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Crime Detection from Pre-Crime Video Analysis with Augmented Activity Information

Crime Detection from Pre-Crime Video Analysis with Augmented...

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Computing and machine Intelligence (ICMI), international conference on

作者： Sedat Kilic Mihran Tuceryan Department of Computer and Information Science Indiana University - Purdue University - Indianapolis Indianapolis Indiana

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

This study focuses on the detection of pre-crime events in videos, specifically shoplifting. In video understanding, visual features, pose, and emotion information are inherently frame-centric, providing insights in understanding within isolated frames. Our approach introduces a novel method by extracting activity information between frames and incorporating visual features augmented with human pose and emotion information to enhance the understanding of pre-shoplifting behaviors. We used a set of CCTV videos in which at the end of some clips the customers shoplift and in others they do not. We used these videos as training data with a transformer machine learning architecture. We augmented low level video analysis with pose, and emotion information on frame level and extracted activity information between frames. We conducted a systematic series of experiments to assess the performance of our model under two distinct configurations. The first configuration involved frame-centric visual features augmented with pose and emotion information, while the second configuration focused on activity information between frames. The results demonstrate improvements, underscoring the pivotal role of extracting temporal dynamics between frames in capturing nuanced activities leading up to the crime. The comprehension and capture of these activity patterns between frames proved to be crucial for a more thorough examination of pre-shoplifting events. The essence of our study's contribution is hinged upon the utilization of extracting temporal dynamics between frames, enabling the capture of crucial activities of individuals prior to engaging in shoplifting.

关键词： Visualization Systematics Training data machine learning Feature extraction Transformers data mining

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Protecting Contactless Credit Card Payments from Fraud through Ambient Authentication and machine learning

Protecting Contactless Credit Card Payments from Fraud throu...

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Advances in Computing, Communication, Embedded and Secure Systems (ACCESS), international conference on

作者： Divneet Singh Chitkara University Institute of Engineering & Technology Chitkara University Punjab India

Credit card fraud (CCF) is a persistent issue in the financial sector with serious consequences. data mining has proven to be extremely useful in detecting fraud in online transactions. However, detecting CCF through data mining is quite a difficult task because of two causes: constant changes in the profiles of normal and fraudulent behaviour, and the highly skewed nature of the data sets. The outcome of fraud detection in credit card transactions depends on the sampling approach, detection techniques, and variable selection. This work studies the performance of K-Nearest Neighbor, Naive Bayes, Logistic Regression and Random Forest algorithms on a highly skewed dataset. The dataset contains 2,84,807 transactions and has been collected from European cardholder transactions. A hybrid of under-sampling and oversampling techniques has been used on the skewed data. The four techniques were utilized on both data namely preprocessed and raw, and the results are evaluated using specificity, accuracy, sensitivity, and F1-score. The outcomes show that the optimal accuracy for Naive Bayes, Logistic Regression, K-Nearest Neighbor and Random Forest classifiers are 98.72%, 52.34%, 96.89%, 91.67%, respectively. The comparative results indicate that K-Nearest Neighbor performs better than Logistic Regression, Random Forest and Naive Bayes techniques.

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Enhancing Digital Forensics: machine learning Techniques for Social Media Investigation

Enhancing Digital Forensics: Machine Learning Techniques for...

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3rd international conference on machine learning and data Engineering, ICMLDE 2024

作者： Vyas, Dhairya Shah, Milind Kothari, Ankita Golakia, Jyoti Parikh, Vruti The Maharaja Sayajirao University of Baroda Gujarat Vadodara India Gujarat Vasad India Gujarat Vadodara India

Due to the increasing growth of social media platforms, improved methods are needed to extract, examine, and interpret digital evidence. Due to the vastness and ever-changing nature of the data that is collected via social media platforms, traditional forensic procedures usually meet challenges. Specifically created for the purpose of conducting forensic analysis of material obtained from social media platforms, the purpose of this research is to build and analyze machine learning models and algorithms. Gathering information from various platforms, such as Kaggle, is the first step in the process. The succeeding processes involve data cleaning and categorization, which includes activities such as text normalization, entity recognition, and sentiment analysis. These steps come after the first data gathering has been completed. The extraction of relevant information, such as user behaviors, temporal trends, and network connections, is accomplished through the use of feature engineering. A significant portion of the investigation is centered on the application of supervised machine learning techniques for the purpose of detecting users and detecting offensive language. In order to determine whether or not the proposed approach is effective, comprehensive experiments are conducted utilizing datasets derived from social media activities. The findings indicate that there have been considerable improvements in the capabilities of forensic investigations, including enhanced accuracy, scalability, and automation. In this research we have investigated social media's data and detected offensive language using various machine learning algorithms such as Support Vector machine (SVM), K-Nearest Neighbours (KNN), Decision Tree, Random Forest, and Extra Tree. The support vector machine (SVM) achieved 86% accuracy where as KNN achieved only 77% accuracy which was far lower than other learning algorithms. However, the Decision Tree, Random Forest, and Extra Tree models achieved 99% a

关键词： Computer forensics

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Application of machine learning in Customer Churn Prediction 3

Application of Machine Learning in Customer Churn Prediction

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3rd IEEE international Virtual conference on Innovations in Power and Advanced Computing Technologies, i-PACT 2021

作者： De, Soumi Prabu, P Paulose, Joy Department of Computer Science Bangalore India

ISBN: (纸本)9781665426916

Retaining customers is the central component of a company's growth strategy. It is evident that several industries are experiencing a surge in customer churn due to the global pandemic. As a result, customer retention that lies at the core of customer relationship management, has become the foundation for every industry to plan for future growth. By reducing customer churn, a company can maximize its profit. Studies suggest that significant advancements are made in the field of customer churn prediction in domains like telecom, banking, e-commerce and energy sector. The focus of the paper is to present a detailed review of the various machine learning techniques applied to address churn. Fifty-five papers related to churn classification published between 2004 and 2020 are collected and analyzed. The reviewed papers are categorized into five main themes. These themes are feature selection techniques, methods to handle class imbalance, experimentation with machine learning algorithms, hybrid models and ensemble models respectively. Finally, few suggestions are presented as direction for future research. © 2021 IEEE.

关键词： data mining

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Chronic Kidney Disease Analysis using Various Biological Parameters under machine learning Techniques

Chronic Kidney Disease Analysis using Various Biological Par...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： M. Suresh E. Nirmala M. Maragatharajan Manish Yadav School of Computing Science and Engineering VIT Bhopal University Kotri Kalan Madhya Pradesh School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Kotri Kalan Madhya Pradesh

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Kidney chronic disease (KCD) has become one of the most critical illnesses which should be diagnosed properly within a certain limit of time for better treatment. Nowadays different algorithms being used for treatment of patients. By using the best predictory model, doctor can easily predict the disease on time. For this verdict, Kidney Chronic Disease Prediction has been discussed in this article. In this article various datasets have been used by the different article authors to predict the disease which are pre-processed with different methods like Univariate Feature Selection (UFS) and Recursive Feature Elimination (RFE), data mining techniques, etc. which are further used to train and test various ML models get the accuracy by predictory models. The scores gained by the predictory models is then validated to check the precision of the output accuracy of by the model using methods like performance matrices, various cross validation techniques, Mathew Correlation Coefficient (MCC). This whole article is all around the analyses of different articles based on the machine learning and validation techniques with respect to the accuracy. Most of the models attained the accuracy of 99 percent and above. In this article, along with machine learning models it also discussed about two neural networks, which attained the accuracy between 93 to 98% with a good precision value.

关键词： Analytical models Renewable energy sources Accuracy Biological system modeling machine learning Predictive models Feature extraction data models Tuning Diseases

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3rd international Workshop on Deep learning Practice for High-Dimensional Sparse data with KDD 2021 21

3rd International Workshop on Deep Learning Practice for Hig...

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27th ACM SIGKDD international conference on Knowledge Discovery and data mining (KDD)

作者： Zhu, Xiaoqiang Lee, Kuang-chih Zhou, Guorui Jiang, Biye Wang, Zhe Tang, Ruiming Ren, Kan Ai, Qingyao Zhang, Weinan Alibaba Grp Hangzhou Peoples R China Roku Inc San Jose CA USA Huawei Noahs Ark Lab Hong Kong Peoples R China Microsoft Res Redmond WA USA Univ Utah Salt Lake City UT 84112 USA Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9781450383325

Recently, we have witnessed that deep learning-based approaches has been widely applied to empower many internet-scale applications. However, the data in these internet-scale applications are high dimensional and extremely sparse, which makes it different from those applications with dense data processing, such as image classification and speech recognition, where deep learning-based approaches have been extensively studied. One of the main applications is the user-centric platform that consists of great deal of users, items and user generated tabular data which are quite high-dimensional. The characteristics of such data pose unique challenges to the adoption of deep learning in these applications, including modeling, training, and online serving, etc. More and more communities from both academia and industry have initiated the endeavors to solve these challenges. This workshop will provide a venue for both the research and engineering communities to discuss and formulate the challenges, utilize opportunities, and propose new ideas in the practice of deep learning on high-dimensional sparse data.

关键词： Deep learning High-dimensional Sparse data

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Advancements in Disease Diagnosis and Prediction Leveraging Insights Extraction and Computational learning Techniques

Advancements in Disease Diagnosis and Prediction Leveraging ...

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： Niveditha Konda Koprada Pydi Durga Lakshmi Sri Vidya Varshith Chavalam Karthikeya Vadde K B Venkata Brahma Rao Department Of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vijayawada Andhra Pradesh India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Technology has revolutionized the health domain largely. Accurate and on-time analysis of any health-related problem It is crucial for the prevention and treatment of the ailment. The traditional way of diagnosis may in the event of a severe illness, it may not prove to be satisfactory. This project aims to design a diagnostic framework for different diseases based on their symptoms. This system utilizes data mining methodologies including classification techniquesin order to achieve such a mode and also ML algorithms for prediction of any diseases can help in a more accurate diagnosis than the conventional method. We have introduced a system for disease prediction that employs several machine learning algorithms. The dataset contains information on symptoms, age, blood group, and gender of individuals, which is used for processing by the diagnosis system. The output of the system is the disease. Here, the SympoDx algorithm, which incorporates Random Forest, Decision Tree, KNN, and Support Vector Classifier, is employed for classification tasks and computes the probabilities of diseases. Following the results, patients can contact the doctor accordingly for further treatment.

关键词： Accuracy machine learning algorithms Medical services Prediction algorithms Vectors Classification algorithms Decision trees

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Big data and Deep learning in Smart Cities: A Comprehensive dataset for AI-Driven Traffic Accident Detection and Computer Vision Systems

Big Data and Deep Learning in Smart Cities: A Comprehensive ...

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Computing and machine Intelligence (ICMI), international conference on

作者： Victor Adewopo Nelly Elsayed Zag Elsayed Murat Ozer Constantinos L. Zekios Ahmed Abdelgawad Magdy Bayoumi School of IT University of Cincinnati Cincinnati USA Dept. of Elec. & Comp. Eng. Florida International University Florida United States School of Eng. & Tech. Central Michigan University Michigan USA Dept. of Elec. & Comp. Eng. University of Louisiana Lafayette Louisiana

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

In the dynamic urban landscape, where interplay of vehicles and pedestrians defines the rhythm of life, integrating advanced technology for safety and efficiency is increasingly crucial. This study delves into the application of cutting-edge technological methods in smart cities, focusing on enhancing public safety through improved traffic accident detection. Action recognition plays a pivotal role in interpreting visual data and tracking object motion such as human pose estimation in video sequences. The challenges of action recognition include variability in rapid actions, limited dataset, and environmental factors such as (Weather, Illumination, and Occlusions). In this paper, we present a novel comprehensive dataset for traffic accident detection. This dataset is specifically designed to bolster computer vision and action recognition systems in predicting and detecting road traffic accidents. We integrated datasets from wide variety of data sources, road networks, weather conditions, and regions across the globe. This approach is underpinned by empirical studies, aiming to contribute to the discourse on how technology can enhance the quality of life in densely populated areas. This research aims to bridge existing research gaps by introducing benchmark datasets that leverage state-of-the-art algorithms tailored for traffic accident detection in smart cities. These dataset is expected to advance academic research and also enhance real-time accident detection applications, contributing significantly to the evolution of smart urban environments. Our study marks a pivotal step towards safer, more efficient smart cities, harnessing the power of AI and machine learning to transform urban living.

关键词： Computer vision Visualization Smart cities Tracking Video sequences Transportation Transforms

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3rd international conference on Intelligent Technologies and Applications, INTAP 2020

3rd International Conference on Intelligent Technologies and...

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3rd international conference on Intelligent Technologies and Applications, INTAP 2020

ISBN: (纸本)9783030717100

The proceedings contain 34 papers. The special focus in this conference is on Intelligent Technologies and Applications. The topics include: Interpretable Option Discovery Using Deep Q-learning and Variational Autoencoders;evaluating Predictive Deep learning Models;Pre-trained CNN Based Deep Features with Hand-Crafted Features and Patient data for Skin Lesion Classification;data-Driven machine learning Approach for Human Action recognition Using Skeleton and Optical Flow;multilingual Voice Impersonation dataset and Evaluation;a Survey on Unknown Presentation Attack Detection for Fingerprint;hierarchical Interpolation of Imagenet Features for Cross-dataset Presentation Attack Detection;cross-lingual Speaker Verification: Evaluation on X-Vector Method;fusion of Texture and Optical Flow Using Convolutional Neural Networks for Gender Classification in Videos;exploring Circular Hough Transforms for Detecting Hand Feature Points in Noisy Images from Ghost-Circle patterns;A PM 2.5 Forecasting Model Based on Air Pollution and Meteorological Conditions in Neighboring Areas;the Aquatic Surface Robot (AnSweR), a Lightweight, Low Cost, Multipurpose Unmanned Research Vessel;empirical Analysis of data mining Techniques in Network Intrusion Detection Systems;deep Neural Network Based Malicious Network Activity Detection Under Adversarial machine learning Attacks;towards Low Cost and Smart Load Testing as a Service Using Containers;securityGuard: An Automated Secure Coding Framework;the Multi-objective Feature Selection in Android Malware Detection System;border Management Systems: How Can They Help Against Pandemics;translating Ethical Theory into Ethical Action: An Ethic of Responsibility Approach to Value-Oriented Design;An Example of Privacy and data Protection Best Practices for Biometrics data Processing in Border Control: Lesson Learned from SMILE;protecting IoT Devices with Software-Defined Networks.

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Breast Cancer Molecular Subtype Detection through Mammograms with machine learning: A Comprehensive Framework Using Radiomics and Metaheuristic Optimization

Breast Cancer Molecular Subtype Detection through Mammograms...

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3rd international conference on machine learning and data Engineering, ICMLDE 2024

作者： Nissar, Iqra Alam, Shahzad Masood, Sarfaraz Department of Computer Engineering Jamia Millia Islamia New Delhi110025 India

machine learning has become pivotal in devising effective solutions for intricate tasks such as cancer detection, owing to its capacity to process extensive datasets with remarkable efficiency and accuracy. This study emphasizes the importance of molecular subtype detection in breast cancer diagnosis. Using the CMMD dataset, we applied various image pre-processing techniques to refine detection capabilities such as cropping, filtering, enhancement, and normalization. The radiomics features such as gray level run length matrix, local binary pattern, and gray level co-occurrence matrix were extracted from the image and optimized using differential evolution (DE) and grey wolf optimization. The evaluation of support vector machine, decision tree, light gradient boosting machine (LGBM), and random forest classifiers revealed that LGBM consistently demonstrated superior performance in most classification tasks. Specifically, when using a median filter and optimized with DE: LGBM achieved an accuracy of 0.81 in predicting the benign or malignant status of breast cancer for coarse-grained classification task and among the fine-grained classification tasks when predicting calcifications that are either benign or malignant, LGBM obtained an accuracy of 0.83. These findings highlight the potential of machine learning, particularly the LGBM classifier, in enhancing breast cancer diagnosis. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Feature Selection

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