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Ayurvedic Ethics: Traditional Foundations and Contemporary Relevance
Ayurveda is a widely practised traditional medical system in India in which clinical practice and moral philosophy are intrinsically linked. Classical concepts, such as Sadv?tta (good conduct) and Chikits? Chatu?p?da (four pillars of treatment), define physician duties, patient care, and community responsibility, reflecting a holistic approach to health. This short communication examines how these frameworks, rooted in historical and cultural contexts, can inform contemporary debates on medical ethics. It explores their continuing relevance in strengthening patientphysician trust, promoting equitable access to care, and shaping the moral responsibilities of healthcare providers in an era of increasing commercialisation. Drawing on these enduring principles, the paper argues that Ayurvedic ethics can offer culturally grounded yet adaptable guidance for modern medical practice. By integrating these insights into present-day discourse, Ayurvedic ethics contribute to inclusive, context-sensitive, and ethically robust approaches to healthcare ethics that address both local traditions and the universal principles of compassionate, ethical medical care. Journal of Research on History of Medicine. -
Awareness and perception of adolescent boys about menstruation: an exploratory study from rural India
Background/objectives: Menstruation, despite being a natural process, remains stigmatized in many patriarchal societies, where taboos and misinformation perpetuate silence and misconceptions. This study investigates adolescent boys awareness and perceptions of menstruation in rural India, examining the influence of cultural, educational, and social factors on their understanding. Methods: Employing an exploratory qualitative design, a focus group discussion (FGD) was conducted with eight 15-year-old boys from a rural secondary school in Kerala, India. The session was audio-recorded and supplemented with detailed field notes to capture verbal and non-verbal insights. Braun and Clarkes thematic analysis framework was used to identify key themes. Results: Three primary themes emerged: Levels of awareness (ranging from correct but insufficient to lack), Sources of knowledge (media, peers, cultural practices), and Desire for further knowledge. Findings indicate that fragmented or inaccurate knowledge, shaped by cultural and societal norms, reinforces stereotypes and stigma around menstruation. Conclusions: Findings underscore the need for inclusive and culturally sensitive educational interventions for boys, aimed at dispelling myths and fostering empathy. Such programs can contribute to improved awareness, reduced stigma, and greater support for menstrual hygiene management (MHM) practices. Future research should assess similar interventions across broader contexts to determine their impact on challenging menstrual taboos. The Author(s) 2025. -
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Available Transfer Capability (ATC) enhancement & optimization of UPFC shunt converter location with GSF in deregulated power system
Available Transfer Capability (ATC) is a measure for transmission system security margin in open access electricity market. Determining the Available Transfer Capability (ATC) of the transmission networks, Repeated Power Flow (RPF) approach have been used since it can satisfy voltage, thermal and stability constraints among all other methods available. The main objectives include identification of best location for UPFC to get maximum ATC enhancement and to propose a novel method for optimizing the UPFC PV bus location using Generation Shift Factor (GSF) so that power system transmission network can deliver more number of power trades. 2016 IEEE. -
Autoregressive Model with Students t-Errors
This paper examines a first-order autoregressive model that incorporates students t-distributed errors. The estimation procedure is developed using the maximum likelihood method, with the solutions demonstrated using a simulation approach. As the estimating equations were not in a closed-form expression, we obtained the parameter estimates using the NewtonRaphson method. For a finite sample size, a parametric bootstrap procedure for the unit root test has been illustrated. To demonstrate the applications of the proposed model, a time series of quarterly GDP percentage changes are analyzed. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Autoregressive Distributed Lag Approach for Estimating the Nexus between Net Asset Value of Mutual Fund and Economic Determinants in India
India has seen a phenomenal growth in cumulative mutual fund investment from Rs 7.93 trillion in 2012 to Rs 40.38 trillion in 2022, which is more than a five-fold increase since last 10 years. Retail investors are now realizing the power of savings and Systematic Investment Plans (SIP) to build long term wealth. A financial literacy wave which is sweeping across India has projected mutual funds as a significant contributor and beneficiary of this phenomenon. The evolving economic landscape of India provides investors with excellent opportunities to capitalize on these fluctuations through systematic investment in safe investment vehicles like mutual funds. The market associated with mutual funds is always subjected to economic risks. The erratic fluctuations in macroeconomic variables can largely explain the Volatility in Net Asset Value (NAV) of equity oriented mutual fund schemes. With this background, this paper examines the impact of select macroeconomic variables on mutual funds performance in India. To analyse this, monthly observations of select macroeconomic variables, average NAV of large cap, mid cap, and small cap funds collected for a period of 10 years starting from January 2013 to November 2022. Descriptive statistics is used to probe the characteristics of the variable. In addition, correlation and ordinary least square method is applied to check the existing relationship and impact level of macroeconomic factors on NAV of select schemes. Lastly, short and long run relationship is analysed using Autoregressive Distributed Lag Model (ARDL). 2023, ASERS Publishing House. All rights reserved. -
Autonomous Systems in Agriculture: An Innovation-Based Framework
The emergence of autonomous systems in agriculture signifies a transformative change in global food production, propelled by developments in artificial intelligence, robotics, and data analytics. This chapter suggests a thorough innovation framework that brings together technological, organizational, institutional, economic, social, and environmental aspects to steer the sustainable development and implementation of autonomous agricultural technologies. The chapter examines essential facilitators and obstacles to innovation, such as infrastructure deficiencies, digital literacy, and regulatory challenges, by analyzing historical trends, case studies, and policy landscapes. It stresses ethical and inclusive innovation to guarantee the involvement of smallholders and ecological resilience. This framework acts as a strategic resource for researchers, practitioners, and policymakers who seek to connect digital transformation in agriculture with sustainability objectives, especially those outlined by the UN Sustainable Development Goals (SDGs). 2026 by IGI Global Scientific Publishing. -
AUTONOMOUS IOT MOVEMENT IN HOSTILE AREAS USING ROBOTICS AND DEEP FEDERATED ALGORITHMS
Innovative solutions are required when Internet of Things (IoT) devices are deployed in hostile or difficult locations to ensure dependable and effective operation. In order to enable autonomous IoT mobility in such challenging circumstances, this study suggests a novel approach integrating robotics and deep federated algorithms. Robotics and IoT can work together to create a system that can adapt to dangerous environments, extreme weather conditions, and unexpected terrain. Deep federated algorithms further improve system performance by facilitating dispersed device collaboration for learning while protecting data privacy. The suggested framework covers the issues of communication stability, energy optimization, and real-time decision-making. We illustrate the practicality of this strategy in strengthening the dependability and efficiency of IoT deployments in hostile situations through simulations and tests. 2023 IEEE. -
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. -
Autonomous AI in Automotive Safety: Rethinking Tortious Liability Paradigms
This paper seeks to examine the application of tort law to the Automated Vehicles. Section I deals with a brief overview of the impact of AVs and tort law and the application of tortious principles. Section II discusses the concept of fault and the multiple parties to whom fault can be attributed. Furthermore, it discusses who should be blamed and to what extent. Section III discusses the tortious principles such as product liability, vicarious liability and apportionment of liability and its application to the AVs. Section IV proposes directions for future research, conclusion and recommendations for the legal challenges posed by the AVs. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Automation using Artificial Intelligence in Business Landscape
The integration of Artificial Intelligence (AI) with automation has sparked a remarkable transformation in the contemporary business landscape, promising elevated efficiency and quality. However, this convergence encounters multifaceted challenges, notably in the adoption of recent AI techniques such as deep learning, reinforcement learning, and natural language processing. These techniques, while potent, grapple with challenges in data quality, interpretability, and ethical considerations. In this study, we aim to delineate the intricate interplay between AI and automation, illuminating their collective potential to augment operational efficiency and confer a competitive advantage. Through a comprehensive review, we will explore the effective integration of these technologies, navigating hurdles such as data bias, system compatibility, and human-machine collaboration. Here, the primary research objective is to provide insights on optimizing the outcomes by synergizing AI and automation while addressing the inherent challenges, ultimately fostering sustainable and impactful implementations in organizational frameworks. 2023 IEEE. -
AUTOMATION OF TEST CASE PRIORITIZATION: A SYSTEMATIC LITERATURE REVIEW AND CURRENT TRENDS
An Important stage in software testing is designing a test suite [18]. The test case repository consists of a large number of test cases. However, only a portion of these test cases would be relevant and can find bugs. Test case prioritization(TCP) is one such technique that can substantially increase the cost-effectiveness of the testing activity. Using test case prioritization, more relevant test cases can be captured and tested in the earlier stages of the testing phase. The objective of the study is to understand different techniques used and a systemic study on the effectiveness of these approaches. The Literature consists of a few relevant articles introducing novel techniques for test case prioritization between 2008 and 2022. Studies show that parameters that are considered for test case prioritization are important. Hence, the current article also focuses on the parameters considered in the literature. 40% of the articles used in the literature review use different test case information as parameters. A systemic review and analysis of data sets involved in the literature are evaluated in the study. The review also focuses on the different approaches used for comparing the newly introduced approach and reveals a novel approach for prioritization. 2023 Little Lion Scientific. All rights reserved. -
Automation of Breast Cancer Diagnosis and Treatment Using Machine Learning
Breast cancer remains a major global health challenge, with the complexity of managing diverse diagnostic tests often hindering timely and accurate detection. This system proposes a solution by unifying various test results, such as imaging, biopsy and genetic data, into a single platform that leverages machine learning (ML) to predict the likelihood of breast cancer. The platform features an intuitive dashboard that visually represents deviations from normal values, enabling healthcare providers to make informed decisions for early detection and treatment planning. In addition, the system includes an interactive chatbot powered by natural language processing, which assists both doctors and patients by interpreting test results, explaining predictions and offering real-time suggestions for treatment options. This comprehensive approach not only integrates ML models to enhance diagnostic accuracy but also provides real-time updates and alerts for critical changes in patient data. By consolidating fragmented information and incorporating predictive analytics, the system aims to improve the precision of cancer monitoring and offer personalized treatment guidance. About 98% early detection accuracy is achieved to do decision-making processes better which leads to efficient treatment planning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Automatic Weld Features Identification and Weld Quality Improvement in Laser Sensor Integrated Robotic Arc Welding
In this study, an integration of point laser sensor in robotic arc welding has been performed for achieving robotic positional accuracy automatically in every welding cycle. With the help of defined focal length of laser sensor, weld seam positions as well as weld gap have been found automatically for any newly positioned work-piece. If there is any change in robot positioning compared to the master job, the shift in every axis is sent as signal to the robot controller so that robot end effector will adjust the shift amount automatically. The welding process parameters are set at optimal values. Taguchi approach so that maximum values of weld quality in terms of depth of penetration, yield strength and ultimate strength can be achieved in every welding cycle. Overall, with the proposed approach, a smart and productive way of operating industrial welding robot has been proposed which can be implemented in any medium to large scale industries for obtaining welding joints with minimum defects. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Automatic vehicle license plate recognizer for kannada script using tensorflow based canvolutional neural network model /
Patent Number: 201941054190, Applicant: Dr. Arish P.
Automatic Vehicle License Plate Recognizer (AVLPR) is used to read license number of a vehicle by itself without direct human control. It is used to track citizens, movements and misidentiflcation of vehicles. Traditional ALVPR is mounted on police cars or on object like road signs and bridges. The challenging tasks in recognizing the license plate numbers in Kannada script are high contrast foreground or background colour, shape of the license plate, location of the plate in the bottom and middle of the vehicle. -
Automatic vehicle license plate recognizer for kannada script using tensorflow based canvolutional neural network model /
Patent Number: 201941054190, Applicant: Dr. Arish P.
Automatic Vehicle License Plate Recognizer (AVLPR) is used to read license number of a vehicle by itself without direct human control. It is used to track citizens, movements and misidentiflcation of vehicles. Traditional ALVPR is mounted on police cars or on object like road signs and bridges. The challenging tasks in recognizing the license plate numbers in Kannada script are high contrast foreground or background colour, shape of the license plate, location of the plate in the bottom and middle of the vehicle. -
Automatic smart attendance management system based on face recognition using deep learning /
Patent Number: 202141025155, Applicant: J Omana.
In this digital era, face recognition system plays a vital role in almost every sector. Face recognition is one of the mostly used biometrics. It can used for security, authentication, identification, and has got many more advantages. Furthermore, face recognition system can also be used for attendance marking in schools, colleges, offices, etc. The conventional method of calling name of each student is time consuming and there is always a chance of proxy attendance. -
Automatic Skin Lesion SegmentationA Novel Approach of Lesion Filling through Pixel Path
Abstract: Lesion segmentation is a vital step in a melanoma recognition system. Many algorithms were developed for the efficient skin lesion segmentation. Most of them fails to realize a perfect segmentation. This paper proposes a novel, fully automatic system, for the lesion segmentation in dermatograms. The proposed approach executes in two steps. Selection of root seed is the first step. All the lesion pixels in the dermatogram are identified during the second step. Traversal through a predefined lesion pixel path ensures the reachability of all lesion pixels irrespective of the possible lesion discontinuity. The proposed algorithm is tested with two publically available dataset, PH2 and images of ISBI2016 challenge. Out of the six evaluation parameters, the proposed method shows the best values for specificity, accuracy, Hammuode distance and XOR. This confirms the merit of the proposal with respect to existing popular methods. 2020, Pleiades Publishing, Ltd. -
Automatic Resume Parsing using Greywolf Algorithm Integrated with Strategically Constructed Semantic Skill Ontologies
The quest for finding the right candidate for their post has made the recruiters employ several methods since the beginning of corporate job recruitment. Apart from the skills and the quality of interview, a thing that matters the most and forms the basis of selection is the candidate's resume. Recruiters and companies have a tough time dealing with the several thousands resumes of the candidates which apply, as manually scanning them and finding the right selection can be tough most of the time. In this paper, Natural Language Processing(NLP) methods have been integrated with ontologies to improve the pace and quality of the recruitment process by proposing an automatic resume parser model. The resume of a candidate, along with his LinkedIn and GitHub profiles are weighted and using the Greywolf algorithm, the global maxima of the most deserving and qualified candidate are found and are recommended with a high accuracy of 96.13%. 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) -
Automatic Measurement and Differentiation of Traffic Volume Count
Traffic volume in India is growing drastically over the past few decades. This leads to an increased need of constructing more highways and underpasses. In order to have the definite knowledge of traffic volume, and to design the width and thickness of the pavements, periodical conduction of traffic census is necessary. At present, the evaluation of traffic volume is conducted manually. This system is tiresome and lacks accuracy. The data obtained from the traffic census decides the sanction of new highways, underpasses, or flyovers which involves huge investments. Hence, the accuracy of this data is very critical. In this paper, we propose an automatic tool that helps to measure the traffic volume and differentiate the vehicles using video processing tools in MATLAB. The proposed algorithm consists of the following steps: i Foreground Detection ii Blob Detection iii Blob Analysis iv Vehicle differentiation Counting. 2018 IEEE.