A Holistic Architecture for a Sales Enablement Sensing-as-a-Service Model in the IoT Environment (original) (raw)
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IAEME Publication, 2024
In today's world of data-centric decision-making, enterprises are progressively using integrated frameworks that amalgamate Machine Learning (ML), the Internet of Things (IoT), and Customer Relationship Management (CRM) to optimize operations and improve customer engagement. This study examines the revolutionary possibilities of integration, emphasizing real-time data analytics, predictive modeling, and tailored consumer interactions. The suggested framework utilizes sophisticated technologies, including edge computing, natural language processing, and blockchain for security, to tackle obstacles such as fragmented data systems, decision-making delays, and interoperability concerns. Case examples illustrate substantial enhancements in operational efficiency, customer happiness, and competitive advantage. This study emphasizes the essential function of ML-augmented CRM systems in utilizing IoT data streams for actionable insights, hence transforming corporate management methods. This research identifies existing limits and proposes creative solutions, offering a path for enterprises seeking seamless integration and optimal value from their data ecosystems.
International Journal of Applied Engineering and Management Letters (IJAEML), 2020
This study examines the emerging fields of data analytics and decision prediction using data collected across different systems using Internet of Things technology. The Internet of Things (IoT) is a collection of interrelated computing devices, mechanical and digital machines, objects, animals, or people that are provided with unique identifiers and the ability to transmit data across a network without needing human-to-human or human-to-computer interaction. A specified aim of predicting the future, along with the explanation of the problem using another high-tech system and model should be used to process the enormous and continuous data produced. The possibility of realizing (design and development) such systems for so-called Tech-Business-Analytics for different real-world applications of predictive business decisions has been addressed in this paper.
CONVERGENCE OF MACHINE LEARNING AND IOT: TOWARDS INTELLIGENT SENSING AND DECISION-MAKING
NTERNATIONAL JOURNAL OF EXPLORING EMERGING TRENDS IN ENGINEERING, 2023
In today's complex business landscape, organizations contend with an avalanche of data. Yet, the true value lies in the ability to transform this extensive data repository into insightful revelations that illuminate more strategic corporate maneuvers. This is precisely where the practice of IoT and Machine Leaning based decision-making emerges. By harnessing the potential of data and leveraging artificial intelligence (AI) capabilities, enterprises can seize the opportunity for well-informed selections that eventually lead to enhanced outcomes. This paper explores the concept of Machine Learning and IoT-powered decision-making and scrutinizes the pivotal role of AI in shaping these astute business resolutions.
IoT Data Analytics in Retail: Framework and Implementation
International Conference on Innovative Intelligent Industrial Production and Logistics, 2020
IoT data analytics has many potential applications in the retail industry. However, relations among ambient conditions at stores as measured by IoT devices and sales performance are not well understood. This paper explores sensory and sales data provided by a large retail chain to quantify the impact of air quality, temperature, humidity and lighting on customer behaviour. It has been determined that the air quality and humidity have a significant impact and temperature appears to have a non-linear effect on customer behaviour. The data analysis findings are used to configure an IoT data analytics platform. The platform is used to monitor the ambient conditions in retail stores, to evaluate a need for improving the conditions as well as to enact improvement by passing them over to a building management system.
A Fine Tuned-based Framework to Predict Salesforce Data using Machine Learning in Business Analytics
Engineering, Technology & Applied Science Research, 2024
Sales forecasting is one of the critical areas in business analytics where business organizations aim to enhance efficiency and, therefore, revenues. An excellent example of a CRM program is Salesforce, which produces massive amounts of sales data that are essential for forecasting and decision-making. Data analysis involves the use of complex and effective tools for its processing. This study proposes a framework based on the following classification algorithms: Support Vector Machines (SVM), Decision Trees (DT), and Random Forests (RF). The proposed framework follows a fine-tuned approach to improve the prediction of sales data. Regarding the fine-tuning of these algorithms, it was observed that specific changes were required within the hyperparameters to better relate to the inherent patterns and other factors that exist in the sales data. The optimization process was very crucial in improving the performance of the model. The proposed framework was used on a sales dataset and evaluated in terms of accuracy, precision, data loss, and F1 score. Fine-tuned algorithms had higher accuracy and lower data loss.
LITERATURE SURVEY ON MACHINE LEARNING WITH INTERNET OF THINGS (IOT
IAEME PUBLICATION, 2020
The innovation through interconnection of various gadgets via web is named as Internet of things (IoT). Creates of IoT has a vast measure of information regarding different attributes and characteristics of information. AI combination with IoT guarantees the inescapable advancement to expand the insight of the IoT gadgets and presentations. The introduction of distinctive brilliant IoT presentations through AI assist in perception, methodical investigation, handling and brilliant utilizations of the huge volume of information in various fields. Numerous enterprises are utilizing the AI and all the more explicitly, Machine learning (ML) as a Services to abuse IoT's latent capacity. This current article comprises of AI essential presentation, AI calculations, surveys of various specialist's investigation, different sensor gadgets and the different utilizations of AI calculations through IoT. Also, the end area of the article comprises of dialog and end
State and Trends of Machine Learning Approaches in Business: An Empirical Review
Chapter in the Book: 'Artificial Intelligence and Applied Mathematics in Engineering Problems', Proceedings of the International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2019). Edition: Springer, ISBN 978-3-030-36178-5. Pages: 1-16., 2019
Strong competition is imposing to enterprises an incessant need for extracting more business values from collected data. The business value of contemporary volatile data derives from the meanings mainly for market tendencies, and overall customer behaviors. With such continuous urge to mine valuable patterns from data, analytics have skipped to the top of research topics. One main solution for the analysis in such context is ‘Machine Learning' (ML). However, machine learning approaches and heuristics are plenty, and most of them require outward knowledge and deep thoughtful of the context to learn the tools fittingly. Furthermore, application of prediction in business has certain considerations that strongly affects the effectiveness of ML techniques such as noisy, criticality, and inaccuracy of business data due to human involvement in an extensive number of business tasks. The objective of this paper is to inform about the role of Machine Learning approaches in enterprises systems. Understanding the vantages and advantages of these methods in the context of business can aid in selecting the suitable technique for a specific application in advance. The paper presents a comprehensively review of the most relevant academic publications in the topic carrying out a review methodology based on imbricated nomenclatures. The findings can orient and guide academics and industrials in their applications within the enterprises systems.
IOT TECHNOLOGIES AND MACHINE LEARNING ALGORITHMS – A STUDY
IoT – The technology buzzword of the internet has gained attention among researchers, scientist, engineers, software developers, academia and much more professionals over the years for its effectiveness and increased use in many applications. IoT technology enables communication among real world objects to interact in an intelligent way. In turn, IoT is all about connectivity and automation that require intelligent algorithms to make the smart objects work together. On this aspect, this paper aims to discuss the technology elements of IoT, applications and how machine-learning algorithms are applied in IoT data classification and prediction.
Towards Marketing 4.0: Vision and Survey on the Role of IoT and Data Science
Societies
According to KPMG, Internet of Things (IoT) technology was among the top 10 technologies of 2019. It has been growing at a significant pace, influencing and disrupting several application domains. It is expected that by 2025, 75.44 billion devices will be connected to the Internet. These devices generate massive amounts of data which, when harnessed using the power of data science (DS) techniques and approaches such as artificial intelligence (AI) and machine learning (ML), can provide significant benefits to economy, society, and people. Examples of areas that are being disrupted are digital marketing and retail commerce services in smart cities. This paper presents a vision for Marketing 4.0 that is underpinned by disruptive digital technologies such as IoT and DS. We present an analysis of the current state of the art in IoT and DS via the three pillars of marketing: namely, people, products, and places. We propose a blueprint architecture for developing a Marketing 4.0 solution ...
Sustainability
Recently, the widespread adoption of the Internet of Things (IoT) model has led to the development of intelligent and sustainable industries that support the economic security of modern societies. These industries can offer their participants a higher standard of living and working services via digitalization. The IoT also includes ubiquitous technology for extracting context information to deliver valuable services to customers. With the growth of connected things, the related designs often suffer from high latency and network overheads, resulting in unresponsiveness. The continuous transmission of enormous amounts of sensor data from IoT nodes is problematic because IoT-based sensor nodes are highly energy-constrained. Recently, the research community in the field of IoT and digitalization has labored to build efficient platforms using machine learning (ML) algorithms. ML models that run directly on edge devices are intensely interesting in the context of IoT applications. The use...