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Leveraging Machine Learning: Advanced Algorithms for Soil Data Analysis and Feature Extraction in Arid and Semi-arid Regions with Expert Systems
India is culturally diverse nation at large. There are two words of symphony one is tradition and second one is inherited agriculture. India has long historical advantage having conventional agricultural practices and the scope for it to dive into day to day life as agriculturist. Happiness shrinks as people grow into modern world current trend of agriculture faces a monument challenge and needs immediate address to survive. Now withstanding with this phrase of human life on earth its necessary to give more importance to soil rather than the existence. Soil health is more paramount in this equation, as it directly influences crop growth and yield. Traditionally, analysing a few key soil properties has been the cornerstone of soil treatment practices. However, this approach often overlooks the complex interplay between various soil characteristics. To overcome the above hurdle present research incorporates the method of multivariate data analysis with selective advanced algorithms in machine learning to find suitability to predict best fit algorithm in real time data sets in arid and semi-arid zones of kolar district in Karnataka. The purpose is to draw the attention of stake holders to leveraging the new technology to deploying them into effective assessment in building expert system to incorporate in regular use on handy devices. This penetrates the results by two extremely good classifications algorithms Decision Tree and Gradient Boosting emerged as winner with 99% accuracy. In contrast, Passive Aggressive and Linear SVC produced below average of 36% accuracy. The ensemble algorithms of SMOTE on Random Forest and Stochastic Decent Gradient produced the acceptable accuracy of 83%. This input helped dynamically to build ready to use expert systems for farmers. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. -
PCRS: Personalized Course Recommender System Based on Hybrid Approach
The traditional system of selecting courses to carry out research work is time consuming, risky and a tedious task, that not only badly affect the performance but the learning experience of a researcher as well. Therefore, choosing appropriate courses in seminal years could help to do research in a better way. This Study presents a recommender system that will suggest and guide a learner in selecting the courses as per their requirement. The Hybrid methodology has been used along with ontology to retrieve useful information and make accurate recommendations. Such an approach may be helpful to learners to increase their performance and improve their satisfaction level as well. The proposed recommender systems would perform better by mitigating the weakness of basic individual recommender systems. 2018 The Authors. Published by Elsevier B.V. -
Embarrassment in the Context of Negative Emotions and Its Effects on Information Processing
Negative emotions are feelings of sadness arising out of negative evaluation of oneself by self or others. Embarrassment is characterized as a negative emotion which is experienced as a threat to ones social identity. This chapter discusses the differences between embarrassment and related negative emotions, namely shame, guilt and humiliation and its effects on information processing. Around 45 articles have been reviewed in the process, which were selected based on their relation to either negative emotions in general or specifically to one or more of them. The study uses the interactional (bio-psycho-social) approach to determine the antecedents and consequences of experiencing embarrassment and how it affects information processing. It further explores gender differences in the experience of negative emotions. Given that the existing evidence reveals many contradictory findings in the experience of negative emotions, this chapter conceptualizes certain factors that might influence this experience. It also provides some reasons for variations in experience of embarrassment and related negative emotions, on the basis of gender. This chapter concludes by proposing the complexity of embarrassment as an emotion and a conceptual framework of a continuum on which the experiences of embarrassment may lie and the factors determining the placement of these experiences with their cognitive implications. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020, Corrected Publication 2020. -
Role of medicinal plants against cancer
Cancer is a fatal disease where uncontrolled multiplication of cells occurs in the body. Radiation therapy, Chemotherapy, and medications are some of the procedures for treating cancer infections, but they are expensive, and the cure is ineffective. Usage of plants for the treatment of cancer can be one of the effective processes as the phytochemical compounds in these plants have the potential of alleviating various malignancies that includes cancer. The phytochemical compounds found in the plants have the medicinal properties like anti-inflammation, apoptotic, anti-oxidative to treat various disease include the cancer. The following chapter will be about the Indian medicinal plants such as Carica papaya, Glycyrrhiza glabra, Morinda citrifolia, Azadirachta indica, Psidium guajava, and Annona reticulate, in treating the cancer and its future perspectives. 2024, IGI Global. -
Cybercrimes in the Associated World
Phrases that scarcely existed a decade ago are now a part of our day-to-day lifestyle, as criminals use malicious new technologies to commit cyber attacks against businesses, individuals, and governments. These crimes cause serious harm and impose real threats to victims worldwide either physically or virtually. There are no borders in cyberspace. Attacks can come from any place and at any time. Cybercrime can take many forms, but they all have a digital platform/environment in common. It can be done with both good and bad intentions. But, nowadays, the most common types of cybercrime activities such as phishing scams, identity theft, Internet frauds, online intellectual property or patent infringements, online harassment, and cyber stalking are sadly very widespread in todays associated world. Cyber bullying and online harassment activities spread casually in social media posts and comments or through direct messages and also via emails. The main motive of these messages is to threaten either an individual or a group. Such kinds of cybercrime activities are extremely damaging to the victims mental health. Government agencies working to investigate cybercrimes have reported multiple records of victims developing mental illnesses and even ending up committing suicide. On the other hand we have phishing scams, one of the widespread crime activities. Organizations have detected an increase in the ratio of phishing emails to professional emails from unknown or anonymous service providers appending fake attachments and invoices. These files and attachments may contain malicious payloads to scam people and to create a backdoor in that system, so the attacker can gain access to the system anytime and from anywhere without the victims knowledge. This has been considered as one of the major advantages for the attacker. Cybercrimes have not restricted to only these forms of criminal activities. A wide variety of new attacks have been created and have spread all over the world through commonly used platforms such as social media sites, blogs, and news portals. We are living in a digital world where all our activities are being monitored by someone, somewhere - even keystrokes are being monitored using keyloggers. Nothing seems to be secret and protected unless you are tech savvy. National agencies are keeping a close watch on all individual online activities to prevent illegal activities from happening. No longer the delete option is possible in this digital world; rather, only migration of data from one location to another or from a local server to a cloud server is possible. In our day-to-day lives, several new viruses and attack mechanisms are triggered by attackers by following very new tactics with the help of more complex algorithms. So, its time to advance our knowledge on protecting our valuable assets by spending time in learning and following proper online practices. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors. -
Modernization of Rural Electric Infrastructure
In the recent digital era, the energy sector in India is truly challenging. But some way or another digital technology has the potential to change the scenario of energy supply in industry. One of the important developments in this decade is the application of Artificial Intelligence (AI). This technology will help us to control smart software and optimize our decision-making and operations. We cannot ignore the need of energy to become sustainable after the introduction of the Internet of Things (IoT). Smart grid technology in IoT is used to detect even minute changes in electricity supply and demand. These two technologies (AI and IOT) jointly provide us a magical tool to improve operational performance in the energy industry. In rural areas, there is a lack of electricity infrastructure supply and demand technologies. A large portion electricity supply is shifting from manufacturing industry to rural areas. They are using grid technology to transform electricity and the load is highly variable. From the demand side, lack of infrastructure and industrial equipment affect consumer devices. An increasing need for electricity in all aspects presents a significant challenge to utilization and cost efficiency. An important issue for the delivery of electricity to rural areas is the infrastructure and administrative policies and regulations. Power plants need to be constructed in rural areas to supply the electricity. This is the modernization of a rural electricity infrastructure. In modernization techniques, smart grid technology can be used to meet low carbon emission and cost-efficiency. It will be interconnected with the traditional grid architecture of electricity energy. Based on recent research, the smart grid should be robust and agile and it might dynamically optimize the grid operations, energy-efficient resources, and so on. Without affecting the nature of village environments, an alternate technology, such as the consumption of solar energy, can also be mutually considered in order to utilize renewable energy. In this chapter we focus on the comparison of traditional and modern technology used for the supply and demand of electricity in rural areas, issues on the implementation of modern technologies, research and development in modernization of electric power systems, and so on. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. -
Improved diabetes disease prediction IWFO model using machine learning algorithms
Diabetic disease is the mostly affected and massive disease on a global level. Diagnosing the diabetic earlier will help the medicalist to give the improved and latest clinical treatment. The healthcare specialist unit uses many machine learning techniques, methodologies and tools for decision making in diabetic field. The machine learning techniques are utilized for the prediction of the diabetic diseases in the initial level. To eliminate such issues, optimized detection techniques are proposed. First of all, the training samples are increased using the sliding window protocol. Further, class imbalanced training data classes are balanced and resolved using the adaptive and gradient booster technique. Further, the diabetic feature selection process is improved by the Intensity Weighted Firefly Optimization firefly techniques (IWFO), in which irrelevant features are reduced based on the correlation between the features that deducts the unwanted features involved in the diabetic disease process. Then the feature transformation problem is faced by the PCA technique, which manages the several types of features. Finally, the improved and optimal hybrid random forest is applied into the normal and diabetes classes respectively. The proposed system predicts the diabetic disease efficiently and maximizes its precision of the prediction system. The present paper is compared with different classifiers to determine the efficiency of the work. Overall, the initiated system improved the present studies accuracy level. 2024 Author(s). -
Word-of-Mouth Promotion: How to Attract Consistent Consumers as a Promoter for the B2C Model
The primary goal of this research article is to discover the consumers' behaviour while spending time on the e-commerce platform and to use the consumers who have positive Word-of-Mouth on the products to motivate them as promoters through positive Word of Mouth behaviour. The behavioural factors considered in this study are Relationship value, Trust value, Satisfaction level and Word of Mouth. The trial model includes the consumers who use an e-commerce platform for their online shopping in India. A proper questionnaire with four components was created and used to collect the sample data. Totally 300 responses have been received and analysed with the help of structured equation model and SPSS and AMOS software. The findings suggest that the 'Word of Mouth' technique can be used as a tool to increase the number of consumers in an online platform, particularly e-commerce. We investigated how Relationship Value and Trust Value can be used as key factors to motivate consumers' positive WoM behaviour. This research would be more beneficial to the B2C model. The research has done only for Indian e-commerce portals for survey. There is a scope to do research for the global level e-commerce market. Future study focuses on dynamic attributes for relationship values. This research work will help the researchers who is working on B2C model and consumer behavioural models. This model would be used for any online transactions-based services. As best of the knowledge of the authors' this study is the novel idea to understand the consumers' behaviour for purchasing items through the positive WoM. This work can be adopted for any e-commerce platform. 2024 IEEE. -
Sixth-Generation (6G) Mobile Cloud Security and Privacy Risks for AI System Using High-Performance Computing Implementation
The exchange of information from one person to another is called communication. Telecommunication makes it possible with electronic devices and their tools. The scientist Alexander Graham Bell has invented the basic telephone in 1876 in the USA. Telephones now have the new format in the form of mobile phones, which are the primary media for communicating and transmitting data. We are using 5th-generation mobile network standards. Still, there are some requirements for the users that are believed to be solved in the 6th-generation mobile network standards. By 2030, all of the people would be using 6G. The computing model in the cloud is not dependent on either the location or any specific device that would provide the service. It is an on-demand computational service-oriented mechanism. Combining these two technologies as mobile cloud computing provides customized options with more flexible implementations. Artificial intelligence is being used in devices in many fields. AI can be used in mobile network services (MNS) to provide more reliable and customized services to the users, such as network operation monitoring, network operation management, fraud detection, and reduction in mobile transactions and security to the cyber devices. Combining cloud with AI in mobile network services in the 6th generation would improve human beings' lives, such as zero road accidents, advanced level special health care, and zero crime rates in society. However, the most vital needs for sixth-generation standards are the capability to manage large volumes of records and excessive-statistics-fee connectivity in step with gadgets. The sixth-generation mobile network is under development. This generation has many exciting features. Security is the central issue that needs to be sorted out using appropriate forensic mechanisms. There is a need to approach high-performance computing for improved services to the end-user. Considering three-dimensional research methodologies (technical dimension, organizational dimension, and applications hosted on the cloud) in a high-performance computing environment leads to two different cases such as real-time stream processing and remote desktop connection and performance test. By 'narrowing the targeted worldwide audience with a wide range of experiential opportunities,' this paper is aimed at delivering dynamic and varied resource allocation for reliable and justified on-demand services. 2022 Srinivasa Rao Gundu et al. -
Quantum-inspired meta-heuristic approaches for a constrained portfolio optimization problem
Portfolio optimization has long been a challenging proposition and a widely studied topic in finance and management. It involves selecting and allocating the right assets according to the desired objectives. It has been found that this nonlinear constraint problem cannot be effectively solved using a traditional approach. This paper covers and compares quantum-inspired versions of four popular evolutionary techniques with three benchmark datasets. Genetic algorithm, differential evolution, particle swarm optimization, ant colony optimization, and their quantum-inspired incarnations are implemented, and the results are compared. Experiments have been carried out with more than 10 years of stock price data from NASDAQ, BSE, and Dow Jones. This work proposes several enhancements to allocate funds efficiently, such as improved crossover techniques and dynamic and adaptive selection of parameters. Furthermore, it is observed that the quantum-inspired techniques outperform the classical counterparts. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
A brief review of portfolio optimization techniques
Portfolio optimization has always been a challenging proposition in finance and management. Portfolio optimization facilitates in selection of portfolios in a volatile market situation. In this paper, different classical, statistical and intelligent approaches employed for portfolio optimization and management are reviewed. A brief study is performed to understand why portfolio is important for any organization and how recent advances in machine learning and artificial intelligence can help portfolio managers to take right decisions regarding allotment of portfolios. A comparative study of different techniques, first of its kind, is presented in this paper. An effort is also made to compile classical, intelligent, and quantum-inspired techniques that can be employed in portfolio optimization. 2022, The Author(s), under exclusive licence to Springer Nature B.V. -
Portfolio optimization using simulated annealing and quantum-inspired simulated annealing: A comparative study
Portfolio optimization has been a highly studied problem in financial investment expert systems. The nonlinear constraint portfolio optimization problem cannot be efficiently solved using traditional approaches. This chapter presents a metaheuristic approach to portfolio optimization using simulated annealing (SA). Experiments have been conducted on over 10 years of NASDAQ stock price data. This first-of-its-kind effort is also made to implement the quantum-inspired version of SA (QiSA) for portfolio optimization, and the results are compared with the classical approach. The optimization parameters are chosen using sensitivity analysis, and the results are compared using different statistical measures. Preliminary results show that the QiSA approach is very promising and faster than SA when applied to the portfolio optimization domain. 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
A Brief Review of Intelligent Rule Extraction Techniques
Rule extraction is a process of extracting rules which helps in building domain knowledge. Rules plays an important role in reconciling financial transactions. This paper presents a brief study of intelligent methods for rule extraction. The paper touches upon heuristic, regression, fuzzy-based, evolutionary, and dynamic adaptive techniques for rule extraction. This paper also presents the state-of-the-art techniques used in dealing with numerical and linguistic data for rule extraction. The objective of the paper is to provide directional guidance to researchers working on rule extraction. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Portfolio Optimization Using Quantum-Inspired Modified Genetic Algorithm
Optimization of portfolios has an additional level of complexity and has been an area of interest for both financial leaders and artificial intelligence experts. In this article, a quantum-inspired version of an improved genetic algorithm is proposed for the task of portfolio optimization. An effort is made to implement two different genetic versions along with their extension in the quantum-inspired space. Improvements to the popular crossover techniques, viz. (i) arithmetic and (ii) heuristic crossover are proposed to reduce computational time. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Explainable artificial intelligence enhanced quantum-inspired spider monkey optimization for a constrained portfolio optimization proble
Optimizing portfolios has consistently posed significant challenges while being an extensively researched subject in finance and accounting. This process requires selecting and distributing appropriate assets in alignment with a set of specified objectives. This nonlinear constraint issue is not effectively solvable using traditional methods. This paper investigates the use of spider monkey optimization, ageist spider monkey optimization, and a newly proposed enhanced spider monkey optimization technique for portfolio optimization problems. The explainability of the spider monkey optimization has been improved without compromising the optimization results. It has been observed that the proposed technique marginally enhances the results of spider monkey optimization and can improve trust and risk management in the portfolio optimization problem. Furthermore, a quantum-inspired version of the proposed method is also implemented, and the results are compared using three benchmarked datasets from Dow Jones, BSE, and NASDAQ. Experimental results obtained using these benchmark datasets demonstrate that the newly introduced technique within the quantum-inspired framework marginally outperforms all other methods in the classical and quantum-inspired domains. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
Multiobjective portfolio optimization using multilevel quantum inspired optimization algorithms: a comparative study
The study of portfolio optimization has been a significant focus for computer science and finance researchers, with frequent publication of innovative methods. Numerous works have illustrated that conventional approaches like quadratic programming struggle with nonlinear constraints. This chapter compares ant colony optimization and particle swarm intelligence optimization within classical and quantum inspired frameworks, utilizing qubits and qutrits. This study analyzes benchmark datasets from the NASDAQ, Dow Jones, and BSE spanning over a decade. A pioneering effort has been made to develop a multiobjective portfolio optimization technique through a multilevel quantum inspired optimization algorithm. The experimental results demonstrate that the quantum inspired metaheuristic technique that utilizes qutrits slightly outperforms classical and qubit based quantum inspired methods. 2026 Elsevier Inc. All rights reserved. -
New Empirical Equation for Fundamental Time Period of RC Moment-Resisting Frame Buildings Using Machine Learning Algorithms
The fundamental time period of reinforced concrete (RC) buildings is a critical parameter in structural engineering, influencing their dynamic behavior and response to seismic and wind loads. This study aims to propose a new empirical formula for estimating the fundamental time period of RC buildings through regression analysis. Leveraging the SAP2000 API with VBA code, a dataset comprising 200 two-dimensional RC building models was rapidly generated, allowing for efficient exploration of various building configurations. Modal analysis was conducted for each model to determine the fundamental time period, and regression analysis was performed using both multiple linear regression and curve estimation regression techniques. The input parameters included total building height and base dimensions, while the output variable was the fundamental time period obtained from SAP2000 results. Multiple linear regression yielded two best-fit models, while curve estimation regression produced logarithmic and exponential models. The proposed models were compared with the fundamental time period values obtained from SAP2000 results and those calculated using the formula specified in the Indian Standards (IS) code. Further the results obtained are used to develop a machine learning model that can be used to estimate the time period of RC structures for a given height. The model is chosen after estimating the coefficient of regression for various individual machine learning algorithms and ensemble algorithms. This research contributes to the advancement of structural engineering by providing a systematic approach to developing empirical formulas tailored to RC buildings. The proposed formula, enabled by the automation capabilities of the SAP2000 API, offers a more accurate and reliable method for estimating the fundamental time period, facilitating improved seismic design and analysis practices. Further validation and verification of the formulas performance using additional datasets and real-world case studies are recommended to enhance its applicability and robustness. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
An exploratory study of Python's role in the advancement of cryptocurrency and blockchain ecosystems
Blockchain is the foundation of cryptocurrency and enables decentralized transactions through its immutable ledger. The technology uses hashing to ensure secure transactions and is becoming increasingly popular due to its wide range of applications. Python is a performant, secure, scalable language well-suited for blockchain applications. It provides developers free tools for faster code writing and simplifies crypto analysis. Python allows developers to code blockchains quickly and efficiently as it is a completely scripted language that does not require compilation. Different models such as SVR, ARIMA, and LSTM can be used to predict cryptocurrency prices, and many Python packages are available for seamlessly pulling cryptocurrency data. Python can also create one's cryptocurrency version, as seen with Facebook's proposed cryptocurrency, Libra. Finally, a versatile and speedy language is needed for blockchain applications that enable chain addition without parallel processing, so Python is a suitable choice. 2023, IGI Global. All rights reserved. -
Bioremediation of Antibiotics as a Pollutant in Soil
The discovery of antibiotics had been a major breakthrough in the field of medicine. Apart from its use in treating disease, it is been used extensively in agricul-tural fields and animal husbandry to improve livestock and crop yield. Improper and overuse of antibiotics have found a route in the food chain and has accumulated in environmental resources like water and soil. This is of serious concern as it leads to the development of drug-resistant microorganisms which is a global threat and also alters the microbial diversity as they are bacteriostatic and bactericidal. Bioaugmen-tation and Biostimulation approaches are effective in the degradation of antibiotics in soil. For enhanced degradation of antibiotics consortia, engineered microbes and enzyme-mediated methods are feasible methods for effective remediation of antibi-otics in soil. Currently, extensive research on the bioremediation of antibiotics is carried out as they are cost-effective and eco-friendly. The present chapter deals with various contamination sources of antibiotics in soil, adverse effects of antibiotics in soil, different bioremediation approaches, and mechanisms, and regulations in the use of antibiotics. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022. -
Identification of Student Programming Patterns through Clickstream Data
In present educational era, teaching programming to the undergraduates is challenging. For an instructor, focusing on each of the aspect of programming like coding language, logical reasoning, debugging errors, troubleshooting code and problem solving is very daunting task. So, educational researchers are identifying ways to easily identify the student's struggles during programming so that timely assistance can be provided. Using programming platforms or software, a lot of programming data is generated in the form of activity logs or clickstream data. Using machine learning along with data analytics over this programming data can reveal programming patterns of students that may help in early interventions. This study focusses on identifying programming patterns of the students through clustering and groups the students into three major categories namely low performers, strugglers, and high scorers. Further, relevant features like test case success, code compile success and failure, finish test etc. that majorly contribute towards the student programming scores are identified through regression analysis. Through this research, educators can early categorize the students based on their programming patterns and provide timely intervention when necessary, ensuring that no student gets left behind in the fast-paced world of programming education. 2024 IEEE.