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An Innovative Method for Brain Stroke Prediction based on Parallel RELM Model
Strokes occur when blood supply to the brain is suddenly cut off or severely impaired. Stroke victims may experience cell death as a result of oxygen and food shortages. The effectiveness of various predictive data mining algorithms in illness prediction has been the subject of numerous studies. The three stages that make up this suggested method are feature selection, model training, and preprocessing. Missing value management, numeric value conversion, imbalanced dataset handling, and data scaling are all components of data preparation. The chi-square and RFE methods are utilized in feature selection. The former assesses feature correlation, while the latter recursively seeks for ever-smaller feature sets to choose features. The whole time the model was being trained, a Parallel RELM was used. This new method outperforms both ELM and RELM, achieving an average accuracy of 95.84%. 2024 IEEE. -
Stocks and throughput Accounting on Material Management and its Impact on Cost Management
Global Journal of Arts and Management, Vol. 2, No. 3, pp. 244-246, ISSN No. 2249-2658 -
A Perspective on Challenges and Opportunities of Supply Chain Management
Global Journal of Arts and Management Vol. 2, No. 3, pp. 227 - 231, ISSN No. 2249-2658 -
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Computational Methods for Detection and Recognition of Coronary Artery Stenosis in Angiogram Images
Coronary Artery Disease (CAD) is caused by stenosis of the coronary artery's lumen. This heart disease is one of the reasons for the highest mortality worldwide. This illness manifests as stenosis or plaque in the coronary arteries and causes atherosclerosis. It damages or clogs the heart arteries, causing a lack of blood flow to the heart muscles and leading to a heart attack. There are different medical modalities to diagnose the heart artery disease. A standard method used by the cardiologist to diagnose the severity of this disease is coronary angiography. An X-ray machine is used to capture the angiogram image at various angles during cardiac catheterization. Experts examine the data and offers different opinions. owever, most of the angiogram videos consist of unclear images with artifacts, and because of the complex structure of the arteries, medical experts fail to get accurate information about the damages and blockages in arteries. Based on the cardiologist's suggestions, a computational model is proposed as a secondary method to detect and recognize the stenosis level from the coronary angiogram images. The proposed model is Coronary Artery Stenosis Detection Using Digital Image Processing (CASDDIP). The proposed research model/framework can identify the stenosis in the cardiogram image with good accuracy of 98.06% precision. This proposed research experimentation can be compared with existing literature methods which outperforms compared to other methods using real time dataset. A dataset, such as angiogram videos and images of patients under varying age groups, is used to train the model. These videos are acquired from the healthcare center with due consent. The proposed CASDDIP model consists of four modules: Keyframe extraction and preprocessing Coronary Artery Segmentation Feature extraction and stenosis detection Initially, a novel keyframe extraction method is proposed to find the keyframe from the angiogram video. Followed by a hybrid segmentation method is presented in this research to extract the coronary artery region from the image. Further a method is proposed to detect the stenosis by extracting and fusing different features. Detected stenosis is categorized using the proposed stenosis level classification method. This CASDDIP model is a supporting tool to help the cardiologist during diagnosis. -
Analysis of benchmark image pre-processing techniques for coronary angiogram images
Coronary Artery supplies oxygenated blood and nutrients to the heart muscles. It can be narrow by the plaque deposited on the artery wall. Cardiologists and radiologists diagnose the disease through visual inspection based on x-ray images. It is a challenging part for them to identify the plaque in the artery in the given imagery. By using image processing and pattern recognition techniques, a narrowed artery can be identified. In this paper, pre-processing methods of image processing are discussed with respect to coronary angiogram image(s). In general the angiogram images are affected by device generated noise / artifacts; pre-processing techniques help to reduce the noise in the image and to enhance the quality of the image so that the region of interest is sensed. The main objective of the medical image analysis is to localize the region of interest by removing the noise. It is essential to find the structure of the artery in the angiogram image, for that preprocessing is useful. 2021 IEEE. -
A Novel Approach for Segmenting Coronary Artery from Angiogram Videos
This paper addresses the research focuses on coronary artery disease; it is one of the major heart diseases affecting the people all around the world in the recent era. This heart disease is primarily diagnosed using a medical test called angiogram test. During the angiogram procedure the cardiologist often physically selects the frame from the angiogram video to diagnose the coronary artery disease. Due to the waning and waxing changeover in the angiogram video, its hard for the cardiologist to identify the artery structure from the frame. So, finding the keyframe which has a complete artery structure is difficult for the cardiologist. To help the cardiologist a method is proposed, to detect the keyframe which has segmented artery from the angiogram video. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Advanced Computational Method to Extract Heart Artery Region
Coronary artery disease, also known as coronary heart disease, is the thinning or blockage of heart arteries, which is generally caused utilizing the build-up of fatty material called plaque. The coronary angiogram test is currently the most utilized method for identifying the stenosis status of arteries in the heart. The objective of the proposed hybrid segmentation method is to extract the artery region of the heart from angiogram imagery. Numerous angiogram video clips have been considered in the dataset in this research work. These video clips were acquired from a healthcare center with the due consent of patients and the concerned healthcare personnel. Most angiogram videos consist of unclear images, or the contents are generally not clear, and medical experts fail to acquire accurate information about the damages or blocks formed in arteries due to the same reason. A hybrid computational method to extract well-defined images of heart arteries using Frangi and motion blur features from angiogram imagery has been proposed to address this issue. Fifty patients' information has been used as the dataset for experimentation purposes in this research work. The enhanced Frangi filter is used on the dataset to obtain edge information to enhance the input image based on the Hessian matrix. Further, the motion blur helps in automatically tracking/tracing the pixel direction using the optical flow method. In this method, the complete structure of the artery is extracted. The results, when compared to the existing methods, have proven to be novel and more optimal. 2022 Seventh Sense Research Group. -
Identification of coronary artery stenosis based on hybrid segmentation and feature fusion
Coronary artery disease has been the utmost mutual heart disease in the past decades. Various research is going on to prevent this disease. Obstructive CAD occurs when one or more of the coronary arteries which supply blood to myocardium are narrowed owing to plaque build-up on the arteries inner walls, causing stenosis. The fundamental task required for the interpretation of coronary angiography is identification and quantification of severity of stenosis within the coronary circulation. Medical experts use X-ray coronary angiography to identify blood vessel/artery stenosis. Due to the artefact, the image has less clarity and it will be challenging for the medical expert to find the stenosis in the coronary artery. The solution to the problem a computational framework is proposed to segment the artery and spot the location of stenosis in the artery. Here the author presented an automatic method to detect stenosis from the X-ray angiogram image. A unified Computational method of Jerman, Level-set, fine-tuning the artery structure, is developed to extract the segmented artery features and detect the arterys stenosis. The current experimental outcomes illustrate that this computational method achieves average specificity, sensitivity, Accuracy, precision and F-scores of 95%, 97.5%, 98%, 97.5% and 97.5%, respectively. 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
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The DREAMS Outbound Leadership Training (DREAMS OLT) for Psychosocial Empowerment Among Youth and Sustainable Development Goals (SDGs)
DREAMS Intervention Program is a three-year curriculum program for schools and Higher Educational Institutions (HEIs), aimed at developing psycho-social leadership skills through mentoring intervention. Its triangulated model connects Schools (under-served students), Universities (youth mentors), and Community (senior leaders) to transfer knowledge, experiences, and leadership skills as tools for social change. Outbound Leadership Training (OLT) is a key instrument in achieving DREAMS' goals. DREAMS OLT is an experiential program to develop higher leadership skills and build effective teams by mentoring children and teens in various DREAMS chapters. Mentor training is ongoing, developing psychosocial competencies through project execution. DREAMS OLT refines these skills with structured, activitybased experiential learning. Integrating sustainability into education advances a holistic approach to developing professional skills and leadership among youth. Such skills help the youth to deal with complex worldly challenges. Initiatives such as DREAMS OLT enable the transformation of SDG frameworks into meaningful action by nurturing responsibility, innovation, collaboration and community engagement. Education and youth leadership are critical drivers of long-term social transformation. DREAMS OLT supports SDGs: 4 (Quality Education), 9 (Industry, Innovation, and Infrastructure), 17 (Partnership for the Goals), 3 (Good Health and Well-Being), and 11 (Sustainable Cities and Communities). This paper explores how DREAMS OLT applies these SDGs through experiential, mentoring-based leadership development and psycho-social empowerment of youth. 2026 RESTORATIVE JUSTICE FOR ALL. -
Engaged institution model: A faculty perspective
This paper attempts to build the engaged institution model from faculty perspective. Data was collected from 200 faculty members across disciplines, who were engaged in community engagement and social responsibility activities in one or the other ways. On analysis of the data, it was found that Instruction and Research, Facilitator, Scholarship factors contribute towards community engagement activities in higher educational institutions and that these factors contribute towards Faculty engagement, Student engagement and Community Engagement. All these factors create Engagement institution model. This work has an implications on theory, practice and policy. Service learning, as a pedagogical tool if implemented in HEIs can effectively bring all the influencing factors together and can help in creating an engaged institution. 2024, IGI Global. All rights reserved. -
Covert Conditioning for Persistent Aggressive Behaviors: A Case Illustration
In psychotherapy practice and training, single case study design plays an indispensable role by effectively articulating the application of textbook knowledge, thereby bridging the gap between theory and practice. This article, on similar lines, illustrates one such successful example of the application of the classical behavioral technique of covert conditioning modified with a component of verbal challenging. A woman in her late-thirties reported with long-standing seemingly-resistant-to-treat symptoms of aggressive behavior of beating children. The client had a total of 10 daily sessions of 6090 minutes each. By the end of one week, she reported not beating children in this period. She felt extremely relieved because it had happened for the first time in 10 years. The intensity of anger had decreased drastically, and she was not shouting any longer. She had to discontinue sessions abruptly due to unavoidable circumstances. Although she was suggested to follow up the intensive sessions again, she was not able to do it due to feasibility issues. The improvement was maintained on follow-up visits after two weeks, four weeks, and three months. 2021 The Author(s). -
Application of Fuzzy-NSGA-II for achieving maximum biodiesel yield from waste cooking oil
The increasing demand for renewable energy and efficient waste management has highlighted the need for innovative biodiesel production techniques. This study optimises biodiesel production from waste cooking oil (WCO) using fuzzy modelling and non-dominated sorting genetic algorithm-II (NSGA-II). The optimisation process focuses on key input parameters: methanol quantity, reaction temperature, reaction time, and catalyst concentration, which were normalised and represented using linguistic variables. Fuzzy logic was employed to predict biodiesel yield, expressed in terms of linguistic variables, and defuzzified to yield crisp output values. The developed model achieved a high R2 value of 96.34%, demonstrating a strong correlation between input variables and biodiesel yield. The NSGA-II algorithm was utilised for multi-objective optimisation, determining the optimal conditions for biodiesel production: 150ml of methanol, a reaction temperature of 62C, a reaction time of 63min, and a catalyst concentration of 7.5g. These parameters resulted in a maximum biodiesel yield of 97.36%. The Box-Behnken experimental design validated the models efficiency, achieving a yield of 96.88%. This study emphasises the practical implications of optimised biodiesel production, such as reducing environmental pollution by recycling WCO and minimising reliance on fossil fuels. The optimised process meets ASTM standards and exhibits scalability potential for industrial-level production with minor modifications. The models robustness makes it suitable for integration into intelligent manufacturing systems, ensuring consistent biodiesel quality and yield through automated monitoring and control mechanisms. Despite its success, challenges such as feedstock variability and initial setup costs must be addressed. Future studies should focus on adaptive models and energy-efficient processing technologies to enhance scalability and sustainability. This research demonstrates a significant step towards sustainable biofuel production, combining waste management with renewable energy generation. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
Modelling and simulation of high-pressure hydrogen storage tank with composite reinforcement
The hydro-carbon fuel disadvantages like cost, pollution and non-renewable source made a way to look for the other energy resources. The carbon neutral fuel hydrogen is one of the promising fuels for all types of locomotives. One of the major challenges is safe fuel filling and storage, since the hydrogen is highly volatile fuel. Based on the travel distance, different environmental conditions, the hydrogen fuel tank subjected to the varying pressure and volume, which needs the cost-effective material for the fuel tank. This paper presents a comprehensive modelling and simulation study of a highpressure hydrogen storage tank reinforced with composite materials. The performance analysis of a hydrogen storage tank with composite reinforcement is conducted and compared to a standard aluminium hydrogen tank. 2026 Author(s). -
Green Synthesized ZnO Nanoparticles as Biodiesel Blends and their Effect on the Performance and Emission of Greenhouse Gases
Pollution and global warming are a few of the many reasons for environmental problems, due to industrial wastes and greenhouse gases, hence there are efforts to bring down such emissions to reduce pollution and combat global warming. In the present study, zinc oxide nanoparticles are green synthesized using cow dung as fuel, through combustion. Synthesized material was characterized by FTIR, XRD, UV, and FESEM. The as-prepared ZnO-GS NPs were employed as a transesterification catalyst for the preparation of biodiesel from discarded cooking oil. The biodiesel obtained is termed D-COME (discarded cooking oil methyl ester), which is blended with 20% commercial diesel (B20). Additionally, this blend, i.e., B20, is further blended with varying amounts of as-prepared ZnO-GS NPs, in order to ascertain its effects on the quality of emissions of various greenhouse gases such as hydrocarbons, COx, NOx. Moreover, the brake thermal efficiency (BTHE) and brake specific fuel consumption (BSFC) were studied for their blends. The blend (B20) with 30 mg of ZnO-GS, i.e., B20-30, displays the best performance and reduced emissions. Comparative studies revealed that the ZnO-GS NPs are as efficient as the ZnO-C NPs, indicating that the green synthetic approach employed does not affect the efficiency of the ZnO NPs. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Analysis of native advertising on buzzfeed and its impact on the brand image of 7 companies /
In today's world, the social web space has become a competitive platform for companies engaged in a plethora of activities to promote and sell their products and, more importantly, create a brand image. In tandem with the rapid development that has been observed in social media, the advertising industry has also evolved to accommodate the needs of the internet. Native advertising has emerged as a viable and lucrative alternative for companies to communicate with their audiences. -
A novel deep learning based multimedia video retrieval framework using may fly optimization
Developing a video retrieval framework in multimedia management is a main challenge due to the massive growth of video content on the internet. A major drawback of video retrieval is its long search response time and low accuracy. To tackle these issues, this paper introduces a novel deep learning-based Multimedia video retrieval system (DL-MVR) to minimize the search response time with high accuracy. The collected video is initially converted into key frames and pre-processed with contrast adaptive histogram equalization to remove noise artifacts thereby improving image quality. After pre-processing, the images are fed to Efficient Net to extract patch features. Finally, to retrieve the similar video, matching is done using may fly optimization (MFO), that compares the query frame features to the video database. Several performance metrics are analysed to measure the effectiveness of the proposed strategy in terms of accuracy and response time. Experimental results indicate that the proposed system has a search response time of 0.71s, which is lower than existing methods. The proposed DL-MVR method achieves 99.26% of accuracy. The proposed method improves the overall accuracy by 9.32%, 22.04%, and 19.40% which is better than CNN-AlexNet (convolutional neural network), Pyramid regional graph network and CBVR respectively. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025. -
Respiratory Motion Prediction of Lung Tumor Using Artificial Intelligence
Managing respiratory motion in radiotherapy for lung cancer presents a formidable and newlinepersistent challenge. The inherent dynamic movement triggered by respiration introduces a notable degree of uncertainty in target delineation, impacting the precision of image-guided radiotherapy. Overlooking the impact of respiratory motion can lead to the emergence of artifacts in images during image acquisition, resulting in inaccuracies in tissue delineation. Moreover, the motion between treatment fractions can induce blurriness in the dose distribution within the treatment process, thereby introducing geometric and dosimetric uncertainties. Additionally, inter-fraction motion can result in the displacement of the distribution of administered doses. Given these complexities, the precise prediction of tumor motion holds the utmost importance in newlineelevating the quality of treatment administration and minimizing radiation exposure to healthy tissues neighboring the pertinent organ during radiotherapy. Nonetheless, achieving the desired level of precision in dose administration remains a formidable task due to the inherent variations in internal patient anatomy across varying time scales and magnitudes. While notable advancements have been witnessed in radiotherapy, attributed to innovations like image guidance tools, which have streamlined treatments, the challenge of accommodating lung tumor motion remains critical, particularly in cases related to newlineradiotherapeutic intervention. Substantial limitations endure despite integrating respiratory-gated techniques in radiation oncology to manage lung tumor motion. Moreover, lung cancer prognosis remains low, irrespective of the recent advancements in radiotherapy. The practice of expanding newlinetreatment margins from the Clinical Treatment Volume (CTV) to encompass the Planning newlineTreatment Volume (PTV) has been adopted as a strategy to amplify treatment outcomes. newlineHowever, this strategy necessitates a trade-off, as it inevitably exposes larger volumes of healthy tissues to radiation. -
Dynamic stress concentrations in piezoelectric materials with semi-elliptical surface notches under shear horizontal waves
Dynamic loading causes high stress concentrations at surface notches, which are further aggravated by piezoelectric effects. This research presents a novel semi-analytical technique for studying dynamic stress concentrations in semi-elliptical surface notches in piezoelectric materials subjected to shear horizontal (SH) wave incidence. The mirror technique is employed to apply traction-free and electrically insulating boundary conditions, converting the half-space problem into its analogous full-space form. Mathieu functions and elliptical coordinate system are adopted to model the geometry of the semi-elliptical notch accurately. By separating the governing equations, the potential function is obtained, and boundary conditions are applied to construct an infinite set of linear algebraic equations. To ensure reliability of the solution, a truncation scheme based on Mathieu function convergence behavior is proposed before solving the system. Numerical simulations are performed with a thorough parametric study to reveal the effects of important parameters like the incidence angle of waves, wave frequency, notch depth, and piezoelectric material characteristics on the behavior of scattered wave fields and dynamic stress concentrations. The presented model enjoys wide geometric applicability, provides necessary theoretical guidelines for the design of piezoelectric elements and serves as a baseline for the validation of computational approximations. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2025.