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Identification of interstitial lung diseases using deep learning
The advanced medical imaging provides various advantages to both the patients and the healthcare providers. Medical Imaging truly helps the doctor to determine the inconveniences in a human body and empowers them to make better choices. Deep learning has an important role in the medical field especially for medical image analysis today. It is an advanced technique in the machine learning concept which can be used to get efficient output than using any other previous techniques. In the anticipated work deep learning is used to find the presence of interstitial lung diseases (ILD) by analyzing high-resolution computed tomography (HRCT) images and identifying the ILD category. The efficiency of the diagnosis of ILD through clinical history is less than 20%. Currently, an open chest biopsy is the best way of confirming the presence of ILD. HRCT images can be used effectively to avoid open chest biopsy and improve accuracy. In this proposed work multi-label classification is done for 17 different categories of ILD. The average accuracy of 95% is obtained by extracting features with the help of a convolutional neural network (CNN) architecture called SmallerVGGNet. 2020 Institute of Advanced Engineering and Science. All rights reserved. -
An efficient deep learning approach for identifying interstitial lung diseases using HRCT images
Interstitial lung disease (ILD) encompasses over 200 fatal lung disorders affecting the interstitium, leading to significant mortality rates. We propose an AI-driven approach to diagnose and classify ILD from high-resolution computed tomography (HRCT) images. The research utilises a dataset of 3,045 HRCT images and employs a two-tier ensemble method that combines various machine learning (ML) models, convolutional neural networks (CNNs), and transfer learning. Initially, ML models achieve high accuracy, with the J48 model at 93.08% accuracy, mainly highlighting the importance of diagonal-wise standard deviation. Deep learning techniques are then applied, with three CNN models achieving test accuracies of 94.08%, 92.04%, and 93.72%. Transfer learning models also show promise, with InceptionV3 at 92.48% accuracy. Ensembling these models further boosts accuracy, with the ensemble of three CNN models reaching 97.42%. This research has the potential to advance ILD diagnosis, offering a robust computational framework that enhances accuracy and ultimately improves patient outcomes. Copyright 2024 Inderscience Enterprises Ltd. -
Reduce Overfitting and Improve Deep Learning Models Performance in Medical Image Classification
A significant role in clinical treatment and educational tasks is played by clinical image classification. However, the traditional approach has reached its peak in terms of implementation. Additionally, using traditional approaches requires a lot of time and effort to remove and choose arrangement features. The deep learning (DL) model is a new machine learning (ML) technique that has proven effective for various classification problems. To alter image classification problems, the convolutional neural network performs well, with the best results. This chapter discusses the importance and challenges of deep learning models in medical image classification and explains some techniques for reducing overfitting and leveraging model performance during model training. 2024 Taylor & Francis Group, LLC. -
A Novel Deep Learning Approach for Identifying Interstitial Lung Diseases from HRCT Images
Interstitial lung diseases (ILDs) are defined as a group of lung diseases that affect the interstitium and cause death among humans worldwide. It is more serious in underdeveloped countries as it is hard to diagnose due to the absence of specialists. Detecting and classifying ILD is a challenging task and many research activities are still ongoing. High-resolution computed tomography (HRCT) images have essentially been utilized in the diagnosis of this disease. Examining HRCT images is a difficult task, even for an experienced doctor. Information Technology, especially Artificial Intelligence, has started contributing to the accurate diagnosis of ILD from HRCT images. Similar patterns of different categories of ILD confuse doctors in making quick decisions. Recent studies have shown that corona patients with ILD also go on to sudden death. Therefore, the diagnosis of ILD is more critical today. Different deep learning approaches have positively impacted various image classification problems recently. The main objective of this proposed research work was to develop a deep learning model to classify the ILD categories from HRCT images. This proposed work aims to perform binary and multi-label classification of ILD using HRCT images on a customized VGG architecture. The proposed model achieved a high test accuracy of 95.18% on untrained data. 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
A Novel Artificial Intelligence System for the Prediction of Interstitial Lung Diseases
Interstitial lung disease (ILD) encompasses a spectrum of more than 200 fatal lung disorders affecting the interstitium, contributing to substantial mortality rates. The intricate process of diagnosing ILDs is compounded by their diverse symptomatology and resemblance to other pulmonary conditions. High-resolution computed tomography (HRCT) assumes the role of the primary diagnostic tool for ILD, playing a pivotal role in the medical landscape. In response, this study introduces a computational framework powered by artificial intelligence (AI) to support medical professionals in the identification and classification of ILD from HRCT images. Our dataset comprises 3045 HRCT images sourced from distinct patient cases. The proposed framework presents a novel approach to predicting ILD categories using a two-tier ensemble strategy that integrates outcomes from convolutional neural networks (CNNs), transfer learning, and machine learning (ML) models. This approach outperforms existing methods when evaluated on previously unseen data. Initially, ML models, including Logistic Regression, BayesNet, Stochastic Gradient Descent (SGD), RandomForest, and J48, are deployed to detect ILD based on statistical measures derived from HRCT images. Notably, the J48 model achieves a notable accuracy of 93.08%, with the diagnostic significance of diagonal-wise standard deviation emphasized through feature analysis. Further refinement is achieved through the application of Marker-controlled Watershed Transformation Segmentation and Morphological Masking techniques to HRCT images, elevating accuracy to 95.73% with the J48 model. The computational framework also embraces deep learning techniques, introducing three innovative CNN models that achieve test accuracies of 94.08%, 92.04%, and 93.72%. Additionally, we evaluate five full-training and transfer learning models (InceptionV3, VGG16, MobileNetV2, VGG19, and ResNet50), with the InceptionV3 model achieving peak accuracy at 78.41% for full training and 92.48% for transfer learning. In the concluding phase, a soft-voting ensemble mechanism amplifies training outcomes, yielding ensemble test accuracies of 76.56% for full-training models and 92.81% for transfer learning models. Notably, the ensemble comprising the three newly introduced CNN models attains the pinnacle of test accuracy at 97.42%. This research is poised to drive advancements in ILD diagnosis, presenting a resilient computational framework that enhances accuracy and ultimately betters patient outcomes within the medical domain. 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Acculturative stress: Psychological health and coping strategies
There is an increasing shift in focus from the causes of immigration to the consequences of immigration, a major aspect being the stress triggered by the myriad changes and challenges experienced during the process of moving into a different culture and settling in. The main aim of this chapter is to introduce the reader to the concept of acculturative stress in detail. The author has gathered the content by doing a keyword search of relevant terms on Google Scholar and choosing articles that provide insight into acculturation, acculturative stress, and psychological health. The chapter will delve into how the different strategies of acculturation are associated with the level of acculturative stress experienced and consequent mental health problems as well as strategies to manage or reduce acculturative stress. 2023, IGI Global. All rights reserved. -
Perfectionism and self-compassion among emerging adults: The role of disciplining experiences
Although the influence of disciplining experiences on a variety of personality factors has been studied, there is less clarity on how disciplining experiences influence the traits of perfectionism and self-compassion in individuals. The purpose of this study was to examine the relationships between different domains of perfectionism and self-compassion, as well as the influence of specific aspects of disciplining experiences, such as parental warmth and punishment experiences, on perfectionism and self-compassion. In this study, a quantitative cross-sectional correlational design was used. A total of 220 Indian emerging adults from the city of Bangalore were surveyed via convenience sampling. The following scales were administered: Disciplining Experiences Measure, Multidimensional Perfectionism Scale, and Self-Compassion Scale. The results showed that (1) Self Compassion has a significant positive relationship with Perfectionism; (2) Punishment experience has an influence on Other-oriented and Socially Prescribed Perfectionism; (3) Disciplining Helped positively predicted Self-oriented Perfectionism; and (4) Parental Warmth positively predicted Self-compassion in individuals. The findings contribute to the literature emphasizing the influence of disciplining experiences on ones self and personality, as well as the potential benefits of self-compassion-based interventions. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Impact of macroeconomic variables on the stock performance of select companies in manufacturing industry /
International Journal Of Economic Research, Vol.14(8), pp.321-328, ISSN: 0972-9380. -
Asset liability management to control the volatility in net interest income and economic value through gap analysis of selected public and private sector banks /
International Journal Of Academic Research In Business And Social Science, Vol.6, Issue 1, pp.123-142, ISSN: 2222-6990. -
Variations in the l-dopa content, phytochemical constituents and antioxidant activity of different germlines of mucuna pruriens (l.) dc.
In this study a 'wonder plant' Mucuna pruriens (L.) DC., which is commercially important medicinal plant of the Fabaceae family known for its treatment in Central Nervous System disorders like Dementia, Parkinson's, Alzheimer's, etc. have been selected. Different germplasms have been collected to analyze the phytochemical variations between them and quantify the L-DOPA in root, stem, leaves and seeds of all the five germlines using HPLC. Along with the biochemical assays, antioxidant activity by DPPH, phosphomolybdneum method, the metal chelating and reductive potential activity of all the germplasms were studied. All parts of the plant have shown the presence of L-DOPA but, seeds have the highest quantity followed by the roots, stem and leaves. Arka Shubra seeds showed high L-DOPA content (51.9 mg/g) while the other germplasms showed L-DOPA ranging from 43-45 mg/g. Highest content of carbohydrates (258.8 mg/g) and phenolics (157.0 mg/g) was seen in the seeds of Arka Aswini. While the seeds and leaves of Arka Charaka showed high protein (332.2 mg/g) and flavonoid (10.2 mg/g) content, respectively. High proline (1.74 mg/g) was observed in the seeds of Arka Shubra. Antioxidant studies revealed that Arka Charaka and Arka Daksha to be having high reductive power and free radical scavenging activity by phosphomolybdate method while high metal chelating activity was observed in Arka Aswini (88.7%) and high antioxidant activity by DPPH method was seen in Arka Shubra (86.5%). 2021 Chemical Publishing Co.. All rights reserved. -
Biotic elicitation mediated in vitro production of L-DOPA from Mucuna pruriens (L.) DC. cell cultures
With the emerging rise in the need for drugs extracted from various plant sources, there also arises the need for the optimum production of the drugs on a larger scale and conservation of those medicinal plants using different in vitro techniques and biotechnological approaches. Plant tissue culture techniques play a prominent role in mass multiplication of the plant. Whereas, strategies such as precursor feeding, elicitation, increases the metabolite content several-fold. Thus, an attempt of using the biotic elicitors for enhancing L-DOPA production, the anti-Parkinsons drug from Mucuna pruriens (L.) DC. cell cultures, has been reported in the present study. Aqueous extracts of algae [Amphiroa anceps (AA), Gracillaria ferogusonii (GF), Kappaphycus striatum (KS), and Sargassum lanceolatum (SL)], fungus [Aspergillus sps. (AS), Penicillium sps. (PE), and Cordyceps sps (CO)], and polysaccharide [Chitosan (CH)] solution were exposed to the cell cultures for 3, 6, and 9 d, respectively, and their effect on biomass and L-DOPA production was noted. This is the first report demonstrating the enhancement of biomass and L-DOPA from M. pruriens cell cultures with the use of various algal and fungal elicitors. Based on productivity (L-DOPA concentration biomass volume), it was observed that Cordyceps showed the best result and enhanced both biomass and metabolite to a greater scale. The elicitors, which showed a significant increase, are as follows: CO > AS > PE > CH > AA > KS > GF > SL. On the whole, it was noted that fungal extracts showed better results than algae. 2022, The Society for In Vitro Biology. -
Establishment of Mucuna pruriens (L.) DC. callus and optimization of cell suspension culture for the production of anti-Parkinsons drug: L-DOPA
It has become a huge challenge to satisfy the emerging demand for levo-3,4-dihydroxyphenylalanine (L-DOPA), an anti-Parkinsons drug in the international drug market. This is attributed to the conventional methods of extraction from the natural sources of Mucuna spp., which has a low germination rate, less viable seeds, and an irritating, itching trichomes on the pods. The need for an alternative method with continuous supply of L-DOPA without affecting the natural biodiversity has been achieved through in vitro procedures. However, there has not been a systematic approach to optimize the cultural conditions for the maximum productivity. Hence, in this study, we aim at optimizing the cultural conditions for high biomass and L-DOPA production. Various plant growth regulators such as auxins (indole acetic acid, indole butyric acid, picloram [Pic], naphthalene acetic acid, and 2,4-Dichlorophenoxyacetic acid), cytokinins (kinetin, benzylaminopurine, 2-isopentenyl adenine, and thidiazuron), and their combinations have been experimented to figure out the best combination to induce callus. At the same time, various factors such as growth kinetics, different media (MS, Gamborgs-B5, Chus-N6, and Nitsch and Nitsch), media strength (0.5, 1.0, and 2.0X), effect of different macro elements and their strength (0, 0.5,1, 1.5, 2, and 3X), inoculum density, different hydrogen ion concentration (pH), ammonium/nitrate concentration, different sucrose concentrations (010%), and other carbon sources have been investigated in detail for optimizing the cell suspension culture. It was found out that 0.5 mg/L Pic gave the best results for callus induction. With respect to biomass, 6-week growth period (135.7 g/L fresh weight [FW]), 1.0X MS media (126.87 g/L FW), 1.5X magnesium sulfate (266.3 g/L FW), ammonium/nitrate ratio of 21.57/18.8 mM (131.4 g/L FW), pH of 6.0 (129.47 g/L FW), 100 g/L of inoculum (222.2 g/L FW), 3% sucrose concentration (125.6 g/L FW), and 3% glucose (183.4 g/L FW) as other carbon sources were found to give the highest biomass. In terms of L-DOPA production, 3-week growth period (5.90 mg/g dry weight [DW]), 0.5X B5 medium (4.27 mg/g DW), 2.0X calcium chloride (5.06 mg/g DW), ammonium/nitrate ratio of 21.57/18.8 mM (3.44 mg/g DW), pH 6.5 (4.02 mg/g DW), inoculum density of 30 g/L (4.79 mg/g DW), and 2% sucrose (5.17 mg/g DW) resulted in a higher L-DOPA yield. 2022 Rakesh and Praveen. -
Role of polyamines in plant tissue culture: An overview
Since the era of plant tissue culture bloomed, we have started approaching from a biotechnological perspective to overcome the massive challenges like inducing embryogenesis and organogenesis, initiating rooting, increasing the number of plantlets, establishing a callus from various organs of plants and also enhancing the metabolite content which was a mind-boggling thought once upon a time. Use of various elicitors, altering the media components, the strength of media, pH, precursor feeding etc. have all contributed tremendously in the in-vitro techniques used for culturing rare, endemic and medicinal plants for the commercial purposes. Owing to the demand for the plant products and drugs, the search for the other superior novel methods to increase its quantity and quality has not been stopped. Thus, one such method is the use of chemical compounds with many amino groups which serves as an additional source of nitrogen in the media and these organic compounds are called polyamines. Polyamines are known to play a wide role in plant physiological processes helping them in differentiation, inducing totipotency, increasing cell division and also in molecular signaling. Polyamines have a versatile application in this field ranging from establishing a callus to the elicitation of secondary metabolites. Thus, polyamines can be considered as a boon to the plant tissue culture field. In this review article, we have mainly focused on the in-depth applications of major polyamines like putrescine, spermidine and spermine in the field of plant tissue culture. 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature. -
Elicitor and precursor-induced approaches to enhance the in vitro production of L-DOPA from cell cultures of Mucuna pruriens
Elicitation and precursor feeding are two important strategies in the in vitro techniques to enhance metabolite production to meet the demand of mankind. The secondary metabolites produced by the plants are extensively used in pharmaceutical, food and agro-chemical industries. One such metabolite is 3,4 dihydroxyphenylalanine (L-DOPA) produced from Mucuna pruriens (L.) DC. is used since ancient times to treat Parkinson's disease. Though all parts produce L-DOPA, the seed has the highest quantity. To overcome the extensive usage of the natural sources whose growth and metabolite production is highly dependent on edaphic and ecological factors, in vitro techniques like establishing cell culture for continuous production of metabolites, precursor feeding and elicitation of cell cultures to enhance the metabolite production has been reported in the present study. Callus was developed from the in vitro leaf explant and cell suspension culture was established in the liquid Murashige and Skoog's medium fortified with 0.5 mg/L picloram. Amino acid precursors like tyrosine, phenylalanine and chemical elicitors like methyl jasmonate, salicylic acid, sodium nitroprusside and silver nitrate were exposed to cell cultures for different periods (3, 6 and 9 days respectively). The precursors showed a better response in enhancing both the biomass and L-DOPA when compared to the elicitors. 500 and 1000 mg/L tyrosine showed a 1.6- and an 8.1-fold increase in biomass and L-DOPA production respectively when supplemented with MS media. However, though all the elicitors enhanced the L-DOPA production by 1.13.3-folds they did not show much significant increase in biomass. Precursor feeding approaches enhanced the metabolite considerably more than the elicitor treatment. Based on the productivity (Biomass L-DOPA conc.) precursors like Tyrosine>Phenylalanine and elicitors like Sodium nitroprusside>Silver nitrate>Methyl jasmonate>Salicylic acid showed better response. 2022 Elsevier B.V. -
UVIT view of NGC 5291: Ongoing star formation in tidal dwarf galaxies at ? 0.35 kpc resolution
NGC 5291, an early-type galaxy surrounded by a giant H I ring, is believed to be formed from collision with another galaxy. Several star forming complexes and tidal dwarf galaxies are distributed along the collisional ring which are sites of star formation in environments where extreme dynamical effects are involved. Dynamical effects can affect the star formation properties and the spatial distribution of star forming complexes along the tidal features. To study and quantify the star formation activity in the main body and in the ring structure of the NGC 5291 system, we use high spatial resolution FUV and NUV imaging observations from the Ultraviolet Imaging Telescope onboard AstroSat. A total of 57 star-forming knots are identified to be part of this interacting system out of which 12 are new detections (star forming complexes that lie inside the H I contour) compared to the previous measurements from lower resolution UV imaging. We estimate the attenuation in UV for each of the resolved star-forming knots using the UV spectral slope ?, derived from the FUV - NUV colour. Using the extinction corrected UV fluxes, we derive the star formation rate of the resolved star forming complexes. The extinction corrected total star formation rate of this system is estimated as 1.75 0.04 M? yr-1. The comparison with dwarf galaxy populations (BCD, Sm, and dIm galaxies) in the nearby Universe shows that many of the knots in the NGC 5291 system have SFR values comparable to the SFR of BCD galaxies. 2023 The Author(s). -
Regression Approach for Predictive Analysis in Cognitive Decline
Cognitive decline refers to the deterioration of cognitive abilities, including memory, thinking, and reasoning, often associated with aging or neurological disorders like Alzheimer's disease. Machine learning (ML) methods can be used for predicting cognitive decline. Techniques such as Generative Adversarial Networks (GANs), feed-forward neural networks, supervised, and unsupervised learning process and analyse data patterns to forecast cognitive changes. By analyzing large datasets, ML algorithms can identify subtle cognitive shifts and predict future decline, enabling early intervention and personalized healthcare strategies. These diverse ML methods provide valuable tools for understanding, detecting, and potentially mitigating cognitive decline, advancing our ability to address cognitive health challenges. Some of these methods have been discussed later. In this research paper, a model to predict cognitive decline using principles of logical regression is proposed. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Open global shadow graph and its zero forcing number
Zero forcing number of a graph is the minimum cardinality of the zero forcing set. A zero forcing set is a set of black vertices of minimum cardinality that can colour the entire graph black using the colour change rule: each vertex of G is coloured either white or black, and vertex v is a black vertex and can force a white neighbour only if it has one white neighbour. In this paper we identify a class of graph where the zero forcing number is equal to the minimum rank of the graph and call it as a new class of graph that is open global shadow graph. Some of the basic properties of open global shadow graph are studied. The zero forcing number of open global shadow graph of a graph with upper and lower bound is obtained. Hence giving the upper and lower bound for the minimum rank of the graph. 2023, Proyecciones. All Rights Reserved. -
On the k-Forcing Number of Some DS-Graphs
Amos et al. introduced the notion of k-forcing number as a generalization of Zero forcing number and is denoted by Fk(G) where k> 0 is any positive integer, the k -forcing number of a graph is the minimum cardinality among all k -forcing sets of a graph G. In this paper, many bounds for k -forcing number of degree splitting graph DS(G) for different graph classes are found. We evaluate the value of k -forcing number of degree splitting graph of some of the Cartesian product graph for different values of k. Also we observed that for Tur graph Tn , t, upper and lower bound is given by, Fk(Tn , t) ? Fk(DS(Tn , t) ) ? Fk(Tn , t) + 1. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Injective coloring of complementary prism and generalized complementary prism graphs
The complementary prism Gof a graph G is the graph obtained by drawing edges between the corresponding vertices of a graph G and its complement. In this paper, we generalize the concept of complementary prisms of graphs and determine the injective chromatic number of generalized complementary prisms of graphs. We prove that for any simple graph G of order n, ?i(G ? n and if G is a graph with a universal vertex, then ?i(G = n. 2020 World Scientific Publishing Company. -
Innovation and Progress: An Insight into the Indian Business Start- Ups and the Promotion of Scientific Temper for Socio-Economic Advancement
The Constitution of India through its 42nd Amendment of 1976, incorporated scientific temper' as one of the Fundamental Duties to every Indian citizen under Article 51 A (h). The first Prime Minister of India, Pandit Jawahar Lal Nehru mentions this term in his book Discovery of India where he characterized scientific temper as a mind-set to change or alter one's intuition in the light of evidences and not to accept anything which appears to be irrational or without proof. Our country has consistently put forth attempts to concede to the scientific temper, time and again and emphasised its significance. In 2013, the Science, Technology, and Innovation policy, developed by the Government of India pushed on the advancement of scientific temper amongst every citizen. However, it needs to go quite far to appropriately show this temperament while fostering the resolutions for achieving socio-economic goals of the country. Presently, it has been noticed that business ventures are intensely reliant uponscientific temper and this will be ultimately essential for the entrepreneurs to succeed. Thus, small and micro undertakings backed by competitive and state of the art technology will be the foundation for greater enterprises in the country, resulting in economic boom. All future businesses will be driven by science and technology and hence, it is called for addition of new avenues and enterprises, with changing time and further with scientific temper as its major ingredient. Start-ups can be considered as one such innovation that has been leading businesses growing by leaps and bounds. In India, the last decade emerged with great start-ups like CRED, Meesho, Swiggy, Zomato, Delhivery, Oyo and many more, making itself one of the most important start-up hubs in Asia and perhaps even in the world. The scientific temper in start-ups can further do wonders to Indias socioeconomic growth in the long run. Hence, in this paper, the authors shall make an attempt to bring out the essence of scientific temper in bringing forth the technologically advanced start-ups in India and its capacity to form the basis of India's future in the global market, in terms of both technological advancements and entrepreneurship. The paper will also highlight the hindrances to its growth and suggest measures in contributing to the growth of start-up ecosystem in India. 2024, Department of Law, University of North Bengal. All rights reserved.