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Modeling of the LiouvilleGreen method to approximate the mechanical waves in functionally graded and piezo material with a comparative study
The present research article studies and compares the surface waves transmission through the functionally graded piezoelectric material (FGPM) club between the piezomagnetic (PM) layer -and half-space, and for a comparative study, lower half-space is assumed to be piezoelectric material. The transmission of mechanical waves in a smart structure is analyzed by following the elastic wave theory of magneto-electro-elasticity. The Liouville-Green (LG) approximation technique is used to solve the differential equation in the FGPM stratum, where exponential variation is assumed in material gradients. It is noticed that the material gradients depend considerably on the angular frequency, which should be a crucial factor in regulating the dispersion characteristics of functionally graded materials (FGM) waveguides. In closed determinant form, the dispersion relation has been obtained for FGPM plate for electrically open and short cases. The profound effect of parameters, such as material gradient, a width of the layer on phase velocity, coupled electromechanical factor, and angular velocity, is observed and delineated graphically. Different parametric plots are sub-plotted into a single figure to increase the readability of the graphs. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Examining the consumption of oil on total factor productivity and CO2 emissions: an analysis of highly oil-consuming countries
Purpose: This study aims to examine the impact of oil consumption on carbon dioxide (CO2) emissions and total factor productivity (TFP) in highly oil-consuming countries of the world from 1995 to 2019. Design/methodology/approach: For this purpose, fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) are applied. Findings: FMOLS and DOLS models reveal that oil consumption, human capital, population, trade openness and nonrenewable energy have a significant positive effect on CO2 emissions. While information and communication technology (ICT), as proxied by mobile and natural resources, has a significant negative effect on CO2 emissions. In the case of TFP, oil consumption, ICT and natural resources have a significant positive effect on the TFP. On the other hand, trade openness, population, human capital and nonrenewable energy have a significant negative effect on TFP. The results of this study can help to provide policy recommendations to reduce CO2 emissions in studied highly oil-consuming countries of the world. Originality/value: Due to the threat to sustainable development, climate change has become a major topic for debate around the world. The influence of oil consumption on CO2 emission and TFP is less known in the available literature. Another significance of this study is that many researchers considered aggregate energy consumption to study this relationship, but the authors have studied the effect of energy consumption, particularly from oil in the top oil-consuming countries, which is a significant shortcoming of the present research. 2023, Emerald Publishing Limited. -
Facial Emotion Detection Using Deep Learning: A Survey
The long history of facial expression analysis has influenced current research and public interest. The scientific study and comprehension of emotion are credited to Charles Darwin's 19th-century publication The Representation of the Sentiment in Man and Animals (originally published in 1872). As Recognition of human emotions from images is one of the utmost important and difficult societal connection study assignments. One advantage of using a deep learning strategy is its independence from human intervention while undertaking feature engineering. This approach involves an algorithm that scans the data for features that connect, then combines them to promote quicker learning without being explicitly told to. Deep learning (DL) based emotion detection outperforms traditional image processing methods in terms of performance. In this analytical study, the creation of an artificial intelligence (AI) system that can recognize emotions from facial expressions is presented. It discusses the various techniques for doing so, which generally involve three steps: face uncovering, feature extraction, and sentiment categorization. This study describes the various existing solutions and methodologies used by the researchers to build facial landmark interpretation. The Significance of this survey paper is to analyze the recent works on facial expression detection and distribute better insights to novice researchers for the upgradation in this domain. 2023 IEEE. -
The Association between Accounting determined and market determined measures of risk: Evidences from Indian Pharmaceutical Industry.
Volume : 3, Issue : 12, pp- 35-44, ISSN: 2249-7307 -
Multi-stage fuzzy swarm intelligence for automatic hepatic lesion segmentation from CT scans
Segmentation of liver and hepatic lesions using computed tomography (CT) is a critical and challenging task for doctors to accurately identify liver abnormalities and to reduce the risk of liver surgery. This study proposed a novel dynamic approach to improve the fuzzy c-means (FCM) clustering algorithm for automatic localization and segmentation of liver and hepatic lesions from CT scans. More specifically, we developed a powerful optimization approach in terms of accuracy, speed, and optimal convergence based on fast-FCM, chaos theory, and bio-inspired ant lion optimizer (ALO), named (CALOFCM), for automatic liver and hepatic lesion segmentation. We employed ALO to guide the FCM to determine the optimal cluster centroids for segmentation processes. We used chaos theory to improve the performance of ALO in terms of convergence speed and local minima avoidance. In addition, chaos theory-based ALO prevented the FCM from getting stuck in local minima and increased computational performance, thus increasing stability, reducing sensitivity in the iterative process, and allowing the best centroids to be used by FCM. We validated the proposed approach on a group of patients with abdominal liver CT images, and the results showed good detection and segmentation performance compared with other popular techniques. This new hybrid approach allowed for the clinical diagnosis of hepatic lesions earlier and more systematically, thereby helping medical experts in their decision-making. 2020 Elsevier B.V. -
Tracing the impact of social media on social cognition: Bibliometric analysis
The words "misinformation, " "fake news, " and "post-truth" have filled social media posts. It is a serious social threat, especially post COVID-19. In this chapter, the authors provide bibliometric analysis of research on social media and its impact on social cognition. This can be useful for identifying gaps for future research in the field. Publication data was obtained from the Web of Science database using a search algorithm. A total of 22,935 articles were extracted, and 22,909 eligible articles were included for analysis. Document co-citation analysis revealed that themes on social engagement, fake news, problematic social media use, and healthcare emerged as trends on shaping the social cognition through social media. Further, India achieved 9th position on the list based on citations and 8th on centrality and did not appear on any of the top-10 lists based on Burst value and Sigma. This indicates that neither sudden trend-setting articles nor scientific novelty-based articles have been published in this domain thus far. There is a considerable research gap in India to counter misinformation. 2024, IGI Global. -
Real-Time Traffic Sign Detection Under Foggy Condition
Traffic congestion becomes high in urban areas and using public and private transportation services. The image of traffic signs gets affected by fog, and the detection of traffic signs has become difficult. To solve this issue, the machine learning technique has been used. Convolution neural network helps to solve real-time problems; hence, it can be used in the study for detecting traffic signs under foggy condition. The study results revealed that the model network has accuracy of 99.8%, and the proposed algorithm detects a traffic sign under foggy conditions in 2s per frame. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A compression system for Unicode files using an enhanced Lzw method
Data compression plays a vital and pivotal role in the process of computing as it helps in space reduction occupied by a file as well as to reduce the time taken to access the file.This work relates to a method for compressing and decompressing a UTF-8 encoded stream of data pertaining to Lempel-Ziv-welch (LZW) method. It is worth to use an exclusive-purpose LZW compression scheme as many applications are utilizing Unicode text. The system of the present work comprises a compression module, configured to compress the Unicode data by creating the dictionary entries in Unicode format. This is accomplished with adaptive characteristic data compression tables built upon the data to be compressed reflecting the characteristics of the most recent input data. The decompression module is configured to decompress the compressed file with the help of unique Unicode character table obtained from the compression module and the encoded output. We can have remarkable gain in compression, wherein the knowledge that we gather from the source is used to explore the decompression process. Universiti Putra Malaysia Press. -
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Microbial Decomposition of Feather Waste
Keratin is generally found as an ?-keratin helix form in hair, nails, horns and ?-keratin sheet form found in feathers, scales, beaks and claws. ?-keratin contains a domain rich in residues favoring to form ?-sheet structures associated with the filament framework. 'N' and 'C' terminal domains are associated with the matrix and forms cross-linking via disulfide bonds. Several million tons of feather waste are generated by poultry industries each year. Since this waste is rich in protein, it contains excellent potential as a protein source for animal feed and other applications.Bacterial and fungal strains used in microbial degradation of feathers are summarized. Various species from the bacterium genus are involved in keratin degradation including Bacillus, Stenotrophomonas, Pseudomonas, Brevibacillus, Fusarium, Geobacillus, Chryseobacterium, Xanthomonas and Serratia which are some keratin degrading bacteria. Actinomycetes and fungi also contribute to feather degradation by the enzyme activity of keratinases. 2022 World Research Association. All rights reserved. -
C-cordial labeling of line signed graphs-I
Let S=(G, ?) be a signed graph. S admits C-cordial labeling if the difference between the number of negative and positive edges (vertices) differ by at most one under canonical marking of S. In this paper, we characterize signed paths and cycles having given number of negative sections where the line signed graphs admit C-cordial labeling. 2020 Author(s). -
Epileptic seizure detection using EEG signals and multilayer perceptron learning algorithm
Purpose: Epileptic is a neurological chronic disorder that causes unprovoked, recurrent seizure. A seizure is a sudden rush of electrical activity in the brain. The central nervous system characterized by the loss of consciousness and convulsions. Epileptic is caused by abnormal electrical discharge that lead to uncountable movements, loss of consciousness and convulsions. 50-80 million people in the world are affected by this disorder. Now a days children and adults are affected the most and it has been medically treated. Sometimes it may lead to death and serious injuries. In this technology world the computerized detection is an enhanced solution to protect epileptic patients from dangers at the time of this seizure. Method: Perceptron learning algorithm is a supervised learning of binary classifiers and also it is a simple prototype of a biological neuron in artificial neural network. EEG is extensively documented for the diagnosing and assessing brain activates and related disorders. In this paper EEG signals are taken as dataset for epilepsy detection. The data is been represented based on three domains namely frequency, time and time-frequency applied by the chebysev filter for processing the signals. Result: Help the patients from dangers at the time of the seizure. Conclusion: The neurological diseases can be divided into two loss of consciousness and convulsions. In this technology world the seizure can be detected by computerized way like EEG and so on. This paper proposes an epileptic seizure detection using EEG (Electroencephalogram) and perceptron learning algorithm. 2020, IJSTR. -
Unveiling the Landscape: A Comparative Study of U-Net Models for Geographical Features Segmentation
Geographical features segmentation is a critical task in remote sensing and earth observation applications, enabling the extraction of valuable information from satellite imagery and aiding in environmental analysis, urban planning, and disaster management. The U-Net model, a deep learning architecture, has proven its efficacy in image segmentation tasks, including geographical feature analysis. In this research paper, a comparative study of various U-Net models customized explicitly for geographical features segmentation is presented. The study aimed to evaluate the performance of these U-Net variants under diverse geographical contexts and datasets. Their strengths and limitations were assessed, considering factors such as accuracy, robustness, and generalization capabilities. The efficacy of integrated components, such as skip connections, attention mechanisms, and multi-scale features, in enhancing the models performance was analyzed. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Heavy metal stress influence the andrographolide content, phytochemicals and antioxidant activity of Andrographis paniculata
Heavy metals (HM) are toxic components present in the earth's crust that can have a negative impact on plants as well as animals. Andrographis paniculata or 'King of bitters' belonging to the family Acanthaceae, is a medicinal herb traditionally used in the treatment of fever, common cold etc. In the present study, the effect of heavy metals (copper, tin and cobalt) on the andrographolide content, biochemical parameters like chlorophyll, carotenoid, protein, Total phenolic content (TPC), Total flavonoid content (TFC) and antioxidant activity in A. paniculata were analysed. Saplings of A. paniculata were treated at 50 and 100 mM concentrations, three different times at a time interval of 7 days. Andrographolide production was found to increase in copper and cobalt treated saplings when compared with the control. From the results, maximum andrographolide concentration was found in the saplings treated with 50 mM copper (8.51 mg/gm of DW) and 50 mM tin (8.10 mg/gm of DW) respectively. 50 mM cobalt treated plants have shown the highest concentration of TPC (17.21 mg/g of extract) and TFC (6.97 mg/gm of extract). Notable variations in other biochemical parameters like total chlorophyll, carotenoid content and antioxidant activities were observed in all treatments compared with the control. Antony & Nagella (2021). This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited (https://creativecommons.org/licenses/by/4.0/). -
Effect of heavy metals on the andrographolide content, phytochemicals and antioxidant activity of Andrographis paniculata
Andrographis paniculata is a medicinal plant that has several medicinal properties and has been traditionally used in different medicinal preparations. The present study deals with the influence of heavy metals (lead, mercury and silver) on andrographolide, phytochemicals and antioxidant activity in Andrographis paniculata. Two months old saplings were subjected to heavy metal stress of two different concentrations (0.2 mM and 0.4 mM) for three different times at 3 day time interval. The results showed that the saplings treated with heavy metals showed increased concentration of andrographolide content. The saplings treated with 0.4 mM silver showed the highest increase in the andrographolide content (24.58 2.85 mg/g of DW) compared with control (9.41 1.26 mg/g of DW) and other treatments. Variations in the biochemical parameters like total phenolic content, total flavonoid content, etc. were also prominent with all the treated samples when compared to that of control. 2020 Chemical Publishing Co.. All rights reserved. -
Enhancing food crop classification in agriculture through dipper throat optimization and deep learning with remote sensing
Remote sensing images (RSIs), a keystone of modern agricultural technology, refer to spectral or visual data captured from drones, satellites, or aircraft without direct physical contact with the Earth's surface. These images provide a wide-ranging view of agricultural landscapes, providing valuable insights into land use, crop health, and environmental conditions. Agricultural food crop classification, a vital application within precision agriculture, includes the detection and classification of different crops cultivated in a certain region. Traditionally reliant on manual techniques, the development of technologies, particularly the incorporation of RSIs, has revolutionized this process. Agricultural food crop classification has become more sophisticated and automated by harnessing the wealth of data received from RS, which facilitates precise management and monitoring of crops on a large scale. Deep learning (DL), a branch of artificial intelligence, plays a more effective role in these synergies. The incorporation of DL into the RSI analysis enables high-precision and efficient detection of various crop types, assisting more informed decision-making in agriculture. This study proposes a new Dipper Throat Optimization Algorithm with Deep Learning based Food Crop Classification (DTOADL-FCC) algorithm using Remote Sensing Imaging for Agricultural Resource Management. The DTOADL-FCC method aims to apply DL algorithms for the classification of different crop types. In the DTOADL-FCC method, fully convolutional network (FCN) based segmentation process is performed. Next, the DTOADL-FCC method exploits the SE-ResNet model for learning intrinsic and complex features. The DTOADL-FCC method makes use of DTOA for the hyperparameter tuning process. Lastly, the classification of crop types takes place using the extreme learning machine (ELM) model. The study utilizes mathematical formulations including activation functions, loss functions, fitness calculations, and iterative update processes. A brief set of simulations showcases that the DTOADL-FCC method achieves remarkable performance over other techniques with much improved results. 2024 The Author(s) -
Paired Domination Integrity of Graphs
The concept of vulnerability in a communication network plays an important role when there is a disruption in the network. There exist several graph parameters that measure the vulnerability of a communication network. Domination integrity is one of the vulnerability parameters that measure the performance of a communication network. In this paper, we introduce the concept of paired domination integrity of a graph as a new measure of graph vulnerability. Let G = (V,E) be a simple, connected graph. A set of vertices in a graph G, say S, is a paired dominating set if the following two conditions are satisfied: (i) every vertex of G has a neighbor in S and (ii) the subgraph induced by S contains a perfect matching. The paired domination integrity of G, denoted by PDI(G), is defined as PDI(G) = min{|S| + m(G - S): S is a paired dominating set of G}, where m(G - S) is the order of the largest component in the induced subgraph of G - S. In this paper, we determine few bounds relating paired domination integrity with other graph parameters and the paired domination integrity of some classes of graphs. 2024 World Scientific Publishing Company. -
Lung tuberculosis detection using x-ray images
This research work is based on the various experiments performed for the detection of lung tuberculosis using various methods like filtering, segmentation, feature extraction and classification. The results obtained from these experiments are discussed in this paper. Lung tuberculosis is a bacterial infection that causes more deaths in the world than any other infectious disease. Two billion people are infected with tuberculosis all around the world. Lung tuberculosis is a disease caused by a bacteria known as Mycobacterium tuberculosis or Tubercle bacillus. This research work strives to identify methods by which patients, who require second opinion for an already identified result, can save a lot of money. Once we receive X-ray image an input, pre-processing methods like Gaussian filter, median filter is applied. These filters help to remove unwanted noise and aid to get fine textural features. The output obtained from this is taken as an input and applied to water shed segmentation and gray level segmentation which helps to focus on the lung area of the obtained results. Output from these segmentation methods is fused to get a Region of Interest (ROI). From the ROI, the statistical features like area, major axis, minor axis, eccentricity, mean, kurtosis, skewness and entropy are extracted. Finally, we use KNN, Sequential minimal optimization (SMO), simple linear regression classification methods to detect lung tuberculosis. The results obtained in this paper suggests KNN classifier performs well than the other two classifiers. Research India Publications. -
Classification and analysis of Alzheimer's Disease using Deep Learning methods on MRI and PET
Alzheimer's disease (AD) falls in the category of neurodegenerative illness in which an individual loses his or her power to remember things and behaviors. It affects memory in younger patients and as it progresses causes diffuse cortical functions. However, a major issue with the diagnosis and treatment of AD symptoms is that it has complex pathogenesis because of which there is no clinical intervention for its treatment. There is no disease-modifying treatment to cure AD symptoms that increases co-morbidities among the patients. The present research identified this gap and focuses on using Deep Learning methods on MRI and PET data so that there is early diagnosis of AD by healthcare experts and they could propose a better treatment process for reducing AD symptoms. The present research identified that by using deep learning-based approaches particularly ResNet50 architecture, there is the execution of quantitative assessment of brain MRI and PET to acquire insights about the internal abnormalities through self-learning features. It will help in initiating proper treatment and avoiding damage to the brain further. 2022 IEEE. -
A novel automated method for the detection of strangers at home using parrot sound
The sound produced by parrots is used to gather information about their behavior. The study of sound variation is important to obtain indirect information about the characteristics of birds. This paper is the first of a series in analyzing bird sounds, and establishing the adequate relation of bird's sound. The paper proposes a probabilistic method for audio feature classification in a short interval of time. It proposes an application of digital sound processing to check whether the parrots behave strangely when a stranger comes. The sound is classified into different classes and the emotions of the birds are analyzed. The time frequency of the signal is checked using spectrogram. It helps to analyze the parrot vocalization. The mechanical origin of the sound and the modulation are deduced from spectrogram. The spectrogram is also used to check the amplitude and frequency modulation of sound and the frequency of the sound are detected and analyzed. This research and its findings will help the bird lovers to know the bird behavior and plan according to that. The greater understanding of birds will help the bird lovers to feed and care for birds. BEIESP.