scholarly journals A noninvasive smart wearable for diaper moister quantification and notification

2019 ◽  
Vol 9 (4) ◽  
pp. 2848 ◽  
Author(s):  
Tareq Khan
Keyword(s):  
Outer Surface ◽  
Nearest Neighbor ◽  
Electronic Device ◽  
Low Cost ◽  
Smartphone App ◽  
Health Issues ◽  
K Nearest Neighbor ◽  
Hybrid Classification ◽  
Smart Wearable ◽  
Urgency Score

A baby feels uncomfortable in a wet diaper and it can cause health issues such as diaper rash. Diaper rash can be avoided by changing the diaper as soon as the baby urinates or passes stool. In this project, a smart wearable gadget is developed which sends an automatic notification to a caregiver’s smartphone whenever the baby urinates. The proposed wearable detects urination event noninvasively by sensing the temperature rise on the outer surface of the diaper and quantifies the event using a decision tree and a midpoint based k-nearest neighbor (KNN) hybrid classification algorithm. The gadget is a small size, low power, low cost and reusable electronic device that is attached externally to the outer surface of the diaper. The gadget can be used with any disposable diaper, thus no change in the diaper production process or price increase is required. The smartphone app shows the diaper change urgency score and logs all the urination events. This record can facilitate treating diseases where accurate records of urination are required. A prototype of the hardware gadget and a smartphone app is developed and tested.

scholarly journals COVIMOS: A Coastal Video Monitoring System

2017 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
I Made Oka Widyantara ◽  
I Made Dwi Asana Putra ◽  
Ida Bagus Putu Adnyana
Keyword(s):  
Monitoring System ◽  
Nearest Neighbor ◽  
Low Cost ◽  
Video Image ◽  
Video Monitoring ◽  
Reference Object ◽  
Self Organizing Map ◽  
K Nearest Neighbor ◽  
Ip Camera ◽  
Class Labels

This paper intends to explain the development of Coastal Video Monitoring System (CoViMoS) with the main characteristics including low-cost and easy implementation. CoViMoS characteristics have been realized using the device IP camera for video image acquisition, and development of software applications with the main features including detection of shoreline and it changes are automatically. This capability was based on segmentation and classification techniques based on data mining. Detection of shoreline is done by segmenting a video image of the beach, to get a cluster of objects, namely land, sea and sky, using Self Organizing Map (SOM) algorithms. The mechanism of classification is done using K-Nearest Neighbor (K-NN) algorithms to provide the class labels to objects that have been generated on the segmentation process. Furthermore, the classification of land used as a reference object in the detection of costline. Implementation CoViMoS system for monitoring systems in Cucukan Beach, Gianyar regency, have shown that the developed system is able to detect the shoreline and its changes automatically.

scholarly journals A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network

PLoS ONE ◽  
2014 ◽  
Vol 9 (11) ◽  
pp. e112987 ◽  
Author(s):  
Nader Salari ◽  
Shamarina Shohaimi ◽  
Farid Najafi ◽  
Meenakshii Nallappan ◽  
Isthrinayagy Karishnarajah
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Nearest Neighbor ◽  
Classification Model ◽  
K Nearest Neighbor ◽  
Backpropagation Neural Network ◽  
Hybrid Classification

scholarly journals An Enhanced Indoor Positioning Algorithm Based on Fingerprint Using Fine-Grained CSI and RSSI Measurements of IEEE 802.11n WLAN

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2769
Author(s):  
Jingjing Wang ◽  
Joongoo Park
Keyword(s):  
Nearest Neighbor ◽  
Low Cost ◽  
Gaussian Function ◽  
Low Complexity ◽  
Correction Method ◽  
Indoor Positioning ◽  
K Nearest Neighbor ◽  
Positioning Accuracy ◽  
Indoor Location ◽  
Positioning Algorithm

Received signal strength indication (RSSI) obtained by Medium Access Control (MAC) layer is widely used in range-based and fingerprint location systems due to its low cost and low complexity. However, RSS is affected by noise signals and multi-path, and its positioning performance is not stable. In recent years, many commercial WiFi devices support the acquisition of physical layer channel state information (CSI). CSI is an index that can characterize the signal characteristics with more fine granularity than RSS. Compared with RSS, CSI can avoid the effects of multi-path and noise by analyzing the characteristics of multi-channel sub-carriers. To improve the indoor location accuracy and algorithm efficiency, this paper proposes a hybrid fingerprint location technology based on RSS and CSI. In the off-line phase, to overcome the problems of low positioning accuracy and fingerprint drift caused by signal instability, a methodology based on the Kalman filter and a Gaussian function is proposed to preprocess the RSSI value and CSI amplitude value, and the improved CSI phase is incorporated after the linear transformation. The mutation and noisy data are then effectively eliminated, and the accurate and smoother outputs of the RSSI and CSI values can be achieved. Then, the accurate hybrid fingerprint database is established after dimensionality reduction of the obtained high-dimensional data values. The weighted k-nearest neighbor (WKNN) algorithm is applied to reduce the complexity of the algorithm during the online positioning stage, and the accurate indoor positioning algorithm is accomplished. Experimental results show that the proposed algorithm exhibits good performance on anti-noise ability, fusion positioning accuracy, and real-time filtering. Compared with CSI-MIMO, FIFS, and RSSI-based methods, the proposed fusion correction method has higher positioning accuracy and smaller positioning error.

scholarly journals Screening of transgenic maize using near infrared spectroscopy and chemometric techniques

2018 ◽  
Vol 16 (2) ◽  
pp. e0203 ◽  
Author(s):  
Xuping Feng ◽  
Haijun Yin ◽  
Chu Zhang ◽  
Cheng Peng ◽  
Yong He
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Low Cost ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Transgenic Maize ◽  
K Nearest Neighbor ◽  
Full Spectrum ◽  
Maize Plants ◽  
Learning Machine

The applicability of near infrared (NIR) spectroscopy combined with chemometrics was examined to develop fast, low-cost and non-destructive spectroscopic methods for classification of transgenic maize plants. The transgenic maize plants containing both cry1Ab/cry2Aj-G10evo proteins and their non-transgenic parent were measured in the NIR diffuse reflectance mode with the spectral range of 700–1900 nm. Three variable selection algorithms, including weighted regression coefficients, principal component analysis -loadings and second derivatives were used to extract sensitive wavelengths that contributed the most discrimination information for these genotypes. Five classification methods, including K-nearest neighbor, Soft Independent Modeling of Class Analogy, Naive Bayes Classifier, Extreme Learning Machine (ELM) and Radial Basis Function Neural Network were used to build discrimination models based on the preprocessed full spectra and sensitive wavelengths. The results demonstrated that ELM had the best performance of all methods, even though the model’s recognition ability decreased as the variables in the training of neural networks were reduced by using only the sensitive wavelengths. The ELM model calculated on the calibration set showed classification rates of 100% based on the full spectrum and 90.83% based on sensitive wavelengths. The NIR spectroscopy combined with chemometrics offers a powerful tool for evaluating large number of samples from maize hybrid performance trials and breeding programs.

scholarly journals HIGH ACCURACY RECOGNITION BIO METRICS BASED ON FINGER VEIN SCREENING SENSOR

2020 ◽  
Vol 3 (2) ◽  
pp. 35-46
Author(s):  
Shereen S. Jumaa ◽  
Khamis A. Zidan
Keyword(s):  
Nearest Neighbor ◽  
Low Cost ◽  
Computation Time ◽  
Recognition System ◽  
K Nearest Neighbor ◽  
Finger Print ◽  
Finger Vein ◽  
Vein Recognition ◽  
Histogram Of Gradients ◽  
Micro Lens

One of the safest biometrics of today is finger vein- but this technic  arises with some specific challenges, the most common  one being that the vein pattern is hard to extract because finger vein images are always low in quality, significantly  hampered the feature extraction and classification stages. Professional  algorithms want to be considered with the conventional hardware for capturing finger-vein images is  by using red Surface Mounted Diode (SMD) leds for this aim. For capturing images, Canon 750D camera with micro lens is used. For high quality images the integrated micro lens  is used, and with some adjustments it can also obtain finger print. Features extraction was used by a combination of Hierarchical Centroid and Histogram of Gradients. Results were evaluated with K Nearest Neighbor and Deep Neural Networks using 6 fold stratified cross validation. Results displayed improvement as compared to three latest benchmarks in this field that used 6-fold validation and SDUMLA-HMT. The work novelty is owing to the hardware design of the sensor within the finger-vein recognition system to obtain, simultaneously, highly secured recognition with low computation time ,finger vein and finger print at low cost, unlimited users for one device and open source.

scholarly journals EEG Motor-Imagery BCI System Based on Maximum Overlap Discrete Wavelet Transform (MODWT) and Machine learning algorithm

2021 ◽  
Vol 17 (2) ◽  
pp. 38-45
Author(s):  
Samaa Abdulwahab ◽  
Hussain Khleaf ◽  
Manal Jassim
Keyword(s):  
Wavelet Transform ◽  
Decision Tree ◽  
Discrete Wavelet Transform ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Low Cost ◽  
Brain Diseases ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Brain Connection

The ability of the human brain to communicate with its environment has become a reality through the use of a Brain-Computer Interface (BCI)-based mechanism. Electroencephalography (EEG) has gained popularity as a non-invasive way of brain connection. Traditionally, the devices were used in clinical settings to detect various brain diseases. However, as technology advances, companies such as Emotiv and NeuroSky are developing low-cost, easily portable EEG-based consumer-grade devices that can be used in various application domains such as gaming, education. This article discusses the parts in which the EEG has been applied and how it has proven beneficial for those with severe motor disorders, rehabilitation, and as a form of communicating with the outside world. This article examines the use of the SVM, k-NN, and decision tree algorithms to classify EEG signals. To minimize the complexity of the data, maximum overlap discrete wavelet transform (MODWT) is used to extract EEG features. The mean inside each window sample is calculated using the Sliding Window Technique. The vector machine (SVM), k-Nearest Neighbor, and optimize decision tree load the feature vectors.

scholarly journals Development of a Low-Cost Portable Electronic Nose for Cigarette Brands Identification

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4239
Author(s):  
Zhiyuan Wu ◽  
Hanying Zhang ◽  
Wentao Sun ◽  
Ning Lu ◽  
Meng Yan ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Low Cost ◽  
Visual Observation ◽  
K Nearest Neighbor ◽  
Sampling Device ◽  
Airflow Distribution ◽  
The Government ◽  
The Cost

In China, the government and the cigarette industry yearly lose millions in sales and tax revenue because of imitation cigarettes. Usually, visual observation is not enough to identify counterfeiting. An auxiliary analytical method is needed for cigarette brands identification. To this end, we developed a portable, low-cost electronic nose (e-nose) system for brand recognition of cigarettes. A gas sampling device was designed to reduce the influence caused by humidity fluctuation and the volatile organic compounds (VOCs) in the environment. To ensure the uniformity of airflow distribution, the structure of the sensing chamber was optimized by computational fluid dynamics (CFD) simulations. The e-nose system is compact, portable, and lightweight with only 15 cm in side length and the cost of the whole device is less than $100. Results from the machine learning algorithm showed that there were significant differences between 5 kinds of cigarettes we tested. Random Forest (RF) has the best performance with accuracy of 91.67% and K Nearest Neighbor (KNN) has the accuracy of 86.98%, which indicated that the e-nose was able to discriminate samples. We believe this portable, cheap, reliable e-nose system could be used as an auxiliary screen technique for counterfeit cigarettes.

scholarly journals A Hybrid Classification Approach That Combines K-Nearest Neighbor and Helps Vector Machine Will Provide Results That Are Closer to The True Value

2021 ◽  
pp. 572-580
Author(s):  
Rajni Bhalla ◽  
Jyoti
Keyword(s):  
Support Vector Machine ◽  
Classification System ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Classification Model ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Classification Approach ◽  
Knn Classification ◽  
Hybrid Classification

To construct a new text message classifier, this paper combines the K-nearest neighbor (KNN) classification approach with the support vector machine (SVM) training algorithm. The hybrid classification system is built by combining KNN and Support Vector Machine is abbreviated as K-VM. Due to its flexibility and reliability in handling different forms of classification activities, the KNN has been stated as one of the most frequently used classification approaches. The KNN faces a significant challenge in determining the acceptable value for parameter K to ensure good classification efficacy. This is because the value of parameter K has a significant effect on the KNN classifier's accuracy. The KNN is a method of learning that is based on laziness that holds the entire training examples before classification time, in addition to deciding the optimum value of parameter K. As a result, as the value of parameter K increases, the KNN's computational method becomes more intensive. This paper proposes the K-VM hybrid classification system to reduce the impact of parameters on classification accuracy. The Euclidean distance function is used to measure the average distance between the testing data point and each range in SVs in various categories. Experiments on a variety of benchmark datasets show that the K-VM approach outperforms the conventional KNN classification model in classification accuracy.

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