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Smart Health Management Application Using IoT and Smart Sensors 2

Smart Health Management Application Using IoT and Smart Sens...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Arnika Shaikh, Asmat Ara Kumar, Mukesh Sharma, Bhasker Yadav, Dinesh Kumar Aeri, Manisha Sharda School of Engineering and Technology Sharda University Department of Computer Science and Applications Up Greater Noida India Parle Tilak Vidyalaya Association's Institute of Management Department of Marketing Management Maharashtra Mumbai India Graphic Era Hill University Department of Computer Science & Engineering Uttarakhand Dehradun India Hitech Institute of Engineering and Technology Department of Computer Science UP Ghaziabad India Lloyd Institute of Engineering & Technology Department of Mechanical Engineering UP Greater Noida India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350371055

Clever fitness management software using the Internet of Factors (IoT) and smart sensors are a modern answer for powerful and personalized health care. This technology integrates innovative sensors with IoT, creating a community of connected gadgets that can music and screen a character's health and well-being. The utility collects facts in actual time, which are then analyzed and processed to provide valuable insights and recommendations for stepped-forward fitness management. The software uses diverse clever sensors, including health trackers, blood strain monitors, and glucose video display units, to constantly display the consumer's vital signs, symptoms, and physical activities. This record is then transmitted to the cloud for garage and analysis. © 2024 IEEE.

关键词： Garage Health care Management Sumptoms Transmitted

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Diagnosing Phytophthora infestans infestations on Solanum tuberosum leaves using Machine Learning Classifiers

Diagnosing Phytophthora infestans infestations on Solanum tu...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Kumar, Pijush Kanti Kumar, Indrajeet Kumar, Sanjeev Kumar, Padmanavan Harshith, John Dutta, Anurag Government College of Engineering and Textile Technology Department of Information Technology Serampore India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India G L Bajaj Institute of Technology and Management Department of Master of Computer Applications Greater Noida India Kalinga Institute of Industrial Technology Department of Computer Science and Engineering Bhubaneswar India Vellore Institute of Technology Department of Computer Science and Engineering Vellore Campus Vellore India Government College of Engineering and Textile Technology Department of Computer Science and Engineering Serampore India

ISBN: (纸本)9798350323917

In reaction to recent technology breakthroughs, modern computational fields such as the IoT, Data science, Machine Learning, AI, and several more have arisen. Humans benefit greatly from these fields. In this study, we would employ parts of machine learning to forecast plant illnesses. Particularly amongst the potato leaves, severe Late Blight Disease was discovered. The potato plant, also known as Solanum tuberosum, is a significant crop that is planted all over the world and yields copious amounts of nutritious tubers. The potato is a frequent staple food and has several health benefits. When ingested in moderation, the fluid extracted from tubers can cure stomach ulcers and reduce inflammation and acidity. Both Early and Late Blight are dangerous and widespread potato diseases. We would concentrate on Late Blight in this effort. A fatal fungus that harms potatoes is called Late Blight. It is accessible everywhere in the world. It can be found in areas of the world where potatoes are grown. Winter is the primary growing season for potatoes in India. It is followed by sweltering summer months in the lowlands. Heat and dryness kill the fungus that lives in the soil. The fungus Phytophthora Infestans, the most prevalent agricultural disease that causes the Late blight of potatoes, can quickly result in crop failure if appropriate control measures are not adopted. Potato crop declines in epidemic years might be as high as 80%. We acquired a collection of roughly one thousand samples of Late Blight-affected leaves of potatoes for this investigation. With the help of conventional classifier algorithms, we have developed a machine-learning classification paradigm that may be able to forecast the onset of Late Blight disease. Medical science has developed significantly. Plant pathology could be able to prevent the threat from happening if we could perhaps predict the illness in its early stages. © 2023 IEEE.

关键词： Diseases

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Optimizing Logistics with Regularization Techniques: A Comparative Study

Optimizing Logistics with Regularization Techniques: A Compa...

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2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Grace, Anne Lourdu Thenmozhi, M. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350310061

Optimising logistics operations is critical for modern businesses as it can significantly impact supply chain efficiency, customer satisfaction, and overall profitability. One way to enhance logistics performance is by employing machine learning models, such as logistic regression, to predict and optimise various logistics outcomes. However, these models often face the issue of overfitting, which can result in poor generalisation and inaccurate predictions. This paper presents a comparative study of various regularisation techniques, such as Ridge and Lasso, applied to logistic regression models for optimising logistics operations. Our results indicate that regularisation techniques can significantly improve the performance of logistic regression models in predicting and optimising logistics outcomes. The Lasso regularisation technique is particularly effective in feature selection, reducing the number of variables needed to achieve high accuracy. Our analysis furthers to the research on machine learning applications in logistics and provides practical insights for logistics managers to optimise their operations. © 2023 IEEE.

关键词： Forecasting

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Unlocking Complex Patterns Through the Power of Deep Learning for Advanced Data Analysis and Prediction

Unlocking Complex Patterns Through the Power of Deep Learnin...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Sasikumar, S. Kumar, M. Guru Vimal Paramesh, J. Sriram, K.P. Anbalagan, E. Saveetha Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai600062 India St. Joseph's Institute of Technology Information Technology OMR Chennai600119 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331542344

Supervised deep learning has brought changes to data analysis and prediction function by leveraging its capacity to make many number of relationships with heterogeneous forms of data. This paper compares deep learning techniques like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks in three tasks: There are three types of tasks, including image classification, time-series prediction, and text classification. In the picture simulation of the CIFAR-10 set for training, the accuracy of the ResNet model was 95% while the accuracy of VGG16 w 93%. When applied to the M4 competition dataset in time-series prediction, the LSTM model was superior to other benchmarks with MAE of 0.40 and RMSE of 0.60, with the RNN model giving MAE of 0.50 and RMSE of 0.65. It is 0.92 of accuracy, 0.91 of F1 score, and 0.921 for precision using the IMDB reviews dataset in text classification of movies using the CNN-LSTM hybrid model while the Bi-LSTM only had an accuracy of 0.89 and an F1 score of 0.889. Even though models, such as ResNet and Bi-LSTM, took slightly more time to train-Approximately 5.0 hours-it was because the epoch models were significantly superior to the baseline model. The present work focuses on the benefits of deep learning architectures in providing interpretable patterns in numerous applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Design and Development of a NLP-Based Assistive Platform to Facilitate Smooth Transition from Bengali Regional to Colloquial Language 3rd

Design and Development of a NLP-Based Assistive Platform to ...

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3rd International Conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Arman, Alif Islam, Md. Jahurul Nowshin, Nadiya Islam, Mohammad Shahidul Department of Electrical and Electronic Engineering Ahsanullah University Of Science and Technology Dhaka Bangladesh Institute of Information Technology Jahangirnagar University Savar Bangladesh Department of Computer Science and Engineering Brac University Dhaka Bangladesh

ISBN: (纸本)9789819739363

Bengali is the State Language of Bangladesh and about 300 million people use this language around the world. The maximum number of Bengali speakers are from Bangladesh. Though there is a common or Colloquial Language to communicate with each other in this country. But there are many Regional Languages in this country. Among the regional speakers one may not or do not understand other regional languages, that’s why it’s a crucial problem to communicate one regional speaker with another regional speaker. In this research, we are trying to make a platform for detecting one’s language region from his given sentences/words and according to his/her region, we are translating his/her sentences/words to Colloquial Language and this platform will train up him/his for learning the Standard Bengali Language as well as. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Translation (languages)

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Analysis of Magnetic Resonance images for the detection of Alzheimer's 4

Analysis of Magnetic Resonance images for the detection of A...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Patil, Harshal Swati Kamala, L. Chaturvedi, Arpana Veeranjaneyulu, Rayavarapu Vekariya, Vipul Maharashtra Pune India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Gitam School of Technology Department of Computer Science and Engineering Karnataka Bengaluru561203 India New Delhi Institute of Management Department of IT/AI/ML New Delhi110062 India Pace Institute of Technology & Sciences Department of Computer Science and Engineering Andhra Pradesh Ongole523272 India Parul University Parul Institute of Engineering and Technology Department of Computer Science and Engineering Post Limda Gujarat 391760 India

ISBN: (纸本)9798350354218

This study aims to design a CAD tool for Alzheimer's disease (AD) detection using linear SVM classifiers on MRI scans. Existing approaches often face challenges with high-dimensional feature spaces and limited data accessibility from repositories like ADNI. To overcome these, three SVM-based classifier proposals were explored. Results demonstrated promising accuracy, sensitivity (Se), and specificity (Sp), particularly with cross-validation techniques. Notably, classifiers trained with image histograms showed superior performance in differentiating between AD, cognitively normal (CN), and mild cognitive impairment (MCI). Despite successes, initial approaches encountered difficulties, necessitating alternative methods. Challenges in data access and processing highlight the need for improved data management tools. Moving forward, refinements such as feature selection and testing with larger datasets could enhance classifier performance. Exploring deep neural networks as an extension may offer further insights into AD detection methodologies for early diagnosis and intervention. © 2024 IEEE.

关键词： Deep neural networks

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IoT-based Patient Monitoring System through Online Cloud and ECG Sensor

IoT-based Patient Monitoring System through Online Cloud and...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Amir-Ul-Haque Bhuiyan, T.M. Ahmed, Shakhir Salahin, Md Pollab, Md Jui, Jannatun Nyeem Ahmmed, Md Tanvir Rahman, Anichur Wadud, Md. Anwar Hussen Bangladesh University of Engineering and Technology Department of Computer Science and Engineering Bangladesh Bangladesh University of Business and Technology Department of Computer Science and Engineering Bangladesh Constituent Institute of University of Dhaka Bangladesh

ISBN: (纸本)9798350316001

The Internet of Things (IoT) is revolutionizing healthcare by enabling remote patient monitoring. An IoT-based patient monitoring system is the main focus of this study that uses an electrocardiogram (ECG) sensor, a wire thermometer sensor, and a DHT11 sensor. The objective of the system is to continuously monitor the ECG signals of patients, monitor patients' body temperature and patients' room temperature and humidity, and transmit them wirelessly to a cloud-based server for real-time analysis. The proposed system comprises an ECG sensor, a one-wire thermometer sensor, a DHT11 sensor, a microcontroller, and a wireless communication module. The ECG sensor captures the electrical signals generated by the heart and converts them into digital signals. The DS18B20 sensor measures patients' body temperature, and the DHT11 sensor monitors room temperature and humidity. The microcontroller processes the digital signals and transmits them to the cloud-based server through the wireless communication module. The smartphone Apps take patients' records from the cloud, analyze the ECG signals, and generate necessary suggestions if any abnormalities are detected. © 2023 IEEE.

关键词： Electrocardiograms

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Tectonism and Drainage Responses: Insights from the Siang River Basin 2nd

Tectonism and Drainage Responses: Insights from the Siang Ri...

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2nd International Conference on River Corridor Research and Management, RCRM 2022

作者： Uma Narayan, M. Bharti, Rishikesh Nair, Archana M. Earth System Science and Engineering Department of Civil Engineering Indian Institute of Technology Guwahati Assam India

ISBN: (纸本)9789819944224

Siang River, the upstream subbasin of the mighty Brahmaputra in northeast India, enters the indian territory from the Tibetan plateau through the Namche Barwa massif carrying the footprints of tectonic perturbations. This study uses morphometric, morphotectonic and profile analyses of the Siang basin to quantify the landscape responses to tectonics and spatiotemporal variability in the responses. The tectonic imprints on the river basin's morphological characteristics were studied using SRTM DEM-based morphometric analysis and geomorphic indices. The hypsometric integral (‘H.I'), asymmetry ratio (‘Af') and stream length gradient index (‘S.L') are to mention a few morphotectonic parameters analysed in this study. The higher asymmetry value with the sigmoidal to slightly convex up hypsometric curve of the Siang River indicates an unstable tectonic setting of the river basin. An increasing trend for the ‘S.L' index is visible along the upstream Siang River. The gradient changes with sharp elevation differences were evident from the river's long profile. The ‘chi' plot displays disequilibrium from the steady state, as diversions from linearity, indicating an imbalance between the rate of erosion and upliftment at the upstream reach of the river. These drainage responses of the Siang basin can be attributed as typical landscape responses to the ongoing upliftment and the active tectonics of the area. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Tectonics

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In Search of a Suitable Epoch Value for the Optimum Result in Image Captioning 3

In Search of a Suitable Epoch Value for the Optimum Result i...

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3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Dinda, Sourav Pal, Alok Ranjan Pal, Antara College of Engineering and Management Kolaghat Department of Computer Science and Engineering Kolaghat India National Institute of Technology Durgapur Department of Computer Science and Engineering Durgapur India

ISBN: (纸本)9781665494007

The proposed work is carried out on retrieving the content of an image using machine learning based approach. In order to gain a deeper insight of this topic, the current state-of-the-art image caption generation technique has been considered as the baseline. The goal of this work is to find a suitable Epoch value during the training phase which can maximize the performance of a system. The proposed model is composed of six major components: 1) Data pre-processing unit;2) Convolutional Neural Network (CNN) as an encoder;3) At-tention mechanism;4) Recurrent Neural Network (RNN) as a decoder;5) Beam search to find most optimal caption;and 6) Sentence Generation and evaluation. The model is trained to maximize the likelihood of the description sentence of a given training image. Experiments on the test dataset show the accuracy of the model and the fluency of the language it learns solely from image descriptions. The challenges of this work are mentioned in detail at the last of this paper. © 2023 IEEE.

关键词： Convolutional neural networks

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Automatic Ship Detection and Classification from Imagery Dataset MASATI Using Deep Learning Framework 5th

Automatic Ship Detection and Classification from Imagery Dat...

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5th International Conference on Advances in Information Communication technology and Computing, AICTC 2024

作者： Patel, Krishna Bhatt, Chintan Department of Computer Science & Engineering Devang Patel Institute of Advance Technology and Research Charotar University of Science and Technology Changa India Gandhinagar India

ISBN: (纸本)9789819761029

A significant challenge is the automatic detection and classification of ships from aerial images for maritime safety and surveillance. There are two techniques, image processing and computer vision, used before the traditional supervised classifiers to extract the features from the images and classify them. The framework proposed in this paper is You Only Look Once (YOLOv8), which was recently developed and implemented for object detection and classification on the COCO dataset. The authors used the framework with some preprocessing and augmentation on the satellite image to detect and classify ships, which helps maritime organizations locate the objects. The MAritime SATellite Imagery dataset (MASATI) of more than 7000 images is used in this paper. The proposed method is tested with two different preprocessing steps, auto-orient and mosaic augmentation, in version 1 and version 2;the preprocessing step includes auto-orient and grayscale along with the augmentation as crop, shear, blur, and mosaic. The framework generates mAP of 57.8% and 63.7% for version 1 and version 2, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Satellite imagery

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