Pub Date : 2023-12-29DOI: 10.37256/rrcs.2220233255
Muhammad Arslan, Waqas Ahmad, Aman Ullah Yasin, Jahanzaib Ali Khan, Muhammad Nadeem, Syeda Wajiha Zahra
The considerable increase in the risk of clinical events associated with chronic renal disease makes it a severe global public health issue. Chronic kidney disease (CKD) is a severe global public health issue, increasing the risk of clinical events and being associated with renal failure, cardiovascular disease, and early mortality. An accurate and timely diagnosis is essential. This research paper focuses on the global public health issue of chronic kidney disease (CKD) and its association with cardiovascular disease. It emphasizes the importance of accurate diagnosis and timely intervention for CKD, which poses significant risks to patients’ health. The study proposes a machine learning (ML) approach using deep neural networks and feature selection methods to diagnose CKD and heart attack disease. The ensemble learning algorithms used in this study are decision tree (DT), logistic regression (LR), Naive Bayes (NB), random forest (RF), support vector machine (SVM), and gradient boosted trees (GBT) classifier, as well as one deep learning technique called recurrent neural network (RNN). Feature selection techniques like correlation coefficient methods are used to identify critical characteristics. The evaluation of the proposed approach was conducted using accuracy, precision, recall, and F1 measure metrics. The study employed all features for grid search and testing in each approach.
与慢性肾病相关的临床事件风险大大增加，使其成为一个严重的全球公共卫生问题。慢性肾脏病（CKD）是一个严重的全球公共卫生问题，它增加了临床事件的风险，并与肾衰竭、心血管疾病和早期死亡有关。准确及时的诊断至关重要。本研究论文重点关注慢性肾脏病（CKD）这一全球公共卫生问题及其与心血管疾病的关联。它强调了准确诊断和及时干预 CKD 的重要性，因为 CKD 对患者的健康构成重大风险。该研究提出了一种使用深度神经网络和特征选择方法来诊断 CKD 和心脏病的机器学习（ML）方法。本研究中使用的集合学习算法包括决策树（DT）、逻辑回归（LR）、奈夫贝叶斯（NB）、随机森林（RF）、支持向量机（SVM）和梯度增强树（GBT）分类器，以及一种称为循环神经网络（RNN）的深度学习技术。相关系数法等特征选择技术用于识别关键特征。使用准确率、精确度、召回率和 F1 测量指标对所提出的方法进行了评估。该研究在每种方法中都采用了网格搜索和测试的所有特征。
{"title":"Comparative Machine Learning Approaches to Analyzing the Illnesses of the Chronic Renal and Heart Diseases","authors":"Muhammad Arslan, Waqas Ahmad, Aman Ullah Yasin, Jahanzaib Ali Khan, Muhammad Nadeem, Syeda Wajiha Zahra","doi":"10.37256/rrcs.2220233255","DOIUrl":"https://doi.org/10.37256/rrcs.2220233255","url":null,"abstract":"The considerable increase in the risk of clinical events associated with chronic renal disease makes it a severe global public health issue. Chronic kidney disease (CKD) is a severe global public health issue, increasing the risk of clinical events and being associated with renal failure, cardiovascular disease, and early mortality. An accurate and timely diagnosis is essential. This research paper focuses on the global public health issue of chronic kidney disease (CKD) and its association with cardiovascular disease. It emphasizes the importance of accurate diagnosis and timely intervention for CKD, which poses significant risks to patients’ health. The study proposes a machine learning (ML) approach using deep neural networks and feature selection methods to diagnose CKD and heart attack disease. The ensemble learning algorithms used in this study are decision tree (DT), logistic regression (LR), Naive Bayes (NB), random forest (RF), support vector machine (SVM), and gradient boosted trees (GBT) classifier, as well as one deep learning technique called recurrent neural network (RNN). Feature selection techniques like correlation coefficient methods are used to identify critical characteristics. The evaluation of the proposed approach was conducted using accuracy, precision, recall, and F1 measure metrics. The study employed all features for grid search and testing in each approach.","PeriodicalId":377142,"journal":{"name":"Research Reports on Computer Science","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139143610","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 : 2023-12-29DOI: 10.37256/rrcs.2220233275
Jahanzaib Ali Khan, Waqas Ahmad, Asad Hussain, S. Zahra, Muhammad Nadeem, Ayesha
Road traffic accidents become more of a problem as there are more automobiles on the road. Accidents are impossible to completely prevent, but there are techniques to lessen their impact and manage their aftereffects to limit harm. One of the biggest issues is exchanging car accident alerts with other vehicles on the road. A number of technologies are in use to prevent traffic collisions. It is planned to develop a system for detecting collisions between vehicles based on the vehicular ad-hoc network (VANET) concept. Additionally, it immediately alerts the neighborhood hospital and social media about the incident. The proposed technique uses a warning system supported by collision potential and is implemented on OpenStreetMaps and at locations of interest. We assess our system’s performance using the simulation of urban mobility (SUMO) software tool, which employs OpenStreetMap data to simulate vehicle-to-vehicle communication and accident detection. SUMO generates traffic on the road for our evaluation. To gauge the effectiveness of our system, we measure the size of messages exchanged within the VANET and demonstrate its feasibility. Furthermore, we compare our results with previously published works in the same field of study for reference.
{"title":"Witness System of Vehicle Accidents Based on the Internet of Things","authors":"Jahanzaib Ali Khan, Waqas Ahmad, Asad Hussain, S. Zahra, Muhammad Nadeem, Ayesha","doi":"10.37256/rrcs.2220233275","DOIUrl":"https://doi.org/10.37256/rrcs.2220233275","url":null,"abstract":"Road traffic accidents become more of a problem as there are more automobiles on the road. Accidents are impossible to completely prevent, but there are techniques to lessen their impact and manage their aftereffects to limit harm. One of the biggest issues is exchanging car accident alerts with other vehicles on the road. A number of technologies are in use to prevent traffic collisions. It is planned to develop a system for detecting collisions between vehicles based on the vehicular ad-hoc network (VANET) concept. Additionally, it immediately alerts the neighborhood hospital and social media about the incident. The proposed technique uses a warning system supported by collision potential and is implemented on OpenStreetMaps and at locations of interest. We assess our system’s performance using the simulation of urban mobility (SUMO) software tool, which employs OpenStreetMap data to simulate vehicle-to-vehicle communication and accident detection. SUMO generates traffic on the road for our evaluation. To gauge the effectiveness of our system, we measure the size of messages exchanged within the VANET and demonstrate its feasibility. Furthermore, we compare our results with previously published works in the same field of study for reference.","PeriodicalId":377142,"journal":{"name":"Research Reports on Computer Science","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139143262","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 : 2023-12-29DOI: 10.37256/rrcs.2220233134
Benjamin Antunes, David Hill
This paper investigates whether simultaneous multi-threading (SMT) can improve performance on modern computing clusters with reproducible results on four types of applications, focused on stochastic simulations with different memory bound and compute bound constraints. We manually set the affinity of processes to compare its efficiency with the computing time obtained by the automatic assignment of the operating system. To measure SMT and affinity impact on a modern multicore processor, we parallelize up to 128 processes of the four types of applications. We expect repeatable numerical results between the sequential and parallel versions of simulations. For the three applications that are not memory bound, SMT is more effective by up to 30%. This represents an interesting increase up to 10% more performance for compute bound applications when compared to the initial papers discussing the efficiency of SMT. However, for the memory-bound application, SMT is less effective and can even decrease performance. The manual setting of core affinity does not show an increase in performance compared to the automatic assignment. All code and data used in the study are available to help reproducible research.
{"title":"Evaluating Simultaneous Multi-threading and Affinity Performance for Reproducible Parallel Stochastic Simulation","authors":"Benjamin Antunes, David Hill","doi":"10.37256/rrcs.2220233134","DOIUrl":"https://doi.org/10.37256/rrcs.2220233134","url":null,"abstract":"This paper investigates whether simultaneous multi-threading (SMT) can improve performance on modern computing clusters with reproducible results on four types of applications, focused on stochastic simulations with different memory bound and compute bound constraints. We manually set the affinity of processes to compare its efficiency with the computing time obtained by the automatic assignment of the operating system. To measure SMT and affinity impact on a modern multicore processor, we parallelize up to 128 processes of the four types of applications. We expect repeatable numerical results between the sequential and parallel versions of simulations. For the three applications that are not memory bound, SMT is more effective by up to 30%. This represents an interesting increase up to 10% more performance for compute bound applications when compared to the initial papers discussing the efficiency of SMT. However, for the memory-bound application, SMT is less effective and can even decrease performance. The manual setting of core affinity does not show an increase in performance compared to the automatic assignment. All code and data used in the study are available to help reproducible research.","PeriodicalId":377142,"journal":{"name":"Research Reports on Computer Science","volume":"21 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139147810","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 : 2023-06-21DOI: 10.37256/rrcs.2120232742
Jiang-Chun Song, Yuan Gu, E. Kumar
Nowadays, the emergence of new technologies gives rise to a huge amount of data in different fields such as public transportation, community services, scientific research, etc. Due to the aging population, healthcare is becoming more important in our daily life to reduce public burdens. For example, manually archiving massive electronic medical files, such as X-ray images, is impossible. However, precise classification is essential for further work, such as diagnosis. In this report, we applied a spectral clustering algorithm to classify chest disease X-ray images. We also employed the "pure" K-means algorithm for comparison. Three types of indexes are used to quantify the performances of both algorithms. Our analysis result shows that spectral clustering can successfully classify chest X-ray images based on the presence of disease spots on the lungs and the performance is superior to “pure" K-means clustering.
{"title":"Chest Disease Image Classification Based on Spectral Clustering Algorithm","authors":"Jiang-Chun Song, Yuan Gu, E. Kumar","doi":"10.37256/rrcs.2120232742","DOIUrl":"https://doi.org/10.37256/rrcs.2120232742","url":null,"abstract":"Nowadays, the emergence of new technologies gives rise to a huge amount of data in different fields such as public transportation, community services, scientific research, etc. Due to the aging population, healthcare is becoming more important in our daily life to reduce public burdens. For example, manually archiving massive electronic medical files, such as X-ray images, is impossible. However, precise classification is essential for further work, such as diagnosis. In this report, we applied a spectral clustering algorithm to classify chest disease X-ray images. We also employed the \"pure\" K-means algorithm for comparison. Three types of indexes are used to quantify the performances of both algorithms. Our analysis result shows that spectral clustering can successfully classify chest X-ray images based on the presence of disease spots on the lungs and the performance is superior to “pure\" K-means clustering.","PeriodicalId":377142,"journal":{"name":"Research Reports on Computer Science","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132794623","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 : 2023-06-20DOI: 10.37256/rrcs.2320232695
S. Jain, Anil K. Gupta
Reference evapotranspiration (ETo) is a valuable factor in the hydrological process and its estimation is a sophisticated and nonlinear problem. In this study, the utility of multilayer perceptron regression is investigated to estimate ETo of Jodhpur city, India which has a hot arid climate. Four different multilayer perceptron regression-based models are created and compared in this study. Multilayer perceptron regression is a popular tool used to predict the results of sophisticated problems. Each created model has a different architecture, in which the size (neurons) of the input and hidden layers is decided by the maximal correlation relationship between meteorological attributes and observed ETo using the Food Agriculture Organization Penman-Monteith method (FAO-PM56). This study found that model with meteorology inputs (namely both high and low temperatures, solar radiation, wind speed at 2 m, and humidity) and nine neurons at the hidden layer achieved high predictive accuracy with mean absolute error (MAE) of 0.08, mean squared error (MSE) of 0.01, root mean squared error (RMSE) of 0.10, Pearson correlation (r) of 0.99, and coefficient of determination (r2) of 0.99. The finding of this study is that the multilayer perceptron regression-based models with at least three meteorological inputs (temperature, solar radiation, and wind speed) can effectively utilize to estimate ETo and may receive attention from agriculturists, engineers, and researchers for irrigation scheduling, water resource handling, crop production enhancement, draught area prediction, etc.
{"title":"Investigation of Multilayer Perceptron Regression-based Models to Forecast Reference Evapotranspiration (ETo)","authors":"S. Jain, Anil K. Gupta","doi":"10.37256/rrcs.2320232695","DOIUrl":"https://doi.org/10.37256/rrcs.2320232695","url":null,"abstract":"Reference evapotranspiration (ETo) is a valuable factor in the hydrological process and its estimation is a sophisticated and nonlinear problem. In this study, the utility of multilayer perceptron regression is investigated to estimate ETo of Jodhpur city, India which has a hot arid climate. Four different multilayer perceptron regression-based models are created and compared in this study. Multilayer perceptron regression is a popular tool used to predict the results of sophisticated problems. Each created model has a different architecture, in which the size (neurons) of the input and hidden layers is decided by the maximal correlation relationship between meteorological attributes and observed ETo using the Food Agriculture Organization Penman-Monteith method (FAO-PM56). This study found that model with meteorology inputs (namely both high and low temperatures, solar radiation, wind speed at 2 m, and humidity) and nine neurons at the hidden layer achieved high predictive accuracy with mean absolute error (MAE) of 0.08, mean squared error (MSE) of 0.01, root mean squared error (RMSE) of 0.10, Pearson correlation (r) of 0.99, and coefficient of determination (r2) of 0.99. The finding of this study is that the multilayer perceptron regression-based models with at least three meteorological inputs (temperature, solar radiation, and wind speed) can effectively utilize to estimate ETo and may receive attention from agriculturists, engineers, and researchers for irrigation scheduling, water resource handling, crop production enhancement, draught area prediction, etc.","PeriodicalId":377142,"journal":{"name":"Research Reports on Computer Science","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115327956","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 : 2023-06-13DOI: 10.37256/rrcs.2320232637
Amrita Khera, U. Kurmi
It is a critical area of study for enhancing the effectiveness of wide-area Cellular Internet of Things (CIoT) networks. One solution is to merge Software Defined Networking (SDN) with Internet of Things (IoT) network to boost efficiency. The main challenge is determining the best location for the SDN controller and evaluating SDN clustering. This paper proposed an Un-Supervised Machine-Learning (US-ML) approach based on silhouette distance along with gap statistic for finding the optimum number of controllers for network under consideration. In addition, the Partition Around Medoids (PAM) approach is opted for allocation of controller locations. Apart from SDN, another approach is to create efficient Low-Power Wide Area Networks (LPWAN). As a result, this research contributed to the study of various LPWAN design approaches and offered a method of optimal controller location for IoT-SDN cellular networks in industries. Several outstanding research challenges are noted, and prospective research objectives for LPWAN are offered. For the case study of wide area networks (WAN), a graphical representation of the SDN controller positioning method is presented. It is determined that effective placement can improve SDN performance in worst-case network scenarios.
{"title":"Enhancing Performance of Wide Area CIoT SDN by US-ML Based Optimum Controller Placement","authors":"Amrita Khera, U. Kurmi","doi":"10.37256/rrcs.2320232637","DOIUrl":"https://doi.org/10.37256/rrcs.2320232637","url":null,"abstract":"It is a critical area of study for enhancing the effectiveness of wide-area Cellular Internet of Things (CIoT) networks. One solution is to merge Software Defined Networking (SDN) with Internet of Things (IoT) network to boost efficiency. The main challenge is determining the best location for the SDN controller and evaluating SDN clustering. This paper proposed an Un-Supervised Machine-Learning (US-ML) approach based on silhouette distance along with gap statistic for finding the optimum number of controllers for network under consideration. In addition, the Partition Around Medoids (PAM) approach is opted for allocation of controller locations. Apart from SDN, another approach is to create efficient Low-Power Wide Area Networks (LPWAN). As a result, this research contributed to the study of various LPWAN design approaches and offered a method of optimal controller location for IoT-SDN cellular networks in industries. Several outstanding research challenges are noted, and prospective research objectives for LPWAN are offered. For the case study of wide area networks (WAN), a graphical representation of the SDN controller positioning method is presented. It is determined that effective placement can improve SDN performance in worst-case network scenarios.","PeriodicalId":377142,"journal":{"name":"Research Reports on Computer Science","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125599646","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 : 2023-06-02DOI: 10.37256/rrcs.2120232577
Qutaiba Ibrahim Ali
In an effort to improve the quality of their academic programs and graduates, an increasing number of academic institutions are obtaining Accreditation Board for Engineering and Technology (ABET) accreditation for their computer engineering programs. This paper acts as a guide for managers and institutions as they get ready to start the accreditation process for their programs. There is an issue with the lack of knowledge regarding the mechanics of implementing student outcome evaluation methodologies since it causes confusion and resource waste, especially in the beginning. Furthermore, there is a paucity of literature available that discuss the methodology and the use of successful accrediting techniques for computer engineering programs. Given this, it is important to document the approaches, teaching techniques, and strategies employed by various computer engineering departments as they pursue accreditation. To the best of our knowledge, such information is not publicly available in published form, although there are fee-based training courses by ABET that provide instruction on how to approach this topic. Here, we investigate the detailed information of five different computer engineering programs and two other related programs using their self-assessment reports (SARs). These SARs span over the last 10 years and represent the outcome of different approaches toward getting accreditation. The study plan involves comparing (objectively and subjectively) the different parameters of the student outcome assessment (criterion 4) to show their convergence and divergence in dealing with accreditation requirements. We found that the selection of an assessment method depends on the goals and context of the educational program. Factors such as the learning outcomes to be assessed, the level of detail needed, available resources, and the preferences of instructors and students should be taken into account. A program may opt to use multiple assessment methods to attain a more thorough and precise evaluation of student outcomes. Ultimately, the most effective approach is one that is customized to the program's specific needs and situation.
{"title":"Surveying Different Student Outcome Assessment Methods for ABET Accredited Computer Engineering Programs","authors":"Qutaiba Ibrahim Ali","doi":"10.37256/rrcs.2120232577","DOIUrl":"https://doi.org/10.37256/rrcs.2120232577","url":null,"abstract":"In an effort to improve the quality of their academic programs and graduates, an increasing number of academic institutions are obtaining Accreditation Board for Engineering and Technology (ABET) accreditation for their computer engineering programs. This paper acts as a guide for managers and institutions as they get ready to start the accreditation process for their programs. There is an issue with the lack of knowledge regarding the mechanics of implementing student outcome evaluation methodologies since it causes confusion and resource waste, especially in the beginning. Furthermore, there is a paucity of literature available that discuss the methodology and the use of successful accrediting techniques for computer engineering programs. Given this, it is important to document the approaches, teaching techniques, and strategies employed by various computer engineering departments as they pursue accreditation. To the best of our knowledge, such information is not publicly available in published form, although there are fee-based training courses by ABET that provide instruction on how to approach this topic. Here, we investigate the detailed information of five different computer engineering programs and two other related programs using their self-assessment reports (SARs). These SARs span over the last 10 years and represent the outcome of different approaches toward getting accreditation. The study plan involves comparing (objectively and subjectively) the different parameters of the student outcome assessment (criterion 4) to show their convergence and divergence in dealing with accreditation requirements. We found that the selection of an assessment method depends on the goals and context of the educational program. Factors such as the learning outcomes to be assessed, the level of detail needed, available resources, and the preferences of instructors and students should be taken into account. A program may opt to use multiple assessment methods to attain a more thorough and precise evaluation of student outcomes. Ultimately, the most effective approach is one that is customized to the program's specific needs and situation.","PeriodicalId":377142,"journal":{"name":"Research Reports on Computer Science","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130229570","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 : 2023-06-01DOI: 10.37256/rrcs.2320232631
Amit Bhandari, M. Ojha, D. K. Choubey, Vaibhav Soni
This paper described the detailed study for the detection and monitoring of accidents in hilly areas. In the past few decades, road accidents are a major cause of death in suburban hilly areas. These accidents not only affect the life of the destitute but also affect the lives of others. Over the last few decades, hilly areas are now ideal locations for holidays, hence an adequate number of travellers are moving towards hill stations for enjoying free time. This movement also invites accidents to occur due to bad driving skills, blind turns, overspeeding, etc. Also, during the peak time of holidays, abrupt climate change occurs and leads to heavy rainfalls, which are the major cause of landslides. As a result, the loss of life of travellers and blocked roads affecting transportation of necessary goods occur in hilly areas which affect the development and living of other people. Internet of Things (IoT) system may be a better solution to detect monitor and prevent these accidents and landslides. IoT system consists of sensors, actuators, a powerful micro-controller, and a network interface. This system detects and monitors accidents and landslides and informs the command centre about the location. Implementation of the IoT system helps us to lower the accident rate and easily locate the affected areas of landslides using global service for mobile (GSM) or Wireless Fidelity (Wi-Fi) connectivity. It is always a challenging task for a rescue team to locate the exact area of an accident and carry out life-saving operations.
{"title":"IoT Based System for Accident Detection, Monitoring and Landslide Detection Using GSM in Hilly Areas","authors":"Amit Bhandari, M. Ojha, D. K. Choubey, Vaibhav Soni","doi":"10.37256/rrcs.2320232631","DOIUrl":"https://doi.org/10.37256/rrcs.2320232631","url":null,"abstract":"This paper described the detailed study for the detection and monitoring of accidents in hilly areas. In the past few decades, road accidents are a major cause of death in suburban hilly areas. These accidents not only affect the life of the destitute but also affect the lives of others. Over the last few decades, hilly areas are now ideal locations for holidays, hence an adequate number of travellers are moving towards hill stations for enjoying free time. This movement also invites accidents to occur due to bad driving skills, blind turns, overspeeding, etc. Also, during the peak time of holidays, abrupt climate change occurs and leads to heavy rainfalls, which are the major cause of landslides. As a result, the loss of life of travellers and blocked roads affecting transportation of necessary goods occur in hilly areas which affect the development and living of other people. Internet of Things (IoT) system may be a better solution to detect monitor and prevent these accidents and landslides. IoT system consists of sensors, actuators, a powerful micro-controller, and a network interface. This system detects and monitors accidents and landslides and informs the command centre about the location. Implementation of the IoT system helps us to lower the accident rate and easily locate the affected areas of landslides using global service for mobile (GSM) or Wireless Fidelity (Wi-Fi) connectivity. It is always a challenging task for a rescue team to locate the exact area of an accident and carry out life-saving operations.","PeriodicalId":377142,"journal":{"name":"Research Reports on Computer Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130744263","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 : 2023-06-01DOI: 10.37256/rrcs.2320232644
Medisetti Yashwanth Sai Krishna, S. Gawre
Machine learning (ML) integrated with development and operations (DevOps) is the key to solving the problem of deploying the latest machine learning models. This paper proposes one of the ways of integrating machine learning with DevOps. The need for this integration is endless as this provides seamless upgradation of the so-created models while also making managing and monitoring simple. The paper also provides light on practices of Continuous Integration/Continuous Deployment (CI/CD) and minimizing the unnecessary loss of time while training an ML model. The procedure followed includes CI/CD that contains jobs to train the models and to roll out the model with maximum performance. The main focus of this paper is the dynamic change of hyperparameters to achieve increased accuracy without the necessity of the physical presence of humans to change it. This research is independent of the type of machine learning model used and can be best followed for neural networks.
{"title":"MLOps for Enhancing the Accuracy of Machine Learning Models using DevOps, Continuous Integration, and Continuous Deployment","authors":"Medisetti Yashwanth Sai Krishna, S. Gawre","doi":"10.37256/rrcs.2320232644","DOIUrl":"https://doi.org/10.37256/rrcs.2320232644","url":null,"abstract":"Machine learning (ML) integrated with development and operations (DevOps) is the key to solving the problem of deploying the latest machine learning models. This paper proposes one of the ways of integrating machine learning with DevOps. The need for this integration is endless as this provides seamless upgradation of the so-created models while also making managing and monitoring simple. The paper also provides light on practices of Continuous Integration/Continuous Deployment (CI/CD) and minimizing the unnecessary loss of time while training an ML model. The procedure followed includes CI/CD that contains jobs to train the models and to roll out the model with maximum performance. The main focus of this paper is the dynamic change of hyperparameters to achieve increased accuracy without the necessity of the physical presence of humans to change it. This research is independent of the type of machine learning model used and can be best followed for neural networks.","PeriodicalId":377142,"journal":{"name":"Research Reports on Computer Science","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130782336","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 : 2023-05-29DOI: 10.37256/rrcs.2320232639
Sai Venkat Boyapati, Maddirala Sai Karthik, K. Subrahmanyam, B. Reddy
It is very important to understand the market drifts in the wake of booming civilization and ever-changing market requirements. The principal purpose of the study is the prediction of house prices based on current conditions. From historical data on property markets, literature attempts to draw useful insights. Business trends must be understood so that individuals may prepare their budgetary needs accordingly. A society that is ever-expanding is driven by the growing real estate industry. A lot of clients have been duped by agents setting up a fake market rate. As a result, the real estate industry has become less transparent in recent years. Due to decreased accuracy and overfitting of data, the previous model reduced efficiency, whereas the newly developed model resolves such issues and provides a rich user interface with a better model. An important part of this study is to develop an extensive model that is beneficial to both business societies and individuals. This is the main objective of this study. In order to simplify the client’s fieldwork and free up his time and money, this software is intended to assist him. Machine learning algorithms enable models to be enlightened such as root mean square error, random forest, support vector machine, k-nearest neighbors, mean squared error, extreme gradient boost, mean absolute error, R-squared score, linear regression, AdaBoost, CatBoost.
{"title":"An Analysis of House Price Prediction Using Ensemble Learning Algorithms","authors":"Sai Venkat Boyapati, Maddirala Sai Karthik, K. Subrahmanyam, B. Reddy","doi":"10.37256/rrcs.2320232639","DOIUrl":"https://doi.org/10.37256/rrcs.2320232639","url":null,"abstract":"It is very important to understand the market drifts in the wake of booming civilization and ever-changing market requirements. The principal purpose of the study is the prediction of house prices based on current conditions. From historical data on property markets, literature attempts to draw useful insights. Business trends must be understood so that individuals may prepare their budgetary needs accordingly. A society that is ever-expanding is driven by the growing real estate industry. A lot of clients have been duped by agents setting up a fake market rate. As a result, the real estate industry has become less transparent in recent years. Due to decreased accuracy and overfitting of data, the previous model reduced efficiency, whereas the newly developed model resolves such issues and provides a rich user interface with a better model. An important part of this study is to develop an extensive model that is beneficial to both business societies and individuals. This is the main objective of this study. In order to simplify the client’s fieldwork and free up his time and money, this software is intended to assist him. Machine learning algorithms enable models to be enlightened such as root mean square error, random forest, support vector machine, k-nearest neighbors, mean squared error, extreme gradient boost, mean absolute error, R-squared score, linear regression, AdaBoost, CatBoost.","PeriodicalId":377142,"journal":{"name":"Research Reports on Computer Science","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126227534","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：