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A literature review on friction stir welding of dissimilar materials
Friction stir welding (FSW) employs a tool that does not require any filler materials; frictional heat is produced and performs a solid-state joining method. Severe plastic deformation causes to join similar and dissimilar materials without melting the workpiece at the welding line. Friction stir welding is the most recent friction welded joining processes with the most surprising features when welding various metal alloys, including magnesium, aluminium, copper, and steel. FSW is victorious of all the other conventional welding methods implied in many industrial applications like automobile, aerospace, fabrication, shipping, marines and robotics. It gives high-quality welds, energy input, and distortion are lower, better retention of mechanical properties; it is eco-friendly and can be performed less operating cost. This research work aims at the FSW process in Al-Cu alloys, highlighting:(a) Optimizing the welding process parameters, welding feed rate, tool rotation speed, (b) Evaluation of Electrical Conductance properties of joints, (c) Mechanical properties and metallography characteristics of joints. 2021 Elsevier Ltd. All rights reserved. -
Enhanced technique for detection and prevention of phishing on websites
Phishing is a kind of assault where cyber criminals trap individuals to gain access to someone's private data like credit card details, passwords, account details, etc. The false e-mails look shockingly genuine and even the Web pages where clients are requested to enter their data may look legitimate. Forgery of a website is a sort of online assault where the phishing person builds a duplicate of a true authorized site, with the objective of misguiding a client by fishing out data that could be utilized to dupe or instigate different assaults upon the victim. In this paper, a new technique is developed using the combination of CORS, Public Repository technique and Heuristic functions. This technique allows only authorized Domain to replicate the original website. Copyright 2019 American Scientific Publishers All rights reserved. -
Revolutionizing Biodegradable and Sustainable Materials: Exploring the Synergy of Polylactic Acid Blends with Sea Shells
This study explores the mechanical properties of a novel composite material, blending polylactic acid (PLA) with sea shells, through a comprehensive tensile test analysis. The tensile test results offer valuable insights into the materials behavior under axial loading, shedding light on its strength, stiffness, and deformation characteristics. The results suggest that the incorporation of sea shells decrease the tensile strength of 14.55% and increase the modulus of 27.44% for 15 wt% SSP (sea shell powder) into PLA, emphasizing the reinforcing potential of the mineral-rich sea shell particles. However, a potential trade-off between decreased strength and reduced ductility is noted, highlighting the need for a delicate balance in material composition. The study underscores the importance of uniform sea shell particle distribution within the PLA matrix for consistent mechanical performance. These results offer a basis for additional PLA-sea shell blend optimization, directing future efforts to balance strength, flexibility, and other critical attributes for a range of applications, including biomedical devices and sustainable packaging. This investigation opens the door to more sustainable and mechanically strong materials in the field of additive manufacturing by demonstrating the positive synergy between nature-inspired materials and cutting-edge testing techniques. 2024 The Authors. -
Natural Language Processing in Medical Applications
Medical applications of machine learning are very new, and there are still several obstacles that limit their widespread use. There is still a need to address issues like high dimensionality data and a lack of a standard data schema. An intriguing way to explore the possibilities of machine learning in healthcare is to apply it to the difficult problem of cardiovascular disease diagnosis. At the present day, cardiovascular disorders account for the majority of deaths worldwide. It is often too late to adopt appropriate treatment for many of them because they progress for a long time without showing any symptoms. Because of this, its crucial to get checked up on routinely so that any developing diseases can be caught early. If the sickness is caught early enough, effective therapy can be put into place to stop the progression of the illness. This is done with the intention of analysing data from many sources and making use of NLP to overcome data heterogeneity. This paper evaluates the usefulness of several machine learning methods (such as the Naive Bayes (NB), Transductive Neuro-Fuzzy Inference, and Terminated Ramp-Support Vector Machine (TR-SVM)) for healthcare applications and suggests using Natural Language Processing (NLP) to address issues of data heterogeneity and the transformation of plain text. The implementation, testing, comparison of performance and analysis of the parameters of the algorithms used for diagnosis have simplified the process of selecting an algorithm better suited to a certain instance. TWNFI is particularly effective on larger datasets, while Terminated Ramp-Support Vector Machine is well suited to lesser datasets with a lower number of magnitudes due to performance difficulties. 2024 Scrivener Publishing LLC. -
A Novel Energy-Efficient Hybrid Optimization Algorithm for Load Balancing in Cloud Computing
In the field of Cloud Computing (CC), load balancing is a method applied to distribute workloads and computing resources appropriately. It enables organizations to effectively manage the needs of their applications or workloads by spreading resources across numerous PCs, networks, or servers. This research paper offers a unique load balancing method named FFBSO, which combines Firefly algorithm (FF) which reduces the search space and Bird Swarm Optimization (BSO). BSO takes inspiration from the collective behavior of birds, exhibiting tasks as birds and VMs as destination food patches. In the cloud environment, tasks are regarded as autonomous and non-preemptive. On the other hand, the BSO algorithm maps tasks onto suitable VMs by identifying the possible best positions. Simulation findings reveal that the FFBSO algorithm beat other approaches, obtaining the lowest average reaction time of 13ms, maximum resource usage of 99%, all while attaining a makespan of 35s. 2023 IEEE. -
Bronchop Neumonia Detection Using Novel Multilevel Deep Neural Network Schema
Pneumonia is a dangerous disease that can occur in one or both lungs and is usually caused by a virus, fungus or bacteria. Respiratory syncytial virus (RSV) is the most common cause of pneumonia in children. With the development of pneumonia, it can be divided into four stages: congestion, red liver, gray liver and regression. In our work, we employ the most powerful tools and techniques such as VGG16, an object recognition and classification algorithm that can classify 1000 images in 1000 different groups with 92.7% accuracy. It is one of the popular algorithms designed for image classification and simple to use by means of transfer learning. Transfer learning (TL) is a technique in deep learning that spotlight on pre-learning the neural network and storing the knowledge gained while solving a problem and applying it to new and different information. In our work, the information gained by learning about 1000 different groups on Image Net can be used and strive to identify diseases. 2023 EDP Sciences. All rights reserved. -
Detection and Robust Classification of Lung Cancer Disease Using Hybrid Deep Learning Approach
Effective lung cancer diagnosis and treatment hinge on the early detection of lung nodules. Various techniques, such as thresholding, pattern recognition, computer-aided diagnostics, and backpropagation calculations, have been explored by scientists. Convolutional neural networks (CNNs) have emerged as powerful tools in recent times, revolutionizing many aspects of this field. However, traditional computer-aided detection systems face challenges when categorizing lung nodule detection. Excessive reliance on classifiers at every stage of the process results in diminished recognition rates and an increased occurrence of false positives. To address these issues, we present a novel approach based on deep hybrid learning for classifying lung lesions. In this study, we explore multiple memory-efficient and hybrid deep neural network (DNN) architectures for image processing. Our proposed hybrid DNN significantly outperforms the current state-of-the-art, achieving an impressive accuracy of 95.21%, all while maintaining a balanced trade-off between specificity and sensitivity. The primary focus of this research is to differentiate between CT scans of patients who have early-stage lung cancer and those who do not. This is achieved by utilizing binary classification networks, including standard CNN, SqueezeNet, and MobileNet. 2023 IEEE. -
The Development and Feasibility of A Self-Efficacy and Self-Esteem Based Intervention on Music Performance Anxiety Among Majors
Music Performance Anxiety (MPA) has been seen to adversely affect a student s goal-setting with respect to careers in music. Two variables that are closely related to the development and maintenance of MPA are self-efficacy and self-esteem of the student. The aim of this research is to present the experiences of students with MPA and the feasibility of an intervention to help reduce MPA among music majors while focusing on building their self-esteem and self-efficacy. The study followed a mixed method, exploratory sequential design. Ten participants were recruited for the newlinequalitative phase and were interviewed using a phenomenological approach. Data from these interviews were analysed using Interpretive Phenomenological Analysis. Themes that were found in the qualitative phase such as blocks growth of performer , Fear of judgment , lacking confidence in skills , comparison with peers , need for appreciation , pointed toward the need to build self-efficacy and self-esteem among the performers. The themes from this analysis were then used, along with previous research evidence to develop a self-esteem and self-efficacy based intervention. A quasi-experimental design was used to carry out and assess the feasibility of this intervention. The intervention lasted for eight sessions, where the experimental group took part in these sessions that consisted of both theoretical and practical components, which lasted for one hour each. The control group on the other hand did not receive any treatment and were not a part of any of these sessions either. The experimental group that consisted of 13 participants and the control group that consisted of 12 participants were assessed on their MPA, Musical Self-Efficacy and Musical Self-Esteem levels at the start of eight weeks and then at the end of eight newlineweeks. The scale used to assess these variable were the K-MPAI (Kenny, 2009), the newlineMusical Self-efficacy scale (Ritchie and Williamon, 2010), and the Self-esteem of newlinemusic ability scale (Schmitt, 1979) respectively. -
The effect of hopeful lyrics on levels of hopelessness among college students
Hopelessness is a product of negative future expectations, negative feelings toward the future, and feeling a lack of control over future improvements. College students are seen to experience hopelessness. This study aimed to reduce levels of hopelessness in college students through an intervention that involved listening to songs having hopeful lyrics. The sample consisted of college students (N = 66), who were randomly assigned to three groups, namely the lyrics-music group, music-only group, and the control group (no intervention). The Becks Hopelessness Scale was used to measure their levels of hopelessness before the intervention and at the end of four weeks. The lyrics-music group and the music group participants were exposed to songs and instrumental tracks, respectively, twice a week, for four weeks. The Wilcoxon Signed-Rank test for related samples was used to analyze the effect of the intervention on levels of hopelessness. The KruskalWallis test was used to analyze the differences across the three groups. Results indicated that the lyrics-music group had a significant decrease in levels of hopelessness after the intervention. However, the music group and the control group showed no significant decrease. There was a significant difference between the three groups with regard to the difference score obtained from pre to post intervention. Thus, the evidence suggests that hopeful lyrics do have an effect on hopelessness and can be seen as differing from the functions of music alone. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Retail landscaping in India - Challenges and strategies /
International Journal in Management and Social Science, Vol. 4, Issue 11, pp. 24-34, ISSN No. 2321-1784. -
Development and implementation of algorithm for image preprocessing and microorganism
The digital revolution has changed most aspects of modern life. Nowhere has the change been more fundamental than in the field of microscopy. Researchers who use the microscope in their investigations have been among the pioneers who applied digital processing techniques to images. Vision is most powerful of the five senses of human being. Digitized visual information provides high impact on the subject. Digital image processing is concerned with the extraction of useful information from images. Visual information from microscopic images of microorganisms is analyzed regularly. This has resulted in a need to understand and implement digital processing on microscopic images. The purpose of this thesis is to bring new digital image processing techniques for the noise removal of microscopic image of microorganisms. The digitized image processing includes image representation; improving image quality by removing noise; and enhancing the quality of microscopic images. -
Development and Implementation of Algorithm for Image Preprocessing of Microorganism
The digital revolution has changed most aspects of modern life. Nowhere has the change been more fundamental than in the field of microscopy. Researchers who use the microscope in their investigations have been newlineamong the pioneers who applied digital processing techniques to images. Vision is most powerful of the five senses of human being. Digitized visual information provides high impact on the subject. Digital image processing is concerned with the extraction of useful information from images. Visual newlineinformation from microscopic images of microorganisms is analyzed regularly. This has resulted in a need to understand and implement digital processing on microscopic images. The purpose of this thesis is to bring new digital image processing techniques for the noise removal of microscopic image of microorganisms. The digitized image processing includes image representation; improving image quality by removing noise; newlineand enhancing the quality of microscopic images. At the outset, the thesis elaborates on the concepts around microscopic images and their digital image processing. Various existing algorithms are studied for their efficacy. This thesis gives three different techniques of image processing based on the noise level in microscopic images. The thesis newlinedevelops the techniques of image processing through Simulation , which is well accepted tool in the field of engineering. MATLAB has been used in this study to simulate the image processing algorithms. The algorithms developed in the study will be helpful in everyday life through better analysis of microscopic images of microorganisms. The thesis is a contribution to the medical field with better analytical techniques. This research work overviews different image processing techniques used in the analysis of microscopic images and other type of images. After reviewing, use of microscopic imaging is presented. Special emphasis is on two types of noise called Gaussian noise and Impulsive noise is given. -
Mathematical models in waste management
Waste management is a major issue faced by municipalities all over the world. The major problem associated with the waste managements newlineis its high cost and main part of the cost comes from its collection and transportation. This problem can be effectively overcome by the application of mathematical models. newlineAn important aspect of waste management is locating facilities like truck locations, transfer stations, compost units etc. The location of facilities when collection of waste happens at multiple time periods is newlineconsidered for cost minimization. The rapid increase in the population of cities as a result of vast urbanization and the corresponding shrinking of land has given rise to increase in apartment complexes in newlineall the cities. The waste management practices here have to be planned carefully as they are sources of large quantities of waste. They are also potential sites for recycling and composting as waste management newlinepractices can be introduced at apartment level itself, so that transportation burden is less. Components like fixed cost /maintenance cost and operational costs are considered for the study in cities as well newlineas in apartments. Testing the mathematical model is done using different scenarios and the results are used to draw conclusions. These results showed that the model works best when processing facilities are nearer to the transfer stations so that there is no additional cost incurred at that point for transportation. In addition, it was clear that the cost of the transportation is brought down using the model, as the newlineamount transported to landfills decreases. newlineScheduling a set of resources to a set of jobs can be done using resource calendar, which shows the availability of resources and the various time periods at which it a particular resource is available. There are different types of jobs and various types of resources. -
A case study of "Parivarthana" - Towards zero waste /
International Journal For Research In Applied Science & Engineering Technology, Vol.4, Issue 6, pp.463-466, ISSN: 2321-9653. -
Some examples in usage of parametric tests /
International Journal of Research In Commerce IT And Management., Vol.5, Issue 11, ISSN No: 2231-5736. -
Some pointers on one way ANOVA in spss /
International journal For Research In Applied Science And engineering Technology, Vol.3, Issue 9, pp.298-301, ISSN No: 2321-9653. -
Some interesting case studies using bayes theorem /
International Journal Of Scientific Research, Vol.4, Issue 4, pp.522-523, ISSN No: 2277-8179. -
Some case studies on importance of variables and scales of measurement in social sciences research /
International Advanced Research Journal In Science, Engineering And Technology, Vol.2, Issue 3, pp.34-37, ISSN No: 2393-8021 (Online) 2394-1588 (Print). -
Some notes on z-scores and t-scores /
International Journal Of Scientific Research And Management, Vol.3, Issue 4, pp.2608-2610, ISSN No: 2321-3418. -
Some case studies for non-parametric tests for ordinal data /
International Journal Of Advanced Research In Engineering Technology & Sciences, Vol.2, Issue 7, pp.309-313, ISSN No: 2394-2819.