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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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Autoencoder for 3D Palm Print Identification 5

Autoencoder for 3D Palm Print Identification

引用

5th International Conference on information technology, Applied Mathematics and Statistics, ICITAMS 2023

作者： Naji, Noora Hadi Al-Juboori, Ali Mohsin University Of Al-Qadisiyah College Of Computer Science And Information Technology Dept. Of Computer Science Diwaniyah Iraq

ISBN: (纸本)9798350335118

The issue of personal identification is growing due to how crucial it is to get over the constraints of the existing methods. The current methods have numerous drawbacks, such as the possibility of password forgetfulness and broken or stolen access cards. The significance of bioinformatics-related identification systems resulted from this. This study devised a new approach for recognizing people from images of their palmprints. The scanning equipment uses contactless 3D imaging to collect the input images. The proposed approach is based on Autoencoder. And the obtained results are promising. With 8 minutes of training, the model gave 98% accuracy for the testing dataset. The results are compared with the accuracy and training time of transfer learning-based models. © 2023 IEEE.

关键词： Deep learning

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SoyNet: Deep Learning Approaches for Automated Soybean Seed Quality Assessment 2

SoyNet: Deep Learning Approaches for Automated Soybean Seed ...

引用

2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Pratap, Harshita Guru Prasad, M.S. Agarwal, Poorvi Bhardwaj, Ayushi Mehra, Ayushi Mathpati, Shilpa Dept. of Computer Science and Engg Dehradun India Reva University School of computing & Information Technology Bengaluru India

ISBN: (纸本)9798350371314

In the realm of agriculture, soybeans have played a vital role in diets for centuries, providing a consistent source of plant protein. Beyond protein, soybeans offer essential components like fibres, vitamins (including B vitamins), minerals (such as iron, calcium, and magnesium), and antioxidants. This necessitates precise quality assessment for factors like size, shape, colour, and damage. Leveraging advanced machine learning techniques such as VGG16, AlexNet and CNN, the model aims to automate the identification and categorization of soybean quality. To implement the proposed work the standard soybean dataset is considered. It consists of five soybean seed types complete, spotted, immature, broken, and skin-damaged. The proposed work effectively categories the quality of soybean seeds and it has the potential to improve the agricultural process as compared to traditional work. In this work different pretrained models are experimented in that VGG16 provides the highest of all showing accuracy 90%, AlexNet accuracy 78.49% and CNN accuracy 74.55%. © 2024 IEEE.

关键词： Damage detection

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Recommendations for Ensuring Equitable and Comparable Paragraphs in a Research Paper 9

Recommendations for Ensuring Equitable and Comparable Paragr...

引用

9th International Conference on Science, technology, Engineering and Mathematics, ICONSTEM 2024

作者： Pandey, Shikha Pandey, Vikas Rsr Rungta College of Engg. And Tech. Dept. of Computer Science And Engg. Bhilai India Bhilai Institute of Technology Dept. of Information Technology Durg India

ISBN: (纸本)9798350365092

Plagiarism is a significant consideration in all areas of research, and is generally considered to be an act of wrongdoing or a violation of official documents or patents. However, in the context of research, it may be appropriate for a researcher to draw on previously published data. It is important to consider whether the researcher's intentions were fair and whether they were aware of any potential plagiarism. Research papers may employ a range of linguistic modifications, such as paraphrasing, summarizing, checking for semantic similarity, and borrowing concepts, but all such modifications must be properly cited. This paper explores how to determine the similarity between paragraphs in potentially suspicious manuscripts and any paragraphs in reference/source manuscripts in scientific research papers. Additionally, this paper provides guidelines and rules for appropriately paraphrasing to avoid plagiarism. © 2024 IEEE.

关键词： Semantics

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SWATI AI : Advancing AI Models from Rule-Based Frameworks to NLP-Driven Prescriptive Analytics to Assist Victims of Domestic Abuse 1

SWATI AI : Advancing AI Models from Rule-Based Frameworks to...

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1st International Conference on AIML-Applications for Engineering and technology, ICAET 2025

作者： Awasekar, Dipali Dilip Lobo, L.M.R.J. WIT-Solapur Punyashlok Ahilyadevi Holkar Solapur University Dept. of Computer Science and Engg. Maharashtra Solapur India Walchand Institute of Technology Dept. of Information Technology Maharashtra Solapur India

ISBN: (纸本)9798350355611

SWATI AI (Support and Welfare Assistance through technology Interface) is an AI-driven chatbot designed to assist victims of domestic abuse by providing accessible, non-judgmental, and actionable legal guidance. This paper introduces the LAMP2 (Legal Analytics Model for Prediction and Prescription), the core framework that powers SWATI, progressing through four distinct research phases aimed at enhancing decision-making capabilities for victims of domestic violence under the Protection of Women from Domestic Violence Act, 2005. Phase 0 focuses on the creation of a curated dataset, PROTECT-JC, which compiles judgments from district courts, high courts, and the Supreme Court of India between 2008 and 2024. This dataset, developed in collaboration with legal experts and judicial professionals, serves as the foundation for training models to predict legal outcomes. Phase 1 introduces a rule-based decision framework in LAMP2 1.0, designed to extract and map predefined intents and entities from user inputs. This phase enables the structured documentation of complaints, providing victims with initial guidance based on the legal provisions of the Domestic Violence Act, 2005. The rule-based approach achieved accuracy of 80%, but with limited customization for individual cases. In Phase 2, LAMP2 2.0 leverages supervised machine learning models to predict legal outcomes, such as protection orders, custody orders, and monetary relief. These models, including Random Forest, XGBoost, and Decision Trees, achieved impressive results, with the Random Forest model showing 96.5% accuracy in predicting protection orders. Phase 3 evolves the model further with NLP-driven predictive analytics to process unstructured legal narratives, such as case judgments, transforming them into structured data for improved predictions. Models like Llama and Mixtral demonstrated strong summarization capabilities and show promise in predicting legal results. The integration of NLP and machine learning te

关键词： Decision making

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A Multi-Objective Hybrid Parallel-Batch Job Scheduling in Edge Computing 3

A Multi-Objective Hybrid Parallel-Batch Job Scheduling in Ed...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Al-Hussaini, Khalid Al-Najhi, Tahani Hamoud Thamar University Information Technology Dept. Dhamar Yemen Thamar University Computer Science Dept. Dhamar Yemen

ISBN: (纸本)9798350305333

Edge computing is an emerging technique for enhancing the computing experience on mobile devices by offloading computationally demanding jobs to the edge cloud. One of the critical scientific issues in edge computing is scheduling jobs on edge servers with restricted computational resources. In order to address this issue, the paper first suggests a parallel batch processing solution to multi-objective task scheduling using a hybrid P-BPM and PSO algorithm (P-MPSO) method that optimizes objectives while minimizing running costs and the amount of failed jobs running behind schedule in edge clouds. In order to maximize the effectiveness of our approach in light of the variable server performance and the various lags in the processing of jobs, this study then defines two parameters: the processing effectiveness of the server's performance by the P-BPM algorithm and the order of importance of the job by PSO algorithm. The simulation examined in this work were designed to compare the hybrid scheduling algorithms with several already-in-use scheduling techniques. Experiments demonstrate that the hybrid algorithms presented in this research are effective and dependable in lowering running costs and ensuring the scheduling jobs in edge clouds in a timely manner. © 2023 IEEE.

关键词： Scheduling algorithms

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Real-Time Helmet Detection System with Vehicle Number Extraction for Two-Wheeler Vehicles using YOLOv8 2

Real-Time Helmet Detection System with Vehicle Number Extrac...

引用

2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Kumar, Abhash Guru Prasad, M.S. Soni, Dheeraj Rani, Vibha Arora, Kanika Ganesh, D.R. Dept. of Computer Science & Engineering Dehradun India REVA University School of Computing and Information Technology Bengaluru India

ISBN: (纸本)9798350371314

In the face of increasing two-wheeler usage and the persistent issue of riders neglecting helmet compliance, this paper proposes a groundbreaking Real-Time Helmet Detection System with Vehicle Number Extraction. The existing literature on helmet detection systems is reviewed, revealing a range of methodologies, accuracies, and limitations in current approaches. Building upon this foundation, the three-stage detector combines the efficiency of YOLO (You Only Look Once) for object detection with a specialized number plate extraction system. The system aims to provide real-time detection of two-wheeler riders, classification of helmet usage, and extraction of vehicle numbers. The proposed methodology, utilizing a pre-trained YOLO model for object detection and EasyOCR library, demonstrates an accuracy rate of 64% for vehicle detection, 78% for helmet detection, and 92% for number plate extraction. The practical implementation of this system showcases its potential for enhancing road safety, automating traffic management, and promoting regulatory enforcement. While showing potential, the ongoing research and development face challenges such as limitation of computational resources and privacy considerations. The Real-Time Helmet Detection System stands as a promising solution for improving road safety, with the potential for broader use in traffic management and safety enforcement. Continuous improvement and adaptation will guarantee its effectiveness in contributing to a safer and more controlled traffic environment. © 2024 IEEE.

关键词： Accident prevention

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A Novel Deep Ensemble Learning Approach to Predict Eye Diseases from OCT Images 5

A Novel Deep Ensemble Learning Approach to Predict Eye Disea...

引用

5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Uddin, Nazim Mia, Md Sajib Uddin, Mohammed Nasir Mahmud, Prince Chandpur Science and Technology University Dept. of Information and Communication Technology Bangladesh Sylhet Engineering College Dept. of Computer Science and Engineering Bangladesh Jagannath University Dept. of Computer Science and Engineering Bangladesh Chandpur Science and Technology University Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350394290

As the world advances in technology, the classification of eye diseases is thought to have clinical applications since it evaluates and analyzes the results. Optometrists research and diagnose age-related eye problems such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), and Drusen using optical coherence tomography (OCT) of the human eye. Because their symptoms are similar, identifying these diseases is challenging and requires hours of expert analysis. The requirements for eye illness classification should be straightforward in nature, structure, and evaluation. Deep Learning Convolutional Neural Network Classification can create a hierarchical representation of images to differentiate between normal and diseased eye patterns for diagnostic purposes. Therefore, this paper aims to implement and define deep learning to classify and identify eye diseases. Using a four-class classification problem, this work investigates the automatic detection of Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), Drusen, and Normal in Optical Coherence Tomography (OCT) pictures. Multi-transfer learning architecture was used in the suggested deep learning Ensemble classification model. The most incredible accuracy of our proposed Ensemble model for multi- class classification was 83%. © 2023 IEEE.

关键词： Optical tomography

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Ensemble Model for Prostate Cancer Detection Using MRI Images 16

Ensemble Model for Prostate Cancer Detection Using MRI Image...

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16th International Conference on Developments in eSystems Engineering, DeSE 2023

作者： Kadhim, Omar Jawad Nafea, Ahmed Adil Aliesawi, Salah A. S. Al-Ani, Mohammed M Modern Uuniversity for Business & Science Dept. Computer Science Beirut Lebanon University of Anbar College of Computer Science and Information Technology Dept. Artificail Inteligence Ramadi Iraq University of Anbar College of Computer Science and Information Technology Dept. Computer Science Ramadi Iraq Selangor Bangi Malaysia

ISBN: (纸本)9798350381344

Prostate cancer is a prevalent form of malignancy impacting a substantial male population and ranks among the primary contributors to cancer-related fatalities globally. The utilization of magnetic resonance imaging (MRI) scans for prostate cancer detection has presented significant difficulties. This proposed, explores the use of machine learning algorithms for prostate cancer detection using MRI scans and addresses the challenge mentioned above. The proposed ensemble models employ a combination of Machine Learning (ML) Algorithms, including Support Vector Machine (SVM), AdaBoost, Decision Tree (DT), and Random Forest (RF) to improve accuracy detection. The results of our rigorous evaluation process revealed that the ensemble model achieved an outstanding accuracy rate of 96% in classifying prostate cancer into Significant and Non-Significant. By comparing our results to existing studies, we have demonstrated that the ensemble model-based method is on par with or even surpasses various techniques used in previous research efforts. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Real-time Detection of Diverse Bangladeshi Traffic Signs Using YOLOv8

Real-time Detection of Diverse Bangladeshi Traffic Signs Usi...

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2024 IEEE International Conference on Future Machine Learning and Data Science, FMLDS 2024

作者： Ehsanu Hoque, Md. Habib Mahbub, Mrittika Bin Md Tomari, Mohd Razali Bashar, Rezaul Rashed, Md. Golam Das, Dipankar University of Rajshahi Dept. of Information and Communication Engineering Bangladesh Pundra University of Science and Technology Dept. of Computer Science and Engineering Bangladesh Dept. of Electronic Engineering Malaysia Science Technology and Management Crescent Sydney Australia

ISBN: (纸本)9798350391213

The need for intelligent traffic management solutions is crucial in the rapidly evolving fields of autonomous traffic control and navigation systems, particularly for minimizing traffic accidents and ensuring the efficient operation of traffic signals. Improving traffic safety is of paramount importance, as road accidents claim over a million lives annually worldwide, with a significant proportion of these fatalities occurring in developing countries like Bangladesh. This research aims to develop a smart assistant capable of identifying and categorizing traffic signs to enhance road safety, especially in areas where drivers may not be fully aware of traffic regulations. To achieve this, we generated a comprehensive dataset containing 5,540 distinct images of traffic signs collected from various regions across Bangladesh, including highways, rural and urban roads, and challenging locations such as steep curves. The dataset comprises 41 categories of traffic signs. The dataset includes challenges such as color variations, blurriness, occlusion, small sign sizes, and low-light conditions. To further enhance the dataset's diversity, data augmentation techniques such as random rotations, shearing, and zooming were applied. We trained the YOLOv8 deep learning model, renowned for its real-time object detection capabilities, on this dataset. The model achieved impressive results, with a mean Average Precision (mAP) of 0.80, a recall of 0.87, a precision of 0.92, and an F1 score of 0.89, demonstrating its effectiveness in real-world traffic sign detection and classification. © 2024 IEEE.

关键词： Traffic signs

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