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An Automatic Detection of Citrus Fruits and Leaves Diseases Using Enhanced Convolutional Neural Network

Remote sensing in Earth systems sciences 2023年第3-4期6卷 123-134页

作者： shastri, Rajveer Chaturvedi, Abhay Mouleswararao, B. Varalakshmi, s. Prasad, G.N.R. Ram, Mylavarapu Kalyan Department of E & T C Engineering Vidya Pratishthan’s Kamalnayan Bajaj Institute of Engineering & Technology Baramati 413133 India Department of Electronics and Communication Engineering GLA University Uttar Pradesh Mathura 281406 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur India Department of ECE Bharath Institute of Higher Education and Research Tamil Nadu Chennai India Department of MCA Chaitanya Bharathi Institute of Technology (A) Gandipet Hyderabad 500075 India Department of Computer Science & Engineering Aditya Engineering College Surampalem India

Accurate and timely detection of diseases present in citrus crops is a crucial task for effective crop management and the prevention of yield loss. traditional methods of disease detection, such as visual inspection, can be time-consuming and prone to human error. in this paper, we propose a novel approach for automatic and accurate disease detection using convolutional neural networks (CNNs). By investigating the numerous images of infected citrus fruits and leaves, the proposed CNN model has attained prominent recognition and classification accuracy results. the proposed Enhanced-CNN (E-CNN) model is trained using diverse collection of three benchmark datasets such as A Citrus Fruits and Leaves Dataset, Citrus Pest and Disease dataset and Citrus Leaf dataset. Due to careful investigation on layer details and image pre-processing techniques, the proposed E-CNN model secures remarkable performance in citrus fruit and leaf disease detection and type classification. the proposed model achieves significant improvement in disease detection and classification performance by securing the average f1 score 92.06, precision score 95.14, recall score 96.67, recognition accuracy 98% and classification accuracy 99%. these results are comparatively higher than earlier approaches and show more than 6% improvements in disease detection and classification performance. We believe that this unique approach has the potential to significantly improve disease management practices in the citrus industry, helping to improve crop yield and reduce the spread of diseases. © 2023, the Author(s), under exclusive licence to springer Nature switzerland AG.

关键词： Automatic detection Citrus fruits Diseases Enhanced convolutional neural network Leaves

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GoferBot: A Visual Guided Human-Robot Collaborative Assembly system

arXiv

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arXiv 2023年

作者： Zhuang, Zheyu Ben-shabat, Yizhak Zhang, Jiahao Gould, stephen Mahony, Robert The College of Engineering and Computer Science The Australian National University Canberra Australia The Faculty of Electrical and Computer Engineering Technion I.I.T Haifa Israel

the current transformation towards smart manufacturing has led to a growing demand for human-robot collaboration (HRC) in the manufacturing process. Perceiving and understanding the human co-worker’s behaviour introduces challenges for collaborative robots to efficiently and effectively perform tasks in unstructured and dynamic environments. integrating recent data-driven machine vision capabilities into HRC systems is a logical next step in addressing these challenges. However, in these cases, off-the-shelf components struggle due to generalisation limitations. Real-world evaluation is required in order to fully appreciate the maturity and robustness of these approaches. Furthermore, understanding the pure-vision aspects is a crucial first step before combining multiple modalities in order to understand the limitations. in this paper, we propose GoferBot, a novel vision-based semantic HRC system for a real-world assembly task. it is composed of a visual servoing module that reaches and grasps assembly parts in an unstructured multi-instance and dynamic environment, an action recognition module that performs human action prediction for implicit communication, and a visual handover module that uses the perceptual understanding of human behaviour to produce an intuitive and efficient collaborative assembly experience. GoferBot is a novel assembly system that seamlessly integrates all sub-modules by utilising implicit semantic information purely from visual perception. Copyright © 2023, the Authors. All rights reserved.

关键词： semantics

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intelligent Campus safety Management using iot and CNNs for surveillance, Access, and Emergency Response 3

Intelligent Campus Safety Management using IoT and CNNs for ...

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3rd international Conference for innovation in technology, iNOCON 2024

作者： sasirekha, V. Ramadevi, R. Presitha Aarthi, M. Malarvizhi, C. Mohankumar, N. Velmurugan, s. Srm Institute of Science and Technology Faculty of Management Studies Vadapalani Campus Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India Shree Sathyam College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Salem India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India B.V. Raju Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India T.J.S. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381931

this paper introduces a novel strategy for improving campus security by combining Convolutional Neural Networks (CNNs) with internet of things (iot) technology. For a smart campus safety management system, the suggested solution integrates advanced monitoring with access control and emergency response methods. iot devices are placed in key locations across the campus to gather data in real-time. this data is then analyzed using CNNs to ensure strong monitoring and the identification of anomalies. implementing CNNs allows for the detection of possible dangers, while access control measures guarantee protected entry points. the system makes use of Ai-powered intelligent emergency response methods to optimize and speed up crisis management. through the implementation of this all-encompassing strategy, want to build a safer and smarter educational ecosystem by guaranteeing the safety of campus settings. the research showcases the possibility for scalable deployment in varied campus contexts by demonstrating the usefulness of the proposed system via simulations and real-world deployments. © 2024 iEEE.

关键词： Anomaly detection

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image Fusion Using Wavelet transformation and XGboost Algorithm

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computers, Materials & Continua 2024年第4期79卷 801-817页

作者： shahid Naseem tariq Mahmood Amjad Rehman Khan Umer Farooq samra Nawazish Faten s.Alamri tanzila saba Department of Information Sciences Division of S&TUniversity of EducationLahore54770Pakistan Artificial Intelligence and Data Analytics(AIDA)Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Sciences University of EducationVehari CampusVehari61100Pakistan Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan Department of Mathematical Sciences College of SciencePrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Recently,there have been several uses for digital image *** fusion has become a prominent application in the domain of imaging *** create one final image that provesmore informative and helpful compared to the original input images,image fusion merges two or more initial images of the same *** fusion aims to produce,enhance,and transform significant elements of the source images into combined images for the sake of human visual *** fusion is commonly employed for feature extraction in smart robots,clinical imaging,audiovisual camera integration,manufacturing process monitoring,electronic circuit design,advanced device diagnostics,and intelligent assembly line robots,with image quality varying depending on *** research paper presents various methods for merging images in spatial and frequency domains,including a blend of stable and curvelet transformations,everageMax-Min,weighted principal component analysis(PCA),His(Hue,intensity,saturation),wavelet transform,discrete cosine transform(DCt),dual-tree Complex Wavelet transform(CWt),and multiple wavelet *** fusion methods integrate data from several source images of an identical target,thereby enhancing information in an extremely efficient *** precisely,in imaging techniques,the depth of field constraint precludes images from focusing on every object,leading to the exclusion of certain *** tackle thess challanges,a very efficient multi-focus wavelet decomposition and recompositionmethod is *** use of these wavelet decomposition and recomposition techniques enables this method to make use of existing optimized wavelet code and filter *** simulated outcomes provide evidence that the suggested approach initially extracts particular characteristics from images in order to accurately reflect the level of clarity portrayed in the original *** study enhances the performance of the eXtreme Gradient Boosting(XGBoos

关键词： image fusion max-min average CWt XGBoost DCt inclusive innovations spatial and frequency domain

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Machine Learning Models for Brain signal Classification: A Focus on EEG Analysis in Epilepsy Cases 2

Machine Learning Models for Brain Signal Classification: A F...

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2nd international Conference on Cyber Resilience, iCCR 2024

作者： Ali, Ali M Nashwan, shadi Al-Qerem, Ahmad Almomani, Ammar sakhnini, Mahmoud Al Aldweesh, Amjad AlAhliyya Amman University Communications And Computer Engineering Department Faculty Of Engineering Amman19328 Jordan Cybersecurity Department Faculty Of Information Technology Amman11831 Jordan Zarqa University Computer Science Department Faculty Of Information Technology Zarqa13110 Jordan School Of Computing Skyline University College Sharjah United Arab Emirates AlHuson University College AlBalqa Applied University I.T. Department Irbid Jordan Shaqra University College Of Computing And It Shaqra Saudi Arabia

ISBN: (纸本)9798350394962

Electroencephalography (EEG) provides critical insights into brain function and neurological disorders. However, analyzing and classifying EEG signals remains challenging. Manual review is time-consuming, prone to bias, and lacks scalability. Hence, developing automated EEG classification has become vital for clinical assessment and treatment of epilepsy. this study presents a machine learning framework for classifying normal and epileptic EEG recordings. the approach involves feature extraction using discrete wavelet transform (DWt) and statistical methods, feature selection to identify the most discriminative attributes, followed by classification across multiple algorithms. specifically, DWt decomposes non-stationary signals for time-frequency representation. Principal component analysis and cosine similarity assist in selecting robust features. supervised classifiers including naive Bayes, decision trees, neural networks, k-nearest neighbors, random forest, and support vector machines categorize the signals. Results demonstrate 100% accuracy with neural networks, indicating highly reliable automated classification is achievable. By comparing multiple techniques, the optimal machine learning pipeline emerges. this epilepsy EEG classification framework demonstrates the potential for Ai to significantly improve screening, diagnosis, treatment, and management of neurological disorders. Ongoing research aims to further enhance efficiency, scalability, and real-time capabilities. © 2024 iEEE.

关键词： Discrete wavelet transforms

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Performance Analysis of Photonics in Wireless Applications 7

Performance Analysis of Photonics in Wireless Applications

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7th international Conference on Electronics, Communication and Aerospace technology, iCECA 2023

作者： Deepika, G. Anushkannan, N.K. Palanivel, Manikannan Dhondiya, shiv Ashish suvitha, s. Ramesh, s. Department of Electronics and Communication Engineering. St. Peter's Engineering College Telangana Hyderabad500100 India Kathir College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore641062 India A.K.T Memorial College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Kallakurichi606213 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India S.A. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai600077 India Institute of Artificial Intelligence and Machine Learning Department of Computational Intelligence Saveetha University Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350340600

the advent of cutting-edge networking technologies like 5G and the internet of things portends well for the launch of innovative, data-intensive applications and services in the not too distant future. to employ these strategies, performance measurements must be quantified. Optical waveguide technology (OWC) has the potential to be a major enabling technology for high data-rate communications, with throughputs of up to tbps. Light cannot propagate above or below the ocean's surface due to turbulence, which is caused by variations in the atmosphere's temperature and pressure, variations in the water's temperature and salinity, and motions of the sea in the marine medium. Other factors that impede light propagation include pointing error from transceiver movement and optical path-loss from scattering and absorption phenomena. Free-space optics (FsO) and underwater optical wireless communication (UOWC) are two examples. On the other hand, the potential application of RF energy harvesting (EH) to create self-sufficient devices and extend node battery life has garnered significant attention. Even while RF and FsO wireless communication systems have been the subject of extensive performance studies in the literature, it may still be difficult to determine their analytical performance requirements for specific setups and schemes. Research on UOWC system performance in the presence of turbulence and path-loss impairments is currently one of the most sought-after areas of study. We provided a numerical approach to compute the UOWC path-loss, which can be utilised to find the UOWC system's bit error rate and received power.. © 2023 iEEE.

关键词： Energy harvesting

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Adaptive Fourier Decomposition of the First three sARs-CoV-2 infection Waves with Epidemic intervention—London,UK,2020-2022

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China CDC weekly 2024年第21期6卷 478-486,I0006-I0008页

作者： Zige Liu Guibin Lu Cheokieng Vong Zhiqi Zeng Wei He Zhijie Lin Cuiyun Lin Kaichin Hsieh Zifeng Yang Arlindo L.Oliveira Chitin Hon Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao SARChina Queen Ethelburga’s Collegiate Thorpe Underwood EstateYorkUK State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory DiseaseGuangzhou Institute of Respiratory HealthThe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications Department of Engineering ScienceFaculty of Innovation EngineeringMacao University of Science and TechnologyMacao SARChina Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Faculty of Innovation Engineering School of Computer Science and EngineeringMacao University of Science and TechnologyMacao SARChina University College London UCL Faculty of Engineering SciencesLondonUK Guangzhou Laboratory Guangzhou CityGuangdong ProvinceChina Instituto de Engenharia de Sistemas e Computadores:Investigação e Desenvolvimento em Lisboa LisboaPortugal Instituto Superior Técnico Universidade de LisboaLisboaPortugal

Background:this study provides a detailed analysis of the daily fluctuations in coronavirus disease 2019(COViD-19)case numbers in London from January 31,2020 to February 24,*** primary objective was to enhance understanding of the interactions among government pandemic responses,viral mutations,and the subsequent changes in COViD-19 case ***:We employed the adaptive Fourier decomposition(AFD)method to analyze diurnal changes and further segmented the AFD into novel multi-component groups consisting of one to three *** restructured components were rigorously evaluated using Pearson correlation,and their effectiveness was compared with other signal analysis *** study introduced a novel approach to differentiate individual components across various time-frequency scales using basis decomposition ***:Analysis of London’s daily COViD-19 data using AFD revealed a strong correlation between the“stay at home”directive and high-frequency components during the first epidemic *** indicates the need for sustained implementation of vaccination policies to maintain their ***:the AFD component method provides a comprehensive analysis of the immediate and prolonged impact of governmental policies on the spread of severe acute respiratory syndrome coronavirus 2(sARs-CoV-2).this robust tool has proven invaluable for analyzing COViD-19 pandemic data,offering critical insights that guide the formulation of future preventive and public health strategies.

关键词： offering sustained London

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Augmented Ensemble Learning Model for Biomarkers Prioritization to Enhance Disease identification Efficiency 6

Augmented Ensemble Learning Model for Biomarkers Prioritizat...

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6th international Conference on information systems and computer Networks, isCON 2023

作者： Vaishali, K. shambharkar, saroj somkunwar, Rachna K. Kolte, Roshan R. Jyothishmathi Institute of Technology and Science Karimnagar India K.I.T.S. Department of Information Technology Ramtek India Dr. D. Y. Patil Institute of Technology Department of Computer Engineering Pimpri Pune 18 India K.D. K. College of Engineering Information Technology Nagpur India

ISBN: (纸本)9798350346961

Biomarkers assist in identification of various diseases present in biological beings. these markers include Lymphatic Vessel Endothelial Hyaluronan Receptor (LVYEi), Regenerating Family Member 1 Beta (REGiB), trefoil Factor 1 (tFF1), Regenerating Family Member 1 Alpha (REGiA), etc. Each of these markers assists in estimation of presence of a particular protein type, which in turn assists in improving disease detection capabilities. Biomarkers are currently used to identify multiple diseases including Alzheimer's disease, pancreatic cancer, posttraumatic stress disorder (PtsD), breast cancer, brain cancer, etc. Algorithms designed to evaluate the presence of these diseases work via prioritization of biomarkers, which assists in evaluating their genomic strength. the currently proposed models for biomarker prioritization work on single type of disease, which limits their scalability and performance when applied to real time clinical applications. Moreover, these models require large training & validation delays, which further reduces their applicability. in order to reduce the delay needed for biomarker prioritization and improve its scalability, this paper proposes design of an augmented ensemble learning model to enhance disease identification efficiency. the model is applied to multiple disease types including Alzheimer's disease, pancreatic cancer, brain cancer and breast cancer. Results indicate that the proposed model has high accuracy, and reduced delay when compared with existing state-of-the-art approaches. this is mainly due to fusion of GoogLeNet and inception net models, which assist in optimizing feature variance, thereby improving efficiency of prioritization. the proposed model is observed to have an average accuracy of 91%, with high precision & recall performance across multiple datasets;thereby it can be facilitating its use for real time applications.. © 2023 iEEE.

关键词： Biomarkers

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A Blockchain-Based intelligent iot Communication system for Enhanced security, Reliability, and Efficiency

A Blockchain-Based Intelligent IoT Communication System for ...

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Advanced Computing technologies (iCoACt), international Conference on

作者： Dinesh Kumar K J. Gobinath Jayavadivel Ravi s. saravanakumar senthil P B. Prabhu shankar Department of Computer Science and Engineering Vel Tech University Chennai India Department of Computer Science and Engineering Rajiv Gandhi College of Engineering Chennai India School of Computer Science and Engineering Presidency University Bengaluru India Department of Information Technology Paavai Engineering College Paachal India Department of Artificial Intelligence and Data Science T.J.S.Engineering college Chennai India Department of Artificial Intelligence and Machine Learning Vel Tech University Chennai India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

this research proposes a new approach for developing and implementing a smart communication system of iot through developmental blockchain technology. Regarding the proposed solution, it is suggested that a distributed ledger can improve security, reliability, and efficiency of communication of iot devices. they tackle issues such as data integrity, privacy and security in iot networks as well as the problem of trust in such networks. the solution provides a decentralised and secure iot ecosystem communication hence allowing monitoring, controlling and administration of the iot build ecosystems in real time. Blockchain technology, however, when combined with nextgeneration communication interfaces and modern data analysis techniques, can offer various superior characteristics such as device identification, origin of information, etc. to iot applications. it also enhances the dependability and capacity of i.t.systems regarding capability and efficiency. Analysis and performance comparisons substantiate the performance benefits of the proposed intelligent communication system based on blockchain technology in addition these theoretical and performance analyses and experimental evaluation confirm the applicability and potential of the blockchain-based intelligent communication system to transform iot communication models and facilitate the new generation of eiOt applications in a number of fields. the key to the solution to incorporate blockchain fundamental infrastructure to augment the communication channel of iot devices. Using cryptography and consensus methodologies with the decentralized framework, integrity and confidentiality of the data is properly maintained because of the barriers of malicious interception or manipulation of the data. the communication system also boosts trust within the network participants because it offers a decentralized and immutable ledger of data transactions on the blockchain. instead, one of the compelling benefits of this solution

关键词： Performance evaluation computers Communication systems Ecosystems transforms Real-time systems Blockchains internet of things Reliability Monitoring

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A Development of 5G Wireless sensor Network using Man Design 4

A Development of 5G Wireless Sensor Network using Man Design

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4th iEEE international Conference on Advance Computing and innovative technologies in engineering, iCACitE 2024

作者： Vennila, C. Almusawi, Muntather Kumar, K. sriram Priya, B. Rawat, Vidhi Amirthayogam, G. Prince Shri Venkateshwara Padmavathy Engineering College Department of S&h TN India The Islamic University of Babylon College of Technical Engineering Department of Computers Techniques Engineering Babylon Iraq Karpagam Institute of Technology Department of Information Technology TN India Nehru Institute of Engineering and Technology Department of Computer Science and Business System Tamil Nadu T.M.Palayam India Ies College of Technology Department of Electrical & Electronics Engg Madhya Pradesh Bhopal India Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350360165

this article investigates the key drivers influencing the design of the forthcoming Metropolitan Area Network (MAN), focusing on the integration of two pivotal elements: Cloud Radio Access Network (C-RAN) for 5G implementation and the deployment of edge computing. As we delve into these aspects, the article systematically uncovers the crucial architectural components essential for effectively addressing the challenges inherent in implementing C-RAN and edge computing. through this strategic approach, the objective is to ensure a cost-effective solution that aligns with the specific requirements of these applications. the article aims to provide insights into the symbiotic relationship between CRAN and 5G, emphasizing their collaborative role in enhancing network efficiency. simultaneously, it underscores the significance of edge computing, highlighting its potential to optimize data processing and reduce latency within the evolving landscape of metropolitan networks. this comprehensive exploration contributes to a nuanced understanding of the future MAN design, grounded in innovative solutions and considerations for seamless integration. © 2024 iEEE.

关键词： Radio access networks

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