Hasil untuk "Information technology"

B. Avolio, S. Kahai, George E. Dodge

Abdul Khair, Bambang Widjanarko Otok, Noraida Noraida et al.

Tanah Bumbu Regency has the highest rate of malaria in South Kalimantan Province. Due to the non-linear fluctuations in malaria cases by ethnicity, a hybrid model combining Autoregressive Integrated Moving Average (MARS ARIMA) and Multivariate Adaptive Regression Splines was proposed for time series forecasting. The purpose of this study is to use the MARS ARIMA hybrid model to predict malaria cases by ethnicity in Tanah Bumbu Regency. The findings demonstrate that the best inputs for MARS modeling are significant lags found using ACF and PACF. The hybrid MARS ARIMA model performs better than standalone ARIMA or MARS models, according to predictions. Key findings show that the number of patients over 35 during the preceding two periods influences increases in malaria cases for the Banjar ethnic group. Cases exceeding 13 in two prior periods and 19 in one prior period are associated with increases for the Javanese group. Cases of more than two or fewer than two in the preceding two periods and more than eleven in one preceding period have an impact on increases among the Bugis. Prior cases below 26 have an impact on Banjar case declines, whereas prior cases below 13 and above 3 have a significant impact on Javanese case declines. This study demonstrates how well the MARS ARIMA hybrid model predicts malaria cases according to ethnicity.

E. Tilley, C. Lüthi, A. Morel et al.

V. Bellotti, A. Sellen

R. Nagaraja

Zeyu Ma, Jianhui Cui, Zhimin Liu et al.

The BeiDou Navigation Satellite System (BDS) has developed rapidly, and the combination of BDS Phase II (BDS-2) and BDS Phase III (BDS-3) has attracted wide attention. It is found that there are code ISBs between BDS-2 and BDS-3, which may have a certain impact on the BDS-2 and BDS-3 combined positioning. This paper focuses on the performance of BDS-2/BDS-3 combined B1I single-frequency pseudorange positioning and investigates the positioning performance with and without code ISBs correction for different types of receivers, include geodetic GNSS receivers and low-cost receivers. The results show the following: (1) For geodetic GNSS receivers, the code ISBs of each receiver is about −0.3 m to −0.8 m, and the position deviation is reduced by 7% after correcting code ISBs. The code ISBs in the baseline with homogeneous receivers has a little influence on the positioning result, which can be ignored. The code ISBs in the baseline with heterogeneous receivers is about −0.5 m, and the position deviation is reduced by 4% after correcting code ISBs. (2) The code ISBs in the low-cost receivers are significantly larger than those in the geodetic GNSS receivers, and the impact on the positioning performance of the low-cost receivers is significantly greater than that on the geodetic GNSS receivers. After correcting the code ISBs, the position deviation of low-cost receivers can be reduced by around 12% for both undifferenced and differenced modes. (3) For low-cost receivers, correcting the code ISBs can increase the number of epochs successfully solved, which effectively improves the low-cost navigation and positioning performance. (4) The carrier-phase-smoothing method can effectively reduce the distribution dispersion of code ISBs and make the estimation of ISBs more accurate. The STD values of estimated code ISBs in geodetic GNSS receivers are reduced by about 40% after carrier-phase smoothing, while the corresponding values are reduced by about 7% in low-cost receivers due to their poor carrier-phase observation quality.

Di ZHU, Fulai WANG, Chen PANG et al.

Distinguishing between ships and corner reflectors is challenging in radar observations of the sea. Traditional identification methods, including high resolution range profiles, polarization decomposition, and polarization modulation, improve radial range resolution to the target by transmitting signals with a large bandwidth. The latter two methods use polarization to improve target identification. Single-carrier pulse signals, often used in civil marine radars owing to their low hardware cost, pose challenges in identifying ships and corner reflectors owing to their low range resolution and pulse compression gain. This article proposes a novel method for identifying ships and corner reflectors using polarization modulation in civil marine radars. This approach aims to fully exploit the target identification potential of the narrowband signal joint polarization modulation technology. Through constructing the polarization-range 2D images, the method differentiates between ships and corner reflectors through their unique polarization scattering characteristics. The process involves calculating the average Pearson correlation coefficient between each polarization image and the range image, which serves as the correlation feature parameter. A support vector machine is then employed to achieve accurate target identification. Electromagnetic simulations show that by increasing the device bandwidth to 2～6 times the original signal bandwidth (2 MHz), civil marine radar can achieve a comprehensive identification rate of 90.18%～92.31% at a Signal to Noise Ratio (SNR) of 15 dB and a sampling rate of 100 MHz. The study also explores the influence of missing 50% of pitch angle and azimuth angle data in the training set, finding that identification rates in all four cases exceed 85% when the SNR is above 15 dB. Comparisons with the polarization decomposition method under the same narrowband observation conditions show that when the SNR is 15 dB or higher and the device bandwidth is increased sixfold, the average identification rate of the proposed method improves by 22.67%. This strongly supports the effectiveness of the proposed method. In addition, two cases with different polarization scattering characteristics are constructed in the anechoic chamber using dihedral and trihedral setups. Five sets of measured data show that when the SNR of the echo is 8～12 dB, the experiments demonstrate strong intra-class aggregation and clear inter-class separability. These results effectively support the electromagnetic simulation findings.

Dalė Dzemydienė, Aurelija Burinskienė, Kristina Čižiūnienė et al.

The problems of developing intelligent service provision systems face difficulties in the representation of dynamic aspects of cargo transportation processes and integration of different and heterogeneous ICT components to support the systems’ necessary functionality. This research aims to develop the architecture of the e-service provision system that can help in traffic management, coordination of works at trans-shipment terminals, and provide intellectual service support during intermodal transportation cycles. The objectives concern the secure application of the Internet of Things (IoT) technology and wireless sensor networks (WSNs) to monitor transport objects and context data recognition. The means for safety recognition of moving objects by integrating them with the infrastructure of IoT and WSNs are proposed. The architecture of the construction of the e-service provision system is proposed. The algorithms of identification, authentication, and safety connection of moving objects into an IoT platform are developed. The solution of application of blockchain mechanisms for the identification of stages of identification of moving objects is described by analysing ground transport. The methodology combines a multi-layered analysis of intermodal transportation with extensional mechanisms of identification of objects and methods of synchronization of interactions between various components. Adaptable e-service provision system architecture properties are validated during the experiments with NetSIM network modelling laboratory equipment and show their usability.

Sundaravadivazhagan Balasubaramanian, Robin Cyriac, Sahana Roshan et al.

The COVID-19 pandemic has been infecting the entire world over the past years. To prevent the spread of COVID-19, people have acclimatised to the new normal, which includes working from home, communicating online, and maintaining personal cleanliness. There are numerous tools required to prepare to compact transmissions in the future. One of these elements for protecting individuals from fatal virus transmission is the mask. Studies have indicated that wearing a mask may help to reduce the risk of viral transmission of all kinds. It causes many public places to take efforts to ensure that its guests wear adequate face masks and keep a safe distance from one another. Screening systems need to be installed at the doors of businesses, schools, government buildings, private offices, and/or other important areas. A variety of face detection models have been designed using various algorithms and techniques. Most of the articles in the previously published research have not worked on dimensionality reduction in conjunction with depth-wise separable neural networks. The necessity of determining the identities of people who do not cover their faces when they are in public is the driving factor for the development of this methodology. This research work proposes a deep learning technique to determine if a person is wearing mask or not and identifies whether it is properly worn or not. Stacked Auto Encoder (SAE) technique is implemented by stacking the following components: Principal Component Analysis (PCA) and Depth-wise Separable Convolutional Neural Network (DWSC-NN). PCA is used to reduce the irrelevant features in the images and resulted high true positive rate in the detection of mask. We achieved an accuracy score of 94.16% and an F1 score of 96.009% by the application of the method described in this research.

Fangwen Yang, Pengfei He, Haiyong Ding et al.

Net primary productivity (NPP), as an indicator of ecological functioning, plays an important role in regional and global carbon cycles. Although many studies have estimated the NPP of vegetation on the Qinghai Plateau (QP), the existing NPP datasets over the QP are either of low spatial resolution or limited-duration time-series. These shortcomings restrict our ability to explore the spatial distribution and long-term trends of NPP at a finer scale. To address this gap, we present a new monthly NPP dataset (QP_NPP30) at a high spatial resolution (30 m) over the QP for the period 1987&#x2013;2021. We constructed this dataset using the Carnegie-Ames-Stanford-Approach (CASA) model and multisource data, including reconstructed normalized difference vegetation index (NDVI) data, reanalysis data, land cover, and other ancillary data. To reconstruct the NDVI, a harmonic regression model based on the Google Earth Engine (GEE) was applied to the NDVI time series data. Statistical analysis of QP_NPP30 showed that the NPP in the QP has increased over the past 35 years (0.92 <inline-formula><tex-math notation="LaTeX">$g C/m^{2}/yr$</tex-math></inline-formula>). Furthermore, we found that NPP is concentrated in June, July, and August, accounting for approximately 73&#x0025; of the annual total. To validate our dataset, we compared it with measured NPP and with the MODIS NPP product (MOD-NPP). Our results demonstrated that QP_NPP30 has similar spatial patterns to MOD-NPP, but offers richer spatial detail. Specifically, QP_NPP30 has a higher accuracy than MOD-NPP, by comparing with the measured data (r &#x003D; 0.695, RMSE &#x003D; 132.823 <inline-formula><tex-math notation="LaTeX">$g C/m^{2}/yr$</tex-math></inline-formula> for QP_NPP30; r &#x003D; 0.328, RMSE &#x003D; 158.586 <inline-formula><tex-math notation="LaTeX">$g C/m^{2}/yr$</tex-math></inline-formula> for MOD-NPP).

D. Lubin Balasubramanian, V. Govindasamy

A wireless sensor network (WSN) encompasses a huge set of sensor nodes employed to collect data and transmit it to a base station (BS). Due to its compact, inexpensive, and scalable nature of sensors, WSN finds its applicability in diverse real-time applications. The battery-operated sensor nodes necessitate the design of a multi-hop routing protocol for the effective utilization of available energy in the network. Routing can be considered an optimization problem and can be solved by the design of bioinspired algorithms. This study introduces an improved deer hunting optimization-enabled multihop routing (IDHO-MHR) protocol for WSN. The major intention of the IDHO-MHR approach is to optimally find the routes to the destination in WSN. The IDHO algorithm is initially derived by the incorporation of the Nelder Mead (NM) concept into the traditional DHO algorithm. In addition, the IDHO-MHR technique primarily derives a fitness function with the inclusion of two major variables, namely residual energy (RE) and distance. The nodes with higher RE and minimum distance have the probability of becoming optimal routes from the networks. The performance validation of the IDHO-MHR approach is performed, and the outcomes are inspected in various aspects. The experimental outcomes reported the supremacy of the IDHO-MHR protocol over the other recent approaches.

Hendra Mayatopani, Nurdiana Handayani, Ri Sabti Septarini et al.

Wild plants or weeds often become enemies or disturb the main cultivated plants. In its development, wild plants or weeds actually have ingredients that are beneficial to the body and can be used as medicine. However, many people still need knowledge about the types of weed plants that have medicinal properties, especially the leaves. The purpose of this research is to classify the image of weed leaves with medicinal properties based on color and texture characteristics with an artificial neural network using a Self-Organizing Map (SOM). To improve information in feature extraction, RGB and HSV color features are used as well as texture features with Gray Level Co-occurrence Matrix (GLCM). Furthermore, the results of feature extraction will be identified as groups or classes with the Self-Organizing Map (SOM) algorithm which divides the input pattern into several groups so that the network output is in the form of a group that is most similar to the input provided. The test produces a precision value of 91.11%, a recall value of 88.17% and an accuracy value of 89.44%. The results of the accuracy of the SOM model for image classification on medicinal weed leaves are in the good category.

Sesenlo Kath, Ruokuovilie Mezhatsu

The agricultural extension services in the North East region of India are not only hindered by limited resources and scarcity of trained staff at state & regional level but also by the remoteness of the villages. Many villages remain inaccessible particularly during monsoon, due to poor road connectivity. The major technology dissemination approach adopted so far had been the traditional direct interaction and field level practical demonstration at the community level. The facility of toll-free modern smart phone based information and communication technology (ICT) service has been started and is gaining momentum. An attempt has been made to collect the data from 200 farmers of Tseminyu district of Nagaland State based on proportionate random sampling (PPS) technique to know the impact of mobile based extension agro advisory services in the region. Majority of the farmer respondents had perceived ‘yield increase’ , and ‘information of new agricultural technology’ as the major benefits of using the mobile -based agro- advisory services.

Lifu Chen, Xingmin Cai, Jin Xing et al.

Water detection from SAR imagery has significant values, such as the flood monitoring and environmental protection. Nowadays, significant progress has been achieved in water detection using deep neural network (DNN) methods, but the blackbox behavior incurs many doubts in the performance of deep learning techniques, which undermines its trustworthiness in water detection from SAR imagery. By integrating SAR domain knowledge, DNN and eXplainable Artificial Intelligence (XAI), an explainable DNN framework for surface water detection is proposed for the first time. This framework includes three parts: the water extraction network containing four backbone networks, the Local and Global Mixed Attribution (LGMA) module for performance evaluation of backbone network, and the Semantic Specific-class Activation Mapping (SSAM) module, which performs geo-visualization for the output layers of high-level features. In the experiment, SAR images from different resolutions and frequency-bands are utilized, which are from millimeter-wave and Sentinel-1 systems. The attribution maps and heatmaps of four backbone networks are assessed towards the final water extraction results. The experiment indicates that the proposed framework can glass-box the decision-making process of DNN in water detection and offer corresponding attribution analytics for given input SAR imagery. This work encourages other scholars to conduct extensive research on the explanation of DNN in SAR domain, gradually establish the trustworthiness of DNN, and promote the development of DNN in SAR images analytics.

Nirmal Choudhary, Henning Clever, Robert Ludwigs et al.

The increasing global demand for high‐quality and low‐cost battery electrodes poses major challenges for battery cell production. As mechanical defects on the electrode sheets have an impact on the cell performance and their lifetime, inline quality control during electrode production is of high importance. Correlation of detected defects with process parameters provides the basis for optimization of the production process and thus enables long‐term reduction of reject rates, shortening of the production ramp‐up phase, and maximization of equipment availability. To enable automatic detection of visually detectable defects on electrode sheets passing through the process steps at a speed of 9 m s−1, a You‐Only‐Look‐Once architecture (YOLO architecture) for the identification of visual detectable defects on coated electrode sheets is demonstrated within this work. The ability of the quality assurance (QA) system developed herein to detect mechanical defects in real time is validated by an exemplary integration of the architecture into the electrode manufacturing process chain at the Battery Lab Factory Braunschweig.

Firdews A.Alsalman, Shler Khorshid, Amira Sallow

Machine learning and deep learning algorithms have become increasingly important in the medical field, especially for diagnosing disease using medical databases. Techniques developed within these two fields are now used to classify different diseases. Although the number of Machine Learning algorithms is vast and increasing, the number of frameworks and libraries that implement them is also vast and growing.&nbsp; TensorFlow is a well-known machine learning library that has been used by several researchers in the field of disease classification. With the help of TensorFlow (Google's framework),&nbsp;a complex calculation can be addressed effectively by modeling it as a graph and properly mapping the graph segments to the machine in the form of a cluster. In this review paper, the role of the TensorFlow-Python framework- for disease classification is discussed.

Michael B. Eisenberg

Information literacy (IL) is the set of skills and knowledge that allows us to find, evaluate, and use the information we need, as well as to filter out the information we don’t need. IL skills are the necessary tools that help us successfully navigate the present and future landscape of information. Information and technology affects every person in every possible setting—work, education, recreation. This paper offers an overview of IL focusing on three contexts for successful IL learning and teaching: (i) the information process itself, (ii) technology in context, and (iii) implementation through real needs in real situations. The author covers conceptual understandings of IL, the range of IL standards and models, technology within the IL framework, and practical strategies for effective IL skills learning and instruction in a range of situations. http://dx.doi.org/10.14429/djlit.28.2.166

Wuxiang Shi, Wuxiang Shi, Yurong Li et al.

Patellofemoral pain syndrome (PFPS) is a common disease of the knee. Despite its high incidence rate, its specific cause remains unclear. The artificial neural network model can be used for computer-aided diagnosis. Traditional diagnostic methods usually only consider a single factor. However, PFPS involves different biomechanical characteristics of the lower limbs. Thus, multiple biomechanical characteristics must be considered in the neural network model. The data distribution between different characteristic dimensions is different. Thus, preprocessing is necessary to make the different characteristic dimensions comparable. However, a general rule to follow in the selection of biomechanical data preprocessing methods is lacking, and different preprocessing methods have their own advantages and disadvantages. Therefore, this paper proposes a multi-input convolutional neural network (MI-CNN) method that uses two input channels to mine the information of lower limb biomechanical data from two mainstream data preprocessing methods (standardization and normalization) to diagnose PFPS. Data were augmented by horizontally flipping the multi-dimensional time-series signal to prevent network overfitting and improve model accuracy. The proposed method was tested on the walking and running datasets of 41 subjects (26 patients with PFPS and 15 pain-free controls). Three joint angles of the lower limbs and surface electromyography signals of seven muscles around the knee joint were used as input. MI-CNN was used to automatically extract features to classify patients with PFPS and pain-free controls. Compared with the traditional single-input convolutional neural network (SI-CNN) model and previous methods, the proposed MI-CNN method achieved a higher detection sensitivity of 97.6%, a specificity of 76.0%, and an accuracy of 89.0% on the running dataset. The accuracy of SI-CNN in the running dataset was about 82.5%. The results prove that combining the appropriate neural network model and biomechanical analysis can establish an accurate, convenient, and real-time auxiliary diagnosis system for PFPS to prevent misdiagnosis.

Jin Zhu, Dayu Cheng, Weiwei Zhang et al.

People spend more than 80% of their time in indoor spaces, such as shopping malls and office buildings. Indoor trajectories collected by indoor positioning devices, such as WiFi and Bluetooth devices, can reflect human movement behaviors in indoor spaces. Insightful indoor movement patterns can be discovered from indoor trajectories using various clustering methods. These methods are based on a measure that reflects the degree of similarity between indoor trajectories. Researchers have proposed many trajectory similarity measures. However, existing trajectory similarity measures ignore the indoor movement constraints imposed by the indoor space and the characteristics of indoor positioning sensors, which leads to an inaccurate measure of indoor trajectory similarity. Additionally, most of these works focus on the spatial and temporal dimensions of trajectories and pay less attention to indoor semantic information. Integrating indoor semantic information such as the indoor point of interest into the indoor trajectory similarity measurement is beneficial to discovering pedestrians having similar intentions. In this paper, we propose an accurate and reasonable indoor trajectory similarity measure called the indoor semantic trajectory similarity measure (ISTSM), which considers the features of indoor trajectories and indoor semantic information simultaneously. The ISTSM is modified from the edit distance that is a measure of the distance between string sequences. The key component of the ISTSM is an indoor navigation graph that is transformed from an indoor floor plan representing the indoor space for computing accurate indoor walking distances. The indoor walking distances and indoor semantic information are fused into the edit distance seamlessly. The ISTSM is evaluated using a synthetic dataset and real dataset for a shopping mall. The experiment with the synthetic dataset reveals that the ISTSM is more accurate and reasonable than three other popular trajectory similarities, namely the longest common subsequence (LCSS), edit distance on real sequence (EDR), and the multidimensional similarity measure (MSM). The case study of a shopping mall shows that the ISTSM effectively reveals customer movement patterns of indoor customers.

Hung-Pin Shih