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An IoT-based agriculture maintenance using pervasive computing with machine learning technique
Purpose: In cultivation, early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates, ensuring that the economy remains balanced. The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification. In this investigation, the accurate prior phase of crop imagery has been collected from different datasets like cropscience, yesmodes and nelsonwisc. In the current study, the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science, yes_modes, nelson_wisc dataset. Design/methodology/approach: In this research work, random forest machine learning-based persuasive plants healthcare computing is provided. If proper ecological care is not applied to early harvesting, it can cause diseases in plants, decrease the cropping rate and less production. Until now different methods have been developed for crop analysis at an earlier stage, but it is necessary to implement methods to advanced techniques. So, the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation. This implemented design is verified on Python 3.7.8 software for simulation analysis. Findings: In this work, different methods are developed for crops at an earlier stage, but more methods are needed to implement methods with prior stage crop harvesting. Because of this, a disease-finding system has been implemented. The methodologies like Threshold segmentation and RFO classifier lends 97.8% identification precision with 99.3% real optimistic rate, and 59.823 peak signal-to-noise (PSNR), 0.99894 structure similarity index (SSIM), 0.00812 machine squared error (MSE) values are attained. Originality/value: The implemented machine learning design is outperformance methodology, and they are proving good application detection rate. 2021, Emerald Publishing Limited. -
COVID-19 outbreak prediction using quantum neural networks
Artificial intelligence has become an important tool in fight against COVID-19. Machine learning models for COVID-19 global pandemic predictions have shown a higher accuracy than the previously used statistical models used by epidemiologists. With the advent of quantum machine learning, we present a comparative analysis of continuous variable quantum neural networks (variational circuits) and quantum backpropagation multilayer perceptron (QBMLP). We analyze the convoluted and sporadic data of two affected countries, and hope that our study will help in effective modeling of outbreak while throwing a light on bright future of quantum machine learning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021. -
Generative AI and its impact on creative thinking abilities in higher education institutions
Generative AI technologies such as ChatGPT have started gaining increased popularity among higher education institutions. Students, as well as teaching professionals, can utilize these tools for various academic purposes due to the immense benefits they provide by way of customization of data generated and ease of access to data. However, this chapter seeks to analyze how such tools may impact students' creative thinking ability. It also analyses the drawbacks faced by teachers after implementation of such tools. The methodology adopted for the study was two surveys: one administered to gather students' opinions and the other for understanding teachers' perspectives. The analysis of the data collected shows that the over-reliance of students on such generative AI tools might hinder students' ability to think creatively to some extent. The chapter also suggests some of the strategies that can be adopted by teachers to ensure students' capabilities are assessed accurately. 2024, IGI Global. All rights reserved. -
Confrontations faced by women in higher education institutions and strategies to overcome the anomalies in the mid-career
Women do succeed in higher positions in the higher education system but only to a certain point, and many women are really motivated by the traditional academic values such as passion to the discipline, pursuit of knowledge, good working environment, and flexibility. Women in higher education .spend the majority of their life at the mid-career stage. Some of them feel wedged, undervalued, and find no motivation to go forward in their mid-career. Hence, the mid-career stage is very much important with women academicians, and they feel there is little support or mentoring. Hence, the mid-career period is increasingly difficult to navigate. Women encounter enormous obstacles in their academic career, including unequal task distribution and balancing caring responsibilities to name a few. The aim of this chapter is to discuss in detail the challenges and obstacles faced by women in their mid-career in higher education and a few strategies to overcome the encounters. 2022, IGI Global. All rights reserved. -
Critical Estimation of CO2Emission Towards Designing a Framework Using BlockChain Technology
The automobile industry is a significant global contributor of carbon footprint this industry has impacted climate change, the research explores the existing methods of carbon footprint tracking and creates a framework by applying blockchain technology by connecting all the countries into one system as blockchain carries the capability to do due to its transparency, security and immutability the proposes of decentralised framework for real time tracking quarterly and implementing the necessary policies to mitigate the raising emission. The methodology encompasses of data analysis of using time series analysis globally and focusing certain parts of the world to show the emissions and creating a design that can help us in tracking the carbon footprint making all over the countries around to participate in suggesting to create a pathway for the future generations a better world as advance technologies come into the world for better ways to save the environment. 2024 IEEE. -
Reshaping the Education Sector of Manipur Through Blockchain
The use of technology in education has been over a century, yet blockchain is in its nascent stage in education. Over the years, technology has enhanced the teaching-learning method, and blockchain can improve even in the administrative section of education. The states of North East, India, in general, lag behind the rest of Indian states in almost all sectors, and the lack of transparency in the administrative sector significantly contributed to it. If blockchain is incorporated into the education department at the administrative level, it could pave the way for a faster, more transparent, and smoother administration. Given the harsh reality that transportation is hard and expensive, a standardised blockchain can alleviate the need to be physically present for any academic-related activity. The attempt of this study would be to show how blockchain can be beneficially used even at the institutional level, where unabated printing could be reduced and adopting to e-paper be maximised. Besides the educational institutions, the administrative sector in education could profitably use them in offices, thereby avoiding red tape for the common people. The chapter points out how blockchain can be a trailblazer in reshaping the education sector in Manipur. Educational institutions must take the lead towards a sustainable future, and blockchain can aid in bringing some visible change in the educational sector. This chapter uses an interdisciplinary approach to substantiate the importance and need for blockchain in the context of Manipur to change for a sustainable future. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Indigenous Beliefs and Practices for Sustainability Among the Mao Nagas
The present society of Mao Nagas is sandwiched between trends to modernity and tendencies to be rooted in the cultural past. Prior to the arrival of Christianity, the Maos were considered animists; the sway of the one Supreme Being, and human relations with nature permeated the social, cultural, and spiritual realm. When the sky represented the father, and the earth, the mother; exploitation becomes inconsequential. Despite the odds of having limited ancestral land, the Maos have proven themselves self-sustainable within the place of habitation. The fact that there are no beggars among the Maos proves that certain aspects of the SDGs are ingrained in the beliefs. The Feast of Merit prevented extreme riches in society. With education, the Mao Nagas learned the harmful effects of shifting cultivation and abandoned its entirety. This paper tries to conceptually prove that if ancient beliefs and practices are tempered with scientific knowledge, life is sustainable. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Community Empowerment through Technology
A UN report estimated that 10% of people on the planet will experience chronic hunger by 2022, with a large share of those people coming from rural origins. In Manipur, a Northeastern state of India characterized by hilly terrain and sparse population, predominantly inhabited by Nagas and Kukis tribes, urban migration threatens to exacerbate poverty. Although hunger is not frequent among Nagas at the moment, one cannot rule it out in future. When smart technologies ably support agriculture, it increases production in a sustainable way, thereby reducing hunger among the tribal populace. For this, the government must assist farmers in discouraging environmentally damaging activities like Jhum cultivation and cultivating poppies. For now, tribals have food to eat; nevertheless, future hunger problems could result from a lack of assistance. This research imagines a future where environmental sustainability is maintained while supplying for the requirements of the people of Manipur by giving technology-assisted farming prime importance. By using a participatory observation approach, the authors highlight how urgent it is to stop more ecological deterioration and protect the biodiversity of the area. Ultimately, a balanced ecological future for Manipurs rural and urban inhabitants depends on the adoption of sustainable smart agriculture. 2025 A. Jose Anand and Saravanan Krishnan. -
Classic Models, Modern Threats: A Study on Adversarial Attack and Defense for Traditional ML Models
Adversarial attacks are a serious threat to machine learning models, both for conventional architectures, like neural networks, and for more sophisticated frameworks, like Vision Transformers (ViTs). Although a lot of work has been done to defend state-of-the-art deep learning models against attacks like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and Gaussian noise perturbations, classical machine learning models like logistic regression, support vector machines (SVMs), and decision trees are relatively less explored despite their extensive use in situations where low computational complexity and high interpretability are needed. This work presents a rigorous evaluation of the adversarial vulnerability of binary and other classical models on the MNIST dataset and explores the effectiveness of various defense mechanisms, including adversarial training, input pre-processing (Gaussian smoothing), and defensive distillation. Experiments demonstrate that adversarial training is the most effective defense that improves model robustness with classification accuracies of up to 96% in all attack scenarios. In contrast, defensive distillation and input preprocessing make modest gains, with accuracy levels ranging from 61 to 81% based on the nature of the attack. Through adversarial threat analysis of typical machine learning models, this work points out their inherent susceptibility to adversarial perturbations and introduces robust defense techniques. These results identify the necessity for robust security and reaffirm the practical viability of typical models in the scenario of resource-constrained environments, contributing towards a more complete picture of adversarial defenses for the entire spectrum of machine learning architectures. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
A Novel Preprocessing Technique to Aid the Detection of Infected Areas of CT Images in COVID-19 Patients Artificial Intelligence (AI) for Communication Systems
An innovative preprocessing method for discerning infected areas in CT images of COVID-19 is described in this abstract. The methodology being suggested exploits the capabilities of artificial intelligence (AI) to improve disease detection communication systems. By employing sophisticated AI algorithms to preprocess CT images, the method seeks to increase the precision and effectiveness of COVID-19-associated area detection. The incorporation of artificial intelligence (AI) into communication systems facilitates enhanced image analysis, resulting in improved diagnostic capabilities and treatment strategizing. The study's findings demonstrate the potential of preprocessing techniques powered by artificial intelligence in augmenting communication systems with the aim of enhancing healthcare outcomes. 2024 IEEE. -
A SIGNIFICANT STUDY ON ROBUST MEASURE OF LOCATION PARAMETERS USING DATA DEPTH APPROACHES
Data depth procedures are statistical methods used to measure the centrality or depth of a point within a multivariate dataset. These procedures provide a way to quantify how deep or outlying a point is relative to the overall distribution of the data. This study explores various data depth procedures to find reliable location estimations in cases like with and without outliers. In this paper, various depth procedures, such as Mahalanobis depth, Halfspace depth, Euclidean depth, Simplicial depth, and Projection depth, are studied and compared. The efficiency of these depth functions is evaluated using real datasets and simulation studies with R software. 2025, Gnedenko Forum. All rights reserved. -
Behavioral Biases in Financial Markets: Understanding the Impact of Cognitive Heuristic-Driven Biases and Emotional Biases in Shaping Investment Decisions
Conventional finance theories believe that the stock market is organized and that stock valuations provide all relevant facts. On the other hand, behavioral finance theories argue that stock valuations can be affected by behavioral biases, explicitly cognitive heuristic-driven biases and emotional biases. The stock market displays the current wellness of an economy, and investment decisions represent it. Investors unveil irrational actions in their investment decision strategies. The investment decision strategy itself is a cognitive procedure, as stock investors must form decisions informed by several possibilities that are available to them. This chapter provides theoretical underpinnings and an overview of the effect of behavioral biases on investors investment decision-making. This research provides an in-depth insight into cognitive heuristic-driven biases (Illusion of Control, Hindsight, Conservatism, House Money Effect, Self-Attribution, Gamblers Fallacy, Confirmation, Recency, Familiarity, and Religiosity) and emotional biases (Disposition Effect, Loss Aversion, Regret Aversion, Risk Perception, and Mental Accounting) impact investment decisions. The implications of this study could be helpful for financial markets and institutions as well as practitioners, such as equity investors and traders, portfolio and asset managers, securities analysts, wealth advisors, money managers, securities bankers, and brokers. In addition, it benefits regulators, policymakers, academicians, and researchers. The overall chapter offers a positive impact between behavioral biases and investment decisions, with distinct themes from earlier research, and contributes to generalization. Copyright 2026 by Nova Science Publishers, Inc. -
A fuzzy approach to project team selection
Project team selection is a complex process in software engineering. The study uses a multiple criteria decision making (MCDM) approach for the selection of a project team under fuzzy environment. In this paper a FRI, FSS approaches are developed to the selection of project team. 2019, International Journal of Scientific and Technology Research. All rights reserved. -
An ordered ideal intuitionistic fuzzy software quality model
Software is one of the major factors in the development of computer - based systems and products. Measurement of the software quality is thus the key factor that has to be taken into account while developing a software system. Many software quality models with numerous quality parameters are under use to measure the performance of a software system, on the basis of which the software is valued. This study intends to make available a fuzzy multiple criteria decision making (FMCDM) approach to measure software quality and to propose new similarity measures between ordered ideal intuitionistic fuzzy sets (OIIFSs). The proposed model is applied to five live software projects so as to quantify the software quality of each project under fuzzy environment. IAEME Publication. -
A fuzzy computing software quality model
Expectation of the quality of a software varies from user to user. A fuzzy approach to measure the quality of a software is very appropriate so that it can deal with non-crisp aspects of the various parameters. In the proposed model, ordered intuitionistic fuzzy soft sets (OIFSS) and relative similarity measures of OIFSS are considered in the backdrop of fuzzy multiple criteria decision making (FMCDM) approach. 2019 Author(s). -
A modified fuzzy approach to prioritize project activities
Project management is an important task in business although project is not just confined to business. Due to the uncertainty of the various variables involved in a project, over several past decades research is going on in the search for an efficient project management model. Although numerous crisp models are easily implementable, the potential of fuzzy models are huge. In the case of software development, the variables involved are highly dynamic. In this paper, we propose a ranking based fuzzy model that can prioritize various activities. We use a popular crisp model to test the effectiveness of the fuzzy model proposed. Simulation is done through Java Server Pages (JSP). There is considerable computational and managerial advantage in implementing the fuzzy model. 2018 Authors. -
A fuzzy soft coronavirus alarm model
The entire world experienced a rampant outbreak of Covid-19 beginning in December 2019. The spread of this disease was so rapid and aggressive that many developed countries struggled to control it. However, some countries such as China and Australia have done a commendable job of controlling this virus. Various studies have been done in parallel to analyze strategies to curb the spread of the virus. In many locations, people displayed swarm intelligence. The collective behavior of people was mixed. Some people followed the instructions of the health authorities. In addition to the instructions, people in some localities developed self-organization to resist the spreading of the virus. This research work mainly focuses on the prediction of coronavirus spread in different districts of Kerala by use of a fuzzy approach as the fuzzy approach is considered the best tool that would not show imprecise data in any situation. The PRONE (Predicted Risk of New Event) indexing algorithm was used for finding the intensity of the spread in five districts of Kerala (Trivandrum, Ernakulam, Kozhikode, Kannur, and Kasargod) and was evaluated under the input parameters of immunity of person, food habits, financial factors, and age with the total number of infected people as the output variable. An eight-step algorithm is provided to determine the PRONE index. Kasargod is more vulnerable to the virus. The final results show that this proposed model better predicts virus spread. 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Health Communication as Capability: Gig Workers Freedoms Through Sens Approach
The rapid growth of the gig economy has increased the number of delivery-platform workers, whose precarious employment conditions expose them to health risks while limiting their ability to utilize existing health resources. This article employs Amartya Sens Capability Approach (CA) to reframe the health challenges faced by delivery workers, arguing that critical barrier is not the absence of medical facilities but the lack of communicative capability to access and use them. Within the CA, resources are only meaningful when individuals can convert them into valued functionings. For delivery workers, constraints such as time poverty, lack of paid leave, and informational asymmetries weaken this conversion process. We argue that health communication must be understood as a capability that directly enlarges workers substantive freedoms by equipping them with the knowledge, confidence, and navigational skills needed to make informed health choices. Health communication thereby turns access into utilization, and utilization into well-being. Health communication operates both as a valued function of being informed and able to engage with health systems and as an instrumental freedom that enhances the conversion of existing resources into achieved health outcomes. Recognizing health communication as a capability reshapes policy debates, highlighting the need to invest in service provision and communicative infrastructures that expand workers agency and real opportunities for well-being. 2025 Taylor & Francis Group, LLC. -
Driving better health outcomes for gig workers through strategic health initiatives
The gig economy offers flexibility and autonomy to workers but also presents significant challenges related to health and well-being, especially for delivery professionals who face irregular hours, physical strain, and limited access to healthcare. Immersive technologies, such as virtual reality (VR), augmented reality (AR), and artificial intelligence (AI), present innovative solutions to bridge these gaps. However, gig workers' adoption of these technologies remains underexplored. This paper applied the diffusion of innovation (DOI) theory to analyse the adoption patterns of immersive technologies within the gig economy and identified barriers, such as digital literacy and cost, alongside facilitators, like perceived usefulness and ease of integration. The paper provides insights into how these technologies can be effectively implemented in the gig workforce. The study highlights the role of platform policies and the broader regulatory landscape in shaping technology adoption, offering valuable recommendations for policymakers and technology developers. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Regulating the gig economy: Addressing worker rights in India's quick commerce sector
The study examines the regulatory challenges faced by India's fast- growing quick commerce gig economy, with a focus on the rights and protections of workers. It examines the issue of gig workers and implementation gaps in existing frameworks and highlights the vulnerabilities of migrant workers. The study uses thematic analysis to explore aspects such as working conditions, workers' legal awareness, algorithmic management, and gender inclusion in the workforce. Findings reveal regional disparities in working conditions, discrepancies between legal recognition and practical enforcement, and persistent gender- based inequalities. The research underscores the need for tailored, collaborative efforts among various stakeholders to improve the situation of gig workers. This study aims to enhance the understanding of regulatory issues in the quick commerce sector and offers valuable insights for policymakers, platforms, and researchers to improve working conditions and ensure fair treatment for gig workers in India. 2025, IGI Global Scientific Publishing. All rights reserved.