Indian journal of science and technology最新文献

{"title":"Studies on Biodiesel and Hydrogen Powered Dual Fuel Common Rail Direct Injection Engine","authors":"Sardar M Shaikh, S. Khandal","doi":"10.17485/ijst/v17i10.3194","DOIUrl":"https://doi.org/10.17485/ijst/v17i10.3194","url":null,"abstract":"Objectives: To evaluate the effect of hydrogen and used temple oil biodiesel (BTO) combination on the performance of Common Rail Direct Injection (CRDi) engine. To report the maximum possible flow rate (HFR) for knock free operation of the engine at a speed of 1500 RPM. Methods: Transesterification process was used to get BTO. was inducted through intake manifold. BTO was injected into engine cylinder using electronically controlled technique. Findings: The study revealed that the peak HFR was for BTO and 0.24 for diesel at an Injection Pressure (IP) of 800 bar and Injection Timing (IT) of before top dead center (bTDC). The Dual Fuel (DF) CRDi engine with Toriodal Reentrant Combustion Chamber (TRCC) shape yielded 6% to lower Brake Thermal Efficiency (BTE) with reduced exhaust gas emissions except 19 to higher oxides of nitrogen (NOx) at and 100% loads. Both Peak Combustion Pressure (PP) and Heat Release Rate (HRR) were 5 to 9% higher than BTO run diesel engine operation. Combustion Duration (CD) and Ignition Delay (ID) were 6 to 15% lower in DF CRDi operation with . Novelty: Diesel engine comes with hemispherical combustion chamber (HCC). Toriodal Reentrant Shaped Combustion Chamber (TRCC) was adopted in place of HCC which results better mixing of air and fuel. and BTO combination was used to power CRDi engine. Electronically controlled fuel injection system developed and fitted to conventional diesel engine. Keywords: Used temple oil biodiesel (BTO); Common rail direct injection (CRDi) engine; Toriodal reentrant combustion chamber (TRCC); Hydrogen flow rate (HFR); Hydrogen-biodiesel energy ratio (H2BER)","PeriodicalId":13296,"journal":{"name":"Indian journal of science and technology","volume":" July","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140092747","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":"An Alternative Method for Finding the Critical Path of the Network in Fuzzy Time Cost Trade off Problem","authors":"B. Abinaya, E. C. H. Amirtharaj","doi":"10.17485/ijst/v17i10.3143","DOIUrl":"https://doi.org/10.17485/ijst/v17i10.3143","url":null,"abstract":"Background : The critical path approach is used to determine the network's longest path, according to historical records. This study examines a different approach to determining the construction network's longest path. Method: Here, the network is viewed as a directed acyclic graph, and the critical path of the network is found using the longest path algorithm of the network. To find the best answer for a building project, the longest path that was found was integrated into a linear programming issue. The triangle fuzzy variable defines all of the project's inputs. The 992 square foot building area is incorporated, and three project manager’s quotes are used as a triangular fuzzy variable. Findings : This work has the options of getting quotation from the project managers, convert them as the fuzzy variables such as triangular fuzzy variable, Trapezoidal fuzzy variable and pentagonal fuzzy variable. After the network is converted into a linear programming problem using the fully fuzzy mathematical model, the best possible solution is found. Novelty and applications : Alternative method for critical path of the network has been incorporated. It has been found that the proposed method reduces the time to find the critical path of the larger networks. Keywords: Directed Acyclic Graph, Longest Path Algorithm, Triangular Fuzzy Variable, Fuzzy Linear Programming Problem, Fully Fuzzy Mathematical Model","PeriodicalId":13296,"journal":{"name":"Indian journal of science and technology","volume":" 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140091328","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":"Lung Cancer Detection and Severity Analysis with a 3D Deep Learning CNN Model Using CT-DICOM Clinical Dataset","authors":"K. J. Eldho, S. Nithyanandh","doi":"10.17485/ijst/v17i10.3085","DOIUrl":"https://doi.org/10.17485/ijst/v17i10.3085","url":null,"abstract":"Objectives: To propose a new AI based CAD model for early detection and severity analysis of pulmonary (lung) cancer disease. A deep learning artificial intelligence-based approach is employed to maximize the discrimination power in CT images and minimize the dimensionality in order to boost detection accuracy. Methods: The AI-based 3D Convolutional Neural Network (3D-DLCNN) method is employed to learn complex patterns and features in a robust way for efficient detection and classification. The pulmonary nodules are identified by 3D Mask-R-CNN at the initial level, and classification is done by 3D-DLCNN. Kernel Density Estimation (KDE) is used to discover the error data points in the extracted features for early removal before candidate screening. The study uses the CT-DICOM dataset, which includes 355 instances and 251135 CT-DICOM images with target attributes of cancer, healthy, and severity condition (if cancer is positive). Statistical outlier detection is utilized to measure the z-score of each feature to reduce the data point deviation. The intensity and pixel masking of CT-DOCIM is measured by using the ER-NCN method to identify the severity of the disease. The performance of the 3D-DLCNN model is done using the MATLAB R2020a tool and comparative analysis is done with prevailing detection and classification approaches such as GA-PSO, SVM, KNN, and BPNN. Findings: The suggested pulmonary detection 3D-DLCNN model outperforms the prevailing models with promising results of 93% accuracy rate, 92.7% sensitivity, 93.4% specificity, 0.8 AUC-ROC, 6.6% FPR, and 0.87 C-Index, which helps the pulmonologists detect the PC and identify the severity for early diagnosis. Novelty: The novel hybrid 3D-DLCNN approach has the ability to detect pulmonary disease and analyze the severity score of the patient at an early stage during the screening process of candidates. It overcomes the limitations of the prevailing machine learning models, GA-PSO, SVM, KNN, and BPNN. Keywords: Artificial Intelligence, Disease Prediction, Lung Cancer, Deep Learning, Cancer Detection, Computational Model, 3D-DLCNN","PeriodicalId":13296,"journal":{"name":"Indian journal of science and technology","volume":"33 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140084581","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":"Stagnation-Point Slip Flow of Hybrid Ferrofluid Past Exponentially Stretching Sheet in Darcy-Forchheimer Space","authors":"Sushil Prasad, Shilpa Sood, Archie Thakur","doi":"10.17485/ijst/v17i10.1910","DOIUrl":"https://doi.org/10.17485/ijst/v17i10.1910","url":null,"abstract":"Objectives: The present article provides a detailed analysis on the Darcy-Forchheimer hybrid nanofluids flow past an exponentially stretching sheet in the presence of mixed convection with slip conditions and the impacts of different relevant parameters of the fluid flow for velocity and temperature profiles. Methods: In order to create hybrid nanofluids, two magnetic nanoparticles, magnetite ( ) and cobalt ferrite ( ), are taken into consideration. The governing boundary layer coupled partial differential equations are transformed into a system of non-linear ordinary differential equations, which are then solved numerically by using the bvp4c solver available in the Matlab software. A comprehensive parametric analysis has been performed to show the effects of the convective parameter, velocity ratio parameter, porosity parameter, forchheimer parameter, solid volume fractions of and , velocity slip and temperature jump on the fluid velocity and temperature profiles as well as the local skin-friction coefficient and local Nusselt number within the boundary layer. Findings: For higher values of , , , , the velocity field grows, and it declines for , Fr, and A. The temperature field thickness is higher for , Fr, , and A, while decreases for and . The local skin friction coefficient diminishes as rise in the values of , , , Fr, , , A and B. The local Nusselt number shows increasing behaviour for increasing amount of , , , Fr, , , A and B. Novelty: The novelty of the current work is the analysis of the flow of Darcy-Forchheimer hybrid nanofluids across an exponentially stretched sheet in the presence of mixed convection with slip conditions. Here, water is used as base fluid and magnetite, cobalt ferrite being used as hybrid nanoparticles for the present study. Keywords: Hybrid nanofluids, Exponentially stretching sheet, Mixed convection, Velocity slip, Temperature jump","PeriodicalId":13296,"journal":{"name":"Indian journal of science and technology","volume":"20 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140084625","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":"Comparative Study of Crank-Nicolson and Modified Crank-Nicolson Numerical methods to solve linear Partial Differential Equations","authors":"Tejaskumar Sharma, Shreekant Pathak, Gargi J Trivedi","doi":"10.17485/ijst/v17i10.1776","DOIUrl":"https://doi.org/10.17485/ijst/v17i10.1776","url":null,"abstract":"Objectives: This paper aims to address the limitations of the Crank-Nicolson Finite Difference method and propose an improved version called the modified Crank-Nicolson method. Methods: Utilized implicit discretization in time and space, with parameters k = 0.001, h = 0.1, and γ = 0.1. Conducted extensive testing on various partial differential equations. Findings: Results, displayed in Table 1, showcase the method's stability and accuracy. Comparative analysis in Table 2 demonstrates the Modified Crank-Nicolson method consistently outperforming the traditional approach, reaffirming its superiority in accuracy. Novelty: The modified Crank-Nicolson method offers a significant enhancement to the traditional Crank-Nicolson finite difference method, making it a valuable tool for effectively solving partial differential equations. Keywords: Crank­Nicolson Method, Modified Crank­Nicolson Method, Finite Difference, Partial Differential Equations, Parabolic Equations, Python Software","PeriodicalId":13296,"journal":{"name":"Indian journal of science and technology","volume":"26 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140087867","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":"An Empirical Study to Analyse The Effect of Bagging and Feature Subspacing on The Performance of A Custom Ensemble Algorithm for Predicting Drug Protein Interactions","authors":"Harshita Bhargava, Amita Sharma, Prashanth Suravajhala","doi":"10.17485/ijst/v17i10.3202","DOIUrl":"https://doi.org/10.17485/ijst/v17i10.3202","url":null,"abstract":"Objectives: The objective of this study is to analyse the effect of bagging and feature subspacing on the performance of a custom ensemble of decision tree classifiers for predicting drug protein interactions. Methods: In our present work we have designed a custom ensemble algorithm with decision trees as the base learner. We analysed the effect of bagging negative samples and feature subspacing on the performance of the custom ensemble in terms of AUCROC and AUPR. The Enzyme dataset from the Yamanishi dataset composed of 445 drugs and 664 proteins was used for the experiments. Findings: It was observed that the effect of bagging negative samples was significant as compared to feature supspacing in terms of AUPR metric. Now since AUPR is a metric that remains unaffected by the presence of negative samples hence the increase in AUPR by increasing the negative to positive ratio clearly indicated that the negative samples do contain the positives which are unknown and are yet to be verified. Novelty: The results give a strong indication that that feature subspacing has no considerable impact on the AUCROC metric performance of the custom ensemble while AUPR metric increases as the negative to positive ratio increases. The results give a foundation to the fact that, finding reliable negative samples from the entire set of negative drug protein pairs can further enhance the performance of the machine learning classifiers. Keywords: Decision tree classifier, Ensemble classifier, Drug discovery, Bagging, Drug repurposing","PeriodicalId":13296,"journal":{"name":"Indian journal of science and technology","volume":"105 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140089835","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":"Attack Analysis on Hybrid-SIMON-SPECKey Lightweight Cryptographic Algorithm for IoT Applications","authors":"Rahul P Neve, Rajesh Bansode","doi":"10.17485/ijst/v17i10.2811","DOIUrl":"https://doi.org/10.17485/ijst/v17i10.2811","url":null,"abstract":"Objective: To perform attack analysis on new developed hybrid-SIMON-SPECKey lightweight cryptographic algorithms and compare its strength with existing SIMON and SPECK Lightweight cryptographic algorithm. Methods: A hybrid-SIMON-SPECKey algorithm is the combination of round function of SIMON and key scheduling of SPECK algorithm. Both SIOMN & SPECK algorithm are used for securing resource constrained devices. In this research work, avalanche effect method is used to analyze attack resistance property of algorithm. Findings: Newly developed Hybrid algorithm shows better results in terms of execution time and memory consumption. As compared to SIMON, hybrid version of algorithm consumes 50% less time and 20% less memory, which makes it efficient. Strict Avalanche criteria for SIMON is 89%, that of SPECK is 90% and in case of hybrid algorithm, it is 90% at start position but when the character is flipped or changed at the end position of plain text then SAC is more (87%) in case of hybrid algorithm as compared as SIMON and SPECK algorithms. Hence, newly developed algorithm showed improved results with equally resistance to the attack as compared to SIMON & SPECK. Novelty and applications: The novelty lies in the creation of a hybrid lightweight cryptographic algorithm that combines the feistel structure of SIMON with the key scheduling function of SPECK. This hybrid approach aims to leverage the strengths of both algorithms, potentially providing a more robust and efficient solution for resource-constrained IoT devices. In section 3.1 comparative analysis is done which show that hybrid algorithm outperforms in term of time and memory consumption as well a strength of newly developed hybrid algorithm is evaluated using avalanche effect which shows that it is at par with base algorithms. Keywords: Attack Analysis, Cipher Code, Decryption, Encryption, Lightweight Cryptography, Iot Devices, And Resource Constraint Devices","PeriodicalId":13296,"journal":{"name":"Indian journal of science and technology","volume":"102 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140089256","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":"Employing Incremental Learning for the Detection of Multiclass New Malware Variants","authors":"Mohammad Eid Alzahrani","doi":"10.17485/ijst/v17i10.2862","DOIUrl":"https://doi.org/10.17485/ijst/v17i10.2862","url":null,"abstract":"Background/Objectives: The study aims to achieve two main objectives. The first is to reliably identify and categorize malware variations to maintain the security of computer systems. Malware poses a continuous threat to digital information and system integrity, hence the need for effective detection tools. The second objective is to propose a new incremental learning method. This method is designed to adapt over time, continually incorporating new data, which is crucial for identifying and managing multiclass malware variants. Methods: This study utilised an incremental learning technique as the basis of the approach, a type of machine learning whereby a system retains previous knowledge and builds upon the information from the newly acquired data. Particularly, this method is suitable for tackling mutating character of malware dangers. The researchers used various sets of actual world malwares for evaluating the applicability of these ideas which serves as an accurate test environment. Findings: The findings of the research are significant. We utilizing 6 different datasets, which included 158,101 benign and malicious instances, the method demonstrated a high attack detection accuracy of 99.34%. Moreover, the study was successful in identifying a new category of malware variants and distinguishing between 15 different attack categories. These results underscore the effectiveness of the proposed incremental learning method in a real-world scenario. Novelty: This research is unique because of the novel use of a tailored incremental learning technique for dealing with dynamic threat environment of malwares. However, with a new threat they cannot be so well adapted using traditional machine learning methods. On the other hand, the technique put forward in this paper facilitates continuous learning that can be modified to match different types of malicious software as they develop. The ability to evolve and adapt is an important addition to current cybersecurity practices that include malware identification and classification. Keywords: Cybersecurity, Malware Detection, Incremental learning","PeriodicalId":13296,"journal":{"name":"Indian journal of science and technology","volume":" February","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140092424","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":"MaliceSpotter: Revolutionizing Cyber Security with Machine Learning for Phishing Resilience","authors":"Shwetambari Borade, Parshva Chetan Doshi, Darsh Bhavesh Patel","doi":"10.17485/ijst/v17i10.148","DOIUrl":"https://doi.org/10.17485/ijst/v17i10.148","url":null,"abstract":"Objectives: To enhance cyber security by implementing advanced algorithms to swiftly identify and neutralize phishing threats. Also, to bolster user protection, fortify data integrity, and ensure a resilient defense against evolving cyber threats. Methods: MaliceSpotter aims in classifying user-entered URLs by analysing 28 features, using algorithms like Logistic Regression, Random Forest, and KNN, combined via a Voting Classifier. Dataset on Kaggle provides diverse samples for evaluation. This methodology's unique aspects include multiple algorithm integration and the utilization of Kaggle as a data source. Findings: MaliceSpotter demonstrates a commendable accuracy of 95%, effectively classifying input URLs as phishing or legitimate. The system's uniqueness lies in its provision of a detailed report on URL behavior, facilitating informed decision-making. The implementation of ensemble learning is notable, particularly the introduction of the Voting Classifier. This approach leverages various algorithms, successfully incorporating bagging and voting concepts. Through the Voting Classifier, MaliceSpotter gains insights into the working of machine learning algorithms, enhancing the scrutiny of URL behavior. This innovative feature sets MaliceSpotter apart, offering a nuanced perspective on the reliability of URLs through the collective input of diverse algorithms. Novelty: MaliceSpotter uniquely combines diverse algorithms, leveraging a voting classifier for robust results. Continuously updating in real time, it meticulously dissects URLs into 28 parts, ensuring thorough scrutiny and effective detection. Keywords: Phishing, Machine Learning, Web Security, Voting Classifier, Bagging","PeriodicalId":13296,"journal":{"name":"Indian journal of science and technology","volume":"116 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140090710","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":"Siamese Neural Networks for Kinship Prediction: A Deep Convolutional Neural Network Approach","authors":"Tukaram Navghare, A. Muley, Vinayak Jadhav","doi":"10.17485/ijst/v17i4.3018","DOIUrl":"https://doi.org/10.17485/ijst/v17i4.3018","url":null,"abstract":"","PeriodicalId":13296,"journal":{"name":"Indian journal of science and technology","volume":"49 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139594720","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}