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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Geluvaraj, B. Anil Kumar, K.N. Vishveshvaran, R. Department of Computer Science and Engineering New Horizon College of Engineering Bangalore India Department of Information Science and Engineering Rajeev Institute of Technology Hassan India

Now the issue of agriculture has become a significant concern in our modern times. The growth of crops and plants is hindered by numerous challenges, and one of the most pressing concerns is ensuring their health. Agriculture plays a pivotal role in human life and the economy, and poor management practices can lead to significant losses in agricultural output. To address this problem, a deep neural network approach for detecting plant leaf diseases has been developed as an alternative to the Densenet121 neural network. This can easily detect the disease of plant leaf. First select the plant village dataset and apply into pre-processing method. In this part it is very useful to identify the disease, then it will process into model selection and classification. In classification it will train the dataset and the disease can analyse and show the status of plant leaf that is healthy or unhealthy. The disease can be detected in the image of plant leaf. In this study, a publicly available dataset of both healthy and diseased plants is employed to classify crop species and identify the presence of various illnesses across different classes. To achieve this, Densenet121 is utilized. The predicted result based on accuracy. © Grenze Scientific Society, 2023.

关键词： Crops

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Fededrated Learning: Research, Challenges, and Future Directions 5

Fededrated Learning: Research, Challenges, and Future Direct...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kaswan, Kuldeep Singh Dhatterwal, Jagjit Singh Yadav, Rishika Malik, Kiran Tripathi, Aanjey Mani Galgotias University School of Computing Science and Engineering UP Greater Noida India Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation AP Guntur India Graphic Era Hills University Department of Computer Science and Engineering Uttarakhand Dehradun India Mriem Department of Computer Science and Engineering Rohtak India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

Federated Learning, a disruptive and novel aspect of machine learning, is at the forefront of decentralized, privacy-conscious data processing. This in-depth review study navigates the complex environment of Federated Learning Ecosystems, attempting to extract the core of the topic while shining light on its research horizons, pressing difficulties, and promising approaches. Our trip begins with a look at the fundamental concepts of Federated Learning, emphasizing its importance in the landscape of distributed machine learning paradigms. We track its development via historical milestones and key contributions, offering context for the field's increasing research. This study focuses on the many features of Federated Learning research, including privacy-preserving techniques, communication protocols, aggregation approaches, and real-world applications. We aggregate major results and advances within these domains, highlighting notable authors and their contributions. However, the route to Federated Learning's full fulfillment is plagued with difficulties, including questions about privacy, communication efficiency, and scalability. In the future, we will enlighten the growing paths of Federated Learning research, forecasting trends, providing insights into overcoming present problems, and visualizing its integration into diverse fields. To summarize, this review article serves as a guidepost for scholars, practitioners, and enthusiasts wanting to understand the vast domain of Federated Learning. It captures the core of this dynamic discipline, offers strategic counsel for resolving its obstacles, and encourages optimism about its bright future within the domain of distributed machine learning paradigms. © 2024 IEEE.

关键词： data Analysis Distributed Environments Edge Computing Federated Learning Machine Learning

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Credit Endorsement Forecast Utilizing Troupe Learning and XGBoost

Credit Endorsement Forecast Utilizing Troupe Learning and XG...

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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Pamula, Narendra Babu Suhana Begum, Shaik Kanakapudi, Upendra Koparaju, Vamsi Krishna Repudi, Rajesh Lakireddy Bali Reddy College of Engineering Department of Artificial Intelligence and Data Science Andhra Pradesh Mylavaram India

ISBN: (纸本)9798350376135

The current approaches to predicting loan approval frequently depend on oversimplified models that are unable to account for the complexity of borrower behaviour and financial risk assessment. One disadvantage is that the model's capacity to identify various patterns and subtleties in the data is restricted by its dependence on a single machine learning technique. This research proposes a framework for predicting loan acceptance that optimizes bank resources by reducing the risk involved in choosing safe candidates for loans through the use of ensemble learning techniques, specifically XGBoost. We make use of a Kaggle dataset with 4269 observations and 13 characteristics, including financial and demographic data. We obtain promising accuracies of 98% and 95%, respectively, by utilizing XGBoost and ensemble learning techniques as Random Forest, SVC, KNN, and Logistic Regression. Our research highlights how machine learning may improve the loan approval process, which can lead to better resource allocation and decision-making in banking operations. © 2024 IEEE.

关键词： Adversarial machine learning

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WU-Net++: A novel enhanced Weighted U-Net++ model for brain tumor detection and segmentation from multi-parametric magnetic resonance scans

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Multimedia Tools and Applications 2024年第28期83卷 71885-71908页

作者： Das, Suchismita Dubey, Rajni Jena, Biswajit Tsai, Lung-Wen Saxena, Sanjay Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar India School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India Division of Cardiology Department of Internal Medicine Taipei Medical University Hospital Taipei111031 Taiwan Department of Computer Science and Engineering Institute of Technical Education and Research SOA Deemed to Be University Bhubaneswar India Department of Medicine Research Taipei Medical University Hospital Taipei111031 Taiwan Department of Information Technology Office Taipei Medical University Hospital Taipei11031 Taiwan Graduate Institute of Data Science College of Management Taipei Medical University Taipei11031 Taiwan

Brain tumor detection and segmentation from multi-parametric magnetic resonance (MR) scans are crucial for the prognosis and treatment planning of brain tumor patients in current clinical practice. With recent technological advancements, artificial intelligence-based deep learning has proven its indispensable image analysis capability in the most challenging tasks. This study proposes an automated WU-Net + + deep learning model for brain tumor segmentation using multiparametric structural MR scans obtained from the BraTS 2018 dataset. The model was validated through cross-dataset testing for intracranial hemorrhage (ICH) classification (ATLAS V2.1 dataset) and multi-organ segmentation from abdominal images of TCIA and BTCV datasets. The WU-Net++ model, a novel version of U-Net, was developed by adjusting its pooling operation as a weighted function of max and average pooling for brain tumor segmentation. The proposed model (WU-Net + +) achieved an F1 score of 0.94 ± 0.124, a dice score of 0.91 ± 0.132, and an AUC value of 0.915 for whole tumor segmentation. The model also achieved a high accuracy of 0.9949 ± 0.121 in ICH classification and dice scores of 0.912 ± 0.21, 0.844 ± 0.25, and 0.893 ± 0.17 for spleen, esophagus, and portal and splenic vein segmentation, respectively. Our study revealed that WU-Net + + has significant potential to improve the accuracy of segmentation and could be an effective method in the era of precision medicine. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Magnetic resonance imaging

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ASLiquid: Non-Intrusive Liquid Counterfeit Identification with Your Earphones 24

ASLiquid: Non-Intrusive Liquid Counterfeit Identification wi...

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22nd ACM Conference on Embedded Networked Sensor Systems, SenSys 2024

作者： Li, Zhaohui Luo, Wei Zhang, Yongmin Chen, Jianxi Shu, Yuanchao Zhang, Yaoxue School of Computer Science and Engineering Central South University China College of Control Science and Engineering Zhejiang University China Department of Computer Science and Technology BNRist Tsinghua University China Zhongguancun Laboratory Beijing China

ISBN: (纸本)9798400706974

As society progresses, liquid identification plays an increasingly important role in human life. But for now, minority of existing liquid identification solutions on the market can meet daily requirements of being ubiquitous, cost-effective and non-intrusive enough. In this work, we propose ASLiquid, the first liquid counterfeit identification system with commercial off-the-shelf earphones. Our core insight is that earphones can effectively induce acoustic resonance in container, and this phenomenon is observed highly associated with the changes in liquid density and solute compositions. Deploying ASLiquid introduces three main challenges: hardware heterogeneity among different earphones, diversity of user operations, and data complexity due to variations in liquid volume and device placement. To address these issues, we first propose to eliminate the existence of hardware noise and frequency response diversity for an earphone-irrelevant solution. Afterwards, we design a user operation adaptation algorithm to extract valuable feature data during each measurement period. To alleviate problems in data complexity, we propose a spectrum projection algorithm that can effectively generate CFR data of unknown liquid volumes and a VAE based anomaly detection model for counterfeit identification. We evaluate our system with six different earphones and under various conditions. Experimental results reveal that ASLiquid can achieve F1 scores of 95%-99.25% for seven frequently occurring liquid counterfeit tasks, even in specialized attacks on liquids with 1% difference in mass fraction and different types of solutions but with the same density. © 2024 Copyright is held by the owner/author(s).

关键词： Earphones

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Development of an AI based Priority Formation Mechanism in Criminal Law 3

Development of an AI based Priority Formation Mechanism in C...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Sharma, Kashish Amburle, Ankita Shelake, Vijay Chauhan, Ashvini Ashok Dept. of Computer Engineering Fr. Conceicao Rodrigues College of Engineering Mumbai India Dept. of Artificial Intelligence and Data Science Fr. Conceicao Rodrigues College of Engineering Mumbai India

ISBN: (纸本)9798331532420

Nowadays, there is a necessity to tackle a number of pending crime cases across different parts of the world through effective scheduling and management. AI and ML can be used in handling unfinalized criminal cases in India can be one of the possible ways of improving the functioning of the criminal justice system in this country. The approach to this project is to create an AI system that will filter through the open cases and then rank them according to complexity, time-sensitivity, prior performances, and location. Since priority in a case is explained through the use of a supervised learning based on selected set, then law enforcement as well as legal personnel are able to shift most of their attention to other significant cases. Despite feature engineering and data preprocessing being in the construction of the system from the data collected from the various sources such as court records. This means that there is than models such as;Random Forest, Apriori and Rule-Based in an effort to establish the most suitable model. Perhaps the application of this AI based mechanism to form priorities could aid India in better organization of resources and with the assistance of backlog decrease – the justice system in India. But as the right touch to measure artificial use is considered, themes of privacy, bias, and fairness must also be addressed appropriately. © 2024 IEEE.

关键词： Crime

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Cloud Based Hostel Facility Automation System

Cloud Based Hostel Facility Automation System

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Sneha Agrawal Sugandh Rastogi Shivani Trivedi Department of Computer Science and Engineering ABES Engineering College Ghaziabad India

Hostel facility management system is a cloud based system was developed to automate hostel administration. The safety of their child is the top concern for parents who send their kids to hostels, whether it’s for college or for other purposes. The entire process might be digital to make things simpler to observe . It will check the cleaning status of rooms, allocate rooms to students online, and give additional capabilities that will increase transparency. It controls the database’s data and retrieves it as needed. The created solution supposedly addresses the shortcomings of manual hostel management and is undependable, user-friendly, and efficient. There will be usage of cloud and MongoDB technologies. In order to eliminate manual paperwork, this study is centeredon the development of a hostel automation system based on cloud computing. To reduce concurrency, all hostel activities will be administered on a single platform.

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IoT Based Optical Sensor Network For Precision Agriculture

IoT Based Optical Sensor Network For Precision Agriculture

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Durai, R. S. Vishnu Vijayakumar, R. Lakshmisridevi, S. Bhanu, Shaik Thasleem Arunkumar, U. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamilnadu Coimbatore641022 India B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600048 India Rajalakshmi Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai602105 India Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore641021 India

ISBN: (纸本)9798350383348

Precision agriculture is a cutting-edge farming strategy that maximizes harvests by using cutting-edge technology and data-driven decision-making. Optical sensors and other Internet of Things (IoT) devices have great promise to revolutionize farming operations in this setting. Sensor networks and Machine Learning (ML) based tracking devices are in great demand because of the precise data extraction and analysis they give. This research was undertaken with the goal of reducing agricultural hazards and promoting smart farming practices. Diseases caused by insects and other diseases may reduce crop yields if not addressed quickly. Thus, in this study, we provide a unique artificial swarm fish optimized naive bayes (ASFONB) method for keeping an eye on the health of the soil and preventing diseases from manifesting in cotton plants' leaves. In this research, numerous important indicators of crop growth and health were monitored using Internet of Things (IoT) devices equipped with optical sensors. The environmental factors like as temperature, humidity, light intensity, and chlorophyll content are recorded by these sensors. The proposed method involves sending the collected data to a central server for processing and analysis via wireless transmission. Once the disease has been detected, the information will be sent to the farmers via Android app. The Android app can show the chemical concentration in a container with soil factors like humidity, temperature, and wetness. Using an Android app, you may control the relay and hence the power supply and chemical sprinkler system. The experimental findings demonstrate that the proposed solution outperforms the status quo in disease identification. © 2024 IEEE.

关键词： Agriculture IoT Optimization Approach

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Hybrid Model for Lung Cancer Prognosis: Integrating SVM and 3D CNN on Nodule data 2

Hybrid Model for Lung Cancer Prognosis: Integrating SVM and ...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Carmel Mary Belinda, M.J. Kannan, E. Alex David, S. Kannan, Sriram Ravi Kumar, S. Vathani, Anstey Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Fidelity Investments Data Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala RD Institute of Science and Technology Department of Civil Engineering India

ISBN: (纸本)9798331534950

In this research, a huge dataset of lung cancer images is processed using advanced image processing techniques gathered from different medical establishments. Images will be edited and color profiles retouched from original look into Strawberry corrected one. Key part of our research is to preprocess the images by using Gaussian preprocessing at initial front levels followed with Gabor filtering which improves clarity and focus in x-ray image. Findings also contribute to the field of biomedical science in studies related to lung cancer diagnosis, by proving that advanced image processing techniques are useful. These techniques are essential tools in many medical fields ensuring high quality imaging, which allows early detection and treatment planning. Lung cancer, characterized by rapid uncontrolled proliferation of cells in the lungs with a potential for distant spread either via blood or lymphatic system requires precise diagnostic techniques. After preprocessing, we apply segmentation and enhancement techniques on images to detect the salient features separating normal from affected lung tissues. Extensive experiments are conducted to validate our method compared with other methods using pixel level percentage and mask labels for anomaly detection. This work highlights the importance of a robust imaging approach for health care, especially in biomedical research where various diseases could be detected and treated. © 2024 IEEE.

关键词： Lung cancer

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Arabic Speech Dialect Classification using Deep Learning 1

Arabic Speech Dialect Classification using Deep Learning

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Alrehaili, Meaad Alasmari, Tahani Aoalshutayri, Areej University of Jeddah College of Computer Science and Engineering Department of Computer Science and Artificial Intelligence Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

The growing use of dialect around the country has recently drawn interest from speech technology and research communities in dialect detection. This article aimed to identify Arabic speech dialects and classify them according to the country of speaking. This study presents an analysis and preprocessing system for audio inputs that express the Arabic dialects within 8 Arab dialects. The dataset contains 672 data and eight main subgroups, 84 samples for each of the eight Arabic dialects. Arabic dialect features are extracted and modeled using Convolutional Neural Network (CNN) techniques. The study shows the suitability and efficiency of the system, deep learning models are used instead of machine learning models. The overall results reveal that CNN's implementation of our proposed system for identifying Arabic dialects reaches a degree of accuracy of 83%. This paper has proposed a system that showed its superiority in performance. The system converts the speech into images using the spectrogram feature, and CNN is used because it can extract features from images automatically. The study contributes to enhancing the classification process of Arabic speech dialects which is an essential issue as many of the studies working on Modern Standard Arabic (MSA), while the majority of Arabs speak local dialects, it is necessary to identify the dialect used by speakers in order to communicate with one another or before machine translation takes place. © 2023 IEEE.

关键词： Convolutional neural networks

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