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Enhancing Indoor Navigation Efficiency with WiFi Signal Mapping 15

Enhancing Indoor Navigation Efficiency with WiFi Signal Mapp...

引用

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

作者： Reddy, N. Sai Pranavdhara Narayanan, Gayathri Singh, Aman Kumar Cheeramvelil, Geo Jolly Sethulekshmi, U.S. Kumar, R. Adithya Tripathi, Akshat Amrita Vishwa Vidyapeetham Dept. Of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350370249

In the contemporary digital landscape, efficient indoor navigation has become imperative for large, intricate spaces like shopping malls, airports, hospitals, and universities. Traditional GPS-based methods often prove inadequate in these indoor settings due to poor reception and a lack of fine-grained location accuracy. To address these challenges, the work employs WiFi signals, pervasive in modern buildings, and harnesses their ubiquity and reliability for sophisticated indoor navigation. The team developed a Python package for collecting WiFi signal details, implementing robust preprocessing to filter out private networks, and storing the data in a database. The core of the system lies in a machine learning ensemble consisting of Gaussian Bayes classifier, decision tree, and K-nearest neighbors (KNN). This ensemble utilizes WiFi signal strengths to predict users' real-time positions within large indoor spaces. The system aims to enhance user experiences and simplify wayfinding in complex indoor environments. © 2024 IEEE.

关键词： Decision Tree Gaussian Bayes Classifier Indoor Navigation K-nearest Neighbors Machine Learning Ensemble WiFi Signals

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Medlet-Block: Secured Health-Based Cloudlet Platform Using Blockchain 16

Medlet-Block: Secured Health-Based Cloudlet Platform Using B...

引用

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

作者： Gupta, Atrayee Agarwal, Varun Techno Main Saltlake Dept. of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350383119

Medical information cannot be easily shared and exchanged between various healthcare systems and providers due to a lack of interoperability, delays, inefficiencies, and above all lack of security. This paper provides a design of an edge-based blockchain for a secured health-care system which takes care of confidentiality, integrity, non-repudiation and other security challenges. © 2024 IEEE.

关键词： Block Health-care Symmetric key Transaction

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Hybrid RNN-CNN model for predicting stock market trends 1

Hybrid RNN-CNN model for predicting stock market trends

引用

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

作者： Bhanujyothi, H.C. Jacob, I Jeena GITAM Deemed to be University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350356816

Stock market prediction has become a challenging task in today's world of valuable and best investment. Simple models cannot do stock market prediction to predict future values with high accuracy. The stock market has becoming more complex and volatile, necessitating accurate models that understand its complex, nonlinear dynamics. This paper creates a model to forecast future price of stock market combining Convolutional Neural Network (CNN) and Long-Short Term Memory model (LSTM). Both CNN and LSTM are used for detecting spatial and temporal patterns. Tesla Datasets is used for model implementation. This paper uses Mean Squared Error (MSE) measures as metrics to demonstrate the model's enhanced performance. displays the expected value, compares it to the actual values, and illustrates the potential improvement of the epochs on this model. © 2024 IEEE.

关键词： Convolutional neural networks

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Intelligent Route Planning for Post-Earthquake Rescues Using IoT 6

Intelligent Route Planning for Post-Earthquake Rescues Using...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Tamzid, Rahat Mahee, Ehfaz Faisal Rahman, Ashikur Tamanna, Tahira Akhtar Rahman, Muntasir Hossain, Md. Mamun Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350385779

Starting rescue operations quickly after an earth-quake is the best way to save lives in such disasters. In the case of large-scale earthquakes, current post-earthquake response systems cannot determine which building or rescue route would save the most lives due to the lack of real-time data availability. Research gaps include a lack of automation in earthquake damage assessment of buildings, location identification, and estimation of the number of trapped people. To fill these gaps, this article proposed an innovative solution using advanced sensors and cloud services for post-earthquake rescue route planning systems. This system aims to improve rescue operations to save the maximum number of lives in the shortest time and minimize impacts by optimizing resource allocation and overall response efficiency. By filling the existing research gap, our system seeks to provide more responsive and effective solutions to post-earthquake emergencies. Tested in a simulated environment with 16 samples, our system successfully collects and processes data from IoT devices deployed in buildings, classifies structural damage, and estimates the number of people affected. Our approach significantly reduces the time required to initiate rescue operations by providing rescuers with optimized routes to disaster sites, which has potential implications for disaster management strategies. © 2024 IEEE.

关键词： Earthquakes

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Streamlining Livestock Surveillance: YOLOv8 Approach to Cattle Body Segmentation

Streamlining Livestock Surveillance: YOLOv8 Approach to Catt...

引用

2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Chugh, Bharti Kumar, Pushepndra Mishra, Upendra Chaudhary, Himanshi Kiet Group of Institutions Dept. of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350375237

This research paper introduces an innovative approach to optimize livestock surveillance by employing YOLOv8 for cattle body segmentation. The study attained a mAP(50), mean Average Precision of 0.598 and 0.464 and mAP(50-95) on a dataset comprising 704 images. Preceding model training, extensive preprocessing and image augmentation techniques were implemented, resulting in a dataset of 1476 training, 171 validation, and 71 testing images. The developed model demonstrates considerable promise in enabling real-time mon-itoring of cattle and their health status. Additionally, to enhance its suitability for edge computing environments, quantization-aware training (QAT) abd weight compression techniques were employed, ensuring efficient deployment on resource-constrained devices. The results highlight the effectiveness and practicality of employing YOLOv8-based segmentation for livestock surveillance, with notable implications for precision agriculture and animal welfare. However, further enhancements are warranted, particularly in achieving higher mAP scores across a broader range of Intersection over Union (IoU) thresholds, to fully address the segmentation challenges in this domain. © 2024 IEEE.

关键词： computer vision

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Enhancing Pneumonia Detection: CNN Interpretability with LIME and SHAP 6

Enhancing Pneumonia Detection: CNN Interpretability with LIM...

引用

6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Hosen, Md. Hamid Saha, Arnob Uddin, Altaf Ashraf, Kahakashan Nawar, Sadia East Delta University Dept. of Computer Science & Engineering Chittagong Bangladesh

ISBN: (纸本)9798350385779

A bacterial or viral infection of the lungs can cause pneumonia, one of the dangerous and potentially fatal illnesses that can have dire repercussions in a short amount of time. Therefore, a key component of a successful treatment plan is an early diagnosis. Therefore, a sophisticated and automated system that can diagnose chest X-rays and make the process of diagnosing pneumonia easier for both specialists and novices is required. This research aims to create a CNN model that will help with the accurate classification of pneumonia. In this work, we have presented our Deep Learning method for the classification challenge, which is taught using modified images through several pre-processing stages. With an overall accuracy of 93.30%, we were able to classify X-ray images of pneumonia using a custom CNN model. Our proposed model is able to precisely detect pneumonia from X-ray images with amazing accuracy and loss. Furthermore, we employed the LIME and SHAP tools of the XAI technique to generate a noteworthy conclusion to persuade medical practitioners. © 2024 IEEE.

关键词： Lime

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'RNN-Driven Prognosis of Heart Disease Based on Health Parameters' 2

'RNN-Driven Prognosis of Heart Disease Based on Health Param...

引用

2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Rahi, Parvez Singh, Inderjeet Singh, Ajay Pal Kumar, Alok Chandigarh University Dept. of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798331519056

Cardiovascular diseases (CVD) are the leading cause of death globally and are estimated to affect 17. 9 million deaths annually. Early diagnosis is very important if one is to prevent adverse consequences. However, traditional diagnostic methods can take time to complete and miss subtle trends in patient information. This research presents an innovative approach utilizing a Recurrent Neural Network (RNN) model, optimized through Stochastic Gradient Descent (SGD), for the prediction of seven distinct types of heart disease: CAD, Heart Failure, AFib, Hypertensive Heart Disease, Congenital Heart Disease, Cardiomyopathy, and PAD. Their architecture is specifically suited for direct modeling of sequential patterns of the health related data that in turn helps to detect temporal patterns and changes in them. This model use ReLU activation function in their hidden layers, dropout in preventing overfitting and batch normalization for stable learning. Moreover, the feature augmentation is used in the model making it use various psychological and physiological parameters that help in making comprehensive predictions on the risks of heart disease. After training the RNN with a Kaggle dataset and properly tuning it, the model obtained an accuracy level of 91%, which means that is highly useful in identifying risk factors of cardiovascular diseases and could be used for early diagnosis and risk factors identification. © 2024 IEEE.

关键词： Recurrent neural networks

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Deep Learning for Tea Leaf Disease Detection: Comparing VGG16, Vision Transformer, EfficientNetB3, and Xception 15

Deep Learning for Tea Leaf Disease Detection: Comparing VGG1...

引用

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

作者： Ayon, Rokonozzaman Mojumdar, Mayen Uddin Mubin, Arif Shahriar Chakraborty, Narayan Ranjan Hridoy, Al-Amin Gazi Md. Jahidul, Islam Dept. of Computer Science & Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9798350370249

The need for efficient disease management in crop production is highlighted by the economic relevance of agriculture. Various leaf diseases cause major losses for tea, a staple product in many places. In this paper, a deep learning-based method is provided for the automated identification of illnesses affecting tea leaves. Using the unique architectural benefits of four models: VGG16, ViT (Vision Transformer), EfficientNetB3, and Xception, their performance is examined. Each model's accuracy and effectiveness in classifying diseases are evaluated by conducting a thorough series of experiments on a dataset of photos of tea leaves. The results show that ViT and VGG16 perform better than expected, with accuracies of 96.09% and 98.29%, respectively. These results highlight the effectiveness of transformer-based designs and conventional CNN in the identification of tea leaf disease. Our research also demonstrates how deep learning approaches can improve agricultural practices by enabling early and accurate disease identification, which would facilitate proactive disease control tactics and increase crop output. ©2024 IEEE.

关键词： Plant diseases

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PassChain: A Blockchain-Based Decentralized Password Manager 3

PassChain: A Blockchain-Based Decentralized Password Manager

引用

3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Anik, Nur Adnan Chowdhury Hossain, Md. Sabbir Rahman, Habibur Rumpa, Ummay Khadiza Datta, Joy Nawaz, Rifat Karmoker, Sagar Khan, Md. Adib Hasan, Mahamudul East West University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350354348

A Nobel approach to the password management system is introduced in this paper, which is through a decentralized system named blockchain. Our goal is to secure people's passwords by providing them with a secure and distributed password management system named "PassChain."In today's digital world, a secure password management system is crucial, but it is still challenging. The traditional system lacks security and is a centralized system, so trust issues also arise. Our proposed blockchain-based solution offers full control over users' passwords. Because of the decentralized method, they are not stored on a single server. Decentralized servers are also more secure than databases. This approach will ensure user security and privacy and will also reduce dependency on third-party services that are prone to vulnerabilities. © 2024 IEEE.

关键词： Decentralized systems

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Negation Handling and Domain Generalization in Sentiment Analysis using Machine Learning Models

Negation Handling and Domain Generalization in Sentiment Ana...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Biradar, Shrnivas Raju, G.T. Divakar, K.M. SJC Institute of Technology Dept. of Computer Science and Engineering Chikkaballapur India SJC Institute of Technology Dept. of Information Science and Engineering Chikkaballapur India

ISBN: (纸本)9798350359688

Conventional lexicon-based approaches to sentiment analysis typically lack the necessary methods to properly identify the negation window, making it impossible to model negation. An enormous increase in sentiment-rich electronic and social media has been observed daily. Negation modifiers cause problems for Sentiment Classification techniques and have the power to entirely change the discourse's meaning. Therefore, it becomes essential to manage them well. Opinion mining or sentiment analysis is the study of people's attitudes, feelings, and views as they are expressed in written language. It is one of the busiest text mining and natural language processing research projects. Even though sentiment analysis research has gained popularity in the field of natural language processing, for this problem, the state-of-the-art machine learning approach is based on Bag of Words. But the BOW model pays little attention to polarity shift, which could have a distinct overall effect. One of the main issues with doing sentimental analysis on any given text or sentence is handling polarity shift, which is what this study attempts to address. Sentiment analysis use Natural Language Processing principles to identify negation in the text. Our goal is to identify the negation effect on customer reviews that, although appearing good, are actually negative. The suggested modified negation methodology helps to increase classification accuracy by providing a method for computing negation identification. In terms of review classification by accuracy, precision, and recall, this approach yielded a noteworthy outcome. When test and training data are from distinct domains, machine learning faces the challenge of domain generalization. Despite the large body of research on cross-domain text classification, the majority of current methods concentrate on one-to-one or many-to-one domain adaptation. Our domain generalization method regularly outperforms state-of-the-art domain adaption methods, a

关键词： Natural language processing systems

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