Browsing by Type "Book chapter"

Agricultural growth needs a newer policy that speeds up plant growth and the nutritional value of the crops. Numerous agrochemicals, pesticides, and fertilizers provide nutrients to crops and enhance plant growth and nutrition quality. However, the demand for food remains a concern. In this context, 2D-nanomaterials or nanosheets have the potential ability to overcome issues associated with agro-chemicals. 2D-nanosheets easily penetrate the seed coats and translocate with the plants using apoplastic and symplastic pathways. The high translocation ability regulates various molecular and biochemical pathways, thereby improving plant growth and development. However, a higher dose of the 2D-nanosheets shows the phytotoxic effects by increasing the production of reactive oxygen species. In this context, 2D-nanosheets-based hybrid materials might be beneficial for improved plant growth with minimal phytotoxicity. Moreover, 2D-nanosheets-based hybrid materials also protect crops against various pathogenic microorganisms. This book chapter focuses on synthesizing 2D-nanosheets, 2D-nanosheets-based hybrid mate-rials, and their interaction with the plants. We also discuss the effect of 2D-nanosheets and 2D-nanosheet-based hybrid materials for plant growth and the protection of crops. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

The Internet of Everything will soon become a reality that is to be acknowledged to 6G, or sixth generation, mobile network research and development efforts. With the help of 6G, a brand-new network would be created that connects almost everyone and everything, including machines, objects, and devices. Additionally, 6G will put an emphasis on quality of experience to deliver rich experiences with 6G technology. Notably, it is crucial to envision the problems and difficulties associated with 6G technologies. Researchers have been investigating numerous alternatives to attain the needed 6G characteristics and, hence, it is imperative to consider a variety of research challenges, from hardware to software capabilities. The major characteristics of 6G are THz-level data communication with a strong emphasis on short-range communications, network-inherent artificial intelligence, high network heterogeneity, and modified radio topology. From possibility to certainty, the main problems of 6G mobile communication technologies are 100% coverage, terahertz communication, optimal spectrum utilization, flexibility, redundancy and self-healing capability, and low-carbon transformation. © 2024 selection and editorial matter, Dr. Abraham George and G. Ramana Murthy; individual chapters, the contributors.

Smart grid describes a network that uses dynamic optimization technology that eliminates network losses in real-time, Retains voltage levels, Enhances reliability, And improves asset management. This grid incorporates advanced technologies and resources from the production, Transmission, And delivery to the system and equipment of end-users. In a structured, Collaborative process that makes the energy generation, Distributing, And consumption efficient, The smart grid integrates infrastructure, Processes, Devices, Information, And markets. The operational data gathered through the whole system and it will trigger system equipment to optimize the solution to make sure that different contingencies are protected from attacks, Vulnerabilities, Etc. This needs the intelligent grid to define and investigate key performance metrics, To design and evaluate suitable resources, And to establish the required educational curriculum. Provide present and future experiences, Experience, And knowledge for this innovative framework to be deployed. A necessity for greater flexibilities in energy systems, Minimizing energy costs, And reducing adverse environmental effects, For consumers in future power systems think of microgrids concept to use. This chapter addresses significant technological challenges for the architecture and implementation of the smart grid by concentrating on the technology and practices necessary to design and integrate various components with smart grids. © 2022 Scrivener Publishing LLC. All rights reserved.

In this study, crop yield forecast is studied by machine-learning methods with respect to accuracy and reliability. Using algorithms like Decision Trees, Random Forests, Support Vector Machines (SVM), Neural Networks, XGBoost, and LightGBM the research scatters environmental elements, for instance rainfall, temperature, type of soil, and agriculture, for example fertilizer and irrigation utilization. With highest performance of key metrics like accurateness, exactness, recall and F1 score, Random Forest model made itself the best model capable of doing complex, nonlinear data relationship. The rainfall and irrigation frequency were found to be the two factors that affect the crop yield significantly using statistical tests such as ANOVA and Chi Square. This suggests that machine learning is a viable means towards improving the productivity of the agricultural resource. The inputs from the study can serve to guide farmers, agricultural policymakers and stakeholders in implementing data based sustainable farming strategies for better food security. © 2026 selection and editorial matter, Pushpa Choudhary, Sambit Satpathy, Arvind Dagur and Dhirendra Kumar Shukla; individual chapters, the contributors

The increasing urbanization and industrial pollution are a serious concern for future generations. Having a proper sewage system in India casts human lives and unfortunately remains the least concern for the policymakers. As per reports, almost 80% of global wastewater is released into the environment without adequate treatment, which can have disastrous health effects. In 2021, The Central Pollution Control Board (CPCB) in its report said that India’s current capacity for treating sewage is 18.6% and its current capacity for treating water is 27.3%. According to government official statistics, 62.5% of urban India’s wastewater is either partially or not at all treated. There are existing research studies that claim “most of the sewage treatment plants that were established under the Ganga Action Plan (GAP) and Yamuna Action Plan (YAP) are not working, and interestingly, out of the 33,000 MLD of waste generated, only 7000 MLD is collected and treated. To underscore the environmental urgency and its pitfalls, it becomes imperative that the centralized wastewater treatment demands a well-developed network of interconnected sewers and drainage for the wastewater to be collected in a central location.” This chapter mainly deals with a critical review of “policies, rules, regulations, on wastewater management in India.” The study will also examine the implementation challenges in enforcing wastewater laws and bye-laws in India. The chapter will also delve into the existing challenges and constraints that impede the development of current waste management practices and propose solutions. Moreover, the study investigate the possibilities for waste management, so complementing centralized treatment plants with less expensive alternatives to reduce potential effects on the aquatic environment and suggest a more effective waste management approach. This chapter seeks to undertake a legal and policy assessment of the reuse of Wastewater in India and establish the challenges and prospects in mitigating water scarcity. Regarding the role of Wastewater reuse policies and regulations, it provides a comprehensive overview of the applied economic, social, and environmental value. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Hadoop has been regarded as the de-facto standard for handling data-intensive distributed applications with its popular storage and processing engine called as the Hadoop distributed File System (HDFS) and MapReduce. Hadoop’s inherent assumption of homogeneity in the cluster is a major cause of performance deterioration due to the huge shuffle required for the processing of data during map phase and reducer phase. This chapter addresses this performance deterioration by proposing a counter placement scheme (CPS) whose main contributions are enumerated as follows; (i) Profiling of nodes based on the completion of maps, (ii) Movement of high-performance nodes into a single rack for tracking higher computation, (iii) Data replacement strategy based on placing at least a single block of file in the rack with the highest computation, and (iv) Finally assigning reducers to the rack and node with highest computation. The experiments performed clearly signify the merits of CPS in terms of reduction in the average completion time, reduce time and off-local shuffle by about (1.9–22.83%), (2.1–21.5%), (4.25–24%) while running several benchmarks. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Swarm intelligence is one of the most modern and less discovered artificial intelligence types. Until now it has been proven that the most comprehensive method to solve complex problems is using behaviours of swarms. Big data analysis plays a beneficial role in decision making, education domain, innovations, and healthcare in this digitally growing world. To synchronize and make decisions by analysing such a big amount of data may not be possible by the traditional methods. Traditional model-based methods may fail because of problem varieties such as volume, dynamic changes, noise, and so forth. Because of the above varieties, the traditional data processing approach will become inefficient. On the basis of the combination of swarm intelligence and data mining techniques, we can have better understanding of big data analytics, so utilizing swarm intelligence to analyse big data will give massive results. By utilizing existing information about this domain, more efficient algorithm can be designed to solve real-life problems. © 2023, IGI Global. All rights reserved.

Recently, several studies have stated that mild weather can perhaps halt the global epidemic, which has already afflicted over 1.6 million people globally. Clarification of such correlations in the worst affected country, the US, can be extremely valuable to understand the function of weather in transmission of the disease in the highly populated countries, such as India. The authors developed a machine-learning approach as logistic regression classification models that used data from several sources to determine whether a patient is at risk of COVID-19 using one of the classification models with the greatest accuracy. They are working on a model that uses simple features available through basic clinical inquiries to detect COVID-19 patients. When testing resources are tight, their approach can be used to prioritize testing for COVID-19, among other things. © 2023, IGI Global. All rights reserved.

The estimate of electricity appeal in modernistic years is becoming progressively relevant thanks to market-free trade and, thus, the initiation of sustainable assets. To satisfy the demands, leading intelligent models are built to form sure explicit power forecasts for multi-time prospects. The load forecasting of electric Power is a crucial process in devising the electric industry and operating electric power systems. Short-term forecasts are adopted to program the power generation and transmission of electricity. Medium-term forecasts are meant to line up the fuel purchases. This necessitates the implementation of the productive determination of algorithms could be a fundamental feature of smart grids and an efficient tool for determining ambiguity for better cost and energy ability decisions like slate the origination, authenticity, power escalation of the system, and monetary smart grid activities. This work introduces a model for the evaluation of the utilization of electricity, which can accurately forecast 68subsequently estimated from minimum to maximum duration with significant improvement in the accuracy of forecasting through advanced deep learning techniques. The analyzes or findings also can provide interesting results for energy consumption with parameters like forecasting efficiency and error with duration of data monitoring algorithms namely (LSTM)-long short-term memory (RNN) - recurrent neural networks and multi-layer perceptron algorithms (MLP). These algorithms furnish the most interesting results with respective to the duration of data. Mainly, MLP and RNN proved to produce favorable results for 24-hour data. Similarly, LSTM has proved better for 15-day data and monthly data with consistency in terms of errors, squared, and mean square. To anticipate data ranging from day to month, the minimal Forecasting error was attained by adopting MLP with R2(0.91). On hour-based data, R2of LSTM holds effective for half-monthly and monthly data with (0.88 and 0.93), RMSE (89.54 and 84.98), MAPE (3.51 and 2.47). RNN has been proven to attain the moderate outputs comparatively. MLP for half-monthly and monthly in terms of R2(0.81 and 0.92), RMSE (90.72 and 85.78) and MAPE (4.25 and 4.01). The result of LSTM acknowledges the enhanced attainment and substantial achievements of electrical load forecasting. © 2024 by Apple Academic Press, Inc.

In this chapter, we investigate the energy efficiency (EE) of the intelligent reflecting surface (IRS)–assisted intelligent transportation system (ITS) under both the Rayleigh and Nakagami-m fading conditions. Since its inception, ITS is growing rapidly, as it helps to provide seamless data transfer between vehicles, avail safe transportation, and avoid accidents. Nevertheless, the amount of data processed in ITS demands more transmission power. To address this, IRS blocks with several passive reflective elements have been recognized as a promising technology to reduce power consumption and enhance EE. In recent years, with the rapid increase in data usage, mobile users demand more transmission power. It is difficult to satisfy all users with limited transmission power; however, IRS has the ability to solve the power requirement problem by using the reflecting elements. In our chapter, two different fading environments (i.e., Rayleigh, Nakagami-m) are adopted to meet the needs for the practical implementations of IRS-assisted ITS. In addition to this, the phase-shift optimization of each IRS element becomes a challenging task which also makes it difficult to estimate the channel for IRS-assisted ITS. To overcome the above challenges and optimize the EE, we develop a novel IRS element clustering method and a passive beamforming technique based on the desired location of the ITS. Furthermore, we analyze the EE and also spectral efficiency of the IRS-assisted ITS with multiple IRS blocks. Numerical results show that the multiple IRS blocks can significantly improve the ITS performance in terms of EE © 2024 selection and editorial matter, Dr. Abraham George and G. Ramana Murthy; individual chapters, the contributors.

Healthcare has been regarded as the pivotal pillar for human prosperity and economic growth which has shifted the focus onto smarter healthcare in smart cities. Despite several efforts, heart diseases pose a dangerous risk to individual life and are the cause of higher mortality rates due to sudden strokes and hence need to be detected in real-time. Researchers have put several efforts to address the overall prediction accuracy rate. However, this has led to the use of innovative and integrated paradigms vis-à-vis cloud computing (CC), fog computing (FC), and mist computing (MC) which have skewed computing and latency requirements and the use of software defined networks (SDN), coupled with federated learning model utilizing ensemble learning to synergize the overall performance through improvements in prediction accuracy and execution time from the data captured from Internet-of-Things (IoT) devices. This chapter proposes a novel use-case of MistFog which utilizes an integrated approach of multiple computing paradigms, and state-of-the-art networks and learning models for heart disease detection and prediction. Finally, the proposed MistFog is also compared with other work through the proof of concept to show its efficacy in terms of network communication. © 2025 selection and editorial matter, Diptendu Sinha Roy, Mir Wajahat Hussain, K. Hemant Kumar Reddy, Deepak Gupta; individual chapters, the contributors.

Focusing on safety attributes becomes an essential practice towards the safety critical software system (SCSS) development. The system should be error free for a perfect decision-making and subsequent operations. This paper presents an analysis on error propagation in the modules through a novel safety metric known as SMEP, which can be characterized depending on the performance rate of the working module. We propose a framework for the analysis of occurrence of error in various modules and the intensity of it is quantified through probabilistic model and universal generating function technique. © Springer International Publishing AG 2017.

In the pay-per-click (PPC) model of online advertising, an advertiser pays an amount to the publishers for every click generated on the published advertisement, which results in click fraud. Click fraud is deliberate clicking by a publisher on the advert. The highly skewed class distribution of the dataset makes the identification of fraudsters more challenging for current machine learning methods. This work thus proposes a reliable click-fraud detection (CFD) system for the efficient investigation of fraudulent publishers. The proposed CFD system has many novel features. First, the problem of class imbalance is overcome using the synthetic minority oversampling technique (SMOTE) and random under-sampling (RUSBOOST). Second, a novel Hybrid-Manifold Feature Subset Selection (H-MFSS) is proposed to obtain optimal informative features. Third, the gradient tree boosting (GTB) model addresses the challenges encountered in investigating and classifying the behavior of fraudsters from balanced and optimally selected user-click data. Experiments are conducted on FDMA2012 mobile advertising user-click data in dual mode: with all features (original data and data sampled through data sampling methods); and with selected features (original data and data sampled through data sampling methods). Classification bias towards the majority class is avoided by evaluating the performance of the models using the average precision (AP), recall (SE), specificity (SP), and G-mean (GM) metrics rather than accuracy. The efficacy of the proposed GTB model is further evaluated by comparing the performance with 12 other conventional machine learning models. The empirical results prove that GTB generalizes well with an achieved AP score of 64.86% without sampling, 65.25% with RUSBoost and 66.78% with SMOTE using significant selected features. A significant improvement in the classification performance is achieved with the impact of sampling methods and selected optimal features. © 2023 selection and editorial matter, Sulabh Bansal, Prakash Chandra Sharma, Abhishek Sharma and Jieh-Ren Chang individual chapters, the contributors.

3D printing is increasingly recognized as a transformative technology in manufacturing, offering unprecedented flexibility and precision in producing complex geometries. The selection of materials significantly influences the mechanical, electrical, and thermal properties of the final products, making it a critical area of study. This review paper delves into the latest advancements in 3D printing materials, focusing on polymer composites, metals, nano-composites, and their applications in additive manufacturing. Recent research highlights the critical role of polymer composites in enhancing biocompatibility, mechanical integrity, and functional properties of printed objects. Notably, the integration of carbon nanoparticles in extrusion-based 3D printing has been shown to drastically reduce print failures and enhance the mechanical strength of the products. The paper also examines the electrical and thermal conductivity improvements that these advanced materials can provide. This review not only synthesizes current research but also discusses persistent challenges in the field, such as material durability and process stability. It aims to serve as a comprehensive guide for both researchers and industry practitioners in navigating the complexities of material selection for additive manufacturing, particularly emphasizing the potential of polymer composites to revolutionize this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

On account of continuous increase in concerns about environmental deterioration and electrical usage, sustainable energy sources are being commonly used to generate power and offer several benefits like clean energy and reduced price. To meet the fast growth in power stipulation, major renewable sources must be attained. Though renewables present a long-term solution as an alternative to fossil fuels, they are associated with uncertainty in generating power. In this chapter, to overcome the above problem, a combination of energy bases will be able to be implemented called as HRES. Due to the relatively low environmental effects, HRES have become a major choice for rural electrification. In this work, an endeavor has been made to present the outline of role of HRES. This chapter will also dispense about the power control strategies, energy assessment of a hybrid system, and battery energy storage devices in electrical networks. Copyright © 2023 by IGI Global.

Recent years have seen a significant increase in the use of artificial intelligence (AI) and machine learning (ML) concepts across a variety of academic domains. AI's major objective is to build intelligent systems and give machines human intelligence. Artificial intelligence is a tool for creating systems, making judgements, solving problems, learning, and linguistic intelligence, as well as for imitating human conduct. Electrical and computer engineers are at the forefront of intellectual creativity as they participate in the planning, creation, evaluation, and production processes for newer generations of gadgets and technology. Even if these professionals want to grow, their objectives might conflict with the consequences of artificial intelligence, which are continually expanding. The practice of Artificial Intelligence (AI) and Machine Learning (ML) applications in industrial industries that have a significant influence on sustainability and the environment, such as renewable energy, smart grids, the catalyzed industry, and power storage and distribution The main popular approaches are artificial neural networks and Machine learning. Demand for energy is skyrocketing at a higher pace than production in the industry between 2004 and 2017, implying a decline in energy efficiency (EE). Under the premise of steady future output, an explicit energy efficiency enhancement target of 26% from 2017 and 2050 is set. Copyright © 2023 by IGI Global.

This study attempts to recognize, investigate, and showcase a research paradigm of cognitive biases impacting leadership decision-making. A questionnaire was systematically framed and distributed among leaders of the Indian corporate sector, and 400 proper responses were accepted at the end. The study utilizes structure equation modelling and partial-least square method (SEM-PLS) to examine the impact of cognitive biases on the leadership decision-making process. It finally suggests various ways to overcome the most prominent biases found in the study. Overconfidence and optimism bias have the largest influence on the leadership decisions of corporate leaders followed by representativeness and hindsight bias. Uniqueness and availability bias had the least impact on the participants and their decisions. The study will contribute to academicians, scholars, analysts, practitioners, policymakers, and firms to make feasible leadership decisions for the cognitive biases considered in it. The impact of the factors on leaders' decision-making process will vary with region and sample size. © 2024, IGI Global. All rights reserved.

Rapid advances in DNA sequencing have converted genomics and made large strides in our expertise in genetic records and the way it pertain to evolution, health, and illness. DNA fragment meeting is the method of reassembling the small, fragmented reads that might be produced by using DNA sequencing to rebuild the authentic genome. DNA fragment meeting is a primary computational issue, especially for huge genomes, due to the full-size extent of statistics and the intrinsic complexity of correctly assembling genomes. The want for innovative solutions is highlighted by using the truth that traditional computational techniques, such as graph-primarily based totally and heuristic approaches, are resource-intensive, have scaling problems, and can be erroneous in complex genome regions. Because of its exquisite capability to do positive computations at formerly unheard-of quotes and efficiently manipulate hard optimization issues, quantum computing has simply come to mild as a likely road for overcoming those computational barriers. In computationally annoying responsibilities like combinatorial optimization and large-scale statistics processing, quantum algorithms just like the Quantum Approximate Optimization Algorithm (QAOA) and Grover`s set of rules for search optimization have verified the capacity to carry out higher than classical approaches. DNA fragments meeting with quantum computing have the potential to boost accuracy and performance even as additionally develop new possibilities for large-scale genomic study. The essential thoughts of quantum computing, current tendencies in quantum-primarily based totally algorithms and their relevance to the meeting system are all included in this chapter, which investigates the capability of quantum computing as an innovative device for DNA fragment assembly. This chapter intends to explore on how quantum strategies would possibly enhance the precision and effectiveness of DNA assembly, starting the door for brand new tendencies inside the subject of genomics with the aid of using reading the capability and constraints of quantum computing for genomics. © 2026 Bipin Kumar Rai, Rupa Rani, and Gautam Kumar

The emerging field of virtual reality (VR) therapies in diverse therapeutic settings is examined in this chapter’s investigation. We provide the most recent literature highlighting an important developments and obstacles in VR therapy research. There are two innovative approaches introduced: a VR-based motor rehabilitation program for stroke patients and an adaptive VR experience rehabilitation scheme for concern disorders. The machine learning algorithms and highly developed haptic feedback are used in this method to improve the treatment results. The result of the proposed approach is compared with the conventional therapeutic approaches. Our proposed system shows a major improvement in both patient engagement and treatment efficiency. The proposed approaches leverage the immersive capabilities of the healthcare metaverse to deliver personalized VR-based motor rehabilitation for stroke patients and adaptive VR exposure therapy for anxiety disorders, utilizing machine learning algorithms and advanced haptic feedback to create a seamless, interactive therapeutic environment that significantly enhances patient engagement and treatment efficacy compared to conventional methods. Many more personalized and successful therapeutic interventions are made possible by the research, which contributes to the continuous development of VR treatments. © 2025 Elsevier Inc. All rights reserved.

Extravagant use of fluorides by taking them through water leads to various fluorosis-based diseases or may be a series of fluorosis diseases. So, the removal of excess fluoride from water is a crucial issue globally. There are various methods of defluoridation; among them, adsorption is a widely used and well-studied technology because of its minimalistic design, ease of use, and inexpensive process. in the last decades, different types of defluoridation adsorbents that were developed were derived from industrial wastes and biomass, natural and mineral adsorbents, metal oxides, metal hydroxides, and carbon-based adsorbents. To elevate the performances of defluoridation adsorbents and to enhance their strategies, it is highlighted that microstructures, regulating and controlling crystalline phases, premises, and other materials incorporated to form composites are some of the proficient methods that enhance the performances of defluoridation adsorbents. A thorough discussion of the water quality following defluoridation and other affecting factors is provided. Additionally, a description and analysis of the various adsorbents’ fluoride removal methods are provided. The study serves as the basis for a discussion of the benefits and drawbacks of various adsorbents, as well as future possibilities and difficulties. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.