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Protecting Medical Research Data Using Next Gen Steganography Approach
In this paper our main aim is to protect medical research information, when data either images or information shared via internet or stored on hard drive 3rd person cant access without authentication. As needs be, there has been an expanded enthusiasm for ongoing years to upgrade the secrecy of patients data. For this we combined different techniques to provide more security. Our approach is a combination of cryptography, Steganography & digital watermarking we named this technique as Next Gen. We used cryptography for encrypting the patients information even if they find image is stegonized and digital watermarking for authenticity and for Steganography we used most popular least significant bit algorithm (LSB). The experimental outcomes with various inputs show that the proposed technique gives a decent tradeoff between security, implanting limit and visual nature of the stego pictures. 2020, Springer Nature Switzerland AG. -
Research asset creation (RAC) model for PhD awarding universities in India
Importance of higher education and research is becoming more prominent and admission for PhD is increasing year by year in India. Most of the time Research done as part of PhD ends with the submission and acceptance of thesis in Universities. This research future work or extension work might be picked by other PhD candidate in the same or from the different Universities but not sure when it will happen. Also not sure the research completed has achieved its end objective and University is fine to stop that research after spending so much of time and resources of University. This paper insists on continuous research with PhD candidates every year in Universities until the completion of research and scope for reducing the waste of resources and time. This paper considers the importance and effectiveness of Indexing parameters, Indexing agencies, review methods, Journals and relevant concepts. Considering the usefulness and facts of the same to research community building a standard process with procedures to control quality, performance and original research in place, I have built a Research Asset Creation (RAC) model for universities offering PhDs in India by making use of University level Indexing, Indexing parameters, Inter and Intra University peer review methods and University Journal as magazine and as well as Indexing agency. Research cannot be considered just for the award of degree. There is a need for making use of research, resources of University and time spent on research till the objective of the research is accomplished. This can be accomplished by adopting Research Asset Creation model. Complete details of the model, components of the model, implementation, its benefits and usage is discussed in this paper. This is a generalized Model and fit for all Countries which are looking for effective use of research and resources in a progressive manner. BEIESP. -
Predicting Stock Market Price Movement Using Machine Learning Technique: Evidence from India
The stock market is uncertain, volatile, and multidimensional. Stock prices have been difficult to predict since they are influenced by a variety of factors. In order to make critical investment and financial decisions, investors and analysts are interested in predicting stock prices. Predicting a stock's price entails developing price pathways that a stock might take in the future. ANN and mathematical Geometric Brownian movement technique were employed in this study to forecast a stock market closing price of Indian companies. The comparative analysis indicates that the Geometric Brownian Method is better than ANN in giving better MAPE and RMSE Values. 2022 IEEE. -
A Bibliometric Analysis of Industry 4.0 and Health-Care Services
A key moment in health care is marked by the Fourth Industrial Revolution, commonly referred to as Industry 4.0. This transformation, driven by the convergence of digital technologies with automation and data driving processes, has led to a paradigm shift in how health care is provided. The integration of the emerging technologies in Industry 4.0, such as Internet of Things, Artificial Intelligence, Big Data Analytics and Advanced Robots, are revolutionizing patient care, improving resource allocation and shaping research's landscape. To learn more about the ever-evolving relationship between Industry 4.0 and health care, this research paper begins with a bibliographic analysis. In this interdisciplinary convergence, our bibliometric analyses serve as a lens through which we can see the key trends, research areas and influential players. The review of literature highlights the profound impact of Industry 4.0 on health care, revealing that Internet of Things technologies for real-time patient tracking, proliferation of artificial intelligence in medical diagnosis and transforming power of big data Analytics are changing health care decision making. Methodologically, we leverage bibliometrics as a quantitative analytical tool, drawing on citation counts, bibliographic coupling, and keyword co-occurrence analysis. The data for this analysis, which covered the period 20152023, was carefully collected from Scopus database. The analysis of the information reveals that, particularly from 2018 onwards, there has been a significant increase in publications concerning Industry 4.0 and health care. In this research landscape India has emerged as a strong contributor, with countries such as the United States and Italy making significant progress. Publication trends and bibliographic coupling among countries and sources shed light on collaborative networks and research focus. The emergence of machine learning, artificial intelligence and data analysis as important themes is illustrated by a co-occurrence analysis of keywords that elucidates evolving research interests. In the complicated terrain of health care converging with Industry 4.0, this research paper serves as a compass. The report highlights this convergence's transformative potential, highlighting the pivotal role that bibliometrics analysis must play in determining future research areas in adopting Industry 4.0 in the health-care sector. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Polyphenol composition and antioxidant activity of andrographis paniculata L. nees /
Mapana Journal of Sciences, Vol.13, Issue 4, pp.481-494, ISSN No: 0975-3303 (Print) -
Bacteriocins as Biotechnological Tools in Food and Pharmaceuticals: Applications and Future Prospects
The World Health Organization (WHO) and FAO have defined probiotics as non- pathogenic living organisms that greatly benefit host cells and have several positive outcomes at the level of gut. The intake of probiotics at an adequate amount confers good health and many times is used for several treatments (Hill et al. 2014; Gibson et al. 2017). Not only the microorganisms as a whole, but the proteins or peptides secreted by these species have tremendous applications in food spoilage, pharmaceuticals, antibiotic development, and much more. Thus, antimicrobial peptides from bacteria have drawn more attention for their wide range of applications. 2023 selection and editorial matter, Arti Gupta and Ram Prasad; individual chapters, the contributors. -
Plant- based Metabolites as Source of Antimicrobial Therapeutics: Prospects and Challenges
Plants are used as traditional medicines from ancient times to today as they are the largest living storehouses of bio- chemicals and pharmaceuticals known on Earth (Abdallah, 2011). The World Checklist of Vascular Plants (WCVP) database reported in April 2021 that there are 1,383,297 plant names with 996,093 plants identified at species level, constituting 342,953 accepted vascular plant species (Govaerts et al., 2021). Around 10% of the reported vascular plants are used as medicines (Salmer- Manzano et al., 2020). According to the MPNS, 33,443 species are recorded as being used for medicinal purpose (MNPS, 2021). Medicinal plants are those that have therapeutic properties which can pose pharmacological effect on the human or animal body (Namdeo, 2018). About 80% of the world's population depends on plant- based medicine for treatment of diseases (Okoye et al., 2014). The medicinal property of a plant is attributed to rich and diverse secondary metabolites (Allemailem, 2021). Secondary metabolites are intermediates or products of primary metabolism that are not involves directly in the growth and development of the plant (Jain et al., 2019). Plants generate secondary metabolites in response to stresses posed by biotic factors (bacteria, fungi, viruses, parasites, pests, weeds, and herbivore animals) and abiotic environmental factors (temperature, salinity, drought, UV radiation etc.) so as to adapt and survive in response to environmental stimuli during their life time (Yang et al., 2018). 2023 selection and editorial matter, Arti Gupta and Ram Prasad; individual chapters, the contributors. -
Experimental Investigation on Density and Volume Fraction of Void, and Mechanical Characteristics of Areca Nut Leaf Sheath Fiber-Reinforced Polymer Composites
Natural fiber-reinforced polymer composite is a rapidly growing topic of research due to the simplicity of obtaining composites that is biodegradable and environmentally friendly. The resulting composites have mechanical properties comparable to synthetic fiber-reinforced composites. In this regard, the present work is formulated with the objectives related to the development, characterization, and optimization of the wt% of reinforcements and the process parameters. The novelty of this work is related to the identification and standardization of the appropriate wt% of reinforcements and parameters for the processing of the areca nut leaf sheath fiber-based polymer composites for enhanced performance attributes. With this basic purview and scope, the composites are synthesized using the hand layup process, and the composite samples of various fiber compositions (20%, 30%, 40%, and 50%) are fabricated. The mechanical characteristics of biodegradable polymer composites reinforced with areca nut leaf sheath fibers are investigated in the present work, with a focus on the effect of fiber composition (tensile properties, flexural strength, and impact strength). The properties of composites are enhanced by combining the areca nut leaf sheath fiber and epoxy resin, with a fiber content of 50% being the optimal wt%. The Scanning electron microscopy (SEM) investigations also ascertain this by depicting the good interfacial adhesion between the areca nut leaf sheath fiber and the epoxy resin. The tensile strength of the composite specimen reinforced with 50% areca nut fiber increases to 44.6 MPa, while the young's modulus increases to 1900 MPa, flexural strength increases to 64.8 MPa, the flexural modulus increases to 37.9 GPa, and impact strength increases to 34.1 k J/m2. As a result, the combination of areca nut leaf sheath fiber reinforced epoxy resin shows considerable potential as a renewable and biodegradable polymer composite. Furthermore, areca nut leaf sheath fiber-reinforced epoxy resin composites are likely to replace petroleum-based polymers in the future. The ecosustainability and biodegradability of the composite specimen alongside the improved mechanical characteristics serve as the major highlight of the present work, and can help the polymer composite industry to further augment the synthetic matrix and fiber-based composites with the natural fiber-reinforced composites. 2022 B. A. Praveena et al. -
Balancing module in evolutionary optimization and Deep Reinforcement Learning for multi-path selection in Software Defined Networks
Software Defined Network (SDN) has been used in many organizations due to its efficiency in transmission. Machine learning techniques have been applied in SDN to improve its efficiency in resource scheduling. The existing models in SDN have limitations of overfitting, local optima trap and lower efficiency in path selection. This study applied Balancing Module (BM)-Spider Monkey Optimization (SMO)-Crow Search Algorithm (CSA) for multi path selection in SDN to improve its efficiency. The balancing module applies Gaussian distribution to balance between exploration and exploitation in the multi-path selection process. The Balancing module helps to escape local optima trap and increases the convergence rate. Deep Reinforcement learning is applied for resource scheduling in SDN. The Deep reinforcement learning technique uses the reward function to improve the learning performance, and the BM-SMO-CSA technique has 30 J energy consumption, where the existing models: DRL has 40 J energy consumption, and Graph-ACO has 62 J energy consumption. 2022 -
CNN-based Indian medicinal leaf type identification and medical use recommendation
Medicinal leaves are playing a vital role in our everyday life. There are an enormous amount of species present in the world. Identification of each type would be a tedious task. Using image processing technology, we can overcome this problem by providing computer vision with the help of a convolution neural network (CNN). The objective of this research is to find out the best CNN model that helps in classifying the plant leaf species and identifying its category. In this research work, the proposed basic CNN model consisting of four convolution layers uses ten different medicinal leaf species each belonging to two categories providing an accuracy of 96.88%. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. -
Time Series Forecasting of Stock Market Volatility Using LSTM Networks
Forecasting stock market volatility is a pivotal concern for investors and financial institutions alike. This research paper employs Long Short-Term Memory (LSTM) networks, a potent class of recurrent neural networks, to predict stock market volatility. LSTM networks have proven adept at capturing intricate temporal dependencies, rendering them a fitting choice for time series data analysis. We commence by elucidating the notion of stock market volatility and its profound significance in financial decision-making. Traditional methodologies, such as GARCH models, exhibit shortcomings in deciphering the convoluted dynamics inherent in financial time series data. LSTM networks, with their capacity to model extended temporal relationships, present an encouraging alternative. In this study, we assemble historical stock price and trading volume data for a diverse array of assets, diligently preprocessing it to ensure its aptness for LSTM modeling. We systematically explore various network architectures, hyperparameter configurations, and input features to optimize the efficacy of our models. Our empirical investigations decisively underscore the supremacy of LSTM networks in capturing the subtleties of stock market volatility compared to conventional techniques. As the study progresses, we delve deeper into the complexities of LSTM network training, leveraging advanced techniques such as batch normalization and dropout to fortify model resilience. Moreover, we delve into the interpretability of LSTM models within the context of stock market forecasting. 2023 IEEE. -
Machine Learning for Smart School Selection and Admissions
Choosing the best school for their kid is an important choice that parents must make, and it is sometimes stressful and unsure. Machine learning is a potential way to improve and streamline the admissions and school selection process in the current digital era. This study investigates the use of machine learning methods in the context of selective admissions and smart school selection. We propose a user-friendly, web-based tool in the early phases of our study that helps parents and guardians locate the ideal school for their kid by using machine learning algorithms. To provide individualized school recommendations, the platform gathers and analyses a range of data, such as extracurricular activity participation, academic achievement, regional preferences, and school reputation. This makes choosing a school easier and supports parents in making wise choices. This paper's second section explores the technical details of the machine learning techniques used, going into the nuances of feature selection, data preparation, and model assessment. We also draw attention to the difficulties and moral issues - such as maintaining impartiality and avoiding bias - that come with using machine learning to school selection. 2023 IEEE. -
Exploring Explainable Artificial Intelligence for Transparent Decision Making
Artificial intelligence (AI) has become a potent tool in many fields, allowing complicated tasks to be completed with astounding effectiveness. However, as AI systems get more complex, worries about their interpretability and transparency have become increasingly prominent. It is now more important than ever to use Explainable Artificial Intelligence (XAI) methodologies in decision-making processes, where the capacity to comprehend and trust AI-based judgments is crucial. This abstract explores the idea of XAI and how important it is for promoting transparent decision-making. Finally, the development of Explainable Artificial Intelligence (XAI) has shown to be crucial for promoting clear decision-making in AI systems. XAI approaches close the cognitive gap between complicated algorithms and human comprehension by empowering users to comprehend and analyze the inner workings of AI models. XAI equips stakeholders to evaluate and trust AI systems, assuring fairness, accountability, and ethical standards in fields like healthcare and finance where AI-based choices have substantial ramifications. The development of XAI is essential for attaining AI's full potential while retaining transparency and human-centric decision making, despite ongoing hurdles. 2023 EDP Sciences. All rights reserved. -
Advanced Technological Improvements in Making of Smart Production Using AI and ML
The necessity for adaptation and creativity in the manufacturing sector demonstrates the importance of sustainable manufacturing by the merging of advanced technologies. To encourage sustainability, a global view on the integration of smart manufacturing procedures is important. Artificial intelligence (or AI) has appeared as a crucial factor in achieving environmentally conscious manufacturing, with methods like the use of machine learning (ML) getting popularity. This study carefully studies the scientific papers related to the usage of AI and ML in business. The emergence of Industry 4.0 as a whole has positioned machine learning (ML) and artificial intelligence (AI) as drivers for the smart industry change. The study categorizes material based on release year, writers, scientific field, country, institution, and terms, applying the Web of Biology and SCOPUS databases. Utilize UCINET alongside NVivo 12 software, thereby the analysis covers empirical studies on machine learning (ML) and artificial intelligence (AI) via 1999 until the present, showing their growth before and after the start of Industry 4.0. Notably, the USA displays a substantial addition to this area, with a noticeable surge in desire following the rise of Industry 4.0. 2024 IEEE. -
Smart Steering Wheel for Improving Drivers Safety Using Internet of Things
Nearly 3700 people every day die on the worlds roads in collisions with trucks, cars, buses, motorcycles, bicycles, or pedestrians.The cause of accidents is drowsiness, drunk driving, breaking the speed limit, Driver health issue and rash driving. The most concept of this venture is to avoid the street mishap so we are utilizing liquor location sensor, eye flicker sensor, over speed control sensor, temperature sensor, beat sensor. To detect drowsiness, speed of the vehicle, drivers health, alcohol consumed by the driver, and rash driving status the model is installed with sensors in steering wheel and camera. The sensors will detect the physical condition of the driver and the camera module will take the live recording of the drivers face part to detect the drowsiness. Simple but effective strategies are used to improve the baseline detection/tracking algorithm and the eye-state classification algorithm, and the results are tabulated to increase the systems dependability and accuracy. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Tadpole domination number of graphs
The graph obtained by joining cycle Cm to a path Pn with a bridge is called Tadpole graph denoted by Tm,n. A subset D of V (G) is said to be a tadpole dominating set of G if D is a dominating set and the set of vertices of D spans a tadpole graph Tm,n where m ? 3, n ? 1. In this paper, we find the tadpole domination number of cartesian product of certain graphs, namely, paths, cycles and complete graphs. Also the tadpole domination number for the Mycielskian of cycles and paths is obtained. 2023 World Scientific Publishing Company. -
Kannada script recognitions from scanned book cover images
Text extraction from the images plays a vital role in providing valuable information. Text extraction from images is still a challenging area specially extracting text from regional scripts of India like Kannada, Malayalam etc. Most of the times the images contain complex background then the cropping of text becomes even more challenging for extracting features. The input image is a scanned document images of Kannada book cover which is scanned with flatbed scanner of 400dpi resolution. The data sets are created by dividing the original images into number of varied size of blocks. Both spatial and frequency features are extracted for classifying images. This paper aims at recognizing the scanned images block which contains text or not by using multiple feature approach. The classification is analysed using Multilayer perceptron, Kstar and KNN. Experiments are performed on different sets of scanned documents of text cover images. Compare to all the classifiers KNN has given the encouraging results. Research India Publications. -
A Comprehensive Study On Detection Of Emotions Using Human Body Movements: Machine Learning Approach
Identifying emotions from human beings is the most challenging area in artificial intelligence. There are different modules used to identify emotions like speech, face, EEG, Physiological Signals, and body movement. However, emotional recognition from body movement is the need of time. The review focuses on identifying various emotions with the help of the full-body movement model and the parts-based model. The aim of the survey is to identify the recent work done by the researchers with the help of full-body movements and body parts-based models. Recently, little research has been done on the identification of emotions using body movements, but most of the time it has succeeded to some extent. Identifying various human emotions using body movements is a really very challenging task. This research work discovers that the various popular machine learning algorithms like Support Vector Machines, Neural Networks, and convolutional neural networks are majorly used to identify basic emotions. 2023 American Institute of Physics Inc.. All rights reserved. -
Impact of chitosan and chitosan nanoparticles on seed germination: probabilities and prospects
Agriculture is the science and practice of making plants and animals for the food for animals, and humans at large. It is used not only for food but also for other needs like wool, silk, leather, etc and now we start to use these agricultural practices for making plants and animals as bioreactors for making valuable pharmaceuticals. But, the main segment of agriculture is related to the cultivation of plants. Even though many types of plant propagation systems are available, the use of seeds is considered to be the most commonly used method in many crop varieties. The productivity of the crops is largely depending on healthy seedling production. There are many methods including the traditional methods to improve seedling development. Recent scientific developments help in the development of better seedlings and thereby the crop yield. Chitosan and its nanoparticles are being used in agriculture in different aspects starting from seed germination and seedling development apart from the use in controlled release of the nutrients. In the present chapter, the use of chitosan and chitosan nanoparticles in agriculture especially in the stages of seed germination and seedling development in different crop varieties is discussed. 2022 Elsevier Inc. All rights reserved. -
Diazanorbornene: A Valuable Synthon towards Carbocycles and Heterocycles
Desymmetrization of meso compounds is well recognized as a powerful method for delivering biologically relevant molecular skeletons in a few synthetic steps. Heterobicyclic olefins are a class of meso compounds which exhibit exceptional reactivity due to their high ring strain originating from the unfavorable bond angles and eclipsing interactions. Extensive research was carried out towards the synthetic transformations of oxa-, aza-, and diazanorbornenes/norbornadienes for the synthesis of a wide variety of carbocycles and heterocycles in a single step, most importantly in a stereo- and chemo-selective manner. This review summarizes the relevant aspects of diazanorbornene reactivity which will inspire the synthetic community for exploiting these highly strained bicyclic systems for the creation of extensive libraries of novel structurally and biologically interesting molecules. The review is divided into several sections based on the type of reactions that diazanorbornenes are subjected to. 2020 Wiley-VCH GmbH