Sri Lanka faces drinking water related issues often due to many reasons like weather changes, floods, droughts, and tsunamis, affecting the drinking water availability. In such inst.nces, people find difficult to gath...
Sri Lanka faces drinking water related issues often due to many reasons like weather changes, floods, droughts, and tsunamis, affecting the drinking water availability. In such inst.nces, people find difficult to gather information regarding consumable drinking water availability. In order to facilitate the meeting of commercial water sellers and customers and to engage the general public and social services in addressing the drinking water problem, this study is focused on building both web and mobile applications. The web application was implemented with a selected set of functionalities such as social login, social sharing, geocoding, reverse geocoding, viewing, and filtering posts using Angular 6, *** web framework, and Leaflet. The mobile application was developed to direct the customers to the water availability location with a shortest path using the Dijkstra's algorithm. Case evaluation was conducted with the Rotaract Club members in Badulla and thinking aloud method was used to pick up the user's ideas while interacting with the system. It can be concluded that the created web and mobile applications would offer the public a clever way to draw attention to an issue with water availability, and the commercial water service providers are given a venue to market their services and locate potential customers.
In contrast to traditional online videos, live multi-streaming supports real-time social interactions between multiple streamers and viewers, such as donations. However, donation and multi-streaming channel recommenda...
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Personal Protective Equipment (PPE) regulations require construction workers to wear safety helmets to ensure site safety. However, monitoring PPE compliance consistently in fast-paced, dynamic construction environmen...
Personal Protective Equipment (PPE) regulations require construction workers to wear safety helmets to ensure site safety. However, monitoring PPE compliance consistently in fast-paced, dynamic construction environments poses a significant challenge. In response, we developed a sophisticated object detection system that automates the real-time verification of helmet use, thereby improving safety standards and reducing the likelihood of accidents. Extensive research was conducted to analyze all feasible algorithms that can be implemented in the safety helmet detection system and compare the proposed model with an existing one to ensure the proposed system can give high accuracy and high inference speed. Therefore, YOLOv5 was identified as the ideal choice in terms of accuracy and speed, and it was then enhanced with optimized transfer learning. We began our methodology by pre-training a comprehensive Kaggle dataset before refining the model using Roboflow on a specialized dataset. Using PyTorch and YOLOv5, we conducted exhaustive model training, testing, and evaluation. Our system achieved a lightning-fast inference speed of 39.8 milliseconds and a remarkable 91.4 percent accuracy in identifying helmet compliance. The implementation of such object detection technologies has the potential to significantly increase safety helmet compliance, thereby creating a safer environment for construction workers.
The first commercial products of 5G will be released within 2020 and therefore, it becomes an absolute necessity to research whether the key enabling technologies are advantageous for the operators to invest in. One o...
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Deep neural networks (DNN) have been widely used in many real-time artificial intelligent (AI) applications because of effective hardware accelerators. However, most present designs either suffer from high area cost o...
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Deep neural networks (DNN) have been widely used in many real-time artificial intelligent (AI) applications because of effective hardware accelerators. However, most present designs either suffer from high area cost or low hardware usage. This paper presents a design of a digital logic accelerator (DLA) for use in PBs (processing block) of an opto-electrical neural network (OENN). The proposed DLA uses processing elements that detects underflow and overflow. Besides, it also increased the processing time to resolve the timing problems. The details of the design together with post-layout simulations are presented in this paper. The DLA is implemented using a typical 40-nm CMOS process. It showed a performance result of 51.2 GOPS and the power consumption is 91.3 mW at 125 MHz.
Gestures constitute an important form of nonverbal communication where bodily actions are used for delivering messages alone or in parallel with spoken words. Recently, there exists an emerging trend of WiFi sensing e...
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This study evaluates ARIMA, Facebook Prophet and a new boosting algorithm framework known as ThymeBoost for time series prediction of monthly precipitation of Belagavi district (semi-arid) in Karnataka. The dataset wa...
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This study evaluates ARIMA, Facebook Prophet and a new boosting algorithm framework known as ThymeBoost for time series prediction of monthly precipitation of Belagavi district (semi-arid) in Karnataka. The dataset was divided into three periods (1901–2002, 1951- 2002, and 1971 - 2002). The first 70% of the data for each period was applied for training while the rest for testing. Also, the datasets were used in two different forms for both training and testing. In the first set, raw data was used as it is, and the second set of data was used after normalizing the time series using the min-max concept (between 0 and 1). However, the normalized data were de-normalized for each period for performance metrics estimation. ThymeBoost is the best model for the first period of raw data and the second period of normalized data. In contrast, Prophet outperforms all other models for the normalized data in terms of all four measures. For the second period of raw data, no model emerged as the best model in terms of all performance metrics. Therefore, all three models performed similarly for the third period of raw and normalized data.
Segmentation of the spinal tissues on MRI is the basis for quantitative analyses, but time-consuming if done manually. In this work, we construct a pipeline for automatic vertebrae segmentation from T2w MRI scans, ass...
Segmentation of the spinal tissues on MRI is the basis for quantitative analyses, but time-consuming if done manually. In this work, we construct a pipeline for automatic vertebrae segmentation from T2w MRI scans, assessing performance and generalizability by external validation. Our study used 15 scans from one site (Haukeland University Hospital, HUH) and 10 scans from another (Sahlgrenska University Hospital, SUH). MRI experts manually delineated the vertebral bodies Th12-L5 on all the HUH data and a subset of six scans from SUH. We trained multiple convolutional neural networks, assessing the performance in an experimental design tailored to small-data contexts and also on external data. Our best model achieved a mean Dice score of 0.899. This is comparable to results in the literature, but our system required much less training data. 1 .
Ahstract-A sensor based on a balloon-like interferometer and a spring-shaped structure for micro curvature measurement is proposed and experimentally demonstrated. The sensor is composed by singlemode fiber inserted i...
Ahstract-A sensor based on a balloon-like interferometer and a spring-shaped structure for micro curvature measurement is proposed and experimentally demonstrated. The sensor is composed by singlemode fiber inserted into a capillary tube. The experimental results show micro-curvature sensitivities of -35.04 $\text{pm}/\mu \mathrm{m}$ , -28.07 $\text{pm}/\mu \mathrm{m} \mathrm{e}-18.7 \text{pn}/\mu \mathrm{m}$ in the range from 0 to 200 $\mu \mathrm{m}$ for three resonants dips $\lambda_{1}, \lambda_{2}$ and $\lambda_{3}$ , respectively. In addition, the sensor has advantages of easy fabrication, low cost, and satisfactory sensitivity, which shows good results of sensing of micro curvature in some applications.
A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine ...
A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis’s results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided.
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