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machine learning Approaches for Marketing Campaign in Portuguese Banks 3

Machine Learning Approaches for Marketing Campaign in Portug...

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3rd international conference on Cybernetics and Intelligent System (ICORIS)

作者： Alexandra, Jennifer Sinaga, Kristina Pestaria Bina Nusantara Univ Dept Master Informat Syst Management Jakarta Indonesia

ISBN: (纸本)9781665425803

Bank is a type of business that deals with saving, circulation of money, deposits and others. The number of services provided by banks is very diverse, this depends on the capabilities of each bank. The more capable and better the bank is, the more services it will offer. Introducing the product directly has been commonly used for various industries, one of them is the banking industry. In directly introducing products, banks can conduct market analysis by utilizing the information technology space that can assist in making decisions. By analyzing bank marketing data, it can be used to select the type of marketing to do. Marketing campaigns can be carried out via email, telephone, and direct email to prospective customers that allow potential customers to decide whether to take the product offered or not. With increasing time, the amount of incoming data continues to grow. With this increasing data, one of the bank institutions found it difficult to predict whether their clients would subscribe to a term deposit or not. Therefore, in this paper, the data mining process will be carried out using classification (Decision Tree, Wye Bayes, and Random Forest) and clustering (K-Means, K-Medoids, and DBSCAN) methods to predict if the client will subscribe a term deposit.

关键词： component machine learning data mining Classification Clustering

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3rd international conference on Smart IoT Systems: Innovations and Computing, SSIC 2021

3rd International Conference on Smart IoT Systems: Innovatio...

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3rd international conference on Smart IoT Systems: Innovations and Computing, SSIC 2021

ISBN: (纸本)9789811628764

The proceedings contain 55 papers. The special focus in this conference is on Smart IoT Systems: Innovations and Computing. The topics include: Requirements in PLS, SSML and SRGS Standard—Hindi as a Case Study;Effectiveness Analysis of Different POS Tagging Techniques for Bangla Language;intelligent Surveillance Systems for Smart Cities: A Systematic Literature Review;the Social and Ethical Implications of Smart Environments in the Age of Big data;Enhanced Energy-Efficient Routing for Wireless Sensor Network Using Extended Power-Efficient Gathering in Sensor Information Systems (E-PEGASIS) Protocol;sentiment Analysis of Amazon Product Reviews Using Hybrid Rule-Based Approach;artificial Intelligence and machine learning Based: Advances in Demand-Side Response of Renewable Energy-Integrated Smart Grid;design of IoT-Enabled Farmer-Centric Multi-Crop Irrigation Support System;intelligent Infrastructure and Transportation: A Case of Passenger Transportation System in Jaipur City of Rajasthan;conceptual machine learning Framework for Performance-Based Resource Allocation to the Institutions: A Hybrid Approach;Criticality Analysis for Safety–Critical Systems Using FMEA;significance of Internet of Things (IoT) in Health Care with Trending Smart Application;greek Handwritten Character recognition Using Inception V3;An Exploratory Analysis on the Unfold of Fake News During COVID-19 Pandemic;a Research on Fake News Detection Using machine learning Algorithm;an Intelligent Model to Suggest Top Productive Seasonal Crops Based on User Location in the Context of Bangladesh;brightness Intensity-Based Transient Motion Prediction;state of the Art and Challenges in Blockchain Applications;swarm Intelligence Techniques for Automated Cryptanalysis of Classical Transposition Cipher: A Review;YOLOv4-Based Monitoring Model for COVID-19 Social Distancing Control;facial Expression recognition System.

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Implementation of Different machine learning Approaches for Heart Disease Diagnosis

Implementation of Different Machine Learning Approaches for ...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Ragav Sharma Anuj Kumar Goel ECE Department Chandigarh University Punjab

Cardiovascular disease is a deadly ailment that can strike unexpectedly and without warning. Despite generating copious amounts of patient and illness data, the healthcare sector often fails to effectively harness this information for precise diagnosis and decision-making. To unlock the wealth of information available, researchers and practitioners can utilize data mining and machine learning approaches. This paper implements a proposed system consisting of data collection, preprocessing, feature selection, model selection, model training, and model evaluation. The study compares different machine learning models for predicting heart disease such as KNN classifier, Support vector machine learning classifier (SVM), Random Forest and Decision tree, where KNN is giving best output of 87.05% with 8 neighbor. An analytical review of existing research on the prediction of heart disease is given in this article and drawing insightful conclusions for future study.

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AI-Enhanced data mining for Fraud Detection in Financial Transactions

AI-Enhanced Data Mining for Fraud Detection in Financial Tra...

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Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Rajan Gupta Raghav Goyal Kanta Malik Indu Sahu School of IT Vivekananda Institute of Professional New Delhi India Application Development Gemini Solutions Private Limited. Gurugram Haryana India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

Financial frauds are on the rise globally, causing significant financial losses. This issue has far-reaching consequences, impacting the investment industry, government, and corporate sectors alike. Manual verifications and inspections are inaccurate, expensive, and time consuming for detecting such fraudulent operations. Artificial Intelligence (AI) and machine learning (ML) techniques should be used intelligently to identify fraudulent transactions by evaluating enormous amounts of financial data. The paper outlines a process of detecting financial fraud with the application of AI and machine learning. Finally, the paper discusses how organizations can leverage AI techniques for the detection of financial frauds.

关键词： Sentiment analysis machine learning algorithms Power measurement Prevention and mitigation Finance Market research Fraud

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Classification of EEG Signal Based on Pre-Trained 2D CNN Model for Epilepsy Detection 2nd

Classification of EEG Signal Based on Pre-Trained 2D CNN Mod...

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3rd international conference on Digital Technologies and Applications

作者： Edderbali, Fatima Harmouchi, Mohammed Essoukaki, Elmaati Ilassan First Univ Settat Lab Radiance Mater & Instrumentat FST Settat Morocco Hssan First Univ Settat Lab Hlth Sci & Technol Settat Morocco

ISBN: (纸本)9783031298561;9783031298578

Epilepsy is a chronic neurological disease due to extreme electric discharge in the brain. Epileptic seizures gravely affect the social life and psychology of patients, knowing the lack of neurologists and the fact that analyzing EEG signals is time-consuming and must be done by an expert;therefore, it is important to correctly diagnose patients with epilepsy automatically. In this work, we start with preprocessing raw EEG data, and we extract the spectrogram of every channel. In the next step, we propose an image category classification method that combines the extractor of the feature pattern, which is AlexNet, which is a pretrained convolutional neural network (CNN), with a trainable classifier support vector machine (SVM). Alex-Net passed the feature vectors to the SVM. The spectrogram images that we extracted are used as the input layer. The number of classes is two: one for normal cases and the second for epileptic cases. We split the training and test samples. Finally, spectrogram images are trained by many classifiers, and we find that the SVM in this study had the best mean accuracy, reaching 97.52%, which means that this model is effective for epilepsy classification.

关键词： Epilepsy EEG Deep learning CNN AlexNet SVM

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Intelligent Tracking Technology of Sensitive data Based on Tag Distribution learning 3

Intelligent Tracking Technology of Sensitive Data Based on T...

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2022 3rd international conference on Computer Information and Big data Applications, CIBDA 2022

作者： Zhou, Xiaoming Yu, Hai Yu, Pengfei Ziyi, Teng State Grid Liaoning Electric Power Supply Co. Ltd State Grid of China Shenyang China State Grid Liaoning Information and Communication Company State Grid of China Shenyang China State Grid Key Laboratory of Information & Network Security Global Energy Interconnection Research Institute Co. Ltd Nanjing China

ISBN: (纸本)9783800758760

The frequent interaction and sharing of massive data in the big data environment brings increasingly serious data security problems, such as the theft of data by illegal users, the unauthorized use of data by legitimate users, and the illegal dis-closure and dissemination of data. In practical application, the phenomenon of data leakage is inevitable due to the influ-ence of environmental and human factors, but there are few studies on the traceability of data after data leakage. data traceability means that when the data leaked to a third party is captured, the source head and relevant responsible person can be traced by analyzing the relevant characteristics of the illegal data. This paper studies the intelligent tracking tech-nology of sensitive data based on tag distribution learning, which consists of three stages: tag generation technology of sensitive data content, tag distribution generation method and tag distribution comparison technology of sensitive data. Firstly, aiming at the problem that the existing machine learning algorithm has low recognition accuracy for sensitive data with no significant features, the sensitive data content tag generation technology is studied, and the tags of structured data and unstructured data are extracted. Secondly, the generation method of label distribution is studied to realize data multi-label identification based on label distribution descriptor model. Finally, the label distribution comparison technol-ogy of sensitive data is studied to realize accurate identification and tracking of sensitive data based on label distribution learning. Aiming at the problem of frequent data flow and difficult monitoring of sensitive data flow, the generation of sensitive digital tags and the construction of association relationship in the process of data flow are studied, and the data tracking technology based on tag recognition is studied to realize the dynamic tracking and security monitoring of sensi-tive data. © VDE VERLAG GMBH -

关键词： Sensitive data

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A Hybrid Approach to Detect Injection Attacks on Server-side Applications using data mining Techniques 3

A Hybrid Approach to Detect Injection Attacks on Server-side...

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3rd international conference on Sustainable Technologies for Industry 4.0 (STI)

作者： Ahmed, Abu Syeed Sajid Shachi, Mehjabeen Brishty, Afsana Afrin Siddiqui, Nurnaby Sakib, Nazmus Ahsanullah Univ Sci & Technol Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781665400091

Cyber attacks are one of the most serious concerns facing individuals at all levels, particularly in enterprises, as they can maliciously destroy systems and steal data. Cyberattacks are normally carried out by a hacker group to attack a single computer or networks. An attacker launches a serverside attack directly at a listening service. Server-side attacks aim to compromise and infringe on a server's data and applications. Attackers are mostly interested in email services, media players, web browsers, office suites, and other similar apps. Attackers can more easily target server-side applications due to malicious requests. In our work, a hybrid approach is implemented inside our proposed two-layer security firewall that includes both machine learning and non-machine learning approaches to detect malicious codes. In the machine learning-based approach, Adaboost and Random Forest are evaluated as the best classifiers with the accuracy 97.9% for detecting SQL injection attacks. On the other hand, SVM performed better than other classifiers with an accuracy of 91.5% for detecting NoSQL injection attacks.

关键词： Cyber Attacks SQL Injection NoSQL Injection Software Security machine learning data mining.

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A Novel Approach for Predict Stock Prices in Fusion of Investor's Sentiment and Stock data 3

A Novel Approach for Predict Stock Prices in Fusion of Inves...

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3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Singh, Harmanjeet Malhotra, Manisha University Institute of Computing Chandigarh University Sahibzada Ajit Singh Nagar Mohali India

ISBN: (纸本)9798350342338

This study employs forecast stock market trends by integrating benchmark sentiment analysis and deep learning techniques. In periods of consistent growth within the stock market, technical analysis is the predominant method for making predictions. Technological prediction involves utilizing stock data obtained through web crawling from' ***' This data is then used to predict a model, compared to the predictions generated by multiple other models. However, technological prophecy fails to align accurately with stock price trends during significant market volatility. Consequently, benchmark prediction emerges as the most reliable approach for forecasting. The utilization of benchmark prediction techniques enables the anticipation of stock market trends based on the dissemination of information by shareholders through social platforms, particularly in situations of urgency. The proposed approach for benchmark prediction involves extracting stock comments from a specified source and subsequently scrutinizing these comments to identify and filter out any instances of false news. Once the verification process has been conducted on the misinformation, the words that have been deemed to be accurate news undergo preprocessing. Once the authentic news comments have been transformed into textual segments, sentiment analysis is applied. The outcomes of the sentiment analysis are then utilized to generate indicators that reflect fluctuations in sentiment. Ultimately, the ascent and descent indicators are integrated with stock price data to construct a graphical representation, from which the forthcoming trajectory of stock prices is projected based on the curve's pattern. This paper addresses the limitation of the technical aspect in accurately predicting outcomes under exceptional circumstances and significantly enhances the accuracy of stock prediction in real-world scenarios. © 2023 IEEE.

关键词： Forecast Opinion mining Reliance Industries Sentiment Analysis Share Market

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Automatic Poincare section 3

Automatic Poincare section

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3rd international conference on Applied machine learning, ICAML 2021

作者： Cheng, Jie Wang, Ruike Li, Wenshi School of Electronics and Information En Soochow University Suzhou215006 China Faculty of Science Beijing University of Technology Beijing100124 China

ISBN: (纸本)9781665421256

An attractor-lock-in Poincare section is formed through three space points of the minimum, mean and maximum of the attractor under test, with parameter needless manual selection. The key algorithm steps of automatic Poincare section include (a) digging out three geometry feature points as minimum, mean and maximum;(b) through three basic statistical points making a plane needed;(c) cut-off points counting by close contact measure and close range fusion;(d) using the spectral entropy complexity and construction creep rate in chaos spring test as gold standards of control experiments. Test data are provided by classical Chua’s equation and unified chaotic equation and random number instruction in Matlab tool. The results show that the automatic Poincare section and its cut-off point number can finish three classifying recognition on period, chaos or random states of the test three dimensional data. This case study will promote economically the information geometry, and further be applied to a wide range of pattern recognition fields. © 2021 IEEE

关键词： pattern recognition

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Efficient FPGA Implementation of Feedback Perceptron for Hardware Acceleration

Efficient FPGA Implementation of Feedback Perceptron for Har...

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Computing and machine Intelligence (ICMI), international conference on

作者： Tamador Mohaidat Azeemuddin Syed Mohammed Alqodah Kasem Khalil Department of Electrical and Computer Engineering University of Mississippi Mississippi USA CVEST International Institute of Information Technology Hyderabad India

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Artificial Neural Networks (ANNs) have revolutionized machine learning, mimicking human cognitive abilities to recognize patterns and make decisions. The perceptron, a fundamental unit in ANNs, forms the basis for complex network structures. This paper introduces a novel approach to perceptrons by incorporating a feedback mechanism using a gain factor, replacing conventional learning rates. The proposed method aims to optimize network performance while adapting to hardware constraints. Implementation of MNIST and Heart Attack datasets showcase the superiority of the proposed approach over tra-ditional methods, revealing substantial accuracy improvements across various activation functions (Sign, Step, and Sigmoid) in both single perceptron and multilayer perceptron (MLP) architectures. The proposed method achieves significant accuracy of 96.80% with the MNIST dataset, and 88.52% with the Heart Attack dataset compared to other methods. It has a small overhead in power consumption. The proposed approach is implemented in Verilog HDL on Xilinx Virtex-7 FPGA XC7A35T-IFGG484C. The proposed method demonstrates remarkable accuracy enhancements in pattern recognition tasks, promising advancements in real-world applications.

关键词： Accuracy Power demand Cardiac arrest machine learning Computer architecture Artificial neural networks pattern recognition

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