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Limaco?n Inspired Particle Swarm Optimization forLarge-Scale Optimization Problem
Large-scale optimization problems are a complex problem in the class of NP-Hard. These problems are not solvable by traditional methods in a reasonable time. Single machine total weighted tardiness scheduling problem (SMTWTSP) is a complex problem in this category. It has a set of different events with varying criteria that need to be scheduled on one machine. The main aim of this problem is to find the minimum possible total weighted tardiness. Particle swarm optimization (PSO) algorithm has performed admirably in the field of optimization. To solve complex optimization problems, several new variants of this algorithm are being developed since its inception. This work proposed an influential local search (LS) technique inspired by limaco?n curve. The new local search is hybridized with PSO and named Limaco?n inspired PSO (LimPSO) algorithm. The efficiency and accuracy of the designed LimPSO strategy are tested over the large-scale SMTWTS problem, which shows that LimPSO can be considered an effective method for solving the combinatorial optimization problems. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Lipase and lactic acid bacteria for biodegradation and bioremediation
Bioremediation is a biotechnological process in which environmental pollutants and solid wastes can be degraded using microbial action to provide a clean free environment without hazards. The process employs microorganisms such as bacteria and fungi for the degradation of wastes. The microbial activity in the bioremediation process degrades the environmental pollutants that are harmful to human health and converts them into less toxic or nontoxic forms. The process mainly focuses on the removal of many types of hazardous materials present in the soil, water, and atmosphere. Microorganisms, especially bacteria, receive great attention in bioremediation as they can mineralize the toxic wastes into other products, such as biomass and water, and make them nonhazardous at times. The activity is not limited only to the degradation of organic wastes, it also degrades crude oil spills in oceans, pesticides, and other industrial wastes. Lactic acid bacteria, Lactobacillus species are Gram-positive and occur mostly in milk and other such products that are highly useful for the human health. They also provide valuable products in the form of foodstuffs for human. Recent findings have shown that the lactic acid bacteria have a better capability to degrade most of the organic wastes and also other industrial contaminations such as dyes. Bioremediation process itself has different methods, such as biosparging and bioventing, grouped as ex situ methods, in which the degradation of wastes can be possible in bioreactors, while on the other hand, in situ methods take place at the site of the pollution or contamination by the microbial growth. 2022 Elsevier Inc. All rights reserved. -
Litigating for Climate JusticeChasing a Chimera?
Across the world, in recent decades, climate litigations have been playing essential roles in shaping domestic policies and legal frameworks on climate change and also in rendering climate justice. There has also been a continuous rise in the development of climate actions, and climate claim litigations by individuals, civil society, and non-state actors. The Indian Supreme Court, High Courts, and the National Green Tribunal have played a significant role in environmental governance by interpreting constitutional and statutory rights to include a right to the environment over the past decades. Nevertheless, with the latest trends in climate litigations, climate challenges have grown across varied climate-related issues, requiring a new judicial approach. In its analysis of climate claims, the justice dispensation mechanism ought to comprehend the shortcomings and be able to generate solutions, similar to those adopted by the courts in the United States, the United Kingdom, and the Netherlands. An analyses of the approach taken by courts in developing nations namely in the Philippines, South Africa, and Pakistan that have compelled governments and corporates to meet their climate commitments are examined. Climate litigation in India has been emerging rapidly over the past decade. As the claims are increasing, the courts and the National Green Tribunal need enhanced capacity building to address climate litigations. This chapter seeks to address the feasibility and implication of equipping courts to address climate litigation. We review the scope of climate litigation and consider the challenges and opportunities to ensure climate justice. This chapter concludes by outlining possible opportunities and challenges in interlinking climate litigation and climate justice in India. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Longitudinal study on noncommunicable diseases using machine learning
This longitudinal case study thoroughly explores the intricate connection between body mass index (BMI) and four key factors: physical health, psychological well-being, lifestyle choices, and the impact of diet on health. Through the analysis of longitudinal data, notable trends emerge, revealing an increase in risk factors for noncommunicable diseases (NCDs) and unhealthy behaviors over time. This highlights the combined impact of these interconnected factors on health outcomes and the risk of developing NCDs like heart disease, diabetes, and cancer. Leveraging machine learning, the study effectively identifies individuals at elevated risk for NCDs and dispels common health misconceptions, underscoring the significance of holistic wellness approaches. Serving as a beacon for the next generation, this study provides insights that contribute to shaping a healthier future. 2025 selection and editorial matter, Arun Kumar Rana, Vishnu Sharma, Sanjeev Kumar Rana, and Vijay Shanker Chaudhary; individual chapters, the contributors. All rights reserved. -
LRD: Loop Free Routing Using Distributed Intermediate Variable in Mobile Adhoc Network
One of the critical challenges in the design of the mobile adhoc networks is to design an efficient routing protocol. Mobility is an unique characteristics of wireless network, which leads to unreliable communication links and loss of data packets. We present a new algorithm, Loop Free Routing with DIV (LRD) is introduced which prevents loops and count to infinity problem using intermediate variables. In addition it finds the shortest path between source and destination. The analysis shows that DIV is compatible with all the routing protocol as it is independent of the underlying environment. The proposed algorithm LRD is compared with the existing algorithm of DIV to prove its applicability in the any routing environment. The simulation results show that LRD excels AODV routing protocol while considering throughput and packet delivery ratio. The new algorithm assures that the routing protocol is shortest loop-free path and outperforms all other loop-free routing algorithms previously proposed from the stand point of complexities and computations. Springer Nature Switzerland AG 2020. -
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. -
Machine Learning and IoT in Smart Agriculture
Smart agriculture is becoming more necessary as food demands quickly rise in response to a growing global population. Additionally, agriculture serves as the primary source of income for almost 60% of India's people. Yet most of our farming practices are still archaic and out-of-date. The fast-expanding population may not be able to be fed using these methods. Smart agriculture uses cutting-edge technology, including Internet of Things (IoT), global positioning systems (GPS), machine learning, robots, and the use of linked gadgets. Smart agriculture could support an artificial intelligence (AI)-integrated agricultural system that gathers data about the agricultural area and then analyses it to help the farmer make the best decisions for producing high-quality crops. The field of AI, with its superior learning capability, is a critical method for tackling several difficulties related to agriculture. AI provides appealing computing and analytical techniques for the better integration of various information-gathering forms from various sources. This paper elaborates the innovative ways AI can be used in the field of Indian agriculture. The study also goes into detail on the impact of smart farming on agricultural research. The analysis demonstrates the range and impact of cutting-edge technology in Indian agriculture, including sensors for rainfall rate prediction, GPS, moisture and temperature sensors, and aerial satellite photos. 2025 selection and editorial matter, Sirisha Potluri, Suneeta Satpathy, Santi Swarup Basa, and Antonio Zuorro; individual chapters, the contributors. All rights reserved. -
Machine Learning and Signal Processing Methodologies to Diagnose Human Knee Joint Disorders: A Computational Analysis
Computer-aid diagnostic (CAD) has emerged as a highly innovative research topic in diverse fields which includes medical imaging systems, radiology diagnostics, and so on. These are the systems that majorly assist doctors by the way of interpretation of medical data or images. In the diagnosis of knee joint disorder technique, both time and frequency-based analysis can be done. These non-stationary and non-linear signals are processed into three important methods, namely VMD, TVF-EMD, and CEEMDAN. To analyze the vibroarthrographic (VAG) signal, the initial stage is to compute the mode strategies termed as intrinsic mode functions (IMFs) which can be attained only after performing the transformations. In our chapter, we analyzed Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for computing the mode signals. The CEEMDAN method utilized the time and frequency data for the available features. The feature extraction depends purely on pixel intensity and the statistical parameters. The classification of available data samples is done through the Least Square Support Vector Machine (LS-SVM) and SVM-Recursion of Feature Elimination (SVM-RFE) for the efficient analysis of healthy and unhealthy data samples. 2024 selection and editorial matter, Hemachandran K., Raul V. Rodriguez, Umashankar Subramaniam, and Valentina Emilia Balas; individual chapters, the contributors. -
Machine learning approaches towards medical images
Clinical imaging relies heavily on the current medical services' framework to perform painless demonstrative therapy. It entails creating usable and instructive models of the human body's internal organs and structural systems for use in clinical evaluation. Its various varieties include signal-based techniques such as conventional X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US) imaging, and mammography. Despite these clinical imaging techniques, clinical images are increasingly employed to identify various problems, particularly those that are upsetting the skin. Imaging and processing are the two distinct patterns of clinical imaging. To diagnose diseases, automatic segmentation using deep learning techniques in the field of clinical imaging is becoming vital for identifying evidence and measuring examples in clinical images. The fundamentals of deep learning techniques are discussed in this chapter along with an overview of successful implementations. 2023, IGI Global. All rights reserved. -
Machine Learning in Cyber Threats Intelligent System
Cybercriminals disrupt services, exfiltrate sensitive data, and exploit victim machines and networks to perform malicious activities against organizations. A malicious adversary seeks to steal, destroy, or compromise business assets that have a specific financial, reputational, or intellectual value. As a result, organizations are complementing their perimeter defenses with threat intelligence platforms to address these security challenges and eliminate security blind spots for their systems. Any type of information useful for identifying, assessing, monitoring, and responding to cyber threats is considered cyber threat intelligence. Organizations can benefit from increased visibility into cyber threats and policy violations. An organizations threat intelligence allows them to prevent or mitigate various types of cyberattacks. The use of machine learning and artificial intelligence is a key component of cybersecurity conflict, which together allows attackers and defenders to function at new speeds and scales. In spear-phishing attacks, relatively frivolous machine learning algorithms have been used to overwhelming effect as adversarial artificial intelligence. This chapter discusses the various cyber threats, cyber security attack types, publicly available datasets for research work, and machine learning techniques in cyber-physical systems. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors. -
Machine learning in smart agriculture
Agriculture is the cultivation of the soil, the growth of crops and the raising of livestock. Agriculture is critical to the economic development of a country. Farming generates nearly 58% of a country's primary income. Previously, cultivators had accepted conventional farming practices. Because these methods were imprecise, they produced less and took longer time. Precise farming boosts productivity by precisely determining which steps must be completed at what time. Precision farming entails forecasting the weather, analyzing soil, recommending crops for cultivation and calculating the amount of fertilizer and pesticides that must be used. Precise farming uses advanced technologies such as IoT, data mining, data analytics, and machine learning (ML) to collect data, train systems and predict outcomes. Precision farming employs technology to reduce manual labor and boost productivity. Farmers have recently faced several difficulties, such as crop failure due to insufficient rainfall, soil infertility and so on. The proposed work in determining the soil, managing crops and harvesting efficiently can solve the problems caused by environmental changes. It guides a person's farming strategy to produce better results through a proper prediction process. The goal of this research is to assist an individual in efficiently cultivating crops, resulting in high productivity at a low cost. It also assists in estimating the total cost of cultivation and forecasting the likely economic barriers. This would help a person plan activities prior to cultivation, resulting in an integrated farming solution. 2023 River Publishers. All rights reserved. -
Machine learning insights into mental health risk factors associated with climate change: Impact on schoolchildren's cognitive abilities
In this chapter, we use machine learning techniques to investigate how the effects of climate change and certain risk factors for mental health affect students' cognitive skills in the classroom. The mental health of at-risk populations, especially students, must be considered in light of the fact that the world's environment is changing significantly. Using state-of-the-art machine learning algorithms, we analyze large datasets that include environmental variables, socio-economic characteristics, and markers of mental health among school-aged persons. We are primarily interested in identifying key relationships and trends that might help us understand the complex relationship between climate change and cognitive health in this population. In order to uncover complex insights, the chapter takes a holistic approach by combining feature selection, model training, and interpretability analysis. The cognitive capacities of school-aged children may be significantly impacted by some climate- related stresses, according to preliminary results. The findings add to our knowledge of the interconnected webs of environmental shifts, psychological susceptibilities, and cognitive consequences. Educators, legislators, and healthcare providers can benefit from this study's use of machine learning insights into the possible effects of climate change on students' mental health. It also paves the way for the creation of tailored treatments and adaptive techniques to deal with the highlighted dangers, fostering resilience and prosperity in the face of a changing environment. 2024, IGI Global. All rights reserved. -
Machine Learning-Based Driver Assistance System Ensuring Road Safety for Smart Cities
Technologies around smart city and green computing are gaining more and more interest from diversified workforce areas. The transportation system is one of them. The transportation vehicles are operating day and night to provide proper support for the need. This is really tiring for the transportation workers, especially the drivers who are driving the vehicle. A slight negligence of a driver may cause a huge loss. The increasing number of road accidents is therefore a big concern. Research works are going on to comfort the drivers and increase the security features of vehicle to avoid accidents. In this chapter, a model is proposed, which can efficiently detect drivers drowsiness. The discussion mainly focuses on building the learning model. A modified convolution neural network is built to solve the purpose. The model is trained with a dataset of 7000 images of open and closed eyes. For testing purpose, some real-time experiments are done by some volunteer drivers in different conditions, like gender, day, and night. The model is really good for daytime and if the driver is not wearing any glass. But with a glass in the eyes and in night condition, the system needs improvements. 2025 selection and editorial matter, Yousef Farhaoui, Bharat Bhushan, Nidhi Sindhwani, Rohit Anand, Agbotiname Lucky Imoize and Anshul Verma; individual chapters, the contributors. -
Magical mushroom Ganoderma-A Promising treatment for cancer
[No abstract available] -
Management practices on execution effectiveness of strategies based on Thirukkural
Thirukkural by Thiruvalluvar contains couplets that speak about the morale necessary for an individual based on the roles played in various circumstances of life. These are applied to various fields including management even today. In this chapter, the authors conduct a narrative analysis on two major aspects of management skills to be inculcated in managers for successful progression of the organization. Execution is one such important aspect of management which plays a significant role in constructing effective doable strategies and executing the strategies without delay after proper analysis, thus sustaining the motivation level of the team and progress. 2024, IGI Global. All rights reserved. -
Managing stress, traumatic experiences, life skill training, and leading with a purpose
This chapter examines the obstacles encountered by minority women pursuing leadership positions in K-12 education, focusing on the interconnectedness of gender and ethnicity. This work explores the intricate terrain of stress and trauma, examining particular obstacles such as marginalization and microaggressions, thereby emphasizing the necessity for specialized assistance. The chapter delivers valuable insights regarding stress management and purposeful leadership, including Mindfulness, life skill training, tension reduction, professional assistance, mentorship, and peer support. The text highlights the significance of effective stress management in cultivating a supportive educational environment. The text culminates in an appeal for empowerment, emphasizing the capacity of obstacles to be catalysts for change in the direction of inclusivity and diversity for minority women leaders who strive to shape a diverse and progressive future. 2024, IGI Global. All rights reserved. -
Mapping Cyclone and Flood Hazard Vulnerability in Puri District, Odisha, India, Using Geoinformatics
India is vulnerable to many natural and human-made disasters due to its unique geo-climatic and socio-economic conditions. This paper focuses on natural disasters; such as cyclones and floods in the Puri district in the Indian state of Odisha. In this study, a number of floods and cyclones that occurred in the district were identified. The thematic maps of the influencing factors such as soil type, flood and cyclone vulnerability, elevation, and 2020 land cover were created using ArcGIS 10.3. Thereafter, the weighted overlay method was adopted based on analytical hierarchy process (AHP) to map the overall vulnerability of the district. The results derived from this study exhibited that the district is highly vulnerable to floods and cyclones. Finally, strategies were recommended for hazard risk reduction covering enhancing awareness towards hazards, improving early warning systems, establishing better communication between various stakeholders, and strengthening environmental protection and disaster risk reduction. Furthermore, measures for mitigation such as creating shelters, post-disaster rehabilitation, better and improved health facilities, incorporating green infrastructure at critical locations, relying on nature-based measures, execution of mangrove plantation along the coastal belt of the district, creating barriers or dykes to prevent water tides, and plummeting leachate due to improper waste disposal near the coast are suggested. The analysis and mapping of hazard vulnerability can act as a reference for urban planners and policymakers to promote Sustainable Development Goal (SDG) number 11 which is sustainable cities and communities. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Mapping Fire, Earthquake and Bio-hazard in Delhi: A Micro-level Study
Delhi, being Indias capital territory, is a massive metropolitan area that is extremely vulnerable to various types of disasters because of the widely spread built-up area that houses the population from all over the country. Delhi lies in Seismic Zone IV14, which makes the area sensitive to disasters. Another major problem that Delhi is currently facing is of proper garbage disposal, since the density of the population is high, tons of waste is generated. A fair share of the waste generated also includes biomedical waste. Delhi generates more biomedical waste than it can process. The area chosen for the present study is Chirag Delhi and Sheikh Sarai, located in south Delhi. This area is urbanized, and a home to a large number of people. The area is populated, poorly managed and highly vulnerable to disasters. The study area also has two colleges situated near the residential area because of which the area is subjected to a lot of traffic jam. The purpose of choosing this area for this study is its vulnerability to disasters like fire, earthquake and biohazard. The study area has pockets with high rise buildings or ill-designed high-risk areas without specific consideration for earthquake resistance. Moreover, the area lacks proper waste management. It has been identified that the area is a highly vulnerable place when it comes to hazards like fire, earthquake and biohazards. The people living there are in a constant threat for their lives. One of the major problems is that the community lacks dedication and determination, which has been tested through a schedule and observation method, to change their circumstances and bring about a change in the area that would benefit them and their families. The Editor(s)(ifapplicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.