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Environmental Monitoring with Machine Learning 1

Environmental Monitoring with Machine Learning

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sikarwar, Shailendra Singh Kumar, Sunil Meghwal, Lakshita Varshney, Manoj Roy, Swapnila Kumar, Ravi PGV College Dept. of Computer Science Gwalior India Madhav University Dept. of Pharmacy Pindwara India Mangalayatan University Dept. of Computer Eng. and Applications Aligarh India Lingayas Vidyapeeth Dept. of Chemistry Faridabad India J.S. University Dept. of Agriculture Shikohabad India

ISBN: (纸本)9798350356816

data science is poised to transform environmental monitoring by boosting the precision, scope, and effectiveness of environmental assessments. Emerging technologies, including powerful machine learning algorithms, big data analytics, and remote sensing, are offering unprecedented capabilities in acquiring and evaluating environmental data. These developments permit more precise projections of ecological changes, real-time monitoring through vast sensor networks, and thorough climate modeling. Looking ahead, data science will drive creative approaches to environmental management, enabling tools for proactive decision-making and more effective resource management. The democratization of data science tools will also broaden stakeholder engagement, enabling communities and policymakers alike. By harnessing these future breakthroughs, data science has the prospect addressing important environmental concerns and creating sustainable solutions for a healthy planet. © 2024 IEEE.

关键词： Environmental monitoring

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Spice Vision: Deep Learning Enhanced Spice Classification System 3

Spice Vision: Deep Learning Enhanced Spice Classification Sy...

引用

3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Pujari, Laxmi Belavatgi, Meenaz Sajjan, Mahantesh M. Kamatar, Vishwanath Surasura, Pradeep Ammanagi, Nausheen K.L.E. Institute of Technology Dept. of Master of Computer Applications Hubballi580027 India K.L.E Institute of Technolog Dept. of Computer Science and Engineering Hubballi580027 India

ISBN: (纸本)9798350364569

Spices are admired for their distinctive flavors, fragrances, and health advantages. They are an essential part of industrial uses, medical procedures, and culinary customs. The automatic classification of 15 different varieties of Indian spices which are essential to both Indian food and traditional medicine is the main subject of this study. To guarantee quality and consistency, these spices underwent a thorough data preparation procedure after being painstakingly gathered. Then, Convolutional Neural Network (CNN) models were trained and tested using the preprocessed dataset. As a family of deep learning algorithms, CNNs are particularly good at classifying images because they extract hierarchical characteristics directly from the input. The study highlights the possibility for automated systems to precisely detect and classify distinct spices based on their visual properties and investigates the efficacy of various CNN architectures for spice classification. Gathering and preprocessing photos of the spices was part of the model creation procedure. Based on how well these models classified the test images, their performance was assessed. The accompanying picture illustrates the findings, which demonstrate the different degrees of accuracy attained by each model. © 2024 IEEE.

关键词： Convolutional neural networks

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Brain-Powered Vehicles for Individuals with Disabilities Leveraging AI 4

Brain-Powered Vehicles for Individuals with Disabilities Lev...

引用

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

作者： Jaison, T. Jisna Thambi, Priyanka E. Pinheiro, Nikita Scms School of Engineering and Technology Dept. of Artificial Intelligence & Data Science Karukutty India Kalamasery India St. Paul's College Dept. of Computer Science Kalamasery India

ISBN: (纸本)9798350387490

This paper provides a novel method for enhancing mobility and independence for people with disabilities via integrating Brain-pc interface (BCI) generation with artificial intelligence (AI) for vehicle control. The proposed device permits customers to control important vehicle features which includes steerage, braking, and acceleration the usage of their Brain indicators, captured through non-invasive EEG sensors. Preliminary checking out with diverse person agencies established an outstanding accuracy price of over ninety% in deciphering person intentions and efficaciously controlling the automobile. The device adapts to person neural styles, ensuring personalized and dynamic control, and extensively improves person autonomy and self-assurance. However, demanding situations which include sign noise, user variability, and long-time period device stability stay. This studies demonstrates the capacity for BCIs and AI to revolutionize transportation for individuals with disabilities, supplying an exceptional level of independence. Future trends will focus on improving signal clarity, improving machine stability, and addressing regulatory issues to make sure safe, real-world application. © 2024 IEEE.

关键词： Neurons

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Estimating Surface Soil Moisture based on Remote Sensing data and Crop Yield Classification using Machine Learning 9

Estimating Surface Soil Moisture based on Remote Sensing Dat...

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Anuradha, G. Susrith, Maddi Siddhardha, Kondreddy Gnana Velagapudi Ramakrishna Siddhartha Engineering College Dept. of computer science and engineering Vijayawada India Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798350377972

Soil moisture plays a critical role in environmental monitoring and sustainable agricultural practices due to its significant impact on crop growth, water availability, and overall ecosystem health. Properly understanding and managing soil moisture is vital for efficient water usage, especially in agriculture. Traditional methods in measuring soil moisture have been confined to ground-based small-scale methods, which are high price due to high labor intensity and limited spatial coverage. This project addresses its limitation in the spatial view by integrating remote sensing data from the Sentinel-1 satellite. Sentinel-1 is particularly effective in monitoring the soil moisture as it captures data in all climate conditions. By combining the satellite data with Google Earth Engine (GEE) we create a soil moisture dataset. For analyzing the joint satellite and ground truth data, the machine learning technique using Random Forest (RF) is applied and classification of crop yield is done. © 2024 IEEE.

关键词： Soil moisture

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Strong Baseline with Auto-encoder for Scale-Invariant Person Re-identification 6

Strong Baseline with Auto-encoder for Scale-Invariant Person...

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6th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022

作者： Bilakeri, Shavantrevva Kotegar, Karunakar Manipal Institute of Technology Manipal Academy of Higher Education Dept. of Data Science and Computer Applications Karnataka Manipal India

ISBN: (数字)9781665487160

ISBN: (纸本)9781665487160

Person Re-identification associates the same person images captured by disjoint cameras. The different training strategies and precise hyper-parameter selection are required for the construction of an effective person re-identification. However, in the current work, the tests are carried out by trial and error, which takes a long time to reach an optimal solution. We experiment with various training strategies with the metric learning model serving as the baseline to address this issue. Existing person re-identification benchmarks have insufficient samples for training the ReID model. Also, the previous approaches fail when the same individual appears in varied sizes. To solve this issue, an auto-encoder module is used to create a single image in three distinct scales to enhance the sample size and tackle the scale variation problem of the person reidentification. In addition, a performance comparison is made between the baseline and the baseline with auto-encoder to demonstrate the influence of an auto-encoder as a data augmentation for the person re-identification. The adoption of auto-encoder module and bestperforming training techniques to a baseline have enhanced the rank1 and mAP on Market1501, DukeMTMC-reID datasets. © 2022 IEEE.

关键词： computer vision

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YOLOv8-Based Deep Learning Approach for Real-Time Skin Lesion Classification Using the HAM10000 dataset

YOLOv8-Based Deep Learning Approach for Real-Time Skin Lesio...

引用

2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Saha, Utsha Ahamed, Imtiaj Uddin Imran, Md Ashique Ahamed, Imam Uddin Hossain, Al-Amin Gupta, Ucchash Das Dept. of Computer Science North Dakota State University FargoND United States Agricultural Data Analytics Unit North Dakota State University FargoND United States Dept. of Computer Science University of South Dakota VermillionSD United States Dept. of Applied Computer Science The University of Winnipeg Winnipeg Canada Dept. of Computer Science Liverpool John Moores University Liverpool United Kingdom

ISBN: (纸本)9798350350548

Skin cancer is a prevalent and potentially fatal disease that requires early detection for effective treatment. We trained and evaluated five YOLOv8 classification model variants (YOLOv8n-cls, YOLOv8s-cls, YOLOv8m-cls, YOLOv8l-cls, and YOLOv8x-cls) on the HAM10000 dataset, which contains 10,015 dermatoscopic images of common pigmented skin lesions. The models were trained for 30 epochs using data augmentation techniques to enhance generalization. Performance was assessed using metrics including accuracy, precision, recall, F1-score, and inference time. The YOLOv8x-cls model achieved the highest accuracy of 86.2% and precision of 82.1%, while the YOLOv8lcls model demonstrated the best balance with the highest F1-score of 77.0%. Compared to previous ensemble approaches, our single YOLOv8 models achieved superior performance with lower computational overhead. The YOLOv8n-cls variant showed the fastest inference time of 0.5 ms, making it suitable for real-time applications. Our results demonstrate the potential of YOLOv8-based models for accurate and efficient skin lesion classification, which could aid in early skin cancer detection and improve patient outcomes. © 2024 IEEE.

关键词： Lung cancer

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Gaze Estimation Using VGG16 Architecture Through XGBoost Classifier 5

Gaze Estimation Using VGG16 Architecture Through XGBoost Cla...

引用

5th International Conference on Electrical, Control and Instrumentation Engineering, ICECIE 2023

作者： Yerramreddy, Dhanvanth Reddy Addanki, Veerababu Durgapu, Sathvik Don, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Amritapuri India Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Applications Amritapuri India

ISBN: (纸本)9798350325041

Gaze Estimation, a technology that has evolved in the modern era that revolves around analysing the movement of eyes and their patterns which covers a wide array of fields and domains. It transformed human-computer interaction by providing hands-free device control and navigation through visual focus. In this study, an unique approach has been introduced for estimating the gazes of the eyes through VGG16 model by modifying it's architecture that preserves it's base layers but replacing the softmax layer with an XGBoost classifier for better classification. For estimating it's efficiency, a comparative analysis has been conducted with various deep learning architectures. Through the implementation of the architecture, the primary aim is to outperform the benchmark models with notable performance improvements. The outcomes are highly encouraging and the modified architecture provides more precise estimation of gazes than the benchmark models. © 2023 IEEE.

关键词： Human computer interaction

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AIML for Road Accident Analysis 4

AIML for Road Accident Analysis

引用

4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Rathore, Saurabh Pratap Singh Sanitha, P.C. Varshney, Manoj Verma, Anupama Vashisht, Manisha Saharan, Aaryan Icapsr Management & Research New Delhi India Dhanalakshmi Srinivasan University Dept. of Ai and Data Science Coimbatore India Mangalayatan University Dept. of Computer Engineering and Applications Aligarh India Usha Martin University Dept. of Business Management Ranchi India Lingayas Vidyapeeth University Dept. of Cse Faridabad India Chandigarh University Dept. of Cse Chandigarh India

ISBN: (纸本)9798350364729

Road traffic accidents pose a significant threat to both developed and developing nations, prompting global efforts to enhance road safety. With cars and drivers on the roads, pedestrians also face risks, often becoming innocent victims of accidents. Various factors contribute to road accidents, including driving under the influence, weather conditions, speed, road type, light conditions, road surface, and design. These accidents can cause severe, sometimes unseen injuries with lasting health implications. This study aims to explore road accidents in India using the random forest classifier algorithm and machine learning techniques. © 2024 IEEE.

关键词： Highway accidents

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Enhancing OCR Accuracy Using Convolutional Neural Networks: A Study on Spelling Correction in Handwritten Numerals 1

Enhancing OCR Accuracy Using Convolutional Neural Networks: ...

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, Priyanka Dewal, Inder Singh Singh Rathore, Sajjan Singh, Vinay Kumar, Sani Bhavani, P.M. Sindhu Chandigarh University Dept. of Computer Science Engineering Chandigarh India Madhav University Dept. of Humanities & S.Science Pindwara India Usha Martin University Dept. of Com. & Information Technology Jharkhand India Mangalayatan University Dept. of Computer Eng. & Applications Aligarh India Meenakshi A.H.Edu. & Research Dept. of Humanities & Science Chennai India

ISBN: (纸本)9798350356816

Artificial neural networks have transformed machine learning and had a significant impact on a wide range of industries, including robotics, deep learning, healthcare, sports, and surveillance. Convolutional Neural Networks (CNNs) are unique among these developments because they combine cutting-edge deep learning methods with conventional Artificial Neural Networks (ANNs). CNNs are essential for tasks like data analysis, text classification, speech and face recognition, and pattern recognition in a variety of fields. In order to improve spelling correction, this article uses a dataset that was upgraded from the National Institute of Standards & Technology (NIST) and includes 70,000 grayscale pictures of handwritten numbers. © 2024 IEEE.

关键词： Convolutional neural networks

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Analysis of Image Restoration techniques on Lunar Surface Images

Analysis of Image Restoration techniques on Lunar Surface Im...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Yerramreddy, Dhanvanth Reddy Marasani, Jayasurya Gowtham, Ponnuru Sathwik Venkata Don, S. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Dept. of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350325188

Lunar surface images captured by Chandrayaan-II, leveraging advanced image restoration techniques. The images obtained from the space craft can subject to various noise influence depending on the data acquisition system. Our goal is to investigate the effect of various types of noise in the image, such as Gaussian, Salt and Pepper, and Speckle noise, necessitating advanced restoration approaches for optimal data extraction. Our primary objective is to employ three restoration techniques such as Wiener, Median and Gaussian filters to restore the lunar images. Through the implementation of these restoration techniques, we aim to mitigate the adverse effect of sensor noise, thermal noise, cosmic ray noise, transmission noise and compression artifacts, all of which impede the precise interpretation of lunar surface features. These restoration methods are then compared in various noise levels and has been evaluated in terms of SNR, PSNR, image fidelity and MSE. The outcomes are encouraging and the suggested technique provides more precise restoration for images of the lunar surface. © 2023 IEEE.

关键词： Chandrayaan-II filter Image restoration lunar data noise suppression

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