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Performance Analysis of a Scalable Federated Learning-Based Agricultural Network 4

Performance Analysis of a Scalable Federated Learning-Based ...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Chakraborty, Shatanik Sarkar, Maharnab Gayen, Avijit Jana, Angshuman Chakraborty, Nilotpal Meghnad Saha Institute of Technology Dept. of Computer Science & Engg. Kolkata India Govt. College Of Engineering & Textile Technology Dept. of Computer Science & Engg. Berhampore India Indian Institute of Information Technology Dept. of Computer Science & Engg. Guwahati India Techno India University Dept. of Computer Science & Engg. West Bengal India

ISBN: (纸本)9798350387063

Although agriculture is a substantial part of the Indian economy, it encounters several challenges. Precision agri-culture is a farming management strategy that aims to overcome these challenges. It optimizes several components of the farming process using data and technology, resulting in increased crop yields while reducing waste and raising total resource use effi-ciency. Thus, in its context, we present a study on the development of a Federated Learning-based Agricultural Network consisting of a popular federated learning algorithm FedAvg to perform image classification on an open dataset, Plant Village. In this work, we specifically analyze the performance of the proposed Federated Learning-based Agricultural Network in terms of its scalable deployment. The proposed network yields an average classification accuracy of 97.52 % on test data (C ≈ 70), and provides a host of other benefits it derives from its federated nature, such as upholding the privacy of local client data as only the model updates are shared between the clients and the aggregation server. The prime objective of our work has been to study and explore the possibility of applying federated learning in the agriculture sector so that more efficient and privacy- preserving smart farming networks could be developed. © 2024 IEEE.

关键词： Agribusiness

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Advancing Diabetic Retinopathy Research with a Dataset from Aizawl region of Mizoram 3

Advancing Diabetic Retinopathy Research with a Dataset from ...

引用

3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Vanlalnunpuia, C. Lalhmingliana Ambeth Kumar, V.D. Kumar Khan, Ajoy Vanlalawmpuia, R. Khiangte, David Lalmuanpuia Lalchhanhima, R. Mizoram University Dept. of Computer Engineering Aizawl India Mizoram University Dept. of Information Technology Aizawl India Eye Care Hospital Aizawl India

ISBN: (纸本)9798331523893

This study presents a high-resolution Diabetic Retinopathy dataset collected from Eye Care Hospital in Aizawl, Mizoram, highlighting its importance for advancing DR detection in underrepresented populations. The dataset includes 495 retinal images captured using an OCT RS 330 device, with quality maintained through a preprocessing pipeline. Images were filtered for clarity using blur metrics and enhanced with adaptive histogram equalization to emphasize diagnostically relevant features. A baseline deep learning pipeline for multiclass DR classification achieved training, validation, and test accuracies of 50%, 49%, and 56%, respectively, reflecting the dataset's complexity. Advanced augmentations like MixUp and CutMix were applied, setting a benchmark for future research. This work underscores the dataset's unique contributions by providing a foundation for robust diagnostic models. This study supports inclusive and practical AI applications in ophthalmology. © 2025 IEEE.

关键词： Transillumination

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Advancing Ophthalmology through Transfer Learning and Channel-Wise Attention for Retinal Disease Classification 6

Advancing Ophthalmology through Transfer Learning and Channe...

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6th International Conference on Electrical engineering and information and Communication technology, ICEEICT 2024

作者： Islam, Sudipta Progga Alam, Ashraf Ul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350385779

The global incidence of ocular disorders, which impacts more than 2.2 billion people, requires innovative approaches to ensure early and accurate diagnosis. This paper explores the integration of artificial intelligence (AI) in ophthal-mology, focusing on retinal diseases like cataracts, glaucoma, and diabetic retinopathy. The utilization of AI enables remote examinations and consultations, providing a crucial advantage in early disease detection. This study introduces a comparative analysis of state-of-the-art models, combining pre-trained convolutional neural networks (CNNs) and incorporating a channel-wise attention mechanism for enhanced discriminative ability. The proposed hybrid model, combining EfficientNetBO and Inception V3 with channel-wise attention mechanism, exhibits a remarkable accuracy of 98.13%, outperforming standalone models and other combinations. The findings underscore the efficacy of combined architectures and the integration of attention mechanisms in advancing the classification of retinal diseases. © 2024 IEEE.

关键词： Ophthalmology

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TrustCaller- Voice-based Fraud Prevention System 4

TrustCaller- Voice-based Fraud Prevention System

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Sonwane, Rushikesh Patil, Mansi Chauhan, Purva Jain, Anushka Nandwalkar, Bhushan Awate, Ashish Shahade, Makarand SVKM's Institute of Technology Dept. of Computer Engineering Dhule India

ISBN: (纸本)9798350349900

An innovative project called 'TrustCaller' aims to improve call security. With the goal of establishing caller verification and building trust in incoming calls, the project makes use of state-of-the-art technologies for voice feature extraction, processing, and pattern matching. Utilizing React Native only for the Android application, 'TrustCaller' assumes a key function in boosting the security of phone communications when combined with Machine Learning (ML) and Speech Synthesis. This program is a vital step in protecting people from possible dangers and promoting safer phone communication. Moreover, it provides scalable security settings to guarantee users' privacy and control over communication. © 2024 IEEE.

关键词： Secure communication

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Navigating the Landscape of Intelligent Systems, Smart Technologies, and User-Centric Evolution 2

Navigating the Landscape of Intelligent Systems, Smart Techn...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Komatireddy, Sanjay Reddy Krishna Bhargavi, Y. Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350327533

Human-computer Interaction (HCI) is a dynamic field pi votal in shaping human-technology relationships. HCI is a multidisciplinary field, essential in understanding and enhancing the interaction between humans and technology. Challenges within HCI encompass design, usability, accessibility, and emotional resonance. The aim of this research study is to highlight the multifaceted nature of HCI, offering insights into its transformative and complementing potential and its continuous influence on the digital landscape. By delving into the challenges faced and the current technologies (Multimodal Affect Recognition, Artificial Intelligence, etc.), this review serves as a foundation for further research and development in this vital domain, offering valuable insights into the dynamic HCI landscape. © 2024 IEEE.

关键词： Affect Recognition Human-computer Interaction (HCI) Multimodal Interaction Personalization Psychology Usability Challenges

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A Machine Learning Model for Predicting Internet Addiction in Teenage and Young Students 3

A Machine Learning Model for Predicting Internet Addiction i...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Johar, Geetika Patel, Ravindra University Institute of Technology Rgpv Dept. of Computer Science & Engineering Bhopal India University Institute of Technology Rgpv Dept. of Computer Applications Bhopal India

ISBN: (纸本)9798350384598

Internet addiction is becoming one of the critical issues among teenagers and young university students. This habit not only negatively impacts the student's learning performance, but also affects the student's physical and mental health. This paper is focusing on designing a Machine Learning (ML) model for accurately identifying internet addiction. The model incorporates data preprocessing and feature selection techniques for reducing the computational complexity. Next, five different ML algorithms have been implemented and compared to identify appropriate ML algorithms. The identified ML model has been trained using the behavioral features of the students for predicting internet addiction. The experiments have been carried out and the performance of the models has been measured. The results demonstrate that a random forest classifier provides 83% accurate detection for university students and 96% accurate results are provided by an SVM-based classifier for teenager internet addiction detection. Therefore, by using the entire observation of experiments it is found that the proposed model can successfully identify internet addiction in students by analyzing the student's behavioral features. © 2024 IEEE.

关键词： Contrastive Learning

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Relaxing Super Localization Frame Rate Requirements Utilizing a Novel 2D Interpolation Technique

Relaxing Super Localization Frame Rate Requirements Utilizin...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Tuccio, Giulia Afrakhteh, Sajjad Iacca, Giovanni Demi, Libertario University of Trento Dept. of Information Engineering and Computer Science Italy

ISBN: (纸本)9798350346459

Ultrasound Localization Microscopy (ULM) by-passes the conventional diffraction limit, allowing the imaging of the vasculature at a microscopic resolution through precise localization of intravenously injected microbubbles. Thanks to their resolution, ULM generated images hold the promise to be the next diagnostic frontier for diseases that modify the vascular structure, such as cancer. However, deploying ULM in clinical settings is currently limited by the prolonged acquisition times and high frame rates needed. To relax the high frame rate requirement, we propose a new time-efficient ULM (TEULM) pipeline. The proposed TEULM introduces the radial basis functions (RBFs) as interpolators upstream of the ULM framework, to recover the missing information consequence of a reduced frame rate. We test TEULM using in vivo Rat's Brain and Kidney datasets acquired at a high frame rate (1 KHz). The results confirm the ability of TEULM to reconstruct super-resolution (SR) images with a similarity score (Dice score) of 80% for both datasets when simulating a frame rate acquisition of 100 Hz;i.e., we can relax the frame rate requirement for ULM by a factor 10 while keeping the SR image quality acceptable. © 2023 IEEE.

关键词： Interpolation

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Predicting Mobile Prices with Machine Learning Techniques

Predicting Mobile Prices with Machine Learning Techniques

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Rana, Deepak Singh Dhondiyal, Shiv Ashish Singh, Sumeshwar Kukreti, Sanjeev Dhyani, Ashish Graphic Era Hill University Dept. Of Computer Science And Engineering Dehradun India Graphic Era Deemed To Be University Dept. Of Computer Science And Engineering Dehradun India Jaypee Institute Of Information Technology Dept. Of Computer Science And Engineering India Graphic Era Deemed To Be University Department Of Hospitality Management Uttrakahnd Dehradun India

ISBN: (纸本)9798350306446

Smartphones are increasingly vital to people on a daily basis. Telephones are utilized in all aspects of life, ranging from personal to professional, due to technological advancements. It serves a function beyond making phone calls. It enables internet connectivity and email reading. when not using the computer. The characteristics of a mobile phone are a crucial consideration when buying *** overall objective of this research is to find the best way to apply machine learning to estimate the retail pricing of smartphones based on their individual specs. Individuals who frequently use their phone are more attentive to selecting features. When purchasing a cell phone, a comparison is done based on the price-performance ratio. Phone features are regarded as performance. This research aims to forecast if mobile phones with certain features are considered economical or *** work is capable of being utilized in various marketing and business contexts to assist in making informed purchasing decisions by maximizing features while minimizing costs. © 2024 IEEE.

关键词： Decision trees

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An Approach in Prediction of Medicine Side Effects Using RNN Networks 8

An Approach in Prediction of Medicine Side Effects Using RNN...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Nargis, Tanzila Nisar Shaha, Suhail Ahamed Hazareena, Asia Nitte Dept. of Information Science & Engineering Karkala India Yenepoya Institute of Technology Dept. of Information Science & Engineering Vidyanagar Moodabidri India P.A. College of Engineering Dept. of Electronics & Communication Mangalore India

ISBN: (纸本)9798350350593

Adverse Drug Reactions (ADRs) are a critical issue in the field of medicine that demands innovative solutions. Various research has been done using machine learning and deep learning approaches to predict the drug side effects. This study delves deep into the realm of Recurrent Neural Networks (RNNs) to revolutionize the prediction of ADRs associated with medications. The groundbreaking RNN-based model harnesses sequential data from both drug structures and patient characteristics to effectively anticipate potential side effects. Through extensive training on a vast dataset of drug-ADR connections and rigorous evaluation, the developed model has demonstrated remarkable accuracy of 98.95% in predicting previously unseen ADRs. When compared to traditional machine learning methods, The proposed RNN model has showcased its process in capturing the intricate relationships between drugs, patients, and side effects. This pioneering work holds immense promise in enhancing patient safety by proactively identifying potential ADRs prior to drug administration. © 2024 IEEE.

关键词： Recurrent neural networks

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IoT-Enhanced Smart Bulletin Board: A Multi-Functional Connectivity Hub 2

IoT-Enhanced Smart Bulletin Board: A Multi-Functional Connec...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Gundavarapu, Mallikarjuna Rao Swapna, Veerlapalii Reddy, Enugu Sriamsh Kumar, Pindi Praveen Prathapani, Abhinay Shivaji, Kothapally Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350327533

The problem with traditional notice boards is their static nature, leading to outdated information and manual effort to update. An IoT-based smart notice board solves this by providing real-time updates but requires addressing challenges such as connectivity, power management, compatibility, user interface, security, and maintenance. Addressing these challenges ensures an effective and user-friendly smart notice board. The proposed model involves using an LED dot matrix, ESP32, Kodular app, Firebase Cloud and DS3231 module. The app sends messages to the Firebase then it passes the message to ESP32, which displays message on the LED dot matrix, We use Firebase cloud which provides infinite range. Firebase provides a NoSQL database that allows developers to store and synchronize data in real-time. It is suitable for applications that require real-time updates, such as chat apps, collaborative tools, and live dashboards. The DS3231 module shows real-time information. This study implements a feature that allows the user to retrieve the current time and temperature by pressing a button in the Kodular app. When the button is pressed, a command is sent to the ESP32, which retrieves the relevant data from the DS3231 module and sends it to display. This study creates a buffer to store messages, accessible by indexes, allowing easy retrieval. The proposed system provides a comprehensive solution for displaying messages, real-time data, and efficient message storage. QR codes are also provided to scan. © 2024 IEEE.

关键词： Database Dot Matrix DS3231 ESP32 Firebase cloud IoT Kodular app LED Module NoSQL QR code Real-Time Updates

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