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Human AI: Explainable and responsible models in computer vision
Artificial intelligence (AI) is being used in all areas of information, research, and technology. Allied parts of AI have to be investigated for understanding the association among them. Human and explainable AI (XAI) are a few examples that can help in the development of understandable systems. Posthoc actions and operations are geared toward explainable AI, which investigates what went wrong in a black box setting. Responsible AI, on the other hand, seeks to avoid such blunders in the first ring. Ontology is defined as the study of existence and has several applications in computer science, specifically in platforms such as Resource Description Framework and Web Ontology Language. In this chapter, we examine both parts of the aforementioned AI and attempt to establish a link between ontology and explainable AI as they complement each other in terms of creating trustworthy systems. As part of the chapter, an applicable literature is also brought in, emphasizing the necessity of current understanding in explainable and responsible AI. For illustrating the lineage of input and output operations in relation to ontology characteristics and AI, a scenario of AI implementation using image processing dataset is studied. Classroom learning is an integral element of every student's daily life. Assessing the interest levels of individual pupils would help in enhancing the process of teaching and learning. This work contributes to the process of explainable AI by eliciting algorithms that can extract faces from frames, recognize emotions, conduct studies on engagement levels, and provide a session-wide analysis. Detailed descriptions of these operations, as well as specific parameters, are provided to relate the theme of work. We feel that this collaboration between ontology and explainable AI is unique in that it acts as a springboard for future study in these domains. 2024 Elsevier Inc. All rights reserved. -
COOPERATIVE FEDERALISM IN A MULTINATIONAL COUNTRY: Examining the Case of Pakistan
Pakistan, as a multilingual and multiethnic country, has had to deal with issues of ethnic conflict and separatism. Cooperative federalism is used as a device by countries across the world to accommodate and manage the immense diversities they possess. This chapter examines the need for cooperative federalism in a multinational country like Pakistan to strengthen its federal model, ensuring that ethnic groups in the country do not feel insecure and alienated from the union, demanding secession. Beyond national security concerns, cooperative federalism in Pakistan will ensure economic security, human rights, social security, effective policymaking and much more, which form the basis of a welfare state. 2024 selection and editorial matter, M.J. Vinod, Stefy V Joseph, Joseph Chacko Chennatuserry and Dimitris N. Chryssochoou; individual chapters, the contributors. -
Positive People and Confident Competitors: Resilient Youth Development Through Sport and Physical Activity
In the altering world scenario, there is a necessity to plan, prepare and progress with youth development. Research has associated positive youth development with the 5Cs model (competence, confidence, connection, character and caring) (Lerner et al., The Journal of Early Adolescence 25:17-71, 2005) to build resilience in youth. Over the past 35 years, sport psychology has established that sport helps in developing necessary psychological skills and attributes among youth. Youth sport is an extracurricular activity that provides young people with unique negative and positive experiences. Within these experiences, the individual goes beyond the self and has to work with a diverse group of others for self-development and achievement of shared goals. In this chapter, our primary objective is to review the foundations of literature concerning confidence, resilience and identity as corner-stones for positive youth development through sport. To achieve this objective, we adopt a global approach blending field experience from participatory sport, developmental sport and elite sport to provide an intervention framework grounded in applied sport psychology. Intervention framework provided is aligned to the COM-B behaviour change model (Michie et al. 2011) for sustainable change. The focus is on a balance between developing stable protective factors for mental health and positive youth development to ensure appropriate cognitive, social, emotional and behaviour skills to thrive in an evolving world. Implications for transferring this learning cross-culturally and in non-sport contexts such as schools and grass-root programs are discussed with recommendation for good practice. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
NONHUMAN VISIONS: From Experimental Cinema to Hollywood
In this chapter, I want to trace the convergences between experimental cinema, video-art practices and Hollywood that has emerged as a result of their mutual investment in capturing the visuality of the Anthropocene through a technologically produced and mediated sensory framework. Through a series of case studies from independent filmmakers and Hollywood blockbusters, I argue that as much as the avant-garde is invested in producing Anthropocenic imaginations, Hollywood has also been pursuing it by creating a series of affective strategies that help us to conceive and relate to an otherwise incomprehensible scales of deep-pasts and futures. 2024 selection and editorial matter, Simi Malhotra, Sakshi Dogra and Jubi C. John; individual chapters, the contributors. -
Bioactive Compounds and Biological Activities of Lotus (Nelumbo nucifera Gaertn.)
Nelumbo nucifera Gaertn. (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. 2023, Springer Science and Business Media B.V.. All rights reserved. -
THE FEDERAL DYNAMICS OF INDIAN FOREIGN POLICY: Issues, Concerns and Trends
Participation of regional governments in foreign policy is a global phenomenon, which has been described as constituent diplomacy or Para diplomacy. It is a phenomenon that includes a cocktail of factors like globalisation, economic liberalisation, diffusion of technology and the decentralisation of political power. Dependence of coalition governments on regional parties in India has resulted in a subtle power shift when it comes to foreign policy prioritisation and perceptions. There is a new activism in the making and implementation of Indian foreign policy. The chapter analyses the domestic imperatives impacting on India's foreign policy. The assertion and expectations of regional parties have been contingent on the leverage they have within a coalition like the NDA and the UPA. States are now even bringing foreign and security issues to the bargaining table, thereby providing the regional parties a variety of participatory opportunities. Conduct of India's foreign policy is no longer the exclusive domain of the federal government in India. At any given time, domestic compulsions influence how states act and react to various foreign policy issues and events, and hence engaging state governments in foreign affairs can be a force multiplier for Indian foreign policy. 2024 selection and editorial matter, M.J. Vinod, Stefy V Joseph, Joseph Chacko Chennatuserry and Dimitris N. Chryssochoou; individual chapters, the contributors. -
Deep learning based federated learning scheme for decentralized blockchain
Blockchain has the characteristics of immutability and decentralization, and its combination with federated learning has become a hot topic in the field of artificial intelligence. At present, decentralized, federated learning has the problem of performance degradation caused by non-independent and identical training data distribution. To solve this problem, a calculation method for model similarity is proposed, and then a decentralized, federated learning strategy based on the similarity of the model is designed and tested using five federated learning tasks: CNN model training fashion-mnist dataset, alexnet model training cifar10 dataset, TextRnn model training thusnews dataset, Resnet18 model training SVHN dataset and LSTM model training sentiment140 dataset. The experimental results show that the designed strategy performs decentralized, federated learning under the nonindependent and identically distributed data of five tasks, and the accuracy rates are increased by 2.51, 5.16, 17.58, 2.46 and 5.23 percentage points, respectively. 2024 selection and editorial matter, Arvind Dagur, Karan Singh, Pawan Singh Mehra & Dhirendra Kumar Shukla; individual chapters, the contributors. -
Detection of toxic comments over the internet using deep learning methods
People now share their ideas on a wide range of topics on social media, which has become an integral part of contemporary culture. The majority of people are increasingly turning to social media as a necessity, and there are numerous incidents of social media addiction that have been reported. Socialmedia channels. Socialmedia platforms have established their worth over time by bringing individuals from different backgrounds together, but they have also shown harmful side effects that could have serious consequences. One such unfavourable result is how extremely poisonous many discussions on social media are. Online abuse, hate speech, and occasionally outrage culture are now all considered to be toxic. In this study, we leverage the Transformers Bidirectional Encoder Representations to build an efficient model to detect and classify toxicity in user-generated content on social media. The Kaggle dataset with labelled toxic comments, was used to refine the BERT pre-trained model. Other Deep learning models, including Bidirectional LSTM, Bidirectional-LSTM with attention, and a few other models, were also tested to see which performed best in this classification task. We further evaluate the proposed models utilising dataset obtained from Twitter in order to find harmful content (tweets) using relevant hashtags. The findings showed how well the suggested methodology classified and analysed toxic comments. 2024 selection and editorial matter, Arvind Dagur, Karan Singh, Pawan Singh Mehra & Dhirendra Kumar Shukla; individual chapters, the contributors. -
Stock market prediction using DQN with DQNReg loss function
There have been many developments in predicting stock market prices using reinforcement learning. Recently, Google released a paper that designed a new loss function, specifically for meta-learning reinforcement learning. In this paper, implementation is done using this loss function to the reinforcement learning model, whose objective is to predict the stock price based on certain parameters. The reinforcement learning used is an encoderdecoder framework that is useful for extracting features from long sequence prices. The DQNReg loss function is implemented in the encoder-decoder model as it could provide strong adaptation performance in a variety of settings. The model can buy and sell the index, and the reward is the portfolio return after the days trading has concluded. To maximize yield the model must optimize reward function. The DQNReg loss implemented DQN network and the Huber loss DQN network is compared with the Sharpe ratio considered for return. 2024 selection and editorial matter, Arvind Dagur, Karan Singh, Pawan Singh Mehra & Dhirendra Kumar Shukla; individual chapters, the contributors. -
Security and privacy aspects in intelligence systems through blockchain and explainable AI
Explainable AI (XAI) is a method of creating artificial intelligence (AI) systems that are transparent and understandable to humans. By allowing people to understand how the system arrived at its conclusions or suggestions, XAI systems strive to make AI more accountable, trustworthy, and ethical. Responsibility, trust, ethics, regulation, and innovation are some of the societal ramifications of XAI. By making AI systems more transparent, XAI fosters accountability. This means that consumers will be able to understand how the system made its decisions and hold it accountable if something goes wrong. By making the decision-making process more transparent, XAI fosters trust between people and AI systems. This boosts user trust in the system and encourages wider adoption of AI technologies. It also contributes to the ethical design of AI systems by making the decision-making process public in order to uncover and mitigate biases and other ethical issues that may occur in AI systems. It aids regulators and policymakers in understanding and regulating AI systems. XAI gives insight into how AI systems operate, which can assist regulators in developing laws that promote ethical and responsible AI use. Because XAI can help developers better and innovate new systems by making it easier for them to design new AI systems and by providing insights into how AI systems work. The proposed chapter will focus on important aspects of algorithmic bias and changing notions of privacy in XAI, which will necessitate the need for AI systems that can adapt accountability, trust, ethics, and compliance with regulations, as well as produce better innovation that can benefit humanity. More openness, greater control over personal data, new types of data privacy, and newer privacy networks are all required. To address algorithmic bias in XAI, it is critical to build the system so that it is aware of the possibility of bias and actively mitigates it. This can involve employing diverse and representative data, inspecting the system for unwanted features, offering detailed explanations, and incorporating a wide range of stakeholders in the system's development and deployment. The envisaged report provides a framework that combines XAI and blockchain to provide a secure and transparent way to store and track the provenance of data used by XAI systems, validate the performance of AI models stored on the blockchain on decentralized systems so that the models are stored and executed on a distributed network of nodes rather than a centralized server, and create a token-based economy that encourages data sharing and AI development. Tokens can be used to compensate individuals and organizations who contribute data or algorithms to the blockchain or who employ AI models stored on the blockchain. Overall, the combination of XAI and blockchain can lead to more trustworthy, transparent, and decentralized AI systems. This approach can have a significant impact on various industries such as finance, healthcare, and supply chain management by increasing efficiency, reducing costs, and improving data privacy and security. 2024 Elsevier Inc. All rights reserved. -
Gaze and Queer Autonomy? Representations and Possibilities on New Visual Media Landscapes in the Indian Context
Representations of sexuality in Indian mainstream cinema tend to reinforce sexual differences, imbalances, and certain stereotypes that put queer identities in a disadvantageous position. The shift and transition from the monopoly of the state to an era of popular forms of entertainment enabled the centrality of debates on representations and sexuality. The study examines the representations of queerness that flourished on new visual media landscapes such as the OTT platforms Netflix and Amazon Prime Video that engaged in a new dialogue on queer representations and possibilities. The study attempts to analyze Super Deluxe (2019), Made in Heaven (2019) and Ajeeb Daastans (2021), as they become the representative form of cinema that marked the shift and engaged in a new dialogue on queer representations and possibilities. The study reads queer representation in light of the frameworks of gaze and homophyly to understand why they might offer different opportunities for representation and gaze. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Current status and future trends on the use of innovative technologies for recovering bioactive from insects
Edible insects hold great potential as human food owing to their nutritional, economic and environmental value. Though, the negative perceptions of insects limit their intake by majority of the insects, their efficient processing and utilization in food products have steadily increased their demand in recent years. This chapter deals with the emerging and advanced extraction techniques for recovering functional and bioactive compounds from insects, considering the various factors which might influence the optimum yields. Apart from their production yields, it is of utmost significance to preserve their nutritional and sensory qualities for their effective utilization in functional food products. In this regard, various emerging technologies such as enzymatic hydrolysis, cold atmospheric pressure plasma, ultrasound-assisted extraction, high hydrostatic pressure have been explored. Mechanisms of action along with their benefits and drawbacks have been thoroughly described in the later part of the chapter which will provide insight to the readers for the selection of optimum technology for insect processing. Overall, this chapter provides the readers a comprehensive view about alternatives to conventional techniques for postprocessing of insects and optimization for case-specific technology. 2024 Elsevier Inc. All rights reserved. -
Machine learning and deep learning techniques for breast cancer detection using ultrasound imaging
One of the greatest causes leading to death in women is breast cancer. Its prompt and precise identification can reduce the mortality risk associated with the disease. With the help of computer-based detection, radiologists can identify irregularities. To identify and diagnose numerous illnesses and anomalies, medical photographs are sources of important information. Various techniques help radiographers to examine the internal system, and these techniques have generated a significant amount of attention across several fields of research. Each of these approaches holds a great deal of relevance in many healthcare sectors. Using artificial intelligence techniques, this article aims to present a study that highlights current developments in the detection and classification of breast cancer. The categorization of breast cancer using many medical imaging modalities is discussed in this article. It initially offers a summary of the various machine learning methodologies, followed by a summary of the various deep learning algorithms used in the detection and characterization of metastatic breast tumors. To give an insight into the field, we also give a quick summary of the various imaging techniques. The chapter concludes by summarizing the upcoming developments and difficulties in the diagnosis and classification of breast cancer. 2024 Elsevier Inc. All rights reserved. -
Applications of Digital Technologies and Artificial Intelligence in Cryptocurrency - A Multi-Dimensional Perspective
The paradigm shift requires spreading the light of decentralized ledger technology, extraordinarily implementing cryptocurrencies, and being visible as a game-changer. Blockchain technology, along with cryptocurrencies like Bitcoin, Ethereum, and Litecoin, is a tool for global economic transformation that is rapidly gaining traction in the finance industry. However, these technologies have had low popularity in the consumer market. Many platforms have been misunderstood and ignored when there is an obvious hole in among them. The basic idea behind cryptocurrency is that it is a network-based, totally virtual exchange medium that utilizes cryptographic algorithms such as Secure Hash Algorithm 2 (SHA-2) and Message Digest 5 (MD5) to secure the data. Transactions within the blockchain era are secure, transparent, traceable, and irreversible. Cryptocurrencies have gained a reputation in practically all sectors, including the monetary sector, due to these properties. The uncertainty and dynamism of their expenses, however, hazard investments substantially despite cryptocurrencies growing popularity amongst approval bodies. Studying cryptocurrency charge prediction is fast becoming a trending subject matter in the global research community. Several device mastering and deep mastering algorithms, like Gated Recurrence Units (GRUs), Neural nets (NNs), and nearly short-term memory, were employed by the scientists to analyze and forecast cryptocurrency prices. As a part of this chapter, we discuss numerous aspects of cryptographic protection and their related issues. Specifically, the research addresses the state-of-the-art by examining the underlying consensus mechanism, cryptocurrency, attack style, and applications of cryptocurrencies from a unique perspective. Secondly, we investigate the usability of blockchain generation by examining the behavioral factors that influence customers decision to use blockchain-based technology. To identify the best crypto mining strategy, the research employs an Analytic Hierarchy Process (AHP) and Fuzzy-TOPSIS hybrid analytics framework. Furthermore, it identifies the top-quality mining methods by evaluating providers overall performance during cryptocurrency mining. 2023 Scrivener Publishing LLC. -
Paradigm shift from AI to XAI of Society 5.0: Machine-centric to human-centric
Artificial intelligence (AI), the Internet of things (IoT), and robotics have gained significant momentum to meet expectations in many applications. Data management has become a tedious job as businesses grow. The interruption of AI in business functions and a growing web-based service economy in the last decade have led the IoT to grow faster, reducing the tedious job. Timely interruption of eXplainable artificial intelligence (XAI) reduces the technical complexities. On the one hand, the AI of Industry 4.0 promises the easiness of business functions. On the other hand, XAI of Society 5.0 tends to ease people's social life. This chapter ascertains the impact of AI on significant business functions and tries to bring out challenges AI faces and ethical values that must be considered in business functions. This chapter also tries to shed some light on the evolution of XAI of Society 5.0 and reasons for the shift from AI to XAI or machine-centric to human-centric and concludes by highlighting the future of XAI. 2024 Elsevier Inc. All rights reserved. -
Doctoral Research by Youth: Analyzing the Role of Socio-Demographic Variables on Flourishing and Grit
The study examines the importance of socio-demographic variables like age, gender, family environment, and relationship with parents and friends in deter-mining non-cognitive traits such as flourishing and grit, during the tenure of doctoral research. The cross-sectional correlational study comprises 400 Ph.D. scholars from a Central University in India, who were given a personal data sheet, the Flourishing Scale and the Grit Scale, for assessment. The results of the F-test showed that flourishing was significantly related to age, family environment and relationship with friends, and grit was significantly related to family environment and relationship with friends. Analysis using Pearson correlation found a weak correlation between flourishing and the three subscales of grit, namely ambition, consistency of interest, and perseverance of effort. Findings suggest that the socio-demographic variables are important contributors in the long-term goal-oriented behaviors and that flourishing and grit are two related but not correlated variables that influence completion and attrition of the doctoral research. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Challenges and Solutions of Using Social Internet of Things (SIoT) in Healthcare and Medical Domains
The revolutionary idea that combines social networks with the Internet of Things (IoT) is called the Social Internet of Things (SIoT). SIoT is a term that refers to the modelling of social networks formed by connecting people and things. SIoT was designed to assist organizations in achieving specific goals, such as boosting usability, scalability, and productivity and satisfying business service requirements. The application layer of the SIoT model performs several tasks like managing the relationship, discovering the services, configuring services, and managing reliability among the devices. The information collected about SIoT is categorized by identifying the relationships between devices. SIoT creates an event identity based on data from IoT applications. This identity may then be transferred with the SIoT network and made available to other IoT apps. Thus, the SIoT network offers guidance services for reusing data from IoT applications across many IoT applications and customizing IoT solutions to meet the unique needs of individual users, hence boosting overall communication. SIoT technology entails the more efficient use of recent data to create favorable patient outcomes in healthcare and medicine. The enormous volume of data generated by SIoT-connected devices has allowed various developments and applications in the healthcare domain. SIoT leverages sensors and other connected devices in these domains to boost social solutions efficiency. Without question, sensors used for creating this kind of network model that can collect vast amounts of data are on the verge of becoming a pervasive part of our lives. If the processing and management are not carried out optimally in SIoT, there is a significant risk that the data will lose its efficacy. This chapter examines SIoT challenges and approaches in the healthcare and medical domain. SIoT approaches may assist users in detecting a patients aberrant behaviour. These approaches are capable of detecting and forecasting patients health states. The SIoTs relational models, such as community sharing, equality matching, and equality matching, also provide IoT services to users. The sensing layers functionalities are compared to those of the network layer and application when assessing SIoT services. The proposed hierarchical network model uses gateways, switches, and IoT devices to establish social relationships. CISCO packet tracer is used to construct and operate this mainly built social network for healthcare. This specially designed social network for the healthcare domain can easily be implemented and controlled by any hospital management. 2023 selection and editorial matter, Gururaj H L, Pramod H B, and Gowtham M; individual chapters, the contributors. -
Youth and Media Literacy in the Age of Social Media
Living in the age of information means information is all pervasive, uncensored, unreliable, and with the potential to influence. The unfettered access to information and communication through social media is a double-edged sword in the hands of youth. The impact of this was explored from sociocultural and mental health perspectives. Specifically, the role of media literacy in combating the challenges posed by usage of social media was explored in this chapter. Various theories, frameworks, models, and components of media literacy were analysed. Impact of the various media literacy interventions on the youth, case studies of specific information literacy programs across regions, and other relevant critiques were reviewed and consolidated. Further to this, recommendations have been presented on creating robust in-school, and outside-school media literacy programs for the youth. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Garbage Management and Monitoring System Using IOT Applications
The main purpose of this application, in addition to boosting the vision of a smart city, is to reduce humankinds effort and resources while simultaneously enhancing the vision. The squashing of the dustbin will occur on a regular schedule. It will be possible to manage waste more efficiently when these sensible garbage bins are implemented on a large scale and replace our previously designed dumpster, as it eliminates the need for waste to be piled up on the roadside in the first place. When managing the trash containers, wireless sensor systems in networks (WSN) in connection with the IoT technologies are used. On the other hand, sensors are used to monitor the container contents in real-time, with results displayed on the website, and the sensed contents are then evaluated to determine the optimal container distribution. This allows for the processing of a variety of waste types depending on the needs of the customer. As a result of installing ultrasonic sensors in each bin, garbage levels are continuously checked. As a result of this notification, the bin will be cleared. 2023, Bentham Books imprint. -
Role of AI in the inventory management of agri-fresh produce at HOPCOMS
Inventory management is vital for maintaining the efficiency of supply chain management. Fruits and vegetables being perishable in nature should involve inventory management to avoid wastage and loss in terms of over stocking and stock out situations. The present study focuses on the role of artificial intelligence (AI)-powered inventory management of fruits and vegetables at HOPCOMS, a cooperative society founded in Bangalore. In the road to satisfy the customers, it is necessary for the society to come up with different strategies to manage the inventories in which a retailer confronts overstock and stock out situation, affecting the profit of the society. Therefore, a study was conducted with the help of structured questionnaire among 122 retailers of HOPCOMS outlets in Bangalore. The results obtained from the study suggest that inventory valuation method positively influences AI-powered demand forecasting and customer order fulfillment, and AI-powered demand forecasting is positively related to customer order fulfillment. 2023, IGI Global. All rights reserved.