEE.

关键词： Contrastive Learning

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Precision Medicine in Oncology - Leveraging Machine Learning for Accurate Neoplasm Diagnosis 5

Precision Medicine in Oncology - Leveraging Machine Learning...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Ashreetha, B. Sumanth, Upputuru Reddy, Lebaka Naveen Kumar Prasad, K.D.V. Srinivas, Kale Naga Venkata Gowda, Dankan V. Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Andhra Pradesh Tirupati India Department of Electronics and Communication Engineering Sree Vidyanikethan Engineering College Mohan Babu University Andhra Pradesh Tirupati India Symbiosis Institute of Business Management Hyderabad India Pune India Department of Computer Science and Engineering Aditya College of Engineering Surampalem India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India

ISBN: (纸本)9798350361155

The precision medicine in oncology provided a novel era where the patients have better chances of being cured and improved outcomes if they adopted the personalized treatment strategies. The core part of this change is the utilization of the ML (machine learning) algorithms which showcases good specificity as compared to humans in making neoplasm diagnoses. This research paper will be exploring an application of ML approaches within precision medicine area for oncology patients and will be trying to fill-up the gap between big genome data and decision making processes in the clinical area. This work proposes a complete study which investigates the usefulness of several strategies of ML in diagnosis under cancer with the help of the detailed dataset that consists of genomic and clinicological profile of patients. In our study we investigated several types of ML algorithms covering decision trees, neural networks as well as support vector machines. Every algorithm underwent strict test and validation for its diagnostic power to be identified. Our approach inaugurated the use of ROC curves, precision-recall curves, as well as survival analysis to objectively evaluate model performance, along with feature importance rankings which allowed for identification of biomarkers as well as genetic signatures known to be predictive of specific cancer subtypes and treatment responses. ML work is seen to show a substantially higher sensitivity, specificity and accuracy compared to conventional diagnostic methods. Having ML integrated with precision medicine, makes the screening of cancer in early stages and the accurate diagnosis possible. Additionally, it provides the opportunity to devise personal treatment plans depending on the various genetic tumor types. Besides that, the results from survival analysis illustrated that these ML-based prognostic models can be used for accurately forecast patient outcomes. This has an implication of not only better management of existing treat

关键词： Diagnosis

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Analyzing the Effectiveness of ML and DL in Combatting Personal Condition Misinformation on Social Media

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第1期52卷 258-267页

作者： Babu, Jaladanki Ravindra Venkataiah, Vaadaala Narayana, Tirumalasetti Lakshmi Rao, Chegudi Ranga Raj, Ch. Deva Sairam, Rallabandi C.H. S. N. P. Anusha, B. Electronics and Communication Engineering Department Prasad V Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada India Computer Science and Engineering Department CMR College of Engineering & Technology Telangana Hyderabad India Electrical and Electronics Engineering Department Aditya University Andhra Pradesh Surampalem India Electrical and Electronics Engineering Department R. V. R. & J. C. College of Engineering Andhra Pradesh Guntur India Mechanical Engineering Department R. V. R. & J. C. College of Engineering Andhra Pradesh Guntur India Department R.V. R. & J. C. College of Engineering Andhra Pradesh Guntur India Electronics and Communication Engineering Department Andhra University Andhra Pradesh Visakhapatnam India

The widespread broadcast of fake health news offers a huge challenge that puts the health and well-being of the general population in peril. This is especially true in a time when there is an excess of information available. In order to develop automated systems that are capable of spotting fraudulent health-related information, researchers have applied artificial intelligence methodologies such as machine learning (ML) and deep learning (DL). These approaches have been utilized in order to construct these systems. In order to address the problem that is now being faced, this is being done. The objective of this research is to give an analysis of the method, performance measurements, and problems that are linked with machine learning (ML) and deep learning (DL) systems for the purpose of recognizing fake health news. This analysis is presented in the course of this research. We study the many ways that are applied in the process of feature engineering, that of model architectural design, and that of evaluation metrics throughout the machine learning (ML) and deep learning (DL) paradigms. These approaches are utilized in the process of feature engineering. Additionally, we evaluate the significance of our findings for the development of the efficiency of systems that are meant to identify false health news and offer prospective areas for additional research in this vitally important sector. Lastly, we conclude that our findings have the potential to improve the effectiveness of these systems. © (2025), (International Association of Engineers). All rights reserved.

关键词： Adversarial machine learning

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A Novel and Effective Ensemble Machine Learning Model for Identifying Healthy and Rotten Fruits

A Novel and Effective Ensemble Machine Learning Model for Id...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Kumar, S. Arun Mary, L. Hubert Varsa, G.V. Shaamili Ulaganathan, M.S. Kannagi, L. Bharathi, P. Shyamala Department of Electrical and Electronics Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering Jeppiaar Institute of Technology Chennai India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Simats Chennai India

ISBN: (纸本)9798350334357

The world's food supply is dwindling at an alarming rate because of inefficient measures used to cope with food waste at all levels of the food system, from farms to households to restaurants. Ripe fruit may now be picked. Predicting when fruit will be ripe and of better condition is crucial not just for farmhand and the food business, but also for grocery shops and corner markets. The term 'fruit maturity classification' refers to the method through which fruits are categorized based on their stage of development. Machine learning (ML) is being used in a wide variety of smart agricultural use cases, including soil and water management, seeding crops, early diagnosis, weed elimination, crop dispersal, crop collection, and productivity prediction. The purpose of this research is to identify the most effective deep learning algorithms for estimating fruit ripeness and quality to extend their storage life. Banana fruit datasets are utilized in this investigation. To increase the total no of images in the collection, image augmentation is utilized. For both datasets, we propose an Ensemble Machine Learning Technique (EMLT) that combines SVM with ResNets. Our analysis led us to the conclusion that the suggested EMLT model is the most efficient technique for determining the quality of bananas and classifying their ripeness. © 2023 IEEE.

关键词： Fruits

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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

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A Ku-Band Omnidirectional High-Gain Printed Antenna Based on Continuous Transverse Stub and Dipole Configuration

A Ku-Band Omnidirectional High-Gain Printed Antenna Based on...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Li, Pingyu Lin, Shu Liu, Zhiheng Wang, Yingqiao Zhang, Xinyue Zhang, Xingqi School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Jilin University Department of Communication Engineering Changchun China Maynooth University Department of Electronic Engineering Maynooth Ireland University of Alberta Edmonton Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9781733509657

The Ku band is one of the most commonly used bands in satellite mobile communication systems. This paper introduces an omnidirectional high-gain printed antenna designed specifically for the Ku band. It is an antenna array composed of a continuous transverse stub (CTS) structure and a dipole formed by a pair of triangular patches. The antenna uses a printed structure for miniaturization and low profile, and is fed by a coplanar waveguide (CPW) and a coaxial line. Simulation results show that the antenna's bandwidth, defined as $\vert S_{11}\vert © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Microstrip antennas

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Large language models for human–robot interaction:A review

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Biomimetic Intelligence & Robotics 2023年第4期3卷 1-15页

作者： Ceng Zhang Junxin Chen Jiatong Li Yanhong Peng Zebing Mao Department of Mechanical Engineering National University of SingaporeSingapore 119077Singapore School of Mechanical Electronic and Control EngineeringBeijing Jiaotong UniversityBeijing 100044China Department of Information and Communication Engineering Graduate School of EngineeringNagoya UniversityNagoya 464-8603Japan Department of Mechanical Engineering Tokyo Institute of TechnologyTokyo 152-8550Japan

The fusion of large language models and robotic systems has introduced a transformative paradigm in human–robot interaction,offering unparalleled capabilities in natural language understanding and task *** review paper offers a comprehensive analysis of this nascent but rapidly evolving domain,spotlighting the recent advances of Large Language Models(LLMs)in enhancing their structures and performances,particularly in terms of multimodal input handling,high-level reasoning,and plan ***,it probes the current methodologies that integrate LLMs into robotic systems for complex task completion,from traditional probabilistic models to the utilization of value functions and metrics for optimal *** these advancements,the paper also reveals the formidable challenges that confront the field,such as contextual understanding,data privacy and ethical *** our best knowledge,this is the first study to comprehensively analyze the advances and considerations of LLMs in Human–Robot Interaction(HRI)based on recent progress,which provides potential avenues for further research.

关键词： Large language models Human-robot interaction Task completion Considerations and challenges

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The Spectral vs. Energy Efficiency Trade-Off in Dynamic User Clustering Aided mmWave NOMA Networks

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IEEE Transactions on communications 2024年第6期73卷 4503-4519页

作者： Rai, Sudhakar Sharma, Ekant Jagannatham, Aditya K. Hanzo, Lajos Indian Institute Of Technology Kanpur Department Of Electrical Engineering Kanpur208016 India Indian Institute Of Technology Roorkee Department Of Electronics And Communication Engineering Uttarakhand Roorkee247667 India University Of Southampton School Of Electronics And Computer Science SouthamptonSO17 1BJ United Kingdom

The spectral efficiency (SE) and global energy efficiency (GEE) trade-off encountered in the design of millimeter-wave (mmWave)-based massive multi-input multi-output (MIMO) non-orthogonal multiple access (NOMA) networks is investigated with a particular focus on user clustering. By exploiting the similarity among user channels a pair of spectral and energy-efficient user clustering algorithms are proposed for dynamically selecting both the number of clusters and the number of users in each cluster. Subsequently, a joint analog precoder/combiner and user clustering technique is developed, followed by a multi-objective optimization (MOO) framework for flexibly balancing the GEE and SE objectives in a mmWave NOMA network subject to specific constraints. The MOO objective is initially transformed to a weighted sum rate maximization problem, followed by a quadratic-transform (QT)-based approach conceived for maximizing the non-convex objective by approximating it as a concave-convex function. Our simulation results demonstrate that the user clustering techniques designed attain a 85% performance gain over random clustering technique and demonstrating the benefits of the algorithm designed for mmWave NOMA networks. © 1972-2012 IEEE.

关键词： Spectrum efficiency

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