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An Association Based Approach to Elicit and Measure Impact of Features on Sales of a Garment Retail

An Association Based Approach to Elicit and Measure Impact o...

2023 International Conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology Conference, ATCON-1 2023

作者： Rout, Dillip Kotangale, Archana Nath, Sayantan Roy, Bholanath Sandip University School of Computer Science and Engineering Department of Computer Science and Engineering Nashik India Mumbai University A P Shah Institute of Technology Department of Computer Science and Engineering Thane India Maulana Azad National Institute of Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9781665456272

In this article, an association-based approach is proposed for determining the feature importance of a given dataset which includes the target variable. In particular, the concept of Market Basket Analysis (MBA) is applied for enumerating the relationships between the target variable and each of the features which lead to the importance of those. Mention that the MBA is generally used for obtaining the recommended items based on the togetherness of the items. Nevertheless, an attempt is made in this paper to correlate the features given a target output by abstracting each feature to be paired with the target variable. The apriori algorithm and association rules are used for accounting for the coupling of features with the target feature. Precisely, Lift metric of MBA is the key to computing the associativity in this context. That is, each feature's importance is the sum of the individual ratio of Lift count of its values (observations) when paired with the target feature. The proposed methodology is tested on a dataset of a garment retail store that has information on several dresses. Each dress contains fifteen features including sales which is the lone numerical feature amidst the categorical features. Note that the sales are influenced by some of the features which generally the customers look for to prefer a particular dress over others. The results of the proposed methodology suggest that a couple of features are instigating sales at a higher rate than others. The outcome of the developed methodology is able to define a clear grouping of features according to the importance related to the target variable. The proposed methodology is applicable to a dataset where the feature selection is with respect to a target feature which is generally done in the case of supervised learning. © 2023 IEEE.

关键词： Sales

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A Host-based Intrusion Detection: Using Signature-based and AI-driven Anomaly Detection for Enhanced Cybersecurity∗ 4

A Host-based Intrusion Detection: Using Signature-based and ...

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4th International Conference on Digital Futures and Transformative Technologies, ICoDT2 2024

作者： Rehman, Fazalur Mushtaq, Farhan Zaman, Hafsah National University of Sciences and Technology School of Electrical Engineering and Computer Science Islamabad Pakistan Department of Electrical Engineering Taxila Pakistan

ISBN: (纸本)9798350391862

This research introduces a Hybrid Intrusion Detection System (HIDS) that merges signature-based detection, with AI-powered anomaly detection to enhance the accuracy and effectiveness of identifying cyber threats. The proposed HIDS demonstrates an ability to detect uncommon and sophisticated cyber threats with an accuracy rate of 90.37%. By combining Gradient Boosting and K-Nearest Neighbors (KNN) algorithms the system improves detection precision, speeds up response times, and expands coverage across network traffic. This comprehensive approach overcomes the limitations of traditional methods by enabling threat responses while reducing false positive rates. The study highlights the potential of integrating signature-based and AI-driven techniques to strengthen cybersecurity defences which emphasizes the benefits of this approach. When the system detects a potential threat, alerts are sent to the Security Operations Center (SOC) or Network Operations Center (NOC), with details such as nature of the threat, and the affected system. This study establishes a foundation for real-time cyber threat detection and intelligence sharing in cloud environments, with future Hybrid IDS versions operating across multiple hosts and supported by a web service for cross-platform compatibility and centralized alert system. © 2024 IEEE.

关键词： Network intrusion

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DP-BERT: A pre-trained deep language model for depression prediction using microarray data

DP-BERT: A pre-trained deep language model for depression pr...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Gao, Junyu Zeng, Min Li, Yiming Wang, Fang Zheng, Ruiqing Liu, Jin Guo, Fei Li, Min Central South University School of Computer Science and Engineering Changsha410083 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China

ISBN: (纸本)9798350386226

In recent years, the increasing number of individuals diagnosed with depression and the growing awareness of its impact on modern society have highlighted the significance of accurate depression diagnosis. Microarray data has played a crucial role in uncovering the genetic mechanisms underlying depression. However, existing methods for depression prediction using microarray data often rely on the selection of differentially expressed genes. This approach disregards important information from other genes and is susceptible to batch effects, thereby limiting generalizability and model stability. To address these limitations, we propose DP-BERT, a depression prediction model based on Bidirectional Encoder Representations from Transformers (BERT). DP-BERT follows a pre-training and fine-tuning paradigm, leveraging a large amount of unlabeled microarray data from diverse sequencing platforms for pretraining to extract comprehensive genetic-level representations of psychiatric disorders. Subsequently, supervised fine-tuning is performed for depression prediction. Experimental results demonstrate that the pre-trained model achieves superior performance in depression prediction. The source code can be obtained from https://***/CSUBioGroup/DP-BERT. © 2024 IEEE.

关键词： Prediction models

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STDFormer: transformer-based network for arbitrary-shaped text detection in natural scenes 3

STDFormer: transformer-based network for arbitrary-shaped te...

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3rd International Conference on computer Vision and Data Mining, ICCVDM 2022

作者： Su, Jiale Zhang, Chongyang School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu China

ISBN: (纸本)9781510661363

Natural scene text detection refers to locating and representing the text in natural scene images. The existing methods of natural scene text detection are based on convolutional neural network (CNN), but it is vulnerable to useless background noise in the process of extracting the features of curved text instances because the convolution kernel of CNN is fixed in size and rectangular in shape. In order to solve this problem, this paper proposes a novel Transformer-based Feature Fusion Module (TFFM) by integrating the transformer structure into feature pyramid network to reduce the influence of background noise in the process of feature fusion. On this basis, combined with the backbone and detection head of transformer structure, a network of natural scene text detection with full transformer structure is constructed. The method proposed in this paper achieves the state-of-the-art result on CTW1500 and Total-Text datasets, and the Transformer-based Feature Fusion Module (TFFM) proposed in this paper can be easily applied to other target detection frameworks in theory. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Convolution

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A ROI Based Self-supervised Strategy for Retinal Image Analysis

A ROI Based Self-supervised Strategy for Retinal Image Analy...

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2022 International Conference on Optical and Photonic engineering, icOPEN 2022

作者： Dong, Shiyun Chen, Qiang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781510661905

With the maturity of deep learning methods and the collection of a large number of retinal images, many deep learning models have been used for retinopathy analysis, but most of them are supervised methods. That is, the training of the models relies heavily on a large number of annotated retinal images, which is sometimes difficult to satisfy in medical fields including ophthalmology. This paper explores the application of self-supervised methods that do not require a large amount of labeled data in the analysis of retinal images. Firstly, we propose a novel self-supervised task of column dislocation and compare the performance of various self-supervised tasks on the retinopathy classification, verifying our proposed task's effectiveness. Then, by considering the characteristics of retinal images, namely the region of interest (ROI) in spectral domain optical coherence tomography (SD-OCT) images is less than half of the whole images, we also propose a ROI based self-supervised strategy to improve the feature representation ability. The experiments on two public datasets for retinal image classification and segmentation demonstrate that the proposed strategy is effective compared with several existed self-supervised learning methods. © 2022 SPIE.

关键词： Ophthalmology

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Stochastic Perturbation Averaging Boosts Transferability of Adversarial Examples 10

Stochastic Perturbation Averaging Boosts Transferability of ...

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10th IEEE International Conference on Data science and Advanced Analytics, DSAA 2023

作者： Yang, Rongbo Li, Qianmu Meng, Shunmei Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350345032

In image, video and even real physics domains, adversarial examples can mislead deep models to produce wrong predictions. Transfer-based attacks against black-box models are more in line with realistic scenarios, but adversarial examples made on surrogate model have a low success rate when transferred to the target model due to overfitting the source model. We study the Stochastic Weight Averaging strategy in the domain generalization process and propose a Stochastic Perturbation Averaging method (SPA). Specifically, we add stochastic perturbations to the examples during the gradient descent attack, and we design a Central Amplification method (CAM) to enhance this random variation, then SPA stabilizes the iteration direction by computing the gradient average of the perturbed examples to find a relatively flat local minimum of the loss function. SPA is an efficient and general strategy which can significantly improve the transferability of the gradient-based attack methods. For instance, the average attack success rate of the adversarial examples produced based on four single models against seven pre-trained models reached 90.10%, which is the best result so far. Code is available at https://*** yangrongbo/SPA. © 2023 IEEE.

关键词： Gradient methods

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Enhanced Fraud Detection in Ethereum Transactions: Fusion of Modified Genetic Algorithms and Deep Learning with Limited Attributes

Enhanced Fraud Detection in Ethereum Transactions: Fusion of...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Srivastava, Atul Kumar, Alok Pillai, Anuradha Sharma, Vijay Shankar AUUP Amity School of Engineering and Technology Lucknow India Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India Manipal University Department of Computer and Communication Engineering Jaipur India

ISBN: (纸本)9798350385199

Ethereum smart contracts, the new way of transactions and a popular name in the world of cryptocurrencies, have gathered a huge base of research and scientific attention. They are so helpful that they allow us to eliminate the need for a separate third-party library to allow unknown parties to see contract details on a computer. But since we can all see that online commerce is growing day by day and will continue to grow, it can never be fully free from scams and unethical operations. So, to correctly detect all such unethical and malicious transactions, this paper used a deep learning model. And to further enhance the model, this study used metaheuristic optimization as well. It employs an algorithm called Genetic Algorithm and to provide better optimization in the explorations phase of Cuckoo Search (CS) to achieve its goal of detecting fraudulent transactions. The algorithm covers the loopholes in the CS strategy. Furthermore, to provide strong grounds for research in our paper, this proposed model was compared with various types of approaches such as Light Gradient Boosting Machine, Support Vector Classification, Multi-Layer Perceptron, XGBoost, Logistic Regression, and Random Forest. The proposed model outperforms other significant models like SVC, KNN, LGBM Classifier, RF etc. with the accuracy of 98.6%. © 2024 IEEE.

关键词： Genetic algorithms

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Enhancing Hybrid Object Identification for Instantaneous Healthcare through Lorentz Force 8

Enhancing Hybrid Object Identification for Instantaneous Hea...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Bhanurangarao, M. Mahaveerakannan, R. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

This research introduces a novel hybrid model that combines deep learning with traditional image processing techniques to enhance medical imaging capabilities. By addressing challenges such as real-time processing, limited dataset diversity, and healthcare-specific privacy concerns, the proposed model offers significant advancements in object recognition. The model considers a large dataset of medical images and employs lightweight algorithms to ensure efficient processing on edge devices and unmanned aerial vehicles (UAVs). Additionally, the model incorporates privacy-preserving techniques to align with healthcare ethics. Extensive evaluation across various medical imaging datasets demonstrates substantial improvements in processing speed (up to 108 ms) and recognition accuracy (up to 96.7%). The goal of this research is to improve diagnostic reliability, enhance patient care, and optimize public health management by seamlessly integrating advanced object recognition algorithms into existing healthcare infrastructures. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Online Payment Fraud Transaction Detection using Machine Learning 3

Online Payment Fraud Transaction Detection using Machine Lea...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Deo, Sakshi Adnan, Mohammad Raj, Manjay Abidi, Ali Imam Bansal, Nidhi School of Engineering and Technology Sharda University Department of Computer Science and Engineering Uttar Pradesh India Nagpur Campus Pune India

ISBN: (纸本)9798331518592

Determining and overcoming the online payment frauds is a very important and challenging step for any business as it may lead to financial losses if it occurs in increasing volume of it. These techniques of machine learning algorithms were used to identify the online fraud transactions in real world. For this, a dataset of online payment transactions was used from Kaggle and machine learning models were created and used for the classification of transactions into fraud or not fraud transactions. The outcomes of the same determined the attainability of these machine learning processes and models in identifying features associated with fraud transactions, which would result in creating an influential mechanism and preventing frauds. These models used not only increases the credibility of the model by increasing the accuracy of it but also allows to implement the bold measures to avoid these frauds. This study above determines and highlights the importance of the explanatory methods in contrast to the online payment fraud of the transactions, underlining the tips for avoiding the frauds in business for protecting their financial interfaces and maintaining the trust of the customers. Accuracy found upto 95% by implementing the things within the machine learning platform. © 2024 IEEE.

关键词： Adversarial machine learning

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Hybridization of Metaheuristics Based Energy Efficient Scheduling Algorithm for Multi-Core Systems

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computer Systems science & engineering 2023年第1期44卷 205-219页

作者： J.Jean Justus U.Sakthi K.Priyadarshini B.Thiyaneswaran Masoud Alajmi Marwa Obayya Manar Ahmed Hamza Department of Computer Science&Engineering St.Joseph’s College of EngineeringChennai600119India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Electronics and Communication Engineering Sona College of TechnologySalem636005India Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The developments of multi-core systems(MCS)have considerably improved the existing technologies in thefield of computer *** MCS comprises several processors that are heterogeneous for resource capacities,working environments,topologies,and so *** existing multi-core technology unlocks additional research opportunities for energy minimization by the use of effective task *** the same time,the task scheduling process is yet to be explored in the multi-core *** paper presents a new hybrid genetic algorithm(GA)with a krill herd(KH)based energy-efficient scheduling techni-que for multi-core systems(GAKH-SMCS).The goal of the GAKH-SMCS tech-nique is to derive scheduling tasks in such a way to achieve faster completion time and minimum energy *** GAKH-SMCS model involves a multi-objectivefitness function using four parameters such as makespan,processor utilization,speedup,and energy consumption to schedule tasks *** performance of the GAKH-SMCS model has been validated against two datasets namely random dataset and benchmark *** experimental outcome ensured the effectiveness of the GAKH-SMCS model interms of makespan,pro-cessor utilization,speedup,and energy *** overall simulation results depicted that the presented GAKH-SMCS model achieves energy effi-ciency by optimal task scheduling process in MCS.

关键词： Task scheduling energy efficiency multi-core systems fitness function makespan

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