International Journal Of Scientific Research In Engineering & Technology最新文献

{"title":"A Genetic Algorithm Based Hybrid Routing Technique for IOT Systems","authors":"Dr Rakesh Kumar","doi":"10.59256/ijsreat.20240401009","DOIUrl":"https://doi.org/10.59256/ijsreat.20240401009","url":null,"abstract":"Internet is gaining new heights in the today’s technological environment. Internet of things (IoT) is a new class of Internet based heterogeneous networked application systems which uses various types of sensor/detectors and devices for the exchange and collection of data. Devices need to be in the radio range and must remain continuously connected to the internet. IoT can sense and control the objects remotely across existing networked architecture and builds chances for direct combination among the physical world and computer-based systems. It is believed that, in future many of the household application will be based on IoT. Due to their applications in situations such as building home automation, emergencies, crisis management, energy management and healthcare, message security becomes of top importance in IoT. An optimized routing scheme using the intelligent mathematical techniques, which includes Genetic Algorithms(GA) and Analytical Hierarchy Process(AHP), is proposed here and an optimized route can be encrypted using cryptanalytic techniques. Simulation results of GA and AHP are also presented here for the proposed network. It has been found that overall efficiency of the IoT system can be greatly improved with the proposed model. A comparison is also provided in discussion section which demonstrates that hybrid algorithms developed for IoT systems performs much better than traditional routing algorithms. Keywords: IoT; AHP;GA; Cryptanalytic Techniques; WSN","PeriodicalId":310227,"journal":{"name":"International Journal Of Scientific Research In Engineering & Technology","volume":"94 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140444207","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}

{"title":"Imploding Detonation Waves in Self-gravitating Ideal Gas","authors":"Pushpender Kumar Gangwar, Rajesh Kumar Verma","doi":"10.59256/ijsreat.20240401008","DOIUrl":"https://doi.org/10.59256/ijsreat.20240401008","url":null,"abstract":"In the present paper, the propagation of strong spherical imploding detonation waves in a reacting ideal gas under the effect of self-gravitation of the non-homogeneous medium has been investigated, by the help of Chester-Chisnell-Whitham (CCW) theory. It is considered that detonation wave is initially Chapman-Jouguet. Initial taking the density distribution law as power decreasing with distance, the analytical expressions for the detonation velocity just behind the front along with other flow variables are derived. Neglecting the effect of overtaking disturbances the variation of non-dimensional detonation velocity, the pressure and density with the propagation distance have been calculated numerically. The effect of change in density parameter at different Alfven Mach number on the convergence of detonation front have been discussed through graphs in details. Finally, it is found that density parameter and Alfven Mach number of gas have a significant role on propagation of strong spherical detonation front in reacting ideal gas with gravitation effect on all the post-flow variables. The software MATLAB have been used for computation of the problem. Keywords: Self-gravitating gas, Strong detonation waves, CCW theory.","PeriodicalId":310227,"journal":{"name":"International Journal Of Scientific Research In Engineering & Technology","volume":"24 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140450058","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}

{"title":"Case Study of Analysis of Wind Load on U and V Shape High Rise Building: A Review","authors":"Ritik Chaudhary, Astha Verma","doi":"10.59256/ijsreat.20240401007","DOIUrl":"https://doi.org/10.59256/ijsreat.20240401007","url":null,"abstract":"Conclusively, this case study provides an exhaustive and methodical investigation of wind load analysis on U and V-shaped high-rise structures, amalgamating conclusions from diverse scholars in the domain. The comprehensive examination and juxtaposition of current research techniques highlight the complex character of wind load assessment, utilizing a range of methods including wind tunnel testing, empirical observations, and Computational Fluid Dynamics (CFD) simulations. The research makes a substantial contribution to our comprehension of the complex relationships that exist between wind forces and the unique architectural arrangements of U and V shapes. By assimilating insights from numerous studies, the research successfully unveils commonalities, identifies divergences, and highlights emerging trends in the analysis of wind-induced effects on these high-rise structures. Professionals in high-rise building design, and architects and engineers. The goal of the research is to make a substantial contribution to the ongoing development of wind load analysis techniques.Understanding the building's response to wind rotation at different incident angles is a specific focus of this study. Validation against experimental data ensures the accuracy of CFD simulations, often involving model refinement based on real-world observations. Overall, this research contributes to a comprehensive understanding of building aerodynamics, offering insights into the intricate interplay between structures and wind forces. Keywords: High-rise building, ANSYS CFX, CFD, wind tunnel, pressure coefficient.","PeriodicalId":310227,"journal":{"name":"International Journal Of Scientific Research In Engineering & Technology","volume":"86 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139959476","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}

{"title":"Pneumonia Detection In Chest X-Rays Using Neural Networks","authors":"Asha Shiny Dr .X.S, Bhavana B, Jyothirmayee A, Sushanth B, Sathish D","doi":"10.59256/ijsreat.20240401006","DOIUrl":"https://doi.org/10.59256/ijsreat.20240401006","url":null,"abstract":"Deep learning techniques are widely used to design robust classification models in several areas such as medical diagnosis tasks in which it achieves good performance. In this paper, we have proposed the CNN model (Convolutional Neural Network) for the classification of Chest X-ray images for Radiological Society of North America Pneumonia (RSNA) datasets. The study also tries to achieve the same RSNA benchmark results using the limited computational resources by trying out various approaches to the methodologies that have been implemented in recent years. The proposed method is based on a noncomplex CNN and the use of transfer learning algorithms like Xception, InceptionV3/V4, EfficientNetB7. Along with this, the study also tries to achieve the same RSNA benchmark results using the limited computational resources by trying out various approaches to the methodologies that have been implemented in recent years. The RSNA benchmark MAP score is 0.25 but using the Mask RCNN model (Region Convolutional Neural Network) on a stratified sample of 3017 along with image augmentation gave a MAP (Mean Average Precision) score of 0.15. Meanwhile, the YoloV3 without any hyperparameter tuning gave the MAP score of 0.32 but still, the loss keeps decreasing. Running the model for a greater number of iterations can give better results. Pneumonia is one of the major causes of cancer-related deaths due to its aggressive nature and delayed detections at advanced stages. Early detection of Pneumonia is very important for the survival of an individual, and is a significant challenging problem. Generally, chest radiographs (Xray) and computed tomography (CT) scans are used initially for the diagnosis of the malignant nodules; however, the possible existence of benign nodules leads to erroneous decisions. At early stages, the benign and the malignant nodules show very close resemblance to each other. In this paper, a novel deep learning-based model with multiple strategies is proposed for the precise diagnosis of the malignant nodules. Due to the recent achievements of deep convolutional neural networks (CNN) in image analysis, we have used two deep three-dimensional (3D) customized mixed link network (CMixNet) architectures for lung nodule detection and classification, respectively. Keywords: X-Rays, Deep learning techniques, CNN model, RSNA.","PeriodicalId":310227,"journal":{"name":"International Journal Of Scientific Research In Engineering & Technology","volume":"11 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139802756","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}

{"title":"Pneumonia Detection In Chest X-Rays Using Neural Networks","authors":"Asha Shiny Dr .X.S, Bhavana B, Jyothirmayee A, Sushanth B, Sathish D","doi":"10.59256/ijsreat.20240401006","DOIUrl":"https://doi.org/10.59256/ijsreat.20240401006","url":null,"abstract":"Deep learning techniques are widely used to design robust classification models in several areas such as medical diagnosis tasks in which it achieves good performance. In this paper, we have proposed the CNN model (Convolutional Neural Network) for the classification of Chest X-ray images for Radiological Society of North America Pneumonia (RSNA) datasets. The study also tries to achieve the same RSNA benchmark results using the limited computational resources by trying out various approaches to the methodologies that have been implemented in recent years. The proposed method is based on a noncomplex CNN and the use of transfer learning algorithms like Xception, InceptionV3/V4, EfficientNetB7. Along with this, the study also tries to achieve the same RSNA benchmark results using the limited computational resources by trying out various approaches to the methodologies that have been implemented in recent years. The RSNA benchmark MAP score is 0.25 but using the Mask RCNN model (Region Convolutional Neural Network) on a stratified sample of 3017 along with image augmentation gave a MAP (Mean Average Precision) score of 0.15. Meanwhile, the YoloV3 without any hyperparameter tuning gave the MAP score of 0.32 but still, the loss keeps decreasing. Running the model for a greater number of iterations can give better results. Pneumonia is one of the major causes of cancer-related deaths due to its aggressive nature and delayed detections at advanced stages. Early detection of Pneumonia is very important for the survival of an individual, and is a significant challenging problem. Generally, chest radiographs (Xray) and computed tomography (CT) scans are used initially for the diagnosis of the malignant nodules; however, the possible existence of benign nodules leads to erroneous decisions. At early stages, the benign and the malignant nodules show very close resemblance to each other. In this paper, a novel deep learning-based model with multiple strategies is proposed for the precise diagnosis of the malignant nodules. Due to the recent achievements of deep convolutional neural networks (CNN) in image analysis, we have used two deep three-dimensional (3D) customized mixed link network (CMixNet) architectures for lung nodule detection and classification, respectively. Keywords: X-Rays, Deep learning techniques, CNN model, RSNA.","PeriodicalId":310227,"journal":{"name":"International Journal Of Scientific Research In Engineering & Technology","volume":"166 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139862968","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}

{"title":"Machine Learning for Web Vulnerability Detection: The Case of Cross-Site Request Forgery","authors":"Sravani N, Sai Raju O, Harish Ch, Anil Kumar B, Anirudh S","doi":"10.59256/ijsreat.20240401005","DOIUrl":"https://doi.org/10.59256/ijsreat.20240401005","url":null,"abstract":"Cross-site request forgery (CSRF) vulnerabilities pose a significant threat to web application security, enabling attackers to execute unauthorized actions on behalf of authenticated users. Conventional CSRF detection methods, such as manual code review and static analysis, are often time-consuming, error-prone, and inefficient. Proposes Mitch, a novel machine learning (ML)-based solution for the black-box detection of CSRF vulnerabilities. Mitch employs supervised learning, trained on a comprehensive dataset of HTTP requests and responses, to effectively identify security-sensitive HTTP requests and uncover CSRF vulnerabilities within them. Rigorous evaluations on a diverse set of real-world web applications demonstrate Mitch's remarkable ability to detect CSRF vulnerabilities with high accuracy, outperforming traditional methods. Mitch's automated nature eliminates the need for manual code review and static analysis, saving time and effort while reducing the risk of human error. Additionally, Mitch's scalability allows seamless integration into continuous integration and continuous delivery (CI/CD) pipelines, enabling continuous security monitoring and vulnerability detection. Mitch's efficacy extends beyond detecting known CSRF vulnerabilities. Its ability to identify patterns and relationships enables it to uncover obscure CSRF vulnerabilities that may have been overlooked by traditional methods, including zero-day vulnerabilities. In conclusion, Mitch emerges as a powerful tool for enhancing web application security, offering a comprehensive and automated solution for detecting CSRF vulnerabilities. Its ability to handle complex web applications, uncover hidden CSRF vulnerabilities, and integrate into CI/CD pipelines makes it an indispensable tool for web security professionals. Mitch's adoption has the potential to significantly reduce the risk of CSRF attacks and safeguard sensitive user data. We propose a methodology to leverage machine learning (ML) for the detection of web application vulnerabilities. We use it in the design of Mitch, the first ML solution for the black-box detection of cross-site request forgery vulnerabilities. Finally, we show the effectiveness of Mitch on real software. In this project, we propose a methodology to leverage Machine Learning (ML) for the detection of web application vulnerabilities. Web applications are particularly challenging to analyses, due to their diversity and the widespread adoption of custom programming practices. ML is thus very helpful for web application security it can take advantage of manually labeled data to bring the human understanding of the web application semantics into automated analysis toolsMitch allowed us to identify 35 new CSRFs on 20 major websites and 3 new CSRFs on production software. Keywords: Mitch, CSRF, CI/CD pipelines, Security Token Service (STS), Same-Origin Policy (SOP).","PeriodicalId":310227,"journal":{"name":"International Journal Of Scientific Research In Engineering & Technology","volume":"46 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139864068","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}

{"title":"Machine Learning for Web Vulnerability Detection: The Case of Cross-Site Request Forgery","authors":"Sravani N, Sai Raju O, Harish Ch, Anil Kumar B, Anirudh S","doi":"10.59256/ijsreat.20240401005","DOIUrl":"https://doi.org/10.59256/ijsreat.20240401005","url":null,"abstract":"Cross-site request forgery (CSRF) vulnerabilities pose a significant threat to web application security, enabling attackers to execute unauthorized actions on behalf of authenticated users. Conventional CSRF detection methods, such as manual code review and static analysis, are often time-consuming, error-prone, and inefficient. Proposes Mitch, a novel machine learning (ML)-based solution for the black-box detection of CSRF vulnerabilities. Mitch employs supervised learning, trained on a comprehensive dataset of HTTP requests and responses, to effectively identify security-sensitive HTTP requests and uncover CSRF vulnerabilities within them. Rigorous evaluations on a diverse set of real-world web applications demonstrate Mitch's remarkable ability to detect CSRF vulnerabilities with high accuracy, outperforming traditional methods. Mitch's automated nature eliminates the need for manual code review and static analysis, saving time and effort while reducing the risk of human error. Additionally, Mitch's scalability allows seamless integration into continuous integration and continuous delivery (CI/CD) pipelines, enabling continuous security monitoring and vulnerability detection. Mitch's efficacy extends beyond detecting known CSRF vulnerabilities. Its ability to identify patterns and relationships enables it to uncover obscure CSRF vulnerabilities that may have been overlooked by traditional methods, including zero-day vulnerabilities. In conclusion, Mitch emerges as a powerful tool for enhancing web application security, offering a comprehensive and automated solution for detecting CSRF vulnerabilities. Its ability to handle complex web applications, uncover hidden CSRF vulnerabilities, and integrate into CI/CD pipelines makes it an indispensable tool for web security professionals. Mitch's adoption has the potential to significantly reduce the risk of CSRF attacks and safeguard sensitive user data. We propose a methodology to leverage machine learning (ML) for the detection of web application vulnerabilities. We use it in the design of Mitch, the first ML solution for the black-box detection of cross-site request forgery vulnerabilities. Finally, we show the effectiveness of Mitch on real software. In this project, we propose a methodology to leverage Machine Learning (ML) for the detection of web application vulnerabilities. Web applications are particularly challenging to analyses, due to their diversity and the widespread adoption of custom programming practices. ML is thus very helpful for web application security it can take advantage of manually labeled data to bring the human understanding of the web application semantics into automated analysis toolsMitch allowed us to identify 35 new CSRFs on 20 major websites and 3 new CSRFs on production software. Keywords: Mitch, CSRF, CI/CD pipelines, Security Token Service (STS), Same-Origin Policy (SOP).","PeriodicalId":310227,"journal":{"name":"International Journal Of Scientific Research In Engineering & Technology","volume":"4 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139803907","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}

{"title":"Optimizing the Future: Unveiling the Significance of MLOps in Streamlining the Machine Learning Lifecycle","authors":"Gorantla Prasanna","doi":"10.59256/ijsreat.20240401002","DOIUrl":"https://doi.org/10.59256/ijsreat.20240401002","url":null,"abstract":"As we stand on the precipice of 2024, the technological landscape is abuzz with the convergence of two revolutionary forces: MLOps (Machine Learning Operations) and Generative AI. This potent cocktail promises to reshape the very fabric of artificial intelligence (AI), ushering in a new era of streamlined workflows, boundless innovation, and redefined value delivery. MLOps, a paradigm shift inspired by DevOps principles, emerges as the knight in shining armor, poised to vanquish the challenges plaguing the machine learning lifecycle. By fostering seamless collaboration, agile deployment, vigilant monitoring, and efficient management of models, MLOps lays the groundwork for robust organizational AI strategies. In this paper, we delve deep into the intricate world of MLOps, exploring its genesis, its potential to revolutionize business operations, and its pivotal role in shaping the future of AI. Keywords: MLOps, Machine Learning, Artificial Intelligence, DevOps, Automation, Collaboration, Deployment, Monitoring, Management, Generative AI, Innovation, Value Delivery, Business Optimization.","PeriodicalId":310227,"journal":{"name":"International Journal Of Scientific Research In Engineering & Technology","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139809911","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}

{"title":"A Comparative Study on Loan Status: Utilizing Machine Learning Algorithms for Predictive Analysis","authors":"Thanneeru Mahesh","doi":"10.59256/ijsreat.20240401003","DOIUrl":"https://doi.org/10.59256/ijsreat.20240401003","url":null,"abstract":"This research delves into a comprehensive comparative study focused on predicting loan status through the application of various machine learning (ML) algorithms. The objective is to assess and compare the effectiveness of Decision Trees, Random Forest, Support Vector Machines (SVM), and Gradient Boosting models in determining the likelihood of loan approval or denial. Leveraging a dataset comprising historical loan application data, including applicant demographics, financial history, and loan characteristics, the study conducts rigorous analysis and interpretation of the models' performance. The results provide valuable insights into the strengths and weaknesses of each algorithm, offering a nuanced understanding of their predictive capabilities in the context of loan status determination. This research contributes to the growing body of knowledge in the application of ML algorithms in the financial sector, presenting practical implications for institutions seeking to enhance their loan approval processes. Key words: Predictive Analysis, Machine Learning Algorithms, Loan Status, Comparative Study, Utilization","PeriodicalId":310227,"journal":{"name":"International Journal Of Scientific Research In Engineering & Technology","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139810025","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}

{"title":"Advancements in Machine Learning: A Comprehensive Exploration of Methods, Applications, and Future Perspectives","authors":"Bommireddy Srikanth Reddy","doi":"10.59256/ijsreat.20240401001","DOIUrl":"https://doi.org/10.59256/ijsreat.20240401001","url":null,"abstract":"Machine learning, a specialized subset of artificial intelligence, imparts the ability to machines to learn, while artificial intelligence (AI) encompasses the broader field dedicated to emulating human capabilities. Within AI, machine learning employs computational techniques to instruct computers on learning from their historical experiences. Unlike models based on predetermined equations, machine learning algorithms derive insights directly from data, progressively improving their performance as the volume of learning examples grows. This paper presents a comprehensive overview of the domain, exploring diverse machine learning methodologies such as supervised, unsupervised, and reinforcement learning, along with an examination of various programming languages employed in machine learning applications. Keywords: Machine learning, Artificial intelligence, Computational techniques, Historical experiences, Learning examples, Supervised learning, Unsupervised learning, Reinforcement learning, Programming languages, Machine learning applications","PeriodicalId":310227,"journal":{"name":"International Journal Of Scientific Research In Engineering & Technology","volume":"17 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139810173","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}