Background: The concept of pill reminders has been discussed and developed throughout the decade. It varies from cascaded plastic pill boxes to complicated robust dispensers. This proposed smart pill reminding system ...
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Background: The concept of pill reminders has been discussed and developed throughout the decade. It varies from cascaded plastic pill boxes to complicated robust dispensers. This proposed smart pill reminding system based on IoT is being designed by considering ease-to-use and cost-effectiveness. Method: A smart pill reminding system is a system that will alert the patient to take their respective pill at the desired time. It will also track the motion of the patient’s hand while taking the pill and will also display the pill count on an LCD Screen. In case a patient forgets/ignores the reminder provided by the system, the system will automatically display the status on the application that will be installed in the relative/caretaker’s phone and through an email on the patient's relative/caretaker’s email address to take subsequent action. The system will monitor the real-time using an RTC module, and as and when the current time matches the medicines time, it will activate its mechanism, and the patient will have a buffer time to take their medicine. In case a patient does take the medicine in the buffer time provided by the system, then one mechanism of the system will be activated. In another case, if a patient does not take the medicine in the stipulated time, further actions will be initiated by the system to benefit the patient. Results: It was tested and found that out of ten times, the system worked accurately nine times, with calculated accuracy as high as 90%. Initially, the Blynk application will display "Welcome Patient" and "You will be updated". Once the RTC matches the scheduled time to take medicine, the buzzer starts buzzing. If the IR sensor detects the movement of the user’s hand, the LCD will update the pill count, and the pill count is reduced by one. The LCD will also display the message "Medicine Taken". If the IR sensor does not detect the movement of the user’s hand, the LCD will display the same pill count. The LCD will also display the me
A prevalent diabetic complication is Diabetic Retinopathy(DR),which can damage the retina’s veins,leading to a severe loss of *** treated in the early stage,it can help to prevent vision *** since its diagnosis takes...
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A prevalent diabetic complication is Diabetic Retinopathy(DR),which can damage the retina’s veins,leading to a severe loss of *** treated in the early stage,it can help to prevent vision *** since its diagnosis takes time and there is a shortage of ophthalmologists,patients suffer vision loss even before ***,early detection of DR is the necessity of the *** primary purpose of the work is to apply the data fusion/feature fusion technique,which combines more than one relevant feature to predict diabetic retinopathy at an early stage with greater *** procedures for diabetic retinopathy analysis are fundamental in taking care of these *** profound learning for parallel characterization has accomplished high approval exactness’s,multi-stage order results are less noteworthy,especially during beginning phase *** Connected Convolutional Networks are suggested to detect of Diabetic Retinopathy on retinal *** presented model is trained on a Diabetic Retinopathy Dataset having 3,662 images given by *** results suggest that the training accuracy of 93.51%0.98 precision,0.98 recall and 0.98 F1-score has been achieved through the best one out of the three models in the proposed *** same model is tested on 550 images of the Kaggle 2015 dataset where the proposed model was able to detect No DR images with 96%accuracy,Mild DR images with 90%accuracy,Moderate DR images with 89%accuracy,Severe DR images with 87%accuracy and Proliferative DR images with 93%accuracy.
Parkinson disease (PD) is a neurodegenerative disease cause by the lack of dopamine hormone secretion. Humans get affected in motor and non-motor activities with Parkinsonism. Motor dysfunction affects speech producti...
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As we are utilizing Conventional vehicles, Electric Vehicles require charging the batteries, so the chargers for batteries are becoming more and more crucial within the automobile industry. While the current chargers ...
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Based on Popov hyperstability, a new Model Reference Adaptive Control System (MRACS) design scheme for high-order system following low-order model using state variables taking from the model is proposed in this paper....
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This paper presents a review of the position-sensitive detector(PSD) sensor, covering different types of PSD and recent works related to this field. Furthermore, it explains the theoretical concepts and provides infor...
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This paper presents a review of the position-sensitive detector(PSD) sensor, covering different types of PSD and recent works related to this field. Furthermore, it explains the theoretical concepts and provides information about its structure and principles of operation. Moreover, it includes the main information about the available commercial PSDs from different companies, along with a comparison between the common modules. The PSD features include high position resolution, fast response, and a wide dynamic range. These features make it suitable for various fields and applications, such as imaging, spectrometry, spectroscopy and others.
In this study, miniaturized Wi-Fi 6E bandpass filters (BPFs) with asymmetrical frequency response were developed on a low-loss organic substrate. The cross-coupling technique can be used to generate one or two transmi...
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In this paper, we develop a novel mobility-aware transformer-driven tiered structure (MASSFormer) based co-operative spectrum sensing method that effectively models the spatio-temporal dynamics of user movements. Unli...
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Classification of speech signals is a vital part of speech signal processing *** the advent of speech coding and synthesis,the classification of the speech signal is made accurate and *** methods are considered inaccu...
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Classification of speech signals is a vital part of speech signal processing *** the advent of speech coding and synthesis,the classification of the speech signal is made accurate and *** methods are considered inaccurate due to the uncertainty and diversity of speech signals in the case of real speech signal *** this paper,we use efficient speech signal classification using a series of neural network classifiers with reinforcement learning *** classification of speech signals,the study extracts the essential features from the speech signal using Cepstral *** features are extracted by converting the speech waveform to a parametric representation to obtain a relatively minimized data *** to improve the precision of classification,Generative Adversarial Networks are used and it tends to classify the speech signal after the extraction of features from the speech signal using the cepstral *** classifiers are trained with these features initially and the best classifier is chosen to perform the task of classification on new *** validation of testing sets is evaluated using RL that provides feedback to ***,at the user interface,the signals are played by decoding the signal after being retrieved from the classifier back based on the input *** results are evaluated in the form of accuracy,recall,precision,f-measure,and error rate,where generative adversarial network attains an increased accuracy rate than other methods:Multi-Layer Perceptron,Recurrent Neural Networks,Deep belief Networks,and Convolutional Neural Networks.
With the advent of modern technologies,IoT has become an alluring field of *** IoT connects everything to the network and transmits big data frequently,it can face issues regarding a large amount of energy *** this re...
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With the advent of modern technologies,IoT has become an alluring field of *** IoT connects everything to the network and transmits big data frequently,it can face issues regarding a large amount of energy *** this respect,this paper mainly focuses on reducing the energy loss problem and designing an energy-efficient data transfer scenario between IoT devices and ***,a layered architectural framework for IoT-cloud transmission has been proposed that endorses the improvement in energy efficiency,network lifetime and ***,an Opposition based Competitive Swarm Optimizer oriented clustering approach named OCSO-CA has been proposed to get the optimal set of clusters in the IoT device *** proposed strategy will help in managing intra-cluster and inter-cluster data communications in an energy-efficient ***,a comparative analysis of the proposed approach with the state-of-the-art optimization algorithms for clustering has been performed.
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