Data mining referred to extracting the hidden predictive information from huge amount of data set. Recently, there are number of private institution are came into existence and they put their efforts to get fruitful admissions. In this paper, the techniques of data mining are used to analyze the mind setup of student after matriculate. One of the best tools of data mining is known as WEKA (Waikato Environment Knowledge Analysis), is used to formulate the process of analysis.
This Student Financial Service system provides an easy, accessible, and seamless to
students whenever they need it. It ensures access to a PU education for all admitted and enrolled
students without regard to their financial circumstances. It provides the best possible solutions and
service to students and their families.
University Student Payment System, USPS is a student financial solution for educational institute. It's our under graduate project. Here is the abstract of this project.
University Student Payment System ‘USPS’ is an online base bespoke application system. It is mainly an accounting system but it is not a conventional accounting system. It has some specialty; it is specific only for student. Students will be able to pay their tuition and other semester fees online using this system. Guardian will able to pay their students fees through online and able to see the student financial statement. It has various message options to notify transaction information like as mobile, emailing also own messaging system. On demand University Student Payment System users will be able to view receipts, payment statement from anywhere in the world using Internet.
At the primary stage of developing University Student Payment System, we have studied similar systems. Most of systems are e-commerce system. USPS has some similarity with e-commerce system, because students have payment their fees using their bank account, credit or debit card and using their mobiles through third party payment gateway.
B tech it project report on attendence management systemVinnie Singh
B tech it project report on attendence management system.
File courtesy Studynama.com India's Mega Education Hub for Free Lecture Notes, eBooks, Projects and Papers. Come Study Smart with Studynama!
This Student Financial Service system provides an easy, accessible, and seamless to
students whenever they need it. It ensures access to a PU education for all admitted and enrolled
students without regard to their financial circumstances. It provides the best possible solutions and
service to students and their families.
University Student Payment System, USPS is a student financial solution for educational institute. It's our under graduate project. Here is the abstract of this project.
University Student Payment System ‘USPS’ is an online base bespoke application system. It is mainly an accounting system but it is not a conventional accounting system. It has some specialty; it is specific only for student. Students will be able to pay their tuition and other semester fees online using this system. Guardian will able to pay their students fees through online and able to see the student financial statement. It has various message options to notify transaction information like as mobile, emailing also own messaging system. On demand University Student Payment System users will be able to view receipts, payment statement from anywhere in the world using Internet.
At the primary stage of developing University Student Payment System, we have studied similar systems. Most of systems are e-commerce system. USPS has some similarity with e-commerce system, because students have payment their fees using their bank account, credit or debit card and using their mobiles through third party payment gateway.
B tech it project report on attendence management systemVinnie Singh
B tech it project report on attendence management system.
File courtesy Studynama.com India's Mega Education Hub for Free Lecture Notes, eBooks, Projects and Papers. Come Study Smart with Studynama!
Presentation by Eija Kujanpää from Aalto University School of Science and Technology. Presentation was held at the EMAP training seminar in Prague for future Erasmus Mundus Master Courses consortia (4-7 February 2010).
This Presentation "Course Registration System" is Implemented in Case Tools. It will Help you to develop Your Project in Technical Manner. Kindly use this presentation for your Reference. If you have any doubts in this presentation mail me baranitharan@gmail.com
The slide was made about Basic Database Management system.
If you are a new student for Database Management then you should read this slide. by this slide you can easily gather a quick knowledge about database Management system. so now open this slide and read it.
My Presentation of Graduation Project
'Library Management System'
using vb.net 2008 and sql server 2008
2013
CS & IT department
faculty of Science
Portsaid Univeristy
A Student Management System Project abstract that contains the basics needs in a student management system. It can also be used as a base to implementing your new creative ideas.
An Empirical Study of the Applications of Classification Techniques in Studen...IJERA Editor
University servers and databases store a huge amount of data including personal details, registration details, evaluation assessment, performance profiles, and many more for students and lecturers alike. main problem that faces any system administration or any users is data increasing per-second, which is stored in different type and format in the servers, learning about students from a huge amount of data including personal details, registration details, evaluation assessment, performance profiles, and many more for students and lecturers alike. Graduation and academic information in the future and maintaining structure and content of the courses according to their previous results become importance. The paper objectives are extract knowledge from incomplete data structure and what the suitable method or technique of data mining to extract knowledge from a huge amount of data about students to help the administration using technology to make a quick decision. Data mining aims to discover useful information or knowledge by using one of data mining techniques, this paper used classification technique to discover knowledge from student’s server database, where all students’ information were registered and stored. The classification task is used, the classifier tree C4.5, to predict the final academic results, grades, of students. We use classifier tree C4.5 as the method to classify the grades for the students .The data include four years period [2006-2009]. Experiment results show that classification process succeeded in training set. Thus, the predicted instances is similar to the training set, this proves the suggested classification model. Also the efficiency and effectiveness of C4.5 algorithm in predicting the academic results, grades, classification is very good. The model also can improve the efficiency of the academic results retrieving and evidently promote retrieval precision.
Study and Analysis of K-Means Clustering Algorithm Using RapidminerIJERA Editor
Institution is a place where teacher explains and student just understands and learns the lesson. Every student has his own definition for toughness and easiness and there isn’t any absolute scale for measuring knowledge but examination score indicate the performance of student. In this case study, knowledge of data mining is combined with educational strategies to improve students’ performance. Generally, data mining (sometimes called data or knowledge discovery) is the process of analysing data from different perspectives and summarizing it into useful information. Data mining software is one of a number of analytical tools for data. It allows users to analyse data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational database. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).This project describes the use of clustering data mining technique to improve the efficiency of academic performance in the educational institutions .In this project, a live experiment was conducted on students .By conducting an exam on students of computer science major using MOODLE(LMS) and analysing that data generated using RapidMiner(Datamining Software) and later by performing clustering on the data. This method helps to identify the students who need special advising or counselling by the teacher to give high quality of education.
STUDENTS’ PERFORMANCE PREDICTION SYSTEM USING MULTI AGENT DATA MINING TECHNIQUEIJDKP
A high prediction accuracy of the students’ performance is more helpful to identify the low performance students at the beginning of the learning process. Data mining is used to attain this objective. Data mining techniques are used to discover models or patterns of data, and it is much helpful in the decision-making.Boosting technique is the most popular techniques for constructing ensembles of classifier to improve the classification accuracy. Adaptive Boosting (AdaBoost) is a generation of boosting algorithm. It is used for
the binary classification and not applicable to multiclass classification directly. SAMME boosting
technique extends AdaBoost to a multiclass classification without reduce it to a set of sub-binaryclassification.In this paper, students’ performance prediction system usingMulti Agent Data Mining is proposed to predict the performance of the students based on their data with high prediction accuracy and provide helpto the low students by optimization rules.The proposed system has been implemented and evaluated by investigate the prediction accuracy ofAdaboost.M1 and LogitBoost ensemble classifiers methods and with C4.5 single classifier method. The results show that using SAMME Boosting technique improves the prediction accuracy and outperformed
C4.5 single classifier and LogitBoost.
Extending the Student’s Performance via K-Means and Blended Learning IJEACS
In this paper, we use the clustering technique to monitor the status of students’ scholastic recital. This paper spotlights on upliftment the education system via K-means clustering. Clustering is the process of grouping the similar objects. Commonly in the academic, the performances of the students are grouped by their Graded Point (GP). We adopted K-means algorithm and implemented it on students’ mark data. This system is a promising index to screen the development of students and categorize the students by their academic performance. From the categories, we train the students based on their GP. It was implemented in MATLAB and obtained the clusters of students exactly.
Student Performance Evaluation in Education Sector Using Prediction and Clust...IJSRD
Data mining is the crucial steps to find out previously unknown information from large relational database. various technique and algorithm are their used in data mining such as association rules, clustering and classification and prediction techniques. Ease of the techniques contains particular characteristics and behaviour. In this paper the prime focus on clustering technique and prediction technique. Now a days large amount of data stored in educational database increasing rapidly. The database for particular set of student was collected. The clustering and prediction is made on some detailed manner and the results were produce. The K-means clustering algorithm is used here. To find nearest possible a cluster a similar group the turning point India is the performance in higher education for all students. This academic performance is influenced by various factor, therefore to identify the difference between high learners and slow learner students it is important for student performance to develop predictive data mining model.
A Survey on the Classification Techniques In Educational Data MiningEditor IJCATR
Due to increasing interest in data mining and educational system, educational data mining is the emerging topic for research
community. educational data mining means to extract the hidden knowledge from large repositories of data with the use of technique
and tools. educational data mining develops new methods to discover knowledge from educational database and used for decision
making in educational system. The various techniques of data mining like classification. clustering can be applied to bring out hidden
knowledge from the educational data.
In this paper, we focus on the educational data mining and classification techniques. In this study we analyze attributes for the
prediction of student's behavior and academic performance by using WEKA open source data mining tool and various classification
methods like decision trees, C4.5 algorithm, ID3 algorithm etc.
Applying K-Means Clustering Algorithm to Discover Knowledge from Insurance Da...theijes
Data mining works to extract information known in advance from the enormous quantities of data which can lead to knowledge. It provides information that helps to make good decisions. The effectiveness of data mining in access to knowledge to achieve the goal of which is the discovery of the hidden facts contained in databases and through the use of multiple technologies. Clustering is organizing data into clusters or groups such that they have high intra-cluster similarity and low inter cluster similarity. This paper deals with K-means clustering algorithm which collect a number of data based on the characteristics and attributes of this data, and process the Clustering by reducing the distances between the data center. This algorithm is applied using open source tool called WEKA, with the Insurance dataset as its input
Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Predictionijtsrd
Data mining techniques play an important role in data analysis. For the construction of a classification model which could predict performance of students, particularly for engineering branches, a decision tree algorithm associated with the data mining techniques have been used in the research. A number of factors may affect the performance of students. Data mining technology which can related to this student grade well and we also used classification algorithms prediction. In this paper, we used educational data mining to predict students final grade based on their performance. We proposed student data classification using ID3 Iterative Dichotomiser 3 Decision Tree Algorithm Khin Khin Lay | San San Nwe "Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26545.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26545/using-id3-decision-tree-algorithm-to-the-student-grade-analysis-and-prediction/khin-khin-lay
Hypothesis on Different Data Mining AlgorithmsIJERA Editor
In this paper, different classification algorithms for data mining are discussed. Data Mining is about
explaining the past & predicting the future by means of data analysis. Classification is a task of data mining,
which categories data based on numerical or categorical variables. To classify the data many algorithms are
proposed, out of them five algorithms are comparatively studied for data mining through classification. There are
four different classification approaches namely Frequency Table, Covariance Matrix, Similarity Functions &
Others. As work for research on classification methods, algorithms like Naive Bayesian, K Nearest Neighbors,
Decision Tree, Artificial Neural Network & Support Vector Machine are studied & examined using benchmark
datasets like Iris & Lung Cancer.
Recommendation of Data Mining Technique in Higher Education Prof. Priya Thaka...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Maintaining Data Confidentiality in Association Rule Mining in Distributed En...IJSRD
The data in real world applications is distributed at multiple locations, and the owner of the databases may be different people. Thus to perform mining task, the data needs to be kept at central location which causes threat to the privacy of corporate data. Hence the key challenge is to applying mining on distributed source data with preserving privacy of corporate data. The system addresses the problem of incrementally mining frequent itemsets in dynamic environment. The assumption made here is that, after initial mining the source undergoes into small changes in each time. The privacy of data should not be threatened by an adversary i.e. the miner and target database owner should not be able to recover original data from transformed data.
Performance and Emission characteristics of a Single Cylinder Four Stroke Die...IJSRD
The current trends in CI engine are to use Water-diesel emulsion as alternative fuel. It can be employed directly to the existing CI Engine system with no additional modifications. This system helps in reduction of NOx as well as PM, which in turn improve the combustion efficiency of the engine. However there are still investigations have to be done. The current work mainly concentrated on diesel engine run on water-diesel emulsions and its effect on engine performance and emissions were studied. The various loads were applied on a constant speed diesel engine run on water-diesel emulsions of varying ratios of 0.2:1, 0.3:1. 0.4:1 and 0.5:1. Emission and performance characteristics were measured and were compared with base diesel operation. The emissions like NOx and smoke density were found to decrease greatly and brake thermal efficiency was found to increase at high loads. Smoke level was 4.2 BSU and 3 BSU for base diesel and water diesel emulsion of 0.4. The ignition delay was found to increase with water diesel emulsions. This also increased the maximum rate of pressure rise and peak pressure. The engine was found to run rough with water-diesel emulsions. The optimal water-diesel ratio was found to be 0.4:1 by weight. HC and CO emissions were found to increase with water diesel emulsions.
Preclusion of High and Low Pressure In Boiler by Using LABVIEWIJSRD
Pressure is an important physical parameter to be controlled in process boiler, heat exchanger, nuclear reactor and steam carrying pipeline. In the article the issue has been face in boiler operation due to pressure is handled. In boiler, the problem is due to maximum and minimum range of pressure. Due to the issues there is a chance to causes the hazop. To avoid such the problem the high and low pressure in boiler has to control. In the paper such the problem has sorted out by implementing ON-OFF control. Here the proposed control action for pressure control is implemented with the help of LabVIEW (Laboratory Virtual Instrument Engineering Workbench) software and NI ELVIS hardware. In the idea the boiler’s low range and high is monitored and controlled valve desirably. And also the high range and low range of pressure in the boiler is signified to plant operator by alarm signal.
Prevention and Detection of Man in the Middle Attack on AODV ProtocolIJSRD
In this paper it is discuss about AODV protocol and security attacks and man in the middle attack in detail. AODV Protocol is use to find route and very important protocol for communication in wireless network. So AODV protocol should be Secured and it is a big challenge. There are various attacks that occur on it. Here in this paper it discussed about the detection and preventions of man-in-the-middle attack in detail.
Comparative Analysis of PAPR Reduction Techniques in OFDM Using Precoding Tec...IJSRD
In this modern era, Orthogonal Frequency Division Multiplexing (OFDM) has been proved to be an explicit promising technique for wired and wireless systems because of its several advantages like high spectral efficiency, robustness against frequency selective fading, relatively simple receiver implementation etc. Besides having a number of advantages OFDM suffers from few disadvantages like high Peak to Average Power Ratio (PAPR), Intercarrier Interference (ICI), Intersymbol Interference (ISI) etc. These detrimental effects, if not compensated properly and timely, can result in system performance degradation. This paper mainly concentrates on reduction of PAPR.A comparisons have been made between various precoding techniques against conventional OFDM.
Evaluation the Effect of Machining Parameters on MRR of Mild SteelIJSRD
Today’s life is totally based on Internet. Now a days people cannot imagine life without Internet. Information and communication technology plays vital role in today’s online networked society. In today’s life, we are very close to the online social networks. Online social networks are used for posting and sharing information across various social networking sites. But user’s privacy is not maintained by online social networks. For maintaining users sensitive information’s privacy online social networks provides little or no support. For filtering unwanted messages we propose a system using machine learning (ML). Using machine learning in soft classifier content based filtering performed. In proposed system filtering rules (FR’s) are provided for content independent filtering.. Blacklists are used for more flexibility by which filtering choices are increased. Proposed system provides security to the Online Social Networks.
Filter unwanted messages from walls and blocking nonlegitimate user in osnIJSRD
Today’s life is totally based on Internet. Now a days people cannot imagine life without Internet. Information and communication technology plays vital role in today’s online networked society. In today’s life, we are very close to the online social networks. Online social networks are used for posting and sharing information across various social networking sites. But user’s privacy is not maintained by online social networks. For maintaining users sensitive information’s privacy online social networks provides little or no support. For filtering unwanted messages we propose a system using machine learning (ML). Using machine learning in soft classifier content based filtering performed. In proposed system filtering rules (FR’s) are provided for content independent filtering.. Blacklists are used for more flexibility by which filtering choices are increased. Proposed system provides security to the Online Social Networks.
Keystroke Dynamics Authentication with Project Management SystemIJSRD
Generally user authentication is done using username and password that is called as login process. This login process is not more secure because, however a login session is still unprotected to impersonator when the user leaves his computer without logging off. Keystroke dynamics methods can be made useful to verify a user by extracting some typing features then, after the authentication process has successfully ended. From the last decade several studies proposed the use of keystroke dynamics as a behavioral biometric tool to verify users. We propose a new method, for representing the keystroke patterns by joining similar pairs of consecutive keystrokes. The above proposed method is used to consider clustering the di-graphs which are based on their temporal features. In this project, authentication system is provide to project management system that make more Secure management system without acknowledging unauthorized user. The Project Management System addresses the management of software projects. It provides the framework for organizing and managing resources in such a way that these resources deliver all the work required to complete a software project within defined scope, time and cost constraints. The system applies only to the management of software projects and is a tool that facilitates decision making.
Diagnosing lungs cancer Using Neural NetworksIJSRD
Artificial Neural Networks is the new technology. It is the branch of Artificial Intelligence and also it is an accepted new technology. Now a days Neural Networks Plays a Vital role in Medicine, Particularly in some fields such as cardiology, oncology etc. And also it has many applications in many areas like Science and Technology, Education, Business, Business and Manufacturing, etc. Neural Networks is most useful for making the decision more Effective. In this Paper, by the use of Neural Networks how the severe disease Lungs Cancer has been diagnosed more effectively. This Paper discussed about how the Lungs cancer can be identified effectively in earlier stages and diagnosed using Neural Networks and some devices. The Neural Networks has been successfully applied in Carcinogenesis. The main aim of this research is by the use of Neural Networks the Carcinogenesis can be diagnosed more cost-effective, easy to use techniques and methods. This Paper discussed about how the Lungs cancer can be identified effectively in earlier stages and diagnosed using Neural Networks and some devices. Sputum Cytology is used to detect the Lungs Cancer in Early stages.
A Survey on Sentiment Analysis and Opinion MiningIJSRD
In Today’s world, the social media has given web users a place for expressing and sharing their thoughts and opinions on different topics or events. For this purpose, the opinion mining has gained the importance. Sentiment classification and Opinion Mining is the study of people’s opinion, emotions, attitude towards the product, services, etc. Sentiment Analysis and Opinion Mining are the two interchangeable terms. There are various approaches and techniques exist for Sentiment Analysis like Naïve Bayes, Decision Trees, Support Vector Machines, Random Forests, Maximum Entropy, etc. Opinion mining is a useful and beneficial way to scientific surveys, political polls, market research and business intelligence, etc. This paper presents a literature review of various techniques used for opinion mining and sentiment analysis.
A Defect Prediction Model for Software Product based on ANFISIJSRD
Artificial intelligence techniques are day by day getting involvement in all the classification and prediction based process like environmental monitoring, stock exchange conditions, biomedical diagnosis, software engineering etc. However still there are yet to be simplify the challenges of selecting training criteria for design of artificial intelligence models used for prediction of results. This work focus on the defect prediction mechanism development using software metric data of KC1.We have taken subtractive clustering approach for generation of fuzzy inference system (FIS).The FIS rules are generated at different radius of influence of input attribute vectors and the developed rules are further modified by ANFIS technique to obtain the prediction of number of defects in software project using fuzzy logic system.
Experimental Investigation of Granulated Blast Furnace Slag ond Quarry Dust a...IJSRD
In this experimental work ninety nine cubes has been prepared having dimension 70.7x70.7x70.7 mm are cast as per IS:4031 (2000). In this experimental investigation cement mortar mix 1:3 by volume were selected for 0%, 20%, 40%, 60%, 80% and 100% partially replacement of natural sand (NS) by Granulated blast furnace slag (GBFS) and quarry dust (QD) [3 cubes on each parameter respectively] for W/C ratio of 0.55 respectively. All the cubes were tested under compressive testing machine. To compare the average compressive strength of natural sand (NS) with granulated blast furnace slag (GBFS) and quarry dust (QD).
Product Quality Analysis based on online ReviewsIJSRD
Customers satisfaction is the most important criteria before buying any product. Technology today has grown to such an extent that every smallest possible query is found on internet. An individual can express his reviews towards a product through Internet. This allows others to have a brief idea about the product before buying one for them. In this paper, we take into account all the challenges and limitations encountered while reading the online reviews and time being consumed in understanding quality of the product from the reviews. We include several methods and algorithms that help the consumer to understand the Quality of the product in better way.
Solving Fuzzy Matrix Games Defuzzificated by Trapezoidal Parabolic Fuzzy NumbersIJSRD
The matrix game theory gives a mathematical background for dealing with competitive or antagonistic situations arise in many parts of real life. Matrix games have been extensively studied and successfully applied to many fields such as economics, business, management and e-commerce as well as advertising. This paper deals with two-person matrix games whose elements of pay-off matrix are fuzzy numbers. Then the corresponding matrix game has been converted into crisp game using defuzzification techniques. The value of the matrix game for each player is obtained by solving corresponding crisp game problems using the existing method. Finally, to illustrate the proposed methodology, a practical and realistic numerical example has been applied for different defuzzification methods and the obtained results have been compared
Study of Clustering of Data Base in Education Sector Using Data MiningIJSRD
Data mining is a technology used in different disciplines to search for significant relationships among variables in n number of data sets. Data mining is frequently used in all types’ areas as well as applications. In this paper the application of data mining is attached with the field of education. The relationship between student’s university entrance examination results and their success was studied using cluster analysis and k-means algorithm techniques.
Fault Tolerance in Big Data Processing Using Heartbeat Messages and Data Repl...IJSRD
Big data is a popular term used to define the exponential evolution and availability of data, includes both structured and unstructured data. The volatile progression of demands on big data processing imposes heavy burden on computation, communication and storage in geographically distributed data centers. Hence it is necessary to minimize the cost of big data processing, which also includes fault tolerance cost. Big Data processing involves two types of faults: node failure and data loss. Both the faults can be recovered using heartbeat messages. Here heartbeat messages acts as an acknowledgement messages between two servers. This paper depicts about the study of node failure and recovery, data replication and heartbeat messages.
Investigation of Effect of Process Parameters on Maximum Temperature during F...IJSRD
In case of friction stir welding, the maximum temperature along the weld line within appropriate range at tool workpiece interface is responsible for quality of welded joint. Through this paper, an attempt is made to establish a relationship between the input process parameters and the maximum temperature along the weld line during friction stir welding of aluminium alloy AA-7075. The design of pre-experimental simulation has been performed in accordance with full factorial technique. The simulation of friction stir welding has been performed by varying input parameters, tool rotational speed and welding speed. The analysis of variance (ANOVA) is used to investigate the effect of input parameters on maximum temperature during friction stir welding. A correlation was established between input parameters and maximum temperature by multiple regression lines. This study indicates that the tool rotational speed is the main input parameter that has high statistical influence on maximum temperature along the weld line during friction stir welding of aluminium alloy AA-7075.
Review Paper on Computer Aided Design & Analysis of Rotor Shaft of a RotavatorIJSRD
The intent of this paper is to study the various forces and stress acting on a rotor shaft of a standard rotavator which is subjected to transient loading. The standard models of rotavator, having a progressive cutting sequence was considered for the study and analysis. The study was extended to various available models having different cutting blade arrangement. The study was carried on different papers and identifies the various forces acting on a Rotor shaft of a rotavator. The positions of the torque and forces applied are varied according to the model considered. The response was obtained by considering the angle of twist and equivalent stress on the rotor shaft. This paper presented a methodology for conducting transient analysis of rotor shaft of a rotavator,
A Survey on Data Mining Techniques for Crime Hotspots PredictionIJSRD
A crime is an act which is against the laws of a country or region. The technique which is used to find areas on a map which have high crime intensity is known as crime hotspot prediction. The technique uses the crime data which includes the area with crime rate and predict the future location with high crime intensity. The motivation of crime hotspot prediction is to raise people’s awareness regarding the dangerous location in certain time period. It can help for police resource allocation for creating a safe environment. The paper presents survey of different types of data mining techniques for crime hotspots prediction.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
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1. IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 10, 2015 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 967
Analysis on Student Admission Enquiry System
Ameya Joshi 1
Manjusha Singh2
1
M.Tech Scholar 2
Assistant Professor
1,2
Chhatrapati Shivaji Institute of Technology, Bhilai, DURG
Abstract— Data mining referred to extracting the hidden
predictive information from huge amount of data set.
Recently, there are number of private institution are came
into existence and they put their efforts to get fruitful
admissions. In this paper, the techniques of data mining are
used to analyze the mind setup of student after matriculate.
One of the best tools of data mining is known as WEKA
(Waikato Environment Knowledge Analysis), is used to
formulate the process of analysis.
Key words: Data Mining, Clustering, KDD, WEKA
I. INTRODUCTION
Data mining define the process of extracting or mining the
knowledge from large amount of data. The term data mining
is formally known as Knowledge mining from data or
Knowledge data mining. It is the process of analyzing data
from different vision and detailing it into useful information.
The overall goal of Data mining process is to extract hidden
information which are potentially useful and structure it for
further use.
Data Mining is an analytic process designed to
explore large amounts of data typically business oriented or
market related also known as "big data" in search of
consistent patterns and/or appropriate relationships between
objects, and then to validate the findings by applying the
detected patterns to new subsets of data. The main goal of
data mining is prediction and Classification. Data mining is
the most common type of direct business applications. The
fundamental and perspective view provided by data mining
move beyond the past event of retrospective tools typical of
decision support systems. Business questions can be easily
answered by data mining tools that traditionally were too
time taking to resolve.
Data mining techniques can be used rapidly on
various existing software and hardware platforms to enhance
the quality of existing information resources that can be
integrated with upcoming products and systems as they are
brought on-line. In this paper we are focusing on techniques
of data mining: Clustering technique is the crucial task for
distinguish the data in to sub sets, Classification technique is
used for predicting the outcomes based on the given inputs.
By combining these two techniques we are analyzing the
activities of the student behavior. There are various
applications of Data mining such as in marketing, web
mining, scientific data analysis and many more.
A. Data Mining Consists Of Five Major Elements:
1) Extract, transform, and load data onto the data
warehouse system.
2) Store and manages the data in various dimensions of
schema
3) Provide data access to business analysts, executives and
information technology professionals.
4) Analyze the data by application software.
5) Present the data in a split charts, diagrams, graphs or
table.
Clustering is the task of nominating a set of objects into
batch (called cluster) so that the objects in the same clusters
are more identical to each other to those in other cluster.
Clustering is an ultimate task of explorative data
mining, and a common technique for statistical data analysis
used in various fields such as information extraction, pattern
evaluation, machine learning techniques, image analysis.
We are using WEKA tools which provide a better solution
for analysis and help to pretend the behavior of the students.
WEKA is a acquisition of machine learning algorithms for
data mining activities.
B. The Key Features Of WEKA Are As Follows:
it facilitate different algorithms for data mining and
machine learning
is is open source and implemented in java
it is platform-independent and portable
it provides flexible facilities for scripting experiments.
Fig. 1: WEKA GUI
II. LITERATURE REVIEW
Rakesh kumar arora, (krishna engineering college
ghaziabad, up ) Dr. Dharmendra badal ( bundelkhand
university, ghaziabad, up)(2013) has presented to analyze
the data obtained from the admission form filled by
admission seekers. Determining the reasons for decline in
quality of admissions taken in the institute over the year. In
this paper, author explained the tools and the methods
applied for admission management procedure.[1]
Dr. Sudhir B. Jagtap SVITN (Udgir) M.H. Dr.
Kodge B. G. SVITN (Udgir) M.H have searched
Knowledge extraction from database is become one of the
key process of each and every organization for their
development issues and plays a vital role in e-governance
for future planning and development issues.[2]
Narendra Sharma, Aman Bajpai , Ratnesh Litoriya
Jaypee University of Engg. & Technology(2012) says that
k-means clustering algorithm is simplest algorithm as
compared to other algorithms. We can’t required deep
knowledge of algorithms for working in WEKA. That’s why
WEKA is more suitable tool for data mining applications.[3]
2. Analysis on Student Admission Enquiry System
(IJSRD/Vol. 3/Issue 10/2015/218)
All rights reserved by www.ijsrd.com 968
R. ROBU, C.HORA University “Politechnica” Romania
(2012) analysis such as images, signals through data mining
techniques aims to extract useful knowledge from the data.
[4]
Suhem Parack, Zain Zahid, Fatima Merchant(2012)
searched about the algorithms such as k-means or Apriori
offers an effective way of profiling students which can be
used in educational systems. We can easily group the
students, identify hidden patterns about their learning
style.[5]
Anand, Vaibhav, Priti Maharashtra, India(2014)
data mining techniques is potentially useful and ultimately
understandable. It involves task like anomaly detection,
classification, regression, clustering etc.[6]
III. CLUSTERING
Clustering is the activity of composing objects into groups
whose members are similar in some properties. A cluster is a
group of objects which are “identical” between them and are
“dissimilar” to the objects belonging to other clusters.
Fig. 2: Process of Clustering
A. K- Means Clustering:
K –Means clustering is one of the best algorithm in terms its
simplicity and efficiency. K-means clustering aims to
splitting n observations into k clusters in which each
observation belongs to the cluster with the nearest mean,
serving as a prototype of the cluster.[12].
In the past work K-Means is being used for the
analysis of the data obtained from the admission procedure.
The data set is collected from the college of Ghaziabad. The
number of student involved is 129 and is done on the basis
of different parameters. The author concluded that this
model will help to assist the management persons to filter
good student who are willing to join the institute.[1]
B. Algorithm Steps for K-Means Clustering:
Input:
D = {d1,d2….dn} set of n data items.
K = number of desired clusters
Output: A set of k clusters.
Steps:
1) Enter the number of cluster K.
2) Find out the centroid.
3) Calculate the distance from object to centroids.
4) Check object moved to the specified group or not?
5) If yes move the object to the respective cluster.
6) If not, then recalculate the distance from object to
centroid until the convergence criteria meet.
But each and every good thing come up with little
disadvantages, K-Means clustering also facing difficulties
while comparing quality of the cluster and it does not work
efficiently with non-globular clusters.
IV. CLASSIFICATION
Classification is a classic data mining technique based on
machine learning. Basically classification is used to classify
each item in a set of data into one of predefined set of
classes or groups. It is considered as supervised learning in
which we have a Training set containing data that have been
previously categorized.
Fig. 3: Classification Strategy
A. Naïve Bayes:
Naive Bayes classifiers are a family of simple probabilistic
classifiers based on applying Bayes theorem with strong
(naive) independence assumptions between the features.
Naive Bayes classifiers are highly scalable, requiring a
number of parameters linear in the number of variables
(features/predictors) in a learning problem.
B. Bayesian Rule:
P(C/X) =
( ) ( )
( )
Posterior=
P(C): independent probability of C: prior probability
P(X): independent probability of D
P(X|C): conditional probability of X given C: likelihood
P(C|X): conditional probability of C given X: posterior
probability.
On the basis of clustering and classification technique we
can judge the attributes of the students. Previously in
admission procedure K-means clustering is used to analyze
and summarize the data and it will also assist the admission
planner to monitor the activity of the admission seekers. We
are expecting to merge this technique which will help to
classify more efficiently
V. OBJECTIVE OF STUDY
To Study of existing data analysis clustering technique.
To Analyze complexity and outlier issue in Algorithm
and to find point of complexity in Algorithm.
To deploy the clustering methodology in the
management process.
To compare the results with K-Means algorithm in
terms of efficiency and throughput.
Here efficiency is in terms of number of student converting
their enquiries in to admission and throughput deals with the
conversion rate of the students.
VI. RESULTS AND CONCLUSION
On the basis of this survey, we found that K-Means
clustering is the easiest algorithm to implement. WEKA is
the efficient tool of data mining for analysis which does not
required deep knowledge about the algorithms. This will
help in identifying the set of students that need to be focused
to actually convert the enquiry into admission. Management
will get beneficial inputs. We wish to develop the algorithm
with the concept of clustering and classification, and to
deploy the same in admission procedure to fulfill the
requirement and to compare the result with existing
algorithm and we try to observe in terms of accuracy and
throughput.
3. Analysis on Student Admission Enquiry System
(IJSRD/Vol. 3/Issue 10/2015/218)
All rights reserved by www.ijsrd.com 969
REFERENCES
[1] Rakesh Kumar Arora, Dr. Dharmendra Badal
“Admission Management through Data Mining using
WEKA” , International Journal, Volume 3, Issue 10,
October 2013. Page No. [674-678].
[2] Dr. Sudhir B. Jagtap and Dr. Kodge B. G., “Census
Data Mining and Data Analysis using WEKA”,
International Conference in “Emerging Trends in
Science, Technology and Management-2013,
Singapore. Page No. [35-40].
[3] Narendra Sharma, Aman Bajpai, Mr. Ratnesh Litoriya
“Comparison the various clustering algorithms of
WEKA tools’ , International Journal of Emerging
Technology and Advanced Engineering (ISSN 2250-
2459, Volume 2, Issue 5, May 2012) Page No-[73-80].
[4] R Robu and C. Hora, “Medical data mining with
extended WEKA ” , IEEE 16TH International
conference on intelligent Engineering Systems , June
13-15, 2012 page No. [347-350].
[5] Suhem Parack, Zain Zahid Fatima Merchant.,
“Application of data mining in Educational databases
for predicting academics trends and patterns. ”, IEEE-
2012.
[6] Anand V Saurkar, Vaibhav Bhujade, Priti Bhagat, Amit
Khaparde “International Journal of Advanced Research
in Computer Science and Software Engineering,
Volume-4, Issue 4, April 2014 ISSN: 2277 128X . Page
No-[98-101].
[7] M Bhoomi, “Enhanced K-Means Clustering algorithm
to reduce time complexity for numeric values ”,
International Journal of Computer Science and
Information Technologies, Volume 5(1), 2014, ISSN:
0975-9646 page No. [876-879].
[8] http://www.ijcsit.com/docs/Volume%205/vol5issue01/ij
csit20140501189.pdf
[9] http://www.iaeng.org/publication/WCE2009/WCE2009
_pp308-312.pdf
[10]http://documents.software.dell.com/Statistics/Textbook/
Data-Mining-Techniques
[11]http://esatjournals.net/ijret/2013v02/i11/IJRET2013021
1019.pdf
[12] https://en.wikipedia.org/wiki/K-means_clustering
[13]https://www.google.co.in/search?q=flowchart+of+kmea
ns+clustering+algorithm&biw=1024&bih=639&tbm=is
ch&tbo=u&source=univ&sa=X&sqi=2&ved=0ahUKE
wiy2fyh2KDJAhUBH5QKHXVGBKIQsAQIIA