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

作者： Janarthanan, Vignesh Pradeep, S. Vidhya, K. Roslin Dayana, K. Bhosale, Supriya Ashok Kumar, Ashok Malla Reddy Institute of Technology and Science Department of Information Technology Hyderabad Secunderabad500100 India S.A. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai600077 India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore641407 India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Thiruvallur601206 India Sandip University School of Computer Sciences & Engineering Maharashtra Nashik422213 India Banasthali Vidyapith Department of Computer Science Rajasthan Banasthali304022 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

The most common type of arthritis is osteoarthritis (OA), particularly in the knee, which causes significant disability for all who suffer from this disease all over the world. Knee Osteoarthritis (KOA) is a degenerative joint ailment caused by the gradual deterioration of cartilage. Hand identification, classification, and annotation of knee joints are still used in clinical processes to detect OA, although they are time-consuming and extremely susceptible to the physician. Because of the complexity of KOA and the lack of understanding of its pathology, there is little requirement for accurate techniques which will minimize physician diagnostic errors. To overcome the limitation of the commonly used strategy, the research provides hybrid Artificial Intelligence (AI) approaches to automate OA prediction without expert assistance. Support Vector Machine (SVM), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and a hybrid (SVM+NB) model were used in this work. To identify OA, AI models must be trained on labelled X-ray images. So, Kaggle is used to gather X-ray images of people without and with OA in various stages (Healthy, minimal, moderate, and severe). The collected image is not fed directly into the AI model for training. Before it can be utilized, the image must be processed. The preprocessed image is being applied for feature extraction to reduce the amount of memory required by the AI model to train. Following feature extraction, the data will be divided into two classes: training and testing. The AI model is trained using images from the training class with labels. The AI model is put to the test by giving unlabelled images of the testing class and asking it to estimate the label. The AI model is compared based on the test results, and the best model is identified using the positive and negative metrics values. The experimental results show that the hybrid AI model outperforms the other three AI models. © 2022 IEEE.

关键词： Forecasting

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Hybrid CNN LSTM Approach for Sentiment Analysis of Bengali Language Comment on Facebook

Hybrid CNN LSTM Approach for Sentiment Analysis of Bengali L...

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Integrated Circuits and communication Systems (ICICACS), IEEE International Conference on

作者： SM Nuruzzaman Nobel S M Masfequier Rahman Swapno Ramachandra A C Hasibul Hossain Shajeeb Md Babul Islam Rezaul Haque Dept of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru Dept. of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Dept of CSE East West University Dhaka Bangladesh

This study addresses the challenge of sentiment analysis in Bengali Facebook comments, where users express their opinions on various topics. Existing sentiment analysis techniques have difficulty achieving high accuracy in this situation, which has inspired the creation of a fresh methodology. To improve sentiment analysis of Bengali Facebook comments, we suggest a model that combines a hybrid convolutional neural network (CNN) and a long short-term memory network (LSTM). Our research adds to the expanding area of sentiment analysis, especially for languages with few resources, such as Bengali. The success of the Hybrid CNN LSTM model opens the door for more precise and context-aware sentiment analysis on social media platforms, allowing for a better understanding of user sentiments and boosting user experiences. Our study focused on dividing comments into five groups according to their emotional content: troll, not bully, sexual, religious, and threat. We carefully generated the handmade dataset and ensured the quality and relevance of a dataset of 50,000 Bengali comments before using it to train and test our algorithm. The Hybrid CNN LSTM model outperforms conventional approaches and achieves an outstanding accuracy rate of 92.09%, demonstrating amazing efficiency in sentiment recognition. This dramatic increase in precision indicates that machine learning techniques are successful in sentiment analysis of Bengali Facebook comments.

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High Isolation Dual Port MIMO Antenna for Vehicle-to-Vehicle communication

High Isolation Dual Port MIMO Antenna for Vehicle-to-Vehicle...

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2023 IEEE International Conference on communications, Information, electronic and Energy Systems, CIEES 2023

作者： Sharma, Preeti Singh, Prabhakar Tiwari, Rakesh N. Kumar, Pradeep Galgotias University Physics Division School of Basic Sciences Greater Noida India Greater Noida Institute of Technology Dept. of Applied Science Greater Noida India Madanapalle Institute of Technology & Science Dept. of Electronics and Communication Engineering Madanapalle India University of KwaZulu-Natal Dept. of Electrical Electronic and Computer Engineering Durban South Africa

ISBN: (纸本)9798350336917

This research paper reports a very thin 2-port multiple-input multiple-output (MIMO) antenna with a small footprint of 14× 10× 0.58mm3. The proposed design works between the frequencies of 8.73 to 15.86 GHz. A modified hexagonal patch is used along with the defected ground plane to obtain wide bandwidth. A conventional decoupling strip technique is utilized to improve the isolation between the ports and it is found >19dB across the whole operating band. In addition to that, 'H'-shaped slot has been incorporated into the grounding plane to enhance impedance matching. The realized gain and radiation patterns of the designed antenna are calculated and found quite acceptable. Furthermore, the diversity parameters of the antenna are computed with envelope correlation coefficient (ECC) 9.97dB across the entire operating frequency range. Finally, the results of MIMO antenna are compared with the calculated data for the purpose of practical validation. © 2023 IEEE.

关键词： Defects

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Cyber Security Intrusion Detection System Based on an Enhanced Gradient Boost Decision Trees with Cosine Kernel Support Vector Machine

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SSRN 2024年

作者： Mary, S. Angel Latha Poonguzhali, A. Sivasubramanian, S. Ramasamy, T. Velmani Dhivya, P. Alagumalai, Suganthi Department of AIML SNS College of Technology Coimbatore India Department of Electronics and Communication Engineering Sri Sairam College of Engineering Bangalore India Department of Electronics and Communication Engineering Rathinam Technical Campus Coimbatore India Department of Computer Science and Engineering Aditya Institute of Technology TamilNadu Coimbatore641 107 India Department of Computer Science and Design Karpagam College of Engineering Coimbatore641032 India JP Morgan Chase Institute India

Organizations that specialize in technological fields, such as cloud computing, big data, Internet of thing (IoT), etc., are significantly troubled by the issue of cyber-attacks in present times. In safeguarding digital systems, the Intrusion Detection System (IDS) performs a critical function by detecting potentially harmful behaviour within the network's traffic. In recent years, significant efforts have gone into researching this topic. However, in the present circumstances, there has been an expansion in both the quantity and types of network attacks. This paper suggests a clever intrusion detection system to anticipate and identify cyber-attacks. An effective IDS is suggested that entails the merging of Enhanced Gradient Boost Decision Tree (EGBDT) and Cosine Kernel Support Vector Machine (CKSVM) techniques through the application of the African buffalo optimization method. The proposed approach has three sequential stages, which comprise investigating the data, preparing the data, and determining the salient characteristics to be utilized. Initially, the type of information is determined, and its excellence is enhanced through a pre-processing procedure. The features undergo ranking, and proficient attributes are elected using Genetic Algorithm (GA) to expedite computation. The EGBDT-CKSVM algorithm categorizes the chosen characteristics as regular or hostile records. Utilizing the UNSW-NB 15 dataset, the proposed method has demonstrated outstanding results, exhibiting a 98% accuracy rate, 97.4% precision score, 96.8% recall rate, and 97.2% Fscore, surpassing both the EGBDT and Improved Decision Tree algorithm using Binary Split (IDTBS) algorithms. © 2024, The Authors. All rights reserved.

关键词： Decision trees

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Correlation and Causation in the Perspective of COVID-19: An Empirical Study on Canonical Correlation Analysis

Correlation and Causation in the Perspective of COVID-19: An...

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2022 International Conference on Smart Applications, communications and Networking, SmartNets 2022

作者： Vinod, Dasari Naga Lebekwe, Casper K. Zungeru, Adamu Murtala Menaka, D. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai600062 India Botswana International University of Science and Technology Department of Electrical Computer and Telecommunications Engineering Palapye Botswana Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai600119 India

ISBN: (纸本)9781665487580

COVID-19 has an immense effect on the Globe, crossing 53,86,95,729 affected in more than 220 nations, with 63,18,093 individuals deceased. Various countries released COVID-19 protocols to enclose its spread to control the pandemic. This research article illustrates the Effect of COVID-19 on aged people (age>50), diabetes individuals, and individuals with smoking habits concerning the cause of death. An attempt has been made to identify the predominant variables for the cause of death due to COVID-19. IBM SPSS statistical tool enabled by Canonical Correlation Analysis (CCA) is used for simulation. Data were gathered from the Kaggle, an open repository for 2020. Based on the results obtained, predictions regarding the Cause and Effect of COVID-19 are discussed. © 2022 IEEE.

关键词： COVID-19

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Rim learning framework based on TS-GAN: A new paradigm of automated glaucoma screening from fundus images

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computers in Biology and Medicine 2025年 187卷 109752-109752页

作者： Chowdhury, Arindam Lodh, Ankit Agarwal, Rohit Garai, Rahul Nandi, Ahana Murmu, Narayan Banerjee, Sumit Nandi, Debashis Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur713209 India Department of Electronics and Communication Engineering University Institute of Technology West Bengal Burdwan713104 India Department of Information Technology University Institute of Technology West Bengal Burdwan713104 India Department of Computer Science and Engineering Government College of Engineering and Textile Technology West Bengal Serampore712201 India Department of Ophthalmology Megha Eye Centre West Bengal Burdwan India

Glaucoma detection from fundus images often relies on biomarkers such as the Cup-to-Disc Ratio (CDR) and Rim-to-Disc Ratio (RDR). However, precise segmentation of the optic cup and disc is challenging due to low-contrast boundaries and the interference of blood vessels and optic nerves. This article presents a novel automated framework for glaucoma detection that focuses on the rim structure as a biomarker, excluding the conventional reliance on CDR and RDR. The proposed framework introduces a Teacher–Student Generative Adversarial Network (TS-GAN) for precise segmentation of the optic cup and disc, along with a SqueezeNet for glaucoma detection. The Teacher model uses an attention-based CNN encoder–decoder, while the Student model incorporates Expectation Maximization to enhance segmentation performance. By combining implicit generative modeling and explicit probability density modeling, the TS-GAN effectively addresses the mode collapse problem seen in existing GANs. A rim generator processes the segmented cup and disc to produce the rim, which serves as input to SqueezeNet for glaucoma classification. The framework has been extensively tested on diverse fundus image datasets, including a private dataset, demonstrating superior segmentation and detection accuracy compared to state-of-the-art models. Results show its effectiveness in early glaucoma detection, offering higher accuracy and reliability. This innovative framework provides a robust tool for ophthalmologists, enabling efficient glaucoma management and reducing the risk of vision loss. © 2025 Elsevier Ltd

关键词： Image segmentation

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Smart Prediction Models for Energy Efficiency in Smart Residential Buildings: A Deep Learning Approach

Smart Prediction Models for Energy Efficiency in Smart Resid...

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2023 International Conference for Technological engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Vasantharaj, A. Ahamed, M. Irshad Prema, K. Mudigonda, Aditya Sapaev, J.B. Thandapani, Kavitha Kalaignar Karunanidhi Institute of Technology Information Electronics and Communication Engineering India E.G.S. Pillay Engineering College Department of Electronics and Communication Engineerin Tamilnadu Nagapattinam India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India JNIAS School of Planning and Architecture Telangana Hyderabad500034 India National Research University Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Tashkent Uzbekistan Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Vgy Technology Avadi Department of Electronics and Communication Engineering Tamil Nadu Chennai600062 India

ISBN: (纸本)9798350336474

Using power in houses has given rise to several ecological issues, including air pollution. Predicting building energy use is essential for better regulating or reducing energy usage. Machine Learning (ML) techniques have been used to estimate the power use of operating buildings. However, there has been little research on using ML algorithms to anticipate the prospective energy usage of buildings during the initial design phase. When lowering energy-inefficient buildings, it is crucial to tackle the underlying issue: accurately anticipating energy consumption before construction. This is necessary to prevent the future development of environmentally detrimental buildings. This research introduces a new and innovative Hybrid Deep Learning Model for Predicting Energy Efficiency in Smart Residential Buildings (HDL-PEE-SRB). The suggested framework has two steps: cleaning the information and constructing the model. The information cleansing step utilizes pre-processing methods to manipulate the original information and incorporates lag values as supplementary characteristics. During model-building, the hybrid model undergoes training using the processed data. The HDL model combines fully linked layers, Unidirectional Long Short-Term Memory (Uni-LSTM), and bi-directional LSTMs (Bi-LSTM), which are stacked together. The suggested model is assessed using standard energy usage databases, demonstrating higher accuracy than commonly used HDL models. The proposed model surpasses other HDL models, demonstrating the most favorable performance with a MAPE of 1.94% and an accuracy of 98.06%. © 2023 IEEE.

关键词： Forecasting

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Broadband usage for rural communities in the North Island of Aotearoa New Zealand

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Intelligent and Converged Networks 2022年第3期3卷 244-259页

作者： Ming Xiang William Liu Edmund Lai Jairo Gutierrez Luca Chiaraviglio Jinsong Wu Control Science and Engineering South China University of TechnologyGuangzhou 510300Chinaand also with the South China Sea Institute of Planning and Environmental ResearchMinistry of Natural ResourcesGuangzhou 510300China Department of Computer Science and Software Engineering School of EngineeringComputer and Mathematical SciencesAuckland University of TechnologyAuckland 1010New Zealand Department of Electronic Engineering University of Rome“Tor Vergata”Rome 00133Italy Consorzio Nazionale Interuniversitario per le Telecomunicazioni(CNIT) Parma 43124Italy Department of Electrical Engineering Universidad de ChileSantiago 92101Chile

New Zealand government’s Rural Broadband Initiative(RBI)aims to invest 400 million New Zealand dollar to provide 99%of New Zealanders with access to 50 Mbps peak broadband speed,with the remaining 1%at 10 Mbps by *** 2017,stage 1 of the RBI has been completed,and research is needed to find out the level of usage,proficiency,and productivity achieved by ***,a survey was carried out to learn whether the rural residents and their communities are making good use of the new fast broadband *** survey collected data from 217 rural residents from North Island,and the results indicate that about half of the interview respondents are satisfied with the new broadband speed and the reliability which is offered by RBI stage ***,there is about 28%of the respondents disagreed with *** majority of the Internet uses for rural residents are information searching,reading news,online entertainment,and online *** that,only a small proportion of respondents know how to utilize the Internet in their work/business and benefit/profit from it,e.g.,using cloud technology capabilities and online marketing campaigns,*** ***,we argue that information and communications technologies(ICT)adoption is not only the availability of the infrastructure but also the beneficial outcomes of Internet *** other words,Internet skills in-depth training and education need to catch up with the infrastructure deployment,which is useful to fuel the digital productivity and inclusion for booming rural *** survey data-driven findings presented in this paper could serve as a reference to inform government policymakers and those who wish to create,invest,and take actions to speed up the economic and social growth of rural communities in Aotearoa New Zealand through the Internet while shifting from the Internet speed and traffic volume-driven to a more effective Internet connectivity and value-added driven rural economy.

关键词： data driven approach data-supported decision and policy making digital exclusion information and communications technologies(ICT)adoption effective connectivity rural broadband usage rural economic development

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Green UAV communications for 6G: A survey

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Chinese Journal of Aeronautics 2022年第9期35卷 19-34页

作者： Xu JIANG Min SHENG Nan ZHAO Chengwen XING Weidang LU Xianbin WANG School of Information and Communication Engineering Dalian University of TechnologyDalian 116024China State Key Laboratory of Integrated Service Networks Xidian UniversityXi'an 710071China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China College of Information Engineering Zhejiang University of TechnologyHangzhou 310023China Department of Electrical and Computer Engineering Western UniversityLondon.ON N6A 5B9Canada

Unmanned Aerial Vehicles(UAVs) have received a wide range of attention for military and commercial applications. Enhanced with communication capability, UAVs are considered to play important roles in the Sixth Generation(6G) networks due to their low cost and flexible deployment. 6G is supposed to be an all-coverage network to provide ubiquitous connections for space, air, ground and underwater. UAVs are able to provide air-borne wireless coverage flexibly,serving as aerial base stations for ground users, as relays to connect isolated nodes, or as mobile users in cellular networks. However, the onboard energy of small UAVs is extremely limited. Thus,UAVs can be only deployed to establish wireless links temporarily. Prolonging the lifetime and developing green UAV communication with low power consumption becomes a critical *** this article, a comprehensive survey on green UAV communications for 6G is carried out. Specifically, the typical UAVs and their energy consumption models are introduced. Then, the typical trends of green UAV communications are provided. In addition, the typical applications of UAVs and their green designs are discussed. Finally, several promising techniques and open research issues are also pointed out.

关键词： Energy efficiency Green communications Sixth Generation(6G)networks Unmanned Aerial Vehicle(UAV) Wireless networks

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Advances in Machine Learning for Crop Yield Prediction: A Comprehensive Review of Techniques, Trends, and Challenges

Advances in Machine Learning for Crop Yield Prediction: A Co...

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International Conference on Electrical, computer and communication engineering (ECCE)

作者： A.M Asik Ifthaker Hamim Abdul Mohaimin Al Fahim Ishmum Rashedul Arefin Ifty M. Jamshed Alam Patwary Department of Computer Science and Engineering International Islamic University Chittagong Kumira Chattogram Bangladesh Institute of Information and Communication Technology Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Addressing food security amidst climate change and population growth requires precise crop yield prediction models that surpass the limitations of traditional approaches. In this field, machine learning has become a potent instrument, utilizing comprehensive data from soil metrics, climatic variables, and crop characteristics to enhance forecasting accuracy. This review examines the application of ML algorithms such as Artificial Neural Networks, Random Forests, and Gradient Boosting Decision Trees for predicting crop productivity. Out of 200 articles initially reviewed from major databases, 47 were rigorously selected based on relevance and methodological rigor. The findings provide an in-depth analysis of ML’s efficacy in predicting yields across diverse agricultural environments, identifying trends, key challenges, and limitations related to data heterogeneity, regional specificity, and interpretability. This paper aims to guide researchers in advancing ML-driven yield prediction, particularly in integrating IoT technologies and improving model generalizability, hence encouraging precision farming and sustainable agriculture.

关键词： Machine learning algorithms Accuracy Predictive models Soil measurement Market research Data models Forecasting Crop yield Random forests Climate change Sustainable development Agriculture

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