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Big Data Paradigm in Cybercrime Investigation
Big Data is a field that provides a wide range of ways for analyzing and retrieving data as well as hidden patterns of complex and large data collections. As cybercrime and the danger of data theft increase, there is a greater demand for a more robust algorithm for cyber security. Big Data concepts and monitoring are extremely useful in discovering patterns of illegal activity on the internet and informing the appropriate authorities. This chapter investigates privacy and security in the context of Big Data, proposing a paradigm for Big Data privacy and security. It also investigates a classification of Big Data-driven privacy and security of each algorithm. In this section, we first define Big Data in the contexts of police, criminology, and criminal psychology. The chapter will look at how it might be used to analyze concerns that these paradigms confront carefully. We provide a conceptual approach for assisting criminal investigations, as well as a variety of application situations in which Big Data may bring fresh insights into detecting facts regarding illegal incidents. Finally, this chapter will explore the implications, limits, and effects of Big Data monitoring in cybercrime investigations. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors. -
Internet of Things-Based Smart Agriculture Advisory System
The Internet era provides a lot of automation tools for data analysis, and it is the need of the hour to develop new analytical tools to manage the big data. For task automation, machine learning and expert systems are of primary importance to study the behavior of computer thinking to involve computers in sensible work, known as computational intelligence. The data involves varied formats such as structured, unstructured, as well as semi-structured, and it is an automation tool that uses computational intelligence to extract valid and potential information from the sources. The specific purpose of this proposed work is to meet out computing demands which highly rely on computational intelligence. Computational intelligenceinvolves the design and deployment of an analytical tool for multidimensional data analytics. The proposed integrated framework focuses on multidimensional data analytics, for crop and plant data, especially plants that contain medicinal values and components. This research works main aim is to create a secured data tool for agriculture crop data management through big data (crops and plants) analytics. The data security is enhanced through applied cryptography, and the final phase prediction on crops is done by various machine and deep learning algorithms. The specific objective of this research work is to help farmers in making informed decisions for the enhancement of cultivation and information. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Deep Learning Decision Support Model for Police Investigation
A police investigation is an exciting task with many complicated processes that may or may not succeed. However, it is the sole duty of a police officer to understand the crime scene, reconstruct the event and predict the criminal with accuracy. There are various methods for interrogations, predictions, and confirmation after identifying a person as a criminal or upon concluding their actions as a criminal act. However, we can see massive growth in crime rates every day. This massive growth rate makes conventional prediction or analysis very strenuous. In such times we can use or take the help of deep learning and machine learning methods for crime analysis and suspect prediction by identifying the data points in a set. This prediction methodology is known as intelligence analysis which simulates the dataset to draw a connection or pattern collectively from millions of data points to identify the instigator and linkman. This chapter will summarize the uses of deep learning and artificial intelligence in a decision support model for police investigation. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors. -
Computer-based Intelligence and Security
There is a massive increase in the incidence of cyberattacks day by day in the modern enterprise environment. Although humans are behind this task due to the rapid growth in the incidence human intervention is unable to control this. Therefore, something more than human intervention is required to have a check over it. With cyberattacks evolving at a rapid pace and with the increase in the use of devices in todays world, Artificial Intelligence (AI) and Machine Learning (ML) can help to have a check over cybercrime incidences, automate the process of detection of threat, and handle these in a better way as compared to the conventional methods used for controlling cybercrime and cyberattack. AI and ML has shown good results in data information security as these technologies are capable of analyzing a large and wide variety of data and can track threats related to cyberattacks that may cause phishing attacks. As these technologies are capable of learning and improving from past experiences, they can even predict and tell the new variety of attacks that may occur in the coming days. This chapter describes the use of AI in controlling cyberattacks and cybercrime and the expert views on this matter. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors. -
Beyond Humour: How Memes Shape Brand Associations and Drive Purchase Choices
Memes are the perfect marketing tools a brand can use while promoting their products or services. In this ever-changing consumer preferences memes are the convenient marketing tools that a consumer pays attention, the usage of memes has become a completely modern approach for brands to seek the attention of consumers. The study examines the impact of internet meme that spreads through social media which catches the consumer attention and improves the intention of purchasing products and also learn about the brands. A structured questionnaire and convenience sampling technique are designed to collect data from frequent internet users who are active in social media from Gen-z and have at least a little knowledge on meme marketing, and responses yielded were 353. This paper gives a general study of meme marketing and if the consumer brand relatability and purchase decisions are affected by meme marketing. The findings state that there is a relationship between branding memes and consumer brand relatability and using the memes in social handles affect the consumer behaviour however there is a discomfort among consumers when brands solely use memes for marketing purposes. Also the study found that there is no significance between Gender and meme motivation into buying products. Thus the study contributes to understand the consumer behaviour, purchase intention and likeliness towards the brands. In addition, the authors contribute to the finding the significance of meme in daily life of a consumer and what type of memes would pursue consumers more towards the brand. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Cybercrimes in the Associated World
Phrases that scarcely existed a decade ago are now a part of our day-to-day lifestyle, as criminals use malicious new technologies to commit cyber attacks against businesses, individuals, and governments. These crimes cause serious harm and impose real threats to victims worldwide either physically or virtually. There are no borders in cyberspace. Attacks can come from any place and at any time. Cybercrime can take many forms, but they all have a digital platform/environment in common. It can be done with both good and bad intentions. But, nowadays, the most common types of cybercrime activities such as phishing scams, identity theft, Internet frauds, online intellectual property or patent infringements, online harassment, and cyber stalking are sadly very widespread in todays associated world. Cyber bullying and online harassment activities spread casually in social media posts and comments or through direct messages and also via emails. The main motive of these messages is to threaten either an individual or a group. Such kinds of cybercrime activities are extremely damaging to the victims mental health. Government agencies working to investigate cybercrimes have reported multiple records of victims developing mental illnesses and even ending up committing suicide. On the other hand we have phishing scams, one of the widespread crime activities. Organizations have detected an increase in the ratio of phishing emails to professional emails from unknown or anonymous service providers appending fake attachments and invoices. These files and attachments may contain malicious payloads to scam people and to create a backdoor in that system, so the attacker can gain access to the system anytime and from anywhere without the victims knowledge. This has been considered as one of the major advantages for the attacker. Cybercrimes have not restricted to only these forms of criminal activities. A wide variety of new attacks have been created and have spread all over the world through commonly used platforms such as social media sites, blogs, and news portals. We are living in a digital world where all our activities are being monitored by someone, somewhere - even keystrokes are being monitored using keyloggers. Nothing seems to be secret and protected unless you are tech savvy. National agencies are keeping a close watch on all individual online activities to prevent illegal activities from happening. No longer the delete option is possible in this digital world; rather, only migration of data from one location to another or from a local server to a cloud server is possible. In our day-to-day lives, several new viruses and attack mechanisms are triggered by attackers by following very new tactics with the help of more complex algorithms. So, its time to advance our knowledge on protecting our valuable assets by spending time in learning and following proper online practices. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors. -
Is ESG the Key to Stimulate Financial Performance? An Empirical Investigation
Environmental, Social, and Governance (ESG) principle is a critical framework for businesses and non-business entities. Recently, investors increasingly concentrate on the ethical impact of their investments in addition to financial rewards. The present study outlines links between a companys ESG practices and its financial performance. By means of performance comparison between the Nifty 100 ESG and Nifty 100 Indices within the Indian context, it issuggested that investing in ESG-adopting companies can lead to better financial performance. The study uses statistical analysis to compare the performance of both indices between 2018 and 2022. The analysis includes financial ratios, such as P/E ratio, ROA, ROE, and ROCE. The findings reveal a relatively modest connection between ESG and P/E ratio. The stronger negative correlations between ESG and key financial metrics (ROA, ROE, and ROCE), signifies a more robust trade-off between ESG focus and financial performance. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Big Data for Intelligence and Security
The name Big Data for Security and Intelligence is a method of analysis that focuses on huge data (ranging from petabytes to zettabytes) that includes all sources (such as log files, IP addresses, and emails). Various companies use big data technology for security and intelligence in order to identify suspicious tasks, threats, and security tasks. They are able to use this information to combat cyber-attacks. One of the limitations of big data security is the inability to cover both current and past data in order to be able to uncover identified threats, anomalies, and fraud to keep the n/wsafe from attacks. A number of organizations are addressing rising problems like APTs, attacks, and fraud by focusing on them. More is better than less! The easier it will be to determine. Nevertheless, organizations which utilize big data techniques make sure that privacy and security issues have been resolved before putting their data to use. Because there are so many different types of data stored in so many different systems, the infrastructure needed to analyze big data should be able to handle and support more advanced analytics like statistics and data mining. The one side of the coin is the collection and storing of lots of information; the other side is protecting massive amounts of information from uncertified access, which is very difficult. Big data is commonly used extensively in the improvement of security and the facilitation of law enforcement. Big data analytics are used by the US National Security Agency (NSA) to foil terrorist plots, while other agencies use big data to identify and handle cyber-attacks. Credit card companies use big data analytics tools to detect fraud transactions, while police departments use big data methods to track down criminals and forecast illegal activity. Big data is being used in amazing ways in todays information world, but security and privacy are the primary concerns when it comes to protecting massive amounts of data. Real-time data collection, standardization, and analysis used to analyze and enhance a companys overall security is referred to as Security Intelligence. The security intelligence nature entails the formation of software assets and personnel with the goal of uncovering actionable and useful insights that help the organization mitigate threats and reduce risks. To identify security incidents and the behaviors of attackers, todays analysts use machine learning and big data analysis. They also use this cutting-edge technology to automate identification and security events analysis and to extract security intelligence from event logs generated on a network. This chapter will discuss how Big Data analytics can help out in the world of security intelligence, what the appropriate infrastructure needs to be in order to make it useful, how it is more efficient than more traditional approaches, and what it would look like if we built an analytic engine specifically for security intelligence. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors. -
India-Maldives Development Partnership: Promises and Possibilities
Indias approach towards Development Partnership with the external world has been inclusive, humanistic, unconditional, comprehensive and futuristic. India-Maldives Development Partnership has to be seen in the context of India-Maldives relations that have been described as close, cordial and multi-dimensional. The Maldives undoubtedly occupies a very special place in Indias Neighborhood First policy. Indias development partnership with the Maldives goes with the `SAGAR (Security and Growth for All in the Region) vision of the Government of India. The partnership is characterised by transparency and as per the needs and priorities of the Maldivians. It touches every facet of the development of the Maldives to enhance stability and prosperity of the atoll state. Involving about US$ 3 billion in terms of grants, loans, budgetary support, capacity building and training assistance, the development partnership support is intended to reach the beneficiaries directly via the local councils. However, there are various challenges in the process of implementation of the projects under the development partnership. Yet, the future of the partnership looks promising. The main objective of the paper is to answer the following questions: What is the context of development partnership between India and Maldives? In what manner India has extended development assistance to its neighbour? Are there any challenges in the process of rendering such assistance? What is the way forward? 2024 Indian Council of World Affairs. -
CRISPR-Cas9 genome editing of crops: Food and nutritional security
The United Nations established the goal of achieving global food security by 2030 as one of its top sustainable development goals in 2015. The current agricultural harvest is insufficient to accomplish the zero-hunger objective and feed the world's growing population. It would require more extensive and consistent crop production. Gene-editing technologies have recently emerged as viable alternatives to permit precise modifications in genomes with increased efficiency and yield higher agricultural productivity. Due to their simplicity, adaptability, and reproducibility across a diverse variety of species, genetic engineering techniques like CRISPR-Cas9 have become quite prominent. CRISPR-Cas9 gene-editing technology can improve crop yields, quality, stress resistance, food safety, nutritional security, and shelf-life, reduce antibiotic resistance, and hasten plant domestication. Cutting-edge techniques like genome editing (GE) allow for the precise introduction or mutation of specified genes into plant genomes. The advent of programmable nucleases like CRISPR-Cas9 has improved gene editing and potentially improved food production and nutritional security. Knock-out, knock-in, gene activation, gene repression, nuclear rearrangements, base editing, molecular breeding, and epigenome engineering are just a few ways that CRISPR systems can target and change genes. For novel applications in plant genetic engineering, CRISPR-Cas systems can be repurposed for GE toward de-novo speciation; mitochondrial and plastid genome engineering toward enhancing photosynthesis, submergence, and drought tolerance. The versatility of CRISPR-associated systems broadens the scope of crop development applications that they can be used for, especially in improving food and nutritional security, which is the focus of this chapter. 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Redefining Organizational Sustainability Through Revamping Digital Capital
[No abstract available] -
Bioactive Compounds and Biological Activities of Lotus (Nelumbo nucifera Gaertn.)
Nelumbo nucifera Gaertn.Nelumbo nucifera Gaertn.Lotus (Nelumbonaceae A. Rich.) is a beautiful aquatic flowering plant with a subterranean rhizome. With a vast array of culinary applications and a storehouse of bioactive compounds in its plant parts, N. nucifera functions as both an underground food crop and a valuable medicinal plant. With a more than 7,000-year history of cultivation, this plant is one of the few aquatic plants used as a vegetable. N. nucifera possesses copious amounts of alkaloids and flavonoids as phytochemicals, along with various other derivatives. The rhizome is consumed as a vegetable since it has more carbohydrates, proteins, and vitamins, and it also possesses phytocompounds that exhibit immunomodulatory, antiviral, and antioxidant properties. Many countries in Asia use N. nucifera starch as a major culinary ingredient. To date, many phytochemicals isolated from this plant are used in many medicinal systems, including traditional, Ayurvedic, herbal, and oriental medicine. The extracts of various organs of this plant are used to treat numerous types of cancers, cardiac diseases, liver ailments, diabetes, and nervous disorders. The flower extracts are effective against fever, adipsia, cholera, and diarrhea. Eaten raw or puffed, lotus seeds are high in protein and contain minerals like calcium, phosphorus, iron, and potassium. The seeds are used as antibiotics to cure skin diseases like leprosy. Chinese medicine uses lotus seeds to treat renal and cardiac problems. Accordingly, N. nucifera is employed in food, medicine, culture, and religion. Furthermore, N. nucifera is an excellent environmental adapter and has the capacity to modify its resistance to environmental stress in order to adapt to a variety of abiotic stresses including flooding, extremely high temperatures, salt, low light, and heavy metals. It can therefore be grown in a variety of environments. Although this aquatic crop is restricted to an extensive geographical region and has a huge variety of cultivars, many parts of the world are still uninformed about this crop. Therefore, it is crucial to comprehend the medicinal and nutritional benefits of this tuberous crop in order to investigate it as a potential replacement for present-day food crops as well as a source of medicine. In order to effectively utilize this aquatic underground crop, this chapter aims to embody the nutritional advantages, traditional uses, phytochemistry, and bioactivity of the phytocompounds from the various parts of N. nucifera. It also emphasizes lotus breeding to date, applications as food, cultural aspects, and future production of potential N. nucifera underground crops of the highest quality. Springer Nature Switzerland AG 2024. -
Bioactive Compounds and Biological Activities of Cassava (Manihot esculenta Crantz)
The most significant tropical tuberous crop, cassava (Manihot esculenta Crantz), is grown extensively around the world. It has a lot of minerals that have been linked to health benefits, is high in calories, and contains vitamin C, an antioxidant that supports the creation of collagen and boosts immunity. It is known to be the biggest generator of carbohydrates among stable crops, with its roots serving as the main source of starch and dietary energy. Currently, cassava flour is being used in gluten-free or gluten-reduced foods as a novel food application. The cassava plant extract is a rich source of major phytochemicals consisting of flavonoids, tannins, cardiac glycosides, anthraquinone, phlobatannins, saponins, and anthrocyanosides along with other antinutritive factors that contribute to its diverse pharmacological activities like antibacterial activity, in vitro ovicidal and larvicidal activity, antioxidant activity, anti-inflammatory activity, and analgesic and antipyretic activities. This chapter provides a comprehensive overview of the botanical features, production statistics, nutritional composition and benefits, phytochemicals present and their biological activities present in different parts of cassava plants, toxicity, food applications, and various strategies of breeding for crop improvement. Springer Nature Switzerland AG 2024. -
Translation of supercapacitor technology from laboratory scale to commercialization
This overview chapter discusses the critical process of transforming supercapacitor technology from the laboratory scale to successful commercialization. Supercapacitors possess remarkable energy storage capacity and fast discharge cycles, making them highly promising for diverse applications, including electric vehicles and renewable energy systems. However, transitioning from small-scale prototyping to mass production presents significant challenges, such as scalability, cost-effectiveness, and maintaining consistent performance. The primary objective of this study is to conduct a comprehensive analysis of the main obstacles in the commercialization process and propose strategies and solutions to expedite the market introduction of supercapacitors. By identifying and addressing these hurdles, this research aims to facilitate the rapid and efficient transition of supercapacitor technology into commercial applications. To achieve this goal, the present chapter examines several aspects, including increasing production output, optimizing manufacturing processes, and reducing costs while upholding performance standards. Additionally, this chapter explores methods to ensure the scalability and reliability of supercapacitors, enabling seamless integration into existing energy storage systems. By bridging the gap between laboratory innovation and large-scale production, this study seeks to make a significant contribution to the realization of efficient and sustainable energy storage technologies across various industries. The successful commercialization of supercapacitors holds the potential to revolutionize the field of energy storage and provide viable solutions to global challenges, such as climate change and the transition to cleaner and more sustainable energy sources. In summary, this chapter addresses the challenges involved in transitioning supercapacitor technology from the laboratory scale to commercialization. 2024 Elsevier Inc. All rights reserved. -
Exploring the Impact of Latent and Obscure Factors on Left-Censored Data: Bayesian Approaches and Case Study
In the realm of scientific investigation, traditional survival studies have historically focused on mitigating failures over time. However, when both observed and unobserved variables remain enigmatic, adverse consequences can arise. Frailty models offer a promising approach to understanding the effects of these latent factors. In this scholarly work, we hypothesize that frailty has a lasting impact on the reversed hazard rate. Notably, our research highlights the reliability of generalized Lindley frailty models, rooted in the generalized log-logistic type II distribution, as a robust framework for capturing the widespread influence of inherent variability. To estimate the associated parameters, we employ diverse loss functions such as SELF, MQSELF, and PLF within a Bayesian framework, forming the foundation for Markov Chain Monte Carlo methodology. We subsequently utilize Bayesian assessment strategies to assess the effectiveness of our proposed models. To illustrate their superiority, we employ data from renowned Australian twins as a demonstrative case study, establishing the innovative models advantages over those relying on inverse Gaussian and gamma frailty distributions. This study delves into the impact of hidden and obscure factors on left-censored data, utilizing Bayesian methodologies, with a specific emphasis on the application of generalized Lindley frailty models. Our findings contribute to a deeper understanding of survival analysis, particularly when dealing with complex and unobservable covariates. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Polyoxometalates and redox-active molecular clusters for supercapacitors
Hybrid electric vehicles and portable electronic devices become inevitable part of our daily life and it is necessary to develop efficient energy storage devices to supply them power. Supercapacitors (SCs) are electrochemical energy storage devices with high power densities. The electrochemical performances of a SC depend mainly on the electrode-active material used in it. An efficient electrode-active material should have qualities such as large surface area, porous structure, uniform pore distribution, good chemical and electrochemical stabilities, and good mechanical strength, to name a few. Mesoporous electrode architecture is highly preferred to obtain maximum electrolyte-ion accessibility that can boost the electrochemical performance of the SC electrode. The various electrode-active materials developed to date are transition metal oxides, electronically conducting polymers, carbon nanomaterials, etc. Polyoxometalates (POMs) are comparatively novel electrode candidates that possess excellent structural stability during the reversible redox reactions. A unique characteristic such as higher oxidation state possessed by POMs makes them an ideal platform to accept and release electrons during the electrochemical charge storage. POMs are considered to be a polyatomic anion, which hold early transition metals like Mo, V, W, etc., and are linked to an oxygen atom in a three-dimensional cluster. The cluster formation of POMs enables higher stability and easy to prepare composites with other materials such as carbon nanomaterial, electronically conducting polymers, etc. The preparation of hybrid electrode architectures by anchoring of POMs helps in producing a large number of electroactive sites for the enhanced electrochemical reactions to occur. This chapter explains the salient features and functionalities of POMs and redox-active molecular clusters that affect the SC performance. 2024 Elsevier Inc. All rights reserved. -
Bioactive Compounds and Biological Activities of Ensete Species
Ensete, commonly known as the false banana, is a plant of the subtropical and tropical regions of Asia and Africa. Ensete has received global attention in the past decade. The various parts of the plant, such as the fruits, fruit peel, corm, pseudostem, seed, leaves, flowers, sap, and roots, have been used in traditional medicine to treat various ailments. Starch and other minor/trace components found in Ensete plants have been used as tablet binders, disintegrants, pharmaceutical gelling agents, and sustained release agents in pharmaceuticals and nutraceuticals. Ensete has been used as a staple and co-staple food by Ethiopians and has many ethnomedicinal uses. The present chapter validates the historic use of various parts of Ensete in treating ailments by providing detailed information on the phytochemicals present in the plant and discussing various biological properties such as antioxidant, antimicrobial, antidiabetic, immunomodulatory, hypolipidemic, cytotoxic, antiurolithiatic, antiestrogenic, nephroprotective, and hepatoprotective properties. Springer Nature Switzerland AG 2024. -
Leveraging Deep Learning in Hate Speech Analysis on Social Platform
The scope and usage of the Internet have surpassed the expected growth and have proven beyond the basic purpose of being used for networking and telecommunications. It serves as the backbone of the web, and one of the predominant domains that uses the Internet is social media. The concept was conceived in the early 1990s and went on to grow as a powerful medium of people networking along with the Internet. Social networking sites (SNS) acquired a predominant element of the Internet owing to their use and services they offer through the Internet. A few of the most used social networking sites include Twitter and Facebook, which are used synonymous to expressions of text. These SNS allow the users to post photos, videos, and other multimedia content along with text and voice messages that are shared among other users. As with any technology or application, these also have the risk of users posting offensive material and textual content. Hate is being spread through messages, which are in the form of text and also through other materials posted. There is no control to check for the message for the hate content as and when it is posted, and by the time it is deleted by admins, it could have already reached millions of users. This chapter proposes a technique for detecting hate texts in reviews from registered users in the Twitter dataset. The proposed work makes use of improved principle component analysis (IPCA) and modified convolution neural network (MCNN) for detecting hate texts. The advantage of natural language processing is used for building an automated system for the analysis of syntax and semantics of the words. The proposed methodology consists of phases like pre-processing, feature extraction, and process to classify the text. The white spaces in the text are removed through normalization in the pre-processing phase, and also remove special characters such as question marks, punctuations, and exclamatory symbols to remove stop words. The features that are pre-processed are then subjected to feature extraction using IPCA. A set of correlated features are made used for identifying more important features in the data set under consideration. Next, the classification is done for identifying the hate text or for any language abuse. MCNN is applied for the classification of the text into HATE and NON-HATE from the text with better accuracy. The experiments prove that the proposed method has a high level of accuracy even for a large dataset. The results show that the proposed method has better performance in terms of precision, recall, and F-measure when compared with other state-of-the-art methods. 2024 Taylor & Francis Group, LLC. -
Challenges and Issues in Health Care and Clinical Studies Using Deep Learning
Deep learning is a subset of machine learning, which has more than three layers of neural networks. Neural networks resemble the functioning of human behavior in nature. These neural networks are capable of producing results with single layers, but multiple layers help in producing accurate results with increased precision rate. Deep learning supports a number of artificial intelligence (AI)-based applications and services, which helps in increased automated devices, data analysis, and many more physical tasks in various fields. Deep learning technology has become part of human day-to-day life. It is involved in every aspect of daily routine like voice-based searches, operating a device, baking transactions, and many more. Deep learning allows the healthcare industry to examine data quickly without compromising accuracy. Deep learning uses mathematical models designed to work almost like the human brain. Multiple layers of networking and technology enable unmatched computing capability and the ability to traverse and analyze through vast sets of data that would have previously been lost, forgotten, or missed. 2024 Taylor & Francis Group, LLC. -
Inkjet printing of MOx-based heterostructures for gas sensing and safety applicationsRecent trends, challenges, and future scope
Volatile organic compounds (VOCs) are pollutants that affect air quality and human health. Detection of VOCs is important for environmental safety. Metal oxide semiconductor (MOS) is a promising product for gas sensors due to its advantages of easy fabrication, low cost, and good portability. Their performance is greatly affected by microstructure, defects, catalysts, heterojunctions, and moisture. Metal oxidebased nanomaterials serve as a platform to identify various VOCs with high sensitivity due to their wide bandgap, n-type transport, and excellent electrical properties. Gas detection devices based on doping, altered morphology, and heterostructure have been shown to be effective against VOCs. Inkjet printing (IJP) is a promising process for the room-temperature deposition of functional metal oxides for sensing applications. However, the development of metal oxide ink requires a careful selection of the precursors, solvents, and additives. This section will focus on the production of various metal oxide (MOx)-based sensors such as ZnO, SnO2, MoO3, CuO, Cu2O, Mn3O4, and WO3 for the detection of VOCs such as acetylene, toluene, ethanol, formaldehyde, and acetone. It will summarize recent research and advances in large-scale printing of MOx-based nanocomposites. This work illustrates the need to explore new composite materials, structures, and morphology as well as other methods for better and faster transformation. The role of solvents in ink stabilization and printing and the behavior of ink rheological parameters in the IJP spraying process will also be discussed. Ink formulations for the synthesis of functional nanocomposites will be analyzed and presented for future scope and challenges. 2024 Elsevier Inc. All rights reserved.