Customer Network Research Articles

PEMANFAATAN HANDPHONE PADA USAHA MIKRO, KECIL, DAN MENENGAH DALAM MENINGKATKAN OMSET DI KELURAHAN ANGGUT DALAM KOTA BENGKULU

Micro, Small and Medium Enterprises (MSMEs) are the largest group of economic players in the economy, can create many entrepreneurs and open up many job opportunities which can become the business sector that contributes the most to economic development. In an effort to increase the productivity and efficiency of MSMEs, they can take advantage of Mobile Technology effectively to have a direct influence on MSMEs. The positive influence on MSMEs can enjoy the various benefits of using mobile phones. Where MSMEs can communicate quickly, increase productivity, build new business opportunities, and they can also connect to a wider global network. The use of mobile phones can be done using social media to improve service quality and expand customer networks. Mobile can also help MSMEs in saving operational costs. The use of mobile phones at MSMEs is currently supported by the availability of infrastructure such as internet connections at competitive prices between operators and internet providers, as well as security in transactions through communication networks. Facing an increasingly open and competitive market mechanism, market control is a prerequisite for increasing competitiveness, one of which can be done by utilizing the use of mobile phones in Micro, Small and Medium Enterprises (MSMEs).

COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images.

The world is still struggling in controlling and containing the spread of the COVID-19 pandemic caused by the SARS-CoV-2 virus. The medical conditions associated with SARS-CoV-2 infections have resulted in a surge in the number of patients at clinics and hospitals, leading to a significantly increased strain on healthcare resources. As such, an important part of managing and handling patients with SARS-CoV-2 infections within the clinical workflow is severity assessment, which is often conducted with the use of chest X-ray (CXR) images. In this work, we introduce COVID-Net CXR-S, a convolutional neural network for predicting the airspace severity of a SARS-CoV-2 positive patient based on a CXR image of the patient’s chest. More specifically, we leveraged transfer learning to transfer representational knowledge gained from over 16,000 CXR images from a multinational cohort of over 15,000 SARS-CoV-2 positive and negative patient cases into a custom network architecture for severity assessment. Experimental results using the RSNA RICORD dataset showed that the proposed COVID-Net CXR-S has potential to be a powerful tool for computer-aided severity assessment of CXR images of COVID-19 positive patients. Furthermore, radiologist validation on select cases by two board-certified radiologists with over 10 and 19 years of experience, respectively, showed consistency between radiologist interpretation and critical factors leveraged by COVID-Net CXR-S for severity assessment. While not a production-ready solution, the ultimate goal for the open source release of COVID-Net CXR-S is to act as a catalyst for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative new clinical decision support solutions for helping clinicians around the world manage the continuing pandemic.

Deep Learning Neural Network Algorithm for Computation of SPICE Transient Simulation of Nonlinear Time Dependent Circuits

In this paper, a special method based on the neural network is presented, which is conveniently used to precompute the steps of numerical integration. This method approximates the behaviour of the numerical integrator with respect to the local truncation error. In other words, it allows the precomputation of the individual steps in such a way that they do not need to be estimated by an algorithm but can be directly estimated by a neural network. Experimental tests were performed on a series of electrical circuits with different component parameters. The method was tested for two integration methods implemented in the simulation program SPICE (Trapez and Gear). For each type of circuit, a custom network was trained. Experimental simulations showed that for well-defined problems with a sufficiently trained network, the method allows in most cases reducing the total number of iteration steps performed by the algorithm during the simulation computation. Applications of this method, drawbacks, and possible further optimizations are also discussed.

Study of the impact of the COVID-19 pandemic on international air transportation

Time Series Forecasting has always been a very important area of research in many domains because many different types of data are stored as time series. Given the growing availability of data and computing power in the recent years, Deep Learning has become a fundamental part of the new generation of Time Series Forecasting models, obtaining excellent results.As different time series problems are studied in many different fields, a large number of new architectures have been developed in recent years. This has also been simplified by the growing availability of open source frameworks, which make the development of new custom network components easier and faster.In this paper three different Deep Learning Architecture for Time Series Forecasting are presented: Recurrent Neural Networks (RNNs), that are the most classical and used architecture for Time Series Forecasting problems; Long Short-Term Memory (LSTM), that are an evolution of RNNs developed in order to overcome the vanishing gradient problem; Gated Recurrent Unit (GRU), that are another evolution of RNNs, similar to LSTM.The article is devoted to modeling and forecasting the cost of international air transportation in a pandemic using deep learning methods. The author builds time series models of the American Airlines (AAL) stock prices for a selected period using LSTM, GRU, RNN recurrent neural networks models and compare the accuracy forecast results.

ONE STOP KITCHEN: managing a multi-brand cloud kitchen

Subject area Marketing; Business Expansion Strategy Case synopsis Rahul Hazarika, the owner of One Stop Kitchen (OSK), was convinced that expansion of his cloud kitchen had to be aligned at various fronts to compete. He was juggling with thoughts of expanding from B2C to B2B model, whether to expand in the same city or other cities or to introduce new variants in existing B2C so as to use and leverage the existing customer base and network. The competition was growing ablaze, and most of the orders were procured from the third-party aggregators, leaving little chance for OSK to connect to its customer base directly. The growth potential in this upcoming sector was undeniable, and Hazarika was continually trying to bring in relevant business practices to maintain the momentum. The case deals growing cloud kitchen business problems and covers business concepts such as business expansion strategy, product profiling and marketing strategies. Learning objectives Students will understand strategies for a Online startup Business in the Cloud Kitchen category. The concept of Product Profiling is also dealt with in the case. Complexity academic level Suitable for PG- and Executive-level courses. Supplementary materials Teaching notes are available for educators only. Subject code CSS 8: Marketing.

An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.

Mathematical model of referral marketing strategy

A marketing strategy is an attempt to campaign for a product so that information about the product becomes viral. Advertising strategy began to be combined by involving customers to campaign for the company’s products. This marketing strategy is known as the referral marketing strategy. Viral information in a community can be analogous to the pandemic condition of a disease. This study aims to examine the relationship between the epidemiological model and the marketing model with a referral strategy. This research is a literature study with the results of a marketing model design with a referral strategy. Based on the research, there are 4 groups in marketing modeling with a referral strategy, namely Unaware, Potential Broadcaster, Broadcaster, and Inert. The epidemiological model that is suitable for this condition is the SEIR model. Adjustments to the SEIR Model need to be made because the dynamics of the Unaware can go directly to the Inert because of the trust factor in marketing information. The simulation results show that the parameters that have an important role in making information viral are the customer network and the incubation time of the information becoming viral.

Computational Biological Modeling Identifies PD-(L)1 Immunotherapy Sensitivity Among Molecular Subgroups of KRAS-Mutated Non-Small-Cell Lung Cancer.

KRAS-mutated (KRASMUT) non-small-cell lung cancer (NSCLC) is emerging as a heterogeneous disease defined by comutations, which may confer differential benefit to PD-(L)1 immunotherapy. In this study, we leveraged computational biological modeling (CBM) of tumor genomic data to identify PD-(L)1 immunotherapy sensitivity among KRASMUT NSCLC molecular subgroups. In this multicohort retrospective analysis, the genotype clustering frequency ranked method was used for molecular clustering of tumor genomic data from 776 patients with KRASMUT NSCLC. These genomic data were input into the CBM, in which customized protein networks were characterized for each tumor. The CBM evaluated sensitivity to PD-(L)1 immunotherapy using three metrics: programmed death-ligand 1 expression, dendritic cell infiltration index (nine chemokine markers), and immunosuppressive biomarker expression index (14 markers). Genotype clustering identified eight molecular subgroups and the CBM characterized their shared cancer pathway characteristics: KRASMUT/TP53MUT, KRASMUT/CDKN2A/B/CMUT, KRASMUT/STK11MUT, KRASMUT/KEAP1MUT, KRASMUT/STK11MUT/KEAP1MUT, KRASMUT/PIK3CAMUT, KRAS MUT/ATMMUT, and KRASMUT without comutation. CBM identified PD-(L)1 immunotherapy sensitivity in the KRASMUT/TP53MUT, KRASMUT/PIK3CAMUT, and KRASMUT alone subgroups and resistance in the KEAP1MUT containing subgroups. There was insufficient genomic information to elucidate PD-(L)1 immunotherapy sensitivity by the CBM in the KRASMUT/CDKN2A/B/CMUT, KRASMUT/STK11MUT, and KRASMUT/ATMMUT subgroups. In an exploratory clinical cohort of 34 patients with advanced KRASMUT NSCLC treated with PD-(L)1 immunotherapy, the CBM-assessed overall survival correlated well with actual overall survival (r = 0.80, P < .001). CBM identified distinct PD-(L)1 immunotherapy sensitivity among molecular subgroups of KRASMUT NSCLC, in line with previous literature. These data provide proof-of-concept that computational modeling of tumor genomics could be used to expand on hypotheses from clinical observations of patients receiving PD-(L)1 immunotherapy and suggest mechanisms that underlie PD-(L)1 immunotherapy sensitivity.

Centroid-based Distance Loss Function for Lamina Segmentation in 3D Ultrasound Spine Volumes.

Ultrasound imaging of the spine to diagnose the severity of scoliosis is a recent development in the field, offering 3D information that does not require a complicated procedure of reconstruction, unlike with radiography. Determining the severity of scoliosis on ultrasound volumes requires labelling vertebral features called laminae. To increase accuracy and reduce time spent on this task, this paper reported a novel custom centroid-based distance loss function for lamina segmentation in 3D ultrasound volumes, using convolutional neural networks (CNN). A comparison between the custom and two standard loss functions was performed by fitting a CNN with each loss function. The results showed that the custom loss network performed the best in terms of minimization of the distances between the centroids in the ground truth and the centroids in the predicted segmentation. On average, the custom network improved on the total distance between predicted and true centroids by 33 voxels (22%) when compared with the second best performing network, which used the Dice loss. In general, this novel custom loss function allowed the network to detect two more laminae on average in the lumbar region of the spine that the other networks tended to miss.

One-dimensional convolutional neural network and hybrid deep-learning paradigm for classification of specific language impaired children using their speech

Background and objectiveScreening children for communicational disorders such as specific language impairment (SLI) is always challenging as it requires clinicians to follow a series of steps to evaluate the subjects. Artificial intelligence and computer-aided diagnosis have supported health professionals in making swift and error-free decisions about the neurodevelopmental state of children vis-à-vis language comprehension and production. Past studies have claimed that typical developing (TD) and SLI children show distinct vocal characteristics that can serve as discriminating facets between them. The objective of this study is to group children in SLI or TD categories by processing their raw speech signals using two proposed approaches: a customized convolutional neural network (CNN) model and a hybrid deep-learning framework where CNN is combined with long-short-term-memory (LSTM). MethodWe considered a publicly available speech database of SLI and typical children of Czech accents for this study. The convolution filters in both the proposed CNN and hybrid models (CNN-LSTM) estimated fuzzy-automated features from the speech utterance. We performed the experiments in five separate sessions. Data augmentations were performed in each of those sessions to enhance the training strength. ResultsOur hybrid model exhibited a perfect 100% accuracy and F-measure for almost all the session-trials compared to CNN alone which achieved an average accuracy close to 90% and F-measure ≥ 92%. The models have further illustrated their robust classification essences by securing values of reliability indexes over 90%. ConclusionThe results confirm the effectiveness of proposed approaches for the detection of SLI in children using their raw speech signals. Both the models do not require any dedicated feature extraction unit for their operations. The models may also be suitable for screening SLI and other neurodevelopmental disorders in children of different linguistic accents.

User-entrepreneurship and innovation: does customer involvement and learning orientation matter?

PurposeThis paper aims to investigate the process of innovation in firms founded by user-entrepreneurs. It also empirically investigates the role of customer involvement and user-entrepreneurs’ learning goal orientation in the innovation process.Design/methodology/approachA survey design is used to collect data from entrepreneurs managing small businesses. A total of 255 entrepreneurs responded to the survey questionnaire. The partial least square structural equation model was used to test the measurement and structural model.FindingsResults suggest a positive association of user-entrepreneurship with innovation and customer involvement. Results also confirmed that customer involvement mediates the relationship between user-entrepreneurship and innovation. In addition, results suggest that the higher learning orientation (LO) of user-entrepreneurs plays a vital role in innovation by strengthening the relationship between user-entrepreneurship and customer involvement.Research limitations/implicationsThe study focuses on small entrepreneurial firms with less than equal to or less than 250 employees. The results may not be generalizable to larger user-entrepreneurial firms. Also, this study is based on American entrepreneurs. Therefore, the results may not be generalizable to other countries.Practical implicationsEvidence for the role of customer involvement and LO in the innovation process can be used by entrepreneurs and small business owners in hiring and training decisions. Also, the findings suggest the important role played by customers in the innovation process. Firms can use this insight to involve their customers in the product development process to secure better innovation outcomes.Originality/valueThis study contributes to the innovation and entrepreneurship literature by emphasizing the critical role of customer networks in user-entrepreneurs’ innovation performance. It offers a process model that offers empirical evidence supporting the positive role of customer involvement in new ventures. It highlights the role of the LO of user-entrepreneurs in the customer engagement process.

Identification of Delighters and Frustrators in Vegan-friendly Restaurant Experiences via Semantic Network Analysis: Evidence from Online Reviews

ABSTRACT The purpose of this paper is to provide an understanding of vegan-friendly restaurant customers’ experiences by investigating the “delighters” and “frustrators” characteristics. A qualitative approach is used to examine the semantic networks of vegan-friendly restaurant customers’ online reviews. Results indicate that salient factors which delight vegan-friendly restaurant customers relate to the attributes of vegan food and menus, social interaction, and unique characteristics of a vegan-friendly restaurant. By contrast, factors that frustrate vegan-friendly restaurant customers associate with perceived overpriced vegan foods, poor restaurant staff attitudes/negative service encounters, and hygiene issues. The results of this study have academic and practical implications through the use of an attribute performance model to understand the experiences of vegan restaurant customers. This research is an initial empirical attempt to thoroughly understand vegan-friendly restaurant experiences using big data.

Information matching model and multi-angle tracking algorithm for loan loss-linking customers based on the family mobile social-contact big data network

This article focuses on the tracking problem of loan loss-linking customers based on the family mobile social-contact big data network. By defining suspected loan loss-linking customer and family member intimacy, a mobile social-contact big data network of the loan loss-linking customers’ family members is constructed, and similarity of mobile phone usage habits, contact similarity, and contact point similarity in the mobile social-contact big data network are defined and studied accordingly. Then, the similarity matching degrees of mobile phone usage habits, contact locations, and contacts between suspected loan loss-linking customers and loan loss-linking customers are analyzed from the perspective of similarity. This establishes an information matching model of loan loss-linking customers, and proposes the multi-angle tracking algorithm of loan loss-linking customers, allowing information matching and multi-angle tracking for loan loss-linking customers, and applies the model and the algorithm to the loan data of a bank in China. The empirical results show that the proposed model and algorithm can track loan loss-linking customers, and the algorithm exhibits rapid convergence. This study has important significance and practical value for financial institutions to track loan loss-linking customers and recover funds.

Measuring Network Conditions in Data Centers Using the Precision Time Protocol

Increased network latency and packets losses can affect substantially application performance. Due to the scale of data centers, custom network monitoring tools have been developed to measure network latency and packet loss. In this paper, we use the Precision Time Protocol (PTP) to estimate one-way delay and to quantify packet loss ratios. We propose PTPmesh as a cloud network monitoring tool which uses PTPd as a building block. We present an analysis of PTPmesh latency and packet loss measurements collected in ten data centers from three cloud providers. Our findings reveal different latency, latency variance, packet loss and path symmetry characteristics across data centers. To foster further research in this area, we make our dataset available at Popescu and Moore (2018).

Dimensioning access link capacity for time-varying traffic with mixed packet streams and circuit connections

Access links that connect cloud data centers or enterprise networks to public networks are important parts of the network infrastructure and make up considerable portions of overall network expenditures. When a customer network is connected to an overlaid elastic optical network, determining the capacity of the access link is difficult. Furthermore, access links carry mixed packet streams and circuit connections, each with different performance requirements. Additionally, traffic loads are time-varying and often exhibit diurnal patterns. In this study, we formulate the access link capacity dimensioning problem as an optimization problem with a nested dynamic constraint satisfaction subproblem. Instead of using hard performance measures, such as packet delays and blocking probabilities, we use the probability of fulfillment with regard to different traffic arrival rates as the performance measure and the performance–cost ratio as our optimization objective. Numerical results suggest that a 7.5% to 19.47% cost savings can be achieved if resources are shared between packet streams and circuit connections. And if resource preemptions are allowed, the delay performance for packet streams can be improved by 12% with more frequent preemptions. Our study provides useful insights into the planning and operation of access links.

Unsupervised feature learning for data classification

Data classification is a critical task for data analysis. However, recent methods aim to model this task as a classification or regression task, which needs ground truth for discriminative representation learning. This paper aims to learn a more efficient and effective feature for data analysis in an unsupervised learning manner. Our method consists of two main components: a customized autoencoder network (C-AENet) and a customized squeeze-and-excitation network (C-SENet). The C-AENet aims to reconstruct the feature using the fewer dimension, and C-SENet is to improve the representation by providing channel attention. Experiment on the Avila dataset shows that both modules are effective in data classification with fewer feature dimension.

Information technology systems implementation and processes of integration and disintegration: case study evidence from Air Greenland

PurposeThis paper aims to analyze the impact of information technology (IT) system implementation on the integration of data and information between sales and accounting departments, and how data integration affects relations with supplier and customers.Design/methodology/approachThe change between three different reservation and distribution systems in an airline company was assessed over 20 years based on qualitative data collected while events unfolded and interviews that traced events retrospectively.FindingsThis study finds that data integration challenges affect the capacity to use revenue and sales data for control purposes and integrating with suppliers and customers. The systems either facilitated the ability to integrate sales and accounting data or enabled integration in wider supplier and customer networks. The implementation of different reservation and distribution systems resulted in a trade-off between integration within the firm and into wider customer and supplier networks.Research limitations/implicationsData were mainly obtained from the focal firm, Air Greenland. The protracted study period meant that the data were not as concentrated as they would have been had the analysis been performed over a shorter duration or had the focus been on one implementation process.Originality/valueExtant research suggests that integration challenges when implementing IT systems are caused by differences in information needs between groups with different logics. The authors illustrate how data integration is also a crucial challenge when implementing IT systems.

Strategi Pemasaran UMKM di Masa Pandemi Covid-19: Studi Kasus UMKM di Kawasan Pondok Pesantren Darul 'Ulum Jombang

Abstract Purpose: This study aims to determine the marketing management strategies of MSME players during the Covid-19 pandemic. This research is included in qualitative research with a case study approach. Research Methodology: The data obtained in this study were qualitative data collected through in-depth interviews, observation and documentation—retrieval of informants using a purposive sampling technique. The source of the data came from 4 MSME informants who trade in the Islamic boarding school area. The informants have been trading in this micro-business sector for at least five years as their main job. The data were analyzed using interactive analysis techniques, namely through the stages of data reduction, data presentation, and drawing conclusions and verification. Results: The result of this research is that the strategy undertaken by MSME actors is to strengthen and expand the network of customers in collaboration with Kyai or Islamic boarding school Teachers by doing "Sowan", which is in the category of social capital. Contribution: The social capital used by the informants as a marketing method during the Corona pandemic is very suitable for the current pandemic conditions. The conditions in the boarding school are that the students are not free to do online transactions like the general public. They can make online transactions via WhatsApp messages or telephone only with business people who are allowed with the permission of the boarding school caretakers.

Major customer network structure and supplier trade credit

PurposeThis study investigates how a firm's structural embeddedness, the structural position in a supply network that consists of major customers, influences the acquisition of supplier trade credit. Specifically, this study examines how network interconnectedness, network integration and network independence of a firm affect its ability to acquire supplier trade credit.Design/methodology/approachThis study utilizes financial data from Compustat to build a longitudinal dataset of manufacturing firms from 1998 to 2013. Customer segment disclosure data are used to construct firm-level network variables. A fixed effect regression approach is used for estimation.FindingsThe study results show that network interconnectedness is negatively associated with supplier trade credit, while network integration is positively associated with supplier trade credit. Network independence does not influence the extent of supplier trade credit. The post hoc analysis shows that the effects of the hypothesized factors vary under different product categories and credit ratings.Originality/valueThis study broadens the supply chain finance literature by showing how a firm's embedded network structural position can influence its ability to obtain supplier trade credit.

A deep-learning-based image forgery detection framework for controlling the spread of misinformation

PurposeWeb users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.Design/methodology/approachThe proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.FindingsThe comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.Research limitations/implicationsThis study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.Practical implicationsThis study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.Social implicationsIn the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.Originality/valueThis study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.