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Artificial Intelligence and Deep Learning-based Model for Indoor Environment: with Virtual Reference Tag Allocation

Procedia computer science 2024年 233卷 254-268页

作者： N. Mathavan Dr. S. Sivaranjani Assistant Professor Department of Electronics &Communication Engineering Nadar Saraswathi College of Engineering and Technology Vadapudupatti Theni Tamil Nadu India Professor Department of Computer Science and Engineering Sethu Institute of Technology Kariyapatti Virudhunagar Tamil Nadu India

By the evolution of context-aware application, indoor location positioning gains much more attention worldwide. Radio Frequency Identification (RFID) is one such wireless positioning technology adopted widely. Moreover, enhancing the tracking accuracy and thus reducing the tracking error remains a challenging task in RFID based indoor environment. So as to overcome this aspect, a localization and tracking method depending on virtual reference tag (VRT) is employed in the indoor environment. This research work includes four modules like (i) data collection and Received Signal Strength Indication (RSSI) estimation;(ii) Optimal allocation of VRT using Spider Monkey Optimization (SMO) at which the allocation of VRT is made by considering number of tags, environmental factors (humidity and temperature), and SNR. Based on this data, the RFID reader allocates VRT for each grid so as to increase the tracking accuracy; (iii) Artificial Neural Network (ANN) module-based localization at which the localization of unknown tags is made; and (iv) tracking of moving target tag is carried based on Aquila Swarm Optimization-Long short-term memory (ASO-LSTM) approach. The optimization at this stage is employed to choose optimal position which enhances the accuracy of tracking. Thus, finally the location estimation is carried by this approach. The simulation is carried in NS3.26 network simulator and the performance are estimated in terms of SNR, tracking error, mean error, tracking accuracy, mean time, and standard deviation. The performance value is then compared with traditional models to show the enhancement of proposed mode over the other conventional techniques .

关键词： Radio Frequency Identification (RFID) Received Signal Strength Indication (RSSI) localization tracking moving target virtual reference tag Spider Monkey Optimization (SMO) Artificial Neural Network (ANN) Aquila Swarm Optimization-Long short-term memory (ASO-LSTM) tracking error tracking accuracy

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A new algorithm for detection of nodes failures and enhancement of network coverage and energy usage in wireless sensor networks

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Materials Today: Proceedings 2023年 80卷 1717-1722页

作者： Satyanarayana, P. Mahalakshmi, T. Sivakami, R. Alahmari, Saad Ali Rajeyyagari, Sivaram Asadi, Srinivasulu ECE V. R. Siddhartha Engineering College Vijayawada India Department of ECE Prasad V Potluri Siddhartha Institute of Technology India Associate Professor CSE Sona College of Technology TN Salem 5 India Department of Computer Science Shaqra University Saudi Arabia Data Analytics Research Laboratory BlueCrest University College Monrovia Liberia

Present natural lives shows rising attention in various applications of the wireless sensor networks (WSNs). Significant applications are isolated and stiff regions where human involvement is hazardous or unfeasible. Some examples are space investigation, battlefield observation, costal, and edge security;nowadays, such networks are needful in several manufacturing and customer applications. A WSN is a device system, indicated as nodes;these nodes intellect the atmosphere and transfer the details collected from the ground through a wireless connection. Statistics are sent, probably via many hops, to a target that can use it nearby or is linked to various networks using the gateway. The sensor nodes might be stationary or changeable. Every node has clear data about the neighborhood or not. Communication over WSN over ecological hazards is a key issue. These constraints might impact the performance of the sensors/direction finding protocols and resource utilization;thus, they may show the way to the node crash situation, i.e., software/hardware breakdown, safety threats, extreme power utilization, etc. It is essential to examine the collision of breakdown over system performance. In the projected method, relay nodes are used as secure nodes for the positioned sensor nodes. The projected method is separated into two phases: the intra segregation stage and inter segregation stage. In the intra segregation stage, the unnecessary nodes reach nearer to the segregation border, and dispersion slowly enlarges the coverage. The projected model is examined through simulations and compares the results with existing methods. © 2021

关键词： Sensor nodes

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Emotion Recognition in Real Time for Personalized Healthcare Using Digital Twin Approach

Emotion Recognition in Real Time for Personalized Healthcare...

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International Conference on Advances in Information technology (ICAIT)

作者： B. Karthikeyani D. Yuvaraj T. Parimalam P. Parthasarathi Nandha Arts and Science College & Assistant Professor @ Shree Venkateshwara Arts and Science (Co Education) CollegeGobi Department of Computer Science and Design Kongu Engineering College Perundurai Department of Computer Science Nandha Arts and Science College Perundurai Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Facial Emotional Recognition (FER) in healthcare can help to monitoring the patient by analyzing the emotional state of the patient through various emotional states. In medical industry it can help to trace the emotional state changes and diagnosis the pattern which are mapped respective to the patient's history. Digital twin technology is transforming healthcare systems and improving patient care, clinical operations, predictive analytics, and training and simulation by integrating virtual simulations and emerging analytic mechanism through ML and DL techniques. By allocating resources, monitoring workflow, and providing individualized patient care, these algorithms reduce healthcare operations and assist in determining an individual's risk. Since FER has lot of technical challenges like illumination, obstruction issues also high implementation cost. To solve these issues, the FER system will embed with the digital twin technology to provide a better result from real time sensing cameras also analyzing the result and guiding for a better treatment of an individual. We propose a system of end to end framework which produces best results in minimum amount of time with maximum accuracy. This real time implementation will help all the healthcare centers/industry to study an individual's health status as well as early prediction of diseases with effective treatment steps.

关键词： Training Emotion recognition Face recognition Medical services Speech recognition Cameras Real-time systems Digital twins Medical diagnostic imaging Monitoring

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An Automated Methodology for Detecting Cercospora Infestations in Eggplant using YOLOv10N

An Automated Methodology for Detecting Cercospora Infestatio...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： S. Deepan Ms. M. Revathy Department of Computer Science and Engineering KIT - Kalaignarkarunanidhi Institute of technology Coimbatore India Assistant Professor / CSE KIT - Kalaignarkarunanidhi Institute of technology Coimbatore India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Cercospora leaf spot, a damaging fungal disease in eggplants, threatens agricultural productivity and farmer livelihoods. This study introduces an automated system for early detection of Cercospora infestations and affected percentages using advanced image processing and the YOLOvlON algorithm. A diverse dataset of annotated eggplant leaf images highlights healthy and infected regions. Preprocessing steps, including noise reduction and feature enhancement, ensure accurate detection. Supervised data augmentation enhances the model's adaptability to real-world conditions. The proposed system achieves high precision with competitive metrics, such as mean Average Precision (mAP), precision, and recall. Its lightweight design ensures compatibility with various devices, promising accessible solutions for farmers. Future developments aim to integrate mobile platforms, advancing sustainable and scalable plant disease management in agriculture.

关键词： Productivity Precision agriculture Performance evaluation Plant diseases Sentiment analysis Noise reduction Object detection Data augmentation Real-time systems Pragmatics

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Optimizing Resource Allocation in Healthcare Facilities Through IoT and Machine Learning Predictive Analysis 3rd

Optimizing Resource Allocation in Healthcare Facilities Thro...

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Dankan Gowda, V. Sharma, Avinash Mukherjee, Saptarshi Rahman, Mirzanur Barshe, Shweta B. Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bengaluru India Professor Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Punjab Mohali140307 India Makaut Maulana Abul Kalam Azad University of Technology West Bengal Nadia Kolkata India Department of Information Technology Gauhati University Assam Guwahati India Department of Computer Engineering Bharati Vidyapeeth College of Engineering Mumbai Navi India

ISBN: (纸本)9789819751457

In the evolving landscape of health care, optimal resource allocation emerges as a critical determinant of patient outcomes and facility efficiency. This study explores the integration of Internet of Things (IoT) devices and machine learning (ML) predictive analysis in healthcare facilities to optimize resource allocation. With the omnipresence of IoT devices and the analytic prowess of ML, our research aims to develop a model that can predict real-time resource demands, ranging from patient care equipment to manpower distribution. Drawing data from wearable sensors, embedded devices, and facility infrastructure, the proposed ML model demonstrates significant improvement in predicting short-term resource needs. Preliminary results indicate a reduction in resource wastage, enhanced patient care, and increased operational efficiency in healthcare facilities that incorporated the proposed system. However, the research also acknowledges potential limitations, including data security concerns and system scalability. The findings presented highlight the transformative potential of IoT and ML in revolutionizing resource management in health care. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data security Embedded devices Internet of Things (IoT) Machine learning (ML) Operational efficiency Wearable sensors

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Stating Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent Face Recognition 33

Stating Comparison Score Uncertainty and Verification Decisi...

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33rd British Machine Vision Conference Proceedings, BMVC 2022

作者： Huber, Marco Terhörst, Philipp Kirchbuchner, Florian Damer, Naser Kuijper, Arjan Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Technical University of Darmstadt Darmstadt Germany Norwegian University of Science and Technology Gjøvik Norway Paderborn University Paderborn Germany

Face Recognition (FR) is increasingly used in critical verification decisions and thus, there is a need for assessing the trustworthiness of such decisions. The confidence of a decision is often based on the overall performance of the model or on the image quality. We propose to propagate model uncertainties to scores and decisions in an effort to increase the transparency of verification decisions. This work presents two contributions. First, we propose an approach to estimate the uncertainty of face comparison scores. Second, we introduce a confidence measure of the system's decision to provide insights into the verification decision. The suitability of the comparison scores uncertainties and the verification decision confidences have been experimentally proven on three face recognition models on two datasets. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Face recognition

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Heart Rate Anomaly Detection in Healthcare Using Elliptic Envelope and Local Forest

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Procedia computer science 2025年 258卷 1677-1687页

作者： Chandralekha E S. Vinodhini Kandasamy V Rama P Assistant Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India-600062 Assistant Professor Department of Information Technology Velammal Engineering College Chennai Tamilnadu India-600062 Assistant Professor Panimalar Engineering College Chennai Tamilnadu India – 600123 Assistant Professor Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai Tamilnadu India-603203

Medical professionals use Anomaly Detection(AD) to identify patients with potential health problems in the heart rate data (HRD), an essential metric related to cardiovascular health. In this research, the most effective AD techniques were identified in detecting unusual variations of the heart rate pattern. Synthetic HRD data with anomalous rates of 15%, 20%, and 25% was generated and standardized it using min-max scaling for consistency among the datasets. Normal HR was based on function with a mean of 70 and a standard deviation of 5 while anomalous values were generated with a mean of 120 and a standard deviation of 10. Among the evaluated models (Elliptic Envelope, one-class SVM, Local Outlier Factor, and Isolation Forest), the Elliptic Envelope (EE) and Isolation Forest (IF) models obtain the best scores in terms of recall and precision – 0.92 and 0.89, respectively. Overall, the results of this research indicate that equally robust AD models should be implemented within clinical systems and wearable devices that serve as a digital space for early cardiac risk identification, prompting adequate intervention and, as a result, health improvement.

关键词： Anomaly Detection Heart Rate Data Isolation Forest Local Outlier Factor One-Class SVM Elliptic Envelope

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Automatic Creation of Quality Images from Text using Multiple Generative Adversial Network

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Procedia computer science 2023年 230卷 955-963页

作者： M.S. Geetha Devasana V. Krishna Kumar R. Selvaraj T. Satheesh Assistant Professor Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore-641022 India Assistant Professor Department of Computer Science and Engineering Dr.N.G.P Institute of Technology Coimbatore-641048 India Assistant Professor Department of Artificial Intelligence and Data Science Dr.N.G.P Institute of Technology Coimbatore-641048 India

Automatic creation of realistic images is a tedious process even though the state-of-the-art AI/ML algorithms are employed. There is a lot of demand for such automatic image generators that could create high quality images. Many have this problem of visualizing things from the explanations they hear about. Thus, the text to image generation problem is necessary because it has significant applications in CAD, art generation and many more. This is a challenging task since the image should be realistic and consistent with the text. One of the most common uses of modern conditional generative models is the generation of visuals from natural language. Viewing an image makes us easily understand what that image is, rather than hearing someone describing the image. To bridge the semantic gap between text and image, Generative Adversarial Network (GAN) systems are used to achieve high accuracy. The proposed system helps in generation of superior quality images that are meaningfully consistent with the text.

关键词： Machine Learning Generative Adversarial Network (GAN) Text to Image and Natural Language Processing

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Improving energy compaction of adaptive fourier decomposition 28

Improving energy compaction of adaptive fourier decompositio...

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28th European Signal Processing Conference, EUSIPCO 2020

作者： Borowicz, Adam Faculty of Computer Science Department of Digital Media and Computer Graphics Bialystok University of Technology Wiejska str. 45A Bialystok15-351 Poland

ISBN: (纸本)9789082797053

Adaptive Fourier decomposition (AFD) provides an expansion of an analytic function into a sum of basic signals, called mono-components. Unlike the Fourier series decomposition, the AFD is based on an adaptive rational orthogonal system, hence it is better suited for analyzing non-stationary data. The most popular algorithm for the AFD decomposes any signal in such a way that the energy of the low-frequency components is maximized. Unfortunately, this results in poor energy compaction of high-frequency components. In this paper, we develop a novel algorithm for the AFD. The key idea is to maximize the energy of any components no matter how big or small the corresponding frequencies are. A comparative evaluation was conducted of the signal reconstruction efficiency of the proposed approach and several conventional algorithms by using speech recordings. The experimental results show that with the new algorithm, it is possible to get a better performance in terms of the reconstruction quality and energy compaction property. © 2021 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Signal reconstruction

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Depth-guided Robust Face Morphing Attack Detection

Depth-guided Robust Face Morphing Attack Detection

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Harsh Rachalwar Meiling Fang Naser Damer Abhijit Das Birla Institute of Technology and Science Pilani (BITS Pilani) Secunderabad Telangana India Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Recently, morphing attack detection (MAD) solutions have achieved remarkable success with the aid of deep learning techniques. Despite the good performance achieved by binary label or binary pixel-wise supervised MAD models, the robustness of such models drops when facing variations in morphing attacks. In this work, we propose a novel process that leverages facial depth information to build a robust and generalized MAD. The depth map, representing the 3D shape of the face in a 2D image, is more informative compared to binary and binary pixel-wise map labels. To validate the idea we synthetically generated 3D depth map ground truth. Furthermore, we introduce a novel MAD architecture designed to capture subtle information from the 3D depth data. In addition, we analyze the training loss formulation to further enhance the MAD performance. Driven by the need for developing MAD solutions while preserving the privacy of individuals for legal and ethical reasons, we conduct our experiments on privacy-friendly synthetic training data and authentic evaluation data. The experimental results on existing public datasets in SYN-MAD 22 competition demonstrate the effectiveness of our proposed solution in terms of both robustness and generalization.

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