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Recent Advancements in Machine Learning and IoT for Health care 15

Recent Advancements in Machine Learning and IoT for Health c...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Vani, K.S. Basavaraju, T.G. Surekha, K.B. Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Karnataka Bangalore India Department of Computer Science and Engineering Government College of Engineering Hassan India Department of Information Science and Engineering BMSIT Bangalore India

ISBN: (纸本)9798331300579

In the current landscape of healthcare, Machine Learning (ML) and the Internet of Things (IoT) are making significant strides. Ongoing research focuses on remote patient monitoring, disease prediction, and diagnostic imaging, utilizing wearable devices and sensors. ML algorithms contribute to drug discovery, personalized medicine, and operational efficiency, predicting equipment failures and optimizing resource allocation. This paper surveys the most recent papers on ML and IoT in health care. This survey of ML and IoT in healthcare highlights diverse applications, from automated diabetes prediction to breast cancer detection. Utilizing wearable devices and sensors, these technologies provide real-time health insights and personalized interventions. Challenges such as data security and limited datasets are identified, prompting the need for future improvements. © Grenze Scientific Society, 2024.

关键词： Electronic health record

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An Approach to Identify Indic Languages using Text Classification and Natural Language Processing 2

An Approach to Identify Indic Languages using Text Classific...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Shetty, Deepthi Sarojadevi, H. Shakeel, Uzma Sanjana, S. Aishwarya, G.M. Nupur, P. Nitte Meenakshi Institute of Technology Computer Science & Engineering Department Bangalore India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

India is one of the most culturally and linguistically diverse nations in the world. India stands second in the world for the most languages spoken by its diverse population, who speak their own regional languages for communication. English is offered as a second additional official language in India. However, there is a communication gap in India because of how little English is used there. It's nearly impossible for humans to bridge this breach by translating from one language into another. However, it is possible to translate languages by taking the help of a machine. As per the literature survey, it was observed that Neural Machine Translation (NMT) is a cutting-edge strategy that significantly outperformed more conventional machine translation methods for translating one language into *** main objective of this proposed work is to achieve accurate identification of Indic language texts and scripts and provide relevant names of the language after the detection process. The entire work is carried out in stages which includes, collection of the dataset from different sources, preprocessing with the help of data mining techniques, identifying the language of input and in future, approaches like rule based, statistical and neural networks will be used followed by post-processing and efficient tasks like Machine Translations, Named Entity recognition, etc. will be carried out. © 2022 IEEE.

关键词： Neural machine translation

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Plant Leaf Disease Detection Using Image Processing 6th

Plant Leaf Disease Detection Using Image Processing

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6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020

作者： Sahana, M. Reshma, H. Pavithra, R. Kavya, B.S. Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (纸本)9789811613371

In India, most of the people are dependent on agriculture. The raw materials obtained from the agriculture are served as food for many people. The crop plantations are being destroyed because of the two main reasons: (i) The natural destructions such as drought, flood, famine, and earthquake. (ii) Pest and pathogens. About 98% of the destruction in crops are caused by pathogens and pests. The remaining 2% of the destruction is due to natural disaster in the surroundings. The rural farmers are severely affected by the crop production problems. In crop's life cycle, leaf plays a major role in getting the information about the growth and production of the plant. In this paper, the proposed system works on the preprocessing of the dataset. The leaf images are collected from the plant village dataset. The feature extraction is applied to the images during the data preprocessing stage. Convolution neural network (CNN) is used for the classification and detection of diseases. The recommendation of pesticides and fertilizers is done by using TensorFlow technique. The convolution neural network with various number of layers is used for training the model, and GUI screen serves as a user interface. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Python

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Deep Learning Solutions for Enhanced Detection for Skin Lesions 2

Deep Learning Solutions for Enhanced Detection for Skin Lesi...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Shashikala, S.V. Anil Kumar, K.T. Chaithra, K.N. Bgs Institute of Technology Adichunchanagiri University Department of Computer Science & Engineering BG Nagara Mandya India Nitte Meenakshi Institute of Technology Department of Electronics & Communication Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350372892

Due of the disease's quick spread, the recent spike in monkey pox outbreaks in more than 40 nations has caused serious public health concerns. Monkey pox can be difficult to diagnose in the early stages because of its similar clinical appearance to measles and chickenpox. When confirmatory Polymerase Chain Reaction (PCR) assays are not available, computer-aided lesion detection for monkey pox could play a major role in surveillance and early case discovery. Although deep learning techniques have demonstrated potential in automating the identification of skin lesions, a challenge is the inadequacy of training data that is especially related to monkey pox. Our research fills this vacuum by developing a deep learning model that can anticipate a range of skin conditions, such as tomato pox, chickenpox, smallpox, and monkey pox. Using mostly photos from publicly accessible websites and platforms, we use data augmentation methods to increase sample size. Moreover, we categorize these illnesses using pre-trained deep learning models like VGG-16, ResNet50, and InceptionV3, supplemented with an ensemble method for increased accuracy. We create a prototype graphical user interface (GUI) screening tool to ensure user-friendliness in clinical settings and to improve simplicity of usage. On an average the models have obtained 97% accuracy rate. © 2024 IEEE.

关键词： Graphical user interfaces

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Enhanced Predictive Model to Diagnose Coronary Heart Disease Using Decision Tree 2

Enhanced Predictive Model to Diagnose Coronary Heart Disease...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Joshi, Sujata Dixit, Ananya Nair, Mydhili K. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India B.M.S. Institute of Technology and Management Department of Information Science and Engineering Bangalore India School of Computer Science Rv University Bangalore India

ISBN: (纸本)9798350373110

As per WHO reports, Heart diseases or Cardiovascular Diseases (CVDs) rank as the leading cause of death worldwide. Approximately 17.7 million individuals succumbed to CVDs in 2015, accounting for 31% of all global fatalities. Among these fatalities, an estimated 7.4 million were attributed to coronary heart disease, one of the prevailing forms of heart disease.. According to the Global Burden of Disease study, nearly quarters (24.8%) of all deaths in India are due to CVD. Hence there is a need for careful and periodic methods of examining heart related diseases. The majority of cardiovascular diseases found in India are coronary heart diseases (CHD) The main contribution of this work is to develop efficient predictive models for Coronary heart diseases using decision trees. In this work, the predictive models are developed for diagnosis of heart disease using the cardiovascular disease dataset from UCI repositories with the objective of improving the accuracy. We have also obtained echocardiograph data obtained from M. S. Ramaiah hospital and used in this work. It is found that the data used in this work is heterogeneous in nature and the decision trees work well on heterogeneous data and also they are expressive and interpretative, we have opted to use decision trees. The predictive model namely Enhanced-Pred-Model CVD-DATA-DT is developed using both the datasets. It is found that the model shows marginal accuracy increase from 77% in baseline model to 80% by the proposed model. The proposed method has better performance in terms of accuracy as well as error rate. The models are also tested on the echocardiograph data for prediction of LVH event and has shown improvement in the performance. © 2024 IEEE.

关键词： Prediction models

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Analysing Machine Learning Techniques in Python for the Prediction of Diabetes Using the Risk Factors as Parameters 6th

Analysing Machine Learning Techniques in Python for the Pred...

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6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020

作者： Akanksha, M.S. Vinutna, Kolachana Thippeswamy, M.N. Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (纸本)9789811613418

Diabetes is a disease caused due to an increased level of glucose in the blood. Many people around the world suffer from this condition. A lot of research works have been published concerning diabetes, using a variety of classifiers and machine learning models, both for predictions and diagnosis. This paper outlines the effects of some risk factors of diabetes, specifically the effect of BMI, blood pressure and physical activity, using machine learning models. A survey was conducted among a random sample of Indians of various age groups and the data of 251 individuals was gathered after which ML algorithms were applied. The analysis is supported by naïve Bayes, logistic regression and random forest algorithms. All the metrics of these algorithms are compared and discussed in this paper, thereby establishing the model having the highest accuracy for the prediction. The model with the highest accuracy and precision can be used or analysed for further computational research on diabetes mellitus. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Enhancing Network Security: A Novel Hybrid ML Approach for DDoS Attack Detection in SDN 15

Enhancing Network Security: A Novel Hybrid ML Approach for D...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Rakesh, V.S. Vasanthakumar, G.U. Department of Computer Science and Engineering Cambridge Institute of Technology Bengaluru Visvesvaraya Technological University Karnataka Belagavi India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru Visvesvaraya Technological University Karnataka Belagavi India

ISBN: (纸本)9798331300579

Software-defined networking represents a ground breaking advancement in network technology, characterized by its desirable attributes such as enhanced flexibility and manageability. Although ongoing, the issue of DDoS assaults in SDN is characterized by malicious and obtrusive network traffic that overwhelms SDN resources. Despite numerous security methodologies aimed at detecting DDoS attacks, the challenge of effectively addressing this issue continues to persist as an active area of research. The XG-Light Hybrid, a unique hybrid system, has been developed in this work as a solution to this problem. This discovery is significant because it has the potential to dramatically increase the reliability of DDoS attack detection in SDN environments, hence boosting network security and stability. Key findings reveal that the proposed hybrid approach outperforms individual machine learning algorithms with respect to DDoS detection. © Grenze Scientific Society, 2024.

关键词： DDoS Detection Light GBM Machine Learning Network Traffic SDN XG Boost

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Object Detection in computer Vision Using Machine Learning Algorithm For Visually Impaired People 2

Object Detection in Computer Vision Using Machine Learning A...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Bolla, Dileep Reddy Trisheela, S. Nagarathana, P. Sarojadevi, H. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Traditionally, blind individuals go to their destinations with a white cane or a guide dog. They were unable to quickly recognize their surroundings, though. Consequently, The development of a navigation system for visually impaired people in an interior environment is the topic of this study. In order to provide an efficient and user-friendly navigation tool, passive radio frequency identification (RFID) transponders are mounted on the floor, such as on tactile pavement, to establish such RFID networks. the navigational system created includes a digital compass to help persons who are blind or visually handicapped walk correctly and in the right direction, especially while turning. This system makes use of the ideas of localization and placement using a digital compass as well as vocal prompts for directions. Others looked into how to get blind people back onto the right path when they go off course and calibrate the digital compass for accuracy. Additionally, in order to determine the practicality of the developed navigation system, a comparison between two subjects - a mobile robot and a human - is done. The results of the experiment show that a mobile robot and a human move at different speeds. People who are blind or visually impaired will benefit from this project since the voice-activated navigation system will improve their travel experience and make it safer and more comfortable. © 2022 IEEE.

关键词： Object detection

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HDRT: Helmet Detection System using Convolutional Neural Network in Real Time 2

HDRT: Helmet Detection System using Convolutional Neural Net...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Vasanthakumar, G.U. Kumar K, Dheeraj Pavan Reddy, K. Shankar B, Shiva Reddy P, Sai Karthik Nitte Meenakshi Institute of Technology Department of Computer Science & Engineering Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Motorcycle accidents have progressively increased in many nations over the years, as riders around the world regularly disregard road safety, resulting in road accidents and casualties. While many countries have made wearing of helmets compulsory, still there need to be special laws to promote safety of vehicle riders. Thus it is critical for motorcyclists to understand the need of wearing helmet to avoid fatal incidents. To enforce wearing of Helmet, in this work, an algorithm is developed using Convolutional Neural Network to enable the detection of helmets in real time. If either of rider is riding without a helmet, the proposed algorithm can accurately track the situation and proclaim a legal violation. The application may be deployed in entrance of offices, marts, schools, and universities, allowing people to enter the premises only if the helmet is detected on the rider. It would almost probably have an impact on the use of helmets, which could save lives. © 2022 IEEE.

关键词： Object detection

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Infant Brain MRI Segmentation Using Deep Volumetric U-Net with Gamma Transformation 7th

Infant Brain MRI Segmentation Using Deep Volumetric U-Net wi...

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7th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2022

作者： Yeshwanth, Gunda Sai Annappa, B. Dodia, Shubham Manoj Kumar, M.V. Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India Department of Information Science Engineering Nitte Meenakshi Institute of Technology Karnataka Bangalore India

ISBN: (纸本)9789811954818

The growth of the brain from infantile to adolescence is very complex and takes a very long period. There are many processes such as myelination, migration, neural induction, and many other time-taking processes to study the development of the brain. This makes it necessary to develop some automatic tools to study the development of the brain. The brain consists mainly of three parts white matter, gray matter, and cerebrospinal fluid. So, quantitative tools will be a great boon for the medical community to deal with the brain if the brain MRI images are segmented into these three different parts. Although there are some tools for segmenting adult MRI images, for 6-month child segmentation, the brain becomes challenging as the white matter and gray matter are almost indistinguishable due to the brain development process. Segmentation of brain MRI images can identify specific patterns that contribute to healthy brain development. The dataset to address this problem had been taken from the Iseg2019 challenge conducted by MICCAI. Segmentation of MRI needs expert doctors. Advancements in computer vision techniques can be used to replace present time-consuming work. This paper proposes a deep learning model for image segmentation using a three-dimensional U-net. The proposed model gives dice values of 93.75, 88.24, and 85.64 for cerebrospinal fluid, gray matter, and white matter. This paper also presents various experimental results of U-net, attention U-net with different modifications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cerebrospinal fluid

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