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Smart Healthcare - IoT and Artificial Neural Network(ANN) 15

Smart Healthcare - IoT and Artificial Neural Network(ANN)

引用

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

作者： Sutar, Laxmikanta Chinara, Suchismita National Institute of Technology Rourkela Dept. of Computer Science and Engineering Rourkela India

ISBN: (纸本)9798350370249

With the use of the Internet of Medical Things (IoMT), the growing accessibility of wearable devices, and the continual reduction in the cost of sensor-based technologies and green computing, internet-connected sensors allow users to track, analyze, and share welfare information employing a cloud-based platform. Artificial Neural Networks (ANN) find extensive use across various applications, including healthcare systems, drawing inspiration from the biological concept of neurons. This study explores the prediction of heart attack risks using a combination of Internet of Things (IoT) sensors and artificial neural networks (ANN). Through the integration of IoT sensors such as the pulse sensor and LM35 temperature sensor, essential physiological data related to heart rate and body temperature was collected. An ANN model was developed, focusing on predictive capabilities using the sigmoid function, feedforward and backpropagation techniques, and random weight initialization. The investigation further utilized Keras with TensorFlow and optimized model parameters using Adam Optimization and Later with One Hot Encoding plus a Genetic Algorithm (GA). Additionally, the study involved training neural networks on higher-quality UCI datasets to evaluate performance. Scatterplots were generated to visualize relationships between variables. Results demonstrated promising performance improvements, with the model achieving close to 90% accuracy on the testing dataset. Encoding techniques were employed to handle categorical variables effectively, contributing to the model's robustness and generalization. Overall, this research offers valuable insights into leveraging IoT sensors and ANN for heart attack risk prediction. © 2024 IEEE.

关键词： Adam Optimisation ANN Back Propagation Dense Layers Feed Forwarding Fuzzy Genetic Algorithm HTTP IoT MQTT One Hot Encoding Protocols UCI Heart Data

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Enhanced Machine Learning Approach for Detecting Spammers in Industrial Mobile Cloud Computing Environment 5

Enhanced Machine Learning Approach for Detecting Spammers in...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Bhavana, K. Sai Abhinandu, B. Sriyesh, P. Sai Rama, K. Satheesh Kumar, S. Institute of Aeronautical Engineering Dept. Computer Science & Information Technology Hyderabad India

ISBN: (纸本)9798331518097

This research study proposes an innovative machine learning algorithm, Spammer Identification Novel K-Means Extension (SINKEX), to fortify IoT mobile networks against commands and potential disruptions in industrial production. Utilizing an industrial mobile cloud dataset, the algorithm employs basic, content, and network features, including follower count, post frequency, message content, and hyperlink usage, for spam detection. After employing Pearson Correlation and Euclidean Distance to eliminate dissimilar records, Principal Component Analysis (PCA) is applied to remove irrelevant features. The SINKEX algorithm then labels unclassified records, designating an account as a SPAMMER or NON-SPAMMER based on features like follower count and post frequency. Comparisons with a hybrid fuzzy c-means clustering algorithm demonstrate SINKEX's superior performance, achieving higher accuracy, precision, and recall. The results are presented through an intuitive graphical user interface, showcasing the algorithm's effectiveness in securing industrial IoT networks against malicious activities. © 2024 IEEE.

关键词： Adversarial machine learning

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AI-Based Indian Currency Detection for Visually Challenged Users

AI-Based Indian Currency Detection for Visually Challenged U...

引用

2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Shopika, S. Senthil Velan, A.S. Dharun Prakash, S.R. Pavithra, S. Lakshmi, R Poornima Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331510022

For denomination recognition, this Indian Currency Detection System will use a CNN trained on an extremely large and diverse set of Indian banknotes from 10 to 2000. It possesses an amicable interface through which images can be captured using the camera of the user's smartphone, providing real-time audio feedback to denominate the banknote without requiring any visual assistance. Made with the features of capturing an image, real-time recognition, and user-friendly design, these are made to add a great experience for smooth navigation by a blind person. The novel and innovative features make this Indian Currency Detection System unique. Using CNN, which has been trained on the comprehensive set of Indian banknotes from 10 to 2000, the software lets a user capture banknote image directly by the smartphone camera. It allows knowing the denomination through real-time audio feedback without any visual assistance. Such characteristics include image capture, real-time identification, and designing user-friendly user interfaces that make easy navigation of the surroundings possible for the visually impaired. It does more than detect currency. It significantly contributes to the discussion regarding the development and availability of more assistive technologies. It is usable and aims to bring about greater accessibility and inclusivity in promoting financial independence among visually impaired people. Contributing to the ability of cash transactions among the visually impaired to gain confidence in freedom and security, this project aligns with a broader discussion related to assistive technologies. © 2024 IEEE.

关键词： Assistive technology

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Relocation Service Provider Application 3

Relocation Service Provider Application

引用

3rd International Conference for Innovation in technology, INOCON 2024

作者： Shobha, Shobha Choudhary, Ankita Kour, Naindeep Prakash, Nitin Tejas, P.R. Nitte Meenakshi Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350381931

This research aims to solve many problems people face when moving to a new place, such as housing, food, transportation, language differences and optimization. By creating recommendations, it is aimed to provide self-support to facilitate the transition process. The system will consider people's interests and consider different languages and cultures and budgets of people. This is useful in war or disaster environment. © 2024 IEEE.

关键词： Budget control

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Deep Learning Based Segmentation of Weed Images in crop fields using U-Net 3

Deep Learning Based Segmentation of Weed Images in crop fiel...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Naik, K. Jairam National Institute of Technology Raipur Dept. of Computer Science & Engineering Raipur India

ISBN: (纸本)9798350354171

Weeds are problems for crop yields because they exert harmful effects on the yields by competing with cultivated crops for necessary resources like nutrients, sunlight, and soil. As a result, continual attempts are being made to create safer and more effective herbicide formulations. For precision weeding and to reduce pesticide pollution, segmenting weeds in photos is extremely important. However, weeds and crops are comparable in the field setting, making it challenging to precisely segregate weed from intricate field photos. A deep learning-based system for segmenting weeds from photos was proposed in this paper. This algorithm can distinguish between weeds and crops and soil in photos. Utilizing a condensed U-net, this semantic segmentation method was created. An image augmentation strategy was suggested because image tagging for the semantic segmentation of weeds is challenging. The classification algorithms used in the modified U-Net are Support Vector Machine (SVM), Random Forrest (RF) and Decision Tree (DT). These were also used for predicting the weed part in a sample image before their segmentation. The threshold accuracy for segmenting the image using to be around 51.85% with a segmentation time of approximately 01.02 ms whereas our modified U-Netis obtained 91.03% accuracy and the segmentation time of 50.35ms. The results showed the modified U-Net successfully differentiated weeds from images with numerous other plants. This study seeks to demonstrate that the weed-associated image segmentation technique is effectively identifying and separating weeds in intricate field conditions. © 2024 IEEE.

关键词： Semantic Segmentation

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Automated Skin Cancer Classification and Detection Using Convolutional Neural Networks and Dermoscopy Images 4

Automated Skin Cancer Classification and Detection Using Con...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Sara, Umme Das, Utpol Kanti Sikder, Juel Chowdhury, Sudipta Roy Dept. of Computer Science and Engineering National Institute of Textile Engineering and Research Savar Dhaka Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Sadar Rangamati4500 Bangladesh

The uncontrolled growth of skin cells in the epidermis producing the creation of a mass termed a tumor is a dangerous condition known as skin cancer. Current developments in deep learning artificial intelligence have greatly improved image-based diagnosis. In this study, we included a Skin Lesion Cancer feature extractor Convolutional Neural Network (SLC-CNN) model, which is used for both classification with the SVM classifier and segmentation with XGBoost for skin cancer. In our proposed system, a test image of skin cancer is taken and pre-processed for both classification and segmentation purposes. After applying pre-processing, the test image features are extracted using the SLC-CNN feature extractor, which features are used in SVM to classify the types of skin cancer (Benign and Malignant), and based on the classification result, a trained XGBoost model is called to segment the cancer region. We have tested our system using the dermoscopy image collection from the International Skin Imaging Collaboration (ISIC) and built it in Google Colab to best use the GPU. Our suggested approach has gained a segmentation accuracy of 95.25% and a classification accuracy of 99.6%. © 2025 The Authors. Published by Elsevier B.V.

关键词： Lung cancer

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Applications and Approaches of Sentiment Analysis: A Current Review 6

Applications and Approaches of Sentiment Analysis: A Current...

引用

6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Sandra, G. Silas, Salaja Rajsingh, Elijah Blessing Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350381689

Limitless amount of data is loaded in every moment in social media platforms in the forms of audio, videos, texts, and photos. They are rich sources of information, which has immense value. Hence extraction of information from them is inevitable. Many methods are employed for this and the popular among them to mine the information is sentiment analysis. To materialize the objectives of this study, a systematic review of articles published from 2013 to 2023 was done. Our study reviewed the relevance of sentiment analysis in social media by probing the different approaches followed, the platforms used and its applications, as its main objectives. Its results show that majority of studies applied machine learning approach or hybrid method for executing sentiment analysis. © 2024 IEEE.

关键词： applications approaches categories dataset sentiment analysis

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Transformer-Based Self-Supervised Imputation for Medical Data 4

Transformer-Based Self-Supervised Imputation for Medical Dat...

引用

4th International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Singh, Aryan Kumar Saikia, Arpit Baro, Pranita Borah, Malaya Dutta National Institute of Technology Silchar Dept. of Computer Science and Engineering Cachar India

ISBN: (纸本)9798350391183

Dealing with missing data is necessary to develop a solid predictive medical research model. This work presents a hybrid dynamic imputation method devoted to addressing the challenge of missing data, specifically focusing on medical datasets. In this work, we investigate the incompleteness of the dataset using a sophisticated measure called the Incompleteness Factor. This metric is used to guide a hybrid dynamic approach. A novel Transformer-based Self-Supervised imputation method is employed to learn complex data dependencies to impute missing data. This paper introduces one potential solution to obtain model-level accuracy increase for tasks like disease prediction, patient prognosis, etc., over medical datasets. The proposed method aims to remove the measurement biases caused by incompleteness and preprocess the data to enhance the credibility and clinical relevance of the experimental results. © 2024 IEEE.

关键词： Self-supervised learning

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Notes Application using Android Studio and Cloud-based Data Management 7

Notes Application using Android Studio and Cloud-based Data ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Dodda, Ratnam Yanala, Maneesh Reddy Pavishetti, Srija Cvr College of Engineering Dept. of Computer Science And Information Technology Hyderabad India

ISBN: (纸本)9798350359299

In today's rapidly advancing technological land-scape, the widespread adoption of mobile applications has become a defining feature of our digital age. This research study presents a on the development of a mobile notes application using Android Studio, with the primary goal of offering users a versatile platform for creating and managing notes. The proposed notes application is designed with a simple and user-friendly interface, facilitating effortless note creation, editing, and deletion. Moreover, it has features like voice-to-text for making notes, text-to-speech for reading the notes aloud, note sharing for easily sharing ideas with others and image insertion for storing the photocopy of the written notes or diagrams based on user interest. This research work uses Android Studio as the integrated development environment (IDE) and utilizes the Java programming language for the app's core functionalities. Furthermore, it incorporates Firebase Authentication to ensure secure user account management and the Firestore for Database for seamless storage and retrieval of notes. This research study provides an essential tool for students, professionals, and anyone seeking a convenient means to keep track of vital information. By combining mobile application development tools and cloud-based data management like Firebase, the application simplifies how information is managed, aligning with the way we live in the digital age. © 2024 IEEE.

关键词： Authentication

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Detection of Specific Language Impairment Using XGBoost Model 15

Detection of Specific Language Impairment Using XGBoost Mode...

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

作者： Rai, Rajnish Thenkanidiyoor, Veena Dept. of Computer Science and Engineering National Institute of Technology Goa Goa India

ISBN: (纸本)9798350370249

Specific Language Impairment (SLI) in children, a neurodevelopmental disorder presenting significant hurdles in language acquisition. Motivated by the prevalence and complexity of SLI diagnosis, this article introduces an methodology using Mel-Frequency Cepstral Coefficients (MFCC) and chroma features combined with the XGBoost algorithm for classification. By analyzing a diverse dataset encompassing both SLI-diagnosed and non SLI children, the proposed model achieves efficiency of 99.3% accuracy on unseen data. Notably, the model’s strategic feature selection and utilization of XGBoost demonstrate superior performance compared to existing methods, highlighting its importance in SLI diagnosis and intervention planning. This work significantly advances machine learning-based diagnostic tools and holds promise for improving the quality of life for individuals impacted by SLI. ©2024 IEEE.

关键词： Adversarial machine learning

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