artificial intelligence

Quality is the driving factor of the manufacturing industry. With the advent of Industry 4.0, the integration of advanced sensors, edge computing, artificial intelligence, digital twins, and 3D printing has great potential to achieve resilient manufacturing. The first part of this study addresses real-time quality control with edge computing. A digital imaging system is introduced, equipped with an edge device for real-time defect detection in additive manufacturing. AI promises great potential for achieving zero-defect manufacturing, which was barely possible in the past. Then, as the main structure provider for Industry 4.0, edge computing is described. The computer is used in quality control features of edge computing by real-time cloud monitoring and real-time elasticity for cloud services. In the second part, a predictive maintenance framework is proposed by integrating the digital twin, advanced data processing, and AI algorithms. AI finally determines the degradation status of products with high confidence to maintain the resilience of the product life cycle. Expensive fault events on products can be avoided with the help of AI-based advanced planning of maintenance at an appropriate time. The approach could mitigate potential risks of shortening lifespan, increasing maintenance costs, triggering catastrophic events, or even causing social impact if combined with real-time edge computing and advanced sensors. Such an approach advances the deployment of the manufacturing industry in the era of Industry 4.0.

This study aimed to investigate the role of artificial intelligence (AI) in predictive data center maintenance practices and strategies. Timely detection of different types of equipment faults and subsequent predictive maintenance can enhance data center availability dramatically and minimize costly outages. The rationale for the study came from the rapid growth of digital services and users, as well as the costly data centers supporting this growth. The costs of a minute (or more) of an unplanned service disruption range from $10,000 to $65,000. The three main Data Center Infrastructure Management (DCIM) components—the common sensors for continuous data acquisition, prevention to power off, and ensuing prediction and solution techniques—are investigated. Multiple other machine learning classification models are suggested to be tested on a similar dataset, in addition to classification, and to quantitatively test a real-life installation in future research. In addition, how these components interact in real-world environments is still not clear and would require advanced statistical analyses, which have not been done in this research. Yet, under the conditions of this study, the results demonstrate how AI elements can provide a reliable solution for enhanced data center DCIM and be applied in deliverable form.

Artificial Intelligence (AI) has become one of the most dominant enablers of digital transformation across a significant number of industries in recent years. With the application of AI technology including natural language processing (NLP), creating human-like chat bots has become easier. Today, a greater portion of India banks have switched over to Chat Bot technology for their customer on-boarding, queries, complaints, fund transfers, etc., due their proficiency in handling numerous queries with high response time and round the clock service. This case study is based on the virtual assistant of State Bank of India (SBI) – State Bank Intelligent Assistant (SIA), which is engaged in providing personalized service to users based on their historical preferences, search frequency etc., via analyzing the data with the assistance of AI techniques. The recent developments and emergence of Virtual Banking in India and the current trends in the modern banking systems are explained along with the features of SBI-SIA virtual assistant. As banking is reshaped by technology, financial stability is a key priority for the Bank of England. The Bank is engaging with FinTech companies to gather information and understanding on the financial stability risks that might emerge from FinTech developments. Such risks are expected from the explosion of technology-enabled financial services. It has now become common for many banks to integrate FinTech, machine learning and AI into their services because customers want more choices, flexibility and control over their banking. AI is a branch of FinTech but not every FinTech is AI. AI is machine intelligence. Machine learning and AI are often treated as synonyms. AI in retail banking is at its nascent stage in the UK, though the potential is extraordinary. In the UK, some banks have launched banking applications using voice recognition. The Royal Bank of Scotland (RBS) has decided to roll out its “Luvo” AI customer service assistant, powered by its partner firm, RBS Group, wider in its branches. Bank of America, Capital One, Société Générale and Swedbank are some of the banks that have experimented with chatbots.

Real-time traffic congestion is a challenging problem in smartcities. An Intelligent Transportation System (ITS) is a big data application integrating sensor hardware and network technologies, which can intelligently capture traffic information, efficiently transform data into knowledge, and organize and manage transportation resources. Urban Traffic Control (UTC) is a critical component of ITS, analyzing real-time traffic information and coordinating traffic signal timing plans to optimize traffic network performance and improve vehicle travel speeds. However, traditional UTC based on centralized architecture would be challenged in data transmission, architecture malfunctions, system bottlenecks, etc. As a solution, the multi-Agent based RTC (ATC) with smart intersections is proposed. Additionally, more comprehensive traffic data can be captured with advanced detection techniques, and Regional Traffic Control (RTC) systems can be designed with advanced optimal control algorithms. Digital Banking (DB) Infrastructures powered with AI can be trained on historical data to simulate human understanding of patterns and trends when leveraging custom models tuned to understand banking transactions. Integrating artificial intelligence (AI) and automation into the digital banking infrastructure can result in a stable pipeline implementation of sorting transactions data for anomalies per the bank thresholds. Moreover, with the integration of AI systems, captured data can be analysed to study the traffic characteristics of the bank and determine how efficiently it is working. AI can also be used to examine this data, identify problematic data, perform risk prediction, timely tracking, and further determine whether it fits the standards of bank transactions. AI can warn of difficulties in bank transactions. When an unauthorised transaction is detected, that transaction can be prohibited in real-time. Furthermore, it can help to significantly increase banks' risk management levels, improved efficiency and a near-zero error output requirement of regular activity.