Pub Date : 2024-03-20DOI: 10.17485/ijst/v17i12.134
P. Lambat, A. M. Pund
Objective: To present a new solution to the field equations for higher-dimensional Bianchi type-III string viscous fluid cosmological model in the context of the Sen-Dunn theory of gravity. Methods: To obtain definite solution of the field equations, we consider a power law relation between the scale factor and scalar field, and we take into account two distinct scale factors, and , in which , are positive constants. This produces a time-dependent deceleration parameter. We analyse the model with constant bulk viscosity constant to explain the current accelerated expansion of the universe. Findings: Spatial volume of the model increases with cosmic time, which shows that universe is spatially expanding. Also, the model possesses a point-type singularity. It is noted that these models depict the universe's accelerated expansion. Novelty: We obtained new solution to the field equations for higher dimensional Bianchi type-III generated by means of a cloud of strings with bulk viscous fluid in Sen-Dunn theory by using quadratic form. Keywords: Bianchi type-III, Cosmic string, Bulk viscosity, Sen-Dunn Theory
目的：以森-邓恩引力理论为背景，提出高维比安奇 III 型弦粘性流体宇宙学模型场方程的新解。方法：为了得到场方程的定解，我们考虑了尺度因子和标量场之间的幂律关系，并考虑了两个不同的尺度因子，和 ，其中 ， ，是正常数。这就产生了一个随时间变化的减速参数。我们分析了具有恒定体积粘度常数的模型，以解释当前宇宙的加速膨胀。研究结果模型的空间体积随着宇宙时间的推移而增大，这表明宇宙在空间上不断膨胀。此外，模型还具有点型奇点。这些模型描述了宇宙加速膨胀的过程。新颖性：我们用二次方程的形式得到了森-邓恩理论中由带有粘性流体的弦云产生的高维比安奇-III型的场方程的新解。关键词比安奇-III型 宇宙弦 体积粘性 森-邓恩理论
{"title":"String Viscous Fluid Cosmological Model in the Framework of Sen-Dunn Theory of Gravitation","authors":"P. Lambat, A. M. Pund","doi":"10.17485/ijst/v17i12.134","DOIUrl":"https://doi.org/10.17485/ijst/v17i12.134","url":null,"abstract":"Objective: To present a new solution to the field equations for higher-dimensional Bianchi type-III string viscous fluid cosmological model in the context of the Sen-Dunn theory of gravity. Methods: To obtain definite solution of the field equations, we consider a power law relation between the scale factor and scalar field, and we take into account two distinct scale factors, and , in which , are positive constants. This produces a time-dependent deceleration parameter. We analyse the model with constant bulk viscosity constant to explain the current accelerated expansion of the universe. Findings: Spatial volume of the model increases with cosmic time, which shows that universe is spatially expanding. Also, the model possesses a point-type singularity. It is noted that these models depict the universe's accelerated expansion. Novelty: We obtained new solution to the field equations for higher dimensional Bianchi type-III generated by means of a cloud of strings with bulk viscous fluid in Sen-Dunn theory by using quadratic form. Keywords: Bianchi type-III, Cosmic string, Bulk viscosity, Sen-Dunn Theory","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":" 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Objectives: This study aims to enhance and optimize a modern communication system for high data rate transmission using Dense Wavelength Division Multiplexing (DWDM). The objectives include employing a 64-channel DWDM system, implementing different data speeds, and utilizing a dispersion compensation method. Methods: To achieve the objectives, we simulate and analyze the effects of Dispersion Compensation Fiber (DCF) in a DWDM system with 64 channels. The system employs Non-Return-to-Zero (NRZ) modulation format at varied bit rates and multiple energy levels. We propose a method for achieving data rates of 10 to 40 Gbps using NRZ modulation and Erbium-Doped Fiber Amplifier (EDFA) over a single-mode fiber transmission distance of 40 to 160 km, along with a dispersion compensation fiber of 8 to 32 km (DCF). The performance of the developed model is evaluated based on Quality Factor, Bit Error Rate (BER), Eye Height, and Threshold. These metrics are measured at two input energy levels from an optical power source, covering a communication capacity ranging from 0.625 Tbps to 2.5 Tbps. Findings: Through the simulations and analyses, we uncover the impact of dispersion and demonstrate the effectiveness of the proposed method in compensating for dispersion effects. Performance analysis of two different input transmitter power levels, -10 dBm is more efficient compared to 0 dBm input transmitter power in terms of bit error rate and quality factor. Novelty: This study presents a novel approach to improving modern communication systems by utilizing DWDM with a dispersion compensation method. The use of a 64-channel DWDM system, combined with the proposed NRZ modulation technique and dispersion compensation fiber, provides an efficient solution for achieving high data rates over long transmission distances while minimizing the effects of dispersion. The findings contribute to the advancement of communication systems for high data rate transmission. Keywords Dispersion effect, Bit Error Rate, Q-factor, Optisystem software, Erbium doped fiber amplifier
{"title":"Optimizing High Data Rate Up to 2.5 Tbps Transmission using 64-Channel DWDM System with DCF and NRZ Modulation","authors":"Ruturaj Thummar, Dharmendra Dhadhal, Vivekanand Mishra","doi":"10.17485/ijst/v17i12.859","DOIUrl":"https://doi.org/10.17485/ijst/v17i12.859","url":null,"abstract":"Objectives: This study aims to enhance and optimize a modern communication system for high data rate transmission using Dense Wavelength Division Multiplexing (DWDM). The objectives include employing a 64-channel DWDM system, implementing different data speeds, and utilizing a dispersion compensation method. Methods: To achieve the objectives, we simulate and analyze the effects of Dispersion Compensation Fiber (DCF) in a DWDM system with 64 channels. The system employs Non-Return-to-Zero (NRZ) modulation format at varied bit rates and multiple energy levels. We propose a method for achieving data rates of 10 to 40 Gbps using NRZ modulation and Erbium-Doped Fiber Amplifier (EDFA) over a single-mode fiber transmission distance of 40 to 160 km, along with a dispersion compensation fiber of 8 to 32 km (DCF). The performance of the developed model is evaluated based on Quality Factor, Bit Error Rate (BER), Eye Height, and Threshold. These metrics are measured at two input energy levels from an optical power source, covering a communication capacity ranging from 0.625 Tbps to 2.5 Tbps. Findings: Through the simulations and analyses, we uncover the impact of dispersion and demonstrate the effectiveness of the proposed method in compensating for dispersion effects. Performance analysis of two different input transmitter power levels, -10 dBm is more efficient compared to 0 dBm input transmitter power in terms of bit error rate and quality factor. Novelty: This study presents a novel approach to improving modern communication systems by utilizing DWDM with a dispersion compensation method. The use of a 64-channel DWDM system, combined with the proposed NRZ modulation technique and dispersion compensation fiber, provides an efficient solution for achieving high data rates over long transmission distances while minimizing the effects of dispersion. The findings contribute to the advancement of communication systems for high data rate transmission. Keywords Dispersion effect, Bit Error Rate, Q-factor, Optisystem software, Erbium doped fiber amplifier","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":" 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.17485/ijst/v17i12.202
Irfan Ahmad Malla, Sehrish Manzoor, Zubair ul Islam Ganie, Maajid Mohi Ud Din Malik
Objective: This study aimed to evaluate MRI efficiency in diagnosing chronic shoulder pain causes. Chronic shoulder pain accounts for 5% of musculoskeletal consultations. Method: Thirty-four chronic shoulder pain patients (18-65 years) underwent shoulder MRI. Sequences included T1-weighted, T2-weighted, proton density, and STIR for comprehensive analysis. All patients were clinically diagnosed with chronic shoulder pain prior to imaging. Finding: One patient (1.96%) had normal MRI findings. Thirty-three patients (98.04%) had abnormalities. Rotator cuff injuries were most prevalent, especially partial supraspinatus tears (18.63%). Other findings included bursitis, joint effusion, AC arthropathy, Hill Sachs deformity, AC joint impingement, rotator cuff fatty atrophy, and biceps tendinopathy. Conclusion: MRI provided excellent visualization of soft tissue pathologies causing chronic shoulder pain, noninvasively without ionizing radiation. Combining MRI sequences accurately diagnosed various shoulder conditions. Fat suppression sequences were key for identifying rotator cuff tears. MRI is the gold standard for diagnosing rotator cuff injuries, the most common chronic shoulder pain cause. Novelty: This study demonstrates MRI's utility for evaluating chronic shoulder pain causes. While no single sequence visualizes all shoulder pathologies, using T1-weighted, T2-weighted, proton density, and STIR sequences together provides a comprehensive analysis to guide appropriate patient treatment. Keywords MRI, Chronic shoulder pain, Rotator cuff, Joint effusion, frozen shoulder
{"title":"Evaluation of the Efficiency and Effectiveness of MRI in the Diagnosis of Chronic Shoulder Pain","authors":"Irfan Ahmad Malla, Sehrish Manzoor, Zubair ul Islam Ganie, Maajid Mohi Ud Din Malik","doi":"10.17485/ijst/v17i12.202","DOIUrl":"https://doi.org/10.17485/ijst/v17i12.202","url":null,"abstract":"Objective: This study aimed to evaluate MRI efficiency in diagnosing chronic shoulder pain causes. Chronic shoulder pain accounts for 5% of musculoskeletal consultations. Method: Thirty-four chronic shoulder pain patients (18-65 years) underwent shoulder MRI. Sequences included T1-weighted, T2-weighted, proton density, and STIR for comprehensive analysis. All patients were clinically diagnosed with chronic shoulder pain prior to imaging. Finding: One patient (1.96%) had normal MRI findings. Thirty-three patients (98.04%) had abnormalities. Rotator cuff injuries were most prevalent, especially partial supraspinatus tears (18.63%). Other findings included bursitis, joint effusion, AC arthropathy, Hill Sachs deformity, AC joint impingement, rotator cuff fatty atrophy, and biceps tendinopathy. Conclusion: MRI provided excellent visualization of soft tissue pathologies causing chronic shoulder pain, noninvasively without ionizing radiation. Combining MRI sequences accurately diagnosed various shoulder conditions. Fat suppression sequences were key for identifying rotator cuff tears. MRI is the gold standard for diagnosing rotator cuff injuries, the most common chronic shoulder pain cause. Novelty: This study demonstrates MRI's utility for evaluating chronic shoulder pain causes. While no single sequence visualizes all shoulder pathologies, using T1-weighted, T2-weighted, proton density, and STIR sequences together provides a comprehensive analysis to guide appropriate patient treatment. Keywords MRI, Chronic shoulder pain, Rotator cuff, Joint effusion, frozen shoulder","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140389112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.17485/ijst/v17i12.2985
S. Uma, S. Prateeksha, V. Padmapriya
Objectives: The objective of this study is to enhance the accuracy of traffic sign detection and recognition in modern intelligent transport systems, addressing real-time challenges under varying conditions. Methods: A two-phase approach is adopted. The first phase employs the You Only Look Once version 8 (YOLOv8) architecture to efficiently detect traffic signs under real-time conditions, considering variables like adverse weather and obstructions. Subsequently, the second phase employs a sequential convolutional network for precise recognition, utilizing the output from the first phase. This integrated method enhances traffic sign detection and recognition, contributing to road safety and efficient traffic management in complex transportation scenarios. Findings: The YOLOv8 architecture, utilized in Phase 1, demonstrated exceptional performance with a mean Average Precision (mAP) of 0.986 during validation. In Phase 2, the Convolutional Neural Network (CNN)-based recognition model achieved an impressive test accuracy of 98.7% on 463 test images, with a low-test loss of 0.1186, indicating consistent accuracy. The robustness of both models is confirmed by successful testing with three unseen images. YOLOv8 accurately detected and classified these images, while the CNN model correctly recognized them. These findings underscore the effectiveness of the two-phase approach in enhancing traffic sign detection and recognition, with significant implications for improving road safety and traffic management in real-world scenarios. Novelty: The novelty of this approach lies in its seamless integration of YOLOv8 for efficient traffic sign detection and a sequential convolutional network for accurate recognition, offering a significant advancement in addressing real-time challenges and contributing to enhancing road safety and traffic management in an increasingly complex transportation landscape. Keywords: Traffic sign detection, Traffic sign recognition, Convolutional Neural Networks, YOLOv8, Object detection
研究目的本研究旨在提高现代智能交通系统中交通标志检测和识别的准确性，解决不同条件下的实时挑战。研究方法采用两阶段方法。第一阶段采用 You Only Look Once version 8（YOLOv8）架构，在实时条件下高效检测交通标志，同时考虑恶劣天气和障碍物等变量。随后，第二阶段采用顺序卷积网络，利用第一阶段的输出进行精确识别。这种集成方法增强了交通标志的检测和识别能力，有助于在复杂的交通场景中提高道路安全和交通管理效率。研究结果第一阶段使用的 YOLOv8 架构在验证过程中表现优异，平均精度 (mAP) 达到 0.986。在第二阶段，基于卷积神经网络（CNN）的识别模型在 463 张测试图像上取得了令人印象深刻的 98.7% 测试准确率，测试损失率低至 0.1186，表明准确率始终如一。使用三张未见图像进行的成功测试证实了这两个模型的鲁棒性。YOLOv8 准确地检测了这些图像并进行了分类，而 CNN 模型则正确地识别了这些图像。这些发现强调了两阶段方法在增强交通标志检测和识别方面的有效性，对改善现实世界场景中的道路安全和交通管理具有重要意义。新颖性：这种方法的新颖性在于它无缝集成了用于高效交通标志检测的 YOLOv8 和用于准确识别的顺序卷积网络，在应对实时挑战方面取得了重大进展，有助于在日益复杂的交通环境中加强道路安全和交通管理。关键词交通标志检测 交通标志识别 卷积神经网络 YOLOv8 物体检测
{"title":"A Two-Phase Approach for Efficient Traffic Sign Detection and Recognition","authors":"S. Uma, S. Prateeksha, V. Padmapriya","doi":"10.17485/ijst/v17i12.2985","DOIUrl":"https://doi.org/10.17485/ijst/v17i12.2985","url":null,"abstract":"Objectives: The objective of this study is to enhance the accuracy of traffic sign detection and recognition in modern intelligent transport systems, addressing real-time challenges under varying conditions. Methods: A two-phase approach is adopted. The first phase employs the You Only Look Once version 8 (YOLOv8) architecture to efficiently detect traffic signs under real-time conditions, considering variables like adverse weather and obstructions. Subsequently, the second phase employs a sequential convolutional network for precise recognition, utilizing the output from the first phase. This integrated method enhances traffic sign detection and recognition, contributing to road safety and efficient traffic management in complex transportation scenarios. Findings: The YOLOv8 architecture, utilized in Phase 1, demonstrated exceptional performance with a mean Average Precision (mAP) of 0.986 during validation. In Phase 2, the Convolutional Neural Network (CNN)-based recognition model achieved an impressive test accuracy of 98.7% on 463 test images, with a low-test loss of 0.1186, indicating consistent accuracy. The robustness of both models is confirmed by successful testing with three unseen images. YOLOv8 accurately detected and classified these images, while the CNN model correctly recognized them. These findings underscore the effectiveness of the two-phase approach in enhancing traffic sign detection and recognition, with significant implications for improving road safety and traffic management in real-world scenarios. Novelty: The novelty of this approach lies in its seamless integration of YOLOv8 for efficient traffic sign detection and a sequential convolutional network for accurate recognition, offering a significant advancement in addressing real-time challenges and contributing to enhancing road safety and traffic management in an increasingly complex transportation landscape. Keywords: Traffic sign detection, Traffic sign recognition, Convolutional Neural Networks, YOLOv8, Object detection","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.17485/ijst/v17i12.156
S. L. Savla, R. G. Sharmila
Objectives: In applied sciences and engineering, fuzzy fractional differential equations (FFDEs) and fuzzy fractional integral equations (FFIEs) are a crucial topic. The main objective of this work is to discover an analytical approximate solution for the fuzzy fractional Volterra-Fredholm integro differential equations (FFVFIDE). In the Caputo concept, fractional derivatives are regarded. Methods: The Shehu transform is challenging to exist for nonlinear problems. So, the Shehu transform is combined with the Adomian decomposition method is called the Shehu Adomian decomposition method (SHADM) and has been proposed to solve both linear and nonlinear FFVFIDEs. Findings: Both linear and nonlinear FFVIFIDEs can be solved using this technique. For nonlinear terms, Adomian polynomials have been used. The main benefit of this approach is that it converges quickly to the exact solution. Figures and numerical examples demonstrate the expertise of the suggested approach. Novelty: The comparison between the exact solution and numerical solution is shown in figures for various values of fractional order . The numerical evolution demonstrates the efficiency and reliability of the proposed SHADM. The proposed approach is rapid, exact, and simple to apply and produce excellent outcomes. Keywords: Fractional calculus, fuzzy number, Mittag Leffler function, Shehu Adomian decomposition method, fuzzy fractional Volterra-Fredholm integro differential equation
{"title":"Solving Linear and Nonlinear Fuzzy Fractional Volterra-Fredholm Integro Differential Equations Using Shehu Adomian Decomposition Method","authors":"S. L. Savla, R. G. Sharmila","doi":"10.17485/ijst/v17i12.156","DOIUrl":"https://doi.org/10.17485/ijst/v17i12.156","url":null,"abstract":"Objectives: In applied sciences and engineering, fuzzy fractional differential equations (FFDEs) and fuzzy fractional integral equations (FFIEs) are a crucial topic. The main objective of this work is to discover an analytical approximate solution for the fuzzy fractional Volterra-Fredholm integro differential equations (FFVFIDE). In the Caputo concept, fractional derivatives are regarded. Methods: The Shehu transform is challenging to exist for nonlinear problems. So, the Shehu transform is combined with the Adomian decomposition method is called the Shehu Adomian decomposition method (SHADM) and has been proposed to solve both linear and nonlinear FFVFIDEs. Findings: Both linear and nonlinear FFVIFIDEs can be solved using this technique. For nonlinear terms, Adomian polynomials have been used. The main benefit of this approach is that it converges quickly to the exact solution. Figures and numerical examples demonstrate the expertise of the suggested approach. Novelty: The comparison between the exact solution and numerical solution is shown in figures for various values of fractional order . The numerical evolution demonstrates the efficiency and reliability of the proposed SHADM. The proposed approach is rapid, exact, and simple to apply and produce excellent outcomes. Keywords: Fractional calculus, fuzzy number, Mittag Leffler function, Shehu Adomian decomposition method, fuzzy fractional Volterra-Fredholm integro differential equation","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.17485/ijst/v17i12.3184
Tilak Raj Sharma, Rajesh Kumar
Objectives: The main objective of this paper is to derive some of the results of lattice ordered semirings, distributive lattice, lattice ideals and morphisms. Methods: To establish the results, we use some conditions like commutativity, simple, multiplicative idempotent, additively idempotent, and finally, use the concept of lattice ideal in semirings. Findings: First we give some examples of lattice ordered semirings and then study some results regarding lattices, distributive lattices, commutative lattice ordered semirings and finally lattice ideals and morphisms. The unique feature of this study is that the concept of gamma is new for the study of lattices. Novelty: We consider a condition (c.f. Theorem 4.1.5) for an additively idempotent semiring due to which it becomes a distributive lattice ordered semiring. Again, in general, the sum of ideals of a semiring need not be ideal. Indeed, and are ideals of is a set of non-negative integers. Clearly, (say) is not a ideal, because , but . However, this condition does not hold in the case of a lattice ordered semiring. AMS Mathematics subject classification (2020): 16Y60. Keywords: Lattices, additive idempotent, multiplicative Γ-idempotent, k-ideal, lattice ideal, Γ-morphism
{"title":"Lattice Ordered GSemirings","authors":"Tilak Raj Sharma, Rajesh Kumar","doi":"10.17485/ijst/v17i12.3184","DOIUrl":"https://doi.org/10.17485/ijst/v17i12.3184","url":null,"abstract":"Objectives: The main objective of this paper is to derive some of the results of lattice ordered semirings, distributive lattice, lattice ideals and morphisms. Methods: To establish the results, we use some conditions like commutativity, simple, multiplicative idempotent, additively idempotent, and finally, use the concept of lattice ideal in semirings. Findings: First we give some examples of lattice ordered semirings and then study some results regarding lattices, distributive lattices, commutative lattice ordered semirings and finally lattice ideals and morphisms. The unique feature of this study is that the concept of gamma is new for the study of lattices. Novelty: We consider a condition (c.f. Theorem 4.1.5) for an additively idempotent semiring due to which it becomes a distributive lattice ordered semiring. Again, in general, the sum of ideals of a semiring need not be ideal. Indeed, and are ideals of is a set of non-negative integers. Clearly, (say) is not a ideal, because , but . However, this condition does not hold in the case of a lattice ordered semiring. AMS Mathematics subject classification (2020): 16Y60. Keywords: Lattices, additive idempotent, multiplicative Γ-idempotent, k-ideal, lattice ideal, Γ-morphism","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":" 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.17485/ijst/v17i12.2055
Hiteshkumar G Bariya, Manisha P Patel
Objective: This paper investigates velocity profile for two-dimensional, incompressible, laminar forced convection flow of the fluid model for Prandtl-Eyring fluid past a stretching sheet in the presence of fluid parameters. Methods: The governing partial differential equation for the flow was transformed into non-linear ordinary differential equation by using the deductive one parameter group theoretic method and numerical solution of non-linear ordinary differential equation (ODE) is solved by MATLAB bvp4c solver. Findings: The solution of velocity profile obtained as a function of parameter and . The effect of the fluid parameter was discussed graphically. Novelty: The main goal of this article is to analyze boundary layer flow of Prandtl-Eyring fluid over a stretching surface. The conservation equations of mass, momentum are converted into non-linear ordinary differential equations along with boundary conditions using deductive one parameter group theoretic method and solved by MATLAB ODE solver. Comparisons with previously published works are made, and results show a high level of agreement. This type of research is applicable to extrusion, paper production, fiber glass production, hot rolling, condensation process, crystal growing, polymer sheets etc. Keywords: Boundary layer, laminar flow, Deductive one parameter Group theoretic method, Absolute invariant, Stretching Sheet, Prandtl-Eyring fluid
{"title":"On the Solution of Blasius Boundary Layer Equations of Prandtl-Eyring Fluid Flow Past a Stretching Sheet","authors":"Hiteshkumar G Bariya, Manisha P Patel","doi":"10.17485/ijst/v17i12.2055","DOIUrl":"https://doi.org/10.17485/ijst/v17i12.2055","url":null,"abstract":"Objective: This paper investigates velocity profile for two-dimensional, incompressible, laminar forced convection flow of the fluid model for Prandtl-Eyring fluid past a stretching sheet in the presence of fluid parameters. Methods: The governing partial differential equation for the flow was transformed into non-linear ordinary differential equation by using the deductive one parameter group theoretic method and numerical solution of non-linear ordinary differential equation (ODE) is solved by MATLAB bvp4c solver. Findings: The solution of velocity profile obtained as a function of parameter and . The effect of the fluid parameter was discussed graphically. Novelty: The main goal of this article is to analyze boundary layer flow of Prandtl-Eyring fluid over a stretching surface. The conservation equations of mass, momentum are converted into non-linear ordinary differential equations along with boundary conditions using deductive one parameter group theoretic method and solved by MATLAB ODE solver. Comparisons with previously published works are made, and results show a high level of agreement. This type of research is applicable to extrusion, paper production, fiber glass production, hot rolling, condensation process, crystal growing, polymer sheets etc. Keywords: Boundary layer, laminar flow, Deductive one parameter Group theoretic method, Absolute invariant, Stretching Sheet, Prandtl-Eyring fluid","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":" 27","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Objective: These eye illnesses can be either internal eye diseases or external eye diseases. The purpose of research is to find the right model for better performance to identify the external eye disease. The model is customised with 16- layers CNN using multiclass classification. Method: The Deep CNN techniques are utilized with multiclass classification, and the model is developed using Vgg16 with different dropout rates of 0.25 and 0.50 to improve accuracy and performance. In this work, a deep convolutional neural network model is proposed to classify and identify external eye diseases like conjunctivitis, blepharitis, and cellulitis. Datasets were taken in 80:20 randomly from blepharitis, cellulitis, and Conjunctivitis to test (242) and train (968) the model after pre-processing. Novelty: The model is novel and unique using deep CNN, Vgg16, and multiclass classification because it has never been classified and predicted previously for external eye disease. Additionally, Vgg16 with dropout rates of 0.25 and 0.50 was not tested. The model is penetrated into fully connected (FC) layers with different dropout rates. Findings: The accuracy of 98.48% and 0.976% for deep CNN and multiclass classification consecutively produced satisfactory results. The efficiency of R2 is evaluated with multiple classes of data that resulted in a range of 0.425 - 0.775 with k = 10 folds. Vgg16 attains the highest performance of 71.54% with changed dropout rates. The effects of fundus in the ocular, such as retinopathy and AMD, can be examined in the future with segmented data using CNN for better optimization. On account of biological changes in eye and retinal structure, models might be constructed or studied. Keywords: Multiclass Classification, Identification, Deep CNN, External Eye Disease, Evaluation
{"title":"Multiclass Classification and Identification of the External Eye Diseases using Deep CNN","authors":"Faizur Rashid, Jamal Abate, Afendi Abdi","doi":"10.17485/ijst/v17i12.21","DOIUrl":"https://doi.org/10.17485/ijst/v17i12.21","url":null,"abstract":"Objective: These eye illnesses can be either internal eye diseases or external eye diseases. The purpose of research is to find the right model for better performance to identify the external eye disease. The model is customised with 16- layers CNN using multiclass classification. Method: The Deep CNN techniques are utilized with multiclass classification, and the model is developed using Vgg16 with different dropout rates of 0.25 and 0.50 to improve accuracy and performance. In this work, a deep convolutional neural network model is proposed to classify and identify external eye diseases like conjunctivitis, blepharitis, and cellulitis. Datasets were taken in 80:20 randomly from blepharitis, cellulitis, and Conjunctivitis to test (242) and train (968) the model after pre-processing. Novelty: The model is novel and unique using deep CNN, Vgg16, and multiclass classification because it has never been classified and predicted previously for external eye disease. Additionally, Vgg16 with dropout rates of 0.25 and 0.50 was not tested. The model is penetrated into fully connected (FC) layers with different dropout rates. Findings: The accuracy of 98.48% and 0.976% for deep CNN and multiclass classification consecutively produced satisfactory results. The efficiency of R2 is evaluated with multiple classes of data that resulted in a range of 0.425 - 0.775 with k = 10 folds. Vgg16 attains the highest performance of 71.54% with changed dropout rates. The effects of fundus in the ocular, such as retinopathy and AMD, can be examined in the future with segmented data using CNN for better optimization. On account of biological changes in eye and retinal structure, models might be constructed or studied. Keywords: Multiclass Classification, Identification, Deep CNN, External Eye Disease, Evaluation","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.17485/ijst/v17i12.211
M. Rajendar, D. M. Reddy, M. Nagesh, V. Nagaraju
Objective: The importance of this research article is to evaluate efficient model for diagnosing pandemic COVID-19 positive cases in Telangana State, India. Method: Neural Network models (Extreme Learning Machine and Multi-Layer Perception), Deep Learning Neural Network model (Long Short Term Memory-LSTM) and traditional Auto Regressive Integrated Moving Average (ARIMA) models were applied and the data was converted from non-linear to linear (stationarity) for forecasting Covid-19 positive cases. The study of the data covered from 1st. Dec 2020 to 30th May 2021. 80% of train data was taken to fit the models and then 20% of the test data was used to predict the values. The deviation between original test data and predicted data led to an error. Among these error values, the model which had minimum errors was considered as the best of the four models. Findings: LSTM model was proved to be the most efficient model, as a result of the least Root mean square error (RMSE = 71.12) compared to ARIMA (258.20), ELM (553.67) and MLP (641.86) values. Novelty: These forecasting methods succour to predict the Covid-19 positive cases in the forthcoming days. This has been suggested for taking the better preventive steps to control the Covid-19 positive cases. Keywords: COVID19, ARIMA, LSTM, MLP, ELM Forecasting
{"title":"Progression of COVID-19 Cases in Telangana State by using ARIMA, MLP, ELM and LSTM Prediction Models by Retrospective Confirmation","authors":"M. Rajendar, D. M. Reddy, M. Nagesh, V. Nagaraju","doi":"10.17485/ijst/v17i12.211","DOIUrl":"https://doi.org/10.17485/ijst/v17i12.211","url":null,"abstract":"Objective: The importance of this research article is to evaluate efficient model for diagnosing pandemic COVID-19 positive cases in Telangana State, India. Method: Neural Network models (Extreme Learning Machine and Multi-Layer Perception), Deep Learning Neural Network model (Long Short Term Memory-LSTM) and traditional Auto Regressive Integrated Moving Average (ARIMA) models were applied and the data was converted from non-linear to linear (stationarity) for forecasting Covid-19 positive cases. The study of the data covered from 1st. Dec 2020 to 30th May 2021. 80% of train data was taken to fit the models and then 20% of the test data was used to predict the values. The deviation between original test data and predicted data led to an error. Among these error values, the model which had minimum errors was considered as the best of the four models. Findings: LSTM model was proved to be the most efficient model, as a result of the least Root mean square error (RMSE = 71.12) compared to ARIMA (258.20), ELM (553.67) and MLP (641.86) values. Novelty: These forecasting methods succour to predict the Covid-19 positive cases in the forthcoming days. This has been suggested for taking the better preventive steps to control the Covid-19 positive cases. Keywords: COVID19, ARIMA, LSTM, MLP, ELM Forecasting","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140389003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.17485/ijst/v17i12.2852
S. N. Therasal, M. Thiagarajan
Objectives: This study aims at (i) introducing the finite capacity of the interdependent queueing model with breakdown and controllable arrival rates, (ii) calculating the average number of clients in the system, and identifying the expected waiting period of the clients in the system, (iii) dealing with the model descriptions, steady-state equations, and characteristics, which are expressed in terms of , and (iv) analyzing the probabilities of the queueing system and its characteristics with numerical verification of the obtained results. Methods: While providing the input, the arrival rates through faster and slower arrival rates are controlled using the Poisson process. Also, the service provides an exponential distribution. The server provides the service on an FCFS basis. In this article, two types of models are used: and which are the system’s conditions, where represents the number of units present in the queue in which their probability is and . All probabilities are distributed based on the speed of advent using this concept. Then, the steady-state probabilities are computed using a recursive approach. Findings: This paper discovers the number of clients using the system on average and the expected number of clients in the system using the probability of the steady-state calculation. Little’s formula is used to derive the expected waiting period of the clients in the system. Novelty: There are articles connected to the finite capacity of failed service in functioning and malfunctioning, but this takes the initiative to provide a link in connection with the rates of the controllable arrivals and interdependency in the arrival and service processes. Mathematics Subject allocation: 60K25, 68M20, 90B22. Keywords: M/M/1/K Queue Model, Finite Capacity, Breakdown, Controllable Arrival rates, FCFS Queue Discipline
{"title":"Poisson Input and Exponential Service Time Finite Capacity Interdependent Queueing Model with Breakdown and Controllable Arrival Rates","authors":"S. N. Therasal, M. Thiagarajan","doi":"10.17485/ijst/v17i12.2852","DOIUrl":"https://doi.org/10.17485/ijst/v17i12.2852","url":null,"abstract":"Objectives: This study aims at (i) introducing the finite capacity of the interdependent queueing model with breakdown and controllable arrival rates, (ii) calculating the average number of clients in the system, and identifying the expected waiting period of the clients in the system, (iii) dealing with the model descriptions, steady-state equations, and characteristics, which are expressed in terms of , and (iv) analyzing the probabilities of the queueing system and its characteristics with numerical verification of the obtained results. Methods: While providing the input, the arrival rates through faster and slower arrival rates are controlled using the Poisson process. Also, the service provides an exponential distribution. The server provides the service on an FCFS basis. In this article, two types of models are used: and which are the system’s conditions, where represents the number of units present in the queue in which their probability is and . All probabilities are distributed based on the speed of advent using this concept. Then, the steady-state probabilities are computed using a recursive approach. Findings: This paper discovers the number of clients using the system on average and the expected number of clients in the system using the probability of the steady-state calculation. Little’s formula is used to derive the expected waiting period of the clients in the system. Novelty: There are articles connected to the finite capacity of failed service in functioning and malfunctioning, but this takes the initiative to provide a link in connection with the rates of the controllable arrivals and interdependency in the arrival and service processes. Mathematics Subject allocation: 60K25, 68M20, 90B22. Keywords: M/M/1/K Queue Model, Finite Capacity, Breakdown, Controllable Arrival rates, FCFS Queue Discipline","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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