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Corner Key Point Extracted Deep Learning Framework Based Birds Species Classification 8

Corner Key Point Extracted Deep Learning Framework Based Bir...

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Devi, M. Shyamala Jamal Mohammed, R. Hemanth, S. Harish Raj, B. Gajapathy, R. Kamalesh, P. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798331505462

Climate change and habitat destruction have caused the extinction of many bird species. Classifying bird species is a crucial aspect of ornithology, as it aids in the comprehension of species distribution, habitat needs, and environmental changes that impact bird populations. Ornithologists can use variations in bird species distributions to measure the health of a particular environment. Expert ornithologists have relied heavily on visual inspection, which can be tedious, time-consuming, and unreliable when classifying bird species. The development of technology can be used to categorize the bird species. This paper proposes Corner Key Filtered VGG16 (CKP-VGG16) that categorizes eight bird species with high accuracy. The execution was carried out using the 4000 bird images from the KAGGLE Birds species dataset. First, the CKP-VGG16 model separates the images of birds, and then it labels each image. After data augmentation on the labeled images, 84,000 bird images are produced. The augmented images are transformed into grayscale versions, which are then used in filtering techniques to produce Corner Key Point Filtered (CKP) bird image. The performance is analyzed by applying the CKP filtered bird images to the current CNN models. The application demonstrates that the suggested CKP-VGG16 model performs significantly well in classifying bird species, with a high accuracy of 99.7%. © 2024 IEEE.

关键词： Birds

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Detectability of hierarchical communities in networks

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Physical Review E 2024年第3期110卷 034306页

作者： Leto Peel Michael T. Schaub Department of Data Analytics and Digitalisation School of Business and Economics Department of Computer Science

We study the problem of recovering a planted hierarchy of partitions in a network. The detectability of a single planted partition has previously been analyzed in detail and a phase transition has been identified below which the partition cannot be detected. Here we show that, in the hierarchical setting, there exist additional phases in which the presence of multiple consistent partitions can either help or hinder detection. Accordingly, the detectability limit for nonhierarchical partitions typically provides insufficient information about the detectability of the complete hierarchical structure, as we highlight with several constructive examples.

关键词： Community structure Complex systems Network structure Patterns in complex systems Block models data analysis

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Diagnosing Dementia Disorder Detection Using an Improved Eliminate Particle Swarm Optimization (IEPSO) Based on Convolutional Neural Networks

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SN computer science 2024年第6期5卷 1-10页

作者： Duraipandian, Kavitha Ambigapathy, Murugan Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Tamil Nadu Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology SRM Nagar Tamil Nadu Kattankulathur Chennai India

Dementia is an un-repairable and continuous disease that affects a person's mental health. Symptoms of Dementia may vary from one person to another, there are no effective treatments for detecting Dementia. Machine learning algorithm has proven to be effectively used by many researchers in assisting optimal detection of Dementia. So, this research introduces an improved eliminate particle swarm optimization (IEPSO) based Convolutional Neural Network (IEPSO-CNN) for diagnosing Dementia without causing severe effects effectively. IEPSO is used to find suitable parameters while training the model to improve Dementia detection accuracy. This IEPSO is used for tuning the parameter of the two-level Visual Geometry Group 16 (VGG 16) model. The integration of IEPSO and two hierarchical layers of VGG 16 increases the model's Dementia detection performance. The IEPSO-CNN model performance is evaluated using 6400 Dementia and non-Dementia images. The performance analysis shows that the IEPSO-CNN obtained a maximum accuracy of 99% for detecting the dementia stages. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural network Deep learning Dementia detection Improved eliminate particle swarm optimization VGG 16

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Emotion Recognition Using Focal Network and Resnet

Emotion Recognition Using Focal Network and Resnet

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Meena, T. Balasai, Sigireddy Anandh Duggireddy Kumar, P. Phaneendhra Velagapudi Ramakrishna Siddartha Engineering College Department of Computer Science and Engineering India Velagapudi Ramakrishna Siddartha Engineering College Department of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350382693

Image emotion recognition involves finding the emotions from visual data, usually done through convolutional neural networks (CNN) or deep neural networks (DNN). The existing methodologies are often high complex or time complex and it is not giving the good considerable results and computationally expensive due to high number of parameters. In this paper we try to accomplish a better output by using focal network which stood among other transformers architecture due to the unique property of the dynamic attention Recent studies have shown the efficacy of transformer architectures in various deep learning tasks and challenged the convolution neural networks on various tasks. but, It has a tradeoff between reduced parameter complexity and the need for extensive training data. Drawing inspiration from recent publications, the study adopts a focal network for training, demonstrating its effectiveness in enhancing the model's attention and optimizing emotional feature extraction. The proposed methodology aims to do the process of image emotion recognition by using the advantages of transformers while mitigating the computational problem associated with the deep neural networks. The incorporation of a focal network addresses attention-related challenges, and it contributes to the development of more efficient emotion recognition systems. This research offers insights into the evolving landscape of image emotion recognition methodologies and underscores the potential of transformer architectures in advancing this field. © 2024 IEEE.

关键词： Convolution

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E-Medical Administration: An Automated Healthcare Management System 4th

E-Medical Administration: An Automated Healthcare Management...

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4th International Conference on data, Engineering, and Applications, IDEA 2022

作者： Awotunde, Joseph Bamidele Misra, Sanjay Abdullahi, Thairu Olanrewaju Agrawal, Akshat Department of Computer Science University of Ilorin Ilorin Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway Amity University Haryana Manesar India

ISBN: (纸本)9789819724505

Almost any service may now be found on the Internet, thanks to advances in web technology. Every major industry is converting and building a digital face for all of its core activities to get closer to the rising digital market. A person or company using redundancy tactics in today's society will not gain from it. All well-run and well-managed businesses today need Internet access. Information digitization is critical in healthcare because of the need to move swiftly and effectively. It lays the groundwork for the creation of software that makes it easier to go from paper documents to electronic ones. Therefore, this paper proposes an automated hospital management system. E-Medical Administration is a web-based platform that removes the need for paper prescriptions in hospitals and offers E-Medical Management, which would enhance patient management, doctor scheduling, and universal access to patient data anywhere in the hospital. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic health record

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Variational quantum neural network with optimized ansatz for predicting malignant ventricular arrhythmias 9th

Variational quantum neural network with optimized ansatz for...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Dominic, Nicholas Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Preventive strategies should be the utmost priority when dealing with diverse patients suffering from malignant ventricular arrhythmia (MVA) that can lead to sudden cardiac death (SCD). Electrocardiogram (ECG) data is commonly used as a predictor for MVA predictive models. In this study, all ECG signals from MIT-BIH databases were fragmented into five-minute durations with a frequency sampling of 128 Hz. To solve the absence of hybrid optimizations in Machine Learning (ML) models, a novel Variational Quantum Neural Network (VQNN) was invented. Empowered by deep learning capabilities and optimized quantum circuits design, VQNN achieved remarkable performances designated by an accuracy of up to 95.1%, a perfect 100% recall, and a 95.2% score of the area under the Receiver Operating Characteristic curve (AUC ROC) with Conjugate Gradient as an optimizer and EfficientSU2 as a quantum ansatz. Despite the susceptibility to quantum noise, this research settles a new trajectory of utilizing quantum variational algorithms to predict and expand its applicability for MVA cases. © 2024 The Authors.

关键词： Quantum computers

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The Complexity of Learning LTL, CTL and ATL Formulas 42

The Complexity of Learning LTL, CTL and ATL Formulas

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42nd International Symposium on Theoretical Aspects of computer science, STACS 2025

作者： Bordais, Benjamin Neider, Daniel Roy, Rajarshi TU Dortmund University Center for Trustworthy Data Science and Security University Alliance Ruhr Dortmund Germany Department of Computer Science University of Oxford United Kingdom

ISBN: (纸本)9783959773652

We consider the problem of learning temporal logic formulas from examples of system behavior. Learning temporal properties has crystallized as an effective means to explain complex temporal behaviors. Several efficient algorithms have been designed for learning temporal formulas. However, the theoretical understanding of the complexity of the learning decision problems remains largely unexplored. To address this, we study the complexity of the passive learning problems of three prominent temporal logics, Linear Temporal Logic (LTL), Computation Tree Logic (CTL) and Alternating-time Temporal Logic (ATL) and several of their fragments. We show that learning formulas with unbounded occurrences of binary operators is NP-complete for all of these logics. On the other hand, when investigating the complexity of learning formulas with bounded occurrences of binary operators, we exhibit discrepancies between the complexity of learning LTL, CTL and ATL formulas (with a varying number of agents). © Benjamin Bordais, Daniel Neider, and Rajarshi Roy.

关键词： Temporal logic

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Watch Product Categorization through Conv2D Sequential Covolutional Neural Network with Truncated Normal Kernel Initializer 2

Watch Product Categorization through Conv2D Sequential Covol...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Shyamala Devi, M. Harivishnu, K.C. Jamal Mohammed, R. Jagan, S. Gurucharan, G. Mohamed Arshad, M. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798350371314

For the sake of customer satisfaction and high profitable revenue, the products are designed in the Ecommerce application along with the product category. Based on the product category, prices may differ in a broad range. So, the application must be able to categorize the products before releasing the price voucher to the customers. The absence of effective product categorization, made it challenging to provide deep learning algorithm that results in the usage of modern tools towards product classification. This paper proposes Truncated Normal Kernel Initializer Conv2D (TNKIC) Sequential model for categorizing the watch product. The suggested TNKIC model includes the Watche Image dataset from KAGGLE which consists of 2000 pictures divided into 10 groups. To classify watch goods, pictures are preprocessed and fitted using the TNKIC model. Three convolutional layers, a max pooling layer, and three dense layers that are weight initialized using a Truncated Normal Kernel initializer are preprocessed on the watch pictures in order to classify the watch products. The watch images are splitted into training and testing images. The TNKIC model is examined to determine how well the suggested model works in the watch product categorization than the current conV2D models after being trained with watch images. When compared to current conV2D models, implementation observations using Python on a Gtx Geforce Tesla V100 Nvidia GPU workstation utilizing 64 and 250 training intervals show that the suggested TNKIC model classifies the watch product category with the accuracy of 97.88%. © 2024 IEEE.

关键词： Deep learning

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A Real-Time Automatic Translation of Text to Sign Language

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computers, Materials & Continua 2022年第2期70卷 2471-2488页

作者： Muhammad Sanaullah Babar Ahmad Muhammad Kashif Tauqeer Safdar Mehdi Hassan Mohd Hilmi Hasan Norshakirah Aziz Department of Computer Science Bahauddin Zakariya UniversityMultan60000Pakistan Department of Computer Science Air UniversityMultan60000Pakistan Department of Computer Science Air UniversityIslamabad44000Pakistan Centre for Research in Data Science Department of Computer and Information SciencesUniversitiTeknologi PETRONASSeri Iskandar32610PerakMalaysia

Communication is a basic need of every human being;by this,they can learn,express their feelings and exchange their ideas,but deaf people cannot listen and *** communication,they use various hands gestures,also known as Sign Language(SL),which they learn from special *** normal people have not taken SL classes;therefore,they are unable to perform signs of daily routine sentences(e.g.,what are the specifications of this mobile phone?).A technological solution can facilitate in overcoming this communication gap by which normal people can communicate with deaf *** paper presents an architecture for an application named Sign4PSL that translates the sentences to Pakistan Sign Language(PSL)for deaf people with visual representation using virtual signing *** research aims to develop a generic independent application that is lightweight and reusable on any platform,including web and mobile,with an ability to perform offline text *** Sign4PSL relies on a knowledge base that stores both corpus of PSL Words and their coded form in the notation ***4PSL takes English language text as an input,performs the translation to PSL through sign language notation and displays gestures to the user using virtual *** system is tested on deaf students at a special *** results have shown that the students were able to understand the story presented to them appropriately.

关键词： Sign language sign markup language deaf communication hamburg notations machine translation

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Priority Based Cyber Incident Handling: Work in Progress 11

Priority Based Cyber Incident Handling: Work in Progress

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11th International Conference on Electrical Engineering, computer science and Informatics, EECSI 2024

作者： Mulyati Stiawan, Deris Widhiarso, Wijang Budiarto, Rahmat Multi Data Palembang University & Faculty of Engineering Universitas Sriwijaya Department of Information System Palembang Indonesia Sriwijaya University Faculty of Computer Science Palembang Indonesia Multi Data Palembang University Department of Informatics Palembang Indonesia College of Computer Science & IT Albaha University Saudi Arabia

ISBN: (纸本)9798350355314

Effective and efficient cyber incident handling is crucial for maintaining the security of information systems and organizational data. This research aims to develop a priority-based cyber incident handling method by adopting the NIST 2.0 framework and utilizing machine learning classification algorithms. This method enables organizations to quickly and accurately identify and respond to incidents based on threat levels and impact, focusing resources on the most critical incidents. The research employs the Support Vector Machine (SVM) classification algorithm to determine incident priorities. The study is still developing for handling cyber incidents, followed by testing and evaluating the system prototype within organizations. Initial results indicate that the priority-based approach using the SVM classification algorithm achieves a True Positive (TP) Rate of 0.999 and a False Positive (FP) Rate of 0.008, with a correlation of 98.88%, resulting in excellent performance in identifying positive cases and a low error rate. © 2024 IEEE.

关键词： Cyber attacks

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