PRASHANT GOAD - Academia.edu (original) (raw)

Papers by PRASHANT GOAD

International Research Journal of Multidisciplinary Technovation, May 14, 2024

Heart-related conditions remain the foremost global cause of mortality. In 2000, heart disease cl... more Heart-related conditions remain the foremost global cause of mortality. In 2000, heart disease claimed around 14 million lives worldwide, a number that surged to approximately 620 million by 2023. The aging and expanding population significantly contribute to this rising mortality trend. However, this also underscores the potential for significant impact through early intervention, crucial for reducing fatalities from heart failure, where prevention plays a pivotal role. The aim of the present research is to develop a prospective ML framework that can detect important features and predict cardiac conditions as an early stage using a variety of choice of features strategies. The Features subsets that were chosen were designated as FST1, FST2, and FST3, respectively. Three distinct methods, including correlation-based feature selection, chi-square and mutual information, were used for picking features. Next, the most confident theory & the most appropriate feature selection were identified using six alternative machine learning models: Logistical Regression (LR) (AL1), the support vector Machine (SVM) (AL2), Knearest neighbor (K-NN) (AL3), Random forest (RF) model (AL4), Naive Bayes (NB) model (AL5), and Decision Tree (DT) (AL6). Ultimately, we discovered that, with 95.25% accuracy, 95.11% sensitivity, 95.23% specificity, 96.96 area below receiver operating characteristic and 0.27 log loss, the random forest model offered the most excellent results for F3 feature sets. No one has investigated coronary artery disease forecasting in depth; however, our study evaluates multiple statistics (specificity, sensitivity, accuracy, AUROC, and log loss) and uses multiple attribute choices to improve algorithms success for important features. The suggested model has considerable promise for medical use to speculate CVD find in Precursor at a minimal cost and in a shorter amount of time as well as will assist limited experience physician to take right decision based on the results of the used model combined with specific criteria.

International journal of engineering research and technology, Dec 13, 2013

Propagation-loss-prediction models play very vital roles in the characterization and design of pr... more Propagation-loss-prediction models play very vital roles in the characterization and design of precise cellular mobile radio communication systems for their specific technical parameters such as transmission power and frequency reuse. This project presents a comprehensive review of the Okumura and Hata propagation-loss-prediction models for heterogeneous terrestrial wireless communication environments. We will conclude by testing the difference in the path loss between predicting values to improve the systems. Our numerical results prove that there occur similar changes with different frequencies for different distances under identical conditions. The power losses are calculated for various wireless parametric combinations such as the operating frequencies from 150 MHz-1.9 GHz, and the distances between transceivers from 50-200m. Other specific parametric combinations such as the electrical-physicalgeographical factors, like different height for transceivers and the operating (suburban or urban) zone related issues are also considered. We analyze all the design features, modeling factors and applicationissues of the Okumura and Hata models separately keeping the standard assumptions unchanged. Our simulation results show that these models are effectively applicable for efficient systems in the Indian wireless environments.

International Journal of Computer Applications, 2010

This paper is based on a novel heuristic algorithm for routing and wavelength assignment in virtu... more This paper is based on a novel heuristic algorithm for routing and wavelength assignment in virtual wavelength path routed WDM network. In this paper, we have considered a wavelength routed WDM optical network and the Heuristic algorithm is implemented on it. The term heuristics is used for algorithm, which finds solution among all possible ones, but they do not guarantee that the best solution will be found. Therefore they may be considered as approximate and not accurate algorithms. Heuristic algorithm has its own structure, so it never runs slowly and never gives very bad results. The results are always close to the best solution. In this paper the objective of this algorithm is to minimize the requirement of wavelength in any network topology demanded by network traffic. It also minimizes the hop length between source and destination nodes in the traffic. As wavelength and number of hops get reduced, the cost of the network also gets reduced and maximizes the resource utilization. In the first phase of this algorithm, we assigned minimum hop length to each route demanded by traffic and also assigned wavelengths to each route. In second phase of algorithm effective rerouting is performed to reduce the number of wavelengths required in the network and it also minimizes the hop length of each rerouted route. By minimizing wavelength requirement, the need of wavelength converter gets reduced, so that the network cost is also reduced. Along with the implementation of heuristic algorithm, we have found out few more parameters such as Network Congestion and Network wavelength converter requirement. This Network Congestion on each link of network is used for calculation of Network Wavelength Requirement, and Network wavelength converter Requirement.

Research Square (Research Square), Dec 20, 2022

The effectiveness of machine learning for a particular activity depends on a variety of parameter... more The effectiveness of machine learning for a particular activity depends on a variety of parameters. The incident database's description and validity come first and primary. Information retrieval even during training cycle is more challenging if there is a lot of repetitious, unimportant information or incomplete information available. It is well knowledge that running time for ML tasks is significantly impacted by conditions are as follows and sorting stages. To increase the accuracy of any model data cleansing is essential. Without sufficient data scrubbing, no predictive model accuracy can begin. EDA, or exploratory data analysis, is the name of this procedure. In this study, we discussed outlier's identification, one of many EDA processes for complete perfect data. In this research, we attempted to use the isolation forest approach to calculate the outlier factor. Then a model known as an outlier finding model is created. The problem of outlier detection leads to a collection of connected supervised learning for binary classification. We carry out in-depth tests on various datasets and demonstrate that in our latest outlier finding technique compare with the old way. Our approach yields superior outcomes in terms of accuracy, precision, recall & F-1 score. Additionally, we successfully lowered the machine learning algorithms' under fitting issue.

Propagation-loss-prediction models play very vital roles in the characterization and design of pr... more Propagation-loss-prediction models play very vital roles in the characterization and design of precise cellular mobile radio communication systems for their specific technical parameters such as transmission power and frequency reuse. This project presents a comprehensive review of the Okumura and Hata propagation-loss-prediction models for heterogeneous terrestrial wireless communication environments. We will conclude by testing the difference in the path loss between predicting values to improve the systems. Our numerical results prove that there occur similar changes with different frequencies for different distances under identical conditions. The power losses are calculated for various wireless parametric combinations such as the operating frequencies from 150 MHz-1.9 GHz, and the distances between transceivers from 50-200m. Other specific parametric combinations such as the electrical-physicalgeographical factors, like different height for transceivers and the operating (suburban or urban) zone related issues are also considered. We analyze all the design features, modeling factors and applicationissues of the Okumura and Hata models separately keeping the standard assumptions unchanged. Our simulation results show that these models are effectively applicable for efficient systems in the Indian wireless environments.

The effectiveness of machine learning for a particular activity depends on a variety of parameter... more The effectiveness of machine learning for a particular activity depends on a variety of parameters. The incident database's description and validity come first and primary. Information retrieval even during training cycle is more challenging if there is a lot of repetitious, unimportant information or incomplete information available. It is well knowledge that running time for ML tasks is significantly impacted by conditions are as follows and sorting stages. To increase the accuracy of any model data cleansing is essential. Without sufficient data scrubbing, no predictive model accuracy can begin. EDA, or exploratory data analysis, is the name of this procedure. In this study, we discussed outlier’s identification, one of many EDA processes for complete perfect data. In this research, we attempted to use the isolation forest approach to calculate the outlier factor. Then a model known as an outlier finding model is created. The problem of outlier detection leads to a collection...

International Research Journal of Multidisciplinary Technovation, May 14, 2024

Heart-related conditions remain the foremost global cause of mortality. In 2000, heart disease cl... more Heart-related conditions remain the foremost global cause of mortality. In 2000, heart disease claimed around 14 million lives worldwide, a number that surged to approximately 620 million by 2023. The aging and expanding population significantly contribute to this rising mortality trend. However, this also underscores the potential for significant impact through early intervention, crucial for reducing fatalities from heart failure, where prevention plays a pivotal role. The aim of the present research is to develop a prospective ML framework that can detect important features and predict cardiac conditions as an early stage using a variety of choice of features strategies. The Features subsets that were chosen were designated as FST1, FST2, and FST3, respectively. Three distinct methods, including correlation-based feature selection, chi-square and mutual information, were used for picking features. Next, the most confident theory & the most appropriate feature selection were identified using six alternative machine learning models: Logistical Regression (LR) (AL1), the support vector Machine (SVM) (AL2), Knearest neighbor (K-NN) (AL3), Random forest (RF) model (AL4), Naive Bayes (NB) model (AL5), and Decision Tree (DT) (AL6). Ultimately, we discovered that, with 95.25% accuracy, 95.11% sensitivity, 95.23% specificity, 96.96 area below receiver operating characteristic and 0.27 log loss, the random forest model offered the most excellent results for F3 feature sets. No one has investigated coronary artery disease forecasting in depth; however, our study evaluates multiple statistics (specificity, sensitivity, accuracy, AUROC, and log loss) and uses multiple attribute choices to improve algorithms success for important features. The suggested model has considerable promise for medical use to speculate CVD find in Precursor at a minimal cost and in a shorter amount of time as well as will assist limited experience physician to take right decision based on the results of the used model combined with specific criteria.

International journal of engineering research and technology, Dec 13, 2013

Propagation-loss-prediction models play very vital roles in the characterization and design of pr... more Propagation-loss-prediction models play very vital roles in the characterization and design of precise cellular mobile radio communication systems for their specific technical parameters such as transmission power and frequency reuse. This project presents a comprehensive review of the Okumura and Hata propagation-loss-prediction models for heterogeneous terrestrial wireless communication environments. We will conclude by testing the difference in the path loss between predicting values to improve the systems. Our numerical results prove that there occur similar changes with different frequencies for different distances under identical conditions. The power losses are calculated for various wireless parametric combinations such as the operating frequencies from 150 MHz-1.9 GHz, and the distances between transceivers from 50-200m. Other specific parametric combinations such as the electrical-physicalgeographical factors, like different height for transceivers and the operating (suburban or urban) zone related issues are also considered. We analyze all the design features, modeling factors and applicationissues of the Okumura and Hata models separately keeping the standard assumptions unchanged. Our simulation results show that these models are effectively applicable for efficient systems in the Indian wireless environments.

International Journal of Computer Applications, 2010

This paper is based on a novel heuristic algorithm for routing and wavelength assignment in virtu... more This paper is based on a novel heuristic algorithm for routing and wavelength assignment in virtual wavelength path routed WDM network. In this paper, we have considered a wavelength routed WDM optical network and the Heuristic algorithm is implemented on it. The term heuristics is used for algorithm, which finds solution among all possible ones, but they do not guarantee that the best solution will be found. Therefore they may be considered as approximate and not accurate algorithms. Heuristic algorithm has its own structure, so it never runs slowly and never gives very bad results. The results are always close to the best solution. In this paper the objective of this algorithm is to minimize the requirement of wavelength in any network topology demanded by network traffic. It also minimizes the hop length between source and destination nodes in the traffic. As wavelength and number of hops get reduced, the cost of the network also gets reduced and maximizes the resource utilization. In the first phase of this algorithm, we assigned minimum hop length to each route demanded by traffic and also assigned wavelengths to each route. In second phase of algorithm effective rerouting is performed to reduce the number of wavelengths required in the network and it also minimizes the hop length of each rerouted route. By minimizing wavelength requirement, the need of wavelength converter gets reduced, so that the network cost is also reduced. Along with the implementation of heuristic algorithm, we have found out few more parameters such as Network Congestion and Network wavelength converter requirement. This Network Congestion on each link of network is used for calculation of Network Wavelength Requirement, and Network wavelength converter Requirement.

Research Square (Research Square), Dec 20, 2022

The effectiveness of machine learning for a particular activity depends on a variety of parameter... more The effectiveness of machine learning for a particular activity depends on a variety of parameters. The incident database's description and validity come first and primary. Information retrieval even during training cycle is more challenging if there is a lot of repetitious, unimportant information or incomplete information available. It is well knowledge that running time for ML tasks is significantly impacted by conditions are as follows and sorting stages. To increase the accuracy of any model data cleansing is essential. Without sufficient data scrubbing, no predictive model accuracy can begin. EDA, or exploratory data analysis, is the name of this procedure. In this study, we discussed outlier's identification, one of many EDA processes for complete perfect data. In this research, we attempted to use the isolation forest approach to calculate the outlier factor. Then a model known as an outlier finding model is created. The problem of outlier detection leads to a collection of connected supervised learning for binary classification. We carry out in-depth tests on various datasets and demonstrate that in our latest outlier finding technique compare with the old way. Our approach yields superior outcomes in terms of accuracy, precision, recall & F-1 score. Additionally, we successfully lowered the machine learning algorithms' under fitting issue.

Propagation-loss-prediction models play very vital roles in the characterization and design of pr... more Propagation-loss-prediction models play very vital roles in the characterization and design of precise cellular mobile radio communication systems for their specific technical parameters such as transmission power and frequency reuse. This project presents a comprehensive review of the Okumura and Hata propagation-loss-prediction models for heterogeneous terrestrial wireless communication environments. We will conclude by testing the difference in the path loss between predicting values to improve the systems. Our numerical results prove that there occur similar changes with different frequencies for different distances under identical conditions. The power losses are calculated for various wireless parametric combinations such as the operating frequencies from 150 MHz-1.9 GHz, and the distances between transceivers from 50-200m. Other specific parametric combinations such as the electrical-physicalgeographical factors, like different height for transceivers and the operating (suburban or urban) zone related issues are also considered. We analyze all the design features, modeling factors and applicationissues of the Okumura and Hata models separately keeping the standard assumptions unchanged. Our simulation results show that these models are effectively applicable for efficient systems in the Indian wireless environments.

The effectiveness of machine learning for a particular activity depends on a variety of parameter... more The effectiveness of machine learning for a particular activity depends on a variety of parameters. The incident database's description and validity come first and primary. Information retrieval even during training cycle is more challenging if there is a lot of repetitious, unimportant information or incomplete information available. It is well knowledge that running time for ML tasks is significantly impacted by conditions are as follows and sorting stages. To increase the accuracy of any model data cleansing is essential. Without sufficient data scrubbing, no predictive model accuracy can begin. EDA, or exploratory data analysis, is the name of this procedure. In this study, we discussed outlier’s identification, one of many EDA processes for complete perfect data. In this research, we attempted to use the isolation forest approach to calculate the outlier factor. Then a model known as an outlier finding model is created. The problem of outlier detection leads to a collection...