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Autonomous green vegetable growth monitoring via YOLOv9 and a vine robot with tracked mobility
Urban agriculture is facing shrinking land while demand for food is increasing. The study introduces a vine-like, soft robot for non-destructive tracking of green vegetable development using a tracked mobile platform. Although an inbuilt camera and YOLOv9 object detector classify in real time and generate results in four size categories, very small, small, medium, and large, a flexible tube is everted into dense greenery through a pneumatic eversion process. Sensor fusion and hierarchical control are integrated to enable navigation through the complex canopies of crops with accurate control of pressure and direction, and steering. A field trial found 91% mAP detection accuracy at 38 FPS, accurate vine extension (1.2 m @ 4 cm/s), and stable locomotion over uneven terrain, resulting in constant coverage without harming the plants. The system provides a scalable solution for precision agriculture, enhancing crop inspection, disease diagnosis, and harvest planning through continuous data insights. 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Path Planning of KR6 R900 Vision Sensor Assisted KUKA Industrial Robot for Pick and Place Application
In this paper, a new multi-objective functions comprising of squared values of joint jerk, acceleration, torque rate and total travel time subjected to kinematic and dynamic constraints have been formulated for achieving optimal trajectory for industrial applications. Then four different multi-objective optimization algorithmsthe multi-objective particle swarm optimization technique (MOPSO), the multi-objective genetic algorithm (MOGA), non-dominated sorting genetic algorithm-II (NSGA-II) and the proposed multi-objective enhanced teaching learning-based optimization (MOETLBO)have been utilized to obtain the optimal solution for trajectory planning. Finally, the experimental validation of the proposed technique and the summarization of simulation results have been done as a comparative study of the four different metaheuristic techniques for pick and place application of KR6 R900 KUKA industrial manipulator. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Optimal setting of arc welding robot and laser sensor variables for getting maximal weld quality, positional accuracy, and smooth trajectory
Abstract: For seam-finding applications, a robotic welding system and laser sensor can be coupled to achieve improved repeatability and shorter cycle times. This manuscript investigates the impact of several robot variables, including robot orientation, robot travel speed, and focal length of the laser sensor, on three key factors: positioning error, associated joint jerk-torque rate, and weld quality. An Enhanced Multi-Objective NSGA-II (EMONSGA-II) is proposed, which combines NSGA-II with Nelder Mead local search to find the best values for robot and sensor variables. The goal is to acquire the lowest values for joint jerk-torque rate, positional error, and maximum weld quality metrics. The maximized weld quality is represented by maximized ultimate strength, yield strength, and penetration of weld joint, as minimized weld bead height and width. Fuzzy logic has been used to transform the multi-performance weld characteristics into one term of the weld quality. The experiments have been performed using the Arc 50 series welding system with AccuFast point laser sensor integrated MOTOMAN MA 1440 arc welding robot system. Finally, the optimal setting of the robot and sensor parameters have been validated through experimentation to observe the weld quality and positional accuracy. The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2025. -
Neuro-fuzzy model optimization for laser sensor-based quality control for robotic welding of AISI 1030 steel
Robotic welding demonstrates considerable potential in the automation of metal joining processes, resulting in enhanced consistency. This study proposes a methodology for evaluating weld quality by utilizing a laser sensor in conjunction with a hybrid neuro-fuzzy model. The system, designed for AISI 1030 mild steel, utilizes a Design of Experimentation (DOE) methodology to collect empirical data and train the model. A MOTOMAN MA1440 robotic arm, integrated with an AccuFast-II laser sensor, was utilized to acquire real-time weld characteristics. The proposed model integrates fuzzy logic with artificial neural networks (ANNs) for predicting weld quality and is subsequently optimized using the Class Topper Optimization (CTO) algorithm. The model exhibited a high level of prediction accuracy, as indicated by R-squared values of 1.0, 0.99677, 0.99851, and 0.97561 for the training, testing, validation, and overall WQCI datasets, respectively. The process parameters obtained from the CTO analysis yielded a WQCI of 0.824, exceeding the highest experimental value of 0.808, which reflects a 1.98% enhancement in weld quality. The system demonstrated strong performance on both straight and curved weld paths, achieving a positional error of less than 0.29 mm, which falls within the acceptable weld gap range of 11.6 mm. This study emphasizes the practical implementation of a neuro-fuzzy prediction system integrated with an innovative metaheuristic for quality control in robotic arc welding. The integration improves weld consistency, minimizes defects, and increases production efficiency, representing a notable advancement in intelligent manufacturing. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2026. -
Semantic segmentation for data validation in unmanned robotic vehicles
Semantic segmentation is a vital aspect of computer vision, widely used in fields such as autonomous driving, medical imaging, and industrial automation. Maintaining high-quality datasets is crucial for enhancing model accuracy and minimizing real-world errors. This paper focuses on developing a comprehensive data validation pipeline for semantic segmentation using OpenCV. The proposed framework integrates automated integrity checks, preprocessing techniques, and consistency verification to manage large-scale datasets effectively. Key validation processes include image quality assessment (detection of blurriness and noise), verification of annotation accuracy, class distribution analysis, and identification of anomalies. Additionally, OpenCV-powered preprocessing steps, such as image resizing, normalization, contrast optimization, and data augmentation, are applied to refine dataset quality for segmentation models. This paper also addresses scalability concerns associated with processing extensive datasets, introducing optimized batch handling and parallel validation techniques. By implementing a structured validation workflow, this research enhances the reliability, robustness, and overall effectiveness of semantic segmentation models, ensuring high-quality training data for deep learning applications. 2026, Intelektual Pustaka Media Utama. All rights reserved. -
Thermomechanical Analysis of Cutting Tool Used for Minimizing Tool Wear During Machining of Inconel 718
During the CNC machining process, high stresses and temperatures are created at the cutting edge during machining of Inconel 718 due to severe tool wear. Inconel 718, one of the most often used Ni alloys, has a low machinability. Hence, determination of proper cutting tool to minimize tool wear and reduce surface roughness becomes an important aspect. Considering the scenario, the model-ling of dry turning of Inconel 718, a 3-Dimensional (3D) numerical model based on Finite Element (FE) is used. Turning tests were used to validate the model. The main wear modes that were dis-covered experimentally (chipping, notching, and built up edge BUE) were linked to variables predicted by the computational model, such as temperature and plastic strain at the chip. In this study, response surface methodology is used to design four features, matrix for a flexible composite design consisting of 5 repetitive levels; planning, implementation, implementation and development of mathematical models. Medium cutting strength is determined by the different feed values in the tangential, radial, and axial directions during the tooth by maintaining immersion and axial depth of the cut as constant. A comparison is shown between modeling and experimentation. In this paper, the principal stress and displacement strain has been seen at the tool-work interface region of three cutting tools during machining Inconel 718. Three directions i.e tangential, radial, and axial directions these stress and displacement has been applied to observe the changes and determine the selection of the suitable cutting tool for optimal machining conditions and parameter selection. It is observed that titanium cutting tool can prove to be a better tool to be used for machining Inconel 718 for longer tool life and improve productivity. Major Findings: The principal stress and Von-Mises stress in the thermomechanical analysis during Machining of Inconel 718 is found to be less in titanium cutting tool. This is the most suitable tool that can be used for machining hard to cut material Inconel 718. 2025, Informatics Publishing Limited. All rights reserved. -
Impact of Digital Storytelling on Motivation in Middle School English Classrooms
Motivation is a key factor in the learning process, especially in language acquisition. This research examines the effects of using digital storytelling (DST) on motivation in English classrooms. The study, which used a mixed methods approach, involved 100 middle school students in Bengaluru selected through convenience sampling. Data collection methods included questionnaires and semi-structured interviews. Students were divided into experimental and control groups, with the former receiving DST-integrated instruction and the latter being taught using traditional methods. The results of the quantitative analysis showed a positive influence on motivation in the experimental group compared to the control group. Qualitative results showed that implementing DST increased students' motivation, engagement, and understanding of the English language more effectively than traditional teaching methods. Further research is encouraged to explore the full potential of DST to improve student language skills and motivation. 2024 IGI Global. All rights reserved. -
Impact of Digital Storytelling on Middle School Students' Attitudes Toward English Language Learning
The integration of digital storytelling (DST) into teaching has significantly influenced educational development, especially English language acquisition. This study examines the impact of DST-integrated pedagogy on students' attitudes and perceptions toward learning English. In a quantitative study using an experimental design, 200 middle school students were purposively selected and divided into control and experimental groups. The control group received the traditional method of language teaching, while the experimental group received the DST method. Data were collected through a survey and analysed using descriptive and Wilcoxon test. The results suggest that exposure to DST positively influenced students' attitudes and led to better understanding, engagement, and motivation in learning English in the treatment group. This suggests that incorporating DST into English lessons can improve teaching quality and students' overall progress. Further analysis is needed to fully explore the potential of DST-based instruction in developing language acquisition skills. 2024 IGI Global. All rights reserved. -
Geometry of Variably Inclined Inviscid MHD Flows
A steady plane variably inclined magnetohydrodynamic flow of an inviscid incompressible fluid of infinite electrical conductivity studied. Introducing the vorticity, magnetic flux density, and energy functions along with the variable angle between magnetic field and velocity vector, governing equations are reformulated. The resulting equations are solved to analyze the geometry of the fluid flow. Considering streamlines to be parallel, stream function approach is applied to obtain the pattern for magnetic lines and the complete solution to the flow variables. Next considering parallel magnetic lines, magnetic flux function approach is applied to obtain streamlines and the complete solution of the flow. A graphical analysis of pressure variation is made in all the cases. 2020, Springer Nature Singapore Pte Ltd. -
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. -
Marketing Research and Market-Focused Production as an Effective Business Tool in Power Sector
Businesses must devote part of their resources to conducting market and marketing research to make good decisions, which will help expand any business and utilize resources effectively. Understanding the intended clients is essential to successfully operating and expanding a firm. For marketers to comprehend consumer value about the product being supplied and therefore add value to their consumers, it is crucial to have this understanding. Organizations can better influence customers to buy niche goods or corporate services after thoroughly understanding their objectives, requirements, and values. In this situation, it is required to restructure the physical system and the related control and planning systems to provide production the tools it needs to become more competitive and customer-focused, acting as a positive and active production process instead of a reactive one. One of the finest techniques for understanding consumers is market research. It provides basic information that a company may utilize to inform its marketing strategy, facilitating and enhancing sales and marketing. This paper reviews the impact of effective market and marketing research and market-focused manufacturing in the power sector. 2023 IEEE. -
Organization justice impact on employee work engagement
Research methodology: For the study 200 employees of selected Educational Institutions in North NCR was taken as respondents. Data was collected using standard questionnaire containing standard scaled of distributive, procedural, interactional, trust and employee engagement. The relationships between justice perceptions and work engagement were analyzed using correlations and regression analysis. Findings: The analysis of the study indicates that there is a strong and positive relationship among organization justice and employee engagement. The study also indicates that procedural, interactional and distributive justice are inter related with each other. Further, distributive and interactional justice take precedence over procedural justice in determining job engagement, while distributive justice plays the most important role in determining organization engagement (OE), followed by procedural and interactional justice. Limitations: This paper adds to the very small number of studies that have investigated the role of interactional justice in enhancing job and OEs. It has also established inter-relationships between the three dimensions of organizational justice and their individual roles in determining job and OEs. 2020 SERSC. -
Determining the Antecedents and Consequences of Brand Experience: A Study to develop a Conceptual Framework
In the marketing literature, one of the most talked- about subjects is brand experience (BE). Through an examination of the numerous studies conducted by BE researchers, this report attempted to determine the significance of BE in the body of recent literature. This paper culminates in the creation of a conceptual framework that prospective investigators might utilize to discern the diverse pathways inside BE. 2024 IEEE. -
Impact of Digital Media Marketing on Consumer Buying Decisions
Digital Marketing has become one of the most discussed topics in the field of management in the recent past. With the advent of social media, digital marketing has even garnered more attention. It has directly or indirectly influenced the buying behaviour of the customers also. This paper has tried to understand the impact of digital marketing in influencing the impulsive buying behaviour of the customers. 2024 IEEE. -
Role of Artificial Intelligence in Influencing Impulsive Buying Behaviour
This research paper investigates the influence of Artificial Intelligence (AI) on impulsive buying behaviour in the digital commerce domain. The study explores how AI algorithms, data analysis, and customized marketing approaches influence impulsive buying decisions, reshaping traditional understandings of this phenomenon. The analysis draws from a confluence of psychological principles, technological advancements, and marketing strategies, aiming to shed light on how AI not only forecasts but also incites impulsive buying behaviours. The study identifies research gaps, such as the integration of AI with emotional triggers, the comparative effectiveness of AI vs. human influence, and cross-cultural and demographic variability. The research methodology involves a descriptive study with a questionnaire-based survey, and data analysis tools such as ANOVA and paired t-tests. This research contributes to the broader discussion on digital-age consumer behaviors, underscoring the revolutionary role of AI in transforming retail experiences and beyond. 2024 IEEE. -
Understanding the use of Regression Analysis in Business Analytics to understand the perceptions of Students about Quality in Higher Education
For a very long time, researchers in a variety of fields have utilized regression analysis as a crucial tool for data analysis and result interpretation. Regression analysis has also been widely employed in the business world to determine what factors influence consumers' decisions to purchase any of the company's products. Comprehending the interplay of these variables will enable the business to conduct a more thorough consumer analysis and boost sales. This essay is an attempt to comprehend students' perceptions on the qualities they consider important while applying to universities. Regression analysis is another approach used in this article to determine how the quality criteria affect the respondents' overall happiness. 2024 IEEE. -
ETHICAL CONFLICTS AMONG THE LEADING MEDICAL AND HEALTHCARE LEADERS
Today, the whole world is fighting the COVID-19 pandemic. In these circumstances, medical professionals are being viewed as the frontline warriors who are risking their lives for the sake of helping, caring, and curing these patients. However, in these difficult times, there are few medical professionals and health care providers who are taking advantage of this situation and taking advantage of distressed and distraught patients at will. A conflict between professional and personal ethical values makes them depressed and puzzled. It is tough for them to maintain a good image of their profession and business. The objectives of this study are to review the ethical conflict amid the ongoing Covid pandemic and post-Covid pandemic (vaccination period) in the context of medical professionals and health care providers. The paper is designed based on a literature review. Almost fifty-two research papers, articles, survey reports, and newspapers were studied in the context of ethics in business/profession. After reviewing moral distress is ongoing and post-pandemic period, the researchers have tried to present the medical professionals and health care providers' critical situation to give priority to their professional ethics or personal interest. School of Engineering, Taylor's University -
Geopolitical Risk, Variability of Oil Price, and the Global Trade Uncertainty: An Economic Perspective
Oil prices are the outcome of a highly integrated, dynamic global system. With geopolitical risk and trade policy uncertainty contribute to the volatile world markets, the study analyses the impact of geopolitical risk, trade policy uncertainty on crude oil price globally. It considers data from 2000 to 2023 and finds out systematic link between the three variables. The data proved a long run impact of trade policy uncertainty and geopolitical risk on the crude oil. This enhances the influence of the variables and leads to implementation of policy measures in global markets. The data are tested through Auto Regressive Distributed Lag (ARDL) model and checked for long run cointegration and short run association. The policy, thus, has been suggested as to improve the transition towards sustainability globally. Though the impact of geopolitical risk and trade policy uncertainty is not strong on crude oil price in world market, it can affect the short run volatility. Thus, mitigation, diversification and transparency are the key factors to strengthen the existing situation in world. 2026, IGI Global Scientific Publishing. All rights reserved. -
Pattern of Carbon Dioxide Emission, Economic Growth and Energy Consumption in South-Asian Countries: An Empirical Analysis
The main aim of this chapter is to analyse the pattern of environmental pollution as represented by per capita carbon dioxide emission (PCCO2), per capita gross domestic product (PCGDP) and per capita energy consumption (PCEC) and their nexus in case of South-Asian countries for the time period 19912014. Econometric tools such as panel co-integration and fully modified ordinary least squares have been used to study the relations. A positive significant relationship has been observed between PCGDP and PCCO2 emission. In addition, an increase in PCEC also has a positively significant impact on PCCO2 emission. Therefore, the governments of all the countries need to come together and take steps to curb the rising carbon emission since neither the problem nor the responsibility is restricted to one country alone. There is a need for countries to increase the consumption of renewable energy and explore alternate options that are fewer dependents on coal or any other fossil fuel. On priority, economies in South-Asian region should focus on sustainable economic activities by balancing growth of economy with clean environment. 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
An Empirical and Statistical Analysis of Classification Algorithms Used in Heart Attack Forecasting
The risk of dying from a heart attack is high everywhere in the world. This is based on the fact that every forty seconds, someone dies from a myocardial infarction. In this paper, heart attack is predicted with the help of dataset sourced from UCI Machine Learning Repository. The dataset analyses 13 attributes of 303 patients. The categorization method of Data Mining helps predict if a person will have a heart attack based on how they live their lives. An empirical and statistical analysis of different classification methods like the Support Vector Machine (SVM) Algorithm, Random Forest (RF) Algorithm, K-Nearest Neighbour (KNN) Algorithm, Logistic Regression (LR) Algorithm, and Decision Tree (DT) Algorithm is used as classifiers for effective prediction of the disease. The research study showed classification accuracy of 90% using KNN Algorithm. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.