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Click Fraud Detection Using Ensemble Classifier 1

3rd International Conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Purwar, Archana Jain, Art Chawla, Indu Gupta, Ishita Raj, Mayank Jain, Divansh Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (数字)9789819748600

ISBN: (纸本)9789819748594

Online advertising fraud is an emerging issue and has drawn researchers’ mind recently because it now poses a significant danger to an online advertising sector. A content publisher signs an agreement to place advertisements on various websites in exchange for commissions on the basis of the amount of traffic these advertisements generate. It may sometimes motivate fraudulent publishers to produce unauthorized clicks on their websites in order to maximize traffic to the advertisements, a practice known as click fraud. Although click fraud is identified as a huge issue, there are not many algorithms that have been able to effectively solve this problem. Our aim in this paper is to overcome the problem of click fraud and come up with an explicit model for the click fraud detection. In this paper, a model is proposed using recursive feature elimination (RFE) and ensemble classifier with Hellinger distance-based decision tree (HDDT). The proposed model is evaluated over the dataset hosted by a Chinese company TalkingData available freely from Kaggle and has resulted in an accuracy of 99.72% as compared to the traditional decision tree. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Marketing

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Spatio-Temporal NDVI Prediction for Rice Crop

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SN computer science 2025年第3期6卷 1-13页

作者： Dey, Anamika Sarkar, Somrita Mondal, Arijit Mitra, Pabitra Centre for Computational and Data Sciences Indian Institute of Technology Kharagpur West Bengal Kharagpur 721302 India Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Bihta 801106 India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal Kharagpur 721302 India

Accurately predicting the Normalized Difference Vegetation Index (NDVI) is crucial for effective agricultural planning and decision-making. Despite much literature on NDVI prediction, most of these methods do not consider spatial-temporal variations. Generating a dataset for predicting NDVI is another major bottleneck, as such data needs to be collected over an extensive period. In this work, we develop a semi-automated framework to generate a comprehensive time-series dataset after necessary preprocessing using the information from Sentinel-2, POWER Data Access Viewer, and the Google Earth Engine (GEE). We propose a deep learning-based model to predict NDVI based on its historical time series and meteorological and spatial information for the rice crop in India. Here, we use a Long Short-Term Memory (LSTM)-based model augmented with spatial information to predict the NDVI of the near-about region for which the data is unavailable. We compared the proposed approach with a method that uses a weighted average of nearest neighbors prediction. Also, we compare the proposed method with existing statistical techniques such as the Autoregressive (AR), AutoRegressive Integrated Moving Average (ARIMA), and Seasonal AutoRegressive Integrated Moving Average (SARIMA). Experimental results show that our methodology outperforms other techniques by a margin of 4% to 8%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Autoregressive integrated moving average Long short-term memory Normalized difference vegetation index Seasonal autoregressive integrated moving average Sentinel-2 Weighted average of nearest neighbors prediction

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Event-Driven Attention Network:A Cross-Modal Framework for Efficient Image-Text Retrieval in Mass Gathering Events

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computers, Materials & Continua 2025年第5期83卷 3277-3301页

作者： Kamil Yasen Heyan Jin Sijie Yang Li Zhan Xuyang Zhang Ke Qin Ye Li School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Information and Software Engineering University of Electronic Science and Technology ofChinaChengdu611731China Kashi Institute of Electronics and Information Industry Kashi844508China

Research on mass gathering events is critical for ensuring public security and maintaining social ***,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting,and there is a relative lack of research on mass gathering *** believe real-time detection and monitoring of mass gathering behaviors are essential formigrating potential security risks and ***,it is imperative to develop a method capable of accurately identifying and localizing mass gatherings before disasters occur,enabling prompt and effective *** address this problem,we propose an innovative Event-Driven Attention Network(EDAN),which achieves image-text matching in the scenario of mass gathering events with good results for the first *** image-text retrieval methods based on global alignment are difficult to capture the local details within complex scenes,limiting retrieval *** local alignment-based methods aremore effective at extracting detailed features,they frequently process raw textual features directly,which often contain ambiguities and redundant information that can diminish retrieval efficiency and degrade model *** overcome these challenges,EDAN introduces an Event-Driven AttentionModule that adaptively focuses attention on image regions or textual words relevant to the event *** calculating the semantic distance between event labels and textual content,this module effectively significantly reduces computational complexity and enhances retrieval *** validate the effectiveness of EDAN,we construct a dedicated multimodal dataset tailored for the analysis of mass gathering events,providing a reliable foundation for subsequent *** conduct comparative experiments with other methods on our dataset,the experimental results demonstrate the effectiveness of *** the image-to-text retrieval task,EDAN achieved the best performance on the R@5 metric,w

关键词： Mass gathering events image-text retrieval attention mechanism

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In-Group Bias in Deep Learning-Based Face Recognition Models Due to Ethnicity and Age

IEEE Transactions on Technology and Society

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IEEE Transactions on technology and Society 2023年第1期4卷 54-67页

作者： Nagpal, Shruti Singh, Maneet Singh, Richa Vatsa, Mayank Ratha, Nalini K. Ratha Indraprastha Institute of Information Technology Delhi Department of Computer Science and Engineering New Delhi110020 India Indian Institute of Technology Jodhpur Department of Computer Science and Engineering Jodhpur342011 India University at Buffalo Department of Computer Science and Engineering BuffaloNY14260 United States

Humans are known to favor other individuals who exist in similar groups as them, exhibiting biased behavior, which is termed as in-group bias. The groups could be formed on the basis of ethnicity, age, or even a favorite sports team. Taking cues from aforementioned observation, we inspect if deep learning networks also mimic this human behavior, and are affected by in-group and out-group biases. In this first of its kind research, the behavior of face recognition models is evaluated to understand if similar to humans, models also encode group-specific features for face recognition, along with where bias is encoded in these models. Analysis has been performed for two use-cases of bias: age and ethnicity in face recognition models. Thorough experimental evaluation leads us to several insights: (i) deep learning models focus on different facial regions for different ethnic groups and age groups, and (ii) large variation in face verification performance is also observed across different sub-groups for both known and our own trained deep networks. Based on the observations, a novel bias index is presented for evaluating a trained model's level of bias. We believe that a better understanding of how deep learning models work and encode bias, along with the proposed bias index would enable researchers to address the challenge of bias in AI, and develop more robust and fairer algorithms for mitigating bias as well as developing fairer models. © 2020 IEEE.

关键词： Face recognition

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Student Placement Prediction Using Machine Learning Algorithms 2nd

Student Placement Prediction Using Machine Learning Algorith...

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2nd International Conference on Emerging Trends in engineering and technology, EGTET 2022

作者： Sajwan, Samarth Bhardwaj, Rudraksh Mishra, Revaan Jaiswal, Shruti Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9789819943616

One of the most perplexing issues confronting higher education institutions today is how to increase student placement performance. Placement estimation becomes more complex as the number of educational institutions increases. Educational organizations explore more efficient technologies to assist them in improving their decision-making practices, as well as in developing creative methods. Providing new insight into instructional processes is a critical component in resolving quality issues. Machine learning methods are used to extract information from historical data contained in the libraries of educational organizations. Our model will generate a recommendation system that forecasts the placement level of a student. This model assists an organization’s selection cell in identifying prospective students, assessing their technical and interpersonal abilities, and assisting them in developing them. Students in their pre-final and final years of B. Tech programs may also use this work to determine their individual placement status and probability of achieving it. This enables them to exert additional effort in order to get placements in organizations with higher hierarchies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Forecasting

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PQTD-TM: Integrating information Retrieval with Machine Learning for effective Placement Topic Discovery 2024

PQTD-TM: Integrating Information Retrieval with Machine Lear...

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16th International Conference on Contemporary Computing, IC3 2024

作者： Sardana, Neetu Kumar, Prabudh Goyal, Kritank Rishi Jain, Nehal Arora, Anuja Varshney, Deepika Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798400709722

Technical interviews, particularly coding rounds, are pivotal in shaping career trajectories, especially for roles in top-tier companies like Amazon, Google, and Microsoft. Understanding the patterns and nuances of coding problems and their associated tags is essential for effective interview preparation. To aid candidates in excelling in these assessments, we analyzed coding problems encountered in such interviews. We compile a comprehensive dataset of coding problems and their associated tags, sourced from real technical interviews, ensuring reliability and relevance. We propose a topic prediction approach for placement related questions, PQTD-TM (Placement question topic discovery using TF IDF and Machine Learning). The proposed technique integrates information retrieval technique (TF-IDF) with six machine learning classifiers for prediction. We evaluated the predictive performance of various machine learning models, including Logistic Regression, Decision Trees, Naive Bayes, KNN, One-vs-Rest Classifier, and Random Forest. Random forest attained the best prediction accuracy of 93%. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Trust and QoS-Driven Query Service Provisioning Using Optimization

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Intelligent Automation & Soft Computing 2023年第5期36卷 1827-1844页

作者： K.Narmatha K.Karthikeyan Department of Computer Science and Engineering SRM Institute of Engineering and TechnologyRamapuramChennaiIndia Department of Information Technology Coimbatore Institute of TechnologyCoimbatoreIndia

The growing advancements with the Internet of Things(IoT)devices handle an enormous amount of data collected from various applications like healthcare,vehicle-based communication,and smart *** research analyses cloud-based privacy preservation over the smart city based on query ***,there is a lack of resources to handle the incoming data and maintain them with higher privacy and ***,a solution based idea needs to be proposed to preserve the IoT data to set an innovative city environment.A querying service model is proposed to handle the incoming data collected from various environments as the data is not so trusted and highly sensitive towards *** handling privacy,other inter-connected metrics like efﬁciency are also essential,which must be considered to fulﬁl the privacy ***,this work provides a query-based service model and clusters the query to measure the relevance of frequently generated ***,a Bag of Query(BoQ)model is designed to collect the query from various *** is done with a descriptive service provisioning model to cluster the query and extract the query’s summary to get theﬁnal *** processed data is preserved over the cloud storage system and optimized using an improved Grey Wolf Opti-mizer(GWO).It is used to attain global and local solutions regarding privacy *** iterative data is evaluated without any over-ﬁtting issues and computational complexity due to the tremendous data handling *** on this analysis,metrics like privacy,efﬁciency,computational complexity,the error rate is *** simulation is done with a MATLAB 2020a *** proposed model gives a better trade-off in contrast to existing approaches.

关键词： Cloud computing IoT privacy security grey wolf optimization

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Enhancement of Alzheimer’s Disease Detection Technique by Fusing Features of Deep Learning and Handcrafted Methods on sMRI Images 3

Enhancement of Alzheimer’s Disease Detection Technique by F...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Bhadra, Sampa Rani Maity, Santi Prasad Sil, Jaya Department of Computer Science Kalyani Mahavidyalaya West Bengal Kalyani741235 India Department of Information Technology Indian Institute of Engineering Science and Technology West Bengal Howrah711103 India Department of Computer Science and Technology Indian Institute of Engineering Science and Technology West Bengal Howrah711103 India

ISBN: (纸本)9798331532420

Automated detection of Alzheimer disease (AD) is evolving rapidly with the advent of artificial intelligence (AI). Machine learning and deep learning models. Detection of AD at an early stage provides advance planning of treatment to the patients that arrest progression of the disease. The aim of this paper is to improve performance of diagnosing AD by fusing features extracted mainly from two sources, such as deep and nondeep frameworks applied on the sMRI images. This paper introduces two customized convolutional neural networks (CNN) (baseline model and hybrid model) and extracted two different sets of deep features. As an alternative source of features, a set of handcrafted features are extracted from the sMRI images by applying the stack assembling of Gray Level Co-occurrence Matrix (GLCM) and Gabor algorithms. In order to select nonredundant important features, we apply cosine similarity metric between the handcrafted and deep features. Next, a Feature Pyramid Network-based feature fusion technique namely- FusionNet is also proposed to fuse the deep features and the handcrafted features in order to further improve the classification accuracy. For comparing the performance of AD diagnosis, fused feature sets without feature selection and with feature selection are considered. It has been observed that the fused features without feature selection achieves the best classification accuracy implying that for AD diagnosis all the features are important and therefore distinct. All the experiments have been applied on a 5-class dataset obtained from ADNI and classified by Support Vector Machine classifier. © 2024 IEEE.

关键词： Convolutional neural networks

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Virtual Fitness Trainer using Artificial Intelligence 2024

Virtual Fitness Trainer using Artificial Intelligence

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16th International Conference on Contemporary Computing, IC3 2024

作者： Gurbuxani, Shrey Gupta, Lakshay Madan, Kapil Department of Computer Science Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798400709722

An AI wellness coach is a software program that guides users through physical health routines by leveraging MediaPipe, OpenCV, and Python features. The program uses OpenCV computer vision techniques to monitor user progress and provide feedback on procedure and frame. To prepare the video data and provide real-time assessments, MediaPipe is used. Additionally, the app makes use of machine learning algorithms to provide users with tailored wellness recommendations and promote adoption. When these advances are combined, clients have an incredibly intelligent and practical way to advance their physical fitness. © 2024 Copyright held by the owner/author(s).

关键词： Python

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An object detection approach with residual feature fusion and second-order term attention mechanism

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CAAI Transactions on Intelligence technology 2024年第2期9卷 411-424页

作者： Cuijin Li Zhong Qu Shengye Wang College of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqingChina College of Electronic Information Chongqing Institute of EngineeringChongqingChina

Automatically detecting and locating remote occlusion small objects from the images of complex traffic environments is a valuable and challenging *** the boundary box location is not sufficiently accurate and it is difficult to distinguish overlapping and occluded objects,the authors propose a network model with a second-order term attention mechanism and occlusion ***,the backbone network is built on *** a method is designed for the feature extraction network based on an item-wise attention mechanism,which uses the filtered weighted feature vector to replace the original residual fusion and adds a second-order term to reduce the information loss in the process of fusion and accelerate the convergence of the ***,an objected occlusion regression loss function is studied to reduce the problems of missed detections caused by dense *** experimental results demonstrate that the authors’method achieved state-of-the-art performance without reducing the detection *** mAP@.5 of the method is 85.8%on the Foggy_cityscapes dataset and the mAP@.5 of the method is 97.8%on the KITTI dataset.

关键词： artificial intelligence computer vision image processing machine learning neural network object recognition

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