data mining 2021
233407671
Proceedings AI/ML for Cybersecurity: Challenges, Solutions, and Novel Ideas at SIAM Data Mining 2021
Malicious cyber activity is ubiquitous and its harmful effects have dramatic and often irreversible impacts on society. Given the shortage of cybersecurity professionals, the ever evolving adversary, the massive amounts of data which could contain evidence of an attack, and the speed at which defensive actions must be taken, innovations which enable autonomy in cybersecurity must continue to expand, in order to move away from a reactive defense posture and towards a more proactive one. The challenges in this space are quite different from those associated with applying AI in other domains such as computer vision. The environment suffers from an incredibly high degree of uncertainty, stemming from the intractability of ingesting all the available data, as well as the possibility that malicious actors are manipulating the data. Another unique challenge in this space is the dynamism of the adversary causes the indicators of compromise to change frequently and without warning. In spite of these challenges, machine learning has been applied to this domain and has achieved some success in the realm of detection. While this aspect of the problem is far from solved, a growing part of the commercial sector is providing ML enhanced capabilities as a service. Many of these entities also provide platforms which facilitate the deployment of these automated solutions. Academic research in this space is growing and continues to influence current solutions, as well as strengthen foundational knowledge which will make autonomous agents in this space a possibility.
ArXiv
John A Emanuello Kimberly Ferguson-Walter Erik Hemberg Una-May O Reilly Ahmad Ridley Dennis M Ross Diane Staheli William W Streilein
233479334
Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2021 Workshops, WSPA, MLMEIN, SDPRA, DARAI, and AI4EPT, Delhi, India, May 11, 2021 Proceedings
PAKDD
233991385
Analysis on Telemarketing Access System Based on C/S Architecture and Data Mining
With the rapid development of information science, the smart system is becoming popular. Hence, this paper conducts the analysis on the telemarketing access system based on C/S architecture and data mining. B/S is far inferior to C/S. Taken together, it can be found that all the strengths of the C/S are the weaknesses of B/S, and vice versa. By using this advantage, the analysis on the CMS is considered. After the OSS processing is successfully returned, the CMS triggers the CDN process, and the CDN process first goes to the CMM. The systematic design shows the robustness of the model.
2021 5th International Conference on Computing Methodologies and Communication (ICCMC)
Junbin Duan
231851420
Research on Application of Online Teaching Performance Prediction Based on Data Mining Algorithm
The application of data mining in teaching has entered the stage of development. During the epidemic, colleges and universities have accumulated a large amount of online teaching statistical data. These data can be used to establish a classification model for predicting student performance. In this paper, the Naive Bayes algorithm is improved to solve the problem of underflow when the data feature values are too large. The student performance prediction classification model is constructed, and the classification efficiency and accuracy are improved to a certain extent. The improved model is used to predict and warn the performance in the mid term stage to prevent The phenomenon of a large proportion of missing subjects, thereby ensuring the quality of students' learning throughout the semester.
2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE)
Dianwei Chi Yinyin Huang
235383684
A Machine Learning Based Approach for Student Performance Evaluation in Educational Data Mining
Astonishing progress in machine learning and data mining techniques has been achieved during the last two decades. Education should benefit from these improvements to discover about how people learn in different educational settings. The aim of the research is comparing artificial neural network (ANN) to random forest (RF) machine learning models for predicting performance of students based on their demographic and assessment information. After analyzing the Open University Learning Analytics Dataset (OULAD) we applied feature engineering techniques, and then the two models were evaluated. Our results showed that the ANN model outperformed the RF model accuracy 91.08& to 81.35 ANN performs well on educational data and can be efficiently used for predicting student performance and in early warning systems.
2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)
Muhammad Sammy Ahmad Ahmed H Asad Ammar Mohammed
234477991
K means Clustering based Data Mining Methodology to Discover the Prosumers' Energy Features
In the paper, a Data Mining methodology is proposed to identify the energy features of the prosumers. The K means clustering algorithm has been used to obtain prosumers' categories considering two specific indicators which can help the Distribution Network Operators (DNOs) in the optimal operating of the networks, namely the injected average hourly power and the total annual energy. Testing the methodology has been done for a database corresponding to the prosumers connected in the low voltage (LV) distribution networks belonging to a Romanian DNO in 2019. The obtained results have confirmed the importance of the Data Mining to extract easy and fast the energy features of the prosumers from the large size databases and used by the DNOs in the Decision Making process associated with the optimal operating of the distribution networks.
2021 12th International Symposium on Advanced Topics in Electrical Engineering (ATEE)
Vasilica Dandea Gheorghe Grigoras Bogdan Constantin Neagu Florina Scarlatache
231852886
A Data Mining Based System For Transaction Fraud Detection
With the deepening of world trade informationization degree, transaction fraud has been endangering the security of world finance and commerce. The frequency and scale of transaction fraud are expanding day by day, which makes the vast number of users and financial practitioners suffer huge economic losses. With the increasing maturity of data mining and machine learning in the field of computer science, the detection of transaction fraud gradually finds a practical solution. This paper adopts a transaction fraud detection system based on random forest and manual detection. The experimental results of IEEE CIS fraud dataset show that the method of this model is better than the benchmark model, such as logistic regression, support vector machine. Finally, the accuracy of our model reached 96.8% and the AUC ROC score was 92.5%
2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE)
Wenkai Deng Ziming Huang Jiachen Zhang Junyan Xu
236920144
Research on Optimization of User Behavior Data Mining Based on Apriori Algorithm
Aiming at the problem of user behavior data mining optimization, an optimization method combining Apriori algorithm and gradient algorithm is proposed. Apriori algorithm has the advantage of global optimization, but there are easy prematurity and local defects of optimal performance is poor, so this article combined with the gradient algorithm, by using the iterative process of the optimal solution, is to accelerate the search speed and guarantee the method of gradient algorithm converges to the global optimal solution, to achieve the global convergence of the method and efficiency of computation speed.
2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)
Tongqing Liu
235616126
Data Mining Classification and analytical model of prediction for Job Placements using Fuzzy Logic
One of the most common issues that new graduates experience is the insufficient understanding of basic concepts. Major skill gaps in this area include a lack of deep comprehension on technical information, lack of customer management abilities, and insufficient knowledge of numerous disciplines. The study has attributed a lack of English communication skills, which they found in 73.63% of applicants, and poor analytical and quantitative skills, which they found in 57.96% of applicants, as a major cause of unemployment. Aptitude tests are conducted to analyze the problem solving skills of the candidate; this evaluation helps to solve a problem at a given point in time. The proposed study has collected data on students, who had different information about their previous and current academic records, and then different classification algorithms along with the Data Mining Tool (VEKA) are used to analyze academic performance in training and accommodation. This study presents a proposed model based on a classification approach to find a better evaluation method in order to predict the student accommodation. There are many basic classification algorithms and statistical methods that can be used as good resources for classifying student datasets in education. In this article, a fuzzy inference system was used to predict the student performance and improve academic performance. This model can determine the relationship between student achievement and campus placement.
2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)
S Venkatachalam
231826583
A Comparative Study on Fake Job Post Prediction Using Different Data mining Techniques
In recent years, due to advancement in modern technology and social communication, advertising new job posts has become very common issue in the present world. So, fake job posting prediction task is going to be a great concern for all. Like many other classification tasks, fake job posing prediction leaves a lot of challenges to face. This paper proposed to use different data mining techniques and classification algorithm like KNN, decision tree, support vector machine, naive bayes classifier, random forest classifier, multilayer perceptron and deep neural network to predict a job post if it is real or fraudulent. We have experimented on Employment Scam Aegean Dataset (EMSCAD) containing 18000 samples. Deep neural network as a classifier, performs great for this classification task. We have used three dense layers for this deep neural network classifier. The trained classifier shows approximately 98% classification accuracy (DNN) to predict a fraudulent job post.
2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST)
Sultana Umme Habiba Md Khairul Islam Farzana Tasnim