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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Banerjee, Deepak Kukreja, Vinay Jain, Ayushi Rawat, Ramesh Singh Hariharan, Shanmugasundaram Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun248002 India Graphic Era Deemed to be University Uttarakhand Dehradun India Department of Computer Science and Engineering Vardhaman College of Engineering India

ISBN: (纸本)9798350305708

This abstract evaluates the efficacy of a model of classification in identifying and classifying different diseases of plants. To measure the model's efficacy across multiple illness classes, recall, precision, F1-score, support, percentage, or accuracy metrics were used. Notably, the model identified "Leaf Spot Diseases" (84.03%) or "Powdery Mildew (Erysiphe spp.)" (77.98%) with excellent precision and recall rates of "Leaf Curl Diseases" (74.45%) or "Viruses" (69.09%). The weighted average measures (precision: 85.40%, retention: 86.00%, F1-score: 84.90%) demonstrate the system's general robustness in dealing with uneven class distributions. The macro average numbers (precision: 70.85%, remembering: 67.45%, and F1-score: 70.11%) show consistent and equitable results across all classes. This study emphasises the model's efficacy for early illness identification and provides insight into effective agriculture disease management tactics. © 2023 IEEE.

关键词： Agriculture Diseases Forest Precision

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Integrating Transformative AI for Next-Level Predictive Analytics in Healthcare

Integrating Transformative AI for Next-Level Predictive Anal...

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2024 IEEE Conference on engineering Informatics, ICEI 2024

作者： Tarafder, Md Tanvir Rahman Ahmed, Nisher Hossain, Zakir Rahman, Md Masudur Rahman, Tahmeed-Ur Ahamed, Asif Westcliff University Irvine College of Engineering and Technology CA United States California State University Northridge College of Engineering and Computer Science CA United States California State University San Bernardino Business Management Department CA United States

ISBN: (纸本)9798331505776

The integration of artificial intelligence (AI) in healthcare has facilitated new predictive analytics, assisting clinicians in making more accurate decisions based on data, rather than guessing. This particular study intends to assess the implementation of AI-based predictive models into the Health Information Exchange (HIE) systems to improve the clinical practice as well as the decision-making processes within health practitioners. Using HIE networks' various datasets, we constructed and tested neural networks, decision trees, and ensemble methods machine learning algorithms that were developed for accurate clinical predictions. A comprehensive analysis of data preprocessing and testing was performed to ensure quality and consistency yielding models that were quite coherent. The results conclude that patient outcomes can be enhanced by applying AI integrated Predictive models and that focus on targeted intervention programs and monitoring disease progression can be achieved. These findings suggest the promise of advanced AI models combined with HIE systems for generating useful real-time decision-making information and effective healthcare personalized care pathways. This study forms the basis for more work using AI in clinical settings and makes a case for AI use across a wider healthcare setting. © 2024 IEEE.

关键词： Electronic health record

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Intelligent Rainfall Prediction and Flood Alert System using ML 5

Intelligent Rainfall Prediction and Flood Alert System using...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Nallametti, Nagarjuna Malarvizhi, C. Ahmed, Sheik Jamil Sathya, R. Vishnu, K. Harish Naveen, J.N. Cvr College of Engineering Csit department Telangana Hyderabad India Rajalakshmi Institute of Technology Department of Ece Tamil Nadu India School of Computer Science and Engineering Presidency University Karnataka Bangalore India Kongunadu College of Engineering and Technology Department of It Tamil Nadu Trichy India

ISBN: (纸本)9798331504403

Forecasting weather has always been a challenge for meteorologists worldwide due to its random nature. An Intelligent Rainfall Prediction and Flood Alert System using Machine Learning endeavours to revolutionize rainfall prediction and flood management by employing machine learning methods. The dataset contains historical rainfall data from 1901 to 2015 for various subdivisions in India. After filling missing values with column means, the data is visualized to understand annual and monthly rainfall trends. The analysis includes a specific focus on Tamil Nadu's rainfall patterns. For modelling, the data is prepared by grouping it by 'SUBDIVISION' and selecting relevant columns. A pipeline is created for preprocessing, including standardization, and Random Forest Regression is performed with hyperparameter tuning using GridSearchCV Hyperparameters, established prior to training, affect factors such as model complexity, learning rate, and regularization strength. Mean absolute error, Mean squared error, Root mean squared error, and R-squared are used to validate the model's precision. The system allows for user input to predict rainfall for a given year and month, with a flood alert triggered if predicted rainfall exceeds a specified threshold. © 2024 IEEE.

关键词： Rain

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Deep learning and ensemble methods for anomaly detection in ICS security

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International Journal of Information technology (Singapore) 2025年第3期17卷 1761-1775页

作者： Hossain, Md. Alamgir Hasan, Tahmid Karovic, jr, Vincent Abdeljaber, Hikmat A. M. Haque, Md. Alimul Ahmad, Sultan Zafar, Aasim Nazeer, Jabeen Mishra, B.K. Department of Computer Science and Engineering State University of Bangladesh South Purbachal Kanchan Dhaka 1461 Bangladesh Department of Computer Science and Engineering Prime University Dhaka Bangladesh Department of Information Systems Faculty of Management Comenius Bratislava Odbojárov 10 Bratislava 25 82005 Slovakia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman 11931 Jordan MEU Research Unit Middle East University Amman 11831 Lebanon Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Computer Science Aligarh Muslim University U.P Aligarh 202002 India P.G. Department of Physics Veer Kunwar Singh University Bihar Ara India

This research addresses the escalating threats to industrial control systems by introducing a novel approach that combines deep learning for feature selection with a robust ensemble-based classification technique to enhance anomaly detection. Our method utilizes a tailored autoencoder architecture to efficiently select features, followed by a Random Forest classifier to ensure reliable and generalizable detection of anomalies. Evaluated on the HAI Security Dataset, the proposed approach demonstrates exceptional performance, with significant improvements in detecting anomalous activities in ICS. The results highlight the method’s potential to enhance ICS security by providing a scalable and adaptable framework that evolves with emerging threats. This study not only advances cybersecurity methodologies for industrial systems but also lays the groundwork for future research, emphasizing the need for innovative and effective security solutions in the face of evolving cyber challenges. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Anomaly detection in ICS Cyber-resilience in critical infrastructure Deep learning for feature selection Industrial control systems security Random forest classifier for ICS

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Revolutionizing Healthcare: A Smart Pill Detection System Empowered by Deep Learning for Intelligent Drug Recognition 2

Revolutionizing Healthcare: A Smart Pill Detection System Em...

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

作者： Dhayanithi, A. Dalali, Shivakumar Madhavi, B. Surla, Govindu Marapelli, Bhaskar Arigela, Arun Kumar Sri Sairam Engineering College Department of Information Technology India Pes University Department of Computer Science and Engineering R R Campus India Srkr Engineering College Department of Information Technology Bhimavaram India Department of Cse Andhra Pradesh Mylavaram India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh India Department of Csit Telangana Hyderabad500043 India

ISBN: (纸本)9798350384277

In the dynamic landscape of healthcare, the accurate identification of medications stands as a critical pillar for patient safety and effective medical management. Traditional methodologies for identifying pills often rely on manual processes, placing a considerable burden on healthcare professionals and leaving room for errors, particularly when patients are unable to furnish precise prescription details. Compounding this challenge is the common practice of patients discarding medication containers, further complicating the identification process. At its core, the system harnesses the cutting-edge capabilities of artificial intelligence, specifically delving into advanced deep learning techniques. The overarching goal is to automate the intricate task of pill detection from images while simultaneously providing intelligent recognition of various medicinal *** by the robust Python programming language and underpinned by the sophisticated MobileNet architecture, the system aspires to achieve unprecedented accuracy and efficiency in the identification of pills and recognition of medicinal drugs. The training and testing processes unfold across a diverse dataset, encompassing a myriad of pill *** underscore the system's adaptability and real-world applicability within healthcare scenarios. The underlying ambition of this project is to furnish healthcare professionals with a state-of-the-art tool that not only mitigates the risk of medication errors but also optimizes time and resource utilization. Beyond its clinical applications, the system extends its impact to patients, affording them access to comprehensive information about their prescribed medications, thereby enhancing overall treatment comprehension and adherence. Over the ensuing sections, this project unfurls its narrative, delving into the intricate details of its methodology, implementation strategies, and the meticulous evaluation that underscores its potential to revolutionize healthcare t

关键词： Deep learning

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A Dynamic Clustering Method for Maximizing Energy Efficiency in C-V2X Metaverse

A Dynamic Clustering Method for Maximizing Energy Efficiency...

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Smart World Congress (SWC), IEEE

作者： Mingyang Ma Xin Feng Jing Zhang Haohua Liu Jiacheng Wang Geng Sun College of Computer Science and Technology Changchun University of Science and Technology Changchun China College of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Technology Jilin University Changchun China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The Metaverse can leverage intelligent traffic management technology to simulate the Cellular Vehicle-to-Everything (C-V2X) environment, integrating closely with the Internet of Vehicles due to its advanced connectivity and real-time data processing capabilities. These features make it possible to create a smart transportation system that closely mirrors the real world. Dynamic clustering is an effective network management method for handling the large number of mobile vehicles within the Metaverse, particularly in emergency scenarios where base stations (BSs) may fail. However, frequent changes in network topology due to high-speed vehicles result in unstable clusters and low communication efficiency. In this study, a dynamic clustering algorithm based on motion vector similarity (DA-MVS) is proposed for constructing stable clusters, and an adaptive adjustment clusters strategy is presented based on the change rate of vehicle numbers to improve energy efficiency (EE). Furthermore, we propose an elite chaotic perturbed sparrow search algorithm (ECPSSA) to optimize the EE of the C-V2X. Compared to the multiple baseline algorithm, the successful transmission rate can be improved by up to 76.9%, and the EE is enhanced by 20.97%.

关键词： Metaverse Heuristic algorithms Simulation Dynamics Clustering algorithms Energy efficiency Vectors Smart transportation Vehicle dynamics Vehicle-to-everything

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Tagging of Quarks to Particle Shower Imagery vis-à-vis Intelligent Learning 3

Tagging of Quarks to Particle Shower Imagery vis-à-vis Inte...

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3rd International Conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Dutta, Anurag Harshith, John Kumar, Pijush Kanti Soni, Yash Voumik, Liton Chandra Ramamoorthy, A. Government College of Engineering & Textile Technology Department of Computer Science Calcutta Serampore India Vellore Institute of Technology Department of Computer Science Tamil Nadu Vellore India Shri Govindram Seksaria Institute of Technology & Science Department of Information Technology Indore India Noakhali Science and Technology University Department of Economics Noakhali Bangladesh Velammal Engineering College Anna University Department of Mathematics Tamil Nadu Chennai India

ISBN: (纸本)9798350300710

The recent technological explosion has greatly aided humanity. We humans have been adopting newer technologies virtually every day, whether it is Artificial Intelligence or the Internet of Things. Among these, Artificial Intelligence is a technology stack or a concept that has attained remarkable heights. From IBM's Deep Blue breakthrough to Google's creation of self-driving cars, AI has become a blanket draped on humans today. In this work, we would make use of Artificial Intelligence, to be specific, Neural Network, to label a Particle Shower Imagery as Quarks' or otherwise. For, the work, we are employing a 4 Layer Convolutional Neural Network, with Adaptive Gradient Optimizer, and a total of 5 epochs only. This research would help in segregation of a Particle Shower Image as being developed of Quark or some other particle. For training the model, we made use of a Particle Imagery Dataset collected using Monte Carlo Simulation. © 2023 IEEE.

关键词： Monte Carlo methods

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Data Visualization in Transforming Raw Data into Compelling Visual Narratives 2

Data Visualization in Transforming Raw Data into Compelling ...

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

作者： Sarkar, Prithu Priya, Yadavalli Devi Patel, Pravina B. Chatterjee Biswas, Prasanta Arigela, Sreenivasulu Sallaram, Sravanthi Amity University Amity School of Communication Kolkata India Computer Science and Engineering Vignan's Institute of Information Technology Duvvada Andhra Pradesh Vishakapatanam India Ganpat University Department of Computer Science Gujarat Mehsana India Cdoe Parul University Vadodara India Mohan Babu University Andhra Pradesh Tirupati India St.Martin's Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798350384277

One of the main ways to evaluate information is through visual learning, which traditionally combines textual reading with visual aids like graphical representations. However, separating the visual method of learning to distinguish between text and visuals has been suggested in light of advancements in learning styles. The ability to swiftly create complex visual information and inexpensively distribute it digitally is another way that technology has improved visual presentation. The purpose of this research paper is to conduct a detailed investigation of the Virtualization of data, storytelling, and Data Visualization's Narrative, understand how Instruments and methods are used in visualization, and select the appropriate tool for presenting visual data stories and upcoming projects. To address this purpose, this research has reviewed a wide range of different journals and articles to gather information about data visualization, storytelling, and data narratives. Based on the analysis, this research paper finds an all-encompassing perspective on the visual data narrative process, incorporating the phases of discovering insights (data exploration), developing these insights into a tale (creating a story), and sharing this story with others (telling a story). Through the pursuit of these novel research directions, visualizations can facilitate more potent data storytelling. © 2024 IEEE.

关键词： Spatio-temporal data

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Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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Semi-asynchronous federated learning algorithm based on clustering and training efficiency 2

Semi-asynchronous federated learning algorithm based on clus...

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2nd International Conference on Digital Signal and computer Communications, DSCC 2022

作者： Lan, Jiayi Zhang, Zhenrong College of Computer Science and Engineering Shandong University of Science and Technology Shandong Qingdao266590 China

ISBN: (纸本)9781510656734

In order to solve the problem of device heterogeneity in federated learning, FedAsync proposes asynchronous federated learning, that is, the server and the client interact in an asynchronous manner, that is, the server updates the global model immediately after receiving the local model. Communication between server and client is non-blocking. Therefore, the server and client can update the model at any time without synchronization, which is advantageous when the devices have heterogeneous conditions. However, when the gap between clients increases, the time difference of the uploaded model parameters becomes larger, which has a great impact on the accuracy. In response to the above problems, this paper first introduces the concept of training efficiency, evaluates the training ability of customers, and determines the training frequency of customers according to the training ability, thereby narrowing the gap between customers. Second, change the semi-asynchronous strategy, that is, the uploaded model is not updated at any time, but aggregated and updated after a certain amount or a certain time is satisfied. Finally, the effect is verified by simulation experiments. © 2022 SPIE.

关键词： Electrocardiograms

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