Browse Items (11810 total)
Sort by:
-
Plasma Sprayed Refractory Coatings from Aluminium Dross
Refractory coatings on metals offer a unique blend of chemical inertness, stability and mechanical properties at temperatures higher than what the metal can normally withstand. However, a balance must be struck with many factors: Thickness, adhesion, performance, durability, economy and suitability for specific end use requirements. The present-day technology requires the coating to give effective service under extreme temperatures while being environmentally friendly and be easily available. One application of refractory coating is in steel industry-pipe linings. This research works highlights the potential to use aluminum dross, an industrial waste material to generate refractory coatings, comprised of Al2O3 and MgAl2O4 after suitable processing. Al dross is a byproduct of the Aluminium smelting process which can be recycled mechanically to separate the residual Aluminium metal from the Aluminium oxide. These are usually produced in tones every year and are found to be dumped in landfills and other empty spaces which generate toxic fumes like methane and other gases when reacted with moisture. The Aluminium dross used in this work was analyzed and found to comprise of its usual constituents such as metallic Al, MgAl2O4, Al2O3, AlN and other oxides and nitrides in minute quantities. Manual procedures were conducted to synthesize plasma spray-able dross which was further introduced to standard laboratory tests for the removal of undesirable constituents like AlN and other nitrides which led to the optimization of quality of powders. Atmospheric plasma spray (APS) coating methodology was used to deposit 250?m thick coatings of re-processed Al dross, involving the spraying of the processed powder onto a bond coated (NiCrAlY) steel substrate. The raw, reprocessed and the plasma sprayed coated Al dross were evaluated for their material characteristics by employing X-ray Diffractometry (XRD) for crystal structural phases, microstructure and chemical composition by employing sophisticated microscopy (SEM) technique and EDS associated with the SEM. The paper is presented keeping in in view the aptness of reprocessed Al dross, an industrial waste material to be utilized as refractories for use in engineering industries. 2019 Elsevier Ltd. -
Synthesis of Value Added Refractories from Aluminium Dross and Zirconia Composites
Results of a developmental study on the potential to synthesize industrial grade refractories from aluminum dross with un-stabilized zirconia are reported. The merit of the developed product to perform as refractories suitable for use at or above 1000C was assessed by studying the thermo-physical behavior as per guidelines of ASTM and IS. Aluminum dross, an industrial waste (slag) is generated in several millions of tons in the production of Aluminum and is dumped into landfills, which releases poisonous gases like methane and ammonia upon contact with moisture present in the land and the heat generated by the earth, warranting stringent mitigation efforts. Rich in aluminum metal (?15%), ?-Al2O3 (7-15%), MgAl2O4 (10-15%) and AlN (20-30%), the general prime dross composition draws interest due to its abundance and presence of ?-Al2O3 and MgAl2O4, for the production of refractories with insulating, shock resistance and stability at high temperature (?1000C and above) characteristics. Nevertheless, presence of AlN, a good thermal conductor acts as a deterrent in the production of refractories. Aluminium dross (after leaching out AlN) was processed with un-stabilized zirconia (monoclinic ZrO2) to synthesize the refractory composites. Conventional process (calcination, ball milling, compaction and sintering (1550C/6 hrs)) was employed. Characterization involved thermal shock cycling (air quench at furnace ambient of 1000C and room temperature) to determine the number of shock cycles endured before failure. Structural phase analyses at various stages of processing were carried out by via XRD. Magnesia present in dross did not appear to stabilize either the tetragonal or cubic ZrO2. Microstructural and chemical composition studies were carried out via SEM and EDS. The favourable results confirm the viability of the process methodology. 2019 Elsevier Ltd. -
A Model to Predict the Influence of Inconsistencies in Thermal Barrier Coating (TBC) Thicknesses in Pistons of IC Engines
LHR (Low heat Rejection) engines comprise of components that are modified with ceramic Thermal Barrier Coatings (TBC) to derive improvements in performance, fuel efficiency, combustion characteristics and life. In addition to engine parameters, the ability of TBCs (250 - 300?m thick) to function favorably depends on materials technology related factors such as surface-connected porosity, coating surface roughness, uniformity and consistency in coating thickness [1]. Right since the nineties, emphasis has been placed on the complexity of piston contours from a coating processing standpoint because the piston bowl geometry although appears simple, is actually quite complex. Robotic plasma gun manipulation programs have been developed to obtain uniform coating properties and thicknesses which are highly classified information. Thicker coatings offer better thermal insulation characteristics but in thickness deficient regions, TBCs may be as thin as ?30 microns. Applied via the 'line of sight' process, in the Atmospheric Plasma Spray System the coating thickness does not get developed adequately if the components comprise of contours with shadow regions. Thus the coating quality of a LHR engine heavily depends upon the shape of the engine components. This affects the barrier effects offered by the TBC and is reflected via generation of unwanted thermal gradients in the combustion chamber and on the external piston walls that adversely influence the engine performance. Extensive diesel engine cycle simulation and finite-element analysis of the coatings have been conducted to understand their effects on (a) diesel engine performance and (b) stress state in the coating and underlying metal substructure. Research work presented here involves the need and developmental efforts made via Computational Fluid Dynamics (CFD) to generate a model via ANSYS - Fluent simulation software that predicts the temperature gradient across TBCs of various ceramics and coating thicknesses. The geometric model was developed using the dimensions obtained using a CMM (Coordinate Measuring Machine) in Solidworks and the mesh was developed in Altair Hypermesh. The generated mesh consists of 221938 elements. Interfaces were created between the piston-bond coat-top coat surfaces. The Ansys-FLUENT CFD code solves the energy equation to find out the temperature drop in the piston for different combustion temperatures. Although most of the cavities presented are not rectangular, incompressible and steady laminar flow was assumed. The Semi-Implicit Method for Pressure-linked Equations (SIMPLE) was used to model the interaction between pressure and velocity. The energy variables were solved using the second order upwind scheme. In addition, the CFD program uses the Standard scheme to find the pressure values at the cell faces. Convergence was determined by checking the scaled residuals and ensuring that they were less than 10-6 for all variables. Two cases with combustion temperatures varying between 700 and 800 K were developed in Ansys FLUENT, wherein the thickness was deficient in the 'shadow' region. The model was validated via experimentation involving thermal shock cycle tests in prototype burner rig facility and measuring the temperature drop across the TBC as well. Non uniform coatings, leading to non-uniform drop in temperature across the thickness are most likely to affect the lubrication system of the engine and therefore the performance. Substantial efforts must be directed towards development of consistent and uniformly thick coatings for optimum performance of the LHR engine. 2017 Elsevier Ltd. All rights reserved. -
Synthesis of high temperature (1150 C) resistant materials after extraction of oxides of Al and Mg from Aluminum dross
Aluminum Dross (Al-dross) is a well-known Industrial waste generated in an Aluminium industry from the melting of the metal itself. It gets made yearly in hundreds of thousands of tons worldwide, due to the wide use and demand of Aluminum in almost every industry. However, Al-dross is not completely a waste as it contains two compounds of interest, namely Aluminum Oxide (Al2O3) and Magnesium Aluminate (MgAl2O4). They are the basic compounds present in any refractory which are products featuring low thermal conductivity and high temperature shock characteristics in the order of 1000 C+. Thus, Aluminum Dross becomes a vital candidate to be considered for the extraction of the two of the aforementioned compounds. Recent studies have shown that Al-dross indeed can be used to extract Al2O3 and MgAl2O4. However, Al-dross also contains Aluminum Nitride (AlN) a compound that exhibits the exact opposite properties demonstrated by refractories. In addition to being technically unsuitable for use as refractory material, AlN also possesses another huge issue. When Al-dross is dumped into landfills, the AlN present in the dross combines with the moisture in the soil and is energized by geothermal heat which leads into an exothermic reaction, thereby releases highly toxic and health hazardous gases. Keeping the above techno-environment challenges in mind, prior to utilizing the beneficiated Al-dross in any industrial application, it is important to leach out the AlN from the dross in an environment friendly manner. This paper deals with the successive leaching of AlN from the Al-dross using two laboratory procedures. Sintered (to be added) pellets made out of the processed powder in the lab were subjected to analysis of structural phases and chemical constituents by employing XRD and EDS. Cyclic thermal shock test cycles were also carried out by subjecting the pellets to 1150 C and quenching in air alternately, to study the refractory characteristics. 2019 Elsevier Ltd. All rights reserved. -
Influence of nano ?-Al2O3 as sintering aid on the microstructure of spray dried and sintered ?-Al2O3 ceramics
Alpha Alumina (?-Al2O3) has traditionally been sintered to near theoretical density by employing variations in raw material properties, particle sizes, grinding methods, compaction pressures, sintering aids or minor quantities of additives and sintering temperatures. All these parameters directly influence the grain growth morphology and microstructure of the sintered alumina ceramic characteristics. Growth of large grained microstructure facilitated by fine grinding of raw material and coalescence of the grains enhanced by dopant additions are well researched. The maximum sintered density and strength of the fired body could be attained through large grained microstructure which include near spheroidal grains. Most of the final sintering is accomplished via additions of suitable aids which also may be promoted by liquid-phase sintering which is considered highly advantageous compared to solid-state sintering for products in many defense applications. In this paper the influence of nano ?-Al2O3 (<100 nm particle size) as sintering aid to obtain the desired microstructure in sintered micron sized (1 to 5 m) ?-Al2O3 is being reported. 1.0 and 1.5 wt% nano ?- Al2O3 powder were spray dried with 99.0 and 98.5% ?-Al2O3 powder respectively, with polyvinyl alcohol binder, compacted into 10 mm dia and 5 mm thick pellets and sintered at 1450 C with 3 h soak time. In addition to the two different sintering aid additive percentages, other variables included are spray dried powders removed from (i) chamber and (ii) cyclone. The sintered ceramics were characterized for bulk density and fracture surface microstructure via SEM analysis. Nano alumina as sintering aid exhibited significant influence that included generation of microstructure with porosity, precipitation or liquid phase sintering. The study was limited to establishing the definitive role played by nano alumina to influence the sintering of micron alumina. 2022 -
Utilization of aluminum dross: Refractories from industrial waste
Aluminum oxide (Al2O3) and Magnesium-Aluminum oxides (MgAl2O4) are well known refractory materials used in engineering industries. They are built to withstand high temperatures and possess low thermal conductivities for greater energy efficiency. Dross, a product/byproduct of slag generated in aluminum metal production process is normally comprised of these two oxides in addition to aluminum nitride (AlN). Worldwide, thousands of tons of aluminum dross are generated as industrial wastes and are disposed of in landfills causing serious environmental hazard. This paper explores the potential to synergize the characteristics of the favourable contents of aluminum dross and its availability (in tons) via synthesis of refractories and thereby develop a value added product useful for the modern industries. In this work, Al-dross as-received from an aluminum industry which comprised of predominantly Al2O3, MgAl2O4 and AlN, was used to develop the refractories. AlN possesses high thermal conductivity values and therefore was leached out of the dross to protect the performance of the developed refractory. The washed dross was calcined at 700 and 1000C to facilitate gradual elimination of the undesired phases and finally sintered at 1500C. The dross refractory pellets were subjected to thermo-physical and structural properties analysis: XRD (structural phase), SEM (Microstructure), EDS (chemical constituents) and thermal shock cycling test by dipping in molten aluminum and exposing to ambient (laboratory). The findings include the favourable prospects of using aluminum dross as refractories in metal casting industries. Published under licence by IOP Publishing Ltd. -
A continuous protocol for the epoxidation of olefins, monocyclic terpenes, and Alpha Beta Unsaturated Carbonyl Synthons using eco-friendly Flow Reactor Conditions
Herein, we report a simple synthetic protocol for selective epoxidation of olefins, monocyclic terpenes, and chalcones using a continuous semi-batch process in good to excellent yields. Mainly, industrial semi-batch epoxidation is an extremely risky process that includes very high safety measures to avoid the accumulation of peroxide species in the reactor during the process, which leads to accidents. To avoid the same, we have established a constant flow reactor protocol for the epoxidation of fore mentioned key synthons using a cyanamide-potassium carbonate catalytic system which helps to reduce the accumulation of the peroxide species, and also yields moderate to high yields of the desired products. The developed methodology was successfully utilized for the epoxidation of a range of aliphatic to aromatic olefins to generate corresponding epoxides. All the products and their structures were examined using 1HNMR, and 13NMR spectroscopy. More importantly, this proposed protocol is recyclable and reproducible where in using similar research conditions. 2022 The Authors -
Augmented Reality-Enabled IoT Devices for Wireless Communication
[No abstract available] -
A Machine Learning- Based Driving Assistance System for Lane and Drowsiness Monitoring
Lane line detection is a vital component when driving heavy vehicles; this concept follows the path for driving a vehicle to prevent the risk of accidentally entering another lane without the drivers knowledge, which could result in an accident. To detect the lane, use frame masking and Hough line transformation with efficient machine learning algorithms, pre-processed and trained adequately for optimum accuracy as per the provided dataset to spot the white markings on both sides of the lane. Long-distance truck drivers suffer from sleep deprivation, making driving extremely dangerous while tired and they ignore the line markings and wander into the wrong lane. This chapter proposes a portable system that does not require any sensors or interference with the vehicles wiring system; instead, a system that fits on a windshield or any surface to monitor the actions of the driver, using computer vision and feature-extracted datasets within a trained neural network model using cameras. This driver-assisted system can detect drowsiness and give an alarm to wake up the driver by identifying the Region of Interest. These predictions are made based on eye movements, and the algorithm generates a score. The higher the score, the longer the time between alarms. 2024 Taylor & Francis Group, LLC. -
Artificial Intelligence Application in Human Resources Information Systems for Enhancing Output in Agricultural Companies
Artificial intelligence (simulated intelligence) apparatuses like master systems, normal language handling, discourse acknowledgment, and machine vision have changed how much work in agribusiness, yet in addition its nature. This is on the grounds that the total populace and interest for food are developing, and the climate and water supply are evolving. Specialists and researchers are presently moving towards involving new IoT advances in shrewd cultivating to assist ranchers with utilizing manmade intelligence innovation to improve seeds, crop security, and composts. This will get ranchers more cash-flow and help the pay of the country in general. In agribusiness, computer-based intelligence is making its mark in three primary regions: checking soil and harvests, prescient examination, and cultivating robots. Along these lines, ranchers are utilizing sensors and soil tests increasingly more to accumulate information that can be utilized by ranch the board apparatuses for additional exploration and examination. This book adds to the field by giving an outline of how computer-based intelligence is utilized in agribusiness. It begins with a prologue to simulated intelligence, including a survey of all the computer-based intelligence techniques utilized in horticulture, similar to AI, the Web of Things (IoT), master systems, picture handling, and PC vision. 2024 IEEE. -
Alpha-Bit: An Android App for Enhancing Pattern Recognition using CNN and Sequential Deep Learning
This research paper introduces Alpha-Bit, an Android application pioneering Optical Character Recognition (OCR) through cutting-edge deep learning models, including Convolutional Neural Networks (CNNs) and Sequential networks. With a core focus on enhancing educational accessibility and quality, Alpha-Bit specifically targets foundational elements of the English language - alphabets and numbers. Beyond conventional OCR applications, Alpha-Bit distinguishes itself by offering guided instruction and individual progress reports, providing a nuanced and tailored educational experience. Significantly, this work extends beyond technological innovation; Alpha-Bit's potential impact encompasses addressing educational inequalities, contributing to sustainability goals, and advancing the achievement of Sustainable Development Goal 4 (SDG 4). By democratizing education through innovative OCR technologies, Alpha-Bit emerges as a transformative force with the capacity to revolutionize learning experiences, making quality education universally accessible and empowering learners across diverse socio-economic backgrounds. 2024 ITU. -
An Interrogation of Android Application-Based Privilege Escalation Attacks
Android is among the most widely used operating systems among consumers. The standard security model must address several dangers while still being usable by non-security users due to the wide range of use cases, including access to cameras and microphones and use cases for sharing information, entertainment, business, and health. The Android operating system has taken smartphone technology to peoples front doors. Thanks to recent technological developments, people from all walks of life can now access it. However, the popularity of the Android platform has exacerbated the growth of cybercrime via mobile devices. The open-source nature of its operating system has made it a target for hackers. This research paper examines the comparative study of the Android Security domain in-depth, classifying the attacks on the Android device. The study covers various threats and security measures linked to these kinds and thoroughly examines the fundamental problems in the Android security field. This work compares and contrasts several malware detection techniques regarding their methods and constraints. Researchers will utilize the information to comprehensively understand Android security from various perspectives, enabling them to develop a more complete, trustworthy, and beneficial response to Androids vulnerabilities. 2023 American Institute of Physics Inc.. All rights reserved. -
Improving Indoor occupancy estimation using a hybrid CNN-LSTM approach
Indoor Air Quality (IAQ) monitoring has been a significant research domain in energy conservation. Many energy resources are required to maintain the IAQ using airconditioning or other ventilation systems. Currently, the research works highly optimize an on-demand driven energy usage depending on the occupant present inside the building. In the last decade, numerous research works have evolved for such an optimization by installing sensors and predicting occupants using machine learning techniques. This research fails to deploy non-intrusive sensors and appropriate machine learning algorithms to predict the occupancy count. Advancement in neural network techniques termed deep learning has made significant performance in recognition and cognitive tasks. Thus, this paper proposes a hybrid deep learning model that stacks the convolutional neural network (CNN) and long short term memory (LSTM) to improve the prediction rate of the occupancy count. Experimentation has been carried out in real-time multivariate sensor data for the occupancy estimation and evaluated the performance in terms of accuracy, RMSE, MAPE, and coefficients of determinants. 2022 IEEE. -
Multi-lingual Spam SMS detection using a hybrid deep learning technique
Nowadays, the incremental usage of mobile phones has made spam SMS messages a big concern. Sending malicious links through spam messages harms our mobile devices physically, and the attacker might have a chance to steal sensitive information from our devices. Various state-of-the-art research works have been proposed for SMS spam detection using feature-based, machine, and deep learning techniques. These approaches have specific limitations, such as extracting and selecting signifi-cant and quality features for efficient classification. Very few deep learning techniques are only used for classifying spam detection. Moreover, the benchmark spam datasets written in English are mostly used for evaluation. Very few papers have detected spam messages for other languages. Hence, this paper creates a multilingual SMS spam dataset and proposes a hybrid deep learning technique that combines the Convolutional Neural Network and Long Short-Term Memory (LSTM) model to classify the message dataset. The performance of this proposed hybrid model has been compared with the baseline deep learning models using accuracy, precision, recall, and F1-score metrics. 2022 IEEE. -
A Review on Artificial Intelligence Techniques for Multilingual SMS Spam Detection
With social networks increased popularity and smartphone technology advancements, Facebook, Twitter, and short text messaging services (SMS) have gained popularity. The availability of these low cost text-based communication services has implicitly increased the intrusion of spam messages. These spam messages have started emerging as an important issue, especially to short-duration mobile users such as aged persons, children, and other less skilled users of mobile phones. Unknowingly or mistakenly clicking the hyperlinks in spam messages or subscribing to advertisements puts them under threat of debiting their money from either the bank account or the balance of the network subscriber. Different approaches have been attempted to detect spam messages in the last decade. Many mobile applications have also evolved for spam detection in English, but still, there is a lack of performance. As English has been completely covered under natural language processing, other regional languages, such as Urdu and Hindi variants, have specific issues detecting spam messages. Mobile users suffer greatly from these issues, especially in multilingual countries like India. Thus, this paper critically reviews the artificial intelligence-based spam detection system. The review lists out the existing systems that use machine and deep learning techniques with their limitations, merits, and demerits. In addition, this paper covers the scope for future enhancements in natural language processing to efficiently prevent spam messages rather than detect spam messages. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
AIFMS Autonomous Intelligent Fall Monitoring System for the Elderly Persons
Falls are the major cause of injuries and death of elders who live alone at home. Various research works have provided the best solution to the fall detection approach during the day. However, falls occur more at night due to many factors such as low or zero lighting conditions, intake of medication/drugs, frequent urination due to nocturia disease, and slippery restroom. Based on the required factors, an autonomous monitoring system based on night condition has been proposed through retro-reflective stickers pasted on their upper cloth and infrared cameras installed in the living environment of elders. The developed system uses features such as changes in orientation angle and distance between the retro-reflective stickers to identify the human shape and its characteristics for fall identification. Experimental analysis has also been performed on various events of fall and non-fall activities during the night exclusively in the living environment of the elder, and the system achieves an accuracy of 96.2% and fall detection rate of 92.9%. Copyright 2022, IGI Global. -
On families of graphs which are both adjacency equienergetic and distance equienergetic
Let A(G) and D(G) be the adjacency and distance matrices of a graph G respectively. The adjacency energy or A-energy EA(G) of a graph G is defined as the sum of the absolute values of the eigenvalues of A(G). Analogously, the D-energy ED(G) is defined to be the sum of the absolute values of the eigenvalues of D(G). One of the interesting problems on graph energy is to characterize those graphs which are equienergetic with respect to both the adjacency and distance matrices. A weaker problem is to construct the families of graphs which are equienergetic with respect to both the adjacency and distance matrices. In this paper, we find the explicit relations between A-energy and D-energy of certain families of graphs. As a consequence, we provide an answer to the above open problem (Indulal in https://icgc2020.wordpress.com/invitedlectures, 2020; http://www.facweb.iitkgp.ac.in/rkannan/gma.html, 2020) The Indian National Science Academy 2022. -
Statistical features learning to predict the crop yield in regional areas
The plethora of information presented in the form of benchmark dataset plays a significant role in analyzing and understanding the crop yield in certain regions of regional territory. The information may be presented in the form of attributes makes a prediction of crop yield in various regions of machine learning. The information considered for processing involves data cleaning initially followed by binning to reduce the missing data. The information collected is subjected to clustering of data items based on patterns of similarity, The data items that are similar in nature is fed to the system with similarity measure, which involves understanding the distance of data items from its related data item leading to hyper parameters for analyzing of information while calculating the crop yield. The information may be used to ascertain the patterns of data that exhibit similarity with nearest neighbor represented by another attribute. Thus, the research method has yielded an accuracy of 89.62% of classification for predicting the crop yield in agricultural areas of Karnataka region. 2022 Institute of Advanced Engineering and Science. All rights reserved. -
Co-sputtered V2O5TiN composite on Ag-network current collector for high-performance flexible transparent thin-film supercapacitors
Next-generation wearables require extremely capable electrochemical energy-storage devices that exhibit improved performance with high flexibility and transparency. Herein, we present a highly flexible and transparent electrochemical thin-film supercapacitor electrode fabricated by co-sputtering V2O5 and TiN on an Ag-network-based current collector. The electrodes' physical properties, optical properties, and structural morphologies are studied using X-ray diffraction, UVvisible spectroscopy, and scanning electron microscopy, respectively. A symmetric device is fabricated using V2O5 and TiN on an Ag network, and the TiN sputter power is varied to optimize the performance. The device performance of the co-sputtered electrodes at various composition ratios is studied. The optimized V2O5TiN (200?40)/Ag electrode device with pseudocapacitive behavior delivers an excellent areal specific capacitance of 98.66 mF cm2 at a current density of 4 mA cm2 with a charge retention of 90.12 % after 6000 cycles. The V2O5TiN (200?40)/Ag electrode device outperforms other reported electrodes, with an energy density and power density of 30.83 ?Wh cm2 and 2999.67 ?W cm2, respectively, and excellent mechanical stability. 2023 -
Mutual Information Pre-processing Based Broken-stick Linear Regression Technique for Web User Behaviour Pattern Mining
Web usage behaviour mining is a substantial research problem to be resolved as it identifies different user's behaviour pattern by analysing web log files. But, accuracy of finding the usage behaviour of users frequently accessed web patterns was limited and also it requires more time. Mutual Information Pre-processing based Broken-Stick Linear Regression (MIP-BSLR) technique is proposed for refining the performance of web user behaviour pattern mining with higher accuracy. Initially, web log files from Apache web log dataset and NASA dataset are considered as input. Then, Mutual Information based Pre-processing (MI-P) method is applied to compute mutual dependence between the two web patterns. Based on the computed value, web access patterns which relevant are taken for further processing and irrelevant patterns are removed. After that, Broken-Stick Linear Regression analysis (BLRA) is performed in MIP-BSLR for Web User Behaviour analysis. By applying the BLRA, the frequently visited web patterns are identified. With the identification of frequently visited web patterns, MIP-BSLR technique exactly predicts the usage behaviour of web users, and also increases the performance of web usage behaviour mining. Experimental evaluation of MIP-BSLR method is conducted on factors such as pattern mining accuracy, false positives, time requirements and space requirements with respect to number of web patterns. Outcomes show that the proposed technique improves the pattern mining accuracy by 14%, and reduces the false positive rate by 52%, time requirement by 19% and space complexity by 21% using Apache web log dataset as compared to conventional methods. Similarly, the pattern mining accuracy of NASA dataset is increased by 16% with the reduction of false positive rate by 47%, time requirement by 20% and space complexity by 22% as compared to conventional methods. 2020. All Rights Reserved.