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Cotton Leaf Disease Classification and Pesticide Recommendation 2

Cotton Leaf Disease Classification and Pesticide Recommendat...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Jamadar, Bhagesh Harikant, Bhavana Manjunath Chaithra, B.J. Mohit, J.S. Vasudevan, Vani Nitte Meenakshi Institute of Technology Computer Science Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350372892

The decline in cotton production due to various diseases poses significant challenges to agriculture. In response, this paper presents the development of a software application titled 'Cotton Leaf Disease Classification and Pesticide Recommendation.' The application aims to classify diseases affecting cotton leaves, recommend suitable pesticides, and suggest nearby shops for pesticide procurement. Focusing primarily on prevalent cotton leaf diseases such as cercospora, bacterial blight, curl virus, fusarium, and Ascochyta, the software employs image preprocessing techniques to process cotton leaf images. Convolutional neural network technologies are utilized for training the datasets to accurately classify diseases. Upon classification, the application suggests appropriate pesticides and nearby vendors, and sends the results to the user's email address. This software endeavors to mitigate the impact of cotton leaf diseases on agricultural productivity, particularly in regions like Karnataka, India. © 2024 IEEE.

关键词： Application programs

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Latest Advancement in Medical Surgery using Artificial Intelligence

Latest Advancement in Medical Surgery using Artificial Intel...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Ujjainkar, Priyanka Anup Raut, Shital A. Computer Science and Engineering Visvesvaraya National Institute of Technology Maharashtra Nagpur India

ISBN: (纸本)9798331517953

Recent advancements in artificial intelligence (AI) have significantly enhanced the use of computer vision, machine learning, natural language processing, augmented reality, and deep learning in surgical video analysis. Laparoscopic procedures generate vast amounts of video data, presenting opportunities for AI to support real-time decision-making and improve surgeon training. AI-based computer vision techniques can extract valuable insights from these videos, facilitating precise intraoperative decisions and enhancing educational systems. Key applications of AI in surgery include robotic surgery, image- guided interventions, and instrument tracking. Emerging fields like automatic surgical action prediction and tool identification are gaining prominence, aiming to refine real-time support systems. The integration of multiple computer vision models, such as Dinov2 and the latest YOLOv9, with balanced classes, holds the potential to address existing challenges in the accuracy and real-time identification of instruments and organs. Future research should focus on further developing these AI applications and utilizing them for more effective surgical training and skill evaluation. © 2024 IEEE.

关键词： Laparoscopy

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Explainable AI for Chest Diagnosis Prediction 2

Explainable AI for Chest Diagnosis Prediction

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Gangam, Manikanta Baghel, Vishal Ali, Mohd Mohsin Raj, Manish Pranav, Ayushman Ranjan, Vaibhav School of Computer Science Engineering & Technology Bennett University Greater Noida India

ISBN: (纸本)9798350328202

Significant advancement has been achieved in medical reasoning (Artificial intelligence). However, the interpretability and confidence of many deep learning model discovery ideas face difficulties in fundamental applications like COVID-19 identification. The compatibility of Explainable AI (XAI) techniques with COVID-19 results according to chest X-ray pictures is examined in this work. Our model achieves uncompromising exactness and provides interpretable experiences in its dynamic interaction by combining Local Interpretable Model Agnostic Explanations (LIME) and the VGG16 architecture in conjunction with a transfer learning technique. Upgrading transparency, confidence, and comprehension in artificial intelligence-driven clinical diagnostics is the goal of the research. © 2024 IEEE.

关键词： COVID-19

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Attendance Management System using Haar Cascades and MTCNN Algorithm

Attendance Management System using Haar Cascades and MTCNN A...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Sharmila, G. Varalakshmi, I. Kaviya, M. Neha, K. Sherin, M.Juhe Suganya, P. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India

ISBN: (纸本)9798331510022

A cloud-based attendance management system utilizing advanced facial recognition algorithms to enhance efficiency and accuracy in educational environments. By employing the CASIA WebFace dataset, which features a diverse collection of facial images, the system leverages deep learning techniques to extract meaningful features and identify individuals effectively. The process begins with image acquisition via webcams or mobile devices, followed by preprocessing to improve image quality. Face detection is accomplished using Haar Cascades, a swift method for identifying facial regions in real-time. Once detected, facial features are extracted using Convolutional Neural Networks (CNNs), which are designed to learn and classify complex patterns inherent in facial data. These extracted features are then compared against a centralized database, allowing for real-time updates to attendance records. The cloud infrastructure supports the processing of large datasets efficiently, ensuring rapid and accurate attendance tracking. This architecture enhances scalability, making it adaptable to various classroom sizes and environments. The findings demonstrate notable improvements in both accuracy and processing speed for attendance monitoring, highlighting the transformative potential of automated facial recognition systems in modern educational settings. This research contributes to the ongoing evolution of intelligent solutions, paving the way for streamlined attendance management practices. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparative Evaluation of Lightweight Cryptographic Algorithms for Secure Data Transmission in IoT Network 3

A Comparative Evaluation of Lightweight Cryptographic Algori...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Verma, Abhay Thokchom, Surmila National Institute of Technology Department of Computer Science and Engineering Meghalaya Shillong India

ISBN: (纸本)9798350372120

Internet of Things (IoT), in a blend with advanced computer vision techniques, models new challenges for end users in securing images and videos. Securing images becomes one of the crucial aspects in the enormous and growing domain of digital transfer. One of the well-known methods for preserving the privacy of photos on an unconstrained public medium is image encryption. IoT devices generally have constrained resources due to low computational potential, bounded memory, and limited power supply. This exhibits the demand for lightweight security schemes instead of traditional cryptographic algorithms to facilitate image security in an intelligent environment. This paper implemented three well-known lightweight encryption algorithms on the actual IoT, such as the Raspberry Pi 3 device, to capture and encrypt images and transmit the encrypted binary over the network. PRESENT, SIMON & XTEA lightweight algorithms are implemented on the Raspberry device. A comparison is made among those to find efficiency in terms of encryption time, PSNR, MSE, and SSIM over the live environment. On comparison over the number of factors, the SIMON algorithm was found to be the most efficient of the other two. © 2024 IEEE.

关键词： Internet of things

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Enhanced Air Quality Prediction Using Optimized Sarimax Model for Coimbatore Region

Enhanced Air Quality Prediction Using Optimized Sarimax Mode...

引用

2024 International Conference on science, technology, engineering and Management, ICSTEM 2024

作者： Revanth, Bande Manoranjitham, R. Division of Computer Science and Engineering Karunnya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350376913

This research delves into examining the forecast of air quality in Coimbatore, India, according to data gleaned from the Central Pollution Control Board (CPCB) specific to the area. The gathered information underwent a preprocessing step, using interpolation to ensure uniformity and completeness. Forecasting methods such as Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors (SARIMAX) and Long Short-Term Memory (LSTM) models were utilized to predict air quality levels. The SARIMAX model was fine-tuned through a grid search process, while the LSTM underwent hyperparameter adjustments to improve air quality predictions. Performance evaluation of these models involved metrics like R-squared, Mean absolute error, and Root mean square error. Based on the selected evaluation criteria, the outcome shows that SARIMAX outperformed LSTM in Coimbatore when it came to predicting the air quality. This analysis improves our understanding of air quality patterns in Coimbatore. Highlights SARIMAX's effectiveness as a modeling tool for atmospheric science applications. © 2024 IEEE.

关键词： Long short-term memory

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High-Sensitivity All-Dielectric Fano Resonance Biosensor with Wavelength-Division Multiplexing 12

High-Sensitivity All-Dielectric Fano Resonance Biosensor wit...

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12th IEEE International Conference on Information and Communication Networks, ICICN 2024

作者： Li, Qiming He, Quanlin Yang, Hongyan Zhi, Wenlong Zhang, Minjie Li, Jianqing School of Optoelectronic Engineering Guilin University of Electronic Technology Guilin China School of Computer Science and Engineering Macau University of Science and Technology 999078 China

ISBN: (纸本)9798350355802

We designed a multifunctional all-dielectric high-sensitivity Fano resonance biosensor. The sensor features an asymmetric structure, comprising an elliptical ring and a rectangular frame, symmetrically aligned along the x-axis. When incident light is perpendicularly directed onto the device, triple Fano resonances are observed at 1547.94 nm, 1974.11 nm, and 2080.94 nm, with calculated Q-factors of 1003.25, 628.9, and 994.3, respectively. This configuration enables triple wavelength-division multiplexing (WDM) functionality in the near-infrared spectrum. Upon varying the background refractive index, the Fano resonances exhibit red shifts, with calculated sensitivities reaching 1395.4 nm/RIU, 1205.2 nm/RIU, and 1052.2 nm/RIU, demonstrating exceptional sensing performance. The WDM sensor possesses high sensitivity and holds potential application value in biomedical sensing, optical communications, and other related fields. © 2024 IEEE.

关键词： Wavelength division multiplexing

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Supervoxels-based Self-supervised Few-shot 3D Medical Image Segmentation via Multiple Features Transfer

Supervoxels-based Self-supervised Few-shot 3D Medical Image ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Jing Wu, Yixuan Zheng, Xiaorou Dong, Shoubin South China University of Technology School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798350386226

In recent years, medical image segmentation technology has made great progress. However, the annotated data of 3D medical images is relatively small, and although fewshot segmentation can solve this problem, there are still many challenges. Too few samples of support images may lead to the fact that it is difficult to fully represent 3D medical images, especially the important 3D spatial information, and the global correlations between support and query images are not fully utilized. In this paper, we propose a novel few-shot 3D medical image segmentation pipeline framework, SMFT-Net, which can efficiently accomplish the 3D medical image segmentation task using only one labeled sample. Specifically, we proposed pretrained feature transfer module (PFTM) and bidirectional feature transfer module (BFTM) for multiple feature transfer of 3D medical image. PFTM can be used for 3D feature transfer to ensure that the 3D spatial information of medical images is preserved. And BFTM can perform bi-directional feature transfer between the query image and the support image to eliminate extraneous information from the surrounding pixels. Extensive experiments on four medical image datasets demonstrate that our method outperforms the state-of-the-art methods. © 2024 IEEE.

关键词： Medical imaging

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Deepfake Detection: Leveraging InceptionResNetV2 and LSTM for Enhanced Accuracy

Deepfake Detection: Leveraging InceptionResNetV2 and LSTM fo...

引用

2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Saini, Aradhna Attiuttama Naaz, Sheenam Shivhare, Anushka Dhuriya, Gaurav Yadav, Neha G L Bajaj Institute of Technology and Management Computer Science and Engineering Greater Noida India G L Bajaj Institute of Technology and Management Artificial Intelligence and Machine Learning Greater Noida India Sharda University Computer Science and Engineering Greater Noida India Netaji Subhas University of Technology Computer Science and Engineering Delhi India

ISBN: (纸本)9798331508685

The antique adage "seeing is believing"is simply no longer actual, which has profound implications for a ramification of sides of our lives. Deepfakes are becoming less complicated and simpler to create as technology advances. In fact, with the right software program, you can do a part of it right out of your palm. it is challenging to pick out deepfakes. The capability of the human eye to differentiate among deepfakes has reduced. however, a few academics have tried to identify deepfake. Deepfakes are media that can be generated by synthetic intelligence (AI) using algorithms. Artificial intelligence algorithms are designed to understand the traits of both the supply and the target photo. Subsequently, the target image is positioned on the pinnacle of the supply photograph. Our goal is to pick out video deepfakes by way of utilizing deep getting-to-know neural networks which include InceptionResNetV2 and LSTM. With the usage of transfer learning and the pre-trained InceptionResNetV2 CNN for characteristic extraction and vector production, we were able to efficaciously assemble a deepfake detection version. The capabilities had been used to train the LSTM layer, and the confusion matrix that was produced offers us the accuracy of testing and validation. The corresponding version's accuracy for 20 and 40 epochs was 84.75% and 91.48%, respectively. © 2025 IEEE.

关键词： Generative adversarial networks

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Exploring the Role of AI in Web Design and Development: A Voyage through Automated Code Generation 2

Exploring the Role of AI in Web Design and Development: A Vo...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Muthazhagu, Veera Harish Surendiran, B. National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India

ISBN: (纸本)9798350306415

Web design now plays a crucial part in how people interact with websites thanks to the quick development of the digital world. Traditional web design methods that rely on manual coding have run afoul of the intricate requirements of contemporary interfaces. This is where artificial intelligence (AI) comes in as a revolutionary force in the development of web design. With a particular emphasis on automated code creation, this literature review launches an informative investigation of the relationship between AI and web *** idea of automated code generation, where AI algorithms allow the conversion of design inputs into useful web code, is inextricably tied to this investigation. In order to enable AI to harness design principles and translate them into concrete, interactive web interfaces, this paper aims to present an integrative review of the methodologies, datasets, and algorithms that enable this. This study outlines the crucial role played by AI in influencing both the present and the future of web design by examining the aggregate insights from the selected research *** paper navigates the past, present, and future of AI-powered web design through a rigorous trip. Historical paradigms, the transformational potential of deep learning, actual case studies, ethical issues, and a comprehensive strategy for future progress are all included in the exploration. This assessment aims to illuminate the successes of the area while also inspiriting novel ideas that will transform the development of digital landscapes as the symbiotic link between AI and web design develops. © 2024 IEEE.

关键词： AI-powered web design automated code generation deep learning literature review web development automation

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