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Smart Crop Cultivation: Harnessing Machine and Deep Learning for Informed Agricultural Practices 3

Smart Crop Cultivation: Harnessing Machine and Deep Learning...

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Meghani, Smit Vele, Jay Madrecha, Kunal Salunke, Abhijeet Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350375190

Amongst various occupations in India, farming is the most common. It boosts the Indian economy significantly and provides Indians with a wide range of job opportunities. The ideal crop for each type of soil is no longer chosen by farmers. This has an immediate effect on agricultural yields. It is causing serious financial losses for the farmers in the area. Cultivating a certain crop on a particular type of soil now involves several factors to take into account. The optimal crop to plant is determined by taking into account a number of soil specifications. This novel opinion mining method will consider the following parameters: temperature, air humidity, soil pH, nitrogen, potassium, phosphorus, and annual rainfall. The model aids in determining the kind of crop that has to be planted by taking into account the data collected from prior years. In order to support the mission, various algorithms are analyzed and evaluated on multiple fronts, including comparative research and analysis. In addition, the ideal fertilizer will be recommended depending on crop-related environmental factors, temperature, pH level, and soil moisture content. © 2024 IEEE.

关键词： Crops

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Advancements in Depression Severity Prediction: A Multi-Modal Machine Learning Approach Integrating MCQs and Audio Data 2

Advancements in Depression Severity Prediction: A Multi-Moda...

引用

2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Pimpalkar, Shilpa P. Chavan, Anish Avinash Gawali, Sadhana Vijay Dalvi, Shreya Sanjay Department of Computer Engineering AISSMS Institute of Information Technology Pune India

ISBN: (纸本)9798350383867

This research presents a machine-learning framework for predicting depression severity by leveraging Multiple-Choice Questions (MCQs) and transcribed audio data. Two distinct data sources are employed, with regression techniques (linear, decision tree, random forest, support vector) applied to both MCQs and audio data. The severity predictions from individual regression models are amalgamated using a weighted average. Additionally, a combined machine learning model integrates classification algorithms (logistic regression, decision tree classification, random forest classification, support vector classification) for both MCQ and audio data. Trained separately, these models contribute to an integrated prediction of depression severity levels-minimal, mild, moderate, and severe. The MCQs utilized originate from the Beck Depression Inventory-2 (BDI-2) questionnaire. This comprehensive approach, encompassing subjective self-report and objective audio indicators, aims to enhance the accuracy of depression severity predictions, offering a valuable tool for early detection and intervention in mental health. The first key component of our approach involves utilizing the structured information derived from the Beck Depression Inventory-2 (BDI-2) questionnaire, which is represented by MCQs. This well-established instrument is widely used for assessing depression, providing a standardized set of questions that capture self-reported symptoms and emotional states. Leveraging the richness of MCQ responses, we employ regression techniques such as linear regression, decision tree regression, random forest regression, and support vector regression to predict the severity level of depression for each participant. © 2024 IEEE.

关键词： Prediction models

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Detection of Thyroid Diseases Using Deep Learning 2

Detection of Thyroid Diseases Using Deep Learning

引用

2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Archana, P. Suhana Anjum, S. Keerthana, S.M. Srusti, P.D. Department of Computer Science and Engineering Adichunchanagiri Institute of Technology Chikkamagaluru India

ISBN: (纸本)9798350383867

Thyroid diseases, including hypothyroidism, hyperthyroidism, thyroiditis, nodules, cancer, and euthyroid disease, are serious health problems that require accurate and timely diagnosis and treatment. This article presents a new method that specifically uses deep learning in convolutional neural networks (CNN) to successfully classify thyroid disease based on X-ray and ultrasound images. Our method, which uses CNN principles and data such as x-ray and ultrasound containing multiple thyroid images, is effective in classifying diseases. Thanks to a careful pre-processing, optimization and elimination process, our CNN model achieved an accuracy of 97.92%, demonstrating its potential as an important diagnostic tool. Early diagnosis and treatment of thyroid disease. © 2024 IEEE.

关键词： Ultrasonic imaging

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Flame Frenzy Fighter

Flame Frenzy Fighter

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2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Unpane, Nikita Jewani, Gunjan Umarvaishya, Manish Kokatey, Akshay Gupta, Ashutosh Nikhare, Amita Jhulelal Institute of Technology Computer Science and Engineering Department Nagpur India

ISBN: (纸本)9798350348460

Our research project aims to develop and implement a revolutionary fire extinguishing robot carefully adapted for the detection and autonomous extinguishing of fires in enclosed spaces. The lack of immediate human intervention in fire alarm systems often causes significant damage, highlighting the need for automated fire protection solutions. Our robot, equipped with flame detection and control sensors, has the ability to quickly detect fire sources and approach them autonomously with unmatched precision. This remotecontrolled marvel ensures a safe and effective response to fires, eliminating the need for human intervention in dangerous situations. This research represents a significant step forward in improving safety, preventing disasters, and saving lives from home fires. This is an important milestone in the field of autonomous robotics and could revolutionize our approach to residential fire safety and disaster management in private homes as well as commercial establishment. © 2024 IEEE.

关键词： Arduino IOT real time robot Sensors snapshot

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Human Activity Recognition in Construction Industry Using Machine Learning Pose Estimation Technique

Human Activity Recognition in Construction Industry Using Ma...

引用

International Conference on Interdisciplinary Approaches in Civil engineering for Sustainable Development, IACESD 2023

作者： Manoj Kumar, M. Hegde, Bhuvaneshwari Veda Murthy, S.P. Akhila, M.K. Bhoomika, A.S. Department of Computer Science and Engineering Jyothy Institute of Technology Bengaluru India

ISBN: (纸本)9789819996094

In many applications, including measuring physical activity, understanding sign language, and controlling full-body gestures, human position estimation from video is essential. This has the potential to be utilized for activity recognition in civil work. By accurately tracking human body posture from video, the technology can identify and classify different tasks and actions being performed by workers in construction, manufacturing, or other industries. This information can be used to monitor worker productivity, optimize workflow, and identify potential safety hazards. The proposed project is a machine learning (ML) solution for high-fidelity body posture tracking, employing current open source research that also drives the Machine Learning Pose Detection Application programming interface to infer predefined 3D landmarks and background segmentation mask on the entire body from RGB (Red, Green, Blue) video frames. The suggested method in this project achieves real-time performance on the majority of modern mobile phones, desktops/laptops, Python, and even the web, in contrast to current state-of-the-art methodologies, which rely mostly on strong desktop environments for inference. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Improving Students' Understanding of Education Using Generative Adversarial Networks: Bridging Text and Visuals 8

Improving Students' Understanding of Education Using Generat...

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8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Shidhaganti, Ganeshayya I. Kavyashree, K.N. Vishwachetan, D. Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331505462

Utilizing technology in the classroom is essential in today's environment to enhance students' learning. The background problem revolves around the necessity to adapt traditional educational methods to meet the evolving needs of students in today's digital age. With rapid technological advancements, classrooms face the challenge of effectively integrating technology to enhance learning experiences and outcomes. This includes addressing issues such as access to technology, digital literacy among students and educators, and the effective utilization of technology to facilitate interactive and engaging learning environments. The need to employ Generative Model to link school students descriptive 'Poem' statements with relevant visual imagery, accommodating diverse learning preferences. Through pre-trained VQGAN and CLIP models, this research work seamlessly integrates textual description with visually captivating images, employing iterative optimization methods to align image representations with textual descriptions. By incorporating advanced technique such as cutout augmentation the project ensures the quality and variety of generated images, catering to the diverse learning needs of school students. The outcome is successfully produced and an image is generated matching the 'Poem' description, where the average approximate percentage of accuracy is approximately 77.80% and the average approximate percentage of loss is approximately 22.89%. Additionally, a video documenting the iterative image synthesis process was created, providing insights into the image's evolution and highlighting the effective integration of technology to enhance educational experiences. © 2024 IEEE.

关键词： Generative adversarial networks

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Enhancement of Data Security using Edge-Preserving Image Interpolation for Reversible Data Hiding 2

Enhancement of Data Security using Edge-Preserving Image Int...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Pandey, Supriya Pandey, Shrawan Kumar Tripathi, Abhinandan Buddha Institute of Technology Department of Computer Science & Engineering Gorakhpur India

ISBN: (纸本)9798350368109

The proliferation of online data, video, and audio transfer over the internet has made Reversible Data Hiding (RDH) a significant area of research. However, embedding data into an image can be challenging as it can cause distortion and degrade image quality. To mitigate this issue, we have proposed edge-preserving image smoothing before data embedding to reduce distortion. The outcomes of proposed schemes are evaluated using metrics such as Peak Signal to Noise Ratio (PSNR), encoding capacity (EC) including incorporation time and embedding speed. The study reveals that the proposed technique surpasses existing state-of-the-art methods. © 2024 IEEE.

关键词： EC PSNR RDH

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BlasT: Blood Clot Classification Model Using Transfer Learning Based Convolutional Neural Network 16

BlasT: Blood Clot Classification Model Using Transfer Learni...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Mishra, Shruti Pal, Sujata Dey, Sweta Indian Institute of Technology Department of Computer Science & Engineering Ropar India

ISBN: (纸本)9798350383119

Blood cells contain a plethora of clotting factors vital for sustaining human life. Blood coagulation manages excessive blood loss from the blood vessels of an injured individual. Blood clots can appear due to various medical reasons in humans, such as platelet aggregation and converting fibrinogen into fibrin. Consequently, blood clots generate multiple disorders and even elevate the risk of death when not promptly diagnosed. Therefore, the prompt identification and treatment of blood clotting are paramount. In this study, we introduced an analytical approach for detecting blood clots. Also, we introduced a blood clot classification model: Blood Clot Classification Model using Transfer Learning and Convolutional Neural Network (BlasT). Our evaluation results demonstrate an impressive accuracy rate of 93.99% for the proposed model: BlasT. The primary objective of this study is to establish an early clot classification model using transfer learning in conjunction with a Convolutional Neural Network (CNN) layer. Our proposed BlasT model can identify blood clots swiftly and accurately. Furthermore, it allows timely notifications to medical professionals. © 2024 IEEE.

关键词： Blood-clot CNN Machine Learning (ML) Red Blood Cells (RBCs) White Blood Cells (WBCs)

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Proposed IoT-Based Smart Garbage Management System 4

Proposed IoT-Based Smart Garbage Management System

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Pujar, A.M. Pardeshi, K.R. Shah, Tanishka A. Shah, Yash N. Lokhande, Mrudula N. Pandhare, Yash S. Walchand Institute of Technology Computer Science & Engineering Department Solapur India

ISBN: (纸本)9798331540364

Considering the rapid growth of the urban population and rapid industrialization, the issue of efficient waste disposal has become very important. In response, authors here propose an idea in the form of an IOT -based waste management system designed to address the challenges at hand autonomously. The paper aims to propose solution for garbage management system. The system leverages IoT technology with sensor-based circuits and machine automation and models to streamline waste management processes. Traditional trash cans require manual operation, you need to press the lever to manually open the lid, monitor the trash can capacity, and adjust the garbage distance to prevent the trash can from overflowing. In contrast, this intelligent waste management model will work autonomously. The sensor will detect the presence of a person and automatically will open and close the lid. Additionally, location tracking will alert the authorities when waste processing capacity approaches its limits, facilitating timely disposal of waste and will prevent overflow. Additionally, there will be features such as voice detection to edit specific commands for executable access. By eliminating manual intervention and implementing intelligent monitoring capabilities, this system will provide a smarter and more efficient approach to waste treatment, ultimately leading to cleaner and more sustainable urban environments. © 2024 IEEE.

关键词： Waste treatment

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Transfer Learning Ensemble Model for Lung Nodule Classification

Transfer Learning Ensemble Model for Lung Nodule Classificat...

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2024 International Conference on Integrated Circuits, Communication, and Computing Systems, ICIC3S 2024

作者： Singh, Vinit Kumar Patel, Anoop Kumar National Institute of Technology Kurukshetra Computer Engineering Department Kurukshetra India

ISBN: (纸本)9798350364088

At present Lung Cancer is the second largest death threat to the world following heart attack. Lung cancer develops a tumor in the lung and the tumor spreads with time if it is malignant. This tumor can be detected in computed tomography(CT) Scan imagery as it shows spots in the lung region. Sometimes it is difficult to detect these spots with human eye as we have some limitation as to how much we can differentiate finer details and most of the time it is detected at later stages. Using Convolution Neural Network we can however detect much finer spots in the lung region and thus can go for further examination. CT-scan images has been used in this paper. The preprocessing operations like median filter for noise reduction and Otsu's method for thresholding has been used to make the images more prudent for classification. Three pretrained Convolutional Neural Network(CNN) models VGG16, ResNet50 and InceptionV3 have been used with modifications. Classification layers of these models have been removed and reconstructed to train on the specific dataset. Images from the preprocessing operations will be fed into the convolutional layers of these models and then output from these will be concatenated and will act as initial layer for the newly constructed dense layer having output nodes of three classes normal, benign and malignant. On the testing dataset this model provides an excellent accuracy of 98%. © 2024 IEEE.

关键词： Tumors

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