Advanced Technologies for Realizing Sustainable Development Goals: 5G, AI, Big Data, Blockchain, and Industry 4.0 Application

The Sustainable Development Goal 11 aims to make cities inclusive, safe, resilient, and sustainable. With economic growth and development, the number cities has increased, and the slums are also increasing, which cause a number of problems like solid waste management, pollution, poor traffic systems, and many more. Among other major concerns, one significant concern is that of energy consumption. To protect the environment, it has become significant to act sustainably and choose sustainable ways to develop cities. Globally, there has been raising concern over the disparities and energy-related CO2 emissions. With an increase in the number of people living in cities, the construction of sustainable cities has become necessary; hence in this regard, it has become crucial to concentrate on technological innovation in energy efficiency. The sustainable urban development path will transform cities to achieve Goal 11 for the path to a sustainable future. Energy efficiency is one of the most immediate aspects on which the whole world needs to work. This study seeks to explain the contribution of technology to support energy-efficient tools for constructing sustainable cities. This study represents dimensions of sustainable development and convergence of energy efficient technology enabling advanced levels of sustainable development along with the challenges and key policy recommendations to achieve Goal 11.

This chapter explores the ways in which blockchain technology can be used to address the challenges of gender inequality in developing countries. It begins with an overview of gender inequality and its persistence in various areas, followed by an introduction to blockchain technology as a potential solution to some of these challenges. The chapter then focuses on three key areas where blockchain can empower women: access to financial services, elimination of bias, and enhancement of safety. Through examples of blockchain-based platforms, the chapter explains how women can use blockchain to participate in financial transactions, report incidents of harassment, abuse, or violence, and identify and eliminate discriminatory practices in various fields. The chapter also highlights the challenges and limitations of blockchain technology in promoting gender equality, including the lack of gender diversity in the tech industry and the risk of reproducing existing gender biases. Additionally, the chapter discusses factors such as connectivity, literacy, and access to technology that can limit the use of blockchain in developing countries. The chapter concludes with a call for diversity and inclusivity in the design and deployment of blockchain solutions and emphasizes the need to address underlying structural inequalities that affect women's ability to use technology and financial services.

CNN has emerged as the de-facto standard for several machine learning (ML) and computer vision applications. It is known for its classification and feature extraction capabilities. Many ML techniques require separate handcrafted feature extraction steps before classification, which are “sub-optimal” in nature. Unlike these, CNN extracts “optimal” features directly from raw data, enabling it to enhance classification accuracy. Two-dimensional CNN (2D-CNN) is the most common one, where inputs to the CNNs are 2D in nature, such as images. Here, we used 1D-CNN for data classification as we used 1D inputs. 1D-CNN has lower computational complexity than 2D-CNN. Mainly for this, we preferred 1D-CNN over 2D-CNN. To demonstrate the superiority of the proposed generic classifier, we compared its classification accuracies with several other generic classifiers. We used 21 benchmark data sets from the UCI machine learning repository to achieve this. Tests prove the superiority of the proposed 1D-CNN-based generic classifier. Many 1D-CNN-based application-specific classifiers are proposed in the literature, but the proposed classifier is applicable for many types of tabular data i.e., it is a generic classifier.

Nowadays, shipping firms have discovered faster connectivity for their ships, not only for the captains but also for the shipping industries. Most of the ships have developed floating offices in their ships that provide crews and captains with email, secure Internet connections, route planners, virtual networks, and a variety of other systems. However, this is the correct time for shipping firms to invest in emerging smart technology that can increase the ship's performance and streamline and even reduce the running costs of the ships. This is possible only with emerging technologies like artificial intelligence and machine learning. Smart computers can store enormous volumes of data and perform even more quickly than humans. The growth of smart technologies like machine learning, artificial intelligence, big data, and data science in collaboration with the shipping industry gives a significant benefit to the ship owners. The greater usage of AI, big data, and ML gives a greater investment. Machine learning algorithms can accommodate data that covers almost the entire history of a vessel's life. The aim of this paper is to provide a technological overview of smart technologies in the shipping industry. 

The next generation of mobile technology, known as 5G, poses a huge challenge to the existing state of the communications industry since it intends to solve the issues that have plagued the 4G network in its current iteration. This cutting-edge technology enables the establishment of multiple connections all at once and maintains network ubiquity even in settings that involve high levels of mobility or densely populated areas. As a result, smart cities and intelligent transport systems may stand to benefit from its use. 5G will make it possible for the actual Internet of Things and the Internet of Vehicles to become a reality if this plan is implemented. The advent of 5G will herald the beginning of a new era of opportunities for networks and services. It will help in maintaining an increased data rate, reduced latency, huge simultaneous connections, and ubiquity of networks around the world. 5G will also be a crucial enabler for a true Internet of Things because of its capacity to connect a vast number of sensors and actuators while following severe energy efficiency and transmission limits.

The Internet of Things (IoT), a new digital communication paradigm, has made it possible for things that are often found in daily life to interact with one another as well as with people [6]. As a result, the objective of the Internet of Things is to expand both the breadth and depth of the Internet. This will be accomplished by making it easier for a wide variety of devices, including vehicles, home appliances, security cameras, industrial actuators, and many more, to interact with one another in an unobtrusive manner. When 5G is applied to the Internet of Things in cities, it may be able to keep track of the total amount of energy that is consumed by all of the city's public services (including lighting, traffic lights, security cameras, and the heating and cooling of public buildings, amongst other things). Municipalities will, among other things, be able to manage their energy resources more effectively if they have access to this information.

Techniques for sentiment analysis are crucial for examining people's opinions. People's attitudes are influenced by the constant and rapid growth in the amount of material provided on social networks. Most research, however, focuses on sentiment analysis to determine how a war will affect the global economy. Consequently, when studying international conflicts, national leaders and other influential figures tend to receive more attention than public opinion and emotions. The purpose of the article is to discuss the analysis of moods and focus on the analysis of emotions and social opinions during the Russian-Ukrainian conflict in order to detect the early signs of post-traumatic stress disorder (PTSD). Post Traumatic Stress Disorder (PTSD) and realizing Sustainable Development Goals (SDGs) are vital. PTSD, with its impact on mental health, poses challenges to achieving Goal 3 of Good Health and Well-being. Additionally, it hampers access to quality education (Goal 4), especially in formative years, and can disproportionately affect women, hindering progress towards Goal 5 of Gender Equality. In the context of conflict, PTSD impedes the establishment of strong institutions (Goal 16) and underscores the importance of collaborative efforts (Goal 17) to address the complex challenges associated with trauma for a resilient and sustainable society. This study is the first to propose a model that reflects the desire to analyze the impact of this war on mental health during the Russian-Ukrainian armed conflict. This can protect people from mental disorders and suicide and provide clues for future research in this area. The method used in this work is a bidirectional LSTM method with a focus-based network approach to analyzing the sentiment of English tweets, using positive, negative, and neutral classification in a multiclass approach to detect signs of PTSD. Sentiment analysis is a method for extracting emotional content from textual information using natural language processing (NLP). Our study aims to identify people with PTSD using sentiment analysis by training a deep learning (ML) model on text data. Based on text mood analysis, trained models can detect post-traumatic stress disorder (PTSD). We attained a 90.02% accuracy by combining the attentional mechanism with a bidirectional shortterm memory layer (Bi-LSTM). The findings of the suggested framework demonstrate greater accuracy than earlier state-of-the-art investigations. By creating an early detection model, post-traumatic stress disorder (PTSD) symptoms may be lessened.

Cybersecurity is a sensitive and critical issue across multiple domains. It is a range of technologies, techniques, and practices designed to protect sensitive, priceless data, well-configured networks, source code snippets, and system programs from attackers, damage and unauthorized access. But we are gyratory our daily lives with these concerns, there is a possibility that we setout truthfully suspicious about our own professional and personal safety. Cyber security is also formulating the process of malware detection more actionable, scalable and effective than traditional approaches, which require human intervention. At the same time web technologies and cyber security can contribute to the implementation of the concept of sustainable development. This study gives a brief description about promising issues like cyber space, cyber-attacks, cyber security, cyber-crime, cyber forensic, cyber defamation, cyber terrorism, cyber law and types of cyber-crimes impacts on professionals in the cyber-world. It also forecast how a professional can conscious of current cyber world and also a theoretical discussion and explore on the relationships between emerging issues of cyber security and sustainable development.

As we rely more on the internet in our daily lives, network attacks pose a severe threat to the safety of computer systems and networks. Cybercriminals utilize a variety of methods to access sensitive data without authorization by taking advantage of network flaws. Firewalls and intrusion detection systems, which are common security measures, have not been found to be effective in preventing network attacks. The connection between network vulnerability detection and realizing sustainable development goals lies in the broader impact of cybersecurity on the economic, social, and environmental aspects of sustainable development.

Deep learning, a kind of machine learning that makes use of neural networks with numerous layers to understand complicated patterns in data, has attracted the attention of researchers and practitioners in an effort to combat the sophistication of cyberattacks that are becoming more sophisticated. Deep learning has demonstrated potential in identifying network attacks due to its ability to automatically extract features from unprocessed data, enhancing its ability to identify previously unknown assaults.

These patterns may include unusually high or low levels of network traffic, adjustments to communication patterns, and other aberrant behavior that could be a sign of an impending attack. The first step in using a deep learning algorithm to detect network attacks is to collect and pre-process the data. In this analysis, we used the NSL-KDD dataset, a freely accessible dataset that includes information on both regular and attack traffic. We can start training our deep learning model once we get the data.

Large amounts of data are fed into the algorithm during training, and the neural network's parameters are changed to reduce the discrepancy between expected and actual results. Different deep learning architectures, such as convolutional neural networks (CNNs), are available to us.

We can use the model to categorize fresh instances of network traffic as either benign or harmful after it has been trained. The model's performance can then be assessed using measures like recall, accuracy, and precision. Our test findings demonstrate that the suggested deep learning technique works better at identifying network assaults than conventional machine learning algorithms. Deep learning algorithms are better equipped to manage the complexity of network traffic data and extract useful information from it.

The security of computer systems and networks is seriously threatened by network attacks, and conventional security measures have not been successful in thwarting them. Our paper offers a thorough manual on how to detect network assaults using deep learning, which has shown potential in this area.

Our daily lives are now completely integrated with internet access. The way we interact with one another, the way we meet new people, the way we communicate information, the way we have fun, and the way we shop have been transformed as a result. They have an impact on many of our daily activities. Today's digital environment has made cybersecurity a requirement rather than a luxury. Cybersecurity refers to a group of security techniques that can be used to protect user data and the internet against attack and penetration. A cyber defense system's primary goal is for data to be confidential, integral, and available.

With one of the world's fastest-rising technological hubs comes a heightened danger of cyberattacks, but India has swiftly emerged as one of them. Indian companies are dealing with a wide range of security issues, such as financial fraud, data breaches, and state-sponsored attacks. One of the biggest and most significant forces of sustainable economic and social growth is information technology.

More precisely, cyber security is now viewed as one of the most important components in guaranteeing global sustainable development. It has been noted that the United Nations Sustainable Development Goals (UNSDG) have prioritized cyber security to protect the cyber environment. To achieve the objectives stated in the Nation for Sustainable Development Goals, trust in ICT is essential. The goals of sustainable development might be challenging to achieve in the absence of a stable and secure cyberspace.

The chapter highlights the part played in establishing stronger cyber security standards as an impetus for sustainable development through the state, business, and other nonstate individuals. In addition to improving online security and protecting India from cyber threats and vulnerabilities, more effective policymaking, improved tools and techniques, cyber architectural designing, and collaborative efforts of private parties like media, industry, civil societies, and other nations and international organizations will also play a vital role towards achieving the global sustainable development goals.

Nations, as well as states, need essential cybersecurity that not only protects from the current threats but also enables proactive protection against emerging and forthcoming threats to be able to confidently respond to latest challenges as cyberattacks become more complex.

The global healthcare ecosystem is being changed thanks to substantial advancements in the fields of machine learning (ML) and artificial intelligence (AI). The potential of ML and AI to improve patient care, increase diagnostic accuracy, optimize treatment regimens, and reduce administrative procedures is covered in this chapter as we investigate the many methods and uses of ML and AI within the healthcare industry. ML and AI have the potential to change healthcare delivery, resource allocation, and disease prevention through the use of large-scale data analysis, predictive modeling, and intelligent decision-making systems. This chapter presents a thorough review of the existing ML and AI methods used in healthcare, emphasizing their advantages, difficulties, and potential future applications. The global healthcare ecosystem might be changed by the introduction of ML and AI, resulting in improved patient outcomes. ML and AI can help expand access to healthcare by enabling remote diagnosis and telemedicine, especially in underserved areas. This aligns with the goal of ensuring healthy lives and promoting well-being for all ages.

Big data is utilized in healthcare to save costs, cure diseases, increase revenues, anticipate epidemics, and improve the quality of life by averting fatalities. This is where the voyage through big data in healthcare gets started, covering some of the most widely utilized applications of big data in the healthcare sector. The source of big data in healthcare is large electronic health databases, which are extremely difficult to maintain with standard hardware and software. Making sense of all this data and using it wisely for treatment plans, clinical operations, and medical research is a problem for the healthcare business because 80% of healthcare data is unstructured. Big data and cloud computing can help healthcare providers optimize resources, reduce administrative costs, and improve operational efficiency, making healthcare more affordable and sustainable (SDG 3). By analyzing big data, healthcare providers can identify and predict disease outbreaks, track the spread of diseases, and develop effective prevention and management strategies, contributing to the goal of reducing the global burden of disease (SDG 3). Cloud computing provides secure and scalable storage solutions for health data, ensuring privacy and security while enabling datadriven decision-making for better health outcomes (SDG 3, SDG 9). Big data analytics and cloud computing support medical research and innovation by providing researchers with access to large datasets and computational resources, leading to the development of new treatments and technologies to address global health challenges (SDG 3, SDG 9).

In recent years, there have been a number of challenges that have posed a danger to the food security of the globe. These issues include the growing population of the world, the consequences of climate change, and the emergence of new pandemics. Because of this, it is of utmost importance that we come up with innovative strategies to strengthen the food system's resilience in the face of these challenges. The technologies that underpin the Fourth Industrial Revolution (also known as Industry 4.0) are causing widespread upheaval throughout a wide range of production and consuming businesses, including the food and agriculture industries. This chapter will provide a general overview of green technology and Industry 4.0 strategies, focusing on how they apply to the food sector. We are going to focus on and investigate the linkages between green food technologies, such as green preserving, processing, extraction, and analysis, and the enablers of Industry 4.0, such as artificial intelligence, big data, smart sensors, robotics, blockchain, and the Internet of Things. Green food technologies include those that preserve food without the use of harmful chemicals, as well as those that extract and analyze food's constituents. The Sustainable Development Goals (also known as SDGs) are relevant to these linkages. The Sustainable Development Goals (SDGs) are becoming increasingly difficult to achieve without the help of environmentally friendly technologies and the Fourth Industrial Revolution. These advances have the potential to speed up digital and ecological transitions in fast moving consumer goods, which will have positive effects not just on individuals but also on the planet. With the assistance of green and digital solutions, which are anticipated to be implemented at a larger rate in the next years, it is conceivable to realize a future that is not only more robust but also healthier, more intelligent, and friendlier to the environment.

In light of the present circumstances, corporate executives, government officials, and academics may now place a higher priority on the collection and analysis of crucial data as a potent instrument for solving the issues of managing the contemporary food supply chain. As food and beverage (F&B) companies place a greater emphasis on collecting, processing, and analyzing relevant data from a variety of sources throughout their respective food systems, data management has become an invaluable resource in modern food supply chains (FSCs). This is because modern FSCs are designed to be more efficient than traditional supply chains. In this context, the phrase “big data” (BD) has only very recently begun to be used to refer to huge quantities of heterogeneous and geographically dispersed data assets that have fast rates of change, a wide variety of sizes, and high volumes of information. Recent research has stated that implementing BD in FSCs might result in a yearly gain in value that ranges from USD 120 billion to USD 150 billion. The current study is focused on analyzing the impact of big data in the food supply chain for realizing sustainable development goals in emerging economies. The researcher intends to collect data from primary and secondary sources. This paper focuses on understanding the conceptual framework that incorporates the relationship between FSC performance and BD applications. 

Smart agriculture is a new sector that integrates cutting-edge technologies for transforming conventional farming methods into sustainable farming methods, such as increasing crop yields, lower expenses, and conserving natural resources. Machine learning (ML) and deep learning (DL) are two significant techniques for smart agriculture that can be used to analyze enormous volumes of data and extract significant insights to enhance agricultural practices. In this context, ML and DL may be utilized for a number of tasks, including crop yield prediction, disease and pest detection, weather pattern monitoring, and irrigation and fertilization management. The proposed chapter investigates the utilization of ML and DL in smart agriculture and highlights some of the most promising uses of these technologies. The study addresses the obstacles and potential of adopting ML and DL in agriculture, such as data quality, privacy problems, and the requirement for specialized hardware and software. The study also looks at some of the most important developments in smart agriculture, including the usage of sensors, drones, and other IoT devices, as well as the integration of ML and DL with other technologies like precision farming and robotics. Overall, we believe that ML and DL have the ability to transform the way we produce food and manage our natural resources by empowering farmers to make better decisions, decrease waste, and boost production.

Pharmaceuticals and associated industries, manufacturers of hospital supplies, equipment, and services, healthcare facilities, managed care, medical services, and health insurance are the basic sub-segments that may be used to classify the core segments of the healthcare business. The goal of technology-assisted virtualization is to personalize healthcare for patients, professionals, and other stakeholders. In brief, the “Health 4.0” movement uses technology to increase communication among healthcare stakeholders and elevate the quality of healthcare services. Healthcare is progressing beyond conventional healthcare resources and toward more virtual, dispersed care that makes extensive use of cutting-edge technologies, such as deep learning (DL), genomics, artificial intelligence (AI), data analytics, robots, home-based healthcare, and 3D printing of tissue and implants.

Healthcare 4.0, often known as the fourth manufacturing revolution, is a new concept. The concept relies on intelligent robots with access to huge amounts of data and the ability to make decisions without the assistance of humans. The employment of robots in healthcare involves the difficulty of integrating new technology into an already complex, highly regulated system. Although robots have made multiple activities easier, some unforeseen consequences have changed ethical rules and pharmacist employment. Lio, a personal assistive robot, was immediately modified during the COVID-19 pandemic to handle additional functions like disinfection and remote detection of elevated body temperature. It complies with ISO13482 - personal care robot safety rules, allowing it to be tested and deployed directly in care facilities. To keep up with the speed of rapidly changing technologies, a low-cost, highly efficient localization solution for wireless sensor technologies is critical. The suggested system provides a solid foundation for future testing and optimization in collaboration with the user, ensuring a useful and appropriate mix of sensors and technical equipment with which the user is familiar.

Public policies like the Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) are designed to mitigate unwarranted unemployment and develop rural infrastructure and resources to have sustainable development in rural India. The pandemics (e.g., flood, drought, and COVID-19) pose the greatest livelihood challenge for these migrant workers. Does MGNREGS provide them with minimum support for the sustainability of their familiesthrough wage employment? Does MGNREGS work to contribute to rural sustainable development? To answer these questions, this study attempted to have an appraisal of the performance of MGNREGS both qualitatively as well as quantitatively across the country by taking a five-year study period (including the COVID-19 period). The study was supported by a focus group interview of migrant laborers who returned to Odisha during the initial phases of the pandemic in India. In the second part, the previous five years’ data was collected from the official website. A comparative cross-sectional analysis of employment generation through MGNREGS in the pre and post-COVID-19 period was made. An administrative efficiency index (AEI) of the program was prepared and compared to the period from April- September 2016 to 2020 for the entire nation. The results explored that there was a significant employment generation through MGNREGS for migrant workers during this pandemic. A conceptual model of ‘migrant employment during the pandemic’ was proposed on the basis of the empirical results. The model has both theoretical and practical implications.

Big data analytics (BDA), as an important tool, is now available to companies who are struggling with problems related to sustainability. However, there are not many case studies of BDA in the academic literature, despite the fact that it has the potential to increase the eco-efficiency of manufacturing. This study focuses specifically on the manufacturing sector to investigate the impact of BDA on green innovation (GI), competitive advantage (CA), and environmental performance (EP) in the context of the manufacturing industry. Big data analytics, also known as BDA, is a relatively new field that has emerged as a result of the growth of contemporary computers and their various uses. The relatively new subjects of business data analytics (BDA) and business analytics (BA) have piqued the attention of both working professionals and academics.

The purpose of this article is to conduct an analysis of the influence that Big Data has had on four important performance indicators: innovation, competitive advantage, productivity growth, and support with decision-making. Big data may help organizations obtain vital insights about their consumers, products, and operations, despite the fact that it does come with a few negatives. Businesses are in a better position to quickly implement new ideas, provide better service to customers, increase efficiency, make decisions that are better informed, and ultimately outperform their rivals when they have access to data insights. The expansion in both the amount and quality of the data that is now accessible has led to improvements in the capacities of organizations as well as the opening of new doors leading to growth. Businesses are renouncing established practices in favor of new, inventive, and innovative techniques in order to redefine creativity, competitiveness, and productivity. It is vital, for the sake of achieving sustainable objectives, to have an understanding of big data analytics as a critical component of the road map for green innovation, competitive advantage, and ecological performance.  

The new advancements in healthcare systems are influenced majorly by the adoption of the Internet of Things (IoT). This is especially important in light of the present state of affairs in the healthcare, social welfare, and energy sectors. By understanding the interconnected problems such as energy efficiency and sustainable development, it may be possible to enhance the well-being of both humans and the environment. The incorporation of sensors and other intelligent devices is crucial to the accomplishment of the aims of sustainable development. In todays rise in population, there is a key area in which the latest scientific developments really need to be put into practice: public health. For the sake of the well-being of future generations, it is essential toconduct research on the ways in which the SDGs have an impact on the uses of sensors and the Internet of Things in human environments. Peoples lives are being influenced by applications of technology, sensor networks, intelligent systems, and the Internet of Things (IoT), all of which are having a positive impact on the environmental sustainability and energy efficiency of the world.

The digitization and application of intelligent systems and the Internet of Things devices are carried out in blocks of analysis, organized in a variety of disciplines, in urbanized settings, and in human-inhabited communities; nonetheless, they all have a similar center of gravity, which is the trilogy: human, technology, and sustainability. The management of effective and healthy resources, enhanced governance, and programs that encourage the adoption of new technological solutions are all necessary for sustainable development in better healthcare services. The study is focused on the major determinants of implementing Internet of Things (IoT) tools in delivering better healthcare services.

Due to its quantitative capacity to differentiate Fermatean fuzzy sets (FFSs), distance measurement is a research hotspot in the Fermatean fuzzy set. For Fermatean fuzzy sets, we used cosine distance, a novel distance metric. In this study, measures of cosine similarity and cosine distance amongst FFSs are taken into account when trying to solve a multi-attribute decision-making problem based on sustainable development goals. In this study, we used the cosine distance measure for Fermatean fuzzy set theory to rank a set of urban cities according to many factors, including poverty, health, industrial development, and climate quality. Additionally, a suitable example is used to illustrate the superiority and logic of the present formulation.

Every nation in the world is striving to achieve the Sustainable Development Goals (SDGs). In keeping with this, a sound global financial system is now required to fulfill its mandate to encourage the mobilization of private capital for the achievement of sustainable development and consistent economic growth. The fintech revolution has significantly altered the financial industry and improved financial inclusion in the nation by offering digital financial services. Fintech has contributed positively to the SDGs by providing increased access to funds and financial services, which, in turn, has resulted in improvised saving opportunities for a large group of people. A wide range of application developments, comprising blockchain, IoT (the Internet of Things), AI (artificial intelligence), big data, and mobile platforms, have recently been part of digital transformation and technological advancement, particularly in the finance sector. These application developments promised to improve performance in the financial sector. It becomes clear that financial applications for digital technology can overcome significant funding challenges for inclusive and sustainable growth. In this scenario, the chapter provides an overview and in-depth examination of the most recent advancements in financial technologies (Fintech) that support the SDGs. It also reviews the significance and effectiveness of fintech in achieving sustainable development goals like education, health, equality, etc.