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A data-Driven machine learning Approach for Human Gender Classification Using Explainable AI

A Data-Driven Machine Learning Approach for Human Gender Cla...

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international conference on Computer, Communication, Control and Information Technology (C3IT)

作者： Shakib Mahmud Dipto Adria Binte Habib Nadia Tasnim Mim Salman Ibne Eunus Nabiha Tasnim Orchi Department of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering BRAC University Dhaka Bangladesh

ISBN: (数字)9798350389128

ISBN: (纸本)9798350389135

Since gender recognition contains a wealth of information about the differences between male and female characteristics, it is crucial and essential for many applications in commercial domains, such as human-computer interaction applications and computer-aided physiological or psychological analysis. there are lots of scientific studies that contain different approaches to classify gender. Apart from those, we have included Explainable Artificial Intelligence to interpret the usability of the model. In this paper, we study a gender classification dataset to predict gender from tabular data of specific human body features such as hair length, forehead length, height, nose width, and length. We have used several machine learning (ML) algorithms such as - Gaussian Naive Bayes (GNB), Support Vector machine (SVM), Decision Tree (DT), and Stochastic Gradient Descent (SGD) to predict gender. After that, we performed a comparison study and implemented Lime on the best performing ML model to interpret the prediction of the model. the Gaussian Naive Bayes architecture with the dataset shows a reasonable accuracy of 98%, proving the usability of the model.

关键词： Biometrics Support vector machines Human computer interaction Accuracy Recurrent neural networks Explainable AI Computer architecture Predictive models Security Usability

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Deep learning-based Worm Detection Method for Polymorphic Networks

Deep Learning-based Worm Detection Method for Polymorphic Ne...

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Electronic Technology, Communication and Information (ICETCI), IEEE international conference on

作者： Wuqiang Shen Yechao Wang Chaosheng Yao Ning Xie Guangdong Power Grid Corporation Guangzhou China Joint Laboratory on Cyberspace Security China Southern Power Grid Guangzhou China Shenzhen E-Link Information Technology Co. Ltd Shenzhen China

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361650

the maintenance of cybersecurity has become increasingly vital with the advancement of the information age, particularly in the detection of polymorphic network worms. Due to their ability to mutate, these worms can evade the capture of traditional detection techniques, for which the application of deep learning provides a new perspective. this paper delves into the exploration of deep learning, especially Convolutional Neural Networks (CNN), in the field of polymorphic network worm detection. By studying deep learning algorithms and understanding how they operate in complex pattern recognition tasks, this study constructs a detection framework based on CNN for the identification and disruption of worm propagation. In addressing network traffic data, a unique method of effective payload matrix processing is proposed to meet the data input requirements of CNN. this method can extract deep-seated features hidden in traffic data, enabling the effective identification of polymorphic worm attacks. through the establishment of an experimental environment and the validation of the proposed method using standard datasets, the experimental results confirm the accuracy and practicality of this approach in polymorphic worm detection. this work significantly enhances the capability of the network security defense system and lays a foundation for defending against future worm attacks.

关键词： Deep learning Weapons Telecommunication traffic Network security Feature extraction threat assessment Convolutional neural networks

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Deep Interaction Feature Fusion for Robust Human Activity recognition 4th

Deep Interaction Feature Fusion for Robust Human Activity R...

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4th international Workshop on Deep learning for Human Activity recognition, DL-HAR 2024, and 1st international Workshop on Anomaly Detection with Foundation Models, ADFM 2024, Held in Conjunction with the international Joint conference on AI, IJCAI 2024

作者： Oh, YongKyung Kim, Sungil Bui, Alex A. T. Los AngelesCA90024 United States Ulsan44919 Korea Republic of

ISBN: (纸本)9789819790029

Human activity recognition (HAR) is vital in healthcare settings, facilitating personalized care and early intervention. Recent advancements in sensor technology and machine learning have paved the way for more accurate and reliable HAR systems. However, the integration of multimodal data and the extraction of informative features remain significant challenges. In this paper, we propose a novel data fusion approach that leverages deep, nonlinear interactions between static and dynamic modalities to enhance the performance of HAR systems. Our method combines three types of interaction features derived from static data, latent representations learned by deep neural networks, and statistical features extracted from time series data. We explore two data fusion strategies: feature-level fusion and decision-level fusion. We conduct extensive experiments on six publicly available datasets, demonstrating the superiority of our approach compared to traditional methods. Our research highlights the importance of capturing complex interactions between multimodal data and opens up new possibilities for the development of advanced HAR systems in healthcare applications. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sensor data fusion

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A Deep-learning Based Automated COVID-19 Physical Distance Measurement System Using Surveillance Video 4th

A Deep-Learning Based Automated COVID-19 Physical Distance M...

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4th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2021

作者： Junayed, Masum Shah Islam, Md Baharul Department of Computer Engineering Bahcesehir University Istanbul Turkey Department of CSE Daffodil International University Dhaka Bangladesh College of Data Science and Engineering American University of Malta Bormla Malta

ISBN: (纸本)9783031070044

the contagious Corona Virus (COVID-19) transmission can be reduced by following and maintaining physical distancing (also known as COVID-19 social distance). the World Health Organisation (WHO) recommends it to prevent COVID-19 from spreading in public areas. On the other hand, people may not be maintaining the required 2-m physical distance as a mandated safety precaution in shopping malls and public places. the spread of the fatal disease may be slowed by an active monitoring system suitable for identifying distances between people and alerting them. this paper introduced a deep learning-based system for automatically detecting physical distance using video from security cameras. the proposed system employed the fine-tuning YOLO v4 for object detection and classification and Deepsort for tracking the detected people using bounding boxes from the video. Pairwise L2 vectorized normalization was utilized to generate a three-dimensional feature space for tracking physical distances and the violation index, determining the number of individuals who follow the distance rules. For training and testing, we use the MS COCO and Oxford Town Centre (OTC) datasets. We compared the proposed system to two well-known object detection models, YOLO v3 and Faster RCNN. Our method obtained a weighted mAP score of 87.8% and an FPS score of 28;both are computationally comparable. © 2022, Springer Nature Switzerland AG.

关键词： Object recognition

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Emotion Detection from Textual data Using Supervised machine learning Models

Emotion Detection from Textual Data Using Supervised Machine...

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Emerging Technology (INCET), international conference for

作者： Rakshit R Malagi Yogith R Sai Prashanth T K Ashwini Kodipalli Trupthi Rao Rohini B R Dept. of Artificial Intelligence & Data Science Global Academy of Technology Bangalore Karnataka India

Emotion detection and recognition from text is a recent field of research that is closely related to Sentiment analysis. Many people express themselves using text, photographs, music, and video. Text communication using web-based networking platforms, however, could be a little overwhelming. Every second, a substantial amount of unstructured data is produced on the Internet as a result of social media sites. this is where sentiment analysis, which recognises polarity in texts, can be useful. It assesses the author’s attitude towards a specific object, administration, person, or location and concludes if it is positive, negative, or neutral. In some cases, sentiment analysis is inadequate, necessitating emotion detection, which precisely ascertains a person’s mental/emotional state. the development of a text-based emotion detection and prediction model is the primary goal of this work. the development of emotion analysis is confronting several market hurdles, with accuracy being the key one. As a result, the Decision Trees, Naive Bayes, Support Vector machine, Logistic Regression, k-Nearest Neighbors and Random Forest, supervised machine learning classification algorithms were examined. the six main emotions recognized by Ekman are joy, fear, anger, love, surprise, and sadness, these were the foundation through which the model was constructed. strategies for preprocessing data containing stemming, stop-words, numerals, and punctuation marks removal, tokenization, and spelling correction were implemented. this review paper delves into the degrees of several emotion models as well as the technique of emotion detection from text.

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Research on Hybrid data Mining Methods for Social Media Sentiment Analysis 23

Research on Hybrid Data Mining Methods for Social Media Sent...

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Proceedings of the 2023 4th international conference on Big data Economy and Information Management

作者： Haoran Li University of Bristol the United Kingdom

ISBN: (纸本)9798400716669

With the popularity of social media in modern society, online opinion analysis has become an important research field. this study proposes a novel hybrid data mining algorithm that combines sentiment analysis and topic modeling techniques, aiming to improve the accuracy and comprehensiveness of social media data analysis. this paper first introduces the important role of social media in shaping public opinion and the major challenges currently faced in data analysis, such as information overload and viewpoint polarization. this is followed by an overview of the main current research trends in online opinion analysis, especially the application of deep learning and machine learning techniques in processing large-scale social media data. this study details the theoretical foundations and implementation of sentiment analysis and topic modeling, and shows how the two techniques can be combined. through experimental results, this paper demonstrates the performance of the hybrid algorithm in sentiment analysis and topic modeling, and verifies its effectiveness in online public opinion analysis. the experimental results show that the algorithm not only exhibits high accuracy in sentiment categorization, but also demonstrates good performance in topic recognition.

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Neologisms recognition Technology Based on a variety of Statistical Indicators

Neologisms Recognition Technology Based on a variety of Stat...

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international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Canrui Feng Jianye An School of Science-TUC Tianjin University of Commerce Tianjin China

In the era of big data, new technical vocabulary is constantly emerging. the construction of a knowledge graph in the recruitment domain requires accurate identification of these neologisms. However, manually constructing a new vocabulary is resource-intensive. Based on the existing research, this paper proposes a neologism discovery technique. through the frequency, mutual information, left and right information entropy and inverse document frequency four statistical indicators. Build a screening index system to screen out neologisms that meet the rules. Automate computer discovery of neologisms and the construction of vocabulary. Finally, through the recruitment text of the ‘‘ 51job’’ platform, the effectiveness and feasibility of neologism discovery technology are verified.

关键词： Vocabulary machine learning Knowledge graphs Big data Indexes Business intelligence Information entropy

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Towards activity recognition in smart homes using multimodal data 4

Towards activity recognition in smart homes using multimodal...

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4th IEEE international conference and Workshop in Obuda on Electrical and Power Engineering, CANDO-EPE 2021

作者： Schlenke, Fabian Kohlmorgen, Fabian Bauer, Jorg Kuller, Markus Karaoglan, Nursi Wohrle, Hendrik Dortmund University of Applied Sciences and Arts Department of Information Technology Dortmund Germany

ISBN: (纸本)9781665420235

Recognizing the activities of residents of a Smart Home is an essential prerequisite to derive their intentions and typical behaviors and, subsequently, to adapt the functions of the Smart Home to these. In this paper, we describe an experimental setup for Human Activity recognition using a special multisensor platform. In the course of the experiment, sensor data and the associated activity labels are acquired and aggregated into a comprehensive dataset. this data will be used to train and validate different machine learning models that analyze the data in real time. these models will be used to perform realtime streaming predictions of activities on an edge device that is located in the home of the user. the goal is to provide activity and context recognition as a basic service for subsequent action routines. Possible use cases can be found in the areas of comfort management and assisted living for, e.g., elderly people. © 2021 IEEE.

关键词： Edge computing

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MLScent: A Tool for Anti-pattern Detection in ML Projects

MLScent: A Tool for Anti-Pattern Detection in ML Projects

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AI Engineering - Software Engineering for AI (WAIN), IEEE/ACM Workshop on

作者： Karthik Shivashankar Antonio Martini Department of Informatics University of Oslo Norway

ISBN: (数字)9798331502195

ISBN: (纸本)9798331502201

machine learning (ML) codebases face unprecedented challenges in maintaining code quality and sustainability as their complexity grows exponentially. While traditional code smell detection tools exist, they fail to address ML-specific issues that can significantly impact model performance, reproducibility, and maintainability. this paper introduces MLScent, a novel static analysis tool that leverages sophisticated Abstract Syntax Tree (AST) analysis to detect anti-patterns and code smells specific to ML projects. MLScent implements 76 distinct detectors across major ML frameworks including TensorFlow (13 detectors), PyTorch (12 detectors), Scikit-learn (9 detectors), and Hugging Face (10 detectors), along with data science libraries like Pandas and NumPy (8 detectors each). the tool's architecture also integrates general ML smell detection (16 detectors), and specialized analysis for data preprocessing and model training workflows. Our evaluation demonstrates MLScent's effectiveness through both quantitative classification metrics and qualitative assessment via user studies feedback with ML practitioners. Results show high accuracy in identifying framework-specific anti-patterns, data handling issues, and general ML code smells across real-world projects.

关键词： Training Analytical models Codes Detectors machine learning Syntactics Libraries data models Complexity theory Faces

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Predictive Analytics in Sports: Using machine learning to Forecast Outcomes and Medal Tally Trends at the 2024 Summer Olympics

Predictive Analytics in Sports: Using Machine Learning to Fo...

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

作者： Jhankar Moolchandani Vikrant Chole Santosh Sahu Rahul Kumar Anshita Shukla Ashok Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Gwalior Madhya Pradesh India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

this paper focuses on using machine learning approaches in predicting the medal projections and analyzing the medal distribution pattern in the 2024 Summer Olympics. Due to the availability of a large number of variables both at the individual athlete level and the country level as well as event specific measures, various statistical models are trained to predict the number of medals and unearthing traits that are associated with success in Olympics. the evaluation of the predictions of the models is done using linear regression and random forest along with support vector classifier, as well as artificial neural network classifiers. Using the research findings, it becomes evident that predictive analytics has the capacity of offering a better understanding of the performance of sport teams and formulation of strategies for future competition events. As is observed from the above findings the random forest model sampled the highest accuracy of 99. 034 percent in the general set out of the four models; thus, it is recommended ideal for complicated set data investigations Based on the results of the experiments, it is clearly observed that the random forest has the highest accuracy of 99. 034.

关键词： Analytical models Adaptation models Accuracy Linear regression Predictive models Market research data models Predictive analytics Random forests Sports

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