Intelligent (Smart) E-Commerce

E-commerce has become an integral part of businesses for decades in the modern world, and this has been exceptionally speeded up during the coronavirus era. To help businesses understand their current and future performance, which can help them survive and thrive in the world of e-commerce, this paper proposes a hybrid approach that conducts performance prediction and evaluation of the e-commerce industry by combining the Grey model, i.e., GM (1, 1) and data envelopment analysis, i.e., the Malmquist-I-C model. For each e-commerce company, GM (1, 1) is applied to predict future values for the period 2020–2022 and Malmquist-I-C is applied to calculate the efficiency score based on output variables such as revenue and gross profit and input variables such as assets, liabilities, and equity. The top 10 e-commerce companies in the US market are used to demonstrate model effectiveness. For the entire research period of 2016–2022, the most productive e-commerce marketplace on average was eBay, followed by Best Buy and Lowe’s; meanwhile, Groupon was the worst-performing e-commerce business during the studied period. Moreover, as most e-commerce companies have progressed in technological development, the results show that the determinants for productivity growth are the technical efficiency change indexes. That means, although focusing on technology development is the key to e-commerce success, companies should make better efforts to maximize their resources such as labor, material and equipment supplies, and capital. This paper offers decision-makers significant material for evaluating and improving their business performance.

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“…Step 5: Construct GM (1, 1) prediction equation, Equations (10) and (11). The predicted results are shown in Table 6 as follows.…”

Section: Results Of Gm (1 1)mentioning

confidence: 99%

Supporting Better Decision-Making: A Combined Grey Model and Data Envelopment Analysis for Efficiency Evaluation in E-Commerce Marketplaces

et al. 2020

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“…AI gives consumers more interest in software-as-a-service (SaaS). Salesforce launched an AI tool called Einstein [20] which translates data into ideas. The tool uses customer data for its sales through advertisement and providing advice to customers by using various social networking means.…”

Section: Role Of Ai In Cloud Computingmentioning

confidence: 99%

Role of artificial intelligence in cloud computing, IoT and SDN: Reliability and scalability issues

Belgaum

Alansari

Musa

et al. 2021

IJECE

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Information technology fields are now more dominated by artificial intelligence, as it is playing a key role in terms of providing better services. The inherent strengths of artificial intelligence are driving the companies into a modern, decisive, secure, and insight-driven arena to address the current and future challenges. The key technologies like cloud, internet of things (IoT), and software-defined networking (SDN) are emerging as future applications and rendering benefits to the society. Integrating artificial intelligence with these innovations with scalability brings beneficiaries to the next level of efficiency. Data generated from the heterogeneous devices are received, exchanged, stored, managed, and analyzed to automate and improve the performance of the overall system and be more reliable. Although these new technologies are not free of their limitations, nevertheless, the synthesis of technologies has been challenged and has put forth many challenges in terms of scalability and reliability. Therefore, this paper discusses the role of artificial intelligence (AI) along with issues and opportunities confronting all communities for incorporating the integration of these technologies in terms of reliability and scalability. This paper puts forward the future directions related to scalability and reliability concerns during the integration of the above-mentioned technologies and enable the researchers to address the current research gaps.

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“…Menurut Turban dan Aronson [2], definisi sistem pakar adalah sistem yang menggunakan pengetahuan manusia yang dimasukan ke dalam komputer untuk memecahkan masalah-masalah yang biasanya diselesaikan oleh pakar. Pengetahuan sistem pakar dibentuk dari kaidah atau pengalaman tentang perilaku elemen dari domain bidang pengetahuan tertentu.…”

Section: Sistem Pakar ( Expert System )unclassified

Perancangan Prototipe Perangkat Lunak Expert System Dengan Metode Backward Chaining Untuk Membantu Proses Pemeriksaan Antenatal Di Tingkat Pelayanan Dasar

Winarsih

Palupi

2020

jitu

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Antenatal care at the Basic Service Level is defined antenatal checks will be undertaken to examine the circumstances of the mother and fetus at regular intervals, followed by an attempt correction of deviations found. The goal is to keep pregnant women can go through pregnancy, childbirth and post-partum properly and safely, and to produce a healthy baby.Procees Antenatal still done manually and is very dependent on the health workers so that the necessary software that can help this problem. The present study emphasizes the design of a prototype expert system software to assist in the level of antenatal examination of basic services.The purpose of this research is to make protitipe design software in order to help workers in antenatal care at the level of basic services.The method used in the design of a prototype expert system software that helps antenatal checks the method used in the design of a prototype expert system software to assist in the level of antenatal examination of basic services. The stages are done systematically in this study is a study phase began with a reference library to find supporters then gathered data, to identify and to determine the extent of the problem konseptualisiasi and experts involved, then formalize the knowledge base includes the manufacture of that relate to the topic, determining ie backward chaining inference method, followed by the drafting of a database, and then to design new prototype expert system software with backward chaining method that can assist antenatal examination in basic service level. Keywords: expert systems, antenatal, backward chaining

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Intelligent (Smart) E-Commerce

Cited by 22 publications

References 1 publication

Supporting Better Decision-Making: A Combined Grey Model and Data Envelopment Analysis for Efficiency Evaluation in E-Commerce Marketplaces

Supporting Better Decision-Making: A Combined Grey Model and Data Envelopment Analysis for Efficiency Evaluation in E-Commerce Marketplaces

Role of artificial intelligence in cloud computing, IoT and SDN: Reliability and scalability issues

Perancangan Prototipe Perangkat Lunak Expert System Dengan Metode Backward Chaining Untuk Membantu Proses Pemeriksaan Antenatal Di Tingkat Pelayanan Dasar

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