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检索条件"机构=School of Engineering Department of Computer Engineering and Informatics"

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Banana Crop Health: A Deep Learning-Based Model for Disease Detection and Classification 27

Banana Crop Health: A Deep Learning-Based Model for Disease ...

引用

27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Singh, Shashank Prakash, Pallav Baghel, Gulshan Singh, Anuj Raj, Dharm Agrawal, Amrit Kumar Sharda University Sharda School of Engineering and Technology Department Of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350392319

Bananas are a major food crop that feeds billions of people and provides an income to countless others. Nevertheless, they are prone to a range of diseases, which can lead to substantial loss in yield and quality as well as market value. The early diagnosis and accurate monitoring of these pathologies play crucial roles in the planning of disease management and control strategies. In this study, a deep learning-based method is proposed for identification and classification of banana diseases with MobileNetV2 architecture. Dataset: The training dataset used for this model is a large repository of 3012 annotated images, from four different diseases - Panama Disease, Black Sigatoka, Yellow Sigatoka and Bacterial Soft Rot along with the healthy plants. This model was validated on another set of images. The model was relatively high accurate after some small through fine-tuning and advanced image augmentation techniques applied to it. The high-level accuracy in predicting disease in banana shows promise for being a key tool at the early stage to control diseases on plant which helps better management, prevention economic loss and improved agriculture productivity. © 2024 IEEE.

关键词： Fruits

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Harnessing Relay Selection and Fluid Antenna Systems (FAS) for Enhanced Cooperative Terahertz Networks 35

Harnessing Relay Selection and Fluid Antenna Systems (FAS) f...

引用

35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Tlebaldiyeva, Leila Arzykulov, Sultangali Nauryzbayev, Galymzhan Nazarbayev University School of Engineering and Digital Sciences Department of Electrical and Computer Engineering Astana010000 Kazakhstan

ISBN: (纸本)9798350362244

Terahertz (THz) communication with a Tbps transmission rate requires novel channel propagation models and designs for antennas and RF components to address the challenges in THz bands, including molecular absorption effects, high penetration loss, and frequent blockages. An extreme data rate, ultra-low latency, and cm-level localization in the THz band will accelerate a massive deployment of IoT devices, where device nodes may re-transmit other users' messages. In this work, we study a THz cooperative network with an N-relay decode-and-forward relay selection scheme, where users are equipped with a fluid antenna system (FAS) receiver technology. FAS offers paramount flexibility and numerous advantages, especially for biomedical applications. Moreover, the proposed system considers non-ideal transceiver hardware that emits residual transceiver hardware impairments (RTHI) noise that notably deteriorates the performance of high-rate networks. Existing research often focuses on ideal transceiver hardware, overlooking the impact of RTHI noise. As a result, we introduce a rapidly converging semi-infinite expression for the coverage probability (CP), validated through extensive Monte Carlo simulations. The results highlight a substantial degradation in the CP performance with each distance meter. Thus, deploying devices for relaying messages and employing FAS receivers significantly enhance the CP performance. © 2024 IEEE.

关键词： Decoding

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Machine Learning Classification of Shoulder Pain Pathologies: A Scoping Review 15

Machine Learning Classification of Shoulder Pain Pathologies...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shukla, Garima Bhagat, Khushi Chaudhari, Vedant Navaneeth Krishnan, E. Bhadauria, Mansi Chauhan Chouksey, Yash Amity University Mumbai Amity School of Engineering and Technology Department of Computer Science and Engineering Mumbai India

ISBN: (纸本)9798350370249

Shoulder problem is a widely known musculoskeletal disorder that impacts heavily on the life of patients. Effective treatment for this must be preceded by accurate and timely diagnosis but traditional diagnostic techniques have always been subjective and less reliable. In recent years, machine learning (ML) techniques have shown promising results in improving the accuracy of diagnosing shoulder pain, addressing the limitations of traditional diagnostic methods. Nonetheless, there is a failure to translate these ML models into practical clinical tools that can easily be employed by healthcare professionals. In addition, this paper will present an overview of current understanding and methodologies employed for diagnosing and managing shoulder pain along with reviewing the use of ML techniques in medical image diagnostics. It also suggests development of a user-friendly and accessible diagnosis tool for shoulder pathologies aimed at linking modern research and daily practice in the medical field. © 2024 IEEE.

关键词： Patient treatment

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Data-driven ML Approaches for the concept of Self-healing in CWN, Including its Challenges and Possible Solutions 8

Data-driven ML Approaches for the concept of Self-healing in...

引用

8th IEEE International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2023

作者： Caleb, S. John Justin Thangaraj, S. Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350347791

The idea of a self-organizing network, or SON for short, was developed to facilitate the autonomous deployment and administration of cellular networks. SON capabilities have the potential to improve service quality, increase network performance, and lower operating and capital expenditures (OPEX and CAPEX). Self-healing refers to a network paradigm in which faults in intended networks are repaired through the execution of a predetermined set of steps, and recompense. SON self-heals as a network paradigm. SON requires self-healing. It is now widely acknowledged that data-driven machine learning is an effective method for infusing networks with intelligence and enabling them to repair themselves. However, there are significant obstacles to overcome before practical implementations of machine-learning approaches for self-healing may be developed. In the first part of this essay, we will begin by dividing these difficulties into Data imbalance, data insufficiency, data cost insensitivity, data reaction time, multi-source data fusion, and data latency are the five main areas of concern. Then, after identifying these difficulties, we provide viable technological solutions to overcome them. In addition, A cost-sensitive defect detection case study employing unbalanced data shows the approaches' feasibility and efficiency © 2023 IEEE.

关键词： Data fusion

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Impact of Social Media On Mental Health 3

Impact of Social Media On Mental Health

引用

3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Anand, Utkarsh Singh, Raj Karan Gupta, Keshav Sharda University Sharda School of Engineering & Technology Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798331528201

This research examines the influence of social media on mental health, particularly highlighting significant instances of students exhibiting high rates of melancholy and anxiety. This research examines the good and negative effects of social media, particularly among teens, in a comprehensive analysis. Social media facilitated social engagement, enhanced communication, and disseminated information. Nonetheless, it remains a significant concern since it facilitates cyberbullying, diminishes self-esteem, and exacerbates mental health issues. The study employed a descriptive research methodology, utilizing data collected from surveys and secondary sources. Additional methodologies employed included statistical analyses such as Crosstabs, ANOVA, and correlation analysis to identify trends and interrelationships between social media usage and mental health issues. This study elucidates the bimodal character of social media, highlighting its function in fostering relationships as well as its association with adverse psychological effects. This necessitates a heightened awareness of the many degrees and patterns of social media activity that influence students' mental health, specifically concerning anxiety and depression. Conclusion This study advocates for heightened awareness and the implementation of intervention measures to alleviate the adverse effects of social media, ensuring that this activity enhances rather than undermines mental well-being. © 2024 IEEE.

关键词： Social psychology

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Lung Cancer Detection: Leveraging Transfer Learning & AI Chatbot for Doctor Prescription 4

Lung Cancer Detection: Leveraging Transfer Learning & AI Cha...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Dixit, Aryan Mishra, Shruti Goyal, Archi Jain, Vishal Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350387490

This research focuses on developing end to end pipeline for the Lung Cancer Detection using Large Language Models (LLMs) & Generative AI. In the field of healthcare there is an advancement of tools and technology used for detection and diagnostics of disease. This project involves mainly two aspects of technology: MLOps and LLMOps. First, it creates a classification model that classifies the disease with the help of user's CT scan image to detect lung cancer using transfer learning model and then build an End-to-End CI/CDI CT pipeline for disease detection by handling new incoming data and version controlling. Afterward, this pipeline is integrated with a chatbot that incorporates a question-and-answer system by utilizing LLM models like GPT and Llama to help patients in receiving appropriate therapy like medicine recommendation and doctor consultancy. A dataset that used comprising of 81,584 images from TCIA is taken which consists of different types of lung cancer images. A pretrained model LLM was utilized to generate accurate doctor prescriptions and the model was fine-tuned using comprehensive medical literature. Four cutting-edge convolutional neural network (CNNs) architecture are used as transfer learning models which are DenseNET121, combination of Inception with Mobile NET, ResNET50, and VGG16 etc. The model, upon implementing, achieved an accuracy of 99.58%, with error rate of Type II being nearly zero by using the combination of Inception and Mobilenet. After identifying the disease, the patient can directly interact with the AI chatbot that is integrated with the pipeline. Both patients and healthcare professionals get the advantage for disease detection and getting diagnostics report related to disease respectively. © 2024 IEEE.

关键词： Lung cancer

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Detection and Taxonomy of Aircraft using Synthetic Aperture Radar Imaging 2

Detection and Taxonomy of Aircraft using Synthetic Aperture ...

引用

2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Sairam, D.V. Tamilselvi, M. Devi, S Gayathri Bhuvaneswari, R. Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Chennai India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350317558

Improvements in aircraft detection are necessary to improve surveillance. This work investigates the exact detection and classification of airplanes using YOLOv8 in conjunction with Synthetic Aperture Radar (SAR) photos. SAR imaging performs well in dimly lit and unfavorable environments. The objective is to use YOLOv8's object detection to identify aircraft kinds and categories in SAR pictures. A variety of SAR datasets are gathered, preprocessed, and trained on the model to allow for a thorough assessment of YOLOv8's performance in differentiating between different types of aircraft. Measures that assess model performance across aircraft classes include mean average precision (mAP), recall, and loss. The results demonstrate the effectiveness of YOLOv8 and show promising results in terms of speed and precision. This work drives progress in aerial surveillance by highlighting the robustness of SAR when combined with cutting edge deep learning for aircraft taxonomy. The achieved 98.5% mean average precision (mAP) attests to exceptional performance in aircraft detection and categorization from SAR images, reinforcing the utility of this integrated approach in bolstering security systems. © 2024 IEEE.

关键词： Aerospace Surveillance Aircraft Detection Aircraft Taxonomy Classification Deep Learning Imaging Techniques Machine Learning Synthetic Aperture Radar (SAR)

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Detecting Fire in Color Images using Convolutional Neural Network Architectures 8

Detecting Fire in Color Images using Convolutional Neural Ne...

引用

8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Tanmayi, Doddapaneni Udaya, Ediga Pati, Peeta Basa Amrita School of Engineering Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350334012

Fire accidents are the most prevalent disasters both indoors and outdoors. Early detection of such accidents helps prevent loss of property and lives. Approaches to detect fire are mostly based on environmental sensors or visual signals. Camera captured images and video sequences are utilized to detect smoke and fires by employing statistical & structural techniques as well as deep learning *** this work, we have employed various standard and custom CNN architectures for detecting fires in both indoor and outdoor environments. We have used 2 different publicly available datasets to train & test our models. These results are compared against already reported results. We find that AlexNet, though a simple architecture, performs the best at 86% accuracy in detecting fire in both indoor and outdoor environments. © 2023 IEEE.

关键词： Fires

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Improving Water Salinity Forecasting in Bang Pakong River with Attention Mechanism 24

Improving Water Salinity Forecasting in Bang Pakong River wi...

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7th International Conference on Machine Learning and Machine Intelligence, MLMI 2024

作者： Saksopit, Thongthai Khawne, Amnach Department of Computer Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (纸本)9798400717833

Seawater intrusion in the Bang Pakong River estuary poses a significant threat to freshwater resources used for agriculture, municipal consumption, and industrial applications. Accurate prediction of salinity fluctuations is crucial for effective water management strategies. This study proposes an enhanced univariate salinity prediction method utilizing a Long Short-Term Memory (LSTM) model augmented with an Attention Mechanism. The Attention Mechanism empowers the LSTM to selectively focus on crucial information within extended historical salinity data sequences. The optimal input sequence length for the model is determined through a training process, aiming for the most accurate predictions. Here, the model forecasts salinity values 24 hours ahead and is evaluated against actual measurements. Performance metrics demonstrate that the Attention-LSTM model achieves the lowest error (MAE: 0.007834, MSE: 0.000094, RMSE: 0.009697, MAPE: 0.048736) and the highest accuracy (R2: 0.782927) at an input sequence length of 504 hours. These findings highlight the potential of the Attention-LSTM model for improved salinity prediction in the Bang Pakong River estuary, aiding water resource management strategies. © 2024 Copyright held by the owner/author(s).

关键词： Estuaries

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Psycho Acoustic Testing to Determine the Optimal Frequency for Audible Safety Alerts for Freediving

Psycho Acoustic Testing to Determine the Optimal Frequency f...

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2024 Systems and Information engineering Design Symposium, SIEDS 2024

作者： Nanda, Arnav Zoraiz, Mohammad Barua, Dhruva Mohamed, Raghad Duke Pratt School of Engineering Department of Electrical and Computer Engineering Duke Marine Lab United States

ISBN: (纸本)9798350385144

Freediving is a captivating sport that involves diving underwater on a single breath, pushing the boundaries of human capability and exploring the depths of the ocean with minimal equipment. While free diving offers a unique and thrilling underwater experience, it also poses several difficulties. Having been in the water for long periods, many divers can develop hypoxia or decompression sickness. To solve this issue many divers use watches that allow them to monitor the heart rate, oxygen levels, and depth levels. Most of these watches can track and monitor vital signs and warn in case of potential harm or risk to the diver. While these watches are helpful, they are not preventative and many divers have passed away due to oxygen-related issues, decompression sickness, and a myriad of other problems. This project aims to address these problems and improve upon current products produced by Garmin Inc. Duke's EGR102 team's goal is to create an effective data collection and signal analysis algorithm dedicated for efficiently analyzing data regarding different pitches, timbers, and audible tones at varying depths of water. More importantly, the product should be able to stay underwater at 6 meters depth for more than an hour. © 2024 IEEE.

关键词： Oxygen

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