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Enhancement of Accuracy for Iris Presentation Attack

Enhancement of Accuracy for Iris Presentation Attack

引用

2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Priyanka, V. Shyam, Gopal K. Presidency University Dept. Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350300826

Biometrics is the assessment and statistical examination of an individual's distinctive physical and behavioural traits. Identification, access control, and identifying those who are being monitored are the major areas where technology is focused. In security systems, biometric-based recognition has been substituting traditional recognition techniques. Iris recognition (IR) is more utilized in today's biometric technologies, which are applied to various devices for various security concerns. The effectiveness of IR systems has significantly improved during the past ten years. This is due to recent developments in deep convolutional neural networks (CNNs) and computer vision, as well as the availability of enormous training datasets. A presentation attack is a kind of attack where an imposter creates artificial or fake out of genuine. This paper proposed an effective approach to increase the accuracy in detecting iris presentation attacks and design the state-of-the-art of existing CNN techniques from 2015 to 2023. The proposed method is a Dual Channel Convolutional Neural Network Presentation Attack Detector(DC-CNNPAD) for increasing the accuracy of detecting real iris. The experiment was performed on the sample LivDet-2017 dataset to identify the effectiveness of the model in order to detect the artifact. The results obtained from the detection model on the sample dataset are extremely good. © 2023 IEEE.

关键词： Biometrics

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N-Queens Convergence: A Tactical Framework for Efficient Target-Directed Drone Formations and Attacks 7

N-Queens Convergence: A Tactical Framework for Efficient Tar...

引用

7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Kumar, Preethika Ajay Pes University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350343144

This study introduces a novel methodology for enhancing the efficiency of Unmanned Aerial Vehicle (UAV) formations in order to achieve accurate target engagements. The concept draws inspiration from the N-Queens puzzle. By incorporating unmanned aerial vehicle (UAV) positioning into a simulated chessboard and expanding it to include a target in the (N + 1)th column, the algorithm produces arrangements that guarantee effective engagement with the target. Significantly, this particular technique ensures the absence of overlapping pathways among UAVs during strikes, hence augmenting operational safety. The methodology employed in this study involves the construction of formations through the generation of permutations for all possible queen positions. Subsequently, the eligible queen configurations are determined by filtering based on the diagonal lines of attack. The method has been tested on N values as high as 10. This study explores the intersection of combinatorial problem-solving with military applications, providing novel insights and tactics for deploying UAVs that minimize collisions. © 2023 IEEE.

关键词： Drones

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Active Learning Using Multifeature Kernel Sparse Representation Classifier for Hyperspectral Image Classification 3

Active Learning Using Multifeature Kernel Sparse Representat...

引用

3rd IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023

作者： Bortiew, Amos Patra, Swarnajyoti Tezpur University Dept. of Computer Science and Engineering Tezpur India

ISBN: (纸本)9798350325591

Active learning is a popular technique that mitigates the need for large training samples for hyperspectral image classification. The success of this technique is dependent on the query function designed to select informative samples for labelling. In the literature, most of the query functions are defined by exploiting spectral information. In this research, a novel query function for active learning is defined that considers both the spectral and spatial information of the hyperspectral image using multiple feature sets. The multiple feature sets provided complementary information for selecting those informative samples which are not only informative in spectral perspective but also from spatial perspective. Experiments conducted using two real hyperspectral datasets show that the proposed technique outperformed many state-of-the-art techniques. © 2023 IEEE.

关键词： Image classification

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Understanding Facial Expression of Children with Autism Using Learning Theory

Understanding Facial Expression of Children with Autism Usin...

引用

2023 IEEE Asia-Pacific Conference on computer science and data engineering, CSDE 2023

作者： Zaman, Anushka Khan, Evea Zerin Rabiul Alam, Md. Golam Chakrabarty, Amitabha Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341072

It is challenging for autistic individuals to gather socially and empathically, as we understand, so this subject matter differs from the identification of a common human facial expression. In our research, we have taken pictures of children and teenagers from school of special children based in Bangladesh, aged between 5 to 20 with Autism Spectrum Disorder (ASD) and identified their emotions from their images. Our objective is to find a technique that will allow their emotions to be accurately perceived and thus, make it easy for them including other individuals around them to interact socially without any barriers. In our paper, we implemented VGG16 and six Machine Learning Algorithms along with one Feature Extraction technique to detect Facial Expression of children with autism. The proposed system has showcased an accuracy of 75% for VGG16, 68% for Random Forest Classifier, 61% for Random Forest Classifier along with PCA, 61% for Support Vector Machine (SVM) and 67% for SVM with PCA, 56% for Logistic Regression and 53% for Logistic Regression along with PCA, 52% for Linear Discriminant Analysis (LDA) and 54% for LDA with PCA, 46% for Decision Tree and 50% for Decision Tree along with PCA and 46% for Gaussian Naïve Bayes and 38% for Gaussian Naïve Bayes along with PCA. © 2023 IEEE.

关键词： Support vector machines

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Predicting the Number of Fatalities in an Air Crash 8

Predicting the Number of Fatalities in an Air Crash

引用

8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Prajwala, T.R. Manoj, Nandana Kumar, Preethika Ajay Harshita, R. Dept. of Computer Science and Engineering PES University Bengaluru India

ISBN: (纸本)9798350334012

Air travel is one of the safest forms of travel today. The mortality rate in air travel is much lesser than that in automobile accidents. Nevertheless, aviation accidents have always been a huge cause of concern for people. Crashes could occur due to multiple factors like injury severity, weather condition, amateur built, make, etc. Air crashes could result in fatalities or serious injuries. The outcome of this research is to predict the number of fatalities when factors like the ones mentioned above have been provided. Air travel has a wealth of historical data available, and by analyzing patterns in flight accidents, there is potential to enhance the safety of air travel further, when compared to other modes of transport. This is essential as this can help the rescue team to respond accordingly. Three models namely, Gradient-boosting-regression, Random-Forest-Regressor, XGBoost have been implemented. After reviewing the results of all three models it has been found that Random-Forest comparatively better results. © 2023 IEEE.

关键词： Correlation methods

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Performance Evaluation of Machine Learning Algorithms for Lung Nodule Detection using Multi-Modal Imaging - A Hybrid Approach 2

Performance Evaluation of Machine Learning Algorithms for Lu...

引用

2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Puneeth, R.P. Amogha Mayya, K.S. Shetty, Agnesh Rao, Akash Sooda, Aditya A. Dept. of Computer Science and Engineering Karnataka Nitte574110 India

ISBN: (纸本)9798350347579

Lung cancer is a major contributor to global mortality rates and identification is critical to improve patient outcomes. In recent years, machine learning algorithms have demonstrated promising results in identifying lung nodules from medical images. The most compelling area of research for scientists is the early detection of lung cancer. This study is a method for lung nodule detection using CT images. The study incorporates a hybrid model that combines multiple machine learning algorithms including CNN, SVM, DTC, ANN, and KNN to improve the accuracy of nodule detection. The hybrid model demonstrated high accuracy in identifying various types of lung nodules, including Adenocell carcinoma, squamous cell carcinoma, and large cell carcinoma. Specifically, the model achieved an accuracy rate of over 90% in detecting and differentiating normal lung tissue and Adenocell carcinomas. Accuracy graphs and priority setting were utilized to assess the model's capability in accurately predicting the presence of lung cancer. Additionally, the efficiency of the hybrid model was compared with other machine learning algorithms, including SVM, Random Forest, and Decision Trees. A large dataset of CT scans was collected for training and evaluation purposes. The results demonstrated the advantages of the suggested hybrid model in terms of accuracy and efficiency. This study highlights the importance of early lung nodule identification using CT scans and demonstrates the effectiveness of the hybrid model in accurately identifying different types of lung nodules. © 2023 IEEE.

关键词： computerized tomography

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Machine Learning Advances in Diagnosing Attention Deficit and Hyperactivity Disorder 14

Machine Learning Advances in Diagnosing Attention Deficit an...

引用

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Deepika Sharma, Meghna Arora, Shaveta The Northcap University Dept. of Computer Science and Engineering Gurugram India

Attention deficit and hyperactivity disorder (ADHD) is worldwide prevalent neurobehavioral ailment affecting kids and adolescents. ADHD is mainly characterized by behavioral and mental abnormalities with its root lying in genetics and neural impairments. Despite prescription of various diagnosis guidelines by American psychiatric association (APA), there is no standard biomarker for ADHD detection which sometimes causes misdiagnosis/overdiagnosis of the symptoms. So, there is crucial need for an accurate and reliable diagnostic standard for ADHD for timely and effective treatment. This paper reviews various machine learning and deep learning models proposed for ADHD diagnosis in recent times. It also lists different factors supporting the use of machine learning for ADHD detection, brain imaging modalities used, novel execution strategies along with the shortcomings of these methods. The conclusion part talks about future scope of machine learning in ADHD diagnosis. © Grenze Scientific Society, 2023.

关键词： Brain mapping

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Finding Type 2 Diabetes Using Sparse Balanced Support Vector Machine in Electronic Health Records

Finding Type 2 Diabetes Using Sparse Balanced Support Vector...

引用

2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Kaur, Ranjanpal Sekhon, Er. Navjot Kaur Chandigarh University Dept. of Computer Science & Engineering Mohali India

ISBN: (纸本)9798350331790

Early Type 2 Diabetes (T2D) diagnosis is crucial for optimal integrated therapy and patient follow-up. There has been a huge growth in the quantity of accessible Electronic Health Record (EHR) data, and developments in Machine Learning (ML) methodologies have been as amazing. Maintaining and modelling a huge data set can cause over fitting, model interpretability, and computational expenditure. We used Sparse Balanced Support Vector Machine (SB-SVM) to detect type 2 diabetes in an original EHR dataset (named FIMMG dataset). In particular, we have chosen just those aspects of the EHR collected from a homogeneous group of participants before the diagnosis of T2D, including exemptions, examinations, and medicine prescriptions. The proposed solution is trustworthy compared to other ML and Deep Learning methods utilised by the state-of-the-art to handle this problem. The SB-SVM offers the best prediction performance-computation time tradeoff, according to the data. In addition, the induced sparsity makes the model more interpretable while automatically handling high-dimensional data and the normally uneven distribution of classes. © 2023 IEEE.

关键词： Support vector machines

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Interpretable MOOC Dropout Prediction using Different Ensemble Methods and XAI 2

Interpretable MOOC Dropout Prediction using Different Ensemb...

引用

2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Khan, Labib Hasan Haque, Mohammed Ashfaqul Ibrahim, Esaba Ahnaf Mostakim, Moin Hossain, Muhammad Iqbal Brac University Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9781665456302

Massive open online course (MOOC) has been around for several years, which started to gain traction in 2012 when Coursera was established. MOOCs use pre-recorded lectures and scheduled weekly tests to provide content and access to students over the internet. The dataset used in this study is taken from the KDDCUP 2015 dataset, a publicly available dataset. Here, 12 features are considered, they are browser access, navigate, average chapter delays, etc to comprehend the possibility of dropout. This research study intends to predict the dropout of a learner so that it can be prevented through manual interaction. Additionally, XAI is implemented to inter-pret the models to suggest MOOC platforms, which feature impact dropout the most. Further, different ensemble learning techniques, namely voting classifier and stacking are used. The proposed voting classifier uses five of the best-performing machine learning models. Then, the performance of the model is evaluated by using multiple metrics such as precision, recall, and F1-score. Finally, a recall of 97.636 % has been obtained with stacking and f1-score of 91.603 % has been obtained with the hard voting classifier. © 2023 IEEE.

关键词： Forecasting

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Bringing computer science Education to Schools in Qatar: Challenges of the implementation 5

Bringing Computer Science Education to Schools in Qatar: Cha...

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5th International Conference on computer and Applications, ICCA 2023

作者： Alhazbi, Saleh Qatar University Dept. of Computer Science and Engineering Doha Qatar

ISBN: (纸本)9798350303254

Many countries have started national curriculum changes to make computer science (CS) as a compulsory subject for all students in schools. In 2018, a new national computing curriculum has been developed in Qatar for all k-12 grades. Comparing to the old curriculum, the new one focuses on teaching computer science instead of digital literacy. This paper discusses the challenges of implementing this new curriculum from CS teachers' perspectives in Qatar public schools. A survey was conducted of over 392 CS teachers who taught the new curriculum during the academic year 2018/2019. From the analysis of the collected data, it is found that teachers face different challenges including more working loads, lack of support to master the new contents, as well as the need of professional developments to improve their pedagogical skills in order to deliver the new CS concepts effectively. Moreover, the results shows that teachers in preparatory and secondary stages struggle with these challenges more than those in the elementary stage. Although all teachers are computing majors, the majority indicated that programming subjects in all grades are the topics where they need the most support. Accordingly, more support in term of improving teachers' content knowledge, and pedagogical content knowledge is essential in order to achieve the objectives of this reform. © 2023 IEEE.

关键词： Curricula

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