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Face Sketch Construction and Recognition

Face Sketch Construction and Recognition

Cognitive Computing in engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Shaik Yacoob B. Sujatha Tamarana Anudeep Bozzam Eesha Sharvi Gande Veera Venkata Siva Sai Koppula Charles Antony Dept. of Computer Science and Engineering Godavari Global University Rajamahendravaram India Dept. of Computer Science and Engineering Godavari Institute of Engineering & Technology (A) Rajamahendravaram India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

As the crime rate increases rapidly, law enforcement organizations are facing the urgent task of quickly and properly identifying suspects. This paper proposes an innovative way to improve crime detection using new facial recognition technologies by describing a newly designed technology that allows users to create precise facial drawings using a user- friendly drag-and-drop interface. In which it will completely eliminate the need for expert forensic artists. This paper allows to create face drawings that can be effortlessly matched to huge police databases by utilizing the powerful deep learning algorithms and cloud infrastructure. By combining these modern technologies this tool speeds up the identification process by overcoming the problems and issues of typical handdrawn drawings. It will not only enhance the speed and accuracy of suspect identification, but it will also give law enforcement officers an efficient resource thereby, it will enhance the overall investigative effectiveness and responsiveness.

关键词： Deep learning computer vision Accuracy Law enforcement Databases Face recognition Forensics Organizations Cognitive systems Bioinformatics

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Waste Generation Forecasting Using Mobility Data During COVID-19 Pandemic 4

Waste Generation Forecasting Using Mobility Data During COVI...

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4th International Conference on Distributed Sensing and Intelligent systems, ICDSIS 2023

作者： Fathabadi, Alireza Toghroli Abdeljaber, Abdulrahman Abdallah, Mohamed Talib, Manar Abu Maraqa, Munjed Dept. of Civil and Environmental Engineering University of Sharjah Sharjah United Arab Emirates Dept. of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781839539954

Municipal solid waste (MSW) generation forecasting serves as the basis for future waste management strategic plans. However, rapid socio-economic and environmental changes lead to fluctuations in the MSW generation patterns. This is particularly exacerbated during unforeseen events, such as the COVID-19 pandemic. This study investigated the effect of COVID-19 and mobility conditions on the behavior of MSW generation predictors. Multiple artificial intelligence models were employed, including generalized linear models decision trees (DTs), support vector machines, deep learning, random forests (RFs), and gradient-boosted trees (GBTs). The models were tested around a 5-year historical MSW generation, incorporated with various socio-economic, seasonal, and climatic factors, in Abu Dhabi, United Arab Emirates. Multiple data split ratios were examined: 1) 60/40, 70/30, and 49/51 to simulate the COVID-19 pandemic. The feature importance analysis indicated that socioeconomic had the highest impact on generation rates, with a Pearson correlation coefficient of 0.30-0.71, followed by seasonal and climatic factors, with 0.12-0.40 and 0.15, respectively. The forecasting results indicated that training the models with 70% of the dataset resulted in higher accuracy compared to using a 60% training split. GBTs and RFs demonstrated superior performance, with root mean squared error (RMSE) of 0.029 and 0.031, respectively. On the other hand, DT achieved the lowest accuracy of 0.057 RMSE. The analysis revealed a significant improvement in the models' predictive capabilities upon the inclusion of COVID-19 and mobility conditions by 17-41%. The findings of this study emphatically highlight the importance of incorporating contextual variables into MSW generation prediction methodologies, affirming their significant impact on enhancing forecasting accuracy in rapidly changing environments. © The Institution of engineering & Technology 2023.

关键词： Prediction models

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XGBClassifier and Explainable AI for Disease Classification from Patient Symptom and Demographic Data with Late Fusion

XGBClassifier and Explainable AI for Disease Classification ...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md. Ismiel Hossen Abir Nurul Bashar Md. Shaek Ahmed Hasibul Islam Peyal Dept. of Computer Science & Engineering International Standard University Dhaka Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

The accurate identification of diseases based on patient symptoms and demographic data is a critical area of healthcare research, with the potential to significantly improve patient outcomes. In this study, we employed machine learning algorithms, specifically the XGBClassifier, to classify diseases based on a dataset containing patient symptoms and demographic information such as age, gender, fever, cough, fatigue, and other health indicators. To ensure model interpretability and transparency, we incorporated Explainable AI (XAI) techniques like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), which allow us to understand and interpret the feature contributions to the model’s predictions. The results demonstrated that the XGBClassifier achieved an accuracy of 81.42%, outperforming other machine learning models tested. This study emphasizes the importance of combining XAI with machine learning for disease classification, offering greater transparency in the decision-making process, which is vital in healthcare settings.

关键词： Support vector machines Electric potential Accuracy Machine learning algorithms Explainable AI Medical services Predictive models Fatigue Convolutional neural networks Diseases

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PPAC Driven Multi-die and Multi-technology Floorplanning

arXiv

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arXiv 2025年

作者： Roman-Vicharra, Cristhian Chen, Yiran Hu, Jiang Dept. of Electrical and Computer Engineering Texas A&M University United States Dept. of Electrical and Computer Engineering Duke University United States Dept. of Computer Science and Engineering Texas A&M University United States

In heterogeneous integration, where different dies may utilize distinct technologies, floorplanning across multiple dies inherently requires simultaneous technology selection. This work presents the first systematic study of multi-die and multi-technology floorplanning. Unlike many conventional approaches, which are primarily driven by area and wirelength, this study additionally considers performance, power, and cost, highlighting the impact of technology selection. A simulated annealing method and a reinforcement learning techniques are developed. Experimental results show that the proposed techniques significantly outperform a naïve baseline approach. © 2025, CC BY.

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PMU Spoof Detection via Image Classification Methodology against Repeated Value Attacks by using Deep Learning 28

PMU Spoof Detection via Image Classification Methodology aga...

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28th International Conference on Information, Communication and Automation Technologies, ICAT 2022

作者： Huseinovic, Alvin Korkmaz, Yusuf Bisgin, Halil Mrdovic, Sasa Uludag, Suleyman University of Sarajevo Faculty of Electrical Engineering Dept. of Computer Science Bosnia and Herzegovina University of Michigan - Flint Dept. of Computer Science United States

ISBN: (纸本)9781665466929

Various devices and monitoring systems have been developed and deployed in order to monitor the power grid. Indeed, several real-world cyberattacks on power grid systems have been publicly reported. For the transmission and distribution, Phasor Measurement Units (PMUs) constitute the main sensing equipment of the overall wide area monitoring and situational awareness systems by collecting high-resolution data and sending them to Phasor Data Concentrators (PDCs). In this paper, we consider data spoofing attacks against PMU networks. The data between PMUs and PDC(s) are sent through the legacy networks, which are subject to many attack scenarios under with no, or inadequate, countermeasures in protocols, such as IEEE 37.118-2. We consider one potential attack, where an adversary may simply keep injecting a repeated measurement through a compromised PMU to disrupt the monitoring system. This attack is referred to as a Repeated Last Value (RLV) attack. We develop and evaluate countermeasures against RLV attacks using a 2D Convolutional Neural Network (CNN)-based approach, which operates in frames for each second mimicking images, in order to avoid the computational overhead of the classical sample-based classification algorithms, such as SVM. Further, we take this frame-based approach and use it with Support Vector Machine (SVM) for performance evaluation. Our preliminary results show that frame-based CNN as well as SVM provide promising results for RLV attacks while the efficacy of CNN over SVM frame becomes more pronounced as the attack intensity increases. © 2022 IEEE.

关键词： Support vector machines

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A Detection Method for Copy-Move Forgery Attacks in Digital Images

A Detection Method for Copy-Move Forgery Attacks in Digital ...

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2022 IEEE Region 10 International Conference, TENCON 2022

作者： Sadu, Chiranjeevi Das, Pradip K. Rajiv Gandhi University of Knowledge Technologies Dept. of Computer Science and Engineering Nuzvid India Indian Institute of Technology Guwahati Dept. of Computer Science and Engineering Guwahati India

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

Digital images are carrying important information in many real-world applications such as surveillance, courts of law as evidence for crimes, journalism, scientific publications, and medical imaging. Manipulating these images becomes easy due to the paramount image processing and editing tools. Thus, the authentication of digital images becomes one of the principal problems in digital image forensics and attracts the attention of the research community. In this paper, we focus on the detection of copy-move forgery attacks in digital images. It is one of the most actively used attacks. It copies some region of the image and pastes it into one or more parts of the same image to cover or emphasize important scenes in the image. We propose a method to detect such forgery regions based on Speeded Up Robust Features (SURF) and Binary Robust Invariant Scalable Keypoints (BRISK) descriptors. Both fused features are matched and performed clustering to reduce the false positives using Hierarchical Agglomerative Clustering (HAC). The proposed method is tested on real-world copy-move datasets. Experimental results show that our method is robust against various geometric transformations and able to precisely determine the forged regions. © 2022 IEEE.

关键词： Medical imaging

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Sentiment Analysis for Curriculum of Independent Learning Based on Naïve Bayes with Laplace Estimator 2

Sentiment Analysis for Curriculum of Independent Learning Ba...

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2nd International Conference on Information Technology Research and Innovation, ICITRI 2023

作者： Ariefah, Amelia Widodo Nugraheni, Murien State University of Jakarta Dept. of Informatics and Computer Engineering Education Jakarta Indonesia State University of Jakarta Dept. of Information System and Technology Jakarta Indonesia

ISBN: (纸本)9798350324945

Curriculum changes have a significant impact on the field of education in Indonesia, especially in universities. This is because the curriculum is used as a means to achieve the goals of educational success as well as guidelines in the implementation of teaching at every level of education. This study aims to determine public sentiment regarding the implementation curriculum of independent learning (MBKM) through Twitter based on Naïve Bayes with Laplace Estimator. Tweet search results use the keywords 'kurikulum mbkm' and 'mbkm'. The data used in this study amounted to 2500 tweet data which will be divided into 2000 data as training data with details of 1000 positive tweets and 1000 negative tweets and 500 data as testing data. The results showed the performance of Naïve Bayes using Laplace Estimator which resulted in an accuracy value of 80.20%, precision value of 82.00%, and recall value of 79.00%. Meanwhile, the performance of Naïve Bayes without Laplace Estimator resulted in an accuracy value of 77.00%, precision value of 80.00%, and recall value of 74.00%. Naive Bayes with Laplace Estimator reached the highest value in all metrics. © 2023 IEEE.

关键词： Curricula

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Refining Lazy Learners of Machine Learning Algorithms Facing Concept Drift 4

Refining Lazy Learners of Machine Learning Algorithms Facing...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Sinha, Nidhi Deepika, S. Singh, Paras Nath CMR Institute of Technology Department of Computer Sc. and Engg. Bengaluru560037 India MVJ College of Engineering Dept. of Computer Sc. And Design Bengaluru560067 India GMR institute of Technology Department of Computer Sc. and Engg. Rajam532127 India

ISBN: (纸本)9798350353266

In machine learning, concept drift is the gradual change in the relationship between the input and the target. Typically, this could be an unanticipated shift in the way that input and output data relate to one another over time. Usually, a machine learning model is trained and validated its performance across several metrics. The findings are also satisfactory but something unforeseen and unexpected happened with input and the output prediction has gone crazy. It fails and fells victim to a phenomenon called concept drift. It happens because 'change is the only constant in life.' Concept drift is now discussed as a strong logic to be considered in the accuracy of machine learning algorithms. The required datasets that are currently available for assessing the working of lazy machine learning algorithms. This paper discusses the detection of 'Concept Drift' faced by lazy learners of Machine Learning (ML) algorithms and proposed its solution. Research directions are also examined and discussed. This will immediately benefit researchers in their comprehension of research under the idea of idea of Concept Drift by offering state-of-the-art knowledge. In order to address the shortcomings of lazy ML algorithms such as K-Nearest Neighbor, Logical Weighted Regression, and Radial Base function, a novel heuristic-weight based technique has been employed. XOR (Exclusive OR) is not linearly separable with Radial Base Function which has been implemented to make it linearly separable in Python successfully. © 2024 IEEE.

关键词： Adversarial machine learning

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Collision Avoidance for AGV Navigation Using the Time Elastic Band Algorithm

Collision Avoidance for AGV Navigation Using the Time Elasti...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Hsu, Pei-En Lin, Cheng-Hung He, Jyun-Hong Chiang, Hsin-Han National Taiwan Normal University Dept. Electrical Engineering Taipei Taiwan National Taipei University of Technology Dept. Computer Science and Information Engineering Taipei Taiwan National Taipei University of Technology Dept. Vehicle Engineering Taipei Taiwan

ISBN: (纸本)9798350324174

This paper presents a design and implementation of an automated guided vehicle (AGV) that addresses the dynamic obstacle avoidance problem in navigation. We employ the TEB path planner, which is based on laser range sensors, as the algorithm for dynamic obstacle avoidance. In the development process, the Stage simulation is previously conducted to obtain suitable parameters. The environment used in the Stage simulation is a flat map of the real vehicle verification site, and the robot specifications and sensors used are the same as those of the actual vehicle. Finally, we apply the parameters obtained from the simulation to the real AGV for validation. © 2023 IEEE.

关键词： Navigation

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Personalized Stress Detection using a Lightweight Machine Learning Framework With Convenient Wrist-Worn Sensors

Personalized Stress Detection using a Lightweight Machine Le...

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2024 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2024

作者： Hasan, M. Mithun, S. Mahmud, M. Baral, S. Poudel, K. Middle Tennessee State University Computational And Data Science MurfreesboroTN United States University Of Memphis Dept. Of Computer Science TN United States University Of Memphis Dept. Of Electrical And Computer Engineering TN United States Middle Tennessee State University Dept. Of Computer Science MurfreesboroTN United States Univ. Of Tennessee Health Science Center Div. Of General Internal Medicine MemphisTN United States

ISBN: (纸本)9798350388572

Over 70% Americans experience daily stress, making real-time, accurate stress detection crucial for timely intervention, and promoting physical and mental well-being. In this study, we detect stress using machine learning from publicly available wrist-worn sensor data. We examined both population and individual levels, focusing on simple, computation friendly statistical features for reduced processing to facilitate adoptions in real-life mHealth applications. Our framework demonstrates that stress detection achieves over 96% accuracy at both the population and individual levels, with the Extreme Gradient Boosting classifier achieving the highest predictive accuracy. We found significant variability between individuals and achieved an average accuracy of 99.63% with area under the curve (AUC) 99.23%, F1-score 99.00%, precision, and recall 99.22%. This approach offers a promising solution for real-life stress detection due to its high accuracy and simplicity. © 2024 IEEE.

关键词： LightGBM Machine learning Personalization Stress detection Wrist-band sensors

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