19c96fd517d854497e8-280e 20220212064516815 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 01 30 2021 9 5 CSE, National Engineering College, Kovilpatti, Thothukoodi, India. Dr. S Maheswari S. Kalaiselvi CSE, National Engineering College, Kovilpatti, Thothukoodi, India. D.Thamarai Selvi CSE, National Engineering College, Kovilpatti, Thothukoodi, India. M. Manochitra CSE, National Engineering College, Kovilpatti, Thothukoodi, India. The administration dispatches different aggressive projects attempting to make the nation more prosperous, yet what they bomb is in fruitful execution and coming to recipients. The fundamental explanation for this issue is the absence of mindfulness among rustic individuals. This paper is to give an answer for this uninformed circumstance. Through this framework the rustic understudies will be instructed such that they can become acquainted with about what are the different plans that are outfitted by the administration and what are the plans they are qualified for. On the off chance that the country understudies came to know and get mindful of the apparent multitude of legislative plans gave by the Government of India for the government assistance of the provincial understudies, at that point their life would venture into next level. At first this framework will investigate the accessible government plans in the instructive for the government assistance of country understudies. Next, the understudy’s information ((i.e.) name, age, station, occupation, annualincome.etc) are accumulated. At that point; both the datasets are brought into the Anaconda Navigator. At that point, investigation and grouping dependent on networks (SC, ST, BC and MBC) of the understudies and the plans are performed. At that point utilizing the forecast calculations (Naïve Bayes, Random Forest and Support Vector Machine (SVM)) what are generally the plans the specific understudy is qualified for are anticipated. An investigation is made on the proficiency of the three calculations. The exactness of the three calculations is broke down and the effective calculation which creates the outcome with most elevated precision is at last used to play out the forecast of the plans that a specific understudy is qualified for. At long last, the anticipated plans anticipated utilizing the most elevated effective calculation among the three calculations will be gotten back to the understudies. Hence, through this undertaking the rustic understudies will come to think about different recipient plans gave by government and they can use those plans for the improvement of the country environmental factors. 01 30 2021 70 79 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5140.019521 https://www.ijrte.org/portfolio-item/E5154019521/ Computer Science and Engineering Dr Akhlesh Das Gupta Institute of Technology and Management, New Delhi, India. Dr Saurabh Gupta Vaishali Computer Science and Engineering Dr Akhlesh Das Gupta Institute of Technology and Management, New Delhi, India. Raghuvansh Tahlan Computer Science and Engineering Dr Akhlesh Das Gupta Institute of Technology and Management, New Delhi, India. Navya Sanjna Joshi Computer Science and Engineering Dr Akhlesh Das Gupta Institute of Technology and Management, New Delhi, India. Ritvik Agarwal Computer Science and Engineering Dr Akhlesh Das Gupta Institute of Technology and Management, New Delhi, India. Stock market prediction is a long-time intriguing topic to researchers from different fields. Stock market data is extremely volatile and hence laborious to model. In particular, innumerable studies have been conducted to predict the movement of stock market using Machine Learning algorithms such as Regression Techniques, Time Series Forecasting, Indices Modelling, Natural Language Processing and more, but there is still room for improvement. Also, Option chain and Options have been the subjects that not many have ventured into, leading us to this subject. Mainly, NIFTY and BANKNIFTY Options account for 70% of total derivatives traded and much more turnover than all stocks combined. This research paper attempts to figure out the utility of Option Chain in predicting the direction of movement in NIFTY. We have tried how different features from Option chain can be extracted, and the resulting problem can be solved using Machine Learning techniques and Deep Learning techniques. 01 30 2021 80 83 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5155.019521 https://www.ijrte.org/portfolio-item/E5155019521/ Research Scholar, Department of Civil , BIT Sindri, Dhanbad, Jharkhand, India. Prerna Priya Dr.Ran Vijay Singh Ddepartment of Civil, BIT Sindri, Dhanbad, Jharkhand, India. Expansive Black cotton clay soils are widely distributed worldwide, and are a significant damage to infrastructure and buildigs. It is a common practice around the world to stabilize black cotton soil using fly ash to improve the strength of stabilized sub- base and sub grade soil. Soil stabilization is the improvement of strength or bearing capacity of soil by controlled compaction, proportioning or addition of suitable admixtures or stabilizers. The Black cotton soils are extremely hard when dry, but lose its strength fully when in wet condition. In monsoon they guzzle water and swell and in summer they shrink on evaporation of water from there. Because of its high Swelling and shrinkage characteristics the black cotton soils has been a challenge to the highway engineers.So in this research paper fly ash has been used to improve the various strength properties of natural black cotton soil.The objective of this research paper is to improve the engineering properties of black cotton soil by adding different percentage of fly ash by the weight of soil and make it suitable for construction. A series of standard Proctor tests (for calculation of MDD and OMC) and California Bearing Ratio (C.B.R) tests are conducted on both raw Black cotton soil and mixed soil with different percentages of fly ash (5%, 10%, 20%, 30%) by weight. A comparison between properties of raw black cotton soil, black cotton soil mixed with fly ash are performed .It is found that the properties of black cotton soil mixed with fly ash are suitably enhanced. 01 30 2021 91 96 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5164.019521 https://www.ijrte.org/portfolio-item/E5164019521/ Department of Mechanical Engineering, University College of Engineering, JNTUK, Kakinada. A.Gopala Krishna R Peddi Raju Research Scholar, Department of Mechanical Engineering, University College of Engineering, JNTUK, Kakinada. Because of stiff competition, industries are in continuous pressure of producing high quality products. To do so, inevitably high quality cutting tools are required. Alumina (Al2O3) is a quality cutting tool that is used for high speed machining. It is a widely used tool for machining cast iron, hard steels and super alloys. Therefore, the present work has taken up to prepare an alumina cutting tool material. One of the greatest drawbacks of alumina cutting tool material is its low fracture toughness. In the present work, Graphene nanoplatelets (GNPs) are considered the reinforcement in the Al2O3ceramic matrix to not only improve the fracture toughness but also the other properties. The composites are fabricated by the powder metallurgy route where the powders of raw materials are essentially subjected to compaction and sintering. Once the Al2O3 composites are fabricated and their properties are tested for their density, hardness and fracture toughness. It is observed that the GNP reinforced composites have much better properties than those of the composites without GNPs. Keywords : Graphene nanoplatelets, Al2O3 cutting tool, powder metallurgy, properties testing 01 30 2021 97 101 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5165.019521 https://www.ijrte.org/portfolio-item/E5165019521/ Interior Design Department, Ahlia University, Manama, Kingdom of Bahrain. Imad Assali Amal Attiya Interior Design Department, Ahlia University, Manama, Kingdom of Bahrain. Recently, the persistent problem in art and design programs in Worldwide universities are visual plagiarism. The main core values of academic institutions in general and Ahlia University is to produce graduates with not only good knowledge but also good values and high academic reputation that encourages intellectual and moral development promoting the image of their universities. Therefore, Ahlia university invests its efforts to create policies and procedures for text-based assignments to control originality of students’ work while handing in assignments, reports, research proposals, and dissertations by using software technology like Turnitin. Conversely, little has been done focused on non-textual materials in art and design education. Besides, there are a plethora of articles when searching the Scopus database, about text-based academic misconduct with a dearth of research devoted to visual plagiarism which often left to the experience of academic faculty. Therefore, the main purpose of this research is to shed light on students’ understanding of visual plagiarism issues and bridge the gap in visual work. Moreover, this research will develop a pedagogical policy that can be used by faculty to control academic dishonesty in visual arts. This research is conducting using two main methods. Firstly, it depended on reviewing different literature from journals, articles, and policies from different universities about plagiarism. Secondly, this research used qualitative and quantitative data. To collect qualitative data, an in-depth interview with the 15 academicians was conducted to triangulate with the students’ findings of reasons of visual plagiarism and prevention solutions. For the quantitative data, an online survey using the Google form survey was used to a sample of 54 students in the design program at Ahlia University and other universities in Bahrain. Finally, this research reveals that the lack of awareness among students in arts and design education about academic integrity leads to visual plagiarism. 01 30 2021 102 106 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5172.019521 https://www.ijrte.org/portfolio-item/E5172019521/ Assistant Professor, Department of Education, Central University of Kerala, Tejaswini Hills, Periye, Kasaragod, Kerala, India. Thiyagu K The primary purpose of the research is to develop and standardize the scale of technostress of Teacher Educator. The researcher had developed the draft statements to measure teacher educators’ technostress based on the psychological experts’ interaction and some theoretical inputs. Thirty-six items have been constructed as a preliminary draft of the tool. The study sample was collected randomly from the 150 teacher educators of Kasaragod and Kannur Districts of Kerala. The item analysis was done through the ‘Cronbach’s Alpha if Item Deleted’ strategies through SPSS 22 Version. After finalizing the item analysis strategies the investigator prepared the final draft of the tool consists of thirty-two items in a five-point scale. The Cronbach Alpha and split-half reliability analysis strategies were used to verify the consistency of the instrument. This tool would be very much useful to measure the technological stress of teacher educators. This paper explains the procedure of technostress scale construction and standardization. 01 30 2021 114 117 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5189.019521 https://www.ijrte.org/portfolio-item/E5189019521/ PG Student, Department of Civil Engineering, Anurag University, Telangana. Axetha Menam K. Sunil Kumar Assistant Professor, Department. Of Civil Engineering, Anurag University, Telangana. P. Rupa PG Student, Department. Of Civil Engineering, Anurag University, Telangana. A new inexperienced constructing material is glass fibre reinforced polymer (GFRP) rebar. GFRP rebars are non-corrosive, non-conductive, light-weight substances and have an excessive longitudinal tensile capacity that is beneficial for use in civil infrastructure applications. In this analysis, the overall performance of GFRP rebar-reinforced concrete beams was assessed. Full scale exams had been conducted underneath four-point bending on eight one hundred fifty x 250 x 1500 mm beams to inspect the influence of GFRP specimens reinforced through both GFRP or metal rebars with flexural reinforcement ratios (ρf) ranging from 0.53 to 1.45 times the balanced ratio (ρfb). In phrases of crack pattern, load deflection behaviour, load strain conduct and peak capacity, the check facts used to be analysed to decide the flexure and shear conduct of GFRP RC beams. The find out about confirmed that the ultimate load capacity of beams is immediately proportional to the flexural reinforcement ratio, and for steel bolstered specimens, cracking moments had been greater, relative to GFRP. For GFRP RC beams, the peak carrying ability is extra than steel beams. GFRP beams confirmed greater deflections than bolstered beams of steel. The findings additionally confirmed that the building of GFRP bolstered beams in concrete with GFRP stirrups can be influenced by means of shear failures. The reinforcement ratio and shear design of GFRP bolstered concrete beams is affected by way of their behaviour. 01 30 2021 229 235 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5191.019521 https://www.ijrte.org/portfolio-item/E5191019521/ Department of Electronics Engineering, Government College of Engineering, Amravati, Amravati, Maharashtra, India. Mrunali D. Mahalle Dinesh V. Rojatkar Department of Electronics Engineering, Government College of Engineering, Amravati, Amravati, Maharashtra, India. In this paper, we proposed an audio based violent scene detection system. As visual based approach has been widely used in identification of violent scenes from video data, audio-based approach; on the other hand, has not been explored as much as visual approach of the video data. In some applications such as video surveillance, visual scenes can be absent because of environmental situations. Also, in many approaches different systems are proposed for movies and real time videos. Therefore, we practiced the audio approach of video data to decide whether a video scene is violent or not from movies and real time videos. For this purpose, we propose an Extreme Learning Machine (ELM) method to detect video scenes as “violent” or “non-violent” using two types of datasets Standardized Media Eval VSD-2014 and other is Customized dataset for the same classifier. After successful training and testing, 85.7% accuracy is achieved by ELM for VSD-2014 dataset and 88.89% for Customized dataset respectively. 01 30 2021 107 113 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5193.019521 https://www.ijrte.org/portfolio-item/E5193019521/ Department of Computer Science and Engineering from National Engineering College, Kovilpatti, India. Ms.A. Sangeetha Mrs.R. Rajakumari Department of Computer Science and Engineering from National Engineering College, Kovilpatti, India. Cracks in concrete buildings may show the total extent of damage or problems of greater magnitude. Causes of cracks depend on the nature of the crack and the type of structure. Crack classification is an approach to using machine learning algorithms to find a particular type of crack. The image is preprocessed by image smoothening and removes noise using a Gaussian filter, whereas the Sobel edge detection method is used to detect the edges. By using k-means clustering, the image segmentation is carried out to identify the Region of Interest. Fractal dimension is an efficient measure for complex objects. Fractal features like fractal dimension, average, and lacunarity are calculated using a differential box-counting algorithm. The classification of the crack classifies the crack based on the characteristics derived from the crack area. 01 30 2021 183 185 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5196.019521 https://www.ijrte.org/portfolio-item/E5196019521/ Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India. Juliet Johny Linda Sara Mathew Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India. The amount of data has risen significantly over the last few years, due to the popularity of some of the data generation sources like social media, electronic health records, sensors and online shopping sites. Analyzing, processing and storing this data is very prominent since it helps to uncover hidden patterns and unknown correlations. A big data analysis and prediction System is proposed in this context, which combines weather observations, health data and social media content in order to forecast the outbreaks of infectious diseases in a locality. Finding information about the determinants of disease outbreaks are required to reduce its effects on populations. An In-mapper combiner based MapReduce algorithm is used to calculate the mean of daily measurements of various climate parameters like temperature, atmospheric pressure, relative humidity, solar and wind. The climatic parameter that may leads to the outbreak of a disease is identified by finding the correlation between the parameters and disease incidence count. To evaluate how user’s tweeting patterns and sentiments matched with the outbreak of diseases, all tweets containing keywords related to diseases are collected using twitter streaming APIs and are analyzed and processed using Spark framework. The performance of proposed model is improved due to the presence of tweet processing. This indicates that the real-time analysis of social media data can provide more effective result rather than working on the historical data. 01 30 2021 118 124 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5204.019521 https://www.ijrte.org/portfolio-item/E5204019521/ Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India. Ansa Baiju Linda Sara Mathew Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India. Neethu Subash Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India. Prediction of drug target interaction is an extrusive domain of drug discovery and repositioning of drugs. Most conventional studies are carried out in early years in the wet laboratory, but it is very expensive and time consuming. So nowadays, the use of machine learning techniques to predict drug target pairs. A new method of interaction targeting drugs is introduced in this paper. Use the Pseudo Position Specific Scoring Matrix (PsePSSM) is used to represent the target, which generate features that describe the original information of protein. The drug chemical structure information can be extracted through FP2 molecular fingerprint which describe the molecular structure information. Then a drug target interaction network is constructed using bipartite graph where in which each node represents a target or drug and each link indicates a drug target interaction. From the above stages, the data contains some noise and redundant data which have a negative impact on the prediction output. So, LASSO (Least Absolute Shrinkage and Selection Operator) method is handle it and reduce the dimension of the extracted feature information of original data. But drug target pair samples have some imbalanced, then cost-sensitive ensemble method is used to address the imbalanced problem between positive and negative samples, and learns about the minority class by assigning higher costs and optimizing their cost error. Finally, the processed data is given as input to the extreme gradient boosting classifier algorithm for predicting new drug target interaction pairs. This method can significantly improve the prediction accuracy of drug target interaction. 01 30 2021 125 129 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5205.019521 https://www.ijrte.org/portfolio-item/E5205019521/ Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India. Nish T P Linda Sara Mathew Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India. Drug repositioning is a compelling technique to find new signs for existing medications. Despite the fact that few exploration have attempted to improve the precision of repositioning by joining information from more than one assets and various levels, it is as yet appealing to additionally review how to effectively abuse significant information for drug repositioning. As contrasted and the customary medication improvement from particle to item, drug repositioning is additional time and worth effective, quickening drug revelation technique. Medication repositioning methods might be ordered as both sicknesses based or drug-based. In this study at, propose an effective strategy, by means of utilizing Adverse Drug Reactions (ADRs) in light of the fact that the middle of the road, a heterogeneous wellbeing network containing drugs, infections, proteins and ADRs is constructed. The repositioning procedure dependent on ADR is equipped for profiling drugs related phenotypic information and can accordingly aid the resulting drugs utilize the disclosure of new recuperating. 01 30 2021 186 190 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5206.019521 https://www.ijrte.org/portfolio-item/E5206019521/ Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India. Anna Joshy Leya Elizabeth Sunny Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India. Linda Sara Mathew Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India. Biosensors calculate the expression pattern of multi- ple genes in experimental work. A unique genomic chip is possible to produce levels of expression from multiple genes. The ability to interpret these high-dimensional samples fuels the creation of methods of automated analysis. Even though the existing methods undergo imbalanced problems and less classification accuracy over gene expression datasets.Therefore, a novel computational method has been developed inorder to increase the classification performance of gene expression dataset and accurate disease prediction.By adding fuzzy memberships, we take into account the features of imbalanced data. Within our work, both the sample entropies and the expense for each class decide the fuzzy memberships in order to understand the different samples with various contributors to the judgment boundary. Thus, on imbalanced genomic datasets, the current proposed approach will result in more desirable classification outcomes. In addition, to build a new algorithm, we integrate the fuzzy memberships into current MKL. The results show that the proposed approach will tackle the imbalanced problem and achieve high accuracy rate. 01 30 2021 130 133 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5208.019521 https://www.ijrte.org/portfolio-item/E5208019521/ Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Ernakulam, India. Reshma M Neena Thomas Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Ernakulam, India. Dr. Surekha Mariam Varghese Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Ernakulam, India. Ant Colony Optimization (ACO) has been commonly applied in solving discrete optimization problems. This is an attempt to apply ACO in a dynamic environment for finding the optimal route. To create a dynamically changing scenario, in addition to distance, constraints such as air quality, congestion, user feedback, etc are also incorporated for deciding the optimal route. Max-Min Ant System (MMAS), an ACO algorithm is used to find the optimal path in this dynamic scenario. A local search parameter ε is also introduced in addition to ρ to improve the exploration of unused paths. Adaptability was studied by dynamically changing the costs associated with different parameters. 01 30 2021 134 138 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5210.019521 https://www.ijrte.org/portfolio-item/E5210019521/ Computer Science Department, Mar Athanasius College of Engineering, Kothamangalam, India. Shirien K A Neethu George Computer Science Department, Mar Athanasius College of Engineering, Kothamangalam, India. Dr. Surekha Mariam Varghese Computer Science Department, Mar Athanasius College of Engineering, Kothamangalam, India. Descriptive answer script assessment and rating program is an automated framework to evaluate the answer scripts correctly. There are several classification schemes in which a piece of text is evaluated on the basis of spelling, semantics and meaning. But, lots of these aren’t successful. Some of the models available to rate the response scripts include Simple Long Short Term Memory (LSTM), Deep LSTM. In addition to that Convolution Neural Network and Bi-directional LSTM is considered here to refine the result. The model uses convolutional neural networks and bidirectional LSTM networks to learn local information of words and capture long-term dependency information of contexts on the Tensorflow and Keras deep learning framework. The embedding semantic representation of texts can be used for computing semantic similarities between pieces of texts and to grade them based on the similarity score. The experiment used methods for data optimization, such as data normalization and dropout, and tested the model on an Automated Student Evaluation Short Response Scoring, a commonly used public dataset. By comparing with the existing systems, the proposed model has achieved the state-of-the-art performance and achieves better results in the accuracy of the test dataset. 01 30 2021 139 144 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5212.019521 https://www.ijrte.org/portfolio-item/E5212019521/