Indian Journal Of Science And Technology最新文献

{"title":"Assessing Molecular Throughput and Efficiency through Simulation in Diffusion-Based Molecular Communication","authors":"Ashwini Katkar, V. Dongre","doi":"10.17485/ijst/v17i6.2814","DOIUrl":"https://doi.org/10.17485/ijst/v17i6.2814","url":null,"abstract":"Objectives: This study investigates the correlation of critical factors influencing throughput and efficiency in diffusion-based molecular communication systems. Method: The study presents a simulation model for 3-D diffusion-based molecular communication, incorporating essential parameters such as molecule size, transmission rate, diffusion rate, and transmitter-receiver distance. Findings: Through comprehensive simulations, the study reveals the effects of different parameters on throughput and efficiency in diffusion-based molecular communication. It highlights the critical trade-offs associated with system design and optimization. The study reveals the key factors influencing the transmission capabilities, the receiver congestion, and the overall efficiency of the communication system. Novelty: In this study, we give a study overview of the latest work of performance metrics in the field of molecular communication. A novel algorithm is proposed to find the throughput and efficiency of molecular communication. The proposed framework analyzes the intricate relationship between system parameters and performance metrics, emphasizing the potential for system optimization. Our simulation work demonstrates how the model parameters influence the performance of the molecular communication system, providing insights for enhancing the system's performance in applications such as targeted drug delivery in the future. Keywords: Molecular Communication, Diffusion, Transmission Rate, Throughput, Efficiency","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"94 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139842630","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":"Big Data Analytics for Heart Disease Prediction using Regularized Principal and Quadratic Entropy Boosting","authors":"P. Muthulakshmi, M. Parveen","doi":"10.17485/ijst/v17i6.2928","DOIUrl":"https://doi.org/10.17485/ijst/v17i6.2928","url":null,"abstract":"Objectives: Over the past few years, there prevails an abundance wealth of big data obtained via patients' electronic health records. One of the leading causes of mortality globally is the cardiovascular disease. Based on the present test and history cardiovascular disease diagnosing of patients can be done. Therefore, early and quick diagnosis can reduce the mortality rate. To address their needs, several machine learning methods have been employed in the recent past in cardiovascular disease diagnosis and prediction. Previous research was also concentrated on acquiring the significant features to heart disease prediction however less importance was given to the time involved and error rate to identifying the strength of these features. Methods: In this work we plan to develop a method called, Regularized Principal Component and Quadratic Weighted Entropy Boosting (RPC-QWEB) for predicting heart disease. Initially in RPC-QWEB, relevant features are selected to avoid missing values in the input database by employing Regularized Principal Component Regressive Feature Selection (RPCRFS). Second, with the obtained dimensionality reduced features, Quadratic Weighted Entropy Boosting Classification (QWEBC) process is carried out to classify the patient data as normal or abnormal. The QWEBC process is an ensemble of several weak classifiers (i.e., Quadratic Classifier). The weak classifier results are combined to form strong classifier and provide final prediction results as normal or abnormal condition with minimal error rate. Findings: Experimental evaluation is carried out on factors with the cardiovascular disease dataset such as heart disease prediction accuracy, heart disease prediction time, sensitivity, error rate with respect to distinct numbers of patient data. The proposed RPC-QWEB method was compared with existing Heart Disease Prediction Framework (HDPF) and Swarm Artificial Neural Network (Swarm-ANN). Novelty: RPC-QWEB method outperforms the conventional learning methods in terms of numerous performance matrices. The RPC-QWEB method produces 3% and 5% increase in terms of accuracy and sensitivity and 7% and 29% reduced prediction time and error rate as compared to the existing benchmark methods. We may use this method to predict the heart disease at early stage there by we can reduce the death rate. Keywords: Big data, Regularized Principal Component, Quadratic Weighted Entropy Boosting, Regressive Feature Selection, Classification","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"77 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139784744","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":"Dynamic Weighted Cumulative Residual Entropy Estimators for Laplace Distribution: Bayesian Approach","authors":"Savita Savita, Rajeev Kumar","doi":"10.17485/ijst/v17i6.1661","DOIUrl":"https://doi.org/10.17485/ijst/v17i6.1661","url":null,"abstract":"Objectives: To develop Bayesian estimators of dynamic weighted cumulative residual entropy (DWCRE) for Laplace distribution and to investigate posterior risks using various priors and loss functions. Methods: Weighted entropy measure of information is provided by a probabilistic experiment whose basic events are described by their objective probabilities and some qualitative (objective or subjective) weights. In this paper, we have used priors (Jeffrey’s, Hartigan, Uniform and Gumble Type II) and several loss functions. Findings: Bayesian estimators and associated posterior risks for Laplace distribution have been derived for different priors and loss functions. Monte Carlo Simulation study and graphical analyses have also been presented along with the conclusion. Through the comprehensive simulation study in the paper, it has been observed that Hartigan prior is better than other priors in terms of the posterior risk whereas Uniform prior has always higher posterior risk. Novelty: The introduction of new Bayesian estimators and their posterior risks for dynamic weighted cumulative residual entropy (DWCRE) of Laplace distribution. Keywords: Bayesian estimators, Laplace distribution, Fisher information matrix, Loss functions, Priors","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"122 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139785211","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":"Dynamic Weighted Cumulative Residual Entropy Estimators for Laplace Distribution: Bayesian Approach","authors":"Savita Savita, Rajeev Kumar","doi":"10.17485/ijst/v17i6.1661","DOIUrl":"https://doi.org/10.17485/ijst/v17i6.1661","url":null,"abstract":"Objectives: To develop Bayesian estimators of dynamic weighted cumulative residual entropy (DWCRE) for Laplace distribution and to investigate posterior risks using various priors and loss functions. Methods: Weighted entropy measure of information is provided by a probabilistic experiment whose basic events are described by their objective probabilities and some qualitative (objective or subjective) weights. In this paper, we have used priors (Jeffrey’s, Hartigan, Uniform and Gumble Type II) and several loss functions. Findings: Bayesian estimators and associated posterior risks for Laplace distribution have been derived for different priors and loss functions. Monte Carlo Simulation study and graphical analyses have also been presented along with the conclusion. Through the comprehensive simulation study in the paper, it has been observed that Hartigan prior is better than other priors in terms of the posterior risk whereas Uniform prior has always higher posterior risk. Novelty: The introduction of new Bayesian estimators and their posterior risks for dynamic weighted cumulative residual entropy (DWCRE) of Laplace distribution. Keywords: Bayesian estimators, Laplace distribution, Fisher information matrix, Loss functions, Priors","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"50 41","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139844840","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":"Clustering-based Recommendations for Enhancing Students’ Academic Performance by Recognizing Prevalent Assessment Method using Exploratory Data Analysis","authors":"G. Sofia, D. Hema","doi":"10.17485/ijst/v17i6.2175","DOIUrl":"https://doi.org/10.17485/ijst/v17i6.2175","url":null,"abstract":"Objectives: To analyse students’ academic performance based on assessment methods and determine the most prevalent one through which students can be categorised for recommending optimal student-centered pedagogies that enhance students’ performance. Methods: Exploratory Data Analysis identifies the implications of the assessment methods based on the marks obtained by students in Continuous Assessments (CA) and the Cumulative Test (CT). Continuous Assessment (CA) and Cumulative Test (CT) marks of three subjects that come under foundation science, elective, and skill-based course of 100 undergraduate students are collected from a reputed Arts and Science Institution using stratified sampling technique, analyzed, and the recommendations are made based on the statistical observations and cluster analysis. Clustering recognises learning patterns of the students’ on the learners’ data. The Elbow method determines the number of clusters where the Silhouette score identifies the best suitable clustering technique for the dataset. K-Means Clustering categorises students based on their performance, that helps to give recommendations to improve. Findings: Based on Univariate and Bivariate analysis on the dataset, this work identifies Continuous Assessment (CA) as a prevalent evaluation strategy that motivates students to get engaged throughout rather than just before the exam. Based on the Silhouette Score (above .5), K-Means clustering is chosen to discover hidden patterns in the assessment marks depending on the three clusters determined by the Elbow method. It helps to identify the underperformers (46%) and suggest personalised recommendations for improving student’s academic performance as per clusters. Novelty: This work integrates Statistical Analysis and Clustering Analysis as per the optimal clusters determined by the Elbow method for identifying patterns hidden in assessment marks based on the prevalent assessment types. As a result, it enables more personalised recommendations for recognising the predominant assessment method and boosting academic achievement. Keywords: Continuous Assessment, Cumulative Test, Statistical Analysis, Exploratory Data Analysis, Univariate, Bivariate, Cluster Analysis, Elbow Method, K­Means Clustering","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"19 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139783332","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":"Irrigation Scheduling Based on Evapotranspiration of Tomato (Solanumm Lycopersicum) Using Atmometer in The Upland Rolling Production Area","authors":"Crisiel Grace M Yambao, R. L. Ines","doi":"10.17485/ijst/v17i6.2754","DOIUrl":"https://doi.org/10.17485/ijst/v17i6.2754","url":null,"abstract":"Objective : The goal is to provide the right amount of water to the crop, at the right time, to meet the crop's needs. It was conducted to estimate evapotranspiration using atmometers for irrigation scheduling that may be highly beneficial in soil water balance, water allocation and budgeting, irrigation management, and improving crop yield and production for greater income. Methods: This study was conducted at the Organic Agricultural Research and Development Innovation Center at the Bataan Peninsula State University – Abucay Campus, Bangkal, Abucay, Bataan, Philippines (North 14°44'28” East 120°27'04”). The effects of using different evapotranspiration estimation methods on the timing and amounts of water application were then evaluated by using a computed irrigation scheduling model. Findings: The atmometer ( and Penman-Monteith ( values were statistically analyzed using linear regression (y=0.8573x + 1.586), coefficients of determination (R² = 0.7236), root mean – squared error (RMSD = 0.73), mean bias error (MBE = 0.03), and the t-statistics (significant at 5% level). The study revealed that the ETo-A data strongly correlated with the ETo-PM data. Using an atmometer for scheduling irrigation on tomatoes would have resulted in an equally similar distribution of irrigation events, more water would have been provided throughout the season compared to using ETo-PM data. Novelty : The calibrated evapotranspiration data from the atmometer may guide appropriate irrigation depth and schedule for tomatoes. By aligning irrigation intervals with other crops’ water requirements, farmers can optimize water usage, minimize water stress, and achieve higher crop yields. Keywords: Atmometer, Bataan Philippines, Evapotranspiration, Tomato, Penman-­Montheith Equation","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139842231","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":"Reliability Analysis of the Shaft Subjected to Twisting Moment and Bending Moment for The Exponential and Weibull Distributed Strength and Stress","authors":"Md Yakoob Pasha, M. Devi, T. Maheswari","doi":"10.17485/ijst/v17i6.1806","DOIUrl":"https://doi.org/10.17485/ijst/v17i6.1806","url":null,"abstract":"Objectives: The purpose of the present work is to design a shaft subjected to twisting moment, bending moment and combined twisting and bending moment by determine the reliability of the shaft. Methods: Probabilistic approach is considered to find the lifetime of shaft by taking stress as random variable. Exponential and Weibull distributions are used to find lifetime of the equipment stress is considered as exponential and Weibull random variable. When the shaft is subjected to combined twisting and bending moment, the stress is found by using the two theories: (i) The maximum shear stress theory is used for ductile materials such as mild steel. (ii) The maximum normal stress theory is used for brittle materials such as cast iron. Findings: Reliability of the shaft is derived subjected to twisting and bending moments. The reliability is computed and compared for changing of the twisting moment, bending moment and diameter of the shaft. Novelty: To design a shaft by using reliability theory and to find reliability of the shaft by using the exponential and Weibull distribution is the novel idea. Keywords: Reliability, Weibull distribution, Shaft, Twisting moment, Bending moment, Combined twisting and bending moment, Exponential distribution, Maximum normal stress theory, Maximum shear stress theory","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"60 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139844443","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":"Absorption Activity Investigation of Saturable Absorber for Dual Wavelengths in Laser Passive Q-Switching System","authors":"Sara Abdul Hussien, Abdul-Kareem Mahdi Salih","doi":"10.17485/ijst/v17i6.2822","DOIUrl":"https://doi.org/10.17485/ijst/v17i6.2822","url":null,"abstract":"Objective: The absorption activity of saturable absorber material (Cr+4: YAG) for dual wavelengths (1.064 μm and 0.946 μm), simultaneously generated in same passive Q-switching system, has been investigated. Methods: This study utilized the mathematical model that was used in our previous study. Rung-Kutta—Fehelberge numerical method has been used to solve this mathematical model. Nd+3: YAG used as an effective medium and Cr+4: YAG used as a saturable absorber in the laser passive Q-switching optical system. Finding: When the population density of saturable absorber increases, the steady state of photons losses occurs at advancement time and the absorption activity reaches to optical bleaching state at advancement time also (it is occurring approximately at time 35 ns from the beginning of the time of pulse construction when the , while at , approximately at time 47 ns). Novelty: The absorption activity of saturable absorber material for a single wavelength of photons oscillating inside the passive Q-switch laser system received attention by some studies. This study verifies or investigates from the behavior of absorption activity of saturable absorber material when encounters photons with two wavelengths oscillating simultaneously inside the laser cavity in order to obtain high power of the pulses. Keywords: Laser, Passive Q­switching, laser, Dual wavelengths laser, Solid state lasers","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"252 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139843235","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":"M/M/1/K Loss and Delay Interdependent Queueing Model with Vacation and Controllable Arrival Rates","authors":"S. P. Subhapriya, M. Thiagarajan","doi":"10.17485/ijst/v17i6.1691","DOIUrl":"https://doi.org/10.17485/ijst/v17i6.1691","url":null,"abstract":"Objectives: In this study, we assume that the vacation is taken while there are no consumers in the queue. There are several servicemen who will take the synchronous multiple vacations in the system. Methods: Assumed some loss and delay in consumers (Elective and emergency) and solve the steady-state probability equations using recursive approach and acquired some obvious iterative expressions. Findings: Carried out some numerical analysis using MATLAB and investigated the movement of , , and through graph. Further, , , and increase when increases; decrease when M increases. Additionally, when L increases remains constant and increase. Novelty: Expanded the preceding models in this study by including vacations and performing the numerical analysis. Using vacation with controllable arrival rates in an optimal way in order to benefit both the server and the customer will minimise waiting time and provide the most feasible, affordable service to the consumer. Keywords: Markovian Queueing System, Vacation, Loss and Delay, Finite Capacity, Interdependent Arrival and Service Rates, Varying Arrival Rates, Bivariate Poisson Process","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"9 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139843119","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":"Big Data Analytics for Heart Disease Prediction using Regularized Principal and Quadratic Entropy Boosting","authors":"P. Muthulakshmi, M. Parveen","doi":"10.17485/ijst/v17i6.2928","DOIUrl":"https://doi.org/10.17485/ijst/v17i6.2928","url":null,"abstract":"Objectives: Over the past few years, there prevails an abundance wealth of big data obtained via patients' electronic health records. One of the leading causes of mortality globally is the cardiovascular disease. Based on the present test and history cardiovascular disease diagnosing of patients can be done. Therefore, early and quick diagnosis can reduce the mortality rate. To address their needs, several machine learning methods have been employed in the recent past in cardiovascular disease diagnosis and prediction. Previous research was also concentrated on acquiring the significant features to heart disease prediction however less importance was given to the time involved and error rate to identifying the strength of these features. Methods: In this work we plan to develop a method called, Regularized Principal Component and Quadratic Weighted Entropy Boosting (RPC-QWEB) for predicting heart disease. Initially in RPC-QWEB, relevant features are selected to avoid missing values in the input database by employing Regularized Principal Component Regressive Feature Selection (RPCRFS). Second, with the obtained dimensionality reduced features, Quadratic Weighted Entropy Boosting Classification (QWEBC) process is carried out to classify the patient data as normal or abnormal. The QWEBC process is an ensemble of several weak classifiers (i.e., Quadratic Classifier). The weak classifier results are combined to form strong classifier and provide final prediction results as normal or abnormal condition with minimal error rate. Findings: Experimental evaluation is carried out on factors with the cardiovascular disease dataset such as heart disease prediction accuracy, heart disease prediction time, sensitivity, error rate with respect to distinct numbers of patient data. The proposed RPC-QWEB method was compared with existing Heart Disease Prediction Framework (HDPF) and Swarm Artificial Neural Network (Swarm-ANN). Novelty: RPC-QWEB method outperforms the conventional learning methods in terms of numerous performance matrices. The RPC-QWEB method produces 3% and 5% increase in terms of accuracy and sensitivity and 7% and 29% reduced prediction time and error rate as compared to the existing benchmark methods. We may use this method to predict the heart disease at early stage there by we can reduce the death rate. Keywords: Big data, Regularized Principal Component, Quadratic Weighted Entropy Boosting, Regressive Feature Selection, Classification","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"58 33","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139844680","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}