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Dynamic and Energy Efficient Data Traffic Scheduling for Earlier Forest Fire Detection using WSN 2

Dynamic and Energy Efficient Data Traffic Scheduling for Ear...

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

作者： Swetha, R. Navaneethan, C. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

In general, data traffic volume is significant in sensor networks, and communication often occurs with variable capacity during early forest fire detection using Wireless Sensor Networks (WSN). Initially, optimal routes are determined by selecting the best prime nodes. Subsequently, packets are dynamically scheduled based on the severity of the data traffic associated with detecting forest fires. The selection of dominant weighted nodes is a crucial initial stage, and these nodes are chosen for optimal routing, taking into consideration factors such as distance and energy criteria. The second stage involves the dynamic scheduling of packets, ensuring that they are sent based on packet priority, queue index value, and the urgency of forest fire detection. This process aims to minimize congestion and prevent unnecessary transmission delays, contributing to the efficient and timely detection of forest fires using WSN. © 2024 IEEE.

关键词： Fires

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A Unified Framework for Dangerous Situation Detection by Integrating Weapon Detection and Distressed Face Analysis

A Unified Framework for Dangerous Situation Detection by Int...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Narayanan, Swathi Jamjala Perumal, Boominathan Mandal, Siddarth Mohandas, Gayathri Jain, Naman Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798331533311

The escalating prevalence of violent threats necessitates proactive security measures to safeguard human life. Within the realm of artificial intelligence (AI), computer vision has emerged as a pivotal tool, leveraging sophisticated algorithms to substantially alleviate the burden on human resources. In this research work, we propose a novel approach for dangerous situation detection that combines object detection and facial emotion recognition. This work includes detecting guns, knives, blood, alcohol, insulting gestures, and cigarettes as these are considered as items that are commonly associated with potential threats or dangerous situations in various contexts, such as crime scenes, conflict zones, or public safety scenarios. To achieve this, the harmful object detection model employs YOLOv8, which has been successful based on the mAP metric. The proposed work also incorporates a CNN model that relies on facial emotions for indicators of distress, fear, or discomfort, enabling the system to identify potential threats even in scenarios devoid of harmful objects. The results obtained demonstrate YOLOv8's superiority over a two-stage detector, and the Facial Expression Recognition (FER) CNN achieves 7 9% test accuracy and 8 4% validation accuracy. The unified framework enhances environmental threat detection, contributing to effective risk mitigation. © 2024 IEEE.

关键词： Emotion Recognition

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Enhanced Artificial Neural Network Algorithm for Predicting Lung Cancer

Enhanced Artificial Neural Network Algorithm for Predicting ...

引用

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Reddy, Narravula Bharathvaz Sai Ganesh Reddy, Settipalle Chidurala, Karthik Krishna Rani Samal, K. Vellore Institute of Technology Department of Computer Science and Engineering Tamilnadu Vellore632014 India

ISBN: (纸本)9798350378092

This study explores the application of deep learning models for the detection of lung cancer subtypes utilizing histopathological images. Leveraging a diverse dataset containing images of adenocarcinoma, squamous cell carcinoma, normal tissue, and large cell carcinoma, our research aims to develop accurate classification models to aid in early diagnosis and treatment planning. The dataset is meticulously preprocessed, ensuring uniform image sizes and balanced class distributions. Two deep learning architectures, VGG16 and MobileNetV2, are employed and trained on the dataset. The VGG16 model is fine-tuned with additional layers for classification, while the MobileNetV2 model is utilized in its pre-trained form. Rigorous training and evaluation procedures are conducted, including stratified train-test splits and performance metric assessments. Experimental results demonstrate the effectiveness of both models in accurately classifying lung cancer subtypes, with the VGG16 model achieving particularly high accuracy. Comparativeanalysis between the two models highlights their respective strengths and performance differences. Overall, this research contributes to the advancement of computer-aided diagnosis systems for lung cancer detection, offering potential solutions for improving patient outcomes and healthcare decision-making. © 2024 IEEE.

关键词： Lung cancer

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CropViT: A light-weight Transformer Model for Crop Disease Detection 3

CropViT: A light-weight Transformer Model for Crop Disease D...

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

作者： Chemmalar Selvi, G. Charan, H.J. Kumar, Dinesh Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372120

Agricultural industry has grown significantly bring sustainable farming practices in improving the food quality, enhancing agricultural productivity and global food security. However, the crop yield and its quality are impacted when the timely identification and management of the crop diseases are not promptly solved. Thus, the crop disease detection becomes the critical aspect in the smart agriculture. Several researchers addressed this crop disease detection by developing optimized solutions through machine learning, deep learning and image processing techniques. However, these techniques face serious challenges when deployed in real-time crop disease identification problems like annotated training images, capturing long-range dependencies, adaption to real-time changes evolving over time and so on. This leads to performance degradation seeking for more powerful applications like some advanced deep learning techniques, in particular, the transformer models. In this paper, a novel crop disease detection model known as CropViT was proposed based on Vision Transformer. This CropViT was developed by fine-tuning the architecture of existing Vision Transformer and PlantVillage dataset was used in the experimental study. Nine crop species were selected and trained using CropViT whose performance was then compared with traditional Convolutional Neural Network model. The experimental study clearly showed the highest mean accuracy of 98.64% for CropViT when compared to 95.52% mean accuracy for traditional CNN. The experimental results also outperformed other existing state-of-the-art models. Finally, the work was concluded with the discussion of experimental results and its future scope of work. © 2024 IEEE.

关键词： Convolutional neural networks

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Dynamic Clustering for Federated Learning: A Reinforcement Learning Approach to Minimize Time Divergence 1

Dynamic Clustering for Federated Learning: A Reinforcement L...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Tiwary, Shresth Singh, Shaarang Srinivasan, Kathiravan Vellore Institute of Technology School of Computer Science and Engineering Vellore632 014 India

ISBN: (纸本)9798350352931

Federated learning (FL) offers a decentralized approach to training machine learning models, allowing data to remain on local devices and preserving privacy. However, the decentralized nature of F Lintroduces challenges such as heterogeneous device performance, leading to straggler devices that slow down the training process. Traditional clustering methods, whether static or dynamic, often fail to adapt effectively to these variations. To address this, the paper proposes FedDy, an advanced algorithm incorporating reinforcement learning (RL) and hybrid clustering techniques. FedDy begins with k-means clustering and dynamically selects the optimal clustering method in each round, guided by RL. Silhouette analysis helps determine the optimal number of clusters, while a hybrid metric considers upload times, computation times, and model divergences to ensure balanced clusters and efficient training. The experiments on the Fashion MNIST dataset demonstrate that FedDy reduces training time by 69.1 %compared to FedAvg, with a similar accuracy of 90.27. The enhanced version, FedDy_Enh, further reduces training time by 65.8% compared to FedAvg and 32.3% compared to FedStat, achieving the highest accuracy of 90.74%. © 2024 IEEE.

关键词： Federated learning

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Facial Attendance Tracker using Enhanced Mean Local Binary Pattern 2

Facial Attendance Tracker using Enhanced Mean Local Binary P...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Charanya, R. Jayasurya, Arnold David Krithika, L.B. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350384277

Facial Attendance Tracker is a mobile application where a student can fetch his/her attendance by scanning his/her face from his/her own mobile. The faculty will project the Dynamic QR code when he/she wants to take the attendance. The students have to login into the mobile application using the login credentials and scan the dynamic QR code provided by the faculty. Once, the QR code is scanned successfully, it will redirect to the camera where the student can fetch his/her face. The system comprises of acquiring the image, followed by sending the image to the database, detection of face, pre-processing, extraction of features, classifying the features and post- processing stage. To detect the face from the image, Viola-Jones algorithm has been used and Enhanced Mean Local Binary Pattern (EMLBP) algorithm is used to extract features as it outperforms the other algorithms in a huge margin. © 2024 IEEE.

关键词： Local binary pattern

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An Accuracy Analysis Of Classical And Quantum Machine Learning Algorithms Using Different Datasets 2

An Accuracy Analysis Of Classical And Quantum Machine Learni...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Bhaskaran, P. Prasanna, S. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350365269

Classical machine learning has practical significant advancements and extensive acceptance across various domains, enabling the growth of precise predictive models. The purpose of this work is to examine the accuracy gained by various machine learning algorithms, both classical and quantum. In addition, the study delves into the fundamental ideas and principles that form the backbone of quantum machine learning algorithms, such as quantum data representation, quantum feature mapping, and quantum optimization strategies. Experiments use classical machine learning models trained and tested on traditional hardware to measure evaluation metrics like classification accuracy, precision, recall, and F1-score for quantum machine learning models executed on quantum computing platforms or simulators. In quantum SVM, the seed dataset obtained 100% accuracy, and the supermarket dataset obtained 69% accuracy when compared to classical SVM. The findings imply that, under some conditions, quantum machine learning algorithms show the capacity to beat their conventional counterparts in terms of accuracy. © 2024 IEEE.

关键词： Quantum entanglement

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A Study on Enhanced Curricula Delivery Using Augmented/Virtual Reality 2

A Study on Enhanced Curricula Delivery Using Augmented/Virtu...

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

作者： Sarath, T. Brindha, K. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

In human perceptive, Augmented Reality (AR) or Virtual Reality(VR) is an effective and deep technology because it creates new replicated world. The AR or VR technique is a distinctive and lucrative application;its imminent will consume a positive influence on technique. Moreover, AR/VR technology is accomplished by persons as amount of new thoughts discoveries and provides us wide concepts like sensory relationship with humans. The AR/VR operates different types of locations which consume different goals and boost the abilities in building, engineers, interior designers and upsurge production in changed business zones and familiarize a novel physical world. By using VR, multiusers developed and implemented collaboration, which highpoints the creativity of human's, and maximum people can carry their choices. Consequently, AR or VR is relatively new technique that is being used at the experimental stage for curriculum building and training design. In this context, here identified some current uses of augmented reality in training and curriculum design. The themes covered in this article are: a brief introduction about AR, the current uses of AR/VR tools in curriculum and training development, how AR/VR works used in education for student engagement, future implications of augmented reality in curriculum and training development. © 2024 IEEE.

关键词： Curricula

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Review on IOMT Security through Distributed Machine Learning 2

Review on IOMT Security through Distributed Machine Learning

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

作者： Bhaskar, Swasthika Vanitha, M. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

In this constructive healthcare industry, AI-based IOMT (Internet of Medical Things) is one of the highly used Technologies. The virtual world is customary to lose responsible data in cyberspace. Without any doubt, IOMT comes under this category. IOMT applications do not intensively focus on Security and privacy and it is a Serious downside of IOMT applications. Considering this crucial issue, we use the Multiple-node machine learning Technique. It is widely used for preserving the privacy of the huge records. It does not want to share its sensitive data from one node to another node, which result in providing security and privacy to medical record by adding protection to input records, output tags, parameters, etc. This paper exclusively focuses on the Security and Privacy of Medical Records and also shows the various roles of Distributed Machine Learning. © 2024 IEEE.

关键词： Machine learning

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A Hybrid Approach to Validate and Retrieve Fingerprint Image for Authenticating Database Sensitive Information Access and to Verify Some of Topological Multi-Granular Rough Sets Properties on Authenticated Database Fields 2

A Hybrid Approach to Validate and Retrieve Fingerprint Image...

引用

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

作者： Raghavan, R. John Singh, K. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

Content based image retrieval has already begun to turn into an intriguing and exceptionally needed field to meet with the client prerequisite. Fingerprint approvals likewise have become a fascinating field to principally uphold the confirmation framework. Notwithstanding foundations regulating guaranteed about information straightforwardly, affiliations are also obliging them to examine the fingerprint endorsement. Despite establishments overseeing ensured about data, directly associations are similarly obliging them to research the fingerprint check. Organizations and foundations both are typically utilizing the idea of fingerprint for the participation and presence reason. Nevertheless, establishments presently managing secured information as like institutes and additionally going along with them to investigate the fingerprint authentication. More than this usual action there are some particular applications where the authentication would be considerable to help the application than using a traditional pattern of password. We proposed a fingerprint authentication system for the scientific based industry database access system. We removed the noise if any found in the input fingerprint using anisotropic Gauss filtering. The noise free fingerprint image is not further passed to the binarization phase as the need of binarization and some form of the drawback of binarization is replaced with a thinning algorithm. The thinned fingerprint image is followed by the matching process and once it is matched, then only the sensitive and highly confidential information are allowed to access from the database. The testing is done using OpenCV and FVC 2004 dataset to assess the projected retrieval method. Further the matched fingerprints are used to prove some of the internal structure properties of multi-granular rough sets (MGRS) on authenticated database fields. The accuracy obtained in this hybrid approach is found to be more satisfactory and hence the security towards acce

关键词： Database systems

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