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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Charan, Chamala Giri Swaraj, Kondawar Gopichand, Devarakonda Reddy, H. Venkateswara Hariharan, Shanmugasundaram Kukreja, Vinay Vardhaman College Of Engineering Department Of Computer Science And Engineering Hyderabad India

ISBN: (纸本)9798350364828

An customized user-friendly website providing comprehensive information on hostels for students attending colleges far from their hometowns. The website focuses on crucial details such as room availability, infrastructure, fees, and images, streamlining the search for affordable and quality hostels near respective colleges. The platform features an intuitive interface with robust search and filter options, enabling users to find hostels based on location and amenities. A unique aspect is the inclusion of detailed owner information, facilitating direct communication between students and hostel owners for better decision-making. Development involves meticulous planning, a user-friendly frontend, and a secure backend. The website incorporates a review and rating system for community engagement, and a map integration feature to visually display hostel proximity to colleges, aiding accessibility assessments. Development involves meticulous planning, a user-friendly frontend, and a secure backend. The website incorporates a review and rating system for community engagement, and a map integration feature to visually display hostel proximity to colleges, aiding accessibility assessments. In conclusion, this website addresses a critical need through leveraging technology. This leads to analysis of results which was more promising and contributes to streamlined and informed decision-making process for students, enhancing their overall college experience. © 2024 IEEE.

关键词： Websites

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Deep Fusion of Vision Transformers, Graph Neural Networks, and LayoutLM for Enhanced Multimodal Detection of Lung Cancer: A Novel Approach in Computational Oncology 1

Deep Fusion of Vision Transformers, Graph Neural Networks, a...

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1st IEEE International Conference on Computing and Data science, ICCDS 2024

作者： Gowri, B. Shyamala Sharan, M. Gehlot, Yemini Varma, Veeralanka Chandrahas Easwari Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350365337

An innovative AI framework has been designed to improve early lung cancer detection by combining Vision Transformers (ViT), Graph Neural Networks (GNNs), and LayoutLM. Utilizing both the IQ-OTH/NCCD and Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening datasets offers a varied and thorough data basis for training and validating models. ViT enables advanced image processing of CT scans by utilizing self-attention processes to capture intricate visual characteristics. Graph neural networks analyze intricate spatial patterns in images to enhance diagnostic precision. Simultaneously, LayoutLM analyzes unstructured text data from patient records, enhancing the depth of the study. This method aims to enhance the diagnostic skills of current models by thoroughly leveraging multimodal data for comprehensive cancer detection. By using two separate datasets, comprehensive testing and improvement are achieved, leading to enhanced performance across diverse patient demographics and conditions. The framework sets a new standard in precision oncology by incorporating advanced AI technologies, leading to significant advancements in early detection, patient care, and personalized treatment approaches. This contributes to transformative improvements in clinical diagnostic methods and patient results in lung cancer treatment. © 2024 IEEE.

关键词： computerized tomography

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A Comprehensive Analysis of Human Action Recognition for Noisy Videos Using CNN and LSTM

A Comprehensive Analysis of Human Action Recognition for Noi...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Suma, S. Patel, Punyaban Tiwari, Rajesh Gundu, Lavanya Devi, Guntoju Kalpana Raju, K. Srujan Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering CMR College of Engineering & Technology Hyderabad India Department of Computer Science and Engineering CMR Engineering College Hyderabad India Department of Computer Science and Engineering CMR Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798331505134

Human action identification has advanced significantly as a result of the development of deep learning algorithms. Convolutional Neural Networks (CNNs) are known for their adeptness at extracting crucial information within video frames, especially shapes and postures. However, understanding movements necessitates not just individual frames as well as the temporal flow of the movement;this is wherein LSTM (long short-term memory) variants of Recurrent Neural Networks (RNNs) come into play. By analyzing frame sequences, LSTMs are particularly effective in gathering temporal connections, recalling prior motions, and overcoming the gap amongst static features as well as dynamic actions. Even with CNNs and LSTMs' remarkable accuracy in human action recognition (HAR), challenges remain. © 2024 IEEE.

关键词： Convolutional neural networks

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An Improved YOLO Detection Approach for Pinpointing Cucumber Diseases and Pests

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computers, Materials & Continua 2024年第12期81卷 3989-4014页

作者： Ji-Yuan Ding Wang-Su Jeon Sang-Yong Rhee Chang-Man Zou Department of IT Convergence Engineering University of KyungnamChangwon51767Republic of Korea Department of Computer Engineering University of KyungnamChangwon51767Republic of Korea College of Computer Science and Technology Beihua UniversityJilin132013China

In complex agricultural environments,cucumber disease identification is confronted with challenges like symptom diversity,environmental interference,and poor detection *** paper presents the DM-YOLO model,which is an enhanced version of the YOLOv8 framework designed to enhance detection accuracy for cucumber *** detection models have a tough time identifying small-scale and overlapping symptoms,especially when critical features are obscured by lighting variations,occlusion,and background *** proposed DM-YOLO model combines three innovative modules to enhance detection performance in a collective ***,the MultiCat module employs a multi-scale feature processing strategy with adaptive pooling,which decomposes input features into large,medium,and small *** approach ensures that high-level features are extracted and fused effectively,effectively improving the detection of smaller and complex patterns that are often missed by traditional ***,the ADC2f module incorporates an attention mechanism and deep separable convolution,which allows the model to focus on the most relevant regions of the input features while reducing computational *** identification and localization of diseases like downy mildew and powdery mildew can be enhanced by this combination in conditions of lighting changes and ***,the C2fe module introduces a Global Context Block that uses attention mechanisms to emphasize essential regions while suppressing those that are not *** design enables the model to capture more contextual information,which improves detection performance in complex backgrounds and small-object scenarios.A custom cucumber disease dataset and the PlantDoc dataset were used for thorough *** results showed that DM-YOLO achieved a mean Average Precision(mAP50)improvement of 1.2%p on the custom dataset and 3.2%p on the PlantDoc dataset over the baseline *** results highlight

关键词： ADC2f C2fe cucumber diseases YOLOv8n MultiCat pest detection

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Parallel Convolutional Neural Network for Brain Tumor Using MR Images 2

Parallel Convolutional Neural Network for Brain Tumor Using ...

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

作者： Lithika, V. Devi, M.K.Kavitha Thiagarajar College of Engineering Department of Computer Science Engineering Madurai India

ISBN: (纸本)9798350328202

We propose a method that utilizes an Auto-Context Convolutional Neural Network (CNN) to learn important local and global image features from 2D patches of varying window sizes. Brain imaging is a crucial first step in many neuroimaging techniques. The precision and efficiency of brain extraction are important to the overall accuracy. Brain extraction algorithms rely on brain atlases that combine questions about anatomy and assume geometry. However, when these assumptions are violated, the effectiveness of this process is limited. As a result, this study presents a CNN-based automated technique for developing an accurate brain extraction tool. We use two different methods to learn local and global images from two dimension patches: 1) Three parallel 2D convolutions based on three different dimensions (axial, coronal and sagittal). This uses a voxel method of implicitly learning 3D data without computationally expensive 3D convolution. 2 U-net architecture-based advanced convolutional network. We employ network post-processing outputs as interactive data, together with raw pictures, to extract local images and brain connections from non-brain tissue. The study proposes a new model for NLM filtering of MRI images. The proposed technique was tested MRI data. We have achieved outstanding results in brain extraction with our CNN algorithm. It has demonstrated better performance than two recently released public reference sets, LPBA40 and OASIS, with Dice overlap coefficients. Our automated content algorithms have enabled us to make significant improvements. We have also evaluated our algorithm's performance in extracting fetal brains from reconstructed MRI datasets with arbitrary orientation Our CNN surpasses previous approaches in extracting fetal brains from MRI. Our algorithm has achieved a Dice coefficient of 95.97%. Iteratively using posterior probability maps and image patches to extract the brain from non-brain tissue. © 2024 IEEE.

关键词： Brain

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Surveillance of species at risk by unarmed aerial vehicles

Surveillance of species at risk by unarmed aerial vehicles

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Vijaya Chamundeeswari, V. Sujith Varshan, G. Sanjay Kumar, Y. Sudarson, A.M. Department of Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9781032669663

A Living being or an organism that is about to vanish from this world will come under the category of Endangered *** Explanation for their state vary from time to time but few important reasons are loss of habitat and loss of genetic variation. These organism will attain the state of endangered when their population is below 30% and it no proper source for its lack of growth. The reason for being endangered species are – Population reduction rate, Geographical range, Population size and Population restriction, Poaching. Many different species are adversely affected by poaching. Different Laws have been sanctioned in order to protect the endangered species in many countries. Forbid hunting, Restrict land development, or Create protected areas are some of the measures for their growth. Some species are even taken away from their habitat in order to ensure their existence, This is called as extensive conservation *** breeding and habitat restoration are the few examples of the above. Many efforts have been made in order to restrict the *** Cameras, Drones and DNA tracking are the some of the methods and their probablitiy of success differs with each other. These systems are static in nature whereas the solution this provides is that with the help of Drone system with high resolution image capturing system the endangered species can be monitored along with their habitat. The advantage of using this system is the range is dynamic and precision is accurate. With the help of image processing technique endangered species is segregated and monitored from the rest of the species. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Antennas

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An Intelligent Solid Waste Classification and Monitoring Alert System using Deep Learning

An Intelligent Solid Waste Classification and Monitoring Ale...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Selvi, S. Elamathy, G.O. Kirana, B. Swetha, S. Government College of Engineering Department of Computer Science and Engineering Bargur India

ISBN: (纸本)9798331541170

Efficient waste control is critical for India's smart cities. This study uses Convolutional Neural network (CNN) technology to classify and reveal waste, integrating sensors, Internet of Things (IoT) gadgets, and system studying to promote sustainability. Also targets to deal with inefficient waste management by using CNN technology for effective garbage classification and tracking. The device integrates sensors, IoT devices, and machine learning algorithms to sort and track waste, promoting sustainable practices and environmental conservation. The proposed system uses an ArduinoMKR1000 microcontroller, ultrasonic sensors, and jumper wires to monitor garbage bin levels and classify waste into biodegradable and non-biodegradable classes the usage of CNN algorithms. The system employs the DenseNet121 model for image type and makes use of ThingSpeak for real-time tracking and notifications. The experimental consequences display an accuracy of 95% for the DenseNet121 version, outperforming traditional CNN and ResNet-50 architectures. The proposed device's overall performance surpasses that of different models because of its dense connectivity and efficient feature map creation. Real-time monitoring and data analytics provide precious insights for waste reduction and sustainability tasks, contributing to cleaner, greener, and more sustainable environments for future generations. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhanced Cardio Vascular Disease prediction model using Contextual Feature Learning approach 15

Enhanced Cardio Vascular Disease prediction model using Cont...

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

作者： Aarthirani, M. Rejitha, D. Arulmozhiselvan, L. Meenakshi College of Engineering Department of Computer science and engineering Chennai India

ISBN: (纸本)9798350370249

Cardio Vascular Disease poses a substantial worldwide health problem, highlighting the urgent requirement for the development of precise and more efficient diagnostic techniques. Existing studies have provided useful insights in this area, but there is still a need to improve the models for prediction and fill the gaps in the current detection methods. Traditional models often overlook the subtle nuances and interdependencies between different health determinants. The Contextual Feature Learning model introduces a sophisticated advancement in Machine Learning, tailored to elevate the accuracy of Cardio Vascular Disease predictions. This study aims to introduce a prediction model for Cardio Vascular Disease using the deep Contextual Feature Learning techniques. The proposed Cardio Vascular Disease model achieves the accuracy, precision, recall, F1 score and AUC of 100%, 95.7%, 97.6%, 96.3% and 96.9%. The findings exhibit that Contextual Feature Learning model effectively captures and use complex feature interferences, prompting highly accurate predictions. © 2024 IEEE.

关键词： Cardio Vascular Disease Contextual Feature Learning Deep Learning Heart Disease Dataset Machine Learning

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Design and Implementation of an AI-Enhanced Automatic Animal Food Dispenser 10

Design and Implementation of an AI-Enhanced Automatic Animal...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Vijayalakshmi, B. Varsheni, S. Sre Ganesh, T.R. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350353068

Animal guardians frequently face the challenge of ensuring that the animals under their care receive timely and adequate nourishment when they are away from home for some period of time. Leaving food in a bowl may result in overeating or shortage of food, and relying on friends or family members to feed the animals may not always be feasible. Additionally, pets with special dietary requirements may require precise feeding schedules and portion control. Hence, there is a growing need for automatic pet feeders to address these concerns. The objective of the project is to provide a convenient and reliable way to feed animals via Artificial Intelligence and IoT concepts, especially when their owners are not able to or available to do so. It can be programmed to dispense designated food tailored for individual animals in precise quantities without caretaker's interference and should also be able to accommodate various pet food types. The feeder must be easy to use and customizable. An automated pet feeder incorporating Internet of Things is a modern solution. It guarantees that their pets receive timely meals, even in the owner's *** systemenables remote monitoring, feeder control and food reordering facility for added convenience, making it an innovative solution for the animal's well-being. © 2024 IEEE.

关键词： Support vector machines

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Logistic-Based OVA-CNN Model for Alzheimer’s Disease Detection and Prediction Using MR Images 1

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4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： John, Chinchu M. Phalgunan, Prafulla Department of Computer Science and Engineering College of Engineering Kerala Trivandrum India

ISBN: (数字)9789819994427

ISBN: (纸本)9789819994410

Alzheimer’s disease (AD) is a form of dementia that diminishes a patient’s cognition and memory. By predicting AD progression early, physicians can begin medication that may reduce the disease’s progression. AD detection and prediction have been extensively performed using convolutional neural networks (CNNs). Despite this, CNN’s use for AD detection is limited by the number of parameters and the extensive training data required. Consequently, this study proposes a logistically based OVA-CNN model for AD detection and prediction using Magnetic Resonance Images (MRI). OVA-CNN consists of the four binary CNN which classified four classes of AD and the logistic regressor predicts the time conversion of pMCI patient who converts AD. The experimental results on the Alzheimer’s Disease Neuro Imaging Initiative (ADNI) demonstrate that the proposed model achieves a high level of accuracy of 93%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Magnetic resonance

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