Browse Items (16488 total)
Sort by:
-
A bibliometric analysis of fruit disease prediction using machine learning
In recent years, there has been a growing interest in leveraging machine learning techniques for the early detection and prediction of diseases affecting fruit crops. This study presents a comprehensive bibliometric analysis of research literature focused on fruit disease prediction using machine learning algorithms. Through systematic review and analysis of a large corpus of scholarly articles, conference papers, and patents, this paper aims to provide insights into the current trends, key research themes, influential authors, and popular machine learning methods in this domain. This paper conducts a literature review and bibliometric analysis to explore a significant increase in research activity in fruit disease prediction using machine learning, indicating the increasing importance of this area in agriculture and plant pathology. Various machine learning and deep learning algorithms, including convolutional neural network (CNN), decision trees, random forests and LSTM have been widely employed for disease prediction tasks. Moreover, the study identifies common datasets, evaluation metrics, and challenges encountered in this field. Overall, this bibliometric analysis provides valuable insights for researchers, practitioners, and policymakers interested in fruit disease prediction, highlighting opportunities for collaboration, innovation, and advancement in agricultural technology and plant health management. 2025 Author(s). -
An ensemble deep learning model for automatic classification of cotton leaves diseases
Cotton plant (Gossypium herbaceum), is one of the significant fiber crop grown worldwide. However, the crop is quite prone to leaves diseases, for which deep learning (DL) techniques can be utilized for early disease prediction and prevent stakeholders from losing the harvest. The objective of this paper is to develop a novel ensemble based deep convolutional neural network (DCNN) model developed on two base pretrained models named: VGG16 and InceptionV3 for early detection of cotton leaves diseases. The proposed ensemble model trained on cotton leaves dataset reports higher training and testing prediction accuracies as compared to the base pretrained models. Given that, deep learning architectures have hyper-parameters, this paper presents exhaustive experimental evaluations on ensemble model to tune hyper-parameters named learning rate, optimizer and no of epochs. The suggested hyper-parameter settings can be directly utilized while employing the ensemble model for cotton plant leaves disease detection and prediction. With suggested hyper-parameters settings of learning rate 0.0001, 20 epochs and stochastic gradient descent (SGD) optimizer, ensemble model reported training and testing accuracies of 98% and 95% respectively, which was higher than the training and testing accuracies of VGG16 and InceptionV3 pretrained DCNN models. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
Effective Groundnut Crop Management by Early Prediction of Leaf Diseases through Convolutional Neural Networks
Groundnut (Arachis hypogaea L.), is the sixth-most significant leguminous oilseed crop grown all over worldwide. Groundnut, due to its high content of various dietary fibers, is classified as a valuable cash, staple and a feed crop for millions of households around the world. However, due to varied environmental factors, the crop is quite prone to many kinds of diseases, identifiable through its leaves, for which Groundnut producers have to suffer major losses every year. An early detection of such diseases is essential in order to save this significant crop and avoid huge losses. This paper presents a novel Machine Learning based Deep Convolution Neural Network (CNN) model CNN8GN. The model uses transfer learning technique for detection of such diseases in Groundnuts at an early stage of crop production. A Groundnut real image data set containing a total of 5322 real images for six different classes of Groundnut leaf diseases, captured in the fields of Gujarat state (India) during September 2022 to February 2023, is generated for training, testing and evaluation of the proposed model. The proposed deep learning model architecture is designed on eight different layers and can be used on varied sized images using simple ReLu and Softmax activation functions. The performance of the proposed CNN8GN model on Groundnut real image dataset is examined using a detailed experimental analysis with other six pre-trained models: VGG16, InceptionV3, Resnet50, ResNet152V2, VGG19, and MobileNetV2. CNN8GN results are also examined in detail using different sets of input parameters values. The proposed model has shown significant improvements for disease detection in comparative analysis with 99.11% training and 91.25% testing accuracy. The Author(s) 2024. -
Purification and Biochemical Characterization of Beta-Hexosaminidase B from Freshwater CnidarianHydra vulgaris Ind-Pune
Beta-N-acetylhexosaminidase (Hex) is a vital lysosomal hydrolase found in all living organisms, playing a crucial role in cellular homeostasis. Dysfunctions in this enzyme are implicated in severe pathological conditions such as Tay-Sachs and Sandhoff diseases in humans. In this paper, we report the purification and biochemical characterization of hexosaminidase from the soluble extracts obtained from the polyps of Hydra vulgaris Ind Pune. The Hydra Hex was purified by two-step sequential chromatography (hydrophobic interaction and gel filtration). Our results suggested that the enzyme isoform purified from Hydra is HexB, most likely to be a homodimer with a subunit mass of 65 kD. The pH optimum was in the range of 5.0 to 6.0 and the temperature optimum in the range of 50 C to 60 C. pH stability and temperature stability were found to be 5.0 and 40C respectively. The homology modelling studies corroborated the homodimeric nature of Hydra HexB, and indicated its structural resemblance to human HexB. This study offers new insights into the biochemical characteristics of Hydra HexB, providing a foundational framework for extensive investigations on this and other lysosomal hydrolases in Hydra. In a broader context, our results significantly contribute to establishing Hydra as a potential model organism to study the lysosomal biogenesis pathway. (2024), (Association of Carbohydrate Chemists and Technologists). All Rights Reserved. -
Machine learning for healthcare
Machine learning currently drives healthcare innovation, enabling novelty in solving complex medical problems. This chapter will present an in-depth critical review of various machine learning techniques applicable in healthcare in general, focusing on practical applications and recent advancements. It will further discuss supervised and unsupervised learning to semi-supervised learning methods, thereby detailing their uses for disease prediction, segmentation of patients, and image analysis in medical science. Among the most important areas in ML includes data preprocessing and feature engineering issues in health-care datasets. This further includes treatments for missing data, dimensionality reduction, and class imbalance. This chapter also discusses extensive case studies with state-of-the-art approaches that give insight into how the ML approach is changing health care decision-making, increasing diagnostic precision, and improving patient outcomes. Interpretability, scalability, and the mitigation of bias are further discussed as some of the challenges in the implementation of ML in healthcare. Ethical considerations regarding the need to develop responsible AI in healthcare and regulatory compliance are also discussed. It aims to serve as a handbook for researchers, practitioners, and policy analysts operating at the intersection between ML and healthcare. 2026 -
Rational design of bifunctional catalyst from KF and ZnO combination on alumina for cyclic urea synthesis from CO2 and diamine
This study is mainly focused on the design of stable, active and selective catalyst for direct synthesis of 2-imidazolidinone (cyclic urea) from ethylenediamine and CO2. Based on the rationale for the catalyst properties needed for this reaction, KF, ZnO and Al2O3 combination was selected to design the catalyst. ZnO/KF/Al2O3 catalyst was prepared by stepwise wet-impregnation followed by the removal of physisorbed KF from the surface. High product yield could be achieved by tuning acid-base sites by varying the composition and calcination temperature. The catalysts were characterized by various techniques like XRD, N2-sorption, NH3-TPD, CO2-TPD, TEM, XPS and FT-IR measurements. It is shown that acidic and basic properties of the solvent can influence the activity and product selectivity for this reaction. Under optimized condition; 180 C, 10 bar and 10 wt.% catalyst in batch mode, 96.3 % conversion and 89.6 % selectivity towards the 2-imidazolidinone were achieved. 2020 Elsevier B.V. -
Mindfulness-based strengths practice: a conceptual framework and empirical review of the literature
This review set out to provide empirical literature on mindfulness-based strengths practice (MBSP), a new approach in positive psychology that integrates mindfulness with character strengths, two positive predictors of well-being. First, the conceptualization of integrating character strengths and mindfulness into MBSP is discussed. The literature on the interrelatedness of character strengths and mindfulness is then described, along with ways that the intervention of MBSP encourages positive outcomes at various levels. The literature search returned 7 (10 samples, N = 3,851) studies supporting a positive association between character strengths and mindfulness (r = 0.30.4) and the mediating role of character strengths/virtues in mindfulness and mindfulnesss role in enhancing character strengths toward psychological well-being. The nine MBSP intervention studies (9 samples, N = 354) conducted in diverse contexts provide evidence of a significant improvement in well-being, engagement, life satisfaction, mindfulness, positive affect, character strengths, work-related outcomes, heightened birthing parents well-being during pregnancy and childbirth, increased academic performance, and enhanced mental health among students. The intervention studies also reported the fostering of mindful positive parenting and contributions to a significant reduction in negative psychological states, such as stress, depression, anxiety, and negative affect. This comprehensive review provides empirical support for the MBSP framework and its positive impact on well-being across various domains, including organizations, education, healthcare, and family. However, it underscores the need for more extensive research, as the current literature on MBSP is limited. The review encourages future studies to explore MBSP applications in diverse domains, thereby paving the way for a deeper understanding of its potential benefits. 2024 Taylor & Francis Group, LLC. -
CHARACTER STRENGTHS INTERVENTIONS IN HIGHER EDUCATION STUDENTS: A LITERATURE REVIEW
This review provides a comprehensive overview of interventions on character strengths in college and university students. Both qualitative and quantitative studies were reviewed. The review showed that focusing on character strengths leads to improved well-being, stronger interpersonal relationships, and reduced levels of stress, depression, anxiety, and academic pressure among students. The review also suggests that such interventions can be integrated into elective courses, first-year programs, and short-term training sessions tailored to address the specific needs of students. The interventions can offer a cost-effective alternative to traditional mental health strategies and could be implemented within college counseling centers. The limitations and practical implications of character strengths intervention modules designed specifically for college students are pointed out. By highlighting positive attributes and nurturing personal growth, character strengths interventions emerge as a valuable tool in bolstering the overall well-being of college students. The Author(s). All articles are licensed under the terms and conditions of the Creative Commons Attribution 4.0 International License (CC-BY 4.0 ). -
Effects of mindfulness-based strengths practice (MBSP) among women undergraduates in enhancing positive mental health
The study investigates the effectiveness of an 8-week Mindfulness-Based Strengths Practice (MBSP) intervention to enhance the positive mental health of women undergraduates by focusing on the development of character strengths, flourishing, mindfulness, and the reduction of psychological distress. Using a quasi-experimental design, the study involved 162 undergraduate women (mean age 18.55) from rural backgrounds; 80 volunteered for intervention and 82 for the control group. Participants completed pre-, post-, and three-month follow-up assessments, and the results showed significant gains in mindfulness, PERMA (Positive Emotion, Engagement, Relationships, Meaning, and Accomplishment) flourishing, character strengths, and a reduction in psychological distress, with moderate to large effect sizes. A follow-up after three months showed persistent effects in certain aspects. This investigation among the Indian population contributes to the literature on MBSP in an Eastern context. It underscores the effectiveness of MBSP as a positive psychological, mindfulness-based intervention on college campuses for promoting well-being and mitigating mental health challenges among college students. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
SIGNIFICANCE OF NURTURING PERMA FLOURISHING IN HIGHER EDUCATION: AN INTEGRATIVE REVIEW
This integrated review explored the significance of PERMA, a multidimensional well-being framework, and PERMA-based interventions in promoting student well-being within higher education contexts. The literature search resulted in 16 studies, and the synthesizing of key research findings supports the effectiveness of PERMA-based intervention on students overall well-being. The interventions centered on cultivating PERMA (positive emotions, engagement, relationship, meaning, accomplishment) offered as semester courses, classroom-based curricula, or intervention programs were found successful in improving wellbeing, happiness, life satisfaction, motivation, relationship building, engagement in learning, and reducing negative emotions, stress, academic boredom, anxiety, depression. Overall, the review findings demonstrate that embedding a PERMA-based well-being program as a holistic approach in education would foster a supportive learning environment and social connection in promoting individual and collective well-being among the students. Future studies could strengthen the present findings and respond to the limitations of the existing studies, which would provide a better understanding of the application and effects of PERMA-based programs. Copyright: The Author(s). -
SmartHealth: Personalized Diet and Exercise Plans Using Similarity Modeling
Due to the growing prevalence of chronic diseases stemming from unhealthy lifestyles, a personalized approach to patient care is crucial. This paper delves into a system that utilizes cosine similarity and Pearson correlation to generate tailored diet and exercise plans, effectively managing chronic diseases. The system focuses on common chronic conditions like diabetes, hypertension, and thyroid disorders. Through sophisticated similarity modeling for diet and exercise, the proposed system provides integrated and personalized lifestyle recommendations, outperforming non-personalized or basic rule-based systems. 2024 IEEE. -
HULA: Dynamic and Scalable Load Balancing Mechanism for Data Plane of SDN
Multi-rooted topologies are used in large-scale networks to provide greater bisectional bandwidth. These topologies efficiently use a higher degree of multipathing, probing, and link utilization. An end-to-end load balancing strategy is required to use the bisection bandwidth effectively. HULA (Hop-by-hop Utilization-aware Load balancing Architecture) monitors congestion to determine the best path to the destination but, needs to be evaluated in terms of scalability. The authors of this paper through artifact research methodologies, stretch the scalability up to 1000 nodes and further evaluate the performance of HULA on software defined network platform over ONOS controller. A detailed investigation on HULA algorithm is analysed and compared with four proficient large-scale load balancing mechanisms including: connection hash, weighted round-robin, Data Plane Devlopment Kit (DPDK) technique, and a Stateless Application-Aware Load-Balancer (SHELL). 2023 IEEE. -
P4 based Load Balancing Strategies for Large Scale Software-Defined Networks
To meet the large demands of future networks, several large-scale Software Defined Networking (SDN) test-beds have been designed. The increasing complexity of networks has resulted in convoluted methods for managing and orchestrating efficiently across a wide range of network environments. The load balance function is impaired when the controller fails to connect with the switches. A traditional Load Balancer (LB) must decapsulate layers one by one and get the information needed to run load balancing algorithms. For instance, OpenFlow, NetConf, Programming Protocol-independent Packet Processors (P4), and Data Plane Developement Kit (DPDK) provide network programmability at both the control and data plane levels. In this paper, authors implement load balancing using the P4 programming language without the need of a controller, the P4 load balancer can operate on its own. Controller's support is used to keep track on the health of the web servers. In this situation, the controller can identify a server failure and notify the P4 load balancer, which will restrict requests to the malfunctioning server, lowering the dispatching failure rate. A detailed investigation of various load balancing mechanisms is analysed in this paper followed by the identification of four potential approaches to large-scale SDN tests, including connection hash, weighted round-robin, DPDK technique, a Stateless Application-Aware Load-Balancer (SHELL). 2022 IEEE. -
Application of Artificial Intelligence on Smart Tourism Eco Space: An Integrated Approach in Post-COVID-19 Era
The AI-integrated approach in recent times has evolved with innovative techniques and gained much importance in the post-COVID-19 scenario. This chapter extends contemporary and exponential research findings for Smart Tourism Practices and the Application of AI-enabled systems for the Tourism Ecosystem. It highlights for various service segments like hotels, motels, resorts, restaurants, cafes, airlines, and destinations under this large umbrella known as the hospitality sector. Smart tourism eco space capacitates an ICT-enabled system consolidates tourism resources and information technologies. Perhaps, with multiple challenges, a successful implementation of smart tourism approaches empowers and supports a smart system in place. The tourism eco space is highly vulnerable, and this situation in the service sector creates an intense requirement of a comprehensive view of digitally enabled smart tourism eco space with innovative mechanisms to remain contact-free with less human intervention. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Impact of Rupee Volatility on the Financials of the Indian IT Companies
International Journal of Advanced Research in Economics and Commerce, Vol-1 (1), pp. 1-8. ISSN-2320-7248 -
Analysis on techniques used to recognize and identifying the Human emotions
Facial expression is a major area for non-verbal language in day to day life communication. As the statistical analysis shows only 7 percent of the message in communication was covered in verbal communication while 55 percent transmitted by facial expression. Emotional expression has been a research subject of physiology since Darwins work on emotional expression in the 19th century. According to Psychological theory the classification of human emotion is classified majorly into six emotions: happiness, fear, anger, surprise, disgust, and sadness. Facial expressions which involve the emotions and the nature of speech play a foremost role in expressing these emotions. Thereafter, researchers developed a system based on Anatomic of face named Facial Action Coding System (FACS) in 1970. Ever since the development of FACS there is a rapid progress in the domain of emotion recognition. This work is intended to give a thorough comparative analysis of the various techniques and methods that were applied to recognize and identify human emotions. This analysis results will help to identify proper and suitable techniques, algorithms and the methodologies for future research directions. In this paper extensive analysis on various recognition techniques used to identify the complexity in recognizing the facial expression is presented. Copyright 2020 Institute of Advanced Engineering and Science. All rights reserved. -
Product specific determinants of electronic gadget purchase intention - a case of the purchase behaviour of Indian youth
This study investigated the impact of product specific features of electronic gadgets on the purchase intention on the Indian youth. The study was quantitative in nature and data was collected from 650 young electronic gadget consumers in Bengaluru, India using structured questionnaires. Descriptive statistics and structural equation modelling (SEM) were used for data analysis. Brand image, product design, and country of origin are referred as product evaluation attributes; and corporate identity were identified as the determinants of purchase intention. Respondents were neutral regarding the role of product evaluation attributes and corporate identity in their purchases, but acknowledged these factors' importance. Findings implied a positive and significant influence of product evaluation attributes on the corporate identity of companies, and purchase intention of the youth. However, corporate identity did not influence purchase intention, clearly indicating that only product specific features, such as brand, design and country of origin are considered when youngsters purchase gadgets. Copyright 2022 Inderscience Enterprises Ltd. -
Load Balancing Strategy for Large Scale Software Defined Networks
Programmability has left its mark on every facet of business, with technology playing newlinean integral role. Social networking industry trends underscore technology s ubiquity in newlinenearly every business transaction. Traditional networks grapple with numerous challenges, rendering them ill-equipped to process and handle the demands of the modern newlinelandscape effectively. The lack of programming in these networks leads to stagnation, newlineinhibiting their ability to evolve or enhance performance. The advent of Software Defined Networks (SDN) has introduced increased flexibility into conventional networks, newlineopening avenues for creating innovative services. newlineSDN technology addresses challenges in large-scale networks, offering solutions for newlinehigh throughput, virtualization, fault detection, and load balancing, providing effective network management. The rapid expansion of network services and applications newlinein SDN environments demands sophisticated load-balancing solutions that adapt to newlinedynamic traffic patterns and varying service requirements. This study presents a pioneering algorithm, the Dynamic Load Balancing Algorithm (DLBA), which utilizes the newlineProgramming Protocol-independent Packet Processors (P4) language. The algorithm is newlinespecifically crafted to tackle the issues associated with optimizing traffic distribution in newlinethe data plane of SDN. newlineP4 programming language, recognized as one of the most robust languages, addresses newlinethe limitations of traditional networking, enhancing programmability and agility by newlinedistributing the load across the network. The research implements a novel quotDynamic newlineLoad Balancing Algorithmquot using the P4 language to instill dynamism and achieve load newlinebalance in large-scale networks. The P4-based implementation showcases dynamicity, scalability, flexibility, and adaptability. This research commences with thoroughly newlineexamining existing load-balancing algorithms implemented using the P4 language, followed by a comparative analysis between these algorithms and DLBA. -
Dynamic Load Balancing on Switches of Software Defined Network Managed by OpenDayLight Controller
In recent times, the world is becoming a global village where connectivity is a new norm irrespective of geographical location. Within corporate networks, huge setbacks are faced due to a lack of efficient resource management. Load balancing is inevitable to cater to a reliable, faster, and congestion-free communication experience for exponentially increasing online enterprises. Dynamic network resource management for high performance and low data transmission latency in a network is necessary. The major issue faced by the traditional network is that it relies on static hardware switches. Software-Defined Network approaches paved a way to overcome the limitations of traditional networks. This research proposes the Dynamic Load Balancing Algorithm for Software-Defined Networks to utilize network resources optimally. The major function of the proposed algorithm is to determine alternative paths and further distribute the incoming and outgoing network flows to achieve optimum network resource utilization with faster traffic flow completion. The experiment is performed with the OpenDayLight Controller on the Mininet simulator, which emulates the network with the novel scheme. The results prove that the proposed solution has accomplished the benchmarks of optimum throughput, reduced redundancy, and reduced flow completion time. 2025 IEEE. -
Optimized score card for mentoring student using artificial intelligence and methods thereof /
Patent Number: 202011040658, Applicant: Dr Priti Verma.
The invention discloses a mentoring system capable of improving student™s performance in the field of Learning in Theoretical, practical, behavioral, sports, cultural activities and life skills for the betterment of the life of an individual.