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IoT and Sustainability Energy Systems: Risk and Opportunity
As IoT (Internet of Things) and smart technologies have developed rapidly, many technological advancements have been made possible. The IoTs main objective is to assist in simplifying processes in a number of different felds, to improve the effciency of technologies and protocols, and ultimately to improve quality of life. Although IoT technologies can beneft the population in numerous ways, their development must be evaluated from an environmental viewpoint to ensure that global resources are used effciently and to prevent negative effects. As previously described, considerable research effort is needed to explore the advantages and disadvantages of IoT technologies. Engineering professionals, industrial experts, and academic researchers successfully interacted at the conference. Several key tracks made up the conference, including smart city, energy and environment, e-health, and engineering modeling. Specifcally, the editorial covered a number of topics including (i) IoT in sustainable energy and environmental management, (ii) smart cities enabled by IoT, (iii) ambient assisted living, and (iv) IoT technologies for transportation and low-carbon products. An important outcome of our introductory analysis has been a greater understanding of both the scientifc developments in IoT applications and the potential ecological consequences associated with increasing IoT applications. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. -
Edge intelligence to smart management and control of epidemic
The effects of COVID-19 vary from person to person. A pandemic is devastating economically and socially. Thousands of enterprises face the possibility of collapse. More than half of the world's 3.3 billion workers may lose their livelihoods if the current crisis continues. The world's healthcare services are facing an unprecedented situation due to the recent outbreak of a novel coronavirus (COVID-19). Community and government health are adversely affected by the COVID-19 pandemic. COVID-19 has continued to spread, and mortalities have risen steadily. The spread of this disease can therefore be controlled utilizing nonpharmacological methods, such as quarantine, isolation, and public health education. Recent breakthroughs in deep learning (DL) have led to an explosion in applications and services relating to artificial intelligence (AI). The rapid advancements in mobile computing and AI have enabled zillions of Bytes of data to be generated at the network edge from thousands of mobile devices and internet of things (IoT) devices connected to the Internet. As a result of the success of IoT and AI technologies, it is of utmost importance that we expand the AI frontiers to the network edge in order for big data to be fully tapped. Edge computing (EC) can help overcome this trend because it allows computation-intensive AI applications to run on edge hardware. The topic of discussion in this chapter is edge intelligence (EI) technology's application in limiting virus spread during pandemics. 2024 Apple Academic Press, Inc. All rights reserved. -
Leveraging machine learning models for intelligent hazard management
[No abstract available] -
Navigating the ethical landscape of artificial intelligence: Challenges, frameworks, and responsible deployment
In artificial intelligence (AI), machine learning (ML) has become a game-changing concept that allows systems to learn from experience and get better without explicit programming. This chapter explores the main ideas, techniques, and applications of ML, offering a succinct introduction to the field. The first step in the process is to gain a basic understanding of supervised learning, which is the process by which algorithms learn to make predictions or judgements from labelled training data. Next, we introduce unsupervised learning, which emphasizes finding patterns in unlabelled data and frequently results in interesting findings and clustering. To emphasize the importance of reinforcement learning in decision-making processes, the paradigm is presented where agents learn by interacting with an environment and receiving feedback. Ideas related to ML, such as feature engineering, model assessment, and the balance between variance and bias, are discussed. The significance of quality data in ML applications is emphasized, along with the impact of data pretreatment on model performance. It also clarifies how neural networks, a branch of ML, simulate the workings of the human brain. The ability of deep learning, a branch of ML that makes use of multi-layered neural networks, to handle challenging tasks such as speech and picture recognition is being investigated. In order to emphasize the necessity of responsible ML model deployment and usage, practical factors are emphasized, including the significance of ethical considerations and responsible AI. The final section of the chapter offers a preview of MLs future, discussing issues and trends that practitioners and researchers should be aware of. This chapter essentially functions as a thorough introduction to ML principles, providing an overview of the wide range of ML approaches, applications, and ethical issues that support the technologys transformative potential across a range of industries. 2025 selection and editorial matter, G. Sucharitha, Anjanna Matta, M. Srinivas and Sachi Nandan Mohanty; individual chapters, the contributors. -
Exploring AI and ML Strategies for Crop Health Monitoring and Management
This chapter offers a thorough examination of machine learning (ML) and artificial intelligence (AI) approaches designed especially for agricultural crop health monitoring. The story starts with a basic introduction to AI and ML ideas and then covers supervised and unsupervised learning approaches, the fundamentals of reinforcement learning, and the significance of high-quality data preparation in agricultural settings. This chapter explores the use of deep learning architectures and neural networks, explaining how they can be used to simulate human brain activity and how they can be used in picture identification to identify crop diseases. A detailed analysis is conducted of the practical aspects of ML for agriculture, encompassing feature engineering and model assessment methodologies. Additionally, the chapter highlights the ethical issues involved in the proper application of AI/ML models in agricultural contexts. These kinds of applications. In conclusion, the chapter discusses obstacles, offers predictions for future developments, and discusses new lines of inquiry for AI and ML research related to crop health monitoring. Through this thorough research, the chapter seeks to offer insightful information on the transformative potential of AI/ML approaches in supporting efficient and sustainable agriculture practices for improved crop health management. (Publisher name) (publishing year) all right reserved. -
Drones for Crop Monitoring and Analysis
Drones are becoming a vital tool for crop monitoring and analysis in contemporary agriculture. With the use of sophisticated sensors, these unmanned aerial vehicles (UAVs) can gather high-resolution pictures and data, giving farmers real-time insights into the growth and health of their crops. Thanks to technological advancements, drones can now more reliably and effectively collect a variety of data points than previous techniques, including plant health, moisture levels, and insect infestations. Drones are a useful tool for crop monitoring because they enable farmers to identify problems early on, such as nutrient deficits, water stress, and disease outbreaks, and take prompt action to optimize yields and avoid losses. Drones can also swiftly and affordably cover vast tracts of agriculture, giving a thorough picture of crop conditions. Farmers may use the information that drones gather to make educated decisions by choices about fertilization plans, pest control techniques, and irrigation schedules, eventually enhancing crop sustainability and output. Drone technology is projected to play an increasingly bigger role in agriculture as it develops, completely changing how farmers monitor and assess their crops. (Publisher name) (publishing year) all right reserved. -
Ant Colony Systems- Enabled Wireless Network Communication
[No abstract available] -
IOT Contribution in Construct of Green Energy
Energy derived from natural sources, such as sunlight, wind, and water, is called green energy. Green energy is a source of energy derived from clean sources such as solar, wind, geothermal, and biomass. The environment benefts from green energy because green energy replaces the harmful effects of fossil fuels with more environmentally friendly options. Green energy sources release far fewer greenhouse gases, as well as little or no air pollutants when looked at in their full life cycle. Taking steps to reduce air pollution benefts not only the planet but also human and animal health. Increasing reliance on the Internet of Things (IoT) has helped modernize the energy industry. Sensor attached to generation, transmission, and distribution equipment is used in IoT applications in green energy production. Alternative energy offers several benefts over traditional energy options. As the demand for clean energy grows and environmental prudence becomes the norm, Internet of Things solutions for energy management keep developing. Using the Internet of Things today benefts green energy, enabling companies in this sector to make the most of their data, and improves effciency and safety. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. -
Edge computing for smart disease prediction treatment therapy
Healthcare systems are increasingly seeking to match patients' pace of life and be personalized, as they are demanding more advanced products and services. The only solution for collecting and analyzing health data in realtime is an edge computing (EC) environment, coupled with 5G speeds and modern computing techniques. The technology in healthcare is currently being used to develop smart systems that can expedite the diagnosis of disease and provide precise and timely treatment. The automated hospital monitoring system and medical diagnosis system enable doctors to monitor and diagnose patients from a variety of locations, including hospitals, workplaces, and homes and provide transportation options. As a result, overall doctor visits are reduced as well as patient care is improved. More than 162 billion healthcare IoT devices are expected to be used worldwide by 2021 thanks to the internet of things (IoT) sensors and applications for general healthcare. With edge intelligence (EI), wearable devices with sensors, like smartwatches or smartphones, and gateway devices, such as microcontrollers, can form edge nodes: smart devices with sensors, as well as gateway devices with sensors, can act as edge nodes. Smart sensor devices are typically installed at a greater distance from personal computers (PCs) and servers, which can be utilized in fog computing (FC). In healthcare, EC and FC are used to deliver reliable, low-latency, and location-aware healthcare services by utilizing sensors located within users' reach. Recently, many researchers have proposed using hierarchical computing for the distribution and allocation of inference-based tasks among edge devices and fog nodes, which could lead to an increase in computing power and compute capability of edge devices. For disease prediction, this chapter discusses a variety of EC techniques. 2024 Apple Academic Press, Inc. All rights reserved. -
Artificial Intelligence for Bio-Inspired Security
[No abstract available] -
Blended Learning and Its Impact on Cognition and Emotion
A lot of research has been conducted to improvise learning by means of smart incorporation of technology and multimedia. There exists a complex relationship between cognition and emotions; technology is used to elicit emotional responses to create an emotional state which people learn best. Given the increasing attention to the important relationship between learning and emotions, this chapter is about blended learning and the emotion experienced by the students. The blended learning model focuses on the learners freedom in the way that they learn and engross in their education. The cognitive goals are the achieved by maintaining learners interest throughout the course. This chapter also explores the intrinsic differences, such as individual characteristics and contextual motivational factors which influence learning. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020, Corrected Publication 2020. -
AI as sustainable and eco-friendly environment for climate change
[No abstract available] -
Nature inspired algorithm approach for the development of an energy aware model for sensor network
The unique and strong characteristics of Wireless Sensor Network (WSN) have paved a way to many real time applications. Nevertheless, the WSN has their own set of challenges likewise data redundancy, resource constraints, security, packet errors and variable-link capacity etc. Among all, management of energy resource is of high importance as the efficient energy mechanism increases the lifespan of the network. Thereby providing good Quality of Service (QoS) demanded by the application. In WSN even though the energy is required for data acquisition (sensing), processing and communication, more energy are consumed during communication where transmission and retransmission of packets are quite often. In WSN data is transmitted from source to destination where at the destination site the data are analyzed using appropriate data mining techniques to convert data into useful information, and knowledge is extracted from that information to aid the user in efficient decision making. The transmission of data can be either through a single hop or via multiple hops. In single hop, a node is just a router where as in multi hop the node acts as both data originator and router. Thus, consuming more amount of energy and in a multi hop if any of the nodes fails it leads to many large retransmissions thus making a system highly susceptible for energy consumption. Many researchers have dedicated and devoted their time, energy and resources in order to come up with better solutions to answer this problem. This chapter is one such effort to provide a better solution to reduce the energy consumption of sensors. Here, the beauty of DBSCAN clustering technique has been fully exploited in order to develop a spatiotemporal relational model of sensor nodes, followed by the selection of representative subset using measure trend strategy and finally meeting the criteria for identifying the best optimal path for transmission of data using few nature inspired algorithms like Ant Colony Optimization (ACO), Bees Colony Optimization (BCO), and Simulated Annealing (SA). 2019, Springer-Verlag GmbH Germany, part of Springer Nature. -
Investigation into the Mechanical, Fatigue and Superplastic Characteristics of Shape Memory Alloys (SMA) in CuAlMn, CuAlBeMn, and CuAlFeMn Compositions and Their Composite Variants
Shape memory alloys (SMAs) exhibit high sensitivity to compositional changes in terms of their super elasticity, shape memory effect, and transition temperatures. A deeper comprehension of SMA composition and its impact on mechanical properties can be attained by differential scanning calorimetry. The current study uses experimental work to assess the energy absorption capacity, mean fracture width, residual strength, and cracking strength of samples made of short shape memory alloy (SMA) fibers that are randomly distributed on the specimens tensile side. In this investigation, three samples were synthesized based on the Cu, Al, and Mn proportions found in CuAlMn shape memory alloys (SMA1, SMA2, and SMA3). Moreover, three samples with different ratios of Cu, Al, Mn, Be, and Fe were synthesized for the shape memory alloys CuAlBeMn and CuAlFeMn (SMA2, and SMA3). The synthesized CuAlMn, CuAlBeMn, and CuAlFeMn SMA alloys showed good strain recovery, ranging from 90 to 95%. The martensite that forms and changes when the alloys are heated and quenched mostly controls the strain recovery by the corresponding SMAs. SMA 2 of the CuAlBeMn has a greater strain recovery rate, rising by 8.5% and 44.38%, respectively, in comparison to SMA 1 and SMA 3. CuAlBiMn shape memory alloys demonstrated superior super elasticity and martensite stability in comparison to SMA 1 and SMA 2 respectively. SMA 1 and SMA 2 demonstrated greater residual strength, cracking strength, and energy absorption capacity for all fiber volume fractions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Ethics of digital journalism
Marshall McLuhan proposed that technological changes impact society. Digital media has enabled journalists to reach their audiences instantly with the news. Journalists need more time to decide what to report and how to present it. Ethics is the belief about what is morally correct or acceptable. Traditionally, newspaper reporters devised moral codes to help them in their professional decision-making. There emerged an almost universal set of principles that guided journalists in their profession. Television journalists were compelled to draw up a code of ethics to ward off criticism about sensationalism. Digital media has blurred the distinction between professional and citizen journalists. Twitter allows the man in the street to break the news as it happens. Privacy and copyright are just two of the significant issues that digital journalists must deal with. Digital media throws up these challenges, and this chapter aims to answer them. Is it acceptable to extend the ethical standards of old media to the digital space, or do we need a new set of ethics to guide digital journalists? New principles and ethical standards are being framed to tackle the unique challenges of digital news media. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. All rights reserved. -
Animal-assisted therapy for children and adolescents with neurodevelopmental disorders: A review
The increase in neurodevelopmental disorders presents the need for complementary and alternative treatment modalities to support well-being in the maximum possible way. This narrative review was conducted with the aim to explore how animal-assisted therapy as a complementary treatment approach is beneficial for children and adolescents with neurodevelopmental disorders. A search in various databases was conducted to identify articles published in the field of animal-assisted interventions. The review comprised of a total of 32 studies. The discussion of the results was presented in terms of different therapy animals incorporated into the therapeutic environment. The review indicated that animal-assisted therapy has the potential to improve symptoms and various psycho-social variables in individuals suffering from different developmental disabilities. 2024, IGI Global. All rights reserved. -
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. -
Cyber secure man-in-the-middle attack intrusion detection using machine learning algorithms
The main objective of this chapter is to enhance security system in network communication by using machine learning algorithm. Cyber security network attack issues and possible machine learning solutions are also elaborated. The basic network communication component and working principle are also addressed. Cyber security and data analytics are two major pillars in modern technology. Data attackers try to attack network data in the name of man-in-the-middle attack. Machine learning algorithm is providing numerous solutions for this cyber-attack. Application of machine learning algorithm is also discussed in this chapter. The proposed method is to solve man-in-the-middle attack problem by using reinforcement machine learning algorithm. The reinforcement learning is to create virtual agent that should predict cyber-attack based on previous history. This proposed solution is to avoid future cyber middle man attack in network transmission. 2022 by IGI Global. All rights reserved. -
Cyber secure man-in-the- middle attack intrusion detection using machine learning algorithms
The main objective of this chapter is to enhance security system in network communication by using machine learning algorithm. Cyber security network attack issues and possible machine learning solutions are also elaborated. The basic network communication component and working principle are also addressed. Cyber security and data analytics are two major pillars in modern technology. Data attackers try to attack network data in the name of man-in-the-middle attack. Machine learning algorithm is providing numerous solutions for this cyber-attack. Application of machine learning algorithm is also discussed in this chapter. The proposed method is to solve man-in-the-middle attack problem by using reinforcement machine learning algorithm. The reinforcement learning is to create virtual agent that should predict cyber-attack based on previous history. This proposed solution is to avoid future cyber middle man attack in network transmission. 2020, IGI Global. -
Ensemble Model of Machine Learning for Integrating Risk in Software Effort Estimation
The development of software involves expending a significant quantum of time, effort, cost, and other resources, and effort estimation is an important aspect. Though there are many software estimation models, risks are not adequately considered in the estimation process leading to wide gap between the estimated and actual efforts. Higher the level of accuracy of estimated effort, better would be the compliance of the software project in terms of completion within the budget and schedule. This study has been undertaken to integrate risk in effort estimation process so as to minimize the gap between the estimated and the actual efforts. This is achieved through consideration of risk score as an effort driver in the computation of effort estimates and formulating a machine learning model. It has been identified that risk score reveals feature importance and the predictive model with integration of risk score in the effort estimates indicated an enhanced fit. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.