AI-Driven Computational Engineering for Sustainable Development

Client retention is now just as important as client acquisition in today's fiercely competitive business climate. Since it is frequently more expensive to acquire new clients than to retain current ones, churn management is an essential corporate priority. Artificial Intelligence (AI) has become an increasingly powerful tool as companies seek more efficient methods to reduce client attrition. Traditional approaches cannot match the capacity to anticipate, evaluate, and react to customer behaviour that AI-driven churn management solutions provide. By foreseeing churn threats and offering useful insights, advanced analytics and machine learning algorithms play a crucial part in this process. Telecom firms take a multifaceted strategy to churn management. To build client loyalty, this includes: 1) personalised offerings, which entail developing service plans and incentives based on unique consumer demands and usage patterns; 2) customer engagement, which includes establishing enduring relationships with customers by being proactive in communication, giving excellent customer service, and responding to issues right away; 3) predictive analytics, which involves using predictive models to pinpoint potential customers who might leave and putting retention plans in place; 4) network quality, which entails ensuring a dependable and high-quality network to reduce reasons for churn connected to services; 5) competitive pricing, which includes drawing and keeping clients that are price sensitive, offering competitive pricing structures while preserving profitability; 6) data security, which inludes protecting consumer information and privacy that is important for maintaining trust and reducing turnover and lastly, 7) value-added services, which includes enhancing the client experience with cutting-edge features and services. The topic of telecom churn management is one thatis continually evolving due to the changing technologies and evolving customer expectations. Successful churn management can have a long-lasting positive impact on a telecom company's bottom line by not only preserving revenue but also fostering customer loyalty and advocacy. This study emphasises the significance of telecom churn management in a highly competitive market and the continual innovation and adaptation required to suit customers' shifting needs. This study implements a paradigm for managing telecom churn, which will assist telecom companies in maintaining low churn rates. To achieve this, the framework forecasts churners with a likelihood percentage, predicts their churn behaviors and causes, recommends consumers who exhibit similar behaviors, and suggests retention strategies for customers based on their behaviors. The administrator of the telecom company can efficiently manage telecom clients through this framework's user-friendly interface. This framework includes: 1) a comprehensive list of all customers, 2) a predictive list of potential churners along with their reasons for leaving, identified using the Random Forest algorithm, 3) detailed customer profiles that highlight individual service usage patterns, and 4) most importantly, an AI-powered retention solution that suggests targeted actions for customers likely to churn, based on behavior predictions generated by the TCCMR framework. The entire system is implemented using the Python programming language. This framework also suggests that similar consumers assist telecom firms in offering the same retention solutions to similar customers. The model performed exceptionally well in predicting churners, achieving outcomes with up to 100% accuracy.

Artificial Intelligence (AI) is increasingly being incorporated into Human Resource Management (HRM), which brings complex ethical and governing issues. This research paper addresses moral and legal problems by methodically investigating Artificial Intelligence's legal and ethical implications in HRM for an effective business. The study addresses the ethical aspects, including bias, justice, accountability, transparency, and the confidentiality and security of worker information. The legal roots are examined, covering information fortification strategies such as the General Data Protection Regulation and anti-discrimination regulations. The Personal Data Protection Bill and the Equal Remuneration Act are two Indian laws discussed in the paper, and their implications for AI in HRM are explored. The major aspects considered in the paper are lawful strategies that signify discrimination in AI-driven HR. The study employed a methodical approach, examining current legal systems and literature.

The main findings include the necessity of model interpretability, the use of fair machine learning algorithms, open communication with employees, and informed consent for sensitive employee data. The study emphasizes the importance of using AI responsibly to uphold human rights that are fair, open, and respectful of individual liberty. The results inform the conversation about AI ethics and legal compliance, directing the responsible application of AI in HR.

In today's technologically advanced era, the integration of communication technologies with the internet has made communication very simple. Email, in particular, is the most reliable and widely used mode of digital communication for both personal and commercial purposes. As of 2021, around 4.1 billion people used email globally. By 2024, this figure is expected to increase. By 2024, this figure is expected to increase. By 2024, this figure is expected to increase. By 2024, this figure is expected to increase to over 4.48 billion. This indicates the continued importance of email as a communication tool in our increasingly digital world. However, the visually impaired and blind people face several difficulties in using this important service, and there are currently around 2.2 billion people who have vision impairment globally, as per a report by the WHO. To address this issue, we propose an effective aid in the form of a voice command-based email system that will help individuals with special needs manage their email accounts through voice commands, making it easier for them to use the service. The proposed system will also benefit others by reducing their time consumption. Python, text-to-speech and speech-to-text conversion, Interactive Voice Response (IVR) technology, and other tools were used in the development of the email system. Through the use of their voice, visually impaired and blind persons will be able to effectively utilize email services and complete tasks without depending on a visual interface. 

Artificial Intelligence (AI) is driving transformative paradigm shifts across industries, offering innovative solutions to pressing societal challenges, including sustainability. By automating repetitive tasks and augmenting human capabilities, AI technologies have catalyzed productivity enhancements in sectors such as manufacturing, services, and education over the past two decades. The rapid evolution of expert systems, underpinned by advancements in AI research, has demonstrated significant potential in addressing complex problems across interdisciplinary domains, including healthcare, engineering, meteorology, and business analytics. This chapter provides a comprehensive examination of AI’s conceptual foundations, applications, and innovations, with a focused analysis of its implementation in educational contexts. Through empirical case studies and critical analysis, it explores AI’s role in optimizing operational efficiency, fostering quality improvements, and shaping future technological trajectories. The discussion concludes with an evaluation of emerging trends and ethical considerations, positioning AI as a pivotal force in advancing both industrial and societal progress. 

Consumer satisfaction and purchasing decisions made by consumers have always been a concern of marketers, taking into account these two aspects can encourage businesses to develop. The purpose of this study is to examine the relationship between AI-driven digital service channels and customer satisfaction, with a focus on the moderating effect of demographic variables. A survey of 189 consumers from Delhi NCR revealed that digital service channels—characterized by ease of use, convenience, and personalization—have a positive impact on customer satisfaction. Age, gender, and education have a moderating effect on the relationship. The existing literature has largely overlooked the role of demographic factors and lacks sufficient emphasis on digital service channels. The findings of this study can be used by businesses and the government to build more consumer/citizen-centric strategies. 

With the advent of technology, it has become an area of interest to forecast stock prices based on the news. Important “new news” is most likely to affect the stock price. The growth of social media platforms has enabled users to express their views on commonplace issues. Hence, the feedback from the public and customers is important. Sentiment analysis, also known as opinion mining, is a popular pre-planning exercise for conversations that seek to identify the underlying feelings associated with various text types. Moreover, public opinion research can provide us useful information. Sentiment analysis is a powerful tool with numerous applications. It can be used to understand user attitudes on social media sites such as Facebook and Twitter.

This sentiment analysis helps determine the customer's inclination, which in turn affects market trends. These trends often indicate a shift in stock prices in response to major announcements and significant news. With the aid of machine learning algorithms, suitably training and testing the data, this effect has been studied, and significant price movements have been observed. This paper employs sentiment analysis, machine learning, and deep learning techniques to examine the influence of news on market prediction, diverging from traditional stock structured data analysis.

However, the efficacy and accuracy of sentiment analysis are being hampered by issues with Natural Language Processing (NLP). Recent studies have shown that deep learning models offer a promising approach to addressing the challenges in natural language processing. 

Online communication on social networking platforms offers new perspectives for identifying various neurological disorder problems. Depression is now considered one of the most prevalent mental disorders. This chapter utilizes various machine learning approaches for text classification that categorize posts as either depressed or non-depressed. In this chapter, six machine learning classifiers, including Support Vector Classifier, Random Forest, Decision Tree, K-Nearest Neighbor, Light Gradient Boosting method, and Catboost, are used to make predictions over the Reddit dataset, which includes various posted contents of an individual over the web. The proposed model performs text preprocessing through stemming and normalization, followed by the application of feature extraction techniques using word embedding models. Feature extraction techniques are applied using word embedding models, which are then fed to a machine learning model. The results demonstrated that the LGBM classifier outperforms other machine learning classifiers with a higher accuracy of 99.9%. However, all chosen ML methods are more adaptive and resilient. LGBM outperforms the others. 

The evolution of Industry 5.0 signifies a transformative shift towards a synergistic integration of advanced technology and human creativity, prioritizing sustainability and environmental stewardship. This chapter examines the critical role of government policies and regulations in fostering the growth of green finance within the framework of Industry 5.0. As industries increasingly adopt automation, smart manufacturing, and networked ecosystems, the emphasis on sustainable practices becomes imperative. Government initiatives, through stringent environmental standards and financial incentives, mobilize capital towards green technologies, facilitating the transition to a low-carbon economy. Green finance, encompassing investments and lending aimed at enhancing environmental sustainability, plays a pivotal role in directing financial resources towards eco-friendly projects. Central banks and financial regulators contribute by incorporating sustainability into their frameworks, guiding investments towards green initiatives, and ensuring transparency and accountability in climate finance. The chapter highlights global efforts and policy measures from countries like India, China, Brazil, France, Indonesia, and Germany, showcasing their commitment to sustainable development through green finance. Despite challenges such as substantial upfront investments and regulatory hurdles, the integration of Industry 5.0 and green finance presents opportunities for innovation, enhanced resource efficiency, and reduced environmental impact. The nexus between Industry 5.0, green finance, and sustainability underscores their collective goal of promoting sustainable development. As industries continue to evolve, collaborative efforts among governments, financial institutions, and industries will be crucial in achieving a sustainable industrial ecosystem. This chapter emphasizes the importance of robust policies, financial incentives, and technological advancements in guiding the industrial sector towards a sustainable and environmentally friendly future. Nowadays, sustainability is essential, and reaching the goal of sustainability is the focus of every industry. Industry 5.0 is developing to accomplish this goal. Industry 5.0 is a humancentric strategy that essentially advances the idea of human-robot cooperation for a smart business ecosystem that enhances resource productivity and social well-being. In the framework of Industry 5.0, this chapter explores the dynamic interactions between government initiatives and the emerging field of green financing. 

The internet has vast amounts of unstructured data. It can be very difficult to analyse these data and be unable to extract meaningful results from them. Due to the abundance of opinionated data on the internet, where people express a wide range of emotions, including happiness, sadness, disappointment, and anger, opinion mining has become increasingly popular. Researchers often use opinion mining and sentiment analysis to understand consumers’ attitudes and emotions, which helps evaluate how effectively different brands are performing.

The article employs the Lexicon-based approach to gather, preprocess, and analyze product review data from Amazon using R. The purpose of the study is to determine how customers feel about Amazon's private-label menswear, both positively and negatively. The study considered two brands: Symbol and Inkast. Sentiment scores and emotional variance estimate the outcomes of the Lexicon-based sentiment, which produces a classification of the emotions present in the text. According to the research, consumers are generally confident in their ability to trust Amazon's men's private clothing brands. Compared to Inkast, people prefer buying under the Symbol brand. Sentiment analysis enhances business expansion and customer experience.

AI's integration in healthcare has led to significant progress in identifying diseases. Recent innovations, especially in machine learning and deep learning, have fostered the creation of advanced models for prompt and precise disease detection. This paper touches upon AI's role in medical imaging, the analysis of electronic health records, and the use of wearables, emphasizing its transformative effect by improving diagnostic precision and efficiency. We explore specific instances like diagnosing cancer, evaluating cardiovascular risks, and spotting infectious diseases, showcasing the value of AI solutions. Nonetheless, this innovation comes with its set of challenges. Concerns related to ethics and privacy, data quality, availability, and the pressing need for regulatory structures are pivotal challenges confronting AI in disease detection. We delve deep into these challenges, providing possible solutions. As we look to the future, we suggest directions such as improving the clarity and justification behind AI model decisions, widening the spectrum of detectable diseases, and endorsing partnerships between healthcare experts, AI specialists, and regulators to guarantee AI's safe integration into healthcare. This paper aims to give a holistic understanding of where AI stands in disease detection, focusing on its advantages and the hurdles ahead. It's an essential guide for researchers, healthcare professionals, and decision-makers aiming to harness AI for diagnosing diseases and enhancing health outcomes.

AI-powered marketing strategies are now crucial to transforming the way businesses interact with their customers. Firstly, it focuses on examining state-of-the-art marketing technologies based on AI with reference to Industry 5.0, which includes social media platforms, big data analytics, augmented reality, virtual reality, with reference to Industry 5.0, which includes social media platforms, big data analytics, augmented reality, virtual reality, and the Internet of Things. Industry 5.0 is a concept with a shift toward machine-human cooperation, which refers to an exchange between mutually beneficial parties related to technological signaling and workers. It highlights the combination of emotional intelligence, problem-solving skills, and artificial intelligence and automation, which is useful in creating more productive industrial environments. This approach facilitates modern industry operations. The analysis of the significance and challenges of integrating new AI-enabled technologies in marketing management is the second main objective of this study. The study focuses on discovering new ways in which Indian companies are utilizing modern technologies to enhance their operations. One of the major goals of the study is to help businesses understand how to effectively utilize AR and VR in their marketing strategies, enabling them to enhance their client experiences. More concerns have been raised about the data privacy associated with the use of AI-enabled products. It provides guidance to marketing managers on how to evaluate the complexities involved in incorporating all new AI-based technologies into their existing workflows. Ultimately, it highlights the importance of companies effectively managing the complexities of technology implementation to maintain their leadership in the modern era of marketing strategies.

The integration of AI into horticulture offers a revolutionary strategy for environmentally responsible resource allocation. The modern horticultural industry must find a way to increase production while reducing its negative effects on the environment. The advent of AI presents a revolutionary chance to optimize resource management procedures. Precision farming, pest and disease management, and crop yield prediction are at the forefront of AI-enabled green and sustainable resource management in horticulture. The basic concept is to establish a networked horticulture ecosystem in which AI collects data from multiple sources and provides real-time insights for intelligent decision-making with the help of Algorithms like Ant Colony Optimization, Particle Swarm Optimization, or neural networks with fuzzy logic. It can simulate natural behaviors to optimize planting patterns and resource allocation. Improvements in resource effectiveness, productivity, environmental protection, and economic sustainability are among the anticipated consequences. Among the outcomes we expect are increases in resource efficiency, productivity, environmental preservation, and economic sustainability. When AI is applied to the horticulture industry, it has the potential to dramatically improve resource management, leading to greater longevity, output, and profitability. 

This chapter focuses on route selection by simultaneously evaluating the safety performance and transportation cost associated with each alternative path. It introduces the concept of a Cost–Safety Matrix as a tool for risk analysis of transportation routes. In this framework, the cost matrix quantifies the transportation expenditure for each route, whereas the safety matrix characterizes the safety-related attributes of those routes. The combined cost–safety matrix incorporates both the economic considerations and the inherent risk present on each individual path. The corresponding risk matrix is developed using a defined numerical scale derived from historical (past) data. The cost of transportation and risk level combine to make a matrix that can be divided into three parts. The categories used to describe the danger level are low, moderate, and high. This matrix may be used to pick a route before commencing the transport. Integrating Artificial Intelligence (AI) into material handling procedures is critical for increasing productivity and maintaining worker safety. The primary purpose of this study is to identify and reduce potential dangers associated with lifting, transporting, and handling products. The primary focus is on using AI to estimate the danger of material handling jobs. The study uses machine learning techniques, data analytics, and sensor technologies to examine large datasets relevant to material handling processes. By harnessing the power of AI, the research aims to develop a robust risk assessment framework that can automatically detect, predict, and mitigate potential risks associated with falling materials, collapsing loads, improper lifting techniques, and struck-by hazards.