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Journal articles on the topic 'Machine Learning Feature Stores'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Researcher. "MACHINE LEARNING FEATURE STORES: A COMPREHENSIVE OVERVIEW." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 82–91. https://doi.org/10.5281/zenodo.13711230.

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This article presents a comprehensive examination of Machine Learning (ML) Feature Stores, their role in modern ML infrastructures, and their impact on the efficiency and scalability of ML operations. We explore the key roles of Feature Stores, including centralization of feature management, ensuring consistency between training and serving environments, promoting feature reusability, enhancing governance, and improving overall efficiency in ML workflows. A detailed reference architecture is proposed, outlining essential components such as data ingestion, feature engineering, storage, serving,

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Liu, Rui, Kwanghyun Park, Fotis Psallidas, et al. "Optimizing Data Pipelines for Machine Learning in Feature Stores." Proceedings of the VLDB Endowment 16, no. 13 (2023): 4230–39. http://dx.doi.org/10.14778/3625054.3625060.

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Data pipelines (i.e., converting raw data to features) are critical for machine learning (ML) models, yet their development and management is time-consuming. Feature stores have recently emerged as a new "DBMS-for-ML" with the premise of enabling data scientists and engineers to define and manage their data pipelines. While current feature stores fulfill their promise from a functionality perspective, they are resource-hungry---with ample opportunities for implementing database-style optimizations to enhance their performance. In this paper, we propose a novel set of optimizations specifically

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Gupta, Robin. "Real-Time Data Pipelines for Feature Stores in Gaming." Journal for Research in Applied Sciences and Biotechnology 2, no. 5 (2023): 253–65. http://dx.doi.org/10.55544/jrasb.2.5.35.

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Machine learning models are used in content creation and generate real-time observations in gaming with a positive effect on both performance and production processes. However, the management and deployment of these features and metrics for the purposes of these benefits are critical. Looking at feature and metric stores data structures that are used for storing and retrieving feature and metric data for machine learning models. Feature stores are responsible for featuring storage and delivery for model training and features needed for model’s inferencing, whereas metric stores contain metrics

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Majeed, Abdul, and Seong Oun Hwang. "Feature Stores: A Key Enabler for Feature Reusability and Availability Across Machine Learning Pipelines." Computer 57, no. 1 (2024): 69–74. http://dx.doi.org/10.1109/mc.2023.3308868.

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Orr, Laurel, Atindriyo Sanyal, Xiao Ling, Karan Goel, and Megan Leszczynski. "Managing ML pipelines." Proceedings of the VLDB Endowment 14, no. 12 (2021): 3178–81. http://dx.doi.org/10.14778/3476311.3476402.

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The industrial machine learning pipeline requires iterating on model features, training and deploying models, and monitoring deployed models at scale. Feature stores were developed to manage and standardize the engineer's workflow in this end-to-end pipeline, focusing on traditional tabular feature data. In recent years, however, model development has shifted towards using self-supervised pretrained embeddings as model features. Managing these embeddings and the downstream systems that use them introduces new challenges with respect to managing embedding training data, measuring embedding qual

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Yi, Siming. "Walmart Sales Prediction Based on Machine Learning." Highlights in Science, Engineering and Technology 47 (May 11, 2023): 87–94. http://dx.doi.org/10.54097/hset.v47i.8170.

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Accurate sales forecasting can improve a company's profitability while minimizing expenditures. The use of machine learning algorithms to predict product sales has become a hot topic for researchers and companies over the past few years. This report features the machine learning sales prediction model that combines the ML algorithm and meticulous feature engineering processing to predict Walmart sales. The following regressions are analyzed in this paper: linear regression, random forest regression, and XGBoost regression. The regression analysis has been tested for the same time period every

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Gujar, Prof Anil D., Nikita B. Sawant, Tejas L. Hake, Aadesh A. Shete, and Shreekar M. Deshmukh. "Face Recognition Open CV Based ATM Security System." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 1114–19. http://dx.doi.org/10.22214/ijraset.2022.42230.

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Abstract: The real-time face detection and recognition has been made possible by using the method of Viola jones, Analysis work. The software first taking images of all persons and stores the information into database. Proposed work deals with automated system to detect person. The methodology comprised of three phases, first face Detection from image, second get all detail of face for the purpose of feature extraction. The most useful and unique features of the camera image are extracted in the feature extraction phase. Find out all facial details are visible. This feature vector forms an eff

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Sujatha, CN. "Coal Production Analysis using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 919–26. http://dx.doi.org/10.22214/ijraset.2021.35130.

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Coal will keep on giving a significant segment of energy prerequisites in the US for at any rate the following quite a few years. It is basic that exact data portraying the sum, area, and nature of the coal assets and stores be accessible to satisfy energy needs. It is likewise significant that the US separate its coal assets productively, securely, and in a naturally mindful way. A restored center around government support for coal-related examination, facilitated across offices and with the dynamic cooperation of the states and modern area, is a basic component for every one of these necessi

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Palma, Catarina, Artur Ferreira, and Mário Figueiredo. "Explainable Machine Learning for Malware Detection on Android Applications." Information 15, no. 1 (2024): 25. http://dx.doi.org/10.3390/info15010025.

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The presence of malicious software (malware), for example, in Android applications (apps), has harmful or irreparable consequences to the user and/or the device. Despite the protections app stores provide to avoid malware, it keeps growing in sophistication and diffusion. In this paper, we explore the use of machine learning (ML) techniques to detect malware in Android apps. The focus is on the study of different data pre-processing, dimensionality reduction, and classification techniques, assessing the generalization ability of the learned models using public domain datasets and specifically

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Rao, Faizan Ali, Muneer Amgad, Almaghthawi Ahmed, Alghamdi Amal, Mohamed Fati Suliman, and Abdulwasea Abdullah Ghaleb Ebrahim. "BMSP-ML: big mart sales prediction using different machine learning techniques." International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (2023): 874–83. https://doi.org/10.11591/ijai.v12.i2.pp874-883.

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Variations in sales over time is the main issue faced by many retailers. To overcome this problem, we attempt to predict the sales by comparing the previous sales data of different stores. Firstly, the primary task is to recognize the pattern of the factors that help to predict sales. This study helps us understand the data and predict sales using many machines learning models. This process gets the data and beautifies the data by imputing the missing values and feature engineering. While solving this problem, predicting the monthly sales value is significant in the study. In addition, an esse

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Akbar, Fahad, Mehdi Hussain, Rafia Mumtaz, Qaiser Riaz, Ainuddin Wahid Abdul Wahab, and Ki-Hyun Jung. "Permissions-Based Detection of Android Malware Using Machine Learning." Symmetry 14, no. 4 (2022): 718. http://dx.doi.org/10.3390/sym14040718.

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Malware applications (Apps) targeting mobile devices are widespread, and compromise the sensitive and private information stored on the devices. This is due to the asymmetry between informative permissions and irrelevant and redundant permissions for benign Apps. It also depends on the characteristics of the Android platform, such as adopting an open-source policy, supporting unofficial App stores, and the great tolerance for App verification; therefore the Android platform is destined to face such malicious intrusions. In this paper, we propose a permissions-based malware detection system (Pe

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Saman, Iftikhar, Alluhaybi Bandar, Suliman Mohammed, Saeed Ammar, and Fatima Kiran. "Amazon products reviews classification based on machine learning, deep learning methods and BERT." TELKOMNIKA 21, no. 05 (2023): 1084–101. https://doi.org/10.12928/telkomnika.v21i5.24046.

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In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inver

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Lee, Seungpeel, Honggeun Ji, Jina Kim, and Eunil Park. "What books will be your bestseller? A machine learning approach with Amazon Kindle." Electronic Library 39, no. 1 (2021): 137–51. http://dx.doi.org/10.1108/el-08-2020-0234.

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Purpose With the rapid increase in internet use, most people tend to purchase books through online stores. Several such stores also provide book recommendations for buyer convenience, and both collaborative and content-based filtering approaches have been widely used for building these recommendation systems. However, both approaches have significant limitations, including cold start and data sparsity. To overcome these limitations, this study aims to investigate whether user satisfaction can be predicted based on easily accessible book descriptions. Design/methodology/approach The authors col

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Chevuri, Rajeev Reddy. "Demystifying MLOps: Core Principles for Scalable Machine Learning." International Journal of Advances in Engineering and Management 7, no. 3 (2025): 884–90. https://doi.org/10.35629/5252-0703884890.

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This article examines Machine Learning Operations (MLOps) as a critical discipline bridging the gap between experimental model development and production-ready AI systems. By integrating principles from DevOps, data engineering, and machine learning, MLOps creates structured frameworks that streamline the entire machine learning lifecycle. The content explores core infrastructure components including cloud computing, containerization with Docker, and orchestration through Kubernetes that form the foundation for scalable AI solutions. It details how continuous integration and deployment pipelin

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Ali, Rao Faizan, Amgad Muneer, Ahmed Almaghthawi, Amal Alghamdi, Suliman Mohamed Fati, and Ebrahim Abdulwasea Abdullah Ghaleb. "BMSP-ML: big mart sales prediction using different machine learning techniques." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (2023): 874. http://dx.doi.org/10.11591/ijai.v12.i2.pp874-883.

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<span lang="EN-US">Variations in sales over time is the main issue faced by many retailers. To overcome this problem, we attempt to predict the sales by comparing the previous sales data of different stores. Firstly, the primary task is to recognize the pattern of the factors that help to predict sales. This study helps us understand the data and predict sales using many machines learning models. This process gets the data and beautifies the data by imputing the missing values and feature engineering. While solving this problem, predicting the monthly sales value is significant in the st

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Alfian, Ganjar, Muhammad Qois Huzyan Octava, Farhan Mufti Hilmy, et al. "Customer Shopping Behavior Analysis Using RFID and Machine Learning Models." Information 14, no. 10 (2023): 551. http://dx.doi.org/10.3390/info14100551.

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Analyzing customer shopping habits in physical stores is crucial for enhancing the retailer–customer relationship and increasing business revenue. However, it can be challenging to gather data on customer browsing activities in physical stores as compared to online stores. This study suggests using RFID technology on store shelves and machine learning models to analyze customer browsing activity in retail stores. The study uses RFID tags to track product movement and collects data on customer behavior using receive signal strength (RSS) of the tags. The time-domain features were then extracted

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Duan, Lianzhai. "Machine Learning Modeling for Forecasting Repeat Purchases in Online Shopping." MALCOM: Indonesian Journal of Machine Learning and Computer Science 4, no. 3 (2024): 863–74. http://dx.doi.org/10.57152/malcom.v4i3.1388.

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Online shopping merchants will conduct a series of marketing activities to increase customers, but in many cases, most of the new customers will not make repeat purchases, which is not conducive to the long-term interests of the merchants. Therefore, it is important for merchants to target users who are more likely to repurchase, as this can reduce marketing costs and increase ROI. Based on the dataset provided by the online shopping website, this paper conducts mining and exploratory analysis of the data, utilizes feature engineering methodology, and modeling analysis using LightGBM, Logistic

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Mohammed Mansoor Ali, Mohd Sahil Ali, Saad Mohammed Mohi Uddin, and Dr. Md Zainlabuddin. "ENSEMBLE-DROID: Optimized Multi-Model Detection of Android Malware." International Journal of Information Technology and Computer Engineering 13, no. 2s (2025): 24–33. https://doi.org/10.62647/ijitce2025v13i2spp24-33.

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Android platform due to open-source characteristics and Google backing has the largest global market share. Being the world’s most popular operating system, it has drawn the attention of cyber criminals operating particularly through the wide distribution of malicious applications. This paper proposes an effectual machine-learning-based approach for Android Malware Detection making use of an evolutionary chi-square algorithm for discriminatory feature selection. Selected features from the chi-square algorithm are used to train machine learning classifiers and their capability in identification

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Naveen Srikanth Pasupuleti. "Transforming healthcare through cloud-native machine learning architecture: A case study in AWS, Spark, and Kubernetes Implementation." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 1622–31. https://doi.org/10.30574/wjarr.2025.26.2.1649.

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This article examines a transformative case study in healthcare data infrastructure, where a skilled data engineer revolutionized operations by implementing an integrated technology stack with advanced machine learning capabilities. Facing challenges of processing diverse and voluminous patient data, the engineer architected a comprehensive solution leveraging AWS services, including S3, Redshift, and Lambda to create a cloud-based data lake optimized for AI workloads. This foundation was augmented with Apache Spark for distributed processing and MLlib for scalable machine learning, Hadoop clu

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Costa, Viviane, Mateus Silva Rocha, Glaucia Amorim Faria, Silvio Fernando Alves Xavier Junior, Tiago Almeida de Oliveira, and Ana Patricia Bastos Peixoto. "Boosting algorithms for prediction in agriculture: An application of Feature importance and Feature Selection." Sigmae 13, no. 4 (2024): 339–48. https://doi.org/10.29327/2520355.13.4-31.

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The Agriculture sector has created and collected large amounts of data. It can be gathered, stored, and analyzed to assist in decision making generating competitive value, and the use of Machine Learning techniques has been very effective for this market. In this work, a Machine Learning study was carried out using supervised classification models based on boosting to predict disease in a crop, thus identifying the model with the best areas under curve metrics. Light Gradient Boosting Machine, CatBoost Classifier, Extreme Gradient, Gradient Boosting Classifier, Adaboost models were used to qua

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Alazab, Moutaz. "Automated Malware Detection in Mobile App Stores Based on Robust Feature Generation." Electronics 9, no. 3 (2020): 435. http://dx.doi.org/10.3390/electronics9030435.

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Many Internet of Things (IoT) services are currently tracked and regulated via mobile devices, making them vulnerable to privacy attacks and exploitation by various malicious applications. Current solutions are unable to keep pace with the rapid growth of malware and are limited by low detection accuracy, long discovery time, complex implementation, and high computational costs associated with the processor speed, power, and memory. Therefore, an automated intelligence technique is necessary for detecting apps containing malware and effectively predicting cyberattacks in mobile marketplaces. I

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Pradhan, Yash, and Sonali Choudhary. "Analog Driven Robot using IoT and Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (2022): 1510–14. http://dx.doi.org/10.22214/ijraset.2022.46434.

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Abstract: Visual impairment is one of the biggest limitations for humanity, especially in this day and age when information is communicated a lot by text messages (electronic and paper based) rather than voice. Facial recognition is category of biometric software that maps an individual’s facial features mathematically and stores the data as a sprint The software uses deep learning algorithms to compare a live capture or digital image to be stored face print in order to verify an individual’s identity vbnnn This project aims to develop a device to help people with visual impairment. In this pr

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Jayabalasubramaniam, Mr P. "Proactive Pathological Assessment Via Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46652.

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Abstract—The Kidney stones afflict millions worldwide, causing severe pain and potential complications such as obstruction and infection. Timely and accurate detection is crucial for effective treatment planning. This work presents a comprehensive, automated pipeline for kidney stone detection in grayscale medical images. The system integrates adaptive preprocessing (contrast enhancement and denoising), segmentation via adaptive thresholding and connected component analysis, feature extraction harnessing gray-level co-occurrence matrix (GLCM) and local binary patterns (LBP), outlier filtering

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Iliou, Theodoros, Christos-Nikolaos Anagnostopoulos, and George Anastassopoulos. "Osteoporosis Detection Using Machine Learning Techniques and Feature Selection." International Journal on Artificial Intelligence Tools 23, no. 05 (2014): 1450014. http://dx.doi.org/10.1142/s0218213014500146.

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Osteoporosis is a disease of bones that leads to an increased risk of fracture and it is characterized by low bone mineral density and micro-architectural deterioration of bone tissue. In this article, the dataset consists of 3426 subjects (1083 pathological and 2343 healthy cases) whose diagnosis was based on laboratory and osteal bone densitometry examination. In all cases, four diagnostic factors for osteoporosis risk prediction, namely age, sex, height and weight were stored for later evaluation with the selected classifiers. In order to categorize subjects into two classes (osteoporosis,

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Stanoev, Boris, Goran Mitrov, Andrea Kulakov, Georgina Mirceva, Petre Lameski, and Eftim Zdravevski. "Automating Feature Extraction from Entity-Relation Models: Experimental Evaluation of Machine Learning Methods for Relational Learning." Big Data and Cognitive Computing 8, no. 4 (2024): 39. http://dx.doi.org/10.3390/bdcc8040039.

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With the exponential growth of data, extracting actionable insights becomes resource-intensive. In many organizations, normalized relational databases store a significant portion of this data, where tables are interconnected through some relations. This paper explores relational learning, which involves joining and merging database tables, often normalized in the third normal form. The subsequent processing includes extracting features and utilizing them in machine learning (ML) models. In this paper, we experiment with the propositionalization algorithm (i.e., Wordification) for feature engin

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Hussain, Mohammad Tehaseen, and Mohammed Abrar Baig. "Human Resource Management by Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 629–32. http://dx.doi.org/10.22214/ijraset.2022.47376.

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Abstract: The main object of Human Resource Management using machine learning is to analyze employees' interest in their respective companies and make predictions about who will stay or leave the organization. The data is extracted using data scraping techniques and stored in CSV format. Data collected through this technique contains different features, and with the help of ML algorithms, it leads to predictions. This analysis helps the manager to make conclusions about who will stay or leave the organization; with this, the manager can approach a way to let stay a worthy employee in the organ

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Rochan,, Patnam. "Identifying Fraudulent Apps on Google Play Store: A Decision Tree and LSTM Approach." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42145.

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The increasing prevalence of fraudulent applications on app stores, such as the Google Play Store, poses severe challenges to user privacy, financial security, and the reputation of legitimate developers. These fraudulent apps often exploit vulnerabilities by mimicking genuine applications, employing deceptive practices such as fake reviews, excessive permissions, and sudden rating spikes to appear trustworthy. Traditional static detection methods struggle to adapt to these evolving fraud strategies.This research introduces a novel hybrid approach that combines Decision Trees for feature impor

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Sai, Kiran Reddy Malikireddy, Algubelli Bipinkumarreddy, and Tadanki Snigdha. "Knowledge Graph-Driven Real-Time Data Engineering for Context-Aware Machine Learning Pipelines." European Journal of Advances in Engineering and Technology 8, no. 5 (2021): 65–76. https://doi.org/10.5281/zenodo.14600600.

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The novel context-aware machine learning is based on state-of-the-art real-time data engineering processes that operate in shifting entity correlations. To this end, this paper presents a new architecture that combines knowledge graph construction with real-time stream processing to underpin the machine learning flow in a context-aware manner. The proposed system uses graph neural networks (GNNs) for updates and embeddings in real-time for dynamic integration of contextual information into the other machine learning models. This makes the approach ideal as changes in the relations of entities

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Lee, Gyeong-Hoon, Jeil Jo, and Cheong Hee Park. "Jamming Prediction for Radar Signals Using Machine Learning Methods." Security and Communication Networks 2020 (January 24, 2020): 1–9. http://dx.doi.org/10.1155/2020/2151570.

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Jamming is a form of electronic warfare where jammers radiate interfering signals toward an enemy radar, disrupting the receiver. The conventional method for determining an effective jamming technique corresponding to a threat signal is based on the library which stores the appropriate jamming method for signal types. However, there is a limit to the use of a library when a threat signal of a new type or a threat signal that has been altered differently from existing types is received. In this paper, we study two methods of predicting the appropriate jamming technique for a received threat sig

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Atif Raza Zaidi, Tahir Abbas, Sadaqat Ali Ramay, Ali Nawaz, Kanwal Ameen, and Muhammad Irfan. "Deep Learning-Based Detection of Android Malware using Graph Convolutional Networks (GCN)." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 6, no. 1 (2024): 57–73. http://dx.doi.org/10.52700/scir.v6i1.159.

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The study is centered around identifying Android malware using deep learning methods through Graph Neural Networks (GNNs) and Graph Convolutional Networks (GCNs). With Android being widely used worldwide ensuring the security of released applications poses a challenge. Conventional malware detection techniques, like dynamic analysis have limitations in recognizing new malware types leading to a shift towards machine learning and deep learning solutions. The research introduces a malware detection system that employs GNNs particularly focusing on GCNs to analyze the relationships within an appl

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Ge, Dongdong, Luhui Hu, Bo Jiang, Guangjun Su, and Xiaole Wu. "Intelligent site selection for bricks-and-mortar stores." Modern Supply Chain Research and Applications 1, no. 1 (2019): 88–102. http://dx.doi.org/10.1108/mscra-03-2019-0010.

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Purpose The purpose of this paper is to achieve intelligent superstore site selection. Yonghui Superstores partnered with Cardinal Operations to incorporate a tremendous amount of site-related information (e.g. points of interest, population density and features, distribution of competitors, transportation, commercial ecosystem, existing own-store network) into its store site optimization. Design/methodology/approach This paper showcases the integration of regression, optimization and machine learning approaches in site selection, which has proven practical and effective. Findings The result w

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Aljrees, Turki, Muhammad Umer, Oumaima Saidani, et al. "Contradiction in text review and apps rating: prediction using textual features and transfer learning." PeerJ Computer Science 10 (January 3, 2024): e1722. http://dx.doi.org/10.7717/peerj-cs.1722.

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Mobile app stores, such as Google Play, have become famous platforms for practically all types of software and services for mobile phone users. Users may browse and download apps via app stores, which also help developers monitor their apps by allowing users to rate and review them. App reviews may contain the user’s experience, bug details, requests for additional features, or a textual rating of the app. These ratings can be frequently biased due to inadequate votes. However, there are significant discrepancies between the numerical ratings and the user reviews. This study uses a transfer le

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Murugesan, G., C. T. Kavitha, G. G. Jabakumar, and E. Swarnalatha. "Prediction of Heart Disease using Machine Learning Algorithms with Feature Selection Techniques." CARDIOMETRY, no. 26 (March 1, 2023): 778–86. http://dx.doi.org/10.18137/cardiometry.2023.26.778786.

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Data is an asset in the digital era, and enormous data was generating day by day in all the fields, including the healthcare industry. The data on the healthcare industry data consists of personal information and disease-related information about a patient and stored in various formats and units. Machine learning and Artificial Intelligence techniques will help us analyze the voluminous amount of data to identify the hidden patterns of a specific disease from the healthcare data and help us predict a particular disease in the future. In this paper, we proposed a decision support system to pred

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Dhindegave, cMs Kalpana, Ms Aishwarya Mane, Ms Vaishnavi Shelke, and Ms Aditi Borade. "Missing Person Identification using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 428–30. http://dx.doi.org/10.22214/ijraset.2022.47747.

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Abstract: From last few years there are many missing person cases reported at police station which are not yet solved. In our project we are solving this issue using various machine learning algorithms like SVM, KNN etc. Basically, using facial expressions, we can train model and depending on matched feature we will get known persons identity. This will generate fast and accurate results. For this we are taking missing person dataset from Kaggle. In output we will get persons identity depending on various classified features like gender, age, geographical location. Results will be forwarded to

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Song, Yubo, Yijin Geng, Junbo Wang, Shang Gao, and Wei Shi. "Permission Sensitivity-Based Malicious Application Detection for Android." Security and Communication Networks 2021 (July 28, 2021): 1–12. http://dx.doi.org/10.1155/2021/6689486.

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Since a growing number of malicious applications attempt to steal users’ private data by illegally invoking permissions, application stores have carried out many malware detection methods based on application permissions. However, most of them ignore specific permission combinations and application categories that affect the detection accuracy. The features they extracted are neither representative enough to distinguish benign and malicious applications. For these problems, an Android malware detection method based on permission sensitivity is proposed. First, for each kind of application cate

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M., Ramnath, and Yesubai Rubavathi C. "Enhancing AppAuthentix recommender systems using advanced machine learning techniques to identify genuine and counterfeit android applications." PeerJ Computer Science 10 (November 20, 2024): e2515. http://dx.doi.org/10.7717/peerj-cs.2515.

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Smartphone app expansion needs strict security measures to avoid fraud and danger. This study overcomes this issue by identifying apps differently. This new solution uses convolutional neural network (CNN), natural language processing (NLP), and the strong AppAuthentix Recommender algorithm to secure app stores and boost customer confidence in the digital marketplace. Since the app ecosystem has grown, counterfeit and harmful applications have risen, threatening consumers and app merchants. These risks need advanced technology that can distinguish malware from legitimate apps. A complex predic

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J, Dr Bhuvana. "A STUDY AND DEVELOPMENT OF APPLICATION ON SENTIMENT ANALYSIS." International Scientific Journal of Engineering and Management 03, no. 03 (2024): 1–7. http://dx.doi.org/10.55041/isjem01354.

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A subfield of natural language processing called sentiment analysis is concerned with locating and obtaining subjective data from text. Analysing and categorising the emotional tone or polarity indicated in text—such as reviews, social media postings, news stories, and consumer feedback—is its primary goal. A subfield of natural language processing called sentiment analysis is concerned with locating and obtaining subjective data from text. Analysing and categorising the emotional tone or polarity indicated in text—such as reviews, social media postings, news stories, and consumer feedback—is

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Tsai, Ming-Fong, Pei-Ching Lin, Zi-Hao Huang, and Cheng-Hsun Lin. "Multiple Feature Dependency Detection for Deep Learning Technology—Smart Pet Surveillance System Implementation." Electronics 9, no. 9 (2020): 1387. http://dx.doi.org/10.3390/electronics9091387.

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Image identification, machine learning and deep learning technologies have been applied in various fields. However, the application of image identification currently focuses on object detection and identification in order to determine a single momentary picture. This paper not only proposes multiple feature dependency detection to identify key parts of pets (mouth and tail) but also combines the meaning of the pet’s bark (growl and cry) to identify the pet’s mood and state. Therefore, it is necessary to consider changes of pet hair and ages. To this end, we add an automatic optimization identi

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B.Meena, Preethi, R.Gowtham, S.Aishvarya, S.Karthick, and D.G.Sabareesh. "Rainfall Prediction using Machine Learning and Deep Learning Algorithms." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 4 (2021): 251–54. https://doi.org/10.35940/ijrte.D6611.1110421.

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he project entitled as “Rainfall Prediction using Machine Learning & Deep Learning Algorithms” is a research project which is developed in Python Language and dataset is stored in Microsoft Excel. This prediction uses various machine learning and deep learning algorithms to find which algorithm predicts with most accurately. Rainfall prediction can be achieved by using binary classification under Data Mining. Predicting the rainfall is very important in several aspects of one’s country and can help from preventing serious natural disasters. For this prediction, Artificial

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Singu, Santosh Kumar. "A Comprehensive Approach to Machine Learning Integration in Data Warehousing." Journal of Technology and Systems 6, no. 6 (2024): 28–37. http://dx.doi.org/10.47941/jts.2239.

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Purpose: This research examines the utilization of machine learning (ML) in data warehousing systems and the extent to which it will transform business intelligence and analytics. It aims to know how ML improves conventional data warehousing systems to support prediction and forecasting. Methodology: This research uses a literature review together with a case analysis. It discusses the issues that may arise when implementing Machine Learning models with data warehouses, such as issues to do with data quality, scalability, and real-time processing. The work examines integration patterns like in

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Kuizinienė, Dovilė, Paulius Savickas, Rimantė Kunickaitė, et al. "A comparative study of feature selection and feature extraction methods for financial distress identification." PeerJ Computer Science 10 (April 30, 2024): e1956. http://dx.doi.org/10.7717/peerj-cs.1956.

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Financial distress identification remains an essential topic in the scientific literature due to its importance for society and the economy. The advancements in information technology and the escalating volume of stored data have led to the emergence of financial distress that transcends the realm of financial statements and its’ indicators (ratios). The feature space could be expanded by incorporating new perspectives on feature data categories such as macroeconomics, sectors, social, board, management, judicial incident, etc. However, the increased dimensionality results in sparse data and o

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Panigrahy, Manisha. "PriceWar." International Journal for Research in Applied Science and Engineering Technology 13, no. 2 (2025): 614–22. https://doi.org/10.22214/ijraset.2025.66915.

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The growth of e-commerce has given rise to many opportunities as well as challenges, such as in the case of dynamic or demand sensitive pricing which changes with demand, competition and market environment. This paper proposes a price prediction system that makes use of machine learning and web scraping techniques in order to offer timely and reliable price estimates for multiple products across various online stores. Using web scraping, we gather useful information such as the latest prices, their corresponding ratings and sales ranks that are key in training machine learning algorithms to es

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Preethi, B. Meena, R. Gowtham, S. Aishvarya, S. Karthick, and D. G. Sabareesh. "Rainfall Prediction using Machine Learning and Deep Learning Algorithms." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 4 (2021): 251–54. http://dx.doi.org/10.35940/ijrte.d6611.1110421.

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Abstract:

The project entitled as “Rainfall Prediction using Machine Learning & Deep Learning Algorithms” is a research project which is developed in Python Language and dataset is stored in Microsoft Excel. This prediction uses various machine learning and deep learning algorithms to find which algorithm predicts with most accurately. Rainfall prediction can be achieved by using binary classification under Data Mining. Predicting the rainfall is very important in several aspects of one’s country and can help from preventing serious natural disasters. For this prediction, Artificial Neural Network u

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Richard, Richard, Muhammad Ammar Marsuki, Gading Aryo Pamungkas, and Felix Irwanto. "ENHANCING MOBILE CRYPTOCURRENCY WALLETS: A COMPREHENSIVE ANALYSIS OF USER EXPERIENCE, SECURITY, AND FEATURE DEVELOPMENT." JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) 10, no. 1 (2024): 15–22. http://dx.doi.org/10.33480/jitk.v10i1.5157.

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The surge in cryptocurrency usage has increased reliance on cryptocurrency wallet applications. However, the usability, security, and feature richness of crypto wallets require significant enhancements. This research aims to identify critical factors that should guide the future design of mobile cryptocurrency wallets. The first step was to collect user reviews on several popular crypto wallets as the dataset. A total of 5,466 mobile wallet-related reviews from mobile application stores were filtered and analyzed. A machine-learning approach was used to cluster the user reviews. The analysis s

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YAVUZ, Murat, and İbrahim TÜRKOĞLU. "Classification of Marble Types Using Machine Learning Techniques." Afyon Kocatepe Üniversitesi Uluslararası Mühendislik Teknolojileri ve Uygulamalı Bilimler Dergisi 6, no. 1 (2023): 33–42. http://dx.doi.org/10.53448/akuumubd.1268931.

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Natural stones are one of the indispensable elements of people from shelter to weapons. Among these stone types, marbles and marble-derived products are among the objects that people always prefer, from bathroom to kitchen, from garden design to small decorative home decorations. While the marbles are named according to the regions where they are extracted, their types and qualities are classified based on observation by people who are qualified as experts in this field. This classification, which is made by experts based on observation, carries risks in economic terms, increases the workload

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Zhuang, Zhutao, Xinqi Zeng, and Zhiguang Chen. "DumpKV: Learning Based Lifetime Aware Garbage Collection for Key Value Separation in LSM-Tree." Proceedings of the VLDB Endowment 18, no. 4 (2024): 1223–36. https://doi.org/10.14778/3717755.3717778.

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Key-value separation is used in LSM-tree to store large values in separate log files to reduce write amplification but requires garbage collection to recycle invalid values. Existing LSM-tree typically adopts a static policy to recycle obsolete values, struggling to achieve low write amplification as it is challenging to predefine the static parameters for garbage collection. In this work we propose DumpKV, a learning-based lifetime-aware garbage collection mechanism which achieves lower write amplification. DumpKV trains a machine learning model based on the access history of keys and accordi

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Chen, Deli. "Walmart sales prediction based on random forest model and application of feature importance." Applied and Computational Engineering 53, no. 1 (2024): 264–73. http://dx.doi.org/10.54254/2755-2721/53/20241461.

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Sales forecasting is crucial for efficient resource allocation and inventory management in retail. This study employs Random Forest to predict weekly sales for 45 Walmart stores, leveraging a diverse dataset with store-specific sales and external factors. Through meticulous preprocessing and model application, one achieves outstanding accuracy, with a Weighted Mean Absolute Error (WMAE) as low as 1.2030 and an impressive accuracy rate of 98.8%. Additionally, integrating feature importance ranking sheds light on influential variables in sales forecasting. This study provides a blueprint for dev

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Dariato, Eri. "Analisa dan Perancangan Machine Learning Untuk Mendeteksi Kegagalan Job di Apache Spark." Arcitech: Journal of Computer Science and Artificial Intelligence 2, no. 1 (2022): 1. http://dx.doi.org/10.29240/arcitech.v2i1.4124.

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A collection of data stored in a database, so the longer the data, the bigger the data, because the data processed is very large, processing time in Apache Spark can take up to a dozen or tens of hours. Sometimes, the Apache Spark application even fails. Therefore, to minimize the waiting time that could have been avoided or reduced, artificial intelligence through Machine Learning will be used to detect whether an Apache Spark application will fail or run smoothly. Factors to determine this failure are called features and are generated through the feature engineering process. The purpose of t

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Petrescu, Livia, Cătălin Petrescu, Ana Oprea, et al. "Machine Learning Methods for Fear Classification Based on Physiological Features." Sensors 21, no. 13 (2021): 4519. http://dx.doi.org/10.3390/s21134519.

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This paper focuses on the binary classification of the emotion of fear, based on the physiological data and subjective responses stored in the DEAP dataset. We performed a mapping between the discrete and dimensional emotional information considering the participants’ ratings and extracted a substantial set of 40 types of features from the physiological data, which represented the input to various machine learning algorithms—Decision Trees, k-Nearest Neighbors, Support Vector Machine and artificial networks—accompanied by dimensionality reduction, feature selection and the tuning of the most r

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Faridi, Muhammad Shakeel, Muhammad Azam Zia, Zahid Javed, Imran Mumtaz, and Saqib Ali. "A Comparative Analysis Using Different Machine Learning: An Efficient Approach for Measuring Accuracy of Face Recognition." International Journal of Machine Learning and Computing 11, no. 2 (2021): 115–20. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1023.

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Feature extracting and training module can be done by using face recognition neural learning techniques. Moreover, these techniques are widely employed to extract features from human images. Some detection systems are capable to scan the full body, iris detection, and finger print detection systems. These systems have deployed for safety and security intension. In this research work, we compare different machine learning algorithms for face recognition. Four supervised face recognition machine-learning classifiers such as Principal Component Analysis (PCA), 1-nearest neighbor (1-NN), Linear Di

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