Customer Network Research Articles

The Impact of Opportunities generated through Q-Commerce on the Performance of Online Fruit Marketers in Pune City

The Maharashtrian fruit market is one of the leading fruit markets in the country. Maharashtra, which is also known as a fruit bowl of the country. It majorly produces 5 fruits namely Banana, Grapes, Mango, Orange and Pomegranate. The online fruit market has grown tremendously in the last few years. The changing phase of e-commerce and introduction of q-commerce brought a fabulous growth in this market. The research is conducted with the basic objective to study whether there is a significant impact of opportunities generated through the q-commerce on the business performance of the online fruit marketers in Pune city. The researchers also intended to study the internal differences between variables of opportunities through q-commerce and performance of online fruit marketers separately. It was also intended to study the correlation between opportunities through q-commerce and performance of online fruit marketers. The parameters to study the opportunities for the sellers and marketers were ‘Allows you to tap the newer and faster market’, ‘Increases the reach in terms of customer base in the existing local market’, ‘Helps you to gain competitive and pricing advantage’, ‘Helps to promote your products and build your own brand’, and ‘Allows you to work on 24x7x361 basis’. Similarly, the parameters for studying the business performance of the online fruit marketers were ‘Helps in improving sales of the product’, ‘Helps in reducing promotional expenses’, ‘Helps in gaining new customers and strengthening the existing customer network’, ‘Helps in gaining advantage over traditional marketing methods’, and ‘Helps in keeping accurate track of all the marketing & selling activities online’. A questionnaire was designed which consists the set of straight forward, limited probing & structured questions. A sample of 100 respondents was interviewed & simple random sampling method was used. Keeping the above objectives in mind, the researchers created the hypotheses and tested those through statistical tests like Friedman Test, Pearson correlation and Regression Analysis with ANOVA. During the study, the researchers found out that there is a significant impact of opportunities through Q-commerce on the performance of the online fruit marketers in Pune city. It was also found that there is a significant positive correlation between opportunities through Q-commerce and performance of the online fruit marketers in Pune city. The researchers also found that there is a significant difference between the internal opportunities’ variables through q-commerce and internal performance’s variables of online fruit Marketers in Pune city respectively.

Enhancing Brain Tumor Detection Through Custom Convolutional Neural Networks and Interpretability-Driven Analysis

Brain tumor detection is crucial for effective treatment planning and improved patient outcomes. However, existing methods often face challenges, such as limited interpretability and class imbalance in medical-imaging data. This study presents a novel, custom Convolutional Neural Network (CNN) architecture, specifically designed to address these issues by incorporating interpretability techniques and strategies to mitigate class imbalance. We trained and evaluated four CNN models (proposed CNN, ResNetV2, DenseNet201, and VGG16) using a brain tumor MRI dataset, with oversampling techniques and class weighting employed during training. Our proposed CNN achieved an accuracy of 94.51%, outperforming other models in regard to precision, recall, and F1-Score. Furthermore, interpretability was enhanced through gradient-based attribution methods and saliency maps, providing valuable insights into the model’s decision-making process and fostering collaboration between AI systems and clinicians. This approach contributes a highly accurate and interpretable framework for brain tumor detection, with the potential to significantly enhance diagnostic accuracy and personalized treatment planning in neuro-oncology.

Forecasting realized volatility with spillover effects: Perspectives from graph neural networks

Forecasting realized volatility with spillover effects: Perspectives from graph neural networks

Research on Algorithm of Composite Material Painting Creation based on Image Processing Technology

In the study we proposed a novel approach is called a Customized Convolutional Neural Network (CCNN) to innovate in the field of art creation, particularly in composite material paintings. This research harnesses the power of image processing technology to analyze and synthesize various artistic elements, thereby facilitating the creation of composite material paintings. The core of the study revolves around the development of a unique algorithm that enables the integration of diverse materials and textures into a cohesive artistic expression. The Customized CNN is trained on a vast dataset of images, encompassing a wide spectrum of textures, colors, and patterns, representative of different materials commonly used in art. The network learns to identify and replicate the aesthetic qualities of these materials, thereby empowering artists to explore new realms of creativity. The algorithm not only recognizes the distinct characteristics of each material but also understands how to blend them effectively, maintaining artistic coherence. The results are evaluated to prove proposed performance.

Digital Protection and Inheritance Path of Intangible Cultural Heritage based on Image Processing Algorithm

This research paper introduces a novel approach in the realm of digital preservation and inheritance of Intangible Cultural Heritage (ICH) through a Customized 3D Convolutional Neural Network (CNN). The core of this study lies in the development of an advanced image processing algorithm tailored to accurately recognize, categorize, and archive diverse forms of ICH, which include traditional performances, ceremonies, oral traditions, and crafts. Utilizing a volumetric 3D CNN, this paper demonstrates how complex ICH elements can be effectively captured and analyzed, overcoming the limitations of traditional 2D image processing methods. The network is trained on a comprehensive dataset of ICH imagery, ensuring sensitivity to the subtle nuances and dynamic nature of these cultural expressions. This paper highlights the algorithm’s capability in not only safeguarding the visual aspects of ICH but also in providing an interactive, digital medium for education and cultural dissemination. The proposed method shows significant promise in aiding the efforts of cultural preservationists and educators, offering a technologically advanced pathway for the protection and inheritance of the world’s rich, yet vulnerable, cultural heritage. This study sets a precedent in the interdisciplinary field of cultural heritage conservation, digital technology, and artificial intelligence, providing a scalable and effective solution for global ICH preservation initiatives.

Building Customer and Product Networks with Cosine Similarity in Graph Analytics for Deep Customer Insight

The goal of social networks is to establish a link that facilitates information sharing and product recommendations between users. For the purpose of comprehending and evaluating links, relationships, and networks, graph theory is indispensable. Our customer network allows us to engage current customers with more products from similar customers and recommend products from one customer to another that are connected by a cosine similarity to the 70% above. If the price of a product is higher than that of comparable products, we can observe the demand for that product in the product network.

Leaf Morphology Classification Using Customized Convolutional Neural Networks

Leaf morphology is a key trait in plant identification and taxonomy, crucial for understanding biodiversity and agricultural studies. Manual classification methods are often labor-intensive and subjective. Machine learning approaches, particularly convolutional neural networks (CNNs), offer automated and precise solutions. To classify plant leaves based on their morphology using CNNs, leveraging image datasets to develop an accurate and scalable model. The Plant Leaves for Image Classification dataset from Kaggle, consisting of leaf images from multiple species, was utilized. Data preprocessing included resizing, normalization, and augmentation. A CNN model was trained and optimized for classification tasks. The proposed CNN achieved an accuracy of 94.85% on the test set. Sample classifications demonstrated the model's ability to distinguish between similar leaf structures effectively. This study highlights the potential of CNNs in automating leaf morphology classification, contributing to advancements in botany and agriculture.

Programmable soft DNA hydrogels stimulate cellular endocytic pathways and proliferation

Hydrogels are pivotal in tissue engineering, regenerative medicine, and drug delivery applications. Existing hydrogel platforms are not easily customizable and often lack precise programmability, making them less suited for 3D tissue culture and programming of cells. DNA molecules stand out among other promising biomaterials due to their unparalleled precision, programmability, and customization. In this study, we introduced a palette of novel cellular scaffolding platforms made of pure DNA-based hydrogel systems while improving the shortcomings of the existing platforms. We showed a quick and easy one step synthesis of DNA hydrogels using thermal annealing based on sequence specific hybridization strategy. We also demonstrated the formation of multi-armed branched supramolecular scaffolds with custom mechanical stiffness, porosity, and network density by increasing or decreasing the number of branching arms. These mechanically tuneable DNA hydrogels proved to be a suitable suitable platform for modulating the physiological processes of retinal pigment epithelial cells (RPE1). In-vitro studies showed dynamic changes at multiple levels, ranging from a change in morphology to protein expression patterns, enhanced membrane traffic, and proliferation. The soft DNA hydrogels explored here are mechanically compliant and pliable, thus excellently suited for applications in cellular programming and reprogramming. This research lays the groundwork for developing a DNA hydrogel system with a higher dynamic range of stiffness, which will open exciting avenues for tissue engineering and beyond.

Factors Affecting for Using Services of Mobile Phone Network for Customer of the Lao Telecommunications Company, Oudomxay Province

The purpose of this study was1) to study the situationof customers using mobile phone network services of Lao Telecommunications Company, 2) to studythe significantbetween customers andthe mobile network of Lao Telecommunications Company, 3) to findout the factors affecting customer's loyalty to the mobile phone network service of Lao Telecommunication Public Company.It was a quantitative research with the target group being customers who use the service through the mobile phone network of customers of Lao Telecommunications Company in Uodomxay province, used402 sample group and questionnaires were usedto collect data and analyzedthe data by usingSPSS program. The results found that most of the users of the Lao Telephony network service wereusing the Internet (Data), spending 50,000-100,000 kip per month, the period of using the number is more than 2 years. The important aspects in makinga relationship with Lao Telecom customers are marketing promotion, service process, customer monitoring, administration, communication between customers, and partnership which are in the high level. Factors affecting customers' loyalty to Lao Telecommunication Public Company's mobile network services include repeat service use factors, referral factors, and non-switching network factors with a statistical significancelevel of 0.01

Implementing machine learning techniques for customer retention and churn prediction in telecommunications

This review paper explores the application of machine learning techniques in predicting customer churn and enhancing customer retention within the telecommunications industry. The paper begins by discussing the significance of customer churn, its causes, and the limitations of traditional churn prediction methods. It then delves into machine learning algorithms, including decision trees, support vector machines, and ensemble methods. It highlights their effectiveness in handling large and complex datasets typical of the telecom sector. The discussion extends to the challenges faced in data quality, model selection, implementation, and ethical considerations in using customer data for predictive analytics. The paper also compares machine learning models with traditional methods, emphasizing the advantages of scalability, accuracy, and real-time processing. Furthermore, it identifies potential innovations, such as improved data integration, interpretable models, and personalized retention strategies. Finally, the paper reflects on future trends, predicting the growing role of AI and machine learning in telecommunications, particularly in customer service automation and network optimization. The review underscores the importance of adopting machine learning to reduce churn and improve customer retention while considering the field's ethical implications and future opportunities. Keywords: Customer Churn Prediction, Machine Learning, Telecommunications, Customer Retention, Predictive Analytics, AI in Telecom

A Study On The Impact Of Digital Marketing On Consumer Buying Behaviour

In customer-oriented marketing environment, the buying pattern of consumer changes at a fast rate. The consumer behaviour is influenced by various factors like price, features, quality, packaging, status, generation, age of the customer, etc. the technological factor influences consumer buying behaviour to a great extent. New innovations in technology have brought challenges and opened up opportunities for existing businesses. Businesses all over the world have been adapting technology related strategies to enable them to maximise customer networks. Technology has revolutionised marketing, moving it from more conventional forms to digital marketing, which uses digital media to communicate marketing material. A significant portion of customer purchasing behaviour is influenced by digital marketing. Digital platforms like blogs, websites and social networks are crucial for boosting a business’s sales and strengthening its clientele. Therefore, its crucial to comprehend how and where these channels affect consumer behaviour. This paper focuses on how consumer purchasing behaviour is impacted by digital ads, social media, blogs and reviews. A survey is made to perform the research and each respondent is given a well-crafted questionnaire. Analysis and interpretation are limited to the data obtained from the questionnaire.

Pemberdayaan Masyarakat Melalui Budidaya Jamur Tiram Berbasis Digital Marketing di Desa Imbanagara

Optimization of community empowerment through mushroom cultivation has a major problem, namely the lack of implementation of digital marketing strategies in oyster mushroom cultivation to increase market access, sales efficiency, and production capacity. With the implementation of digital marketing strategies, the Sukahurip II Farmer Group can promote their products more widely, effectively, reach a larger market and increase sales. The methods used include training and assistance in the use of digital platforms such as social media, e-commerce, and online marketing applications. The results of this implementation show that with the support of digital marketing, the community is able to increase income, expand customer networks, and strengthen their position in the market. This study provides practical guidance for the implementation of digital marketing strategies in oyster mushroom cultivation businesses, as well as providing recommendations for the development of similar empowerment models in other sectors.

A lightweight security defense algorithm for multimodal attack blocking in industrial control networks based on momentum enhanced feature graphs

AbstractAs the infrastructure of modern industry, the safe and stable operation of lightweight industrial control network is crucial for industrial production. Multi‐mode attack is a kind of highly customized and low resource consumption network attack method, which can target the specific vulnerabilities and configuration weaknesses of lightweight industrial control network. In order to improve the defense capability of industrial control network against multimodal attacks, a lightweight security defense algorithm based on momentum‐enhanced feature graph is proposed. The lightweight industrial control network operation mode is determined by abstract relationship, and the ontology structure of industrial control network multi‐mode attack is established; with reference to the ontology structure, the multi‐mode attack map of industrial control network is defined; the normal behavior and lightweight attack behavior are divided, and the convolutional network is used to enhance the characteristics of lightweight attack; and the momentum‐enhanced feature map technology is used to correspond to the enhancement process, and to block and defend against the multi‐mode attack of industrial control network. The results show that the studied algorithm can realize accurate prediction of attack behaviors with different attack behaviors as test objects, and ensure accurate blocking of all kinds of attack behaviors with better security defense effect.

A robust near-field body area network based on coaxially-shielded textile metamaterial

A body area network involving wearable sensors distributed around the human body can continuously monitor physiological signals, finding applications in personal healthcare and athletic evaluation. Existing solutions for near-field body area networks, while facilitating reliable and secure interconnection among battery-free sensors, face challenges including limited spectral stability against external interference. Here we demonstrate a textile metamaterial featuring a coaxially-shielded internal structure designed to mitigate interference from extraneous loadings. The metamaterial can be patterned onto clothing to form a scalable, customizable network, enabling communication between near-field reading devices and battery-free sensing nodes placed within the network. Proof of concept demonstration shows the metamaterial’s robustness against mechanical deformation and exposure to lossy, conductive saline solutions, underscoring its potential applications in wet environments, particularly in athletic activities involving water or significant perspiration, offering insights for the future development of radio frequency components for a robust body area network at a system level.

Business model of international consulting: main characteristics and elements

Based on the analysis of the essence and modern types of business models of enterprises, the basic professional service business model of the international consulting "Consultant" and reveals its main characteristics is substantiates: the B2B model, the modification of the "Producer" business model, the integration of elements of service and digital business models, customer orientation. Based on the Business Model Canvas, the main components of the basic business model of international consulting are revealed: key partners: partner intra-service and inter-service networks of the consulting firm; key activity: providing professional assistance to business organizations in solving problems of international economic activity; key resources: intellectual human, organizational, market; value proposition: the value services of international consulting for the client; relations with clients: client orientation, client-centricity, positive client experience, cooperation between consultants and clients at all stages of the consulting process; channels: omnichannel, cross-channel communications; customer segments: target audiences, customer networks; cost structure; income streams. Client networks as a component of the basic business model of international consulting are characterized and measures to optimize them are disclosed: forming a loyal attitude of clients to the consulting firm, consultants, and services; accumulation and protection of a network of regular customers; forming a positive customer experience; improving the efficiency of consultant-client relations; development of omnichannel provision of professional assistance to clients; ensuring client orientation of the personnel of the consulting firm; management of the value and quality of consulting services; introduction of new consulting services and technologies based on consulting innovations.

Eksplorasi Pemetaan Pelanggan dan Prioritas Pengembangan Jaringan Internet : Pendekatan Sistem Informasi Geografis Berbasis Web dan AHP

ArsyaNet, a local internet service provider, strives to deliver fast and reliable internet access to its customers. This study aims to implement a web-based Geographic Information System (GIS) for customer mapping and network development prioritization using the Analytic Hierarchy Process (AHP) method at ArsyaNet. Leaflet is employed to map the distribution of ArsyaNet customers within the RT/RW network in the Pringgasela District. The main objective of this research is to accurately and efficiently map the distribution of internet customers to support optimal network development. By integrating AHP into the GIS, customer preferences and other relevant factors such as access speed, service quality, and infrastructure needs in specific areas will be considered in determining the priority locations for network development. The AHP method allows for the grouping of customers based on their preferences and determining locations by considering weights on each relevant aspect. Integrating Leaflet into the GIS enables accurate and interactive mapping results using a lighter map compared to the Google Maps API. This research is conducted to enhance the quality of internet services provided by ArsyaNet by better understanding the needs and preferences of customers in selecting internet service providers and their customer distribution.

A novel ensemble deep network framework for scene text recognition

In recent years, scene text recognition (STR) has always been considered a sequence-to-sequence problem. Attention-based techniques have a greater potential for context-semantic modelling, but they tend to overfit inadequate training data. STR is one of the most important and difficult challenges in image-based sequence recognition. A novel framework ensemble deep network (EDN) is proposed, EDN comprises customized convolutional neural network (CNN), and deep autoencoder. Customized CNN is designed by introducing the optimal spatial transformation module for optimizing the input of irregular text to read for same size. Further, deep autoencoder is introduced with effective attention mechanism utilizing the inherent features. The proposed ensemble deep network-proposed system (EDN-PS) approach outperforms the existing state-of-art techniques for both irregular and regular scene-texts and upon further simulations, the proposed model generates better results for IIIT5K, ICDAR-13, ICDAR-15, and CUTE dataset in comparison with the existing system hence our proposed EDN-PS model outperforms the existing state-of-art methods.

Linear Programming Model for Sunflower Oil Supply Chain Network

Food supply networks are vital to the global economy, producing and consuming products worldwide. Supply chain management (SCM) has emerged as a crucial technique to optimize the integrated network of suppliers, factories, warehouses, customers, and distribution channels. SCM aims to ensure efficient raw material sources, transformation, transportation, and timely customer delivery. Edible oil, a crucial ingredient for cooking and food preparation, is utilized by people from all social classes. Vegetable oils, particularly sunflower, soybean, peanut, and others, are the main source of edible oils, which have less unsaturated fatty acids compared to animal oils. The production and consumption of edible oils have substantial environmental impacts, necessitating increased research to address these implications. Sunflower, a valuable oilseed crop, is grown worldwide due to its short growing season and adaptability to various climates and soil types. Sunflower seeds have high oil and protein content, making them popular for their health benefits. This article uses a linear optimization model for the sunflower oil supply chain from the farmer to the end user. The objective is to minimize the overall transportation cost subject to capacity, demand fulfillment, and waste management.

Firm Networks and Asset Returns

Abstract Changes in the propagation of shocks along firm networks are important to understanding aggregate and cross-sectional features of stock returns. When calibrated to match key characteristics of supplier–customer networks in the United States, a model in which firms are interlinked via enduring relationships generates long-run consumption risks, high and volatile risk premiums, and a small and stable risk-free rate. The model also matches cross-sectional patterns of portfolio returns sorted by firm centrality, a feature unaccounted for by standard asset pricing models. (JEL C67, E30, G12, L14)

Automatic Detection of Acute Leukemia (ALL and AML) Utilizing Customized Deep Graph Convolutional Neural Networks.

Leukemia is a malignant disease that impacts explicitly the blood cells, leading to life-threatening infections and premature mortality. State-of-the-art machine-enabled technologies and sophisticated deep learning algorithms can assist clinicians in early-stage disease diagnosis. This study introduces an advanced end-to-end approach for the automated diagnosis of acute leukemia classes acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML). This study gathered a complete database of 44 patients, comprising 670 ALL and AML images. The proposed deep model's architecture consisted of a fusion of graph theory and convolutional neural network (CNN), with six graph Conv layers and a Softmax layer. The proposed deep model achieved a classification accuracy of 99% and a kappa coefficient of 0.85 for ALL and AML classes. The suggested model was assessed in noisy conditions and demonstrated strong resilience. Specifically, the model's accuracy remained above 90%, even at a signal-to-noise ratio (SNR) of 0 dB. The proposed approach was evaluated against contemporary methodologies and research, demonstrating encouraging outcomes. According to this, the suggested deep model can serve as a tool for clinicians to identify specific forms of acute leukemia.